The Future of Commerce: Linking Modern Retailing Characteristics

with Cloud Computing Capabilities

Christian Daase

, Matthias Volk

, Daniel Staegemann

and Klaus Turowski

Institute of Technical and Business Information Systems, Otto-von-Guericke University, Magdeburg, Germany

Keywords: Modern Retailing, Future e-Commerce Characteristics, Cloud Computing, Systematic Literature Review.

Abstract: The future of retail will be shaped by the rapidly evolving digital landscape. New technologies such as big

data analytics, artificial intelligence, virtual reality, and cloud computing are expected to play a crucial role

in advertising products, making personalized offers tailored precisely to customers' needs, and thus meeting

rising expectations in connection with the general improvement in living standards. In this paper, the

characteristics of modern retailing and e-commerce in particular are examined in detail. Based on a systematic

literature review, the nature of current and future retail business models is analyzed step by step, starting from

more conceptual aspects to concrete underlying technological capabilities with the ultimate goal of leveraging

cloud computing tools to realize them. Common proven practices are categorized, described, and assessed to

provide a comprehensive overview of opportunities, challenges, and consequences in this area. Finally, the

identified technological capabilities are aligned with adequate cloud services, exemplarily presented with

tools of the Google Cloud.

1 INTRODUCTION

Digitization, as one of the major buzzwords of our

time, is rapidly transforming almost every aspect of

our daily lives. Thus, it may seem obvious that the

quote “The only constant in life is change”, which is

attributed to the Greek philosopher Heraclitus, also

applies partly to the sector of commerce (Sims, 2018).

While the traditional role of a retailer was to serve

customers and physically distribute products, more

and more stages of the value chain, such as payment

and delivery, are nowadays handled by third-party

companies (Reinartz and Imschloß, 2017). But not

only the businesses’ perspective regarding the pursuit

of financial efficiency is changing. Since the retail

industry in general is dependent on the overall

national economic situation and the living standards

therein (Xue et al., 2017) and today’s customers tend

to adopt increasingly busy lifestyles (Shankar et al.,

2021), the behavior of clients is evolving as well.

While becoming more demanding, customers expect

smooth and tailored processes, services, and offerings

(Alexandrova and Kochieva, 2021; Farah et al., 2019;

https://orcid.org/0000-0003-4662-7055

https://orcid.org/0000-0002-4835-919X

https://orcid.org/0000-0001-9957-1003

Marchand and Marx, 2020) across a multitude of

channels (Tyrväinen et al., 2020). The modern retail

industry faces a new generation of consumers that has

been grown up with digital technologies and therefore

expects companies that are after them as potential

new customers to align with their preferences of how

business is conducted (Kahn et al., 2018). The process

that Grewal et al. summarize with the sentence “The

worlds of online and offline are converging.” (Grewal

et al., 2017) is closely related to the phenomenon of

the classic stages of consumption, meaning need

occurrence, actual transaction, and consumption

itself, moving closer together (Reinartz and Imschloß,

2017). While the ability to buy products around the

clock seven days a week across multiple online and

offline channels might be a convenience for

customers, the provision of these e-commerce

platforms places pressure upon retailers (Kahn et al.,

2018; Lim et al., 2022; Reinartz and Imschloß, 2017;

Sasidhar and Mallikharjuna Rao, 2020).

In this article, the transformation of the retailing

landscape is investigated from a technological point

of view with respect to social and economic

418

Daase, C., Volk, M., Staegemann, D. and Turowski, K.

The Future of Commerce: Linking Modern Retailing Characteristics with Cloud Computing Capabilities.

DOI: 10.5220/0011859600003467

In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS 2023) - Volume 2, pages 418-430

ISBN: 978-989-758-648-4; ISSN: 2184-4992

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

developments. The data richness of the sector

(Dekimpe, 2020), the explosive growth of social

media and networking platforms (Grewal et al., 2017;

Kahn et al., 2018), and external events such as the

COVID-19 pandemic (Guha et al., 2021; Shankar et

al., 2021; X. Wang et al., 2021) drive the need for

adjustments up to the exploitation of the big shifts in

business and society. Big data, the Internet of things

(IoT), cloud computing, machine learning (ML), and

artificial intelligence (AI) are just a few examples of

technologies whose interplay might be the most

promising revolution of the retail sector of the future

(e.g., (Dekimpe, 2020; Grewal et al., 2017; Kahn et

al., 2018; Sasidhar and Mallikharjuna Rao, 2020;

Shankar et al., 2021)). Since cloud computing, as a

paradigm in which networks of remote servers are

used to store, process, and manage data (Shankar et

al., 2021), has the ability to connect different digital

technologies and can also be extensively utilized by

smaller companies due to dynamic pricing models

(i.e., pay-per-use, pay-as-you-go) (Adewumi et al.,

2015; Chan et al., 2017; Khidzir et al., 2018), the

focus of this work is on how to leverage cloud

technologies for especially e-commerce-related

retailing activities. Thus, the following research

questions are in the center of investigation:

RQ1: Which characteristic trends shape the current

and future sector of retailing and which

consequences for managers and policy makers

arise from these?

RQ2: Which necessary technological capabilities

that could be handled or supported by cloud

computing tools can be derived from these

developments?

Subsequently to this introductory section, the

methodology that is followed to propose possible

answers to these questions is explained in more detail.

This includes an overview of the overall research

approach in accordance with the design science

research methodology (DSRM) proposed by Peffers

et al. (2007) and a detailed review protocol for the

systematic literature review (SLR) conducted in this

paper. Section 3 presents results from the SLR on

expected technological key points of future retailing

that should also be considered in e-commerce, thus

answering RQ1. In section 4, explicit recommendable

capabilities of modern (e‑)retailing are categorized

and matched against cloud computing tools of leading

cloud service providers in section 5, thus answering

RQ2. Finally, an outlook on future research and

improvements is given in section 6.

2 METHODOLOGY

The research steps adopt a subset of the six activities

described by Peffers et al. (2007) for the DSRM,

considered as the theoretical part of a DSR project

(Daase et al., 2022). The research in this paper

includes the problem identification and motivation,

defining objectives of a solution, and a preliminary

design step. The development, demonstration, and

evaluation are outlined in the conclusion as potential

practical implementations of the findings of this

work. The step of communication is conducted by

sharing and discussing the results with the academic

community. To achieve a higher scientific rigor, the

first three steps, which are performed by means of an

SLR, additionally factor in the recommendations of

vom Brocke et al. (2009). The summarized research

process is visualized in Figure 1.

Figure 1: Design science research process with methods and

outcomes of each step.

The first step for organizing the SLR is to define a

search strategy in terms of textual queries, databases,

inclusion and exclusion criteria, and a procedure for

extracting the target information. Vom Brocke et al.

(2009) note, that usually the quality of journal

contributions can be considered higher than that of

conference proceedings. Therefore, primarily

established peer-reviewed journals covering a broad

spectrum of diverse topics in sales-oriented business

areas are included in the review. Secondarily,

SpringerLink, with its collection of in-house journals

The Future of Commerce: Linking Modern Retailing Characteristics with Cloud Computing Capabilities

419

and conferences, is included due to its widely

recognized quality, as well as the abstract and citation

database Scopus, which claims to be the largest

database of its kind (Kitchenham and Charters, 2007).

As a tertiary source for articles that may not be

indexed in one of the largest databases, the first page

of a Google Scholar search ranked by relevance is

also included in the review.

As the primary source, the database ScienceDirect

of the publisher Elsevier is chosen because of its

extensive publication base of over 19 million items

and its well-organized publication browser. The

domain of business, management and accounting,

and more specifically the subdomain of marketing, is

considered as the starting point for this investigation.

The determination for screening the journals to

identify a suitable selection for further review is to

consider the criteria of being established (more than

5 years active), geographically unaffiliated, and

covering a wide range of topics related to businesses

that are associated with retail. The four journals

meeting these criteria are Electronic Commerce

Research and Applications, International Journal of

Research in Marketing, Journal of Retailing, and

Journal of Retailing and Consumer Services.

Since the only journal in SpringerLink including

the term retail in its title stopped publishing in 2010,

the general advanced search of SpringerLink for all

types of publications was used instead. The other

secondary source of literature is Scopus due to its

inclusion of abstracts and citation data from various

full-text databases. Google Scholar is considered as

an addition under reservation because it also

references to uploads of papers on collaborative

networks with possible unverified assertions.

However, elaborations commissioned by a reputable

institute or similar are reviewed.

To identify abstract capabilities and

characteristics of future retailing from the Elsevier

journals, the review workflow refrains from defining

more restrictive search phrases for the journals

Electronic Commerce Research and Applications and

the Journal of Retailing. For the International

Journal of Research in Marketing, the terms retail or

retailing must appear in the abstract, because the

concept of marketing is otherwise too broad, although

it is an indispensable part of retailing. Publications

orienting from the Journal of Retailing and Consumer

Services must contain a form of retail* (i.e., with an

asterisk as wildcard) in the title, since sample

searches have shown that the spectrum of articles is

too vast for an unrestricted approach. Furthermore,

only contributions marked as research article are

considered. The investigation of publications

included in the database of SpringerLink more

specifically targets the influence of cloud computing

technologies on retail and related areas. Therefore,

using the advanced search, the title of articles must

contain the terms retail, retailing, or retailer,

respectively (i.e., requiring three separate searches),

and at least the text body must contain the term cloud.

The content type is restricted to articles and

conference papers. Similarly, in Scopus, the title must

contain the construct retail* and the abstract must

include cloud. The limitation to the abstracts is

conditioned by the type of the database Scopus while

the asterisk is a placeholder for characters, thus

including terms such as retail, retailing, or retailer.

Document types are limited to the same as

SpringerLink. In Google Scholar, the simplified

search consists of the phrase future of retailing with a

sorting according to relevance (i.e., the provisioned

mechanism of the search engine). Only the first page

with its ten articles is added to the literature base.

Regarding the time frame, it is determined that the

publications should not be released before 2017 to do

justice to the designation of investigating the state of

the art up to future-oriented assumptions on the retail

industry. From the Elsevier journals, only the

volumes published starting from the year 2017 are

reviewed. The search ends with November 2022 and

the respectively last completed volume. Table 1

summarizes the above sources of literature and the

restrictions regarding the search process.

Table 1: Literature sources and specifications.

Source Restrictions

All - Published between Jan

2017 an

Nov 2022

ScienceDirect - marke

as research article

Electronic Commerce

Research and

pplications

---

International Journal

of Research in

arketin

- Abstract contains retail*

ournal o

etailin

---

Journal of Retailing

and Consume

Services

- Title contains retail or

retailin

SpringerLink - Title contains retail,

retailing, or retailer

- Text body contains cloud

rticle o

con

erence

Scopus - Title contains retail*

- Abstract contains cloud

rticle o

con

erence

Google Scholar - Search future of retailing

- Sorted by relevance

- Onl

first hit

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420

The criteria for the process of including and

excluding publications that cannot be filtered with the

build-in mechanisms of a database have to be

transparent to allow readers to assess the

exhaustiveness and reusability of the review’s results

for their own endeavors (vom Brocke et al., 2009). To

do so, the criteria collected and presented by

Kitchenham and Charters (2007) are adopted for this

review. In summary, the inclusion and exclusion

criteria can be described as in Table 2.

Table 2: Inclusion and exclusion criteria for the SLR.

Inclusion Exclusion

The goal of the business

area under investigation is

to sell items and/or serve

customers.

No abstract is available, it

does not state any

information on the content

of the paper, or the paper is

unavailable.

The study concentrates on

one core section of the

value realization chain in

retail (e.g., trend

forecasting, supply chain,

customer management)

after manufacturing

(except when the

manufacturer itself is the

distributor) and before

consum

tion o

dis

osal.

The study is an exact or a

semantic duplicate,

whereas the latter is

considered as a publication

on the same subject, by the

same authors within a short

period of time.

The research is conducted

from the company's

perspective rather than

from the customer's

standpoint.

The study specifically

observes one country or

geographic region, with no

general or global

applicability of the results

to the retail

ecto

Statements on the state of

the art and/or future

directions are provided.

No assessment is made of

the impact of a concrete

technological innovation or

phenomenon.

3 RELATED WORK

The literature review workflow is structured into

three stages. First, the automatically retrieved articles

are collected, counted, and sorted in a project internal

table structure. Second, within a fast-screening

review stage, the publication titles and abstracts are

read to allow an exclusion of apparently unsuitable

ones. Third, by reading the remaining articles in

detail, the most important details for explaining the

scientific fundamentals of the present project are

extracted. Figure 2 illustrates the review process with

numerical notes on how many articles where

originally retrieved and then excluded based on the

criteria established in section 2. From the initial 1106

potential contributions found in the Elsevier journals

and databases using the above stated search queries

and specifications, 205 were examined in detail of

which in turn 49 were deemed as sufficiently useful

for the explanations in the subsequent sections.

Figure 2: Literature review workflow visualization.

3.1 Modern Retail Environments

In this subsection, both currently established

transformations of retail business models and

development trends whose realization will be targeted

more forcefully in the future are synthesized from the

available literature. Thereby, essential parts of a

possible answer to the first research question (RQ1)

formulated in the introduction are compiled and put

into context.

3.1.1 Changed Characteristics

The value chain of the future is characterized by a

reordering of how and by whom products get into the

hands of customers. The trend toward

disintermediation (Gielens and Steenkamp, 2019;

Shankar et al., 2021), which enables the elimination

The Future of Commerce: Linking Modern Retailing Characteristics with Cloud Computing Capabilities

421

of layers in the distribution channel through online

market platforms where manufacturers can sell their

products to consumers with fewer intermediaries, has

led not only to a reduction in the cost of

intermediaries but also to the rise of new powerful

players. Since companies such as Amazon and

Alibaba are well aware of their established position in

the middle of retailing processes, aspirations of

pushing own private labels turn these enterprises

partially from facilitators into competitors, thus

deeply changing the retail landscape (Alexandrova

and Kochieva, 2021; Gielens and Steenkamp, 2019;

Grewal et al., 2017; Reinartz et al., 2019; Shankar et

al., 2021). This development is largely driven by a

generally changing customer behavior. Due to the

extent of goods available online at various prices, it

became increasingly normal to extensively search for

the assumingly best purchase option, price-wise as

well as regarding the quality of a product by

comparing reviews of other consumers (Gielens and

Steenkamp, 2019; Reinartz and Imschloß, 2017).

More conveniently, platforms such as Amazon

combine these capabilities so that it is only a small

step for online shoppers to click on the perceived best

offer instead of buying offline. The trend that

platforms such as Amazon are able to compete with

traditional retailers and the entirety of their business

models, including payment systems and delivery

strategies, is supported by the simultaneous rise of

specialized third-party companies taking over

substantial parts of the commercial processes, for

example DHL (a German delivery service) for

physical distribution or PayPal for payment

transactions (Reinartz and Imschloß, 2017).

However, it is worth mentioning that the stationary

retail does incorporate new approaches to outsource

operational activities for the sake of increasing

profitability as well. Under the umbrella term

autonomization, several service-, payment-, and

delivery-related innovations have been introduced by

the retail industry, such as chatbots, self-checkouts,

and delivery drones (Bellis and Johar, 2020; Grewal

et al., 2018; Shankar et al., 2021).

As a remarkable variation of disintermediation,

Direct-to-Consumer (D2C) models allow brands to

engage directly with customers by means of digital

channels (Gielens and Steenkamp, 2019). In this

context, due to today’s widespread use of mobile

devices, an increasing number of transactions is

processed through websites or tailored mobile

applications (Khidzir et al., 2018; Lim et al., 2022;

Nikhashemi et al., 2021; Reinartz et al., 2019; Haiyue

Zhang et al., 2021). One advantage compared to

traditional sales points is that the brand does not need

to compete with other brands on the same store shelf.

However, as tempting as the opportunities of pure

online shops may be, this development is not

expected to replace physical stores entirely as

consumer interfaces but to put a certain pressure on

them to adapt more closely to customers’ needs and

desires (Ferracuti et al., 2019; Huddleston et al.,

2018; Reinartz et al., 2019). Furthermore, traditional

stores enable customers to evaluate a number of

attributes of goods that simply cannot be experienced

in online retailing, for example fit and wearing

comfort of garments (Smink et al., 2019). Yet, online

shops can reach customers living farther away from

the store, which is why they can serve as sensible

supplements (van Heerde et al., 2019). In conclusion,

a partial redefinition of the customer journey in terms

of an increasing number of touchpoints towards so-

called omnichannel retailing can be observed

whereas customers tend to seamlessly switch between

the channels (Liao and Yang, 2020; Rudkowski et al.,

2020; Tyrväinen et al., 2020).

While these developments relate to the most

dominant part of retailing, the actual transaction,

retailers should keep in mind that it builds upon

another stage of the classic consumption model, the

prepurchase phase of need occurrence (Reinartz et al.,

2019; Reinartz and Imschloß, 2017). Attracting and

directing customers’ attention therefore is an essential

task of any retailer (Huddleston et al., 2018). From

the literature findings, the reformation of marketing

strategies can be divided into two branches: the

gathering of necessary data to create purposeful

advertising concepts and the effective presentation of

these. Summarized under the key term of big data,

massive amounts of analyzable data from all available

sources, such as social media, sales data,

demographics, or other historical internal or external

data, are collected and processed (Dekimpe, 2020;

Grewal et al., 2017; Grewal et al., 2018) in order to

increase efficiency and profits, improve customer

experience and demand prediction, and to gain a

deeper understanding of customer behavior in general

(Grewal et al., 2017; Kaur et al., 2020; Sasidhar and

Mallikharjuna Rao, 2020).

The literature indicates that some of today’s

retailers are becoming increasingly interested in

micromanaging the optimization of their stores. For

example, examining customer behavior through in-

store video analytics (Kaur et al., 2020; Liciotti et al.,

2017) and customizing displayed advertisement (Han

et al., 2022), or studying that customers are used to

counter-clockwise aisle arrangements in countries

with right-handed traffic (Ferracuti et al., 2019) are a

reality nowadays. Worth noting, the underlying

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capabilities of data analysis (e.g., storage,

networking, and computation power) require

dedicated digital infrastructures that are associated

with costs.

With the overarching theme of this article in mind,

the effort and cost of deploying efficient and scalable

solutions can be mitigated by taking advantage of the

emerging field of cloud computing (Adewumi et al.,

2015; Chandrashekhara et al., 2019; Lee, 2017).

Modern marketing, or more broadly termed

dissemination of information that can stimulate a

purchase, resorts to a variety of newer phenomena,

such as social media and video-to-retail applications

(Alexandrova and Kochieva, 2021; Dolega et al.,

2021; Grewal et al., 2017; Huaizheng Zhang et al.,

2020), and old but adapted institutions, like

temporary pop-up shops (Rudkowski et al., 2020;

Shankar et al., 2021). Personalized advertising using

enhanced data analysis techniques to craft

individually tailored advertisements can improve

customer experience and loyalty (Tyrväinen et al.,

2020). Note that it should not to be confused with

personalization, meaning the provision of

personalized offers based on (historical) customer

data (Reinartz et al., 2019; Tyrväinen et al., 2020)

only when a customer actively visits a store or

website (Gironda and Korgaonkar, 2018), thus

transforming retailing from a product-centric to a

customer-centric environment (Gupta and

Ramachandran, 2021). However, uncertainty about

how customer data were obtained can have a serious

negative impact on trust, whereby studies suggest that

this threat could be alleviated by simply disclosing

that an advertisement is in fact personalized and

where the respective data were found (Grewal et al.,

2018; Marchand and Marx, 2020). Despite this

general statement, if the data are private and not

directly consciously or unconsciously shared with the

company (e.g., data orienting from social media

profiles), the personalized advertisement can be

expected to elicit strong negative reactions (Gironda

and Korgaonkar, 2018).

Lastly in this subsection, the adaptation of supply

chains is not neglected by modern retail enterprises.

Significant cost reductions of up to about 12 percent

by means of effective information sharing (Chan et

al., 2017), substantial savings by adopting 3D

technologies for virtual prototyping (Shankar et al.,

2021), and augmented reality (AR) to allow

customers to get realistic ideas of a product before

ordering and eventually returning it (Nikhashemi et

al., 2021) are just a few examples provided by the

literature. More commonly, forecasting demand as

part of efficient logistics has been widely established

whether to reduce costly unnecessarily storage

capacities (Sasidhar and Mallikharjuna Rao, 2020) or

to avoid stock outs and missed selling opportunities

(Dekimpe, 2020; Wolters and Huchzermeier, 2021).

Besides these organizational considerations, the

adoption of robotics within the journey of a product

in the form of warehouse robots and delivery drones

has an increasing impact of the modern retail world

(Grewal et al., 2017; Shankar et al., 2021).

Concluding these observations, two hypotheses can

be set up. First, domains traditionally handled by

service employees are currently in a phase of

transformation, in a way that staff members can add

less and less value as consultations are overtaken by

online reviews, payment transactions by third-party

systems, and delivery and returns by robots (Bellis

and Johar, 2020). Second, the competition of the

future is technological (Reinartz and Imschloß,

2017). Retailers unable to exploit vital technologies

can be expected to face serious difficulties and, in the

long term, a decline in their business.

3.1.2 Potential Challenges

Available infrastructure and knowledge can evolve

into the main bottlenecks while integrating

technological advances (Chan et al., 2017; Dekimpe,

2020; Gottlieb and Bianchi, 2017). In addition, if the

perceived benefits for the retailer are not tangible

enough, the adoption is further inhibited (Shankar et

al., 2021). However, the outbreak of the COVID-19

pandemic has not only strengthened the role of

technologies for online retailing but has also urged

the industry to build up and switch to innovative

business models, preferably with reduced physical

contact (Guha et al., 2021; Shankar et al., 2021; X.

Wang et al., 2021). A clash between the inertia to

stick to established models and the pressure to adapt

to a new reality therefore seems conceivable.

Thereupon, two existing issues might gain intensity.

On the one hand, a phenomenon called showrooming,

where customers visit physical shops solely to inspect

a product that they are likely to buy online later under

better price conditions (Kokho Sit et al., 2018; Lee,

2017; Schneider and Zielke, 2021; J. Wang and

Wang, 2022), may become a more vexing problem as

the stationary retailer invests time and money without

reaping the rewards. Occasionally, a counter-concept

called webrooming can be observed, where

consumers visit online shops only for the purpose of

assessing products informationally but then make the

actual purchase offline (Smink et al., 2019; Z. Wang

et al., 2021). This practice indicates that the desire to

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423

be consulted in person is still present and is

furthermore valued.

A second issue, product returns, was ever existing

but has reached a new level through customers’

online shopping behavior (Smink et al., 2019). The

main reason is the intrinsic uncertainty whether a

product, for example garments, meets the

expectations (e.g., fit and coloring) when buying

online (Li et al., 2019; Haiyue Zhang et al., 2021). To

overcome these challenges, new strategies to

communicate the attributes and benefits of a product

and to handle the natural behavior of consumers must

be developed.

From the largely established characteristics, a few

possible development tendencies outlined in the

literature can be derived. The demographic change

towards a generation of digital natives as well as the

successful integration of technologies by industry

leaders (early adopters) will foster the adoption by

rather risk-averse retailers (fast followers) (Shankar et

al., 2021). Hazardous events such as the pandemic

and financial fraud will drive the need for enhanced

safety, contactless payment, and security systems

(Liao and Yang, 2020; Rudkowski et al., 2020;

Shankar et al., 2021; X. Wang et al., 2021).

Furthermore, legal regulations will certainly force the

industry to conceptualize strategies to track the

movement of goods to ensure, for example, food

safety (Shankar et al., 2021), but also to limit oneself

to anonymized customer data when it comes to

segmentation and behavior analysis (Gironda and

Korgaonkar, 2018; Kakatkar and Spann, 2019).

Regarding the issue of showrooming, retailers might

rethink their physical stores as showrooms

intentionally to give customers the chance to

experience their products while the transaction is

performed online on purpose (Li et al., 2019; Shankar

et al., 2021; Haiyue Zhang et al., 2021). Whether

forecasting trends or demand, optimizing prices,

stores, or customer experience, the future of retailing

will hold fascinating developments from which all

have one aspect in common: digital technologies

based on buzzwords such as artificial intelligence, big

data, and the Internet of Things (Alexandrova and

Kochieva, 2021; Bellis and Johar, 2020; Guha et al.,

2021; Marchand and Marx, 2020; Sasidhar and

Mallikharjuna Rao, 2020; Shankar et al., 2021). The

characteristic evolutional tendencies in the retail

sector, connected to their overarching domain, are

summarized in Figure 3 along with associated

consequences and important issues that need to be

considered by retailers.

Figure 3: Overview of development tendencies in retail and

associated consequences.

4 MODERN (E-)RETAILING

CAPABILITIES

In the following, the four fields of marketing and

customer engagement, store management and

representation, prediction and forecasting, and

product assessment and shopping experience are

connected with corresponding enabling technologies.

These elaborations serve as the first part of an answer

to RQ2.

4.1.1 Marketing and Customer Engagement

Mobile applications by retailers have been

established as vital tools and more specialized

interfaces than websites to get in contact with

customers (Lim et al., 2022; van Heerde et al., 2019).

Through such apps, both sides of personalization (i.e.,

data collection and presentation of individual

offerings) can be efficiently realized. Based on the

phenomenon of big data analytics (BDA),

personalization is assumed to provide a number of

advantages. Studies indicate that the provision of

offers that are likely to fulfill a customer’s current

needs is valued (Farah et al., 2019; Reinartz et al.,

2019), leading to an improved customer experience

and loyalty (Tyrväinen et al., 2020), and ultimately

tends to increase the chances of sales (Kaur et al.,

2020). From a legal point of view, data for the

purpose of marketing research often must be

anonymized. Due to the wide availability, however, a

segmentation of customers is possible, although the

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granularity is limited (Jansen et al., 2021; Kakatkar

and Spann, 2019).

Recommending specialized offers as a

combination of identifying consumers’ needs and

matching products accordingly based on big data also

involves a realistic potential use of artificial

intelligence (Bellis and Johar, 2020; Grewal et al.,

2017; Zhu et al., 2022). Moreover, a report by

McKinsey & Company (a major management

consulting firm) projects that, out of 19 industries, the

most valuable impacts of AI will occur in the retail

sector (Bellis and Johar, 2020; Guha et al., 2021).

Marchand and Marx conclude that the current

developments, especially the widespread use of smart

devices, are moving the society “into an age of

artificial intelligence-based assistance provided by

automated recommender systems” (Marchand and

Marx, 2020). Social media networks such as

Facebook and Instagram have emerged as suitable

platforms for advertising campaigns and - depending

on their scope, complexity, and brand status -

generally have a positive impact on sales figures

(Dolega et al., 2021; Grewal et al., 2018). For

example, data on emerging trends and customer

behavior could be analyzed in a cloud environment,

and the knowledge gathered could be used to

automatically and regularly post relevant content on

a social media presence (Kaur et al., 2020).

Since this kind of marketing is rather impersonal

without giving customers or other interested parties

much space for interaction, companies are also trying

to find a compromise between exploiting the modern

technological capabilities, especially those enabled

by the Internet, and traditional events to push

products, such as trade fairs. Virtual reality (VR)

paved the way for virtual trade shows in which

exhibitors can connect with visitors remotely without

having to physically attend the event (Gottlieb and

Bianchi, 2017). With respect to the lessons learned

during the COVID-19 pandemic, VR-enabled

opportunities to stay in close contact with customers,

partners, or even competitors by means of trade

shows while avoiding critical physical proximity can

be expected to attract further interest.

4.1.2 Store Management and Representation

As explained in section 3.1.1, mere online retailing is

not expected to replace stationary shops entirely. The

management of a store and its representation to the

outside, however, is assumed to be reformed based on

the reviewed literature. Besides the automation of

marketing activities, as described in the previous

section, consumer processes such as purchasing can

be increasingly automated by means of the IoT and

smart household appliances that order certain

products (e.g., food or detergent) when the stock is

low (Reinartz et al., 2019). Robotics in the form of

checkout automation, drones, and driverless vehicles

further reduce the human effort needed to complete

the distribution of purchased items (Grewal et al.,

2017; Noble et al., 2022) while e-wallets for mobile

payment facilitate financial transactions, thus saving

time and potentially personnel (Liao and Yang,

2020).

Inside a store, video analytics can help to better

understand customers and to design the interior as

well as the assortment accordingly (Kaur et al., 2020;

Liciotti et al., 2017). Outside of stores, video

analytics can help assess customer sentiment in

review videos (Agrawal and Mittal, 2022). The

central component of such systems might be a cloud

environment, including capacities to store the vast

amount of visual data and analytical capabilities to

draw the right conclusions from the consumers’

movements and product selections (Ferracuti et al.,

2019; Liciotti et al., 2017). One step further towards

micromanaging a store, eye tracking made it possible

to evaluate exactly what a customer is looking at, in

what order, and for how long (Huddleston et al.,

2018). In all these applications, it can be argued that

AI in general and ML as a subcategory form the

backbone of such analyses.

4.1.3 Prediction and Forecasting

Keeping inventory for no specific reason simply

because the demand does not correspond with the

number of ordered items is an unnecessary yet

avoidable expenditure since storage capacities are

usually not for free (Sasidhar and Mallikharjuna Rao,

2020). On the other hand, ordering to small quantities

can lead to stock-outs that also result in financial

losses (i.e., generating lower profits than possible)

(Dekimpe, 2020). Extracting purchase patterns from

historical data in cloud environments to predict future

buying behavior for anticipatory shipping is already a

reality in a number of successful businesses such as

H&M and Zara, two fashion companies from Sweden

and Spain, respectively (Alexandrova and Kochieva,

2021; Lee, 2017). Additional to organizational data,

information acquired from social media (i.e., social

media data scraping) can be used to better forecast

sales, design campaigns based on trend analyses, and

ultimately increase customer satisfaction (Kaur et al.,

2020). Another aspect adding to the complexity of

inventory management is pricing, which must be

factored into the interdependencies between supply

The Future of Commerce: Linking Modern Retailing Characteristics with Cloud Computing Capabilities

425

and demand. ML models, trained with different

features of a product and historical sales data, can

serve as a decision support to choose an appropriate

pricing policy for a specific product

(Chandrashekhara et al., 2019).

4.1.4 Product Assessment and Shopping

Experience

In order to make an informed purchase decision with

minimal risk of returning the product, certain

properties of a good must be carefully assessed by the

customer. Studies suggest that technologies

supporting the realistic perception of an item, such as

AR and VR, can have a positive impact on the

intention to buy it (Arghashi, 2022; Arghashi and

Yuksel, 2022; Nikhashemi et al., 2021; Z. Wang et

al., 2021). As a side effect, a more direct shopping

experience can promote trust, leading to a higher

willingness to disclose personal information (Smink

et al., 2019). The two key technologies, AR and VR,

differ in the way the visualization is presented and

thus in which scenarios a particular approach is most

suitable. VR places the user in a computer-generated,

entirely artificial environment while hiding the

surrounding real world and thus creates a highly

immersive experience (Farah et al., 2019; Gottlieb

and Bianchi, 2017). With that in mind, VR in retail is

primarily appropriate for promoting products that

either do not require to be examined in context with

other present items or do require a specific

atmosphere to be optimally presented. For example, a

virtual world consisting of an open road at sunset to

get a realistic feel for a customized car would likely

create greater anticipation for purchasing the vehicle

than just a virtual model of the car in a real-world

showroom. The latter approach corresponds to the

definition of AR where a product can be inspected by

overlaying a virtual model onto the real-world

surroundings or even the own body (Smink et al.,

2019). One possible application therefore is to try on

clothes before buying via virtual fitting rooms (Li et

al., 2019). Both technologies can help to prevent the

issue of showrooming, although stationary retailers

might not be the beneficiaries of this development.

Since the customers’ uncertainty about a product due

to the lack of tangible information can be viewed as

the main reason for showrooming (Kokho Sit et al.,

2018), a direct realistic vision of an item provided by

the capabilities of VR or AR can help to reduce the

doubts about buying it. Thus, the perceived need for

the customer to physically inspect the product could

be reduced and a retailer's resources (e.g., time to

consult with a customer) might be spared. Figure 4

summarizes the findings derived from the literature

that was available following the strict methodological

approach, without claiming completeness. Firstly, the

outermost boxes wrap the four categorized parts of

retailing, which are further divided into several more

specific activities of the respective parts. Lastly,

concrete technologies associated with these activities

are stated. The different gray colorings link

technologies that are mentioned more than once.

Figure 4: Categorization of identified future technological

(e-)retailing capabilities.

5 MATCHING CLOUD TOOLS

WITH THE CAPABILITIES

The following brief explanations originate from the

official Google Cloud website, including the

documentation and blog posts. Even though the

information given there may be glossed over for

marketing reasons, the presentation of an exemplary

selection of suitable tools for the technological

capabilities stated above does serve the purpose of

proving that such cloud-based solutions exist.

Big data is a broad field concerned with acquiring,

storing, analyzing, and presenting large quantities of

data, to some extend in real time. The GC offers a

range of services suitable for these capabilities or

specific parts of them. For example, Cloud Spanner

can be used as a relational database with practically

unlimited volume, Bigtable, on the other hand, serves

as a NoSQL database, and Cloud Storage might be

ICEIS 2023 - 25th International Conference on Enterprise Information Systems

426

used as an extensive storage for binary objects. For

data streaming, Pub/Sub is a service usable for fast

voluminous data transfers based on a

publisher/subscriber model, as the name suggests.

Dataflow, thereupon, can be used for stream and

batch processing of the gathered and stored data.

Visualizing and exploring data is made possible by

using Cloud Datalab. One tool particularly

appropriate for storing and analyzing big data in

combination is BigQuery since this service links the

capacities of a data warehouse with the analytical

capabilities of machine learning through the

integration of BigQuery ML. Thus, huge amounts of

data can be stored and employed to train and evaluate

machine learning models.

Considering AI and ML more generally, Google

Cloud provides a wide range of tools for different use

cases. With text conversion via Speech-to-Text and,

inter alia, Text-to-Speech APIs, Google’s Natural

Language API to extract information from

unstructured texts, and Vision AI to identify texts,

objects, properties, and logos on images, a large

selection of solutions can be specified for a variety of

use cases that revolve around the topic of text and

image analytics. Additionally, AutoML and Vertex AI

are provided as tools for the creation of general

machine learning models to classify, predict, and

forecast data values. Since this is only an exemplary

overview, it must be noted that Google does provide

an even larger collection of earmarked products as

well as rather uncommitted ML tools such as

BigQuery ML, which can be used for any given

tabular dataset.

Mobile and web applications can be made

available by using any Google Cloud tool that can

provide an application to an audience through the

public Internet. App Engine, for example, is a fully

managed serverless platform, meaning that a

developer does not have to care about underlying

infrastructure since it is handled as well as

automatically scaled by Google. The standard version

supports several popular programming languages and

frameworks (e.g., NodeJS, PHP, and Python). The

developers’ task hereby is solely to write the

application and, if necessary, adjust the service

account for the App Engine to enable access to other

services inside the cloud since Google allows for an

easy connectivity between cloud services without

complex authentication techniques as required when

developing locally. This way, data storage, analytics

tools, and App Engine to present something to an

external user could be linked. Other exemplary

options for offering an application would be via

Compute Engine’s virtual machines or by

containerizing the application and deploying it on a

Google Kubernetes Cluster. Not limited to cloud

products but also usable in combination with them,

the Google Pay API adds a way for users to pay with

their Google accounts for offered services in apps or

on websites.

Since the technologies AR and VR are mostly

reliant on massive data computing and real-time

rendering (i.e., high-performance computing),

Google Cloud does not offer one particular tool but a

variety of combinations of services. For instance,

with Compute Engine, compute-optimized virtual

machines with up to 96 vCPUs with 8 gigabyte

memory each can be created. The same applies to the

Google Kubernetes Cluster whose nodes run on

Compute Engine instances themselves. Thus,

developers of AR and VR projects can benefit from

the high-performance computing capabilities offered

by Google Cloud.

Regarding social media integration, a field report

by the company Computerlogy shows how cloud

tools can be utilized for data collection and analytics

from popular social media networks. As foundational

infrastructure, a Google Kubernetes Cluster running

on Compute Engine instances delivers a highly

flexible and cost-efficient basis through embedded

autoscaling capabilities. With a deployed

Elasticsearch search engine, the developers track with

this approach information from, for example,

Facebook, Twitter, and Instagram that are

subsequently further processed with BigQuery’s

analytical tools. Visualizations are realized with

Google Data Studio. At this point, it is worth noting

that cloud tools are often suitable for multiple

scenarios, while the ultimate benefit depends on the

exact architecture. Google Cloud offers a high degree

of flexibility and thus a reduction in the effort

required to familiarize with specialized tools.

Especially for IoT systems, IoT Core is a fully

managed service to connect and manage IoT assets.

The cloud handles security, communication, and

device management for the users and links the

necessary tools to process the data. For example, in

an approach taken from the documentation, Dataflow

might be used for streaming and batch analysis, while

Pub/Sub is responsible for managing input

connections. Again, BigQuery and Vertex AI store

the data and help develop machine learning models

for analytics. In addition, with the Google Maps

Platform, Google offers the ability to integrate

location intelligence for geographic tracking of IoT

devices.

For video analytics (i.e., the last of the

technological capabilities mentioned in Figure 4),

The Future of Commerce: Linking Modern Retailing Characteristics with Cloud Computing Capabilities

427

Google offers two solutions, Video Intelligence API

and AutoML Video Intelligence, which differ in their

ability to annotate videos with custom or predefined

labels. Both products help extract information from

video material, such as objects, locations, actions,

texts, logos, faces, and others. The outlined tools are

summarized in Table 3 below and constitute the

second part of an answer to RQ2.

Table 3: (E-)retailing capabilities matched with Google

Cloud tools.

Capability /

technolo

Exemplary Google Cloud tools

Big data Cloud Spanner, Bigtable, Cloud

Storage, BigQuery (storage);

Pub/Sub, Dataflow (processing);

Datalab (exploration)

Artificial

intelligence and

machine

learning

Speech-to-Text/Text-to-Speech,

Natural Language AI (text

analytics); Vision AI (image

processing); AutoML, Vertex AI,

BigQuery ML (general use cases)

(Mobile/tailored)

applications and

payment

App Engine, Compute Engine,

Google Kubernetes Cluster (app

provision); Google Pay API

(payment)

Virtual and

augmented

realit

Compute-optimized virtual

machines (Compute Engine,

Google Kubernetes Cluster)

Social media

integration

Compute Engine, Google

Kubernetes Cluster (foundation);

BigQuery ML (analytics); Google

Data Studio

(

visualization

)

Internet of

Things

IoT Core (foundation); Dataflow,

Pub/Sub (processing); BigQuery,

Vertex AI (analytics); Google

s Platfor

(

trackin

Video Analytics Video Intelligence API, AutoML

Video Intelligence

6 CONCLUSION AND FUTURE

IMPROVEMENT

In the near future, traditional stationary retail and its

business models are expected to undergo massive

changes due to new digital technologies such as big

data, artificial intelligence, virtual reality, and social

media networks (Alexandrova and Kochieva, 2021;

Bellis and Johar, 2020; Guha et al., 2021) and due to

rising customer expectations, living standards, and

the desire for more freedom of choice (Alexandrova

and Kochieva, 2021; Farah et al., 2019; Marchand

and Marx, 2020; Xue et al., 2017). On the journey into

the digital age, cloud technologies can provide a

useful, cost efficient, flexible, and easily accessible

addition to present retail channels. Based on an

extensive body of literature reviewed through a

systematic literature search, this paper provides

possible answers to the questions of what

characteristics and technological capabilities are

likely to shape the future of retailing. In addition, an

exemplary cloud tool selection from Google Cloud is

presented and explained. In future research

endeavors, more of the identified technological

capabilities are to be integrated into purposeful cloud

reference architectures.

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