See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/322878809
The Evolution and Future of Retailing and Retailing Education
Article in Journal of Marketing Education · February 2018
DOI: 10.1177/0273475318755838
CITATIONS
READS
164
13,398
3 authors, including:
Dhruv Grewal
Scott Motyka
Babson College
Babson College
275 PUBLICATIONS 55,657 CITATIONS
9 PUBLICATIONS 494 CITATIONS
SEE PROFILE
All content following this page was uploaded by Dhruv Grewal on 22 July 2018.
The user has requested enhancement of the downloaded file.
SEE PROFILE
755838
research-article2018
JMDXXX10.1177/0273475318755838Journal of Marketing EducationGrewal et al.
Article
The Evolution and Future of Retailing and
Retailing Education
Journal of Marketing Education
2018, Vol. 40(1) 85­–93
© The Author(s) 2018
Reprints and permissions:
sagepub.com/journalsPermissions.nav
https://doi.org/10.1177/0273475318755838
DOI: 10.1177/0273475318755838
journals.sagepub.com/home/jmed
Dhruv Grewal1, Scott Motyka2, and Michael Levy1
Abstract
The pace of retail evolution has increased dramatically, with the spread of the Internet and as consumers have become more
empowered by mobile phones and smart devices. This article outlines significant retail innovations that reveal how retailers
and retailing have evolved in the past several decades. In the same spirit, the authors discuss how the topics covered in
retail education have shifted. This article further details the roles of current technologies, including social media and retailing
analytics, and emerging areas, such as the Internet of things, machine learning, artificial intelligence, blockchain technology,
and robotics, all of which are likely to change the retail landscape in the future. Educators thus should incorporate these
technologies into their classroom discussions through various means, from experiential exercises to interactive discussions
to the reviews of recent articles.
Keywords
learning approaches and issues, marketing education issues, experiential learning techniques, retailing, e-commerce/Internet
marketing, course content, teamwork/projects/issues
Retailing and retailers face a rapidly changing market landscape, due largely to the ways modern customers shop: instore, online, through mobile channels, and even with emerging
machine-to-machine commerce (Grewal, Roggeveen, &
Nordfält, 2017; Rafaeli et al., 2017; Roggeveen, Grewal,
Townsend, & Krishnan, 2015; Roggeveen, Nordfält, &
Grewal, 2016). Such shifting consumption patterns are partly
a function of technological advances associated with novel
offerings and capabilities provided by the Internet of things
(IoT), artificial intelligence (AI), blockchain technologies, and
robotics (Rafaeli et al., 2017).
These impending technological promises follow from the
relatively recent emergence of now familiar advances in the
Internet, computing and storage capabilities, big data, and
retail analytics. These trends have prompted the enormous
growth of Internet-based retailing, as well as tremendous
challenges and opportunities for the retail sector. Amazon
has led the way, establishing a powerful competitive advantage over most retailers. Big data and retail analytics are less
obvious in everyday consumers’ lives, but their influence has
been equally important (Bradlow, Gangwar, Kopalle, &
Voleti, 2017). Retailers are developing their analytical capabilities to understand and serve customers better, price products and services dynamically, and manage the flow of
merchandise in the supply chain.
As retailing and retailers change, it is equally important
for retailing education to evolve in parallel. Retailing education must reflect technological advances, in all its various
forms. First, the content covered needs to be up-to-date.
Second, digital, online pedagogical solutions should integrate mobile and smart devices in the classroom. Third,
retailing education continues to need relevant case studies
and vignettes on novel and emerging issues (e.g., Amazon’s
acquisition of Whole Foods), along with relevant experiential exercises.
Accordingly, we structure this article to acknowledge the
past, the present, and the future of retailing and retailing
education. First, we examine major changes in a key retailing textbook (Retailing Management; Levy, Weitz, &
Grewal, 2014, 2018) and the supporting material it has provided, from its first through 10th editions, to highlight how
retail education has and continues to evolve.1 In this analysis, we first cover significant retailing innovations that have
changed the retail and consumer landscapes and tie them to
changes in retail education. Second, we discuss current
challenges and shifts in retailing that are influencing retail
education. Third, we pursue insights into several new technologies that have the promise of changing retailing and its
teaching, such as the IoT, machine learning, AI, and blockchain technologies.
1
Babson College, Babson Park, MA, USA
Keck Graduate Institute, Claremont, CA, USA
2
Corresponding Author:
Dhruv Grewal, Toyota Chair of Commerce and Electronic Business,
Professor of Marketing, Marketing Division, Babson College, 213 Malloy
Hall, Babson Park, MA 02457, USA.
Email: dgrewal@babson.edu
86
Journal of Marketing Education 40(1)
Figure 1. Retailing technology timeline.
Note. Adapted from Braun (2015).
Figure 2. Technology topics covered in Retailing Management (1992-2018 [10 editions]).
Retailing in Textbooks: The Past
To assess the potential of technology to change both retailing
and retailing education, we take a historical perspective and
map key technological advances in retailing, as indicated by
each edition of Retailing Management, a popular retailing
textbook, according to when the innovations were introduced
(Levy et al., 2014; see Figures 1 and 2).
Although core retailing concepts (e.g., visual merchandising, pricing, atmospherics) have largely remained the same,
the ways retailers deliver on these principles have changed
tremendously, particularly due to technological innovations.
The widespread introduction of the Internet in 1994 eventually led to the notion of omnichannel retailing, defined as a
consistent, integrated shopping experience across all channels
maintained by the retailer. In the face of these rapid developments, not just retailers but also educators have had to constantly refine their understanding.
The second edition of Retailing Management (Levy &
Weitz, 1995) was published a year after the popular introduction of the Internet. It reflected the then-timely technology
changes, such as quick-response delivery systems and their
uses of electronic data interchange, enabled by the Internet,
to reduce inventory costs. Amazon also launched in 1995,
initiating the start of the e-tailing revolution. In the third and
fourth editions of Retailing Management, the authors
describe the Internet channel as “non-store and electronic
retailing,” but as the importance of this channel grew, so did
the terminology. As the Internet took wider hold, retailers
began to understand the value of online retailing, leading
87
Grewal et al.
them to prioritize this channel equally with brick-and-mortar
stores. As a result, educators shifted from describing an
“electronic channel” and instead began referring to “multichannel retailing,” such that the fifth to ninth editions of
Retailing Management (2003-2014) acknowledge their equal
importance.
Then in 2007, the Apple introduced the iPhone, and by
2009, the smartphone, which included competing Android
models, was widely adopted. Consumers no longer had to
choose whether to shop in either a brick-and-mortar store or
at their desks on a computer in their homes or offices.
Smartphones with mobile applications empowered consumers to shop not only anytime but also anywhere.
This expansion represented a great opportunity for retailers, but it also meant new challenges. Consumers demand
consistency from the retail experience. If a customer can
compare in-store prices with online prices in real time on a
smartphone, retailers cannot manage their Internet and brickand-mortar channels independently. Thus, the concept of
“omnichannel retailing,” which appeared in Retailing
Management’s 10th edition is thus more than just offering
customers products through multiple channels. It is the coordination of offerings across retail channels that provides a
seamless and synchronized customer experience, using all of
the retailer’s shopping channels (Witcher, Swerdlow, Gold, &
Glazer, 2015). Recognizing that online retailing was changing how consumers bought, competitors have responded
actively, as recently exemplified by Walmart’s massive
investment to acquire Jet.com ($3.3 billion in cash and stock),
to enhance its competitive ability relative to Amazon and the
rest of the marketplace (Stone & Boyle 2017). At almost the
same time, Amazon acquired Whole Foods for $13.4 billion
to enhance its brick-and-mortar capacities (Wingfield & de la
Merced, 2017)
Brick-and-mortar retailers and online channels also
increasingly feature a cooperative effort, such that Amazon
obtained Whole Foods for $13.4 billion to enhance its brickand-mortar capacities (Wingfield & de la Merced, 2017).
Overall, conventional brick-and-mortar retailers need to
coordinate their activities across multiple channels, in formats that reflect multi- or omnichannel integration, to leverage the power of their traditional channels, exploit the
benefits of advanced technology channels, and achieve
seamless integration across all their customer touchpoints
(Ailawadi & Farris 2017; Grewal, Bart, Spann, & Zubcsek,
2016; Inman & Nikolova 2017).
Retailing in and Beyond the Classroom:
The Present
To establish the current state of the art, this section first outlines real-world retail developments, then describes how
those developments and advanced pedagogy should be integrated into retailing curricula.
Retailing Analytics’ Growing Importance
When retailers gain greater insights into their customers by
combining data sets, such as details about purchase data,
location data, and social media data, they can leverage this
information to create value for consumers. Retailers such as
Kroger, CVS, and Target are investing heavily in retail analytic capabilities and moving from traditional loyalty programs to systems that integrate and make effective use of
varied data to engage customers (Grewal, 2018). They are
also spending more of their promotional budgets on online
and social media-based promotions, such as advertising
through Google AdWords, Instagram, and Facebook. At the
same time, customers are making increased use of digital
channels, by posting reviews about products, services, and
providers, which can help other consumers make informed
purchase decisions. After purchasing, customers also post
information to their social network feeds about the purchases
they have made, as well as their opinions and reviews of the
product and overall shopping experience.
Kumar, Anand, and Song (2017) have linked profitability
to the increased use of analytics, such that retailing analytics
promise to take an ever-growing role, in both retailing and
retailing education. The strategic use of analytics can drive
insights at four levels: market, firm, store, and customer
(Kumar et al., 2017). For example, retailers might analyze
store-level data to create geo-fenced mobile ads (delivered to
mobile users in predefined geographic areas) or to understand the effects of specific store atmospherics on sales, customer satisfaction, or basket size. To facilitate such analyses,
retailing students will need to learn Python (used for machine
learning and natural language processing [NLP]; www.
python.org) and SQL (used to access data from databases)
programming languages. To facilitate managerial decision
making, the results of these analytics also must appear in
easy-to-understand, dynamic dashboards using visualization
programs such as Tableau. As these topics continue to grow
in importance and complexity, we expect the development of
new elective courses, related to retailing management but
focused primarily on retail analytics, dashboards, and data
visualization through tools such as Tableau. Educators can
access free software licenses for students, sample data sets,
and classroom activities through its Tableau for Teaching
program (https://www.tableau.com/academic/teaching).
The primary method retailers use to analyze social media
data is sentiment analysis, often monitored through various
social media listening dashboards, as offered by companies
such as Salesforce Radian6 and Brandwatch. Social media
analysts search for key terms related to their brand and use
NLP to score each post as positive or negative (Pang & Lee,
2008), then aggregate the scores across consumers to gain a
measure of their overall attitude toward the brand (Roggeveen
& Grewal, 2016). For example, Yelp provides an excellent
opportunity for restaurants to understand their customers and
88
develop innovative, appealing offerings. Regional managers
can analyze sentiments about their restaurants to identify
customers’ concerns about prices, atmospherics, service
quality, product offerings, and other aspects of the retail
offering.
Sentiment analysis is rapidly evolving, with the focus
shifting from categorizing posts as positive or negative to
identifying the strength of both positive and negative sentiments (Ordenes, Ludwig, de Ruyter, Grewal, & Wetzels,
2017). People rarely express a completely positive or negative view; instead, consumer reviews tend to highlight both
positive and negative aspects (e.g., “I love how inexpensive
the store is, but hate that it’s so disorganized” (Ordenes et al.,
2017). Current sentiment analysis methods classify mixed
sentiment posts as “neutral,” but doing so ignores rich information about their customers’ complex sentiments. By
assessing the strength of positive and negative sentiment
separately, retailers can gain a better understanding of their
customers and incorporate these insights into their predictive
models and strategy.
Big Data
As the cost of data storage and processing continues to
decline, retailers are collecting more data, including purchase data from enterprise systems (e.g., quantity purchased,
price and cost of each item, size of discounts applied, composition of shopping basket, and time and date of purchase;
Grewal et al., 2017) as well as social media and demographic
information about customers. Big data provide a wealth of
both opportunities and challenges. In particular, they allow
retailers to create massive data warehouses that combine
multiple data sets and thus uncover unique insights. For
example, retailers can combine customer loyalty data, demographic information (e.g., age, gender), and geographic data
(e.g., store locations, weather forecasts) to build better
demand models that enable them to better manage inventory
and labor costs.
Big data originally were defined by the “three Vs”: volume
(amount of data), variety (types of data), and velocity (rate at
which data are generated; McAfee, Brynjolfsson, & Davenport,
2012). In recent years, researchers and practitioners have
called for adding a fourth V: veracity (Mattmann, 2013).
Retailers accustomed to working with market research firms
that provide well-validated measures and weighted samples
will need to learn to cast a more skeptical eye on big data analytics. When data are gathered from multiple sources with
varying degrees of validity, examining the accuracy or veracity of those data, before making managerial decisions, is critical. Furthermore, big data create significant security risks. The
2017 breach of Equifax, in which the personal information of
145 million U.S. consumers was stolen, the 2016 hack of Uber
in which 57 million riders’ and drivers’ data were stolen, and
the 2017 hack of Forever 21’s point-of-sale system are all
Journal of Marketing Education 40(1)
prime examples. As the amount of data stored by retailers
grows exponentially, so do the demands for retailers to adopt a
common set of procedures to protect the security of their customer data and to ensure ethical uses of the related analytics.
Any solutions retailers implement also need to pursue
win–win outcomes, for the firm and the customer. For example, website ads are often customized on the basis of data,
which customers did not know were being collected. They do
not like shopping for an air mattress on Amazon, then seeing
ads for air mattresses on every website they browse for
weeks afterward. Research has shown though that retailers
can reduce or eliminate consumer aversion to personalized
ads, simply by disclosing that their ads are personalized
(Aguirre, Mahr, Grewal, Ruyter, & Wetzels, 2015).
Active Learning and Digital Pedagogy
As smartphones, tablets, and laptops have been more widely
adopted, retailing education also has begun to shift, away
from traditional pedagogical models that use static business
cases and toward a more dynamic, active learning model.
Unlike traditional, lecture-based classes, active learning
requires students to engage and become partners in their
learning experience. Research has found active learning can
increase grades on examinations and concept inventories and
significantly increase the odds of passing the class (Freeman
et al., 2014). For example, case studies and vignettes pertaining to innovative topics and relevant experiential exercises
(e.g., retailer analyses, simulations) encourage students to
apply the knowledge they have gleaned from their textbooks
and class discussions to real-world problems. Through active
learning, students gain a better understanding and appreciation of core concepts and are more motivated to engage with
the learning process. The following sections describe some
example exercises that educators can incorporate into their
classes to encourage active learning.
Using Simulations. Textbooks are a cornerstone of retailing
education, but students often struggle to connect the various
topics in a holistic fashion. Educators tend to cover a different retailing topic each week, and though it is clear to the
educator how the concepts relate, inexperienced students
learn these topics in a disconnected fashion. To make the
content “come alive,” students might engage in retailing simulations that force them to combine multiple concepts covered in the course to manage a virtual business, perhaps in a
dynamic, competitive environment. By applying the concepts
actively, students learn the content better; research shows that
overall class grades improve with simulation performance
over the course of a semester (Woodham, 2017). Educators
can choose from simulations that focus on managing a retail
location, such as Interactive Simulation’s Entrepreneur
(https://www.interpretive.com/business-simulations/entrepreneur/), or managing a manufacturer and its retail channels,
89
Grewal et al.
such as McGraw Hill’s Practice Marketing (http://www.
mhpractice.com/products/Practice_Marketing).
There are two general options for integrating simulations
into a course: benchmark and direct competition. In benchmark simulations, each group competes against a computer
in an independent but identical business environment. This
option supports easy assessments across groups. However,
such simulations are less dynamic and engaging, because
teams are playing against a computer. Because the simulation is standardized, students can readily discuss strategies
across groups or search for optimal solutions online, which
may diminish the value and power of this learning experience. In a direct competition simulation, the student groups
instead compete against one another. Students thus are more
engaged, because they know their competitor is a real person. The business environment also is dynamic, changing
according to each group’s decisions, so students are less
likely to discuss strategies with other groups (i.e., their competitors) and cannot find solutions online, because each simulation game is unique.
The use of simulations can greatly enhance learning experiences (Woodham, 2017), but simulations also require careful integration into the class. An instructor should establish 8
to 10 groups of three to four students each (depending on
class size). Then two separate simulation games can be run,
with four or five teams assigned to each. It is possible to run
10 separate groups within a single simulation, but teams that
underperform at first often face too much competition, such
that they struggle throughout the simulation and begin to disengage from the learning process. A business environment of
four to five groups makes it possible for underperforming
teams to improve, which keeps them engaged in active learning. We also recommend that rather than using simulation
decisions as homework assignments, educators devote an
entire class period to each decision, to highlight the importance of the activity and encourage students to take it seriously. In addition, such a structure gives the instructor
sufficient time to interact with each group individually and
help them make decisions, in a strong active learning environment. The final deliverable can feature 15- to 20-minute
presentations of the virtual firm to a board of trustees (i.e.,
the class), in which students argue for why they should
remain in a leadership role, by detailing the strategies they
used, their successes and failures, and what they would do
differently in the future. Students also might write papers to
provide deeper, research-based insights into the retail analytics they used to make their decisions.
Enhanced In-Class Discussions and Breakout Groups. The digital revolution has provided retail educators with a wide array
of digital options, including e-books and a host of interactive
exercises. For example, starting with its eighth edition,
Retailing Management has integrated McGraw-Hill’s Connect Marketing platform with the digital version, offering
access to a host of retailing exercises. The ninth edition
expanded the newsletter content available and provided
online access to articles and videos specifically linked to
each chapter topic in a blog format (www.theretailingmanagement.com). Synopses of popular press articles also offer
suggested discussion questions, such that students can consider relevant, topical issues without needing to purchase
subscriptions to sources such as The New York Times or The
Wall Street Journal. The abbreviated length also supports the
use of these synopses to stimulate active learning discussions
in the classroom.
For example, consider a recent article synopsis discussing
Amazon’s foray into brick-and-mortar retailing, which will
involve groceries, as well as showrooms for Amazon products, furniture, and appliances.2 To engage students in active
learning, classes might be divided into breakout groups, each
of which receives a print version of the article and related
discussion questions, such as the following:
1.
2.
3.
4.
How is Amazon using its technology to compete in
the brick-and-mortar arena?
How important is location for Amazon’s showrooms?
Its grocery stores?
Perform a SWOT analysis to understand the risks and
benefits of Amazon opening brick-and-mortar
locations.
If you were the Chief Marketing Officer of Target,
how would you respond to Amazon’s move?
Then groups of two to four students each take about 10 minutes to read the article and consider their answers to the questions. By answering them in small groups, each student gains
an opportunity to discuss and develop her or his own opinion, which enhances student engagement, classroom discussions, and learning. While the groups are discussing the
concepts presented in the synopses, the educator can visit
each group and provide individual comments on their
insights, which establishes a scaffold for learning and provides the educator with information about students’ effective
learning and areas that may need more coverage in class.
After the groups finish their analyses, the class comes back
together to discuss the reading as a whole. Each group has
had time to analyze the reading independently, so these class
discussions tend to be dynamic and diverse.
Retailer Analysis. Another example assignment, assigns students to groups of two to three people, each of which chooses
a retailer to analyze throughout the semester. Each week, students undertake an assignment related to the course content
covered in that period (e.g., location analysis, omnichannel
strategy, merchandise planning, financial strategy), such that
they are required to visit a brick-and-mortar location of their
chosen retailer and evaluate it according to that topic. Groups
provide brief, 5-minute presentations the following week. By
90
Journal of Marketing Education 40(1)
Figure 3. Example analysis of tweets by @Walmart and @Patagonia.
Source. AnalyzeWords.com.
limiting the presentations to a maximum of 5 minutes, this
method forces students to distill their analyses into key takeaways and learn to be concise. For example, the instructions
for a retailer analysis assignment that accompanies the class
discussion of location analysis were the following:
Go to “your store.” If “your store” doesn’t have an Internet
store, choose another store for this assignment. Evaluate “your
store’s” bricks and mortar assortment. Is it the best assortment
for the space and trade area, that is, are they carrying the “right”
depth and breadth? Why or why not? Be as specific as possible.
Choose one major merchandise category and compare the
store’s assortment with the assortment on its website. How does
the depth compare? Should there be items in the store that are
currently only on the website? If so, what would you remove
from the store to make room for the Internet items?
Social Media Analytics. Another form of analysis that students
can undertake explicitly reflects modern trends in which customers increasingly connect with retailers and other consumers via social media networks, such as Instagram, Snapchat,
Facebook, and Twitter. To expose students to sentiment analysis concepts, active learning exercises are key, because they
ensure those students understand social media platforms and
how retailers can use them to drive traffic (e.g., Bal, Grewal,
Mills, & Ottley, 2015). During in-class assignments, students
can be introduced to social media analytics and NLP, perhaps
through the demo website AnalyzeWords.com (see Pennebaker & King, 1999). In an example assignment, students
identify the Twitter handles of two retailers with disparate
brand identities, such as Walmart (@Walmart) and Patagonia
(@Patagonia), then undertake a comparative analysis of
tweets by each retailer (see Figure 3). As a semester-long
project, students instead might work in teams to create fan
pages for the retailer of their choice. They can post content
on the fan page and other social media sites that they choose
to create, using Facebook, Twitter, or Instagram. The data
analytics provided by these social media sites help students
understand how different types of content (e.g., text, pictures, videos) drive traffic and engagement. Such an exercise
thus provides insights into the uses of both social media and
data analytics.
Important Forthcoming Innovations in
Retailing Technology: The Future
In this section, we discuss several emerging technologies
that have the potential to exert substantial effects on various
facets of retailing. They also promise to become interesting
discussion topics in retailing education classrooms.
Artificial Intelligence
Whether it is Apple’s Siri, Microsoft’s Cortana, Amazon’s
Alexa, IBM’s Watson, or Alphabet’s Deep Mind, AI is being
widely adopted. It already is present in the pockets of most
smartphone owners. Despite its early development stage,
91
Grewal et al.
retailers have begun to adapt their business models to include
AI. With the recognition that customers often prefer to search
on their phones rather than interact with a salesperson,
Macy’s launched On Call, a mobile application that uses
IBM’s Watson and smartphone location services to help customers navigate its stores (Arthur, 2016). Because it also features NLP, customers can ask questions of the app, using
natural phrases in either English or Spanish. The AI-based
responses then influence how customers shop in both stores
and online. Product recommendation engines, physically
locating items in a store, answering questions about store
hours and returns, and supply chain optimization all represent realistic potential uses of AI (Grewal et al., 2017).
Frontline Service Robots
The retail landscape also promises to be altered by the introduction of frontline service robots. Today, robots are used
extensively in large fulfillment centers; they offer the potential to replace human labor in other areas too, including
frontline service. For example, retailers are experimenting
with drones and driverless vehicles for deliveries (Van Doorn
et al., 2017), and McDonald’s has announced that it plans to
install automated kiosks in 2,500 of its stores to eliminate the
need for cashiers and create the “experience of the future”
(Kim, 2017).
Internet of Things
In the 1990s, users connected to the Internet through desktop
computers and dial-up modems with slow connection speeds.
But by 2009, smartphones had heavily penetrated markets
and the Internet had expanded to include mobile websites
and mobile applications. Technology continues to grow
smaller and cheaper, enabling a host of “smart devices” connected to the Internet, including Bosch Home Connect ovens,
Samsung smart washers and dryers, Nest thermostats, Ring
video doorbells, SimpliSafe security system, GPS and accelerator sensors on smartphones, and radio frequency identification tags on products. When data from a multitude of
Internet-connected sensors combine, the IoT emerges
(Gubbi, Buyya, Marusic, & Palaniswami, 2013).
Supported by decreasing storage costs, retail analytics,
and advanced visualizations, IoT promises to provide a
wealth of data to retailers that should help them optimize
their processes. Among many other applications, they can
achieve inventory optimization, predictive preventative
maintenance, and distribution center efficiencies (Siebel,
2017). Students need to be aware that IoT has not only the
potential to improve processes but also the potential to abuse
consumer privacy. If in-home appliances gather personal
data about the consumers who use them, for example, retailers have ethical responsibilities to use that data appropriately, and students should be prompted to consider such
issues carefully.
Blockchain/Distributed Ledger Technology
Predicted to be worth $7.74 billion in the next few years,
blockchain technology is poised to disrupt many industries,
including retailing (Conick, 2017; Nowiński & Kozma,
2017). In its simplest form, blockchain or distributed ledger
technology (DLT) is an immutable spreadsheet or ledger that
is stored on thousands of computers and publicly available
for anyone to search at any time (Conick, 2017). By replicating the ledger so vastly, it becomes nearly impossible for the
data contained in the spreadsheet to be hacked or altered. The
result is trust in the immutable data, such that data intermediaries are no longer necessary.
The applications of blockchain technologies pertain to
three main areas: authenticating traded goods, disintermediation, and lower transaction costs (Nowiński & Kozma, 2017).
By combining blockchain and IoT technologies, retailers can
track precisely where products are at any moment in the supply chain. If product provenance is important (e.g., wine,
coffee), consumers can scan a quick-response code on the
packaging and see the entire journey of their coffee beans,
from farm to cup (Conick, 2017). Thus, DLT can affirm customers’ trust in retailers’ claims, eliminating the need for
intermediaries that currently function to verify those claims.
For example, in the digital advertising space, fraud is rampant today, but if they turn to DLT, retailers can eliminate
intermediaries, buy ads directly, and confirm through the
blockchain that their marketing communication actually took
place. Being able to track products better and eliminate intermediaries promises to lower operations and transaction costs
substantially, providing a compelling opportunity to increase
profitability.
Discussing New Technologies in The Classroom
Within the classroom, educators must reinforce topics related
to technology that are not yet covered in textbooks. A simple
way to incorporate these concepts would be to start each
class with a brief discussion of “retailing technology in the
news.” These discussions can also incorporate recent retailing articles published in journals, such as Journal of
Retailing. Students would bring to each class popular press
retailing-related articles or academic articles pertaining to
big data, analytics, robotics, blockchain, IoT, and so on. The
instructor can randomly call on two to three students at the
start of each class meeting, who summarize the article they
read and why they think it is important to retailing. A brief
class discussion then ensues, tying the article to relevant
course concepts.
Conclusions
In the 1960s, Gordon Moore (1965), cofounder of Intel, proposed Moore’s law, which asserted that computing technology would double every 24 months—a prediction that has
92
proved surprisingly accurate. Technological revolutions have
irrevocably altered the way retailers conduct business,
requiring retailing textbooks and educators to update their
curricula constantly to stay abreast of the latest advances. We
have taken a historical view on retailing education, to highlight its past, its present, and the likely future of retailing
technologies and education.
One of the most notable changes in retailing textbooks is
their treatment of e-tailing channels. We analyzed all 10
editions of Retailing Management, including the third edition, which followed soon after Amazon sold its first book
and included a discussion of “electronic retailing” and its
potential. As the importance of the online channel grew,
Retailing Management altered its discussion, from the
potential of electronic retailing to discussing “multichannel
retailing” and how to develop retailing strategies for different channels (fifth edition). As smartphone penetration
increased and consumers became untethered from their
computers, retailers again had to shift their strategy, away
from a multichannel approach and toward merging all of
their channels into a single, seamless experience, termed
omnichannel retailing. The 10th edition of Retailing
Management thus discusses how retailers can develop
omnichannel strategies.
Just as technology has changed the retailing landscape, it
has enabled innovative new approaches that retailing educators can adopt. Instead of issuing lectures based on business
cases, educators can engage students and help them take
responsibility for their education through active learning. We
offer suggestions and summaries of several active learning
activities related to different topic areas, including retailer
analyses, competitive simulations, sentiment analysis, social
media campaigns, and big data and privacy issues. Digital
learning platforms also provide opportunities for educators
to supplement their instruction, with elements such as digital
textbooks, flashcards, and interactive lessons. Blogs, such as
www.theretailingmanagement.com, provide short summaries of important modern retailing developments that can be
distributed as mini case studies to foster classroom discussions about relevant topics.
Retailing and retailing education will continue to evolve
too. We note three key technological advances that educators
need to follow and potentially integrate into their classes.
The AI revolution may produce frontline service robots that
replace human counterparts. As the number of Internetconnected products increases, the IoT will take on increased
importance. Combining AI and retailing analytics, a revolution can optimize retailing strategies, including supply chain
efficiency, inventory management, pricing, and perhaps even
visual merchandising. As more retailers adopt blockchain
technology, new levels of transparency and closer relationships with consumers are likely to emerge, with benefits and
challenges that retail actors and the educators who teach
about them need to address.
Journal of Marketing Education 40(1)
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Notes
1.
2.
The textbook was authored by Michael Levy and Barton A.
Weitz from the first to eighth editions and by Michael Levy,
Barton A. Weitz, and Dhruv Grewal for the ninth and 10th
editions.
See http://www.theretailingmanagement.com/?p=1577.
References
Aguirre, E. M., Mahr, D., Grewal, D., Ruyter, K. D., & Wetzels, M.
(2015). Unraveling the personalization paradox: The effect of
information collection and trust-building strategies on online
advertisement effectiveness. Journal of Retailing, 91(1), 34-49.
Ailawadi, K. L., & Farris, P. W. (2017). Managing multi- and omnichannel distribution: Metrics and research directions. Journal
of Retailing, 93, 120-135.
Arthur, R. (2016, July 20). Macy’s teams with IBM Watson for
AI-powered mobile shopping assistant. Forbes. Retrieved from
https://www.forbes.com/sites/rachelarthur/2016/07/20/macysteams-with-ibm-watson-for-ai-powered-mobile-shoppingassistant/#4aad3b8b7f41
Bal, A., Grewal, D., Mills, A., & Ottley, G. (2015). Engaging students with social media. Journal of Marketing Education, 37,
190-203.
Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017). The
role of big data and predictive analytics in retailing. Journal of
Retailing, 93(1), 79-95.
Braun, S. (2015, May 8). The history of retail: A timeline. Retrieved
from https://www.lightspeedhq.com/blog/2015/05/the-historyof-retail-a-timeline/
Conick, H. (2017, November/December). What marketers need to
know about blockchain. Marketing News, 12-14. Retrieved from
https://www.ama.org/publications/MarketingNews/Pages/whatmarketers-need-know-about-blockchain.aspx
Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K.,
Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active
learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of
Sciences, 111, 8410-8415.
Grewal, D. (2018). Retail marketing management: The 5Es of retail
today. Manuscript in preparation.
Grewal, D., Bart, Y., Spann, M., & Zubcsek, P. P. (2016). Mobile
advertising: A framework and research agenda. Journal of
Interactive Marketing, 34, 3-14.
Grewal, D., Roggeveen, A., & Nordfält, J. (2017). The future of
retailing. Journal of Retailing, 93(1), 1-6.
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013).
Internet of things (IoT): A vision, architectural elements, and
future directions. Future Generation Computer Systems, 29,
1645-1660.
Grewal et al.
Inman, J. J., & Nikolova, H. (2017). Shopper-facing retail technology: A retailer adoption decision framework incorporating
shopper attitudes and privacy concerns. Journal of Retailing,
93(1), 7-28.
Kim, T. (2017, June 20). McDonald’s hits all-time high as wall street
cheers replacement of cashiers with kiosks. CNBC. Retrieved
from https://www.cnbc.com/2017/06/20/mcdonalds-hits-all-timehigh-as-wall-street-cheers-replacement-of-cashiers-with-kiosks.
html
Kumar, V., Anand, A., & Song, H. (2017). Future of retailer profitability: An organizing framework. Journal of Retailing, 93(1),
96-119.
Levy, M., & Weitz, B. A. (1995). Retailing management (2nd ed.).
Burr Ridge, IL: McGraw-Hill/Irwin.
Levy, M., Weitz, B. A., & Grewal, D. (2014). Retailing management (9th ed.). Burr Ridge, IL: McGraw-Hill/Irwin.
Levy, M., Weitz, B. A., & Grewal, D. (2018). Retailing management (10th ed.). Burr Ridge, IL: McGraw-Hill/Irwin.
Mattmann, C. A. (2013). Computing: A vision for data science.
Nature, 493, 473-475.
McAfee, A., Brynjolfsson, E., & Davenport, T. H. (2012). Big
data: The management revolution. Harvard Business Review,
90(10), 60-68.
Moore, G. E. (1965). Cramming more components onto integrated
circuits. Electronics, 38, 114-117.
Nowiński, W., & Kozma, M. (2017). How can Blockchain technology disrupt the existing business models? Entrepreneurial
Business and Economics Review, 5, 173-188.
Ordenes, V. F., Ludwig, S., Ruyter, K. d., Grewal, D., & Wetzels,
M. (2017). Unveiling what is written in the stars: Analyzing
explicit, implicit, and discourse patterns of sentiment in social
media. Journal of Consumer Research, 43, 875-894.
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1-2),
1-135.
Pennebaker, J. W., & King, L. A. (1999). Linguistic styles: Language
use as an individual difference. Journal of Personality and
Social Psychology, 77, 1296-1312.
Rafaeli, A., Altman, D., Gremler, D. D., Huang, M.-H., Grewal,
D., Iyer, B., . . . Ruyter, K. d. (2017). The future of frontline
View publication stats
93
research: A glimpse through the eyes of thought leaders. Journal
of Service Research, 30(1), 91-99.
Roggeveen, A. L., & Grewal, D. (2016). Engaging customers:
The wheel of social media engagement. Journal of Consumer
Marketing, 33(2). doi:10.1108/JCM-12-2015-1649
Roggeveen, A. L., Grewal, D., Townsend, C., & Krishnan, R.
(2015, November). The impact of dynamic presentation format
on consumer preferences for hedonic products and services.
Journal of Marketing, 79, 34-49.
Roggeveen, A. L., Nordfält, J., & Grewal, D. (2016). Do digital displays enhance sales? Role of retail format and message content.
Journal of Retailing, 92, 122-131.
Siebel, T. M. (2017, December). Why digital transformation is now
on the CEO’s shoulders. McKinsey Quarterly. Retrieved from
https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/why-digital-transformation-is-now-on-theceos-shoulders
Stone, B., & Boyle, M. (2017, May 4). Can Wal-Mart’s expensive
new e-commerce operation compete with Amazon? Bloomberg
Businessweek. Retrieved from https://www.bloomberg.com/
news/features/2017-05-04/can-wal-mart-s-expensive-new-ecommerce-operation-compete-with-amazon
Van Doorn, J., Martin, M., Noble, S. M., Hulland, J., Ostrom, A.
L., Grewal, D., & Petersen, J. W. (2017). Domo Arigato Mr.
Roboto: Emergence of automated social presence in organizational frontlines and customers’ service experiences. Journal of
Service Research, 20(1), 43-58.
Wingfield, N., & de la Merced, M. J. (2017, June 16). Amazon to
buy Whole Foods for $13.4 billion. New York Times. Retrieved
from https://www.nytimes.com/2017/06/16/business/dealbook/
amazon-whole-foods.html
Witcher, B., Swerdlow, F., Gold, D., & Glazer, L. (2015). Mastering
the art of omnichannel retailing. Retrieved from https://www.
forrester.com/report/Mastering+The+Art+Of+Omnichannel+
Retailing/-/E-RES129320.
Woodham, O. P. (2017). Testing the effectiveness of a marketing simulation to improve course performance. Marketing
Education Review. Advance online publication. doi:10.1080/
10528008.2017.1369356