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The path-to-purchase is paved with digital opportunities: An inventory of shopper-oriented retail technologies


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This study focuses on innovative ways to digitally instrument the servicescape in bricks-and-mortar retailing. In the present digital era, technological developments allow for augmenting the shopping experience and capturing moments-of-truth along the shopper's path-to-purchase. This article provides an encompassing inventory of retail technologies resulting from a systematic screening of three secondary data sources, over 2008-2016: (1) the academic marketing literature, (2) retailing related scientific ICT publications, and (3) business practices (e.g., publications from retail labs and R&D departments). An affinity diagram approach allows for clustering the retail technologies from an HCI perspective. Additionally, a categorization of the technologies takes place in terms of the type of shopping value that they offer, and the stage in the path-to-purchase they prevail. This in-depth analysis results in a comprehensive inventory of retail technologies that allows for verifying the suitability of these technologies for targeted in-store shopper marketing objectives (cf. the resulting online faceted-search repository at . The findings indicate that the majority of the inventoried technologies provide cost savings, convenience and utilitarian value, whereas few offer hedonic or symbolic benefits. Moreover, at present the earlier stages of the path-to-purchase appear to be the most instrumented. The article concludes with a research agenda.
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The path-to-purchase is paved with digital opportunities:
An inventory of innovative retail technologies
Kim Willems
Pleinlaan 2 (Room C2.09), 1050 Brussels, Belgium
Agoralaan Building D, 3590 Diepenbeek, Belgium
[Corresponding author]
Kim Willems (PhD Applied Economics: Business Engineer, UHasselt & VUB) is Assistant
Professor Marketing at the VUB since October 2012. Her research pertains to retail
differentiation strategies. She studied among others environmental- and evolutionary
psychological effects of store atmospherics on customers and currently focuses her research
around HCI in retailing and particularly the customer value and returns for retail management
a strategic exploitation of the convergence between digital and physical retail channels can
entail. She has published for example in Journal of Business Research, Psychology &
Marketing and the International Review of Retail & Distribution Management.
Annelien Smolders1
Pleinlaan 2 (Room C2.12), 1050 Brussels, Belgium
Annelien Smolders (MSc Applied Economics, KULeuven) is Doctoral Researcher in
Marketing and Consumer Behavior at the VUB since September 2015. Her research pertains
to digitalization of traditional bricks-and-mortar retailing.
Malaika Brengman1
Pleinlaan 2 (Room C2.09), 1050 Brussels, Belgium
Malaika Brengman (PhD in Applied Economics, University of Ghent), is Associate Professor
Marketing and Consumer Behavior at the VUB. Her scientific research focuses on the impact
of store atmospherics and shopper motivations and behavior, in offline and online retail
contexts, with special attention to new technologies and their impact on consumer behavior.
She has presented her findings globally at numerous conferences and published her work in
Vrije Universiteit Brussel, Faculty of Economic and Social Sciences & Solvay Business School, Department
Business Marketing & Consumer Behavior
Hasselt University, Faculty of Business Economics, Department Marketing & Strategy
several well-respected international scientific journals. She is member of the ‘New Store’
expert panel of ‘Shopping Tomorrow’ (NL) and is a renowned speaker at (inter)national
industry meet-ups on shopping of the future.
Kris Luyten
Wetenschapspark 2, 3590 Diepenbeek, Belgium
Kris Luyten is a professor in Computer Science at Hasselt University and member of the
Human-Computer Interaction lab of the iMinds research institute Expertise Centre for Digital
Media. His research interest is finding new techniques and methods to engineer and use
context-aware interactive systems. Since a few years, he is working on creating more
accessible, usable and approachable ubiquitous systems, focusing on intelligibility in its
various shapes and forms. He thinks writing a bio in third person is a bit weird and likes to
point to his website for more information on his endeavours in research and teaching:
Johannes Schöning3,4
Wetenschapspark 2, 3590 Diepenbeek, Belgium
Johannes Schöning is a professor in Computer Science with a focus on HCI at Hasselt
University, working within the Expertise Centre for Digital Media (EDM) the ICT research
Institute of Hasselt University. In addition, he is a visiting lecturer at UCL London within the
Intel Collaborative Research Institute for Sustainable Cities. Before that, he was a
senior consultant at the German Research Centre for Artifical Intelligence (DFKI) within the
Innovative Retail Lab (IRL). As of October 2016, he continued as professor at Universität
Bremen (Germany), in the Faculty 03: Mathematics/Computer Science.
Hasselt University, iMinds research institute Expertise Centre for Digital Media
4 Universität Bremen, Faculty 03: Mathematics / Computer Science, Bibliothekstr. 5, MZH 5235, 28359 Bremen,
The path-to-purchase is paved with digital opportunities:
An inventory of innovative retail technologies
This study focuses on innovative ways to digitally instrument the servicescape in bricks-
and-mortar retailing. In the present digital era, technological developments allow for
augmenting the shopping experience and capturing moments-of-truth along the shopper’s
path-to-purchase. This article provides an encompassing inventory of cutting-edge retail
technologies resulting from a systematic screening of three secondary data sources, over
2008-2014: (1) the academic marketing literature, (2) retailing related scientific ICT
publications, and (3) related business practices (e.g., publications from retail labs and R&D
departments). An affinity diagram approach allows for clustering the retail technologies from
an HCI perspective and categorizing the technologies in terms of the type of customer value
that they offer, and the stage in the path-to-purchase they mainly pertain to. This in-depth
analysis results in a comprehensive inventory of innovative retail technologies that allows for
verifying the suitability of new technologies for targeted in-store shopper marketing
objectives (cf. to consult the resulting online repository, using faceted
search). The results of the analyses indicate that the majority of the inventoried technologies
provide utilitarian value, whereas few offer hedonic benefits. Moreover, at present the earlier
stages of the path-to-purchase appear to be the most instrumented. The article concludes with
a research agenda.
Retail technology, path-to-purchase, customer value, smart retailing, shopper marketing
The path-to-purchase is paved with digital opportunities:
An inventory of innovative retail technologies
1. Introduction
While online retail sales still represent a minority of the total sales across all channels,
their growth rates are exceedingly outperforming those of bricks-and-mortar stores, year after
year (U.S. Census Bureau, 2016). The continuing and explosive rise of e-commerce of about
20 percent on average each year sharply contrasts with the rather stable situation in traditional
retailing (Deloitte, 2011). As a result, many researchers devote their attention to online
shopping which is hot and ‘on’, as if bricks-and-mortar shopping is ‘off’ and on its demise.
However, despite the many merits of electronic and mobile commerce, it is unlikely that
traditional retail settings will disappear; rather both channels will complement each other in
satisfying shopper needs (Zhang et al., 2010). Nevertheless, a proper strategic response is
necessary in order for offline retailing to sustain its role.
One of the main drivers of these changes in multichannel shopping behavior, and
consequently also in optimizing shopper marketing actions, is technology (Shankar et al.,
2011). Technology has always played a role as the primary enabler of change in the evolution
of retailing (Hopping, 2000). Today, as bricks-and-mortar retailers are preparing for battle
with online merchants, there are several areas they can draw upon in order to gain a
competitive advantage (IBM, 2012). This article sheds light on the fairly underexplored topic
of the promising role of technology for traditional retailers to survive in today’s fierce
multichannel competition. Indeed, as Bodhani (2012, p. 46) suggests ‘[…] rather than
diminishing the traditional shopping experience, techniques that have been the preserve of the
online shop are to some extent now informing the new in-store retail technology’. In this vein,
Pantano & Timmermans (2014) introduced the concept of ‘smart retailing’, referring to the
use of technology in retail to improve the quality of shopping experiences. In this smart
retailing scenario, technologies are considered as ‘[…] enablers of innovation and
improvements in consumers’ quality of life’ (Pantano & Timmermans, 2014, p.103). Or how
a digital infusion in physical retailing may serve well, switching the old-school retail stores
from ‘off’ back to ‘on’.
From barcodes, over in-store kiosks, ATMs, and self-scanning, to virtual fitting rooms, the
technological possibilities for retailers to optimize their in-store experience offering are vast
and continuously increasing. After providing an appropriate theoretical background for this
study (cf. Section 2), this paper’s first empirical aim is to compose a multi-disciplinary
inventory of these kinds of advanced retail technologies, drawing from academic marketing
and IT literature as well as from developments in the field (cf. Section 4), made available via
web appendix A as well as via an easily accessible online repository of that data. The URL allows for faceted search of the inventory.
However, as Pantano and Timmermans (2014, p. 101) note, ‘the idea of smartness goes
beyond the concept of application of new technologies’. Since technology in retailing is and
should remain a means to an end rather than an end in se, the most important objective of this
article is to add to this inventory in terms of when these technologies entail potential for
augmenting the shopping experience and eventually also the retailer’s bottom line. History
has shown that trying to capitalize on technological trends without appropriate business model
evolution is a pitfall for many businesses (Rayna & Striukova, 2016). Clearly, ‘technology
can play a role in enhancing the shopping experience’ but ‘consumers are not interested in
technology for its own sake’ (Burke, 2002, p. 426-427). This study contributes to elaborating
on the conditions when it will enhance the offline shopping experience, by evaluating (1) at
what stage of the customer’s path-to-purchase the technology is likely to add value, and (2)
what type of consumer value the technology foremost entails. Section 5 offers a summary of
this exercise, guided by affinity diagram clustering methodology, and supported by inter-rater
reliability evaluations. All this information is made available through an online repository
offering faceted search, thus allowing for directed search for technologies that are suitable for
a given context and set of expectations (cf. After conducting and
discussing a deep analysis of the current situation in retail technologies (cf. Section 6), a
research agenda is compiled with specific avenues for future research on shopper marketing
and advanced technologies for smart retailing, at the intersection of both the marketing and
the IT bodies of research, conclude this paper (cf. Section 7). As such, this paper serves as a
starting point for smart retailing research.
2. Theoretical background
2.1 Fighting fire with fire: Offline retailing digitally trading-up
Bricks-and-mortar retailers face an erosion of their sales productivity as they struggle to
redefine their role in a multichannel world. In order to avoid that consumers shift their
purchases even more online, they do need to find a way to create a differentiating value
proposition that exploits the physical store advantages in a more compelling way (IBM, 2012;
Rapp et al., 2015; Pantano & Viassone, 2015).
Trying to make the difference in terms of price may no longer be the best idea. The current
trend of retailers shifting their focus towards prices, especially in recent times of economic
crisis, has led to a predominantly price-based competition, undermining the retailer’s
profitability and resulting in ‘commoditization’ of retail outlets (Corstjens & Corstjens, 1995;
Rigby & Vishwanah, 2006). Moreover, online shops have an operational cost-advantage over
traditional retailers (cf. cost savings on physical store space, frontline employees, stock
management and so on). Furthermore, the Internet offers an unprecedented price transparency
that only rarely identifies the offline player as being the cheapest. So, price competition is a
battle that traditional retailers can hardly win against their online counterparts.
Another potentially relevant basis to differentiate physical service encounters from online
shopping trips is the face-to-face interaction with friendly, knowledgeable sales people (cf.
SERVQUAL e.g., Parasuraman, Zeithaml, & Berry, 1988). However, with the advent of
technology infusion in service encounters, retailers have moved from traditional interpersonal
service encounters to non-interpersonal service encounters where consumers can often
perform the service themselves through ‘self-service technologies’ (cf. Hilton et al., 2013;
Meuter et al., 2000). As such, the ubiquity and sophistication of new information and self-
service technologies have fundamentally altered and are continuing to alter the way in which
retail organizations interact with their customers (Froehle & Roth, 2004; Pantano and
Timmermans, 2014).
As a result, ‘[…] retailers increasingly realize the benefits of utilizing the store itself as a
means of marketing, i.e., the concept of shopper marketing,’ (Ligas & Chaudhuri, 2012: 2).
Shankar et al. (2011) suggest two unique characteristics that physical retailers can still exploit
in their quest of creating a competitive advantage over online channels by means of shopper
marketing, namely in-store atmospherics on the one hand and in-store merchandising
innovations on the other hand. First, as Eroglu et al. (2001) note, online retailing is deprived
of certain sensory appeals, contributing to a higher perceived purchase risk (Lee and Tan,
2003). Second, regarding in-store merchandizing, Shankar et al. (2011) and Newell (2013)
suggest ‘technology utilization’ as a promising weapon in face of the stiff competition from
online players that allows offline retailers to ride the waves of the digital revolution that is
currently shaking up the retailing landscape. After all, as Kukka et al. (2014, p. 29) note,
‘people ever more blend their online and offline worlds into a single reality’. It is now up to
the physical retailer to follow-up on this evolution by assuming its reinvented role in the
smart cityscape, reaping the rewards of the convergence between the physical and virtual
shopping environment (Kent et al., 2015).
2.2 The strategic role of advanced technologies in retailing
On an operational level, the use of technologies in retailing is obvious (Newell, 2013;
Efendioglu, 2015). Finne and Sivonen (2008) confirm cost efficiency as a priority focus for
retailers in implementing technologies. The current advances in technologies are however also
able to enhance both consumers’ shopping experience and retailers’ performance as they
enable business competitive advantages (Pantano, 2014; Pantano & Di Pietro, 2012; Zhu et al.,
2013). Particularly advanced technologies, like virtual and augmented reality, RFID,
biometric technology, and 3D scanning entail strategic potential as they provide retailers
constantly with real-time feedback about customers, that can inspire better informed
marketing strategies (Renko & Druzijanic, 2014; Pantano & Naccarato, 2010). The strategic
importance of investing in such emerging smart retail technologies is outlined in annual
reports of pioneering retailers (see, for example, MetroGroup, 2014a; Tesco PLC, 2014).
Innovative retail technologies moreover also directly modify customer behaviour in the store
by enhancing consumers’ shopping experience and increasing satisfaction (Pantano &
Naccarato, 2010; Bharadwaj, Walker, & Hofstede, 2009), and by improving the in-store
service and increasing store appeal (Newsom, Collier, & Olsen, 2009; Liljiander et al., 2006).
A wide array of previous studies has focused in particular on self-service technologies in this
regard (cf. e.g., Weijters et al., 2007; Nilsson, 2007; Fisher et al., 2009).
Nevertheless, investments in retail technologies do not always provide the expected returns
(Sethuraman & Parasuraman, 2005). Several studies illustrate how the current retailer
strategies towards using advanced technologies at the point-of-sale do not capture their full
potential in meeting and exceeding customer expectations (Pantano & Viassone, 2012).
Varadarajan and colleagues (2010, p. 105) contend that ‘[…] a retailer with superior insights
into the potential of a new interactive technology (relative to its competitors) and/or
complementary resources may be able to effectively leverage the fullest potential of a generic
technology, and thereby achieve a competitive advantage’. The present paper contributes to
the generation of exactly those insights that may leverage advanced technologies for smart
retailing to become the basis of a competitive advantage, by examining what kind of customer
value they provide and at what stage of the shopper’s path-to-purchase they tend to be most
2.3 Retail technology as a source of customer value
This study directly responds to Burke’s (2002, p. 427) suggestion that ‘[…] it is not the
technology per se but how it is used to create value for customers that will determine
success’. So, to use technological innovations as the cornerstone of a successful business
strategy, retailers are to leverage technology in function of specific types of customer value
(Padgett & Mulvey, 2007). Customer value is a cornerstone in the marketing literature. One
of the most generally accepted definitions of customer value is the one by Zeithaml (1988, p.
14): “the consumer’s overall assessment of the utility of a product based on perception of
what is received and what is given”. In retailing, customer value tends to be considered as a
multidimensional construct. An offering can save consumers time and money (i.e., the ‘cost’
side of the value equation), and/or it can provide consumers with benefits (i.e., added value).
Basically, two distinct albeit not per se mutually exclusive types of value are common in
academic literature, namely functional versus hedonic value (cf. Babin, Darden, & Griffin,
1994), referring respectively to the utilitarian and experiential benefits that an offering
provides (i.e., does it help the shopper to achieve his goal or is it pleasant?).
2.4 Retail technology along every stage in the path-to-purchase
Shankar et al. (2011, p. 29) define shopper marketing as ‘the planning and execution of all
marketing activities that influence a shopper along, and beyond, the entire path-to-purchase
from the point at which the motivation to shop first emerges through to purchase,
consumption, repurchase, and recommendation’. As such, shopper marketing targets the
shopper while (s)he is in a shopping mode (i.e., in an active decision mode, ready to make a
choice) by influencing triggers in the shopping cycle (Shankar et al., 2011). This paper
focuses on the role of advanced retail technologies as triggers in the shopping cycle, thereby
building on Pantano and Naccarato’s (2010) suggestion that advanced technologies might
influence the customer shopping experience by affecting the different stages in the path-to-
In general, a full shopping cycle consists of 5 stages; (1) the need recognition stage, (2) the
information search stage, (3) the stage where the customer evaluates alternatives in his/her
consideration set, (4) the actual purchase stage, and (5) the post-purchase stage (Hoyer,
MacInnis, & Pieters, 2012). Advanced retail technologies can address one or more of these
stages (Pantano & Naccarato, 2010). For instance, in the need recognition stage, a technology
‘[…] can inform consumers about the new arrivals in the stores, and suggest them the
products capable to stimulate the emerging of new needs’ (Pantano & Naccarato, 2010, p.203;
Pantano, 2016), whereas in the information search stage, ‘[…] technologies become a useful
tool for consumers to achieve fast and detailed information about products in the store’
(Pantano & Naccarato, 2010, p.203).
Where manufacturers and retailers traditionally focused on increasing brand equity and
sales by influencing shoppers early in the shopping cycle (i.e., often before they even enter
the store), shopper marketing takes on a different approach by emphasizing that ‘[…]
marketing activities should be relevant to shoppers’ needs as they emerge over the entire
shopping cycle’ (Shankar, 2011, p. 27). Since retailers are both in close proximity to shoppers
and in control of the in-store environment, traditionally ‘[…] they are better able to influence
the shopper near the end of the shopper cycle (Shankar, 2011, p. 30). With the increasing
adoption of digital technologies by physical retailers, technological aspects become ever more
important elements in customers’ encounters, and this at different touch points along the
customer journey. Recent qualitative, sequential incidence research by Stein and Ramaseshan
(2016), based on 34 customer journey narratives, deduced in this regard for example a relative
presence of technology along the path-to-purchase as follows: about 45 percent pre-purchase,
36 percent in the purchase stage, and 19 percent post-purchase.
2.5 Defining the scope of retail technologies in this study
Technology in retail can encompass different environments such as point-of-sale, online or
supply chain (Renko & Druzijanic, 2014). The focus in this study is on technology at the
point-of-sale, since a substantial part of the purchase decisions are cue-prompted or
unplanned and triggered in the store (Neff, 2008). In particular, this study predominantly
examines self-service technologies or technology-mediated customer contact, where ‘[…] the
human customer service representative component of the service encounter is entirely
replaced by technology’ (Froehle & Roth, 2004, p. 3). Moreover, the present study focuses on
customer-oriented technologies, or technologies with which the customer interacts, rather than
on behind-the-scenes (back-end) technologies such as RFID in function of supply chain
management (Renko & Durzijanic, 2014). So, the emphasis is on how IT can serve as pillar of
the ‘customer intimacy’ strategy in retailing (rather than the ‘operational excellence’ strategy,
cf. Efendioglu, 2015).
Pantano and Viassone (2014) classify the most recent retail technologies in three
categories according to their technical characteristics: (1) touch screen displays/in-store
totems (e.g., ATMs or virtual garment fitting systems enabled by 3D body scanning systems;
Choi & Cho, 2012), (2) mobile applications (e.g., product comparison apps on the shopper’s
own mobile phone; Rudolph & Emrich, 2009; Bennet & Savani, 2011) , and (3) hybrid in-
store systems that users can move around with in the store only (e.g., RFID recommendation
systems, Wong et al., 2012; intelligent shopping trolleys, Black, Clemmensen, & Skov, 2009).
Given this paper’s focus on in-store shopper marketing, the empirical part (i.e., composing the
inventory of retail technologies) centers on Pantano and Viassone’s (2014) first and third
category of technologies. As such, this study discards purely mobile applications that bear no
link to the in-store shopping experience, and can thus function on a stand-alone basis. This
delineation is in line with Shankar’s (2011, p. 33) classification of social and mobile media as
being largely ‘out-of-store activities’. Both generic and proprietary technologies qualify for
potential inclusion in this study’s inventory (cf. Varadarajan et al., 2010).
2.6 Adoption and maturity of retail technologies
Retailers are usually not perceived as pioneers with innovative technologies (Hopping,
2000; Pederzoli, 2015). As Pantano and Viassone (2014, p. 43) note: ‘Despite the large
number of technologies for points of sale and the potential benefits emerging from the
introduction of these advanced systems, still only a limited number of retailers adopted them’.
Factors that tend to discourage retailers to adopt technology-based innovations are uncertainty
of consumer acceptance, an obsolescence risk, huge monetary investments, and rather long
term and difficult to measure returns on investments (Evans, 2011; Alkemade & Suurs, 2012;
Pantano, Lazzolino, & Miggliano, 2013; Zhu et al., 2013; Pantano & Di Pietro, 2012).
Without detailing on drivers of technology adoption in retailing (cf. Pantano, 2014; and
Pantano & Viassone, 2014, for more information), retail sectors or formats seem to differ in
terms of which technologies they tend to embrace. Table 1 provides an overview of Pantano’s
(2014) findings in this regard.
Table 1. Technology diffusion in retailing industries (Source: adapted from Pantano, 2014)
Number of
Amount of
Digital signage
(mainly in
Groceries and
department stores
(mainly in
mobile apps
(mainly in
Small and
frequently ad-hoc
new retailers
(mainly in
Carr (2003, p. 41) contends that ‘[…] as information technology’s power and ubiquity
have grown, its strategic importance has diminished’. However, in retailing, IT’s potential is
still not exploited to the full extent. The pace of change is accelerating, causing competitive
advantage to erode ever quicker (IBM, 2012). Although many retailers believe IT strategy and
investments should be a top priority, only one in five (18%) indicates to be presently ahead of
the curve (Deloitte, 2011). However, retail executives do recognize the importance of
incorporating emerging technologies into the next-generation stores as their intentions toward
future investments indicate (Deloitte, 2011).
3. Research objectives
This study provides a systematic overview and inventory of digital retail technologies,
tailored for retailers to make informed decisions about what technologies to select given their
specific goals and situation. As such, this research contributes to educate retailers in terms of
when and with what objective the many existing retail technologies can serve for shopper
marketing purposes. ‘In order to stay ahead, retailers must bring the best of this digital
technology into the store environment’ (Newell, 2013, p. 37), but how does one select ‘the
best’ technology? To this end, this study combines both marketing literature and literature
from computer science to inform readers beyond the currently available body of knowledge
on this subject. To optimize the managerial relevance of this retail technologies inventory, a
classification of the existing technological possibilities and innovations is performed from a
marketing perspective, bearing in mind (1) the stage in the shopping cycle in which they are
most powerful to be of influence and (2) the type(s) of customer value that they contribute to.
These insights are essential in order for retailers to leverage advanced retail technologies to
become the basis of a competitive advantage.
Starting from this study’s scope of retail technologies, the paper also aims to provide a
finer overview of how to group the inventoried technologies into clusters based on the
perspective of the consumer (or "user") of these technologies. We map technologies by
characteristics and knowledge prevalent in the human-computer interaction (HCI) domain.
HCI can be situated at the intersection of computer- and behavioral sciences and aims to
optimize the user experience when interacting with digital systems. As such, HCI is also
applicable for the shopper-oriented retail technologies in this paper. Taking on a HCI lens, a
further elaboration of the existing but fairly broad-based schemes, such as the one of Pantano
and Viassone (2014) for example, is a second objective of this paper. A more fine-grained
technical clustering allows for identifying which underlying technologies/platforms and
stakeholder interactions seem to be dominating in a given situation. Studying what common
grounds they share, enables to detect potential drivers for success as well as barriers, inspiring
further developments in the (academic) IT discipline.
An online repository of the resulting inventory of retail technologies is composed that is
searchable according to these three above mentioned facets (i.e., customer value type it offers,
stage in the shopping cycle is matters most, and HCI cluster). This repository intends to create
awareness among both scholars and retail managers of the existing retail technologies and,
more importantly, their strategic potential. Finally, a research agenda is composed in order to
further guide academic interest in the domain of smart retailing.
To sum up, the following research questions are central in this study:
RQ1: What is the current state-of-art in terms of retail technologies?
RQ2: What type of customer value can these retail technologies offer?
RQ3: At which stage of the customer journey do these retail technologies matter most?
RQ4: Can we cluster these retail technologies from an HCI perspective?
RQ5: What are research priorities to further advance the knowledge on smart retailing?
4. Research methodology
In order to compile a comprehensive inventory of technological advancements in retailing,
this study consults publications that stem from the intersection of marketing science and
computer science, as well as practitioner reports. In particular, three sources of information
have been systematically screened: (1) publications in the field of ICT for retailing as
presented in the digital library of the Association for Computing Machinery (ACM, see
paragraph 4.1), (2) publications in the marketing discipline (cf. paragraph 4.2), and (3)
working papers and vulgarizing publications available from applied retail labs, retail R&D
departments and related newsletters (cf. paragraph 4.3). This literature review covers 7 years
of relevant research, with data collection starting from the beginning of 2008 up to December
2014. The idea behind this specific timeframe comes from Foster (1986) and Johnston (2013).
In essence, Foster (1986) discovered that supplanting an old technology by a new one
generally takes between 5 and 15 years. Now, thirty years later, due to the sheer volume of
new technology releases, the pace of technological innovation even seems to further increase
(Johnston, 2013). Therefore a timeframe of seven years seems to be ideally suited for the
intended research purposes. Starting from this retail technologies inventory, a team of
researchers in the area of human-computer interaction (HCI) as well as a team of marketing
scholars have clustered the technologies by means of an affinity diagramming approach (cf.
Paragraph 4.4).
4.1 Academic literature review: IT in Retailing
In the first place, we consulted the Association for Computing Machinery (ACM, 2014)
Digital Library, which is the largest scientific computing association world-wide that provides
a vast digital library. On top of offering cutting-edge publications, this computing society also
includes conferences and workshops. The review of this literature consists in particular of a
systematical screening of the ACM Digital Library, for the timeframe 2008-2014. The search
basis consists of the following keywords: retail” OR “supermarket” OR grocery. A
relevancy screening on the resulting hits (cf. scope definition in Section 2.5) subsequently
took place in a stepwise basis, first by assessing the title, and second by examining the
abstract. Search output beyond this study’s scope is discarded for further analysis. In
summary, this literature review resulted in an output of 47 papers dealing with a total of 53
technologies retrieved from 20 ACM sources.
4.2 Academic literature review: Marketing
The next step in this systematic screening was reviewing the academic marketing literature.
In order to find relevant papers in the marketing domain about new in-store technologies, the
52nd edition of Anne Harzing’s (2014) Academic Journal Quality Guide served as a guide in
finding journals with an appropriate standard in different domains. More specifically, all the
journals in the domain of Marketing, General Management & Strategy, and Innovation
incorporated in the Association of Business School (ABS) were screened for the period 2008-
2014 (i.e., same timespan as for the computing discipline search). Together, these 3 domains
contain 107 ABS recognized journals. After completing this protocol driven methodology of
searching according to a pre-defined strategy, the literature review in the marketing discipline
continued with a ‘snowballing technique’ searching to capture published output that might
have escaped our first review round (cf. e.g., Ravasi and Stigliani, 2012; Greenhalgh and
Peacock, 2005). In particular, we supplemented the composition of our inventory in two ways.
First, two additional special issues dedicated to the use of technologies in retail were screened
(i.e., International Journal of Electronic Commerce 18(4) and Journal of Retail and Consumer
Services 17(3)). The keywords in these searches were: technology AND retail OR
computing AND retail’ OR supermarket AND technology’ OR ‘grocery AND technology’. If
no output resulted with the above-mentioned keywords, the search term technology was
used stand-alone. The resulting hits were again screened for relevancy. Second, as she is
considered an authority in the research domain of technology-based retail settings, all
publications of Eleonora Pantano were screened, and forward as well as backward
snowballing was applied on these publications. This research resulted in total in the
identification of 22 additional technologies retrieved from 13 papers published over 8 journals.
One reason for the - in comparison to the results from the ACM database - limited search
results from the marketing literature, is that a lot of technologies are still in too early stages of
IT development to allow for conducting field research (e.g., experimental field studies in real
stores, with real shoppers).
4.3 Review of practitioner publications and developments in the field
A final source of input for composing the envisioned inventory of retail technologies
consists of publications and best practices by practitioners in the field as well as by shopper
labs. First, several online newsletters in the retail domain (i.e.,,,,,,,,,,, and retail- as well as newsletters provided by trade fairs for retail technologies
(e.g., EuroCIS) were screened. The keywords in these searches were ‘technology AND retail’
OR ‘innovation AND retail’ OR ‘digital AND retail’ and the resulting hits were again
screened for relevancy. Second, a screening of industry living/shopper labs renowned for their
achievements in the field of retail technology (e.g., SAP Future Retail Center, IBM, HP, Intel,
Lowe’s Innovation Lab) was performed. However, probably due to data confidentiality and
non-disclosure agreements, the results of this search were meager. There is only sufficient
information available from the Philips Shopper Lab and Lowe’s Innovation Lab. Finally, the
website of Innovation Boulevard, a project of Belgian digital communication company
Digitopia, as well as the website of the academic Innovative Retail Laboratory (IRL), run by
the German Research Center for Artificial Intelligence (DFKI GmbH), were screened to
further substantiate the inventory of retail technologies. These searches resulted in an output
of 90 technologies within 12 sources.
Summarizing the overall literature review in search of retail technologies (cf. Section 4.1
4.3), the resulting inventory contains a total of 165 technologies that are retained, stemming
from 20 ACM sources, 8 marketing journals and 12 practitioner sources. However, it should
be noted that within the total of 165 inventoried technologies, some instances may in fact be
considered applications of a same underlying technology. For instance, several of the
inventoried technologies are based on facial recognition technology (e.g., OptimEyes, cf.
Retail Business Review, 2013; Facial Detection, cf. EssentialRetail, 2013; FaceDeals, cf.
Harrison, Mennecke, & Peters, 2014). Our online repository provides a complete searchable
overview of these 165 technologies, including a brief explanation, the discipline of retrieval,
and further classification information.
4.4 Affinity diagram clustering
We used an affinity diagramming approach to find appropriate clusters of technologies in
order to further improve the organization of the 165 in-store technologies. This method,
introduced in the 1960s by Jiro Kawakita, is a powerful tool for collaborative qualitative data
analysis (Harboe et al., 2012). Affinity diagramming is commonly used to address research
questions that at first sight seem hard to structure due to the lack of a generally accepted
classification scheme, which is clearly the case in the present research. Constructing an
affinity diagram starts with recording each technology on paper notes, and organizing these
into groups/clusters according to intuitive relationships, such as for example similarity,
dependence and proximity between technologies (Moggridge & Atkinson, 2007). Next, based
on resemblances and differences between the clusters, mergers and reorganizations take place
to arrive at a parsimonious, actionable affinity diagram scheme. The affinity diagramming
was conducted by an interdisciplinary team, bringing experience in marketing, computer
science, economics and user experience to the table. This strengthens the value of the clusters
that are uncovered, encompassing the agreement of multiple viewpoints of different types of
experts. Sections 5.1 and 5.2 further elaborate on the specific clusters that were uncovered.
5 Results
5.1 Retail technologies clustering from a Human-Computer Interaction perspective
Let us start by retaking Pantano and Viassone’s (2014) framework consisting of three
categories (i.e., ‘fixed’ touch screen displays/in-store totems, mobile applications, and hybrid
systems), of which the first and third are relevant, given this study’s scope (cf. Section 2.5).
Of the 165 inventoried technologies, a majority of around 55.15% (i.e., 91 of the 165
technologies) pertain to the hybrid class, implying a combination of both in-store fixtures and
mobile aspects. Some examples are the Mobile Productlens (IRL, 2014), or the Lambent
Shopping Trolley (Kalnikaite et al., 2011). The remaining 74 technologies pertain to the class
of fixed touch screen displays or in-store totems and comprise examples such as the
Monopulse System of RFID-equipped Fitting Rooms (Parada et al., 2013), or Kahl’s (2013)
ACES application for Electronic Shelf Labels. This dual classification into fixed versus
hybrid retail technologies is open for further elaboration to enrich our insights from a HCI-
perspective, which is one of this study’s objectives.
Two HCI scholars involved in this study conducted an affinity diagram clustering
technique on the inventory of 165 retail technologies, resulting in ten overarching clusters in
which these technologies can be aggregated. Natural relationships that underlie these ten
resulting clusters draw on technical characteristics as well as user-benefits. Moreover, this
affinity diagram incorporates the fact that there are three parties that interact: the customer,
the retailer and the product. Technologies can exist on all three levels. For example, product
augmentation is possible using augmented reality techniques such as Layar or Junaio (Olsson,
& Salo, 2012). Technologies on the level of the retail environment entail either the more
‘ambient’ (often lighting related; cf. Philips, 2014) technologies, or smart retail ‘furniture’
comprising technologies like electronic price tags, the Smart Cheese Counter and smart
shelves (cf. IRL, 2014). Technologies in support of payment form a separate cluster (e.g.,
Wallet phone; Swilley, 2010). Then, there are five clusters of technologies that relate more
closely to the customer and technology-provided user benefits in the shopper’s personal
decision making process, namely (1) a cluster of ‘context-aware data pool technologies’ like
the Digital Grocery Shopping List (Heinrichs, Schreiber, & Schöning, 2011), (2) the cluster of
‘product finding technologies’ (e.g., SoloFind; Wiethoff & Broll, 2011), (3) ‘personal product
assistants’ such as the IRL SmartCart and the Mobile Productlens (IRL, 2014), (4) product
decision support systems’, comprising technologies such as the Ecofriends app (Tholander et
al., 2012) or the Digital sommelier (IRL, 2014), and finally (5) price comparison technologies
(e.g., Bargain Finder app; Karpischek, Geron, & Michahelles, 2011).
5.2 Retail technologies clustering from a marketing perspective
The inventoried technologies were also clustered from a marketing perspective. Section
5.2.1 starts by mapping the 165 inventoried technologies in terms of the stage in the path-to
purchase they pertain to. Subsequently, a classification in terms of the type of customer value
that the inventoried technologies offer is discussed in section 5.2.2. Finally, section 5.2.3
summarizes the marketing clustering results by confronting the path-to-purchase and the
customer value type classifications.
5.2.1 Mapping advanced retail technologies in terms of the path-to-purchase
Three independent raters classified the 165 inventoried technologies into one of the five
stages in the shopper’s path-to-purchase. Based on Perreault and Leigh’s (1989) formula for
nominal data stemming from qualitative judgments, an inter-rater reliability score of 87.47%
was obtained (i.e., 31 inter-rater disagreements on the total of 165 technologies that were
classified in one of the five stages in the customer journey). A commonly experienced
difficulty in the independent raters’ classifications was the unique allocation of a technology
to either the ‘information search’ stage or the ‘evaluating alternatives’ stage. Many of the
information providing technologies also aid shoppers directly or indirectly in the stage of
comparing alternatives in their consideration set. In such cases, the raters opted for the stage
farthest in the shopper’s path-to-purchase, namely the stage of evaluating alternatives.
Whereas the digitalization of shopping lists (e.g., Digital Grocery List by Heinrichs et al.,
2011) may aid in need recognition, translating natural language into SKUs known in store
data warehouses or in-store information kiosks (e.g., RFID-Based Smart Shopping Assistant
by Chen et al., 2014) may help in searching external information upon request. In order to
evaluate alternatives, smart shopping trolleys equipped with touch screens (e.g., Lambent
Shopping Trolley by Kalnikaite et al., 2011) are an example of how technology at the point of
sale can help shoppers in succeeding their mission. The actual purchasing stage can be
technology-supported by means of near field communication technology (e.g., Pre-paid https-
based mobile NFC payment, by Park et al., 2012) or even biometric identification systems
(e.g., Biometric Authentication technology by Clodfelter, 2010). After the purchase, shoppers
can easily share reviews with their friends and relatives via numerous apps (e.g., Taggle, cf.
Retail Business Review, 2011) and social media platforms.
5.2.2 Mapping innovative retail technologies in terms of customer value
In line with previous studies on retail technologies from a consumer’s point of view (cf.
e.g., Renko and Druzijanic, 2014), technology is able to provide three broad types of
consumer value, namely (1) saving ‘costs or energy’ (e.g., price comparison apps; smart
shopping trolleys to navigate efficiently through the store and as such save time and effort), as
well as in terms of (2) offering utilitarian benefits (e.g., in-store information kiosks to
compare products and optimize one’s choice, smart fitting rooms with RFID-enabled touch
screens that recommend suited accessories to the dress the consumer may be trying on), and
(3) providing hedonic benefits (e.g., lighting technologies drawing attention to certain
products or reacting to consumers’ movements and behaviors, apps to share pictures of a
potential purchase to get social feedback in the process).
Each of these three broad value types can be split-up into a more fine-grained view. First,
cost or energy savings can occur in three ways: (1) saving time or effort for the shopper at
home, (2) in-store, or by (3) offering monetary savings. Second, functional benefits can be
disentangled into (1) product information and comparisons in function of choice optimization
by mobile phones, or (2) by in-store aid, (3) personalized recommendations, and (4)
customization in function of optimizing one’s choice. Third, the retailer can offer hedonic
benefits by (1) inspiring or educating the shopper, (2) providing aesthetics or a nice ambient
store atmosphere, (3) offering social value in terms of connecting people, or (4) generating a
playful dimension in the shopping experience. On this finer-grained level, in total 11
alternative value types can be established.
Again, three independent marketing scholars classified the 165 technologies, using table
2 as a classification guideline. In case a technology provides multiple types of customer value,
the value type highest on the hierarchical customer value ladder was chosen. For instance,
Philips’ (2014) Smart Lights Solution System offers both cost savings and shopping
convenience (namely by providing in-store navigation and targeted coupons) as well as the
functional benefit of personalized recommendations (namely by providing targeted
information such as recipes or matching products). Since the functional benefit of
personalized recommendation is considered highest on the customer value type ladder, the
technology is classified under this value type. The inter-rater reliability of these classifications
equals 90.18% (based on 28 disagreements).
Table 2. Customer value type classification guidelines
a. convenience at home
- Saving time or effort for the shopper at home
- Shopping lists
Amazon Dash: provides a service to facilitate the
composition of shopping lists at home
b. convenience in-store
- Saving time or effort for the shopper in-store
- In-store navigation or product finding
- Convenience in payment
- Personalized check-out services (e.g., saving personal data/recognizing the customer with a smart
interface so that an extended log-in procedure can be avoided)
The Product Finder: provides in-store navigation by
helping consumers find the articles they are looking
for, thereby saving time and effort for the shopper in-
Uniqul: uses facial recognition to make payment at
the checkout counter more convenient
c. money
- Offering monetary savings
- Price comparison apps
- Personalized coupons/promotions
LiveCompare: provides an app to improve interstore
grocery price comparisons
FaceDeals: sends personalized coupons to consumers
d. product information and comparison by
mobile phone
- Product comparison in function of optimizing one’s choice by means of
mobile phones
- Information pull
- NOT: receiving coupons (=c), local event information (=g), price comparison apps (=c), etc. by
mobile phones
Mobile Shopping Assistant: provides information
about products scanned by consumers by means a
mobile device
e. product information and comparison by
in-store fixtures
- Product comparison in function of optimizing one’s choice by aid of in-store fixtures
- Information pull
Cereal Assistant: shows information about the
products taken from the shelf
f. personalized recommendation
- Using customer profile or in-store location information to push or pull
customized information
IRL SmartCart: takes into account previous purchases
in order to display personalized product
g. customization
- Adapting to the consumer
- Providing answers to questions such as ‘how do I look in this product, what color suits me best,
what are my sizes,…?’
- Producing or displaying customized products
Augmented Reality Makeup Mirror: simulates how
makeup products look on a shopper’s face
Soap App: allows to create and order customized soap
h. inspiration/education
- Inspiring or educating the shopper
- Providing attention to certain products (e.g., via lighting)
- Information push
OLED: turns objects such as mannequins or coat
hangers into light sources to highlight and promote
certain products
i. aesthetics
- Providing aesthetics or a nice ambient atmosphere
- Not focused on one particular product
Swarm: produces variable lighting and communicates
messages, images, text, colors or a specific mood
j. social
- Offering social value in terms of connecting people
- Sharing and reviewing products via social media
Mobile Mirror: allows customers to share pictures
and video via social media and to receive feedback on
items they are considering to buy
k. play
- Generating a playful dimension in the shopping experience
Shopping Cart Game: offers a shopping cart game to
let toddlers participate in the shopping process
An example of a disagreement is that of Melià-Seguí et al.’s (2013) RFID enabled racks
with contextual media detecting which garment is being examined by a customer and showing
photographs and videos of that garment being worn by a model. Two raters interpreted this
technology as offering the shopper with the functional benefit of product information and
comparison, facilitated by an in-store fixture (namely the RFID enabled racks with contextual
media, providing information on how the garment looks on a model). The other rater,
however, classified this technology as offering the shopper with the functional benefit of
providing personalized recommendations (namely by providing customized product
information in response to a consumer’s interest in a specific garment). A discussion between
the three raters led to the classification in line with the former’s interpretation. In this way, all
28 value disagreements have been resolved in order to allocate each of the technologies to a
single type of value.
Note that upon aggregating the eleven value dimensions into the three overarching value
dimensions, the reliability amounts to 92.93% (i.e., 15 disagreements). As such, the reliability
of the technology classifications along both dimensions (i.e., value and path-to-purchase)
largely surpasses Nunally’s (1978) cut-off value for exploratory purposes (i.e., 70%),
demonstrating the trustworthiness of the technology classifications.
5.2.3 Confronting the path-to-purchase and the customer value type clustering results
Table 3 represents the integrated technology clustering by three marketing scholars,
documenting the inventoried 165 advanced retail technologies in terms of the stage in the
shopper’s path-to-purchase where they prevail (i.e., horizontal dimension), and in terms of the
type of customer value they offer (i.e., vertical dimension). Table 3 furthermore provides an
overview of the number and percentage of technologies classified under each stage of the
path-to-purchase and each customer value type. A more in-depth discussion of these numbers
is provided in section 6.
Table 3. The marketing affinity diagram clustering result
31 (18.79%)1
38 (23.03%)
38 (23.03%)
56 (33.94%)
2 (1.21%)
55 (33.34%)
Convenience at home
6 (3.64%)
A Grocery Product Retrieval
System; Intelligent Shopping
List; Digital Grocery List;
Amazon Dash
Store View
Convenience in-store
41 (24.85%)
UbiPay; Wallet Phone; Biometric Authentication
Technology; IBM Personal Shopping Assistant;
PIRAmIDE BlindShopping; Robot Shopping Cart;
NCR Personalized Self-Checkout; Smart Cart
Application; Pre-Paid https-based Mobile NFC
Payment; Bitcoins; PayPal’s API; Starbucks’ Square
Wallet; RFID enabled POS; ProFI Product Finding
Assistant; Indoor Positioning by Wifi Fingerprinting;
StoreMode; 360 Degrees Scanner; Mirco-Store Self-
Service Systems; RFID-Based Smart Shopping
Assistant; Smart Lights Solution Systems; Mobile
Payment; Easy Checkout; Product Finder; Smart Cart;
Zero-Effort Payments (ZEP); Uniqul; Interactive
Touchpoint; Meijers’s Find-It; Stop & Shop
Supermarket’s Scan it! Application; OSHbot; Qless;
Instore E-Commerce; Touch&Go; Sainsbury’s App; A
Smartphone Application for Visually Impaired Persons;
Smart Shelves; Enhanced Mobile Payment Options;
Toshiba TCxAmplify; Digital Money; Retail Site
8 (4.85%)
Plot Plugin; FaceDeals
LiveCompare; SurfaceWare; Bargain Finder
App; Ubira; eBay’s RedLaser Comparison
Shopping App
Mobile Commerce Application Tilly’s; Store Mode
for Kohl’s Mobile App
83 (50.31%)
Product information
and comparison by
mobile phone
18 (10.91%)
CAST a context-aware shopping trolley; Mobile
Shopping Assistant; Layar; Junaio; Google Goggles;
ShopSavvy; The Ecofriends Application; Cobra;
Atierre’s NFC-enabled Price Tags and Digital Signs;
Signature Mobile App; Mobile App Nutritional
Balance; Mobile Productlens; I-Space; Endless
Aisle App
Screen Codes; Retail Interactive Fashion
Experience; Mobile Shopping Assistant;
HarvestMark; Image Search App
Window QR & Augmented Reality Codes
Product information
and comparison by in-
store fixtures
21 (12.73%)
Must-D; Solofind; Adiverse Interactive Digital
Shopping Walls; I-PrOSTM; ACES Application
and Controlling Framework for Electronic Shelf
Labels; RFID-enabled Smart Shelves; RFID enabled
Racks with Contextual Media; Digital Sommelier; A
Mobile & Functional Lambent Display;
Lambent Shopping Trolley; Mobile Sales
Assistants; Cereal Assistant; Smart Cheese
Counter; Interactive Digital Signage
Shopping Experience
1 Note: The numbers and percentages mentioned in the row- and column headings of this tables are interpreted as in the following example. A total of 31 technologies or
18.79% of the 165 technologies were classified as pertaining to the ‘need recognition’ stage.
Mobile AR System to Browse Physical Reality;
Browsable Physical Space with Clicking Solution;
Touchscreen Tablet Computer for Grocery Carts;
Tablet Shopping Experience; Shelfbucks
25 (15.15%)
Ubiquitous Market Platform;
OptimEyes; Facial Detection;
RFID-enabled Personal
Shopclub Ring; Beacon
Technology; Miraview;
Product Experience Wall;
Touchless Large Interactive
Displays; ShopBeacon
Virtual Shopping Assistant; Interactive terminal for
Cooking Recipes; Myhmv; IBM Shopping
Application; Smart Retail Environment; Gamestop
RFID cellphone; weShop Mobile
Application; Monopulse System (RFID)
Fitting Room Application; RFID enabled
Interactive Fitting Rooms; Smart Dressing
Room; Cross-Selling & Recommendation
Tool; AR-assisted Mobile Grocery Shopping
IRL SmartCart; Smarter Checkout Solution
19 (11.52%)
Tracking Biometrics; Futuristic
Smart Mirror by Panasonic
Clark’s Feet Measurement Technology
Color-Match; Smart Mirror for Optical
Products; RFID-enabled Magic Mirror;
Scanning Computer and Mirror (SCAM)
System; Soap App; The Box; Holoroom;
Body Scanning; Augmented Reality Makeup
Mirror; 3D Mirror Measuring Bra Size; Oak
Fitting Room; Intel MemoMi Memory
Smart Mirror; 3D Printing Service; Eye Candy Vending
Machine; 3D Printing
27 (16.36%)
12 (7.27%)
Dynamic Digital Menu Board;
Shelf Vision Refrigerator;
Reactive Spotlight; Dynamic
Balustrade; Holographic
Display; Instore LED Wall;
Magic Mirror; Digital
Shopping Window
Intelligent Shop Window; OLED; Look
Interactive ‘Coming Soon’ Wall
4 (2.42%)
Lightung; Easytool; Interface
6 (3.64%)
Clothes Racks with Online
Buzz; FourSquare
Mobile Mirror; Lacoste’s Augmented
Reality App
Nedap twittering
Mirror; Taggle
5 (3.03%)
Engaging Digital Window
Shopping Cart Game; Wanagogo Kids Corner;
Augmented Reality for Halloween; Bloomingdale’s
Clothing To-Go Window
5.3 An Online Retail Technology Repository with Faceted Search
The complete list of results of our analysis has been serialized as a collection of structured
data records and made available through an interactive online repository at www.retail- Our aim with this online repository is to support both exploration and directed
search of possible solutions based on either a specific customer value type, a stage in the path-
to-purchase cycle, a preferred technology cluster, or a combination of these. For this purpose
the Exhibit framework (Huynh, Karger & Miller, 2007) was used, which allows to publish
structured data on the web and provides a faceted search facility (see Yee, Swearingen &
Hearst, 2003; Hearst, 2009). The faceted search exploits the orthogonal categories that appear
in the metadata of the structured data for finding resources (solutions) that adhere a set of
given characteristics. In the current implementation, three primary facets are used (i.e., value
type, path-to-purchase, and cluster). However, additional facets (e.g., discipline, year, etc.)
can be easily supported when even more fine-grained control over search is required. An
additional benefit of this approach is extensibility. When new technologies become available,
their data records can be added to the online repository and included in the search results
without any further effort.
6. Conclusion and discussion
This study provides an interdisciplinary inventory of retail technologies, gathered from the
academic fields of computing and marketing, as well as from business practice. A
classification of the technologies along the stages in the shopper’s path-to-purchase shows
that all five stages are (or can) to some extent (be) digitally instrumented in the servicescape.
The most technology-supported stage in the customer journey is that of the actual purchase,
comprising many in-store payment and navigation technologies. Moreover, the earlier stages
of the shopping cycle (i.e., before the purchase decision is made), comprise in total 64.85% of
the 165 inventoried technologies (cf. Table 3). As such, the strategic potential of retail
technology is high, since according to Shankar (2011, p. 30), retailers traditionally are better
at influencing the shopper near the end of the customer journey. The technologies inventoried
in this paper include instruments to also digitally equip the retailer for targeting the shopper
earlier on. Remarkably underrepresented however, is the post-purchase stage, with only 2 out
of 165 technologies serving to target the shopper at that stage (cf. Table 3). This void entails
opportunities for IT and retailing to join forces in order to develop suitable technology-
support to close the customer journey loop. Managers can benefit from digital support to
capture insights in post-purchase behavior and leverage them to encourage repurchase and
word-of-mouth intentions (Shankar, 2011).
Regarding the value of retail technologies, it is efficiency in-store (i.e., time saving
technologies) that tops the list with 41 out of 165 technologies with this purpose (cf. Table 3).
This finding is in line with Pantano and Viassone’s (2014) finding that retailers tend to focus
on the utilitarian benefits of technologies, and seem to neglect the proposal of recreational
tools for improving the shopping experience per se. The hedonic benefit providing
technologies cluster comprises a meager 16.36% (i.e., 27 of the total of 165 technologies),
whereas utilitarian benefit providing techs comprise 50.31% (or 83 technologies) and
cost/time-saving technologies 33.34% (i.e., 55 technologies). The reasons for this finding are
twofold. First, the retail technologies in the present study to a large extent focus on
applications in grocery retailing, which is the utilitarian retail context par excellence. When
doing grocery shopping, consumers tend to be ‘on a mission’, striving to complete the
purchase of their shopping list as efficiently as possible (Geuens et al., 2001). From that
respect, technologies that aid in saving time, money, and contributing in optimizing one’s
product choice are indeed the ones that serve shoppers goals in such supermarket contexts
best (Geuens et al., 2003). After all, many shoppers are not looking for hedonic distractions
upon doing grocery shopping. Second, the bulk of functional benefit- and cost-/time-saving
technologies also reflects retailers’ main preoccupation with operational efficiency matters
(e.g., Finne & Sivonen, 2008).
7. Limitations, research and managerial implications
7.1 Extending the present inventory of retail technologies
Notwithstanding the comprehensive nature of the literature review in this study, the use of
rather generic search terms such as ‘retail’ and ‘technology’, may have resulted in the fact that
some technological advancements in shopping are not captured in this inventory. Furthermore,
the inventory of 165 technologies could be further enriched by examining additional industry
labs (such as SAP The Future Retail Center, IBM, HP, Intel; cf. Narayanaswami et al., 2011).
The restriction that the present study however faced is the lack of disclosure of such lab
findings. Another source of additional inspiration to complement this study’s inventory are
real-life stores of the future, like Metro Group’s Real Future Store (Metro Group, 2014b) or
Globus Warenhaus (IRL, 2014), although the latter implements innovations stemming from
cradle IRL, that have been incorporated in this paper. The innovations are vast and evolve
continuously and so does the information that is spread about them. This paper does not claim
to present an exhaustive overview but is nevertheless, to the best of our knowledge, the first
attempt to integrate findings from the intersection of the marketing and the computing
discipline, providing a state-of-the art and outlook for the future, in academic and business
practice terms.
Choosing is losing, so the saying goes. In respect to the scope delineation of retail
technologies in this paper (cf. Section 2.5) some interesting related fields are left open to be
explored in future studies. One significant example is that of the booming rise of mobile
marketing and location-based advertising in retail settings (e.g., Tin et al., 2016; Kim & Lee,
2015). Entire special issues are devoted to this matter and new studies appear in many
marketing journals on a continuous basis. Consulting the Appstore for example, points out
that there are a wide amount of apps on offer upon searching for ‘shopping’ (i.e., 2163 hits),
or ‘retail’ (i.e., 1190 hits), or ‘price’ (i.e., 2171 hits; all as of September 2014). Such mobile
shopping applications for smartphones are sometimes called ‘mobile shopping assistants’, and
have been subject to research in the field of ubiquitous and pervasive computing for many
years (Karpischek et al., 2011). The mobile apps in this paper’s inventory all meet the
criterion of serving as one of the multiple components in the digital system of optimizing the
shopping process while not on a stand-alone basis.
Besides the notable scholarly interest in mobile marketing, the extant marketing research
on effects of user-generated content, and in particular (online) product reviews, is also vast.
Since this type of (typically) post-purchase behavior most often occurs via social media
online, which falls outside this study’s scope on retail technologies as being out-of-store
(Shankar, 2011), part of the digital instrumentation of shoppers’ post-purchase behavior may
be lacking in this article’s inventory (cf. only 2 technologies aimed at this stage of the path-to-
purchase are retained). This finding however entails fruitful opportunities for future research.
Furthermore, whereas the above mentioned scope extensions would still focus on the value
of technology from a consumer perspective, the same documentation of the inventoried
technologies can take place from a retailer perspective (cf. Pantano & Viassone, 2014).
Another axis along which the present research can relevantly be extended is to examine how
such digital augmentations of the consumer’s shopping experience can also capture moments-
of-truth along the shopper’s path-to-purchase (cf. e.g., Johnston, 2015). For example, Smart
RFID-equipped Shelves can sense consumer-product interactions, and as such peek into the
retailer’s black box of which traditionally only product in- and outflows have been
documented. This real-time information could inform and optimize shopper marketing actions
at the point-of-sales (cf. Willems et al., 2014). Another illustration is that of Tesco’s
OptimEyes technology (N.A. - Retail Business Review, 2013) or wearable technology like
Apple’s SmartWatch (Cavus & Munyavi, 2016), allowing the retailer to interact with
customers in a context-aware manner, and addresses shoppers while accounting for their
socio-demographic as well as situational characteristics such as mood (Pucinelli, Deshpande,
& Isen, 2007). In a similar approach to how the inventory in the present study has been
documented in terms of customer value and stage in the path-to-purchase, documentation
could be provided in terms of marketing intelligence capabilities (cf. analogy to webshop
metrics) to feed retail shopper marketing strategy development (e.g., a dashboard of particular
retail KPIs). Beyond organizational challenges resulting from these revolutionary changes in
knowledge management (cf. e.g., Pantano & Timmermans, 2014; Rohrbeck, Battistella, &
Huizingh, 2015; Vecchiato, 2015), another facet that merits further research attention pertains
to challenges in terms of selling activities, such as the study of how to motivate and engage
salespersons upon implementing smart retail technologies (cf. Pantano & Timmermans, 2014;
Yurova et al., 2016). After all, ICT triggers the need for new knowledge and specialized skills
(Gallouj et al., 2015), particularly in a traditionally ‘low technology’ sector such as the retail
industry (Pederzoli, 2016).
7.2 Empirical further research opportunities on retail technologies
Besides extending the current inventory exercise to a wider scope, empirical research
opportunities inspired by the knowledge generated in the present study are also numerous. As
Ray et al. (2005, p. 626) state, ‘[…] while a number of case studies do highlight the critical
role of IT in customer service (Elam & Morrison, 1993; El Sawy & Bowles, 1997), empirical
research examining the link between IT and customer service performance has been lacking.
For the stages in the customer journey that are well-instrumented (e.g., info search 38 of
165 technologies, cf. Table 3), marketing scholars can start to examine which technology
customers prefer to interact with in order to satisfy their needs in that particular stage of the
shopper pathway, to ultimately also further advance IT fine-tunings in that direction. The
same goes for customer value. Where 19 out of the 165 technologies are aimed at
customization, marketing researchers can study drivers of customer adoption of such popular
systems, making use of well-established models like the Technology Acceptance Model
(TAM; Davis, Bagozzi, & Warshaw, 1989; Pantano & Di Pietro, 2012) or the (extended)
Unified Theory of Acceptance and Use of Technology (UTAUT 2; Venkatesh, Thong and Xu,
2012) to empirically examine how shoppers (users) evaluate ‘perceived usefulness’ and ‘fun
or pleasure derived from using a technology’ (Venkatesh et al., 2012, p. 161), for example.
Pioneering examples in this direction include (1) Kourouthanassis, Giaglis, and Vrechopoulos
(2007) research on user experience evaluations of pervasive retailing, (2) the study by
Evanschitzky et al. (2015) on consumer trials, continued usage and benefit perceptions of
personal shopping assistants, and (3) Gurtner, Reinhardt & Soyez’ (2015) TAM test of mobile
business applications for ageing consumers.
To this end, the development of user scenarios may be a first necessary step, particularly
for evaluating adoption of technologies that are not yet commercially or widely available and
which as such may not yet enjoy sufficient familiarity among consumers (Pederzoli, 2016).
Furthermore, return on investment in technology needs to be examined by quantifying the
benefits that can be gained in terms of among others customer satisfaction (as one of the
drivers of service firms’ financial performance, cf. e.g., Heskett & Schlesinger, 1994) via an
enhanced shopping experience (Pantano & Naccarato, 2010). Moderator analysis is warranted
to set the boundaries for generalization of the findings resulting from such quantitative studies
(e.g., including consumer characteristics, such as technology readiness; retail sector
characteristics, such as level of self-service and technology-mediation, think/feel - high/low-
involvement products, etc…).
Furthermore, additional IT developments to further instrument the post-purchase stage in
the customer journey, and to add to the offering of hedonic value to shoppers, could be
inspired by advances in other industries (cf. Pantano, 2014), such as the game industry (e.g.,
haptic technologies, augmented reality scenarios, multimodal interaction; cf. e.g., Poncin et al.,
2015), the education sector (e.g., digital storytelling, collaborative 3D environments), and
tourism and hospitality (e.g., ubiquitous computing and improved connectivity for data
7.3 Managerial implications
The managerial relevance of the present study lies in the insights that are provided on (1)
what technologies exist for retailers to consider (cf. RQ1),, (2) what type of customer value
they mainly offer (cf. RQ2),, and (3) in what stage of the customer journey are they most
likely to be effective (cf. RQ3). The fact that the main emphasis of retail technologies
currently still appears to be on functional benefits and cost/time savings, may over time erode
the differentiating potential of such technologies, inducing retailers to move a step up on the
hierarchical customer value ladder (e.g., hedonic value providing technologies). Furthermore,
given retailers traditional excellence in influencing shoppers nearby the end of the path-to-
purchase, this technologies inventory provides inspiration on how technology can be used to
also leverage retail power to trigger shopper responses earlier on in their shopper pathway. Or
how the shopper’s path-to-purchase indeed is - and will become ever more - paved with
digital opportunities.
The authors would like to thank Randy Lauriers for her input in the data collection process for
this study and An Hutjens for proof reading the resulting manuscript.
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... The authors identified their benefits and limitations for both retailers and customers, with the underlying conclusion that congruence of technology with shopper goals increases the perceived technology utility. In the extensive inventory analysis of smart retail technologies, the authors indicated that most inventoried technologies provide cost savings, convenience, and utilitarian value, while only a few technologies offer hedonic or symbolic benefits [41]. ...
... The deployment of beacons should fulfill the utilitarian role the technology can provide to customers [41,46]. The relevance of location-congruent ads has been confirmed in multiple studies [40,63,74]. ...
... Shoppers are willing to use beacons in hypermarket and shopping mall environments [43]. The expectation regarding such activations is to leverage the unique communication tool during the path to purchase, addressing the pillar of proximity-based consumer engagement [50] and facilitating the conversion to purchase by providing the cost and effort reduction value to shoppers [41], thus generating incremental sales that the IoT-based personalised offer service increases sales versus traditional methods [13]. ...
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This paper studies shopper acceptance for using beacons in the purchase process. The main goal is to examine shopper response to beacon-triggered promotions and propose a model that would help retail practitioners plan the implementation of beacons in stores. The model was evaluated via an in-market test to examine the effects of beacon-triggered promotion on shopper attention, technology acceptance, and the decision to purchase. The test was conducted in Belgrade, Serbia in 10 representative stores where beacons were implemented with 10 twin control stores. The SimplyTastly mobile application was used for sending notifications. Furthermore, two more in-market beacon activations were analysed in Croatia and Bulgaria. The results showed that shoppers accepted beacon technology and that beacon-triggered promotion had a positive impact on shopper attention, purchase behaviour, and the decision to purchase. The results show that the proposed model could serve as a sound basis for the implementation of beacon technology in retail.
... Such benefits include improved customer services, more effective, less costly internal operations (Renko and Druzijanic, 2014) and more engaging (Pantano and Viassone, 2015) and even extraordinary (Pelletier and Collier, 2018) shopping experiences. However, while research broadly agrees that technologies are dramatically changing the retail business and the retailing experience (Guha et al., 2021;Pantano et al., 2018;Shankar et al., 2020;Willems et al., 2017), luxury retailing is a specific sector for which technology has not traditionally been a priority. ...
... Because technological progress is ripe with innovative solutions to support retailers' operations, the choice of which technologies to adopt is a key challenge. On the one hand, adopting technological innovation might give the company certain advantages over competitors; on the other hand, adopting these technologies involves risks associated with uncertainty, consumer rejection, monetary investment, and possible failure (Pantano and Vannucci, 2019;Willems et al., 2017). Retailers oriented primarily toward process innovation that frame technological advances as a resource are frequently first movers. ...
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Luxury organizations have traditionally resisted technology, as they perceived it to be antithetical to the values of luxury. Recently, however, competitive and market pressures, compounded by the global pandemic, have prompted luxury organizations to utilize significant technological innovations to enhance their customer experience, mostly on an ad hoc basis. Across four case studies in the luxury fashion retail sector, we conduct 12 interviews with managers. This paper advances a framework that encourages luxury organizations to consider technological innovation in retailing from a strategic point of view. Such a view involves contemplating questions regarding what technology type to adopt (radical vs. incremental) and when the best timing is to adopt the technology (pioneering vs. following technological leaps). The framework identifies four retailer roles that emerge from the innovation process: facilitator, enabler, explorer, and initiator. Each role comprises a different set of risks, resource implications, and expected returns.
... The boundaries between digital and physical are dissolving (Piotrowicz and Cuthbertson, 2014) and technology has become a dimension of the in-store experience (Alexander and Alvarado, 2017). The literature then commonly promotes ISTs (Adapa et al., 2020;Bertacchini et al., 2017;Blázquez, 2014;Foroudi et al., 2018;Pantano and Gandini, 2017;Priporas et al., 2017;Roy et al., 2017Roy et al., , 2018, and their benefits: improved shopping efficiency (Roy et al., 2017), experiential aspects (Pantano and Laria, 2012), value (Boudkouss and Djelassi, 2021;Feenstra and Glérant-Glikson, 2017), higher purchase intention (Willems et al., 2017), satisfaction (Fernandes and Pedroso, 2017) or advocacy intention (Lee, 2015). ...
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To improve the in-store experience, physical retailers increasingly adopt in-store technologies (ISTs). However, questions are being raised about the use of these technologies, their ROI, and to what extent and how they affect the customer experience… for better or for worse. Studies tempering their adoption, or even tackling negative perceptions, of ISTs are rare, as the literature tends to emphasize their positive effects. This research then tackles the specific question of the impact of a proactive IST-namely, a robot-on the shopping experience. We suggest that the proactive nature of an IST could lead to a perceived coolness feeling as well as intrusiveness, which could lead to a paradoxical or ambivalent appreciation of such an IST. An experiment on 131 respondents is conducted, and findings indeed demonstrate parallel paths with both a positive influence of perceived coolness and a negative influence of perceived intrusiveness on attitude toward the shopping experience. Contributions of this work, managerial issues, limits and perspectives are then discussed.
... Second, the positive association between effort expectancy and attitude toward autonomous decision-making processes highlights the need for such systems to be easy for customers to use. Modern customers are often unable to allocate a significant amount of time to learning to use new autonomous decision-making processes [127]. Therefore, AI systems developers should ensure that such systems can learn and adapt to customers' needs and expectations without requiring much effort from the customers themselves. ...
Smart technologies promise to enhance customer experience to new levels in next-generation retail stores. Offline retailers increasingly employ technology-enabled personalization (TEP) strategies to digitally enhance in-store customer experience. To send personalized messages to in-store customers, retailers can choose from two types of smart devices: customer-owned smartphones or retailer-owned immersive screens. Although these smart devices may largely determine customers’ experiences in future retail, research rarely addresses device-related determinants of the effectiveness of personalized messages in stores. Building on assemblage theory, the authors consider the role of these devices in influencing customer experience and eventually consumer shopping behavior. Through two experiments and a mediated moderation analysis, they investigate the interplay of personalized content and device technology in customers’ response to TEP. The results illustrate that consumers react differently to message content depending on the device through which it is conveyed; that is, personalized (standardized) messages are more effective on customer-owned smartphones (retailer-owned screens) because they become integrated into (remain separate from) the customer's extended self. Relational customer experiences, or the extent to which a customer feels positively connected to store assemblages, mediate the effect on shopping behavior. To build TEP strategies, retailers should therefore use smart devices integrated into customers’ extended selves.
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The holistic perspective model is a concept of three stages that considers the whole of online consumer behavior. It is based on dynamic aspects and overview measurement to demonstrate the preliminary of three stages, including “Pre-purchase”, “Purchase” and “Post-purchase”. There is a shared purpose for all the positive, negative, and hesitation factors that inhibit or encourage online shopping decisions. This model can capture the dynamic and fast-changing elements in online shopping platforms. Most online buying-selling platforms are gaining popularity and growing rapidly. Thus, they should maintain good levels of online customers’ satisfaction. This research presents a balanced investigation model of online consumer purchasing behaviors under uncertainty through the integration of Push-Pull Mooring (PPM) theory and the three stages of online consumer behaviors. In this study, questionnaires were collected from 525 online applications from experienced users of electronic business platforms. The outcome reveals that PPM affects three stages of online consumer behaviors. This means that PPM factors influence online customers during and after online shopping. This research can be used to develop attractive online shopping applications for prospective customers while retaining existing customers, which is the challenge faced by online shopping platforms.
Increased digitalization enabled innovation and practical examples from the retail industry have captured the attention of marketing scholars, with rapid development in the academic field. The pace of change has significantly accelerated during the Covid-19 crisis. In seeking to (1) investigate the unique characteristics of digitalization enabled retail business model innovation, (2) understand how does digitalization influences changes to the retail business model innovation dimensions, and (3) identify the areas for future research related to retail business model innovation, this study systematically reviews the literature. Employing four databases, a sample of 170 articles were chosen. Based on bibliometric and network analysis and visualization, the major researchers, articles, and topics were rigorously identified. Finally, the results revealed the unaddressed issues in this research area. The study ends with theoretical and managerial implications.
Most research related to the reliability and validity of marketing measures has focused on multi-item quantitative scales. In contrast, little attention has been given to the quality of nominal scale data developed from qualitative judgments. Judgment-based (“coded”) nominal scale data are important and frequently used in marketing research-for example, in analysis of consumer responses to open-ended survey questions, in cognitive response research, in meta-analysis, and in content analysis. The authors address opportunities and challenges involved in evaluating and improving the quality of judgment-based nominal scale data, with specific emphasis on the use of multiple judges. They review approaches commonly used in other disciplines, then develop a new index of reliability that is more appropriate for the type of interjudge data typically found in marketing studies. Data from a cognitive response experiment are used to illustrate the new index and compare it with other common measures. The authors conclude with suggestions on how to improve the design of studies that rely on judgment-coded data.
The aim of this chapter is to develop knowledge of retail environments through an overview of the most used technologies in retailing and the contribution of in-store technologies to the experience of the fashion store environment. The chapter commences with an overview of the influence of multichannel development, consumer-facing technologies, and their adoption by fashion retailers. The second part examines the use of digital signage and its contribution to atmospherics in a department store. The researchers have used a mixed method approach, with observational techniques drawn first from ethnographic methodology, and second, a quantitative approach to consumers’ environmental response behavior. The results indicate a limited use of innovative in-store technologies and reliance on conventional technological media in fashion stores. Secondly, digital signage demonstrates both communication and experiential effects. The chapter concludes with a discussion of convergence between the virtual and physical store environments and the implications for theory and management.
This chapter provides a review and synthesis of information related to technologies available at the retail POS (point-of-sale) checkout. Several POS technologies available to retailers are described, detailing their benefits and drawbacks for both retailers and consumers. The five technologies described and analyzed are barcode scanning, electronic shelf tags, shelf-checkouts, RFID tags, and fingerprint authentication. The extent to which retailers have implemented these available technologies is described, and perspectives on the future implementation of these technologies and emerging trends are also presented. Findings would indicate that there will continue to be innovations in retail technology at POS, and shopper expectations will continue to change. At the same time, retailers will probably remain cautious in deciding if and when to adopt new technologies. They must be convinced that the innovations will deliver sufficient value to offset their expenses.
In this paper, we analyze the diffusion of technologies in the retail sector. Technologies are disrupting the traditional way of selling products and services and the relations between companies and consumers. In our paper we categorize four different fields for technologies impacting retail activities and we analyze some examples for each category that can illustrate these trends.
Purpose – The purpose of this paper is to profile grocery retailers in terms of seven value types based on Holbrook’s value typology; to link these value types to three key outcomes (i.e. satisfaction, repurchase intention, and word-of-mouth); and to evaluate the impact of the retail format on performance and importance of the seven value types. Design/methodology/approach – For each retail format, the authors administered a consumer survey, resulting in an aggregate sample of 392 respondents. The authors used partial least squares structural equations modeling to test the relationships between the value types and key outcomes (i.e. importance) and ANOVAs to examine cross-format differences between latent variable scores of the value types (i.e. performance). Findings – The three retail formats included in the study perform differently on Holbrook’s value types (e.g. non-discounters excel in terms of aesthetic value and play, compared to hard and soft discounters). Furthermore, this study reveals that the strategic importance of each value type depends on the key outcome (e.g. whereas efficiency is the main source of satisfaction, play mainly drives the other two outcomes). Research limitations/implications – The authors randomly assigned respondents to one of the three retail formats irrespective of their personal preference or patronage. To conduct value-based segmentation, respondents should evaluate either their preferred format or all supermarkets. Practical implications – This study offers positioning advice to retail managers, according to their format and strategic objectives. Originality/value – Unlike previous research, this paper provides a cross-format comparison of retailers based on a three-dimensional value typology and its key outcomes.
In the preceding chapters we’ve seen many examples of adaptive marketers using real-time data to develop better products and experiences, enhance customer service, and improve advertising. However, the use of data doesn’t stop there. In fact, real-time data can be used to gain competitive advantage in arguably the most important part of the consumer journey: the actual purchase of a product or service. This chapter explores how adaptive marketers are exploiting real-time data to adapt both online and off-line commerce. In fact, the line between these two is increasingly becoming blurry as shoppers get more sophisticated in their use of data and technology.