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Mobile Apps for Omnichannel Retailing:
Revealing the Emerging Showroom Phenomenon
Chris Lazaris
ELTRUN, School of Business, Department of Management Science & Technology, Greece(4-%.0!#-
Adam Vrechopoulos
ELTRUN, School of Business, Department of Management Science & Technology, Greece1-!$+,0!#-
Georgios Doukidis
ELTRUN, School of Business, Department of Management Science & Technology, Greece#& 0!#-
Aikaterini Fraidaki
ELTRUN, School of Business, Department of Management Science & Technology, Greece"-% '%0!#-
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Ninth Mediterranean Conference on Information Systems (MCIS), Samos, Greece, 2015 1
MOBILE APPS FOR OMNICHANNEL RETAILING:
REVEALING THE EMERGING SHOWROOMING
PHENOMENON
Complete Research
Lazaris Chris, ELTRUN - The E-Business Research Center, Athens University of Economics &
Business, School of Business, Department of Management Science & Technology, Greece,
lazaris@aueb.gr
Vrechopoulos Adam, ELTRUN - The E-Business Research Center, Athens University of Economics
& Business, School of Business, Department of Management Science & Technology, Greece,
avrehop@aueb.gr
Doukidis Georgios, ELTRUN - The E-Business Research Center, Athens University of Economics &
Business, School of Business, Department of Management Science & Technology, Greece,
gjd@aueb.gr
Fraidaki Katerina, ELTRUN - The E-Business Research Center, Athens University of Economics &
Business, School of Business, Department of Management Science & Technology, Greece,
fraidaki@aueb.gr
Abstract
The transformation of the smartphone into a key integrating factor of the online & offline retailing
environment has lead to the development of mobile applications that shape the omniretailing land-
scape. The present study provides evidence of the mobile retailing apps frequency of use inside physi-
cal stores and explores mobile retailing app assisted shoppers’ preferences of in-store omniretailing
practices & technologies. Results reveal that price comparison that could lead to showrooming is of
utmost important for consumers. In parallel, consumers that attach great importance to such practice
significantly differ from the rest, in terms of the importance they attach to salespeople & omnichannel
integration criteria, in order to purchase offline. In contrast, there weren’t found statistically signifi-
cant differences in terms of the importance they attach to online & offline store atmosphere. Neverthe-
less, the importance attached to online store atmosphere is high among mobile retailing app assisted
shoppers. Drawing on these results, the study provides feedback to retail entrepreneurs regarding the
optimal design and features of the future physical retail store.
Keywords: Omnichannel Retailing, Mobile Apps, Consumer Behaviour, Showrooming, Store Atmos-
phere.
1 Introduction
It is evident that smartphones have become an important part of everyday life. They could be de-
scribed as life companions, since users seem to integrate them into their daily activities
1
. This phe-
nomenon can be attributed to several factors. First of all, having internet access at all times provides
value added services that enhance the users’ physical activities. In addition to this, users are able to
1 http://www.emarketer.com/Article/Smartphones-In-Store-Shopping-Companions/1010800
Lazaris et al. / Mobile Apps for Omnichannel Retailing
Ninth Mediterranean Conference on Information Systems (MCIS), Samos, Greece, 2015 2
take advantage of hardware-assisted internet features that smartphones provide them, such as sensors,
location based services and cameras. Finally, the combination of always-on internet access and hard-
ware supported features is complemented with new user interfaces that outperform the conventional
web environment experience provided by modern web browsers: the mobile applications (apps) GUI.
Mobile apps seem to be the driving force of smartphones, creating ecosystems that engage users and
influence their behaviour.
From a retailing perspective, smartphones play an important role integrating retailing channels, blend-
ing online with offline, since consumers inside physical stores are at the same time mobile-assisted
online shoppers. Multichannel retailing transforms into a complex, diversified form of retailing, re-
cently characterized as “omnichannel retailing” or “omniretailing”. On the one hand, reports
2
show
that physical retail stores will continue to be consumers’ preferred point of purchase and that online
sales will only account for a small portion of total sales. While there are several reasons for that fore-
cast, an obvious one is the clear superiority of the physical environment in comparison to the online
one: it attracts more physical senses, with tactile being the most important one, according to lab ex-
periments (Spence & Gallace, 2011).
Conversely, e-commerce provides unique benefits to shoppers that are absent from the physical store.
Online features such as instant price comparison, fast checkouts, recommender systems and product
reviews accessibility are quite popular in e-tailing. In early m-commerce era, such practices were im-
possible or difficult to perform within the physical store since mobile phones were not smart enough
(software and hardware-wise) for consumers to take fully advantage of them. Whilst smartphones’
hardware specifications continually evolve along with innovative software features in the form of mo-
bile apps, they provide consumers with a convenient access to the online retailing environment, inside
the physical one, transforming them into “omnishoppers”.
The purpose of this study is to explore the consumers’ retailing mobile app & in-store internet penetra-
tion within the physical store. Furthermore, the study attempts to clarify which omniretailing tech-
nologies & practices are most important for mobile app assisted in-store internet users. Finally, con-
sumers’ preferences are analyzed providing feedback for retailing entrepreneurs that are interested in
designing the future retail store and enhancing it with omniretailing features.
2 Literature Review, Research Hypotheses and Methodology
At first, internet services were meant to be utilized by the use of internet browsers. Early browsers
were optimized for the desktop environment, whereas web content consisted mainly of document files
(html) and few multimedia elements (e.g. images, audio). Next, the Web 2.0 era emerged as the inter-
net experience included dynamic web pages, asynchronous network communication and richer con-
tent, converting the web into an application environment (Mikkonen & Taivalsaari, 2011). Since the
introduction of the mobile as a new internet access device, several attempts were made in order to
transfer the internet experience on the move. Early mobile phones featured small, non-touch screens
and low hardware specs which resulted in poor internet browsing, which followed the WAP protocol.
As the devices became more powerful and their screens improved, both in size and in quality, the
internet experience could be offered by html browsers. Still, usability remained an issue, since small
keyboards, or even touch pens could not provide efficient and effective human-computer interaction.
It was the advent of the Apple iPhone that brought true revolution to this domain. The success of the
iPhone was not only because of its superior hardware (capacitive multi-touch screen, sensors, etc), but
also because of the mobile apps ecosystem it introduced, creating the true smartphone. Users were
now able to benefit from online services not only by using the internet browser, but also by utilizing
2 http://www.emarketer.com/Article/Retail-Sales-Worldwide-Will-Top-22-Trillion-This-Year/1011765?ecid=1001
Lazaris et al. / Mobile Apps for Omnichannel Retailing
Ninth Mediterranean Conference on Information Systems (MCIS), Samos, Greece, 2015 3
mobile applications, downloaded from the application store of the platform. Mobile apps soon became
a strong alternative to web sites, and the latter also became mobile friendly. Nowadays, online services
can be encountered in the following variants in smartphones: Standard web site (desktop version),
Web site featuring responsive design (desktop version adapts to smaller screens), Mobile web site
(separate from the desktop version), Native mobile app and Hybrid mobile app (web pages wrapped
into native mobile app). Overall, the key differences between mobile app and web site implementa-
tions are the following:
Mobile apps run compiled code, written in C & Objective C (Apple iOS), Java (Google Android),
.NET (Windows Phone), whereas mobile web sites typically utilize mobile frameworks, running
interpreted code (Charland & Leroux, 2011). Therefore, mobile apps perform faster than mobile
web sites (Huy & van Thanh, 2012).
Mobile apps are device-specific and difficult to implement and maintain, mobile web sites, on the
other hand, are typically cross-platform and can be instantly updated (Wisniewski, 2011).
Mobile apps offer deep mobile OS integration (e.g. alerts and notifications), featuring specific APIs
that access device hardware (sensors, cameras, gps, etc) directly, whereas web sites have limited
hardware API support, although HTML5 seems to gradually adapt to this situation (Charland &
Leroux, 2011; Wisniewski, 2011).
Mobile apps provide superior user interface (Charland & Leroux, 2011), suitable even for one-
handed operation, featuring hardware acceleration and customized software buttons and gestures,
web sites, conversely, rely on the web browser interface in order to interact with the user.
Based on the previous differences, it can be assumed that for retailing purposes mobile apps seem to
be a more suitable choice for consumers in-store, since they can assist users with more natural interac-
tion (e.g. augmented reality, camera-based product recognition), less clicks featuring one-hand opera-
tion (retailing optimized interface), employing more hardware functions (e.g. sensors, bluetooth), and
with faster response (Wisniewski, 2011; Mikkonen & Taivalsaari, 2011). In addition to this, mobile
apps seem to be the most appropriate way to seamlessly integrate online & offline features, due to di-
rect hardware API & OS support (Huy & van Thanh, 2012). In fact, this could be the key point achiev-
ing omnichannel state within the physical store.
Omnichannel stems from the latin word omnis (meaning: all, everything) and it was first introduced by
practitioners in order to differentiate from multichannel. The concept was that consumers utilize retail-
ing channels simultaneously and not just in parallel (Parker & Hand, 2009; Ortis & Casoli, 2009). In
academic literature, it was first encountered by Rigby (2011, p.4) who defined omnichannel retailing
as “an integrated sales experience that melds the advantages of physical stores with the information-
rich experience of online shopping”. Besides, Omniretailing was introduced as “a coordinated mul-
tichannel offering that provides a seamless experience when using all of the retailer’s shopping chan-
nels” (Levy, et al., 2013, p.67). Recently, Fairchild (2014, p.1) states that “omnichannel commerce
involves combining traditional commerce with online commerce by integrating processes in a harmo-
nious and complementary way throughout the organizational and IT chain, and includes external logis-
tics partners in these processes”. Finally, recent omnichannel-specific literature poses specific mention
to mobile apps and the mobile channel referring to it as a “disruptive change in the retail environment”
(Verhoef et al., 2015). Consequently, omnichannel includes several aspects of retailing ranging from
the consumer point to the retailer or even the whole supply chain. In the remaining of this study we
attempt to explore the consumers’ perspective regarding the simultaneous use of channels, inside the
physical retail store, utilizing mobile apps as the key integrating technology of the online & offline
environment.
There are several studies & reports that depict consumers’ omniretailing practices within the physical
store. Some refer to them as mobile-assisted shoppers (Luo et al., 2014; Quint et al., 2013) focusing on
Lazaris et al. / Mobile Apps for Omnichannel Retailing
Ninth Mediterranean Conference on Information Systems (MCIS), Samos, Greece, 2015 4
consumers’ efforts to assist themselves in-store. Other focus on the online practices they use (Wurm-
ser, 2014; Lazaris et al., 2015) and there are also other studies that attempt to explain their behaviour
(Agrebi & Jallais, 2015; Lazaris et al., 2014). All studies and reports agree that mobile plays an impor-
tant role in in-store shopping and that there is a growing percentage of consumers that adopts omnire-
tailing practices (Adobe, 2013). Following these directions, our initial research question is whether
consumers utilize not only mobile internet in-store but also mobile apps. Also, if there is a relationship
between in-store internet frequency of use and retailing mobile apps frequency of use by shoppers.
Related literature shows that enjoyment, behavioural intention to use mobile internet, educational
level, subscription of a flat rate and ease of use are correlated with mobile internet usage criteria (Ger-
pott & Thomas, 2014). Ease of use is attributed to mobile apps (Charland & Leroux, 2011) and there-
fore we could assume that apps correlate with mobile internet usage criteria. In addition to this, it was
found that mobile apps increase internet traffic to the provider’s corresponding mobile website, and
therefore mobile internet use (Xu et al., 2014). For that reason, if mobile apps were used in-store, it
would also lead to increased in-store internet use. Therefore, the following research hypothesis is for-
mulated:
H1: There are statistically significant differences between shoppers with different levels of retailing
mobile app frequency of use, in terms of their in-store Internet frequency of use levels.
An aftermath of this hypothesis is what retailing-assisting mobile app users want to do with internet
in-store. Previous insights suggest that they want to engage in omniretailing practices utilizing e-
commerce technologies that they are familiar with from the online channel, seeking for the omnichan-
nel experience. But which online practices & technologies are most important for them? In a recent
related business report, several omniretailing practices inside physical store are presented, with price
comparison appearing to be the most favourite (Wurmser, 2014, p.11). The report referred to price
comparison in-store, in relation to showrooming. Quint et al. (2013) were among the first that pre-
sented this topic in a report entitled: “Showrooming and the Rise of the Mobile-Assisted Shopper”,
where they also enlist other accompanying consumer practices. Showrooming, was only recently de-
fined in academia by Rapp et al. (2015, p.360) as “a practice whereby consumers visit a brick-and-
mortar retail store to (1) evaluate products/services firsthand and (2) use mobile technology while in-
store to compare products for potential purchase via any number of channels”. The study investigated
the role of the salesperson towards this behaviour. Similarly, Luo et al. (2014) examined the show-
rooming intention of mobile-assisted shoppers in a multichannel retailing environment, regarding it as
an important phenomenon, with pricing and employee knowledge competency to play an important
role in it. At the same time, Willmott (2014) presented several statistical findings and reports that
showrooming goes mainstream among mobile shoppers as a common practice.
Nonetheless, price comparison was also a favourite online practice years ago, when Burke (2002) in-
vestigated 128 different aspects of the shopping experience online & in-store, conducting a national
survey with 2.120 online users. Price comparison online was considered “must have” for 28,1% of
respondents and “should have” for 66,9% of them. Although the study incorporated in-store shopping
features, price comparison was not included among them at that time probably because neither smart-
phones nor efficient online price comparison shopping engines existed. Also, price comparison was
not included by Mahatanankoon et al. (2005) who explored consumer perception of 44 mobile applica-
tions at early days of m-commerce. Apart from price comparison and showrooming, several other re-
search papers offer recommendations about mobile app features. Zhao & Balagué (2015) provided
recommendations for branded mobile apps features and categorized them in tool-centric, game-centric,
social-centric, m-commerce centric & design centric. Similarly, Magrath and McCormick (2013) pre-
sented a product & services design m-marketing design framework depicting several features for mo-
bile fashion retail apps. Based on the previous studies and business reports, we selected 18 online
practices and technologies that are compatible with omnichannel retailing, in order to rank and explore
the importance that mobile retailing app consumers attach to them in-store. Consequently, based on
previous literature, the following research hypotheses are formulated:
Lazaris et al. / Mobile Apps for Omnichannel Retailing
Ninth Mediterranean Conference on Information Systems (MCIS), Samos, Greece, 2015 5
H2: There are statistically significant differences across a series of online practices & technologies
applied inside physical stores in terms of the importance that in-store Internet retail mobile app as-
sisted shoppers attach to them.
In addition to this, we postulate that between retail mobile app and non mobile app assisted in-store
internet shoppers significant differences exist. The reason for this is the nature of mobile apps, the ad-
ditional hardware-assisted features that they support and the overall differentiated smartphone fea-
tures, as presented above. Therefore, we also propose the subsequent hypothesis:
H3: There are statistically significant differences between retail mobile app and non mobile app as-
sisted in-store Internet shoppers in terms of the importance they attach to a series of online practices
& technologies applied inside physical stores (18 sub-hypotheses in total, as the number of online
practices & technologies).
Next, a subsequent research question emerges: if price comparison and more specifically showroom-
ing intention is high among in-store internet users, what could be done to prevent it? This subject re-
mains an open issue and literature reveals several approaches that could be followed. Chiu et al.
(2011) provided 3 effects that have an impact on cross-channel free-riding behaviour, a term similar to
showrooming: searching for product information in retail channel and then purchasing it in another
one (Chiu et al., 2011, p.1). According to their study, the “push” effect is consumers’ perceived mul-
tichannel self-efficacy that positively influences showrooming. The “pull” effect is the attractiveness
of competitor’s physical retail store, which also has a positive effect on showrooming. In other words,
a consumer may leave a store in order to purchase from another one which has more attractive store
atmosphere. Finally, the “mooring” effect, which are lock-in levels within the retailer negatively im-
pact showrooming. That is, factors that make it difficult for the consumer to switch to another retailer
(e.g. time consuming or involving complicated procedures). Nevertheless, we should mention that this
study only examined free-riding from the online channel to the offline one (research online, purchase
offline). Next, Shukla & Babin (2013) discovered that regarding store switching behaviour, hedonic
values are more important that utilitarian ones and, therefore, retailers should pay attention to the retail
store environment in order to reduce consumer defection. In contrast, Heitz-Spahn (2013) addressed
three motives to cross-channel free-riding behaviour: shopping convenience, flexibility and price
comparison. Interestingly, they discover that channel aesthetics as components of store atmosphere,
although important, do not influence retailer & channel choice and therefore showrooming. They also
suggested that utilitarian motives (e.g. pricing) are more important than hedonic ones (e.g. design, er-
gonomics) towards this issue. They also proclaimed that mobile applications are turning to be a sig-
nificant research direction towards this area.
At this point it should be noted that store atmosphere notion is applicable both online and offline, with
different components and definitions characterizing it throughout the years. Eroglu & Machleit (1993),
reported that store atmospherics consist of “all of the physical and non-physical elements of a store
that can be controlled in order to enhance (or restrain) the behaviors of its occupants, both customers
and employees”. In parallel, Dailey (2004, p.796) stated that a web atmospheric cue is “comparable to
a brick-and-mortar atmospheric cue and can be defined as any web interface component within an in-
dividual’s perceptual field that stimulates one’s senses”. In fact, atmospherics also extend to the mo-
bile domain in the form of m-atmospherics (Manganari et al., 2007).
Only recently, Pantano & Viassone (2015) considered store atmosphere & channels availability to im-
pact purchase intention. These factors are also found to affect service quality perception, which is also
affected by technology and/or salesperson interaction. The study concluded indicating that consumers
evaluate all channels simultaneously and therefore retailers should integrate them seamlessly through
the use of mobile technologies such as iBeacon, mobile apps and smartphones. In fact, they suggested
that multichannel integration is the right step towards avoiding cross-channel free riding behaviour.
Also, regarding multichannel integration, Zhang & Oh (2013) exploring customer switching behavior,
proposed that retailers should focus on providing innovative cross-channel services in order to retain
customers and enhancing service convenience. As far as service is concerned, Monteleone & Wolf-
Lazaris et al. / Mobile Apps for Omnichannel Retailing
Ninth Mediterranean Conference on Information Systems (MCIS), Samos, Greece, 2015 6
erseberger (2012) suggested that although pricing is an important showrooming aspect, store associ-
ates and in-store assisting technologies play important role as well. Correspondingly, Rapp et al.
(2015) elaborated on the relationship between showrooming and the salesperson and found out that
retailers should invest in salesperson-consumer interaction through specific strategies and behaviours.
In our case, we selected four criteria for consumers in order to purchase from a physical store, based
on the previous showrooming-related factors: conventional store atmosphere, online store atmosphere,
service support by salespeople utilizing sales supporting electronic technologies and a store’s mul-
tichannel integration in order to create a seamless shopping experience. Store atmosphere in both off-
line & online variants was included, since in our case we investigate the omniretailing environment.
The effect of retail salesperson was empowered with electronic technologies, in order to test omnire-
tailing effects to him, too. It should be noted that although salespeople could be regarded as part of the
conventional store atmosphere (human factor), in our case we examine them separately. The reason is
twofold: to test human (e.g. personal selling techniques) vs environmental atmospheric effects and to
separately examine the combination of human-technology effects on consumers’ preferences. Om-
nichannel effects to showrooming were also incorporated as a criterion, based on its definition: a mul-
tichannel integration in order to create a seamless shopping experience (Levy, et al., 2013, p.67). As a
result, our hypothesis is formulated as follows:
H4: There are statistically significant differences between in-store internet users with different levels
of showrooming intention, in terms of the importance they attach to conventional (H4.1) & online
store atmosphere (H4.2), salespeople (H4.3) & omnichannel integration (H4.4) criteria in order to
purchase from the physical store.
Finally, it would be intriguing to discover if there are any differences regarding retailing mobile app
and non app shoppers in relation to the previous offline purchase intention criteria. In other words, if
retail mobile app assisted in-store internet shoppers attach more importance to each of these criteria in
order to purchase from the physical store, in relation to non mobile app assisted in-store internet shop-
pers. That could be attributed to the enhanced mobile app UI and features, which could make these
users to differ in terms of the previous criteria in relation to the others. Therefore, the following hy-
pothesis could be originated:
H5: There are statistically significant differences between mobile app and non mobile app assisted in-
store internet shoppers, in terms of the importance they attach to conventional (H5.1) & online store
atmosphere (H5.2), salespeople (H5.3) & omnichannel integration (H5.4) criteria in order to pur-
chase from a physical store.
For testing the research hypotheses, the study employs an exploratory quantitative empirical research
design that took place in Greece in November 2014, in the context of an annual ELTRUN - The E-
Business Research Center eCommerce survey. The data collection instrument of the national survey
was an online questionnaire which received 815 valid answers from Internet users. The questionnaire
was created in the Google forms platform and internet users were invited to participate via e-mail
campaigns, display banners on popular Greek news sites & e-shops and social media. Questions in-
cluded frequency of internet use at various channels, retailing-assisting mobile app utilization, as well
as questions regarding 18 omniretailing practices & technologies within the physical store. These om-
niretailing practices & technologies were sorted according to the shopping process encounter, i.e. from
the store entrance to the store checkout. Finally, they were asked about the importance they attach to
the four aforementioned criteria, in order to purchase from a physical store. Statistical analysis was
performed using SPSS version 20, and its outputs are presented and discussed at the following sec-
tions.
3 Findings And Discussion
Descriptive statistics confirm the forecast that was made back in 2011 that mobile internet will surpass
desktop internet usage by 2014 (Wisniewski, 2011): 86% choose mobile phones for internet utiliza-
Lazaris et al. / Mobile Apps for Omnichannel Retailing
Ninth Mediterranean Conference on Information Systems (MCIS), Samos, Greece, 2015 7
tion, whereas 78% use desktops. It was also found that 80% of retailing mobile app users use internet
in-store and 46% of them do it often. What’s more, 70% of in-store internet users use retailing mobile
apps in order to assist their purchases & 56% of them attach great importance to in-store retail-
assisting mobile app or mobile sites. Besides, 39% use them often, whereas 31% rarely. It seems obvi-
ous that they use them along with internet inside physical stores, in order to facilitate shopping, and
60% of them respond that they attach great importance to them. Our initial research question is par-
tially answered by the previous descriptive statistics. Retailing mobile app consumers definitely utilize
them in-store. However, in order to validate hypothesis H1 additional statistical tests should be ap-
plied. Specifically, ANOVA was performed in order to test whether there are significant statistical
differences between shoppers with different frequency of retailing mobile apps use, in terms of their
in-store internet use. Shoppers were separated into three groups in order to perform the test: Group 1:
Non-app users, Group 2: Rare mobile retailing app users, Group 3: Frequent mobile retailing app us-
ers. Frequency of in-store internet use was measured on a 5-point likert scale. Descriptive statistics of
these groups are shown in Table 1.
N
Mean
Std. Deviation
Std. Error
95% Confidence Interval
for Mean
Minimum
Maximum
Lower Bound
Upper Bound
Non
264
2,080
,9419
,0580
1,965
2,194
1,0
4,0
Rare
285
2,102
,9606
,0569
1,990
2,214
1,0
4,0
Frequent
266
2,733
,8469
,0519
2,631
2,835
1,0
4,0
Total
815
2,301
,9658
,0338
2,234
2,367
1,0
4,0
Table 1: Descriptives of the Frequency of Internet use inside Physical Stores
ANOVA results (Table 2) reveal that the null hypothesis is rejected (p value < .05), and that signifi-
cant statistical differences exist only between frequent user group and all the others (Table 3).
Sum of Squares
df
Mean Square
F
Sig.
Between Groups
73,922
2
36,961
43,786
,000
Within Groups
685,428
812
,844
Total
759,350
814
Table 2: ANOVA for Groups of Different Frequency of Internet use inside Physical Stores
Dependent Variable: Frequency of Internet use inside Physical Stores - Tukey HSD
(I) Frequency
of Mobile Re-
tailing Apps
use
(J) Frequency
of Mobile Re-
tailing Apps
use
Mean Difference
(I-J)
Std. Error
Sig.
95% Confidence Interval
Lower Bound
Upper Bound
Non
Rare
-,0222
,0785
,957
-,206
,162
Frequent
-,6535
,0798
,000
-,841
-,466
Rare
Non
,0222
,0785
,957
-,162
,206
Frequent
-,6313
,0783
,000
-,815
-,447
Frequent
Non
,6535
,0798
,000
,466
,841
Rare
,6313
,0783
,000
,447
,815
Table 3: ANOVA Post Hoc Multiple Comparisons Test
Lazaris et al. / Mobile Apps for Omnichannel Retailing
Ninth Mediterranean Conference on Information Systems (MCIS), Samos, Greece, 2015 8
Figure 1: Frequency of Mobile Retailing Apps - In-Store Internet Use Plot
Drawing from the ANOVA results and also Figure 1 (Frequency of Mobile Retailing Apps - In-Store
Internet Use Plot) we predict a positive correlation between mobile apps use and in-store internet use,
which is statistically significant. Therefore, we validate our hypothesis by performing a correlation
test. A Pearson product-moment correlation was run (Table 4) to determine the relationship between
shoppers’ mobile apps use and their in-store internet use, which was found to be significant (r = .273,
n = 815, p < .05). Consequently, hypothesis H1 is accepted.
Frequency of Internet use
inside Physical Stores
Frequency of Mobile Re-
tailing Apps use
Frequency of Internet use
inside Physical Stores
Pearson Correlation
1
,273
Sig. (2-tailed)
,000
N
835
815
Frequency of Mobile Retail-
ing Apps use
Pearson Correlation
,273
1
Sig. (2-tailed)
,000
N
815
965
Table 4: Pearson Correlation for Frequency of Mobile Retailing Apps - In-Store Internet Use
In order to test hypothesis H2, we compared the 18 online practices & technologies inside the physical
stores separating them at 18 groups, while performing ANOVA between them, in terms of the impor-
tance that in-store Internet retail mobile app assisted users attach to them. In this way not only we will
grade them in terms of mean scores, but also we can identify significant statistical differences between
them.
Online practices & technologies applied inside physical stores
Mobile Retailing
App Consumers
Means
Non App
Consum-
ers Means
Importance
attached by
consumers
16. Fast electronic checkouts without queues
4,27
3,96
3. Ability to buy in-store with internet prices, as a result of an elec-
tronic check-in in the physical store
4,20
3,99
6. In-store price comparison, which could lead to showrooming
4,09
4,11
1. Free in-store wifi
3,91
3,64
11. Product stock electronic availability
3,89
3,83
Lazaris et al. / Mobile Apps for Omnichannel Retailing
Ninth Mediterranean Conference on Information Systems (MCIS), Samos, Greece, 2015 9
15. Product electronic search & map navigation to them
3,82
3,75
4. Special prices, coupons, offer alerts at the store’s entrance
3,80
3,93
12. Loyalty points electronic access
3,67
4,01
14. In-store location-based offers
3,59
3,76
8. Access to user opinions, product presentations & reviews
3,57
3,28
18. In-store retail-assisting mobile site (accessible via wifi) or mobile
app
3,55
3,53
17. Mobile payments
3,52
3,11
5. Access to electronic profile & purchase history
3,46
2,95
13. Self-service assisting technologies
3,43
3,17
7. Electronic recommender systems
3,28
2,51
9. Product/service posts and comments on social networks
3,13
2,65
10. Email send & receive
3,06
2,51
2. Electronic check-in in the physical store (e.g. via wifi, foursquare,
swarm, facebook, etc)
2,83
2,51
Table 5: Mean Ranking of Importance attached to Online Practices & Technologies Applied In-Store
by Mobile Retailing App Consumers Vs Non App Consumers
The ranking of these practices and technologies according to their mean scores is presented in Table 5.
The three most preferred ones are “Fast electronic checkouts without queues”, the “Ability to buy in-
store with internet prices, as a result of an electronic check-in in the physical store” and “In-store price
comparison, which could lead to showrooming”. It should be noted that they are the only ones with
mean scores above 4 in the 5-point likert scale and that ANOVA post-hoc comparison showed that
there are no significant statistical differences between them. In contrast, significant statistical differ-
ences do exist between these three practices and all the rest. The “Ability to buy in-store with internet
prices, as a result of an electronic check-in in the physical store” also depicts consumers’ price sensi-
tivity, which strikingly elevates the “Electronic check-in in the physical store” feature from the last
place (score: 2,83) to the second one (score: 4,20). In addition to this, we calculated the percentage of
consumers that attach great importance (over 4 points at the 5-point likert scale) to the previous prac-
tices. It appears that “In-store price comparison, which could lead to showrooming” now comes first,
surpassing the other two (Table 6). All these results advocate that hypothesis H2 is accepted.
Next, we aim at exploring hypothesis H3, that is whether the previous results differ in terms of
whether the users utilize mobile apps for retailing or not. Our test sample consisted only in-store inter-
net users, therefore it would be interesting to explore if web-only users have the same technology
preferences with mobile app users. Table 5 depicts their preferences (mean scores) by comparison. We
performed an independent samples t-test which showed that null hypothesis is rejected for 12 sub-
hypotheses. As a result there are significant statistical differences between mobile app and non mobile
app assisted in-store internet shoppers, in terms of the importance they attach to these 12 online prac-
tices & technologies applied inside physical stores. “In-store price comparison, which could lead to
showrooming” practice ranks first among non retailing mobile app consumers and wasn’t among the
12 ones supported by our hypotheses. Overall, the practices that didn’t show significant statistical dif-
ferences, and thus the related sub-hypotheses were rejected, were the following:
1. In-store price comparison, which could lead to showrooming
2. Product stock electronic availability
3. Product electronic search & map navigation to them
4. Special prices, coupons, offer alerts at the store’s entrance
5. In-store location-based offers
6. In-store retail-assisting mobile site (accessible via wifi) or mobile app
Lazaris et al. / Mobile Apps for Omnichannel Retailing
Ninth Mediterranean Conference on Information Systems (MCIS), Samos, Greece, 2015 10
Therefore, these practices are considered by both retail mobile app and non retail app customers to be
of equal high importance. Furthermore, half of these technologies feature location-based services (#3,
#4, #5). It seems that the attached importance to these services is equal to both user groups, although
only mobile app consumers have full access to them (e.g. iBeacon, gps). This finding shows that these
technologies should be implemented into both technology approaches (#6 verifies that, too). On the
other hand, there is low availability of these types of apps in the application stores. Therefore, non app
consumers may utilize apps if retailing apps that lie in these categories are available, since they share
the same preferences for them as the other mobile app consumer group.
Mobile Retailing
App Consumers
Percentage
Non App Con-
sumers Percent-
age
Percentage
Differences
6. In-store price comparison, which could lead to
showrooming
83%
89%
-6%
16. Fast electronic checkouts without queues
81%
67%
14%
3. Ability to buy in-store with internet prices, as a
result of an electronic check-in in the physical store
76%
78%
-1%
11. Product stock electronic availability
71%
71%
0%
4. Special prices, coupons, offer alerts at the store’s
entrance
66%
76%
-10%
8. Access to user opinions, product presentations &
reviews
63%
44%
18%
1. Free in-store wifi
62%
53%
9%
12. Loyalty points electronic access
60%
67%
-6%
15. Product electronic search & map navigation to
them
59%
63%
-4%
18. In-store retail-assisting mobile site (accessible via
wifi) or mobile app
56%
63%
-7%
14. In-store location-based offers
54%
58%
-5%
17. Mobile payments
51%
39%
12%
5. Access to electronic profile & purchase history
49%
39%
10%
13. Self-service assisting technologies
49%
34%
15%
7. Electronic recommender systems
45%
22%
22%
10. Email send & receive
38%
6%
32%
9. Product/service posts and comments on social net-
works
35%
23%
12%
2. Electronic check-in in the physical store (e.g. via
wifi, foursquare, swarm, facebook, etc)
33%
22%
11%
Table 6: Percentage of Consumers that attach great importance to the Online Practices & Technolo-
gies Applied In-Store
Finally, we calculated the percentage of non retail app users that regarded each technology of utmost
importance (4 & 5 in the 5-point likert scale of preference). At Table 6 we rank these preferences, in
comparison with mobile app users. In-store price comparison, which could lead to showrooming gath-
ers the highest percentage of the sample that consider it to be of utmost importance, highest than re-
tailer mobile app users (89% vs 83%). In addition to this, non app consumers score higher than mobile
app ones regarding “In-store price comparison, which could lead to showrooming” in the mean scores
(Table 5), which is striking since mobile apps feature easier price comparison techniques, e.g. through
camera barcode recognition. That could be attributed to either low performance of mobile apps in this
Lazaris et al. / Mobile Apps for Omnichannel Retailing
Ninth Mediterranean Conference on Information Systems (MCIS), Samos, Greece, 2015 11
category (e.g. troublesome barcode recognition) or higher desire for appropriate apps by non app con-
sumers because they do not have them available.
Finally, we observe that “Email send & receive” & “Electronic recommender systems” are the two
technologies with the highest differences in percentages of users that regarded each technology of ut-
most importance (32% & 22% respectively). The differences are in favour of retailing mobile app con-
sumers, which, in the case of email, indicates that these users attach more importance to checking
emails via apps in-store than the others, since apps provide push mechanism though the OS, which is
more efficient. However, email activities rank 17
th
at our standings. The higher percentage of user
preference to “Electronic recommender systems” probably indicates that it is a feature best imple-
mented through apps, since it involves more complicated functionalities and UI.
Next, drawing from our results regarding in-store price comparison, which could lead to showroom-
ing, we aim at testing hypothesis H4 regarding showrooming intention. For this purpose we performed
an independent samples t-test between the respondents group that attach high importance to in-store
price comparison, which could lead to showrooming and those that don’t. Results show that in-store
internet users that attach great significance to in-store price comparison, which could lead them to
showrooming, consider service support by salespeople utilizing sales supporting electronic technolo-
gies (H4.3) and a store’s multichannel integration in order to create a seamless shopping experience
(H4.4) more important than those that don’t attach great significance to it (Table 8). Salespeople utiliz-
ing sales supporting electronic technologies is considered to be the most important (Table 7). In con-
trast, there are no statistically significant differences between these consumer groups in terms of the
importance they attach to online & offline store atmosphere in order to purchase from a physical store
(Table 8). Therefore, sub-hypotheses H4.1 & H4.2 are rejected.
In-store price comparison, which
could lead to showrooming
N
Mean
Std. Devia-
tion
Std. Error
Mean
Store’s conventional atmosphere
>= 4,0
523
3,780
1,1225
,0491
< 4,0
78
3,885
,6026
,0682
Service support by salespeople utilizing
sales supporting electronic technologies
>= 4,0
523
4,340
,9091
,0398
< 4,0
88
3,886
,8767
,0935
Online store’s atmosphere
>= 4,0
523
3,975
1,1130
,0487
< 4,0
88
4,000
,6781
,0723
Multichannel integration in order to
create a seamless shopping experience
>= 4,0
523
4,036
,9926
,0434
< 4,0
78
3,756
,9828
,1113
Table 7: Descriptives of offline purchase intention criteria between in-store internet users with differ-
ent levels of showrooming intention
t-test for Equality of Means
t
df
Sig.
(2-
tailed)
Mean Differ-
ence
Std. Error
Difference
95% Confidence Interval of
the Difference
Lower
Upper
Store’s conven-
tional atmos-
phere
Eq. var.
assumed
-,805
599
,421
-,1045
,1299
-,3595
,1505
Eq. var. not
assumed
-1,243
170,572
,215
-,1045
,0841
-,2704
,0614
Service support
Eq. var.
assumed
4,356
609
,000
,4540
,1042
,2493
,6587
Lazaris et al. / Mobile Apps for Omnichannel Retailing
Ninth Mediterranean Conference on Information Systems (MCIS), Samos, Greece, 2015 12
by salespeople
utilizing sales
supporting elec-
tronic technolo-
gies
Eq. var. not
assumed
4,470
120,674
,000
,4540
,1016
,2529
,6550
Online store’s
atmosphere
Eq. var.
assumed
-,203
609
,839
-,0249
,1223
-,2651
,2154
Eq. var. not
assumed
-,285
177,683
,776
-,0249
,0871
-,1968
,1471
Multichannel
integration in
order to create a
seamless shop-
ping experience
Eq. var.
assumed
2,326
599
,020
,2799
,1203
,0436
,5162
Eq. var. not
assumed
2,344
101,863
,021
,2799
,1194
,0430
,5168
Table 8: T-Test between in-store internet users with different levels of showrooming intention regard-
ing offline purchase intention criteria
We also performed Pearson’s correlation for each of the aforementioned criteria, regarding showroom-
ing intention. Results reveal that there is a positive correlation between showrooming intention and
importance attached to service support by salespeople utilizing sales supporting electronic technolo-
gies in order to purchase from a physical store, which is statistically significant (r = .244, n = 611, p <
.05). In addition, there is a positive correlation between showrooming intention and importance at-
tached to a store’s multichannel integration in order to create a seamless shopping experience in order
to purchase from a physical store (r = .187, n = 611, p < .05). On the contrary, there is no positive cor-
relation between showrooming intention and importance attached to a store’s online (r = .033, n = 611,
p=0.410) & offline (r = .043, n = 611, p=0.292) store atmosphere in order to purchase from a physical
store.
Last but not least, regarding hypothesis H5, we perform an independent samples t-test between mobile
retailing app users and non app users regarding the same criteria of hypothesis H4. It turns out that
there are statistically significant differences between mobile app and non mobile app assisted in-store
internet shoppers, only in terms of the importance they attach to online store atmosphere (H5.2) in or-
der to purchase offline (Table 9). This result probably indicates that due to the additional and superior
UI that mobile app assisted in-store internet shoppers interact, they respond more to online atmospher-
ics, in order to purchase from the physical store. Thus, only sub-hypothesis H5.2 is accepted.
t-test for Equality of Means
t
df
Sig.
(2-
tailed)
Mean
Differ-
ence
Std. Error
Differ-
ence
95% Confidence Inter-
val of the Difference
Lower
Upper
Store’s conventional
atmosphere
Eq. var.
assumed
,302
608
,763
,0289
,0957
-,1590
,2168
Eq. var.
not as-
sumed
,279
279,119
,780
,0289
,1036
-,1750
,2328
Service support by
salespeople utilizing
sales supporting elec-
tronic technologies
Eq. var.
assumed
,952
618
,341
,0774
,0813
-,0822
,2369
Eq. var.
not as-
sumed
,921
305,154
,358
,0774
,0840
-,0880
,2427
Online store’s atmos-
phere
Eq. var.
assumed
3,923
609
,000
,3663
,0934
,1830
,5497
Eq. var.
not as-
sumed
3,355
246,478
,001
,3663
,1092
,1513
,5814
Lazaris et al. / Mobile Apps for Omnichannel Retailing
Ninth Mediterranean Conference on Information Systems (MCIS), Samos, Greece, 2015 13
Multichannel integra-
tion in order to create a
seamless shopping ex-
perience
Eq. var.
assumed
,135
608
,893
,0121
,0903
-,1651
,1894
Eq. var.
not as-
sumed
,136
309,261
,892
,0121
,0891
-,1632
,1875
Table 9: T-Test between Mobile Retailing App Consumers and Non App Consumers regarding offline
purchase intention criteria
4 Implications for Retail Entrepreneurs
The present study provides several managerial implications for mobile app entrepreneurs, as well as
retail managers alike, towards developing the future retail store. Retailing mobile apps that provide
consumer assisting features are still at their infancy. Mobile app developers should focus not only pro-
viding a supplemental shopping assistant interface, but also integrate this interface with hardware fea-
tures that blend the physical and the online world seamlessly. In that way, they could provide the best
of the two worlds, creating a superior shopping environment that could deter showrooming and pro-
vide added-value services. It should be noted that some retailers have already leveraged mobile apps
as omniretailing assisting technologies either by providing in-store location based services & promo-
tions (e.g. Apple stores app, Macy’s Shopbeacon, Carrefour China app), loyalty points & social media
integration (Guess Mobile app) or augmented reality support (American Apparel).
Observing Tables 5&6 we can extract several guidelines regarding the features that omniretailing mo-
bile apps should offer. More specifically, they should offer deep integration with backend IS and POS
systems in order to facilitate fast checkouts, unified pricing and realtime stock availability. Additional
technologies such as in-store location based services are second runners, but shouldn’t be neglected,
either.
In addition to this, it seems that mobile apps could prove to be even more beneficial for store associ-
ates. Consumers value the salespeople-technology combination the most, therefore mobile apps could
empower employees in a more powerful fashion. In that case, apps could be more effective by utiliz-
ing them on tablets, in order to provide a more spacious UI. Regarding, showrooming avoidance, lit-
erature also shows that specific personal selling techniques & strategies should also be adopted by
salespeople, since technology on its own is not enough (Rapp et al., 2015). Therefore, retailers should
invest on their human capital, while transforming into omniretailers, embracing omnichannel retailing
principles and guidelines. Taking into consideration Table 6, we should advice mobile app entrepre-
neurs to offer anti-showrooming services for salespeople’s mobile devices. Indicatively, these could
include price comparison and price matching functionalities. Towards these directions, new apps,
mainly for salespeople tablets, start to emerge (e.g. Shopkeep, Entersoft Mobile Retail Sales Assis-
tant).
In sum, the future retail store should offer deep omnichannel integration, providing a 360 degree view
of the customers (e.g. incorporating universal analytics), unifying the offline and the online shopping
experience. To that end, new omniretailing software platforms were recently introduced aiming at
merging online & offline operations providing universal analytics (e.g. Euclid Analytics, Index, Re-
tailNext, Prism). This integration could additionally be assisted with the use of apps, but stores should
be also enhanced with supplemental technologies that offer location-based services (e.g. iBeacon),
efficient & beneficial electronic check-in for consumers, as well as fast electronic checkouts without
queues. Towards the last direction, the store could support mobile payments, or even eliminate check-
outs completely. In vision of that, a recent Amazon patent (Amazon, 2015), employing RFID technol-
ogy and ubiquitous video cameras, shows that the online retailer may attempt to disrupt the physical
Lazaris et al. / Mobile Apps for Omnichannel Retailing
Ninth Mediterranean Conference on Information Systems (MCIS), Samos, Greece, 2015 14
retail domain towards that direction by opening bricks ‘n’ mortar stores
3
that offer automated check-
outs
4
.
5 Conclusions, Limitations and Future Research
As E-Commerce practices are desired by consumers in the physical stores, retailing mobile apps seem
to play an important role in this behaviour, integrating retail channels. Increased use of these apps has
been found to take place in-stores, accompanied with increased use of mobile internet. Price-centric
apps dominate users’ preferences with “In-store price comparison, which could lead to showrooming”
to gather the largest percentage of them that regard it of utmost importance. Hence, showrooming in-
tention is high among in-store internet users, both retailing mobile app and non app ones. Interestingly,
for that target consumer group (high showrooming intent), service support by salespeople utilizing
sales supporting electronic technologies and omnichannel integration were found to be regarded as
more important than the group that didn’t care about showrooming. This finding leads us to believe
that apart from price-matching strategies (since these consumers seem to be price-centric), increased
importance should be placed at the role of salespeople in the physical store, as well as at omnichannel
integration strategies.
Regarding the role of salespeople, the results are consistent with related studies (Zhang & Oh, 2013;
Monteleone & Wolferseberger, 2012; Rapp et al., 2015; Pantano & Viassone, 2015) which empha-
sized on the dominant role that store associates play, coping with that emerging consumer behaviour.
In particular, Zhang & Oh (2013) stressed on the role of service support, Monteleone & Wolferseber-
ger (2012) on salespeople assisting technologies, Rapp et al. (2015) on salesperson-consumer interac-
tion and Pantano & Viassone (2015) on service quality perception as an outcome of technology and/or
salesperson interaction. Therefore, our criterion of “Service support by salespeople utilizing sales sup-
porting electronic technologies” is validated as a means of battling showrooming, since users that tend
to engage in such behaviour attach significantly more importance to it (more than any other criteria) in
order to purchase from the physical store that they have visited.
As far as the omnichannel integration criterion in concerned, that is, a store’s multichannel integration
in order to provide a seamless shopping experience, Pantano & Viassone (2015) provided empirical
evidence that it can prevent showrooming and suggested the use of channel integrating technologies to
accomplish it (iBeacon, mobile apps and smartphones). This finding is also consistent with our statis-
tical findings regarding these technologies that gather increased attention by consumers. In addition to
this, Zhang & Oh (2013) also suggested that cross-channel services lead to customer retain. Neverthe-
less, Chiu et al. (2011) found that multichannel self-efficacy positively affects showrooming; therefore
multichannel integration should be carried out cautiously. On the other hand, store atmosphere, both in
conventional and online variants, though considered important, it doesn’t attract consumers with
showrooming intention more than the others. However, these results are not consistent with Shukla &
Babin (2013), Pantano & Viassone (2015) and Chiu et al. (2011) findings indicating that store atmos-
phere affects showrooming. On the contrary, they are in line with Heitz-Spahn (2013) claims that
channel aesthetics as components of store atmosphere do not influence cross-channel free-riding be-
haviour. However, they propose that this behaviour could be fought with appropriate mobile applica-
tions.
3 http://www.theguardian.com/technology/2015/feb/03/amazons-first-store-opens-indiana
4http://www.retailwire.com/discussion/18195/could-amazons-brick-and-mortar-invention-eliminate-checkout-
lines?utm_campaign=RW%20Discussions&utm_content=13995290&utm_medium=social&utm_source=twitter
Lazaris et al. / Mobile Apps for Omnichannel Retailing
Ninth Mediterranean Conference on Information Systems (MCIS), Samos, Greece, 2015 15
Captivatingly, retailing mobile apps in-store internet users regard a store’s online store atmosphere of
utmost importance in order to purchase from that store’s physical counterpart. The importance mean
score they attach is even higher than the store’s conventional store atmosphere equivalent. What’s
more, among this consumer group and those that don’t use retailing mobile apps, there were found to
be statistical differences only regarding the online store atmosphere’s importance in order to purchase
offline. As a result, that could mean that proper mobile app atmospherics could also influence retailing
mobile app consumers’ showrooming intention in-store.
The study encloses several limitations that are mainly attributed to the research setting and method.
First of all, our sample consisted of solely internet users and, therefore, our results cannot be general-
ised to the whole population, who may not be interested in online practices within physical stores. In
addition to this, consumers were asked which practices & technologies they considered most important
in-store and not which they actually employ. The reason for that was the availability of most practices
& technologies, which were too advanced at that time for stores to support them, especially in the
form of mobile apps. For that reason, consumers could respond differently if they had actual experi-
ence of them in the conventional shopping environment. Last but not least, to the best of our knowl-
edge, the store atmosphere notion has not been transferred to the mobile apps domain. While web at-
mospherics components could be applied to mobile web one with little modifications (Manganari et
al., 2007), mobile apps, as discussed, provide features unique to the online world that may influence
our online atmosphere-related results.
In order to verify and expand our findings, researchers are encouraged to employ experimental design
approaches in real physical stores, in order to test omniretailing effects in practice. Field experiments
should definitely exploit the use of retailing mobile apps, since they are the most suitable choice to-
wards blending physical with virtual experiences. Also, the interplay of multiple atmospheric cues,
both online & offline, through omnichannel integration remain unexplored. Hence, in would be in-
triguing to explore consumer behaviour and the showrooming phenomenon specifically, inside the
future retail store, where the Omnichannel Retailing Store Atmosphere is present.
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