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Exploring the "Omnichannel" Shopper Behaviour



This study explores omnichannel shoppers’ behaviour through an online questionnaire with 1324 respondents executed in November 2013 in Greece. The study classifies shoppers according to their “omnichannel retailing intensity” and tests whether the resulted groups differ in terms of a series of relevant to the omnichannel retailing phenomenon key behavioural patterns. The results indicate that omnichannel retailing intensity affects the frequency of mobile Internet usage, the research online - purchase offline behaviour, the importance shoppers attach to the offline retail stores’ assisting technologies and the research offline - purchase online behaviour. The paper provides implications for practice and future research.
Chris Lazaris, Adam Vrechopoulos, Katerina Fraidaki and Georgios Doukidis,
ELTRUN - The E-Business Research Center, Department of Management Science &
Technology, Athens University of Economics & Business, Greece
Nowadays, consumers are interacting with an increasing number of touchpoints as they search, buy,
and get support. For example, they can use their mobile devices while there are in a physical store, in
order to instantly search for availability and price, comparing multiple retailers. Then, they can easily
move across different retail channels (online or offline) of the same or a competitor retailer. They are
characterised by retail practicioners as “omnichannel” shoppers: “an evolution of multichannel
consumers who want to use all channels (store, catalog, call center, web, and mobile) simultaneously,
not each channel in parallel” (Ortis, 2010, p.1). “Omni” stems from the Latin word “Omnis” which
means “all” “everything”, or “universal”. In comparison, “multichannel” comes from the word
“Multus”, meaning “multiple”, “much” or “many”. The term “omnichannel retailing” was first
introduced in a 2009 study by IDC’s Global Retail Insights research unit (Ortis & Casoli, 2009). Since
then, omnichannel retailing remained a buzzword, until enabling Information and Communication
Technologies (ICT) made this notion a reality. These technologies (e.g. mobile devices, in-store
technologies, augmented reality, location-based services) appeared both online and offline, blending
all the retail channels together, providing a seamless integrated experience for the consumers, while
empowering retailers with valuable tools, often only available to e-commerce environments. Indeed,
as Chen & Mersereau (2013, p.3) point out, “a significant challenge of modern in-store retailing, seen
in the push for “omnichannel retailing”, is learning how best to compete with, complement, and learn
from the e-commerce channel”. As a result, retailers should reengineer their business processes so as
to place the customer at the center of their business and provide omnishopping experiences. Similarly,
merchandise and promotions should not be channel specific, but consistent across all channels. In fact,
offline marketers begin to adopt mobile marketing and experiment with in-store marketing efforts
enabled by e-commerce platforms (Walker, 2010). Apart from the previous business sources,
omnichannel retailing has recently appeared in academic literature, too. It is defined by Rigby (2011,
p.67) as “an integrated sales experience that melds the advantages of physical stores with the
information-rich experience of online shopping”. Aubrey & Judge (2012, p.31) report that “a huge
opportunity is realised for brands to reinvent the physical store so that it actively drives growth”. They
also suggest that instead of considering e-commerce as a threat to their offline retail networks, brands
need to develop online operations that cooperate and support the physical channel, as part of an
integrated “omnichannel ecosystem”. Finally, Brynjolfsson & Rahman (2013, p.1) explained how “the
distinctions between physical and online retailing are vanishing” and they pointed out how “advanced
technologies on smartphones and other devices are merging touch-and-feel information in the physical
world with online content, creating an omnichannel environment”.
The research need of the present study is clearly documented by the fact that while consumer
behaviour has been thoroughly investigated in the multichannel retailing environment, relevant
research in the context of the emerging omnichannel retailing landscape is limited. Kourouthanassis et
al. (2007) found that in-store retail technologies positively affect shopping experience within the
physical store. Also, Van der Heijden, (2006) introduced a decision support system for consumers “on
the go” when they are located inside a retail store, which was found useful for shopping. Similar
results where found by Jan-Willem et al. (2010) regarding the influence of mobile recommendation
agents in in-store consumer behaviour. In parallel, Broeckelmann & Groeppel-Klein, (2008) studied
the usage of mobile price comparison sites at the point of sale and its influence on consumers'
shopping behaviour. Their research revealed that consumers recognise differences in prices, which in
turn influence their evaluation of the shop's price competence, their trust in the shop and their
patronage of it. Using a different research approach, Verhoef et al., (2007) discovers that Internet
search, followed by store purchase, is the “most popular form of research shopping”. Along these
lines, Chiu et al. (2011) revealed that when consumers have more multichannel self-efficacy
perception, then cross-channel free-riding behaviour (i.e. when consumers visit a retailer’s channel
only for product information & evaluation and switch to another retailer to purchase) increases. They
report (p.268) that “perceived service quality of competitors’ offline store and the reduced risk in the
brick-and-mortar channel influence the attractiveness of this behaviour and increase cross-channel
free-riding intentions”. Nevertheless, this study investigated only the behaviours associated with a
specific purchase path: searching online and purchasing offline. However, searching offline and then
purchasing online is another kind of multichannel free-riding, which remains relatively scarcely
researched. It is partly addressed by Van Baal & Dach (2005). Elaborating on these research insights,
the research objective of the present study is to explore omnichannel shopper behaviour placing
particular emphasis on exploring the “intensity” of omnichannel retailing practices used by shoppers
(i.e. the degree that shoppers use these capabilities) and explore any potential relationships between
the omnichannel retailing intensity and other shoppers’ behavioural characteristics. Thus, the
following research hypotheses focus on exploring whether consumers with different levels of
omnichannel retailing intensity differ, in terms of a series of relevant to the omnichannel retailing
phenomenon key behavioural patterns, as derived through the review of the literature:
Η1: Shoppers’ omnichannel retailing intensity affects the frequency of their mobile Internet usage
Η2: Shoppers’ omnichannel retailing intensity affects the research online - purchase offline behaviour
Η3: Shoppers’ omnichannel retailing intensity affects the importance shoppers attach to the offline
retail stores’ assisting technologies
Η4: Shoppers’ omnichannel retailing intensity affects the research offline - purchase online behaviour
For testing the research hypotheses, it is, firstly, attempted to classify/segment the respondents into
distinctive groups according to the degree they use omnichannel retail capabilities and practices (i.e.
“omnichannel intensity”). Multichannel shopper segmentation has already been addressed in the
multichannel literature, but not in relation to the omnichannel concepts. For example, Nunes &
Cespedes (2003) introduced 4 segments: Habitual Shoppers, High-value Deal Seekers, Variety-loving
Shoppers & High-involvement Shoppers. Similarly, Keen. et al., (2004) discovered 4 clusters:
Generalists, Formatters, Price Sensitives & Experiencers. Moreover, Kumar & Venkatesan (2005)
classified multichannel shoppers according to the number of channels they use. Furthermore, Thomas
and Sullivan (2005) identified 5 shopper categories according to the impact of product type, customer
lifestyle, and price sensitivity on their channel choice. Finally, Konus, Verhoef, & Neslin, (2008),
identified 3 multichannel shopping segments (Multichannel Enthusiasts, Uninvolved Shoppers, and
Store-focused Consumers), while Quint & Ferguson (2013) classified mobile shoppers to
Traditionalists, Experience-Seekers, Exploiters, Savvys & Price-Sensitives. The present study
employed an exploratory quantitative empirical research design that took place in Greece, in
November 2013. The data collection instrument of the survey was an online questionnaire which
received 1324 answers from Internet users. The groups of the present study are classified according to
the observed level of the omnichannel retailing intensity of the sample’s participants. Then, the study
investigates whether there are statistical significant differences among the groups, in terms of the
variables included in the aforementioned research hypotheses. A detailed profiling of each of the
resulted groups is also attempted by utilizing relevant information provided through the descriptive
statistics and by the hypotheses testing results.
The sample comprised 62,53% male and 37,47% female. It originated mostly from Attiki region
(63,87%), but it also included 3,23% respondents from abroad. Descriptive statistics reveal that
71,27% of the consumers use in-store Internet access. This finding alone shows the blending of online
and offline shopping environments. In other words, the consumer is online, even in physical stores
and, therefore, the purchase process can become differentiated and complex. In addition to this,
consumers have mobile phone Internet access at a percentage of 89,84% (62,10% of them daily),
which partly explains the previous statistic. Also, 43,17% of consumers spend more than 30 hours per
week on the Internet, a figure that implies that they are experienced Internet users. Their shopping
profile shows that they shop online at a percentage of 91,94% (52,30% often). Similarly, 94,36% of
the sample, use the Internet for product search and price comparison (53,18% often). Furthermore,
more than 50% of offline purchases took place after having searched online, for more than half of the
respondents (53,55%). In contrast, more than 50% of online purchases took place after having
searched offline, for 23,68% of the respondents. This is aligned with literature findings (Verhoef et al.,
2007). The results regarding consumer multichannel preferences are very interesting, too. In addition
to the physical store, consumers’ supportive channels of choice are: call center (56,77%), online store
(51,72%), catalogs (37,49%), in-store retailing assisting technologies (34,13%), store social media
(28,41%), store mobile version (24,11% ) and store mobile apps (22,52%). The omnichannel notion is
best depicted in the results which showed that 39,29% of consumers used their mobile phones for
price comparison, while in store (in order to negotiate or buy elsewhere), whereas 17,79% of the
sample would also act that way, if their mobile supported it and they had Internet access. In parallel,
32,46% of respondents have used their mobile phones to search for product presentations and
reviews/comments, while in store, in order to make a purchase. Table 1 summarizes the results of the
classification process conducted according to the omnichannel intensity of the study’s participants,
characterizing as “omnishoppers” those that comply with omnichannel behaviour. It should be noted
that from the 1324 respondents (i.e. total sample size), 894 questionnaires were employed for this
classification due to several reasons (e.g. missing values, invalid or contradicting data).
Group Labels Group Classification Characteristics/Criteria %
Group #1:
Full Omnishoppers
These shoppers have mobile Internet access within the store and use it to compare
prices (i.e. for negotiations or for free-riding) and search for product information
(i.e. presentations and reviews/comments for products) 23%
Group #2:
Partial Omnishoppers
These shoppers have mobile Internet access within the store and use it either to
compare prices (i.e. for negotiations or for free-riding) or search for product
information (i.e presentations and reviews/comments for products) 21%
Group #3:
In-Store Internet Users and
Potential Omnishoppers
These shoppers have Internet access within the store but they don’t use it for
comparing prices and search for product information. However, they report that
they are willing to use it for the previous purposes if their mobile phones supported
this function
Group #4:
In-Store Internet Users but
Omnishopping Avoiders
These shoppers have Internet access within the store but they are not interested to
use it for performing the previous tasks 26%
Group #5:
Non In-Store Internet Users
but Potential Omnishoppers
These shoppers don’t have Internet access within the store but they are willing to
have, in order to perform the previous tasks 6%
Group #6: Omnishopping
Avoiders These shoppers neither have Internet access within the store, nor they are willing to
have, in order to perform the previous tasks 14%
Table 1: Classification of Shoppers according to their Omnichannel Retailing Intensity
Descriptive statistics regarding the Full Omnishoppers’ profile reveal that they spend more time on the
Internet per week than any other group. Also, this is the only group in which every member has
conducted online transcactions. Finally, most of them age between 28-34 years. For testing the
research hypotheses of the study, the Independent-Samples Kruskal-Wallis non-parametric test with
pairwise comparisons was employed, since the data do not follow the normal distribution (i.e. instead
of the ANOVA with Post-Hoc comparisons test). This test examines potential differences between two
or more groups and, thus, it is suitable for the present study objectives. The results indicate that there
are statistical significant differences among the groups for each of the research hypotheses of the study
(all asymptotic significances are equal to ,000) implying that shoppers with different levels of
omnichannel intensity significantly differ in terms of a series of behavioural patterns. All research
hypotheses are accepted. Specifically, as far Hypothesis #1 is concerned, the pairwise comparisons
show that the significant differences lie among almost all groups except the groups #5 and #6, groups
#3 and #4 and groups #2 and #4. As far as groups #5 and #6 are concerned this finding could be
explained by the fact that both groups do not have Internet access within the store. Regarding the other
two relationships, observing Table’s 1 results it is clear that groups #3 and #4 and groups #2 and #4
have Internet access within the store and thus this may explain the unobserved significant differences.
Furthermore, as it was expected, group’s #1 respondents (full omnishoppers) are the ones reporting the
highest average score in the the frequency of mobile Internet use (with significant differences
observed between this and all the other groups) confirming existing theory reporting that one of the
dominant characteristics of omnichannel retailing is the simultaneous mobile Internet use for retailing
purposes within the physical store. Hypothesis #2 aims to discover whether the combined use of the
online and the offline channels is related to the omnichannel intensity of shoppers. More specifically,
it is attempted to explore whether higher omnichannel intensity reflects a higher use of the online
channel for information search before conducting offline channel purchases (i.e. purchases in the
physical retail store). The main results indicate that almost all groups use the Internet to search for
product information (either in store or in other locations – e.g. home) before buying offline. This
finding is in line with the trends regarding the shopping behavioural patterns dominating today’s
retailing environment (i.e. consumers use the Internet for information search either they buy online or
offline) and with the descriptive statistics discussion included at the beginning of this section.
However, as expected, it was found that group #6 (i.e. the omnishopping avoiders) significantly differ
with both #1 and #2 groups of respondents. This finding confirms existing knowledge in the sense that
if any difference was expected to be observed in this test, this was expected to lie between the higher
and the lower levels of omnishopping intensity (i.e. full and partial omnishoppers vs. omnishopping
avoiders). Finally, the other significant relationship was found to lie between group #1 and group #4.
It should be noted that group’s #4 respondents are omnishopping avoiders and despite the fact that
they have Internet access also within the store, they are not interested to search online before buying
offline at least at the extend the groups’s #1 respodents are. This finding implies that this group does
not use the Internet for retailing purposes at least at the extent other groups do or willing to do.
Regarding hypothesis #3, it is observed that almost all groups’ respondents attach high importance to
the existence of technology applications that support their purchases within the physical store.
However, also in this case, as expected, significant differences are observed between group’s #1 and
group’s #6 participants with full omnichannel shoppers attaching significantly higher scores in this
dimension. Also, group’s #3 and group’s #6 participants (i.e. potential omnishoppers vs.
omnishopping avoiders) significantly differ without, however, lower than the average scores in the
likert scale observed for avoiders. Furthermore, significant differences are observed between groups
#3 and #4 (i.e. potential omnishoppers attach higher scores than avoiders). Also, it should be
underlined that group’s #3 respondents provided the highest from all other groups score in this
dimension (even from the full omnishoppers) implying that the fact that these shoppers are not
classified as full omnishopping ones is attributed to the limitations of their mobile phones’ browsing
capabilities. Finally, as far as hypothesis #4 is concerned, the results indicate that omnishoppers tend
to research offline and then purchase online. To be more specific, most significant differences where
found between groups #1-#6, #2-#6, and #3-#6, which are the pairs with the highest differences
observed as far as omnichannel intensity is concerned. This finding implies that omnishoppers may
use the store for showrooming and, therefore, engage in free-riding behaviour. This is consistent with
multichannel free-riding behaviour literature previously referenced (Chiu et al., (2011), Van Baal &
Dach (2005)) and with several recent business reports (e.g. Quint & Ferguson, 2013).
The present paper contributes to the emerging omnichannel literature in two ways: by executing a
segmentation study of omnishoppers and by discovering behavioural patterns that are related to the
degree that these shoppers engage in omnichannel behaviour. Interpreting the results we can conclude
that more than the half (approximately 60%) of the sample’s respondents (i.e. the ones used in the
shoppers’ classification in terms of their omnishopping intensity – Table 1) are either current or
potential users of the omnishopping retailing capabilities. The evolution of smart phones capabilities,
along with the product life cycle dynamics that may convert these products/services to commodities,
(e.g. lower prices both for smart mobile devices and Internet connections and/or diffusion of in-store
availability of free wi-fi access) could constitute the enabling omnichannel retailing mechanisms that
may potentially accelerate the diffusion and adoption of these practices. Moreover, also for the case of
omnichannel avoiders, these shoppers may adopt omnichannel practices in the future, motivated by a
series of factors that probably differ from the ones that motivate current and potential omnichannel
shoppers (e.g. price vs. reference groups’ effects, speed vs. entertainment, etc.). All these indications
pose great implications for retailers, especially for their offline stores. However, it should be
underlined that since the data collection of the present study was conducted online (i.e. the population
from which the sample was drawn comprised only Internet users), these results can not be generalized
to non-Internet users. Evidently, there are quite many future research directions and opportunities in
this emerging topic. Indicatively, since the complexity of that issue is high (mainly due to the
simultaneous involvement of more than one channels in the shopping process), the employment of a
shoppers’ “panel” logic/mechanism (e.g. collaboration of different loyalty schemes programs,
combined use of current market research offline and online consumers’ panels, exploitation of
advanced ICT capabilities, etc.) for shoppers that are active both online and offline could contribute to
the effective investigation of their behaviour in the omnichannel retail environment. Also, further
research could examine omnichannel usage in relation to products/services categories and
corresponding retailers/service providers. Moreover, it would be interesting to analyse if such usage
affects loyalty and how that could be dealt with. To sum up, an integrated view and deep
understanding of shoppers’ omnichannel behaviour, following permission Marketing guidelines and
legal frameworks regarding consumers’ data exploitation, is critical in today’s evolving multichannel
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Prior research demonstrates links between the maximizing tendency in decision making and online shopping behaviour, with maximizers spending considerable time on their online shopping yet being somewhat dissatisfied with their shopping decisions. Our research extends prior knowledge to the multichannel shopping context. Multichannel shopper journeys are an important form of shopping, whereby the activities comprising a shopping event occur in more than one channel. Our quantitative study examines relationships between two dimensions of maximizing, maximization as a strategy and maximization as a goal, multichannel shopper journey configuration and subsequent affect. Maximization as a strategy directly and positively relates to the numbers of channel switches and of pauses in a shopper journey and to the use of product and retailer reviews. It is indirectly associated with increased counterfactual thinking and regret, and with decreased satisfaction. Maximization as a goal has no effect on multichannel shopper journey configuration or on affect. Our findings have managerial relevance for multichannel retailers. We demonstrate that product and retailer reviews are of particular importance to those employing maximization as a shopping strategy, as they mitigate against their increased tendency to engage in counterfactual thinking. As counterfactual thinking leads maximizers to increased regret and decreased satisfaction, multichannel retailers can improve shopper satisfaction by actively directing their customers to reviews. Shoppers using maximization as a strategy could be helped to configure their shopper journeys with fewer channel switches and fewer pauses, as these provide maximizers with opportunities to doubt their decisions.
The paper reviews omni-channel retailing strategy to differentiate between omni-channel retailing and its precursors, multi and cross-channel retailing; delineate omni-channel strategies evolving from a retailing perspective; and present a research agenda to address a lack of research on and inconsistencies in this topic. By means of an extensive literature review, the authors focus on papers that define the concept and approach the practical aspects of implementing the strategy. We identify three approaches to defining omni-channel retailing: integrated selling channels, seamless shopping experience, and a combination of the two. Finally, this paper reveals inconsistencies in understanding implementation of the strategy. It highlights the main areas that need to be addressed by a retailer when shifting to an omni-channel retail strategy. In contrast with the existing literature, this review combines the logistic and management perspectives. It is the only study that emphasises the imperatives and alternatives related to implementing omni-channel strategy.
The omnichannel varies across countries due to different retail environments and retailers’ growth strategies. The Japanese big retailers’ omnichannel can be characterized by having multiple retail formats, such as department stores, general merchandise stores, convenience stores, specialty stores, Internet stores, and so on. They have grown by multiplying retail formats to appeal to different customer segments, and they have unique challenges in managing an omnichannel with many retail formats. These are (1) extremely wide variety of merchandise, (2) enormous quantity of data from transaction, inventory, logistics, and customers, (3) different organization structures and management, and (4) unique organizational capabilities in each retail format. From these challenges, we could propose further research issues as follows: (1) theoretical consideration of boundary-spanning functions among retail formats, (2) international comparative analysis reflecting the different conditions in each country, and (3) clarifying the characteristics of the omnichannel shopper in the Japanese omnichannel environment.
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The retail sector has changed radically in the last years, driven by possibilities created by the internet. Consumers adapted their behaviour accordingly, purchasing online as well as in-store and combining several retail channels for a single purchase. This type of behaviour is called “omnichannel” and spurred the development of retail models accommodating this behaviour, by integrating physical stores with web-shops and mobile shops. Transport associated with retailing is investigated with great attention, particularly because it is a significant contributor to climate change and air polluting emissions, among others. An unresolved yet crucial question remains which retail model is more sustainable from an environmental point of view: online retail or retailing instore. While research has focused mostly on retail logistics to address this matter, this PhD advocates to consider the broad omnichannel retail context, explicitly including consumers’ purchase, travel and choice behaviour.
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This research investigates the influence on purchase decisions of the reference prices given by an Internet site providing comparison prices, which could be accessed by shoppers at the point of sale via a mobile device. We manipulated the reference prices given on the mobile device such that one group of shoppers was shown on-line prices that were slightly higher than those in the shop, another group was shown prices that were slightly lower, and a third group was shown prices that were clearly lower. The research reveals that consumers recognise differences in price. Furthermore, these differences influence their evaluation of the shop's price competence, their trust in the shop and their patronage of it. The findings have serious implications for retailers.
Armed with a number of modern and emerging visibility technologies and facing increased competition from the internet channel, retail managers are seeking ever deeper visibility into store operations. We review two established streams of operations management research that try to overcome shortcomings of common retail data sources. The first is demand estimation and inventory optimization in the presence of data censoring, where imperfect data may cause significant estimation biases and inventory cost inefficiencies. The second is inventory record inaccuracy, where intelligent replenishment and inspection policies may be able to reduce inventory management costs even without real-time tracking technologies like radio frequency identification (RFID). Common themes of these literatures are that lack of visibility can be costly if not properly accounted for, that intelligent analytical approaches can potentially substitute for visibility provided by technology, and that understanding the best possible policy without visibility is needed to properly evaluate visibility technologies. We include a survey of modern and emerging visibility technologies and a discussion of several new avenues for analytical research.
A decade after the dot-com implosion, traditional retailers are lagging in their embrace of digital technologies. To survive, they must pursue a strategy of omnichannel retailing—an integrated sales experience that melds the advantages of physical stores with the information-rich experience of online shopping. Retailers face challenges in reaching this goal. Many traditional retailers arenʼt technology-savvy. Few are adept at test-and-learn methodologies. They will need to recruit new kinds of talent. And theyʼll need to move away from analog metrics like same-store sales and focus on measures such as return on invested capital. Traditional retailers must also transform the one big feature internet retailers lack—stores—from a liability into an asset. They must turn shopping into an entertaining, exciting, and emotionally engaging experience. Companies like Disney, Apple, and Jordanʼs Furniture are leading the way. Artwork: Rachel Perry Welty, Lost in My Life (wrapped books), 2010, pigment print Photography: Rachel Perry Welty and Yancey Richardson Gallery, NY Itʼs a snowy Saturday in Chicago, but Amy, age 28, needs resort wear for a Caribbean vacation. Five years ago, in 2011, she would have headed straight for the mall. Today she starts shopping from her couch by launching a videoconference with her personal concierge at Danella, the retailer where she bought two outfits the previous month. The concierge recommends several items, superimposing photos of them onto Amyʼs avatar. Amy rejects a couple of items immediately, toggles to another browser tab to research customer reviews and prices, finds better deals on several items at another retailer, and orders them. She buys one item from Danella online and then drives to the Danella store near her for the in-stock items she wants to try on. As Amy enters Danella, a sales associate greets her by name and walks her to a dressing room stocked with her online selections—plus some matching shoes and a cocktail dress. She likes the shoes, so she scans the bar code into her smartphone and finds the same pair for $30 less at another store. The sales associate quickly offers to match the price, and encourages Amy to try on the dress. It is daring and expensive, so Amy sends a video to three stylish friends, asking for their opinion. The responses come quickly: three thumbs down. She collects the items she wants, scans an internet site for coupons (saving an additional $73), and checks out with her smartphone.
This article presents a marketing communications process that uses customer relationship management ideas for multichannel retailers. The authors describe and then demonstrate the process with enterprise-level data from a major U.S. retailer with multiple channels. On the basis of the results, the authors develop an initial marketing communications strategy for the retailer.
The growth of Internet technology and electronic commerce has not been matched by theoretically guided social science research. Clear and well-designed consumer research is needed to describe, explain, and predict what will happen to this changing landscape. The primary purpose of this study is to investigate the structure for consumer preferences to make product purchases through three available retail formats—store, catalog, and the Internet. Conjoint analysis was used to assess the structure of the decision and the importance of the attributes in the decision-making process. The results from this study noticeably show that the structure of the consumer decision-making process was found to be primarily one of choosing the retail format (store, catalog, or Internet) and price of product (set at low, medium, or high) desired. The strength of the retail store format suggests that fears that the Internet will take over the retail arena seem, at least at this point in time, overblown and exaggerated. However, there seems to be an identifiable segment of customers that has a preference for the Internet as a retail shopping alternative.
We develop a conceptual framework, which identifies the customerlevelcharacteristics and supplier factors that are associated with purchase behavior across multiple channels. We also propose that multichannel shoppers provide benefits as measured by several customer-based metrics. We conduct an empirical analysis of our propositions using the customer database of a high technology hardware and software manufacturer. We find that customers who buy across multiple product categories, initiate more contacts with the firm, have past experience with the supplier through the online channel, have longer tenure, purchase more frequently, are larger and receive communication from the supplier through multiple communication channels, especially through highly interpersonal channels. We also find evidence for a nonlinear relationship between returns and multichannel shopping, and that there is a positive synergy towards multichannel shopping when customers are contacted through various communication channels. Customers who shop across multiple transaction channels provide higher revenues, higher share of wallet, have higher past customer value, and have a higher likelihood of being active than other customers.We derive several implications for managers who wish to target customers for a multichannel strategy.
The proliferation of channels has created new challenges for research, including understanding how consumers may be segmented with respect to their information search and purchase behavior in multichannel environment. This research considers shopping a dynamic process that consists of search and purchase phases, in which the total utility of shopping process is determined by the perceived consumer utility toward channel use, which is mainly driven by consumer characteristics. The authors (1) segment consumers on the basis of their attitudes toward multiple channels as search and purchase alternatives; (2) investigate the association among psychological, economic, and sociodemographic covariates and segment membership; and (3) explore how multichannel behavior might differ across different product categories. Using survey data from 364 Dutch consumers and Latent-Class Analyse, they identify three segments – multichannel enthusiasts, uninvolved shoppers, and store-focused consumers – and covariates, such as shopping enjoyment, loyalty, and innovativeness that predict segment membership. The category-specific analysis suggests that overall segment descriptions apply generally to a variety of categories, though some differences exist, including the impact of covariates, across categories. The authors discuss implications for further research and practice.