Journal of Business Models (2023), Vol. 11, No. 1, pp. 78-81

The Role of Digital Technologies in a Data-driven Circular
Business Model: A Systematic Literature Review
Malahat Ghoreishi1,2

Abstract

The Circular Economy (CE) has been identified as a promising solution for reducing emissions, waste,
and achieving sustainable development goals while offering economic values for companies. How-
ever, the move towards CE requires managers and decision-makers to rethink and redesign their
Business Models (BMs) incrementally or radically. In order to achieve proper decisions on resource
usage, product designs, material flows, and recirculation of materials, data plays a significant role
in CE. Accessible data is considered as an essential enabler of circular solutions and at the heart of
circular business models. In this regard, digital transformation can offer innovative tools for efficient
execution and sharing of data to help companies generating new business models and to increase
their competitive advantages. This study explores different data-driven BMs enabled by digital tech-
nologies in CE.

Keywords: Circular economy, data-driven business model, digital technologies

Please cite this paper as: Ghoreishi, M. (2023), The Role of Digital Technologies in a Data-driven Circular Business Model:
A Systematic Literature Review, Journal of Buisness Models, Vol. 11, No. 1, pp. 78-88

1 LUT University, Finland, malahat.ghoreishi@lut.fi
2 LAB University of Applied Sciences, Finland

ISSN: 2246-2465
DOI https://doi.org/10.54337/jbm.v11i1.7245

Introduction
The move towards Circular Economy (CE) requires
systemic change in how companies create and
deliver value to customers (value proposition) and
how they can capture and generate revenue (value
capture) (Bocken et al., 2016). Therefore, innovat-
ing Business Models (BMs) are the fundamentals
of the CE concept (Centobelli et al., 2020). Bock-
en and Ritala (2021) defined two strategic choices

in developing circular BM initiatives as innovation
and resource strategies. While resource strategy
focuses on narrowing, slowing, closing, and regen-
erating resource and energy loops (Geissdoerfer
et al., 2018), innovation strategy focuses on firm-
driven internal processes (closed innovation) and
collaboration with external partners and stakehold-
ers (open innovation). The value creation in circular
BMs for narrowing the loops happens by delivering

https://doi.org/10.54337/jbm.v11i1.7245

Journal of Business Models (2023), Vol. 11, No. 1, pp. 78-88

7979

value to customers through efficient design and
production, reducing the extraction of virgin mate-
rials and resources (Li et al., 2010). In slowing the
loops, circular BMs aim to create value by extend-
ing products’ life by designing products that can
have more than one use cycle and are more dura-
ble, upgraded, repairable, and easy to disassemble
recyclable (Zhu et al., 2010). Finally, BMs for closing
the loops generate value through recycling and re-
covering materials for reuse in new production pro-
cesses (Bocken and Ritala, 2021). Processes such
as resource optimisation, manufacturing products,
extending the lifetime of products, offering new use
cycles, and improving material flow, include high-
volume data, which, if implemented efficiently, can
enhance circularity (Ingemarsdotter et al., 2020).
Data can create value when transferred to informa-
tion, which can be integrated with other data sourc-
es and interpreted as knowledge. When knowledge
is further enriched and developed, it forms wisdom
(Kristoffersen et al., 2020). A data-driven CE gives
companies more opportunities to develop innova-
tive BMs, create networks and partnerships, as well
as expanding ecosystems (Kauppila, 2022). Trans-
parent data on material and components enables
measuring the impact of production and opera-
tions on material, creating pure and high-quality

feedstock by preventing toxic, contaminant and
non-renewable material, as well as reducing the
cost of material extraction and usage (Blomsma et
al., 2020). Hence, it helps companies to make more
efficient and accurate decisions on material and
process choices to achieve CE goals. In addition,
accurate data on material flow internally and across
the whole value chain can ensure proper recycling
opportunities at the end of product’s life while en-
hancing the recovery processes of materials (Sitra,
2021). Accordingly, data for circular BMs can be
categorised as follows: data on product design and
production, data on use phase and customer be-
haviour, data on product and service lifetime, data
on system performance, and data on material flows.
Implementing such data in circular value creation
develops BMs such as servitisation-based models,
product as a service model, sharing economy mod-
els, collaborative consumption models, product life
extension models, and resource recovery models
(Luoma et al., 2021).

As shown in Figure. 1, data is at the core of the CE
model, which can be collected, stored, measured
and analysed by digital technologies such as big
data, Artificial Intelligence (AI), Blockchain and the
Internet of Things (IoT), also known as Industry 4.0

• Figure 1. Role of data and digital technologies in CE ( based on Ghoreishi et al., 2022)

Waste sorting optimization, Recycling

Material supply Circular design Distribution & use End of first life Reverse logistics

Optimize, share,
reuse

Repair, Maintenance

Remanufacturing

2
3

1 Durable design tool
Big Data, AI, Digital Twin

2 Customer services, Service-based models
AI, Machine learning, IoT, Blockchain

Value assessment tool for used products
Big Data, IoT, Blockchain, AI

Value-based return incentives, revers logistics optimization
Big Data, IoT, Blockchain, AI, Machine vision

Figure 1: Role of data and digital technologies in CE ( based on Ghoreishi et al., 2022)

Journal of Business Models (2023), Vol. 11, No. 1, pp. 78-88

8080

(Ghoreishi et al., 2022). Digital technologies are rev-
olutionising BMs in CE by:

• enabling tracking and tracing products and
materials to develop product-as-a-service sys-
tem which reduces product ownership while
increasing reuse, repair and refurbishment op-
portunities (Alcayaga et al., 2019);

• enabling data sharing within the whole supply
chain that improves retaining of value from prod-
ucts and materials (de Sousa Jabbour et al., 2019);

• enabling higher efficiency and circularity in
manufacturing products and material process-
es (Ranta et al., 2021);

• enabling platforms that connect companies
and customers, support development of ser-
vice and dematerialisation, and facilitate indus-
trial symbiosis (Täuscher and Laudien, 2018);

• enabling shared databases for sharing waste
information and reusing waste as a resource
(Radamaekers et al., 2011).

Although research on the role of data in CE has re-
cently gained the attention of practitioners and re-
searchers (Luoma et al., 2021), only limited studies

were conducted on the role of digital technologies
in data-driven circular BMs (Ranta, 2021). Therefore,
the research questions of this study are as follows:

RQ1. What are the existing data-driven business
models in CE?
RQ2. What is the role of digital technologies in
data-driven circular BMs?

Methodological approach
To answer the research question and to understand
the existing literature on the role of digital technolo-
gies on data-driven circular BMs, a systematic liter-
ature review was conducted in this study (Xiao and
Watson, 2019). Scopus and EBSCO Business Source
Complete were the selected academic databases.
The search was conducted using the main terms
‘circular business model’, ‘digitalization’, and ‘data-
driven business model’. The set of keywords for each
term was selected based on the domain literature
(Table 1). The search terms were selected in the ti-
tle, abstract, keywords, or subject, with ‘data’ chosen
as any part of the text. In addition, the search was
limited to articles and reviews published in peer-re-
viewed journals, English language, between January
2000 and March 2022 (1.1.2000-31.03.2022).

Table 1.

Terms Keywords

Circular business model (circular*) AND “business model*” OR “Value creat*”)

Digitalization (digitali*ation OR “digital technolog*” OR “digiti*ation” OR “digital transfor-
mation” OR “big data” OR “IT” OR “Industry 4.0” OR “Internet of Things” OR
“IoT” OR “remote control” OR “remote monitoring” OR “RFID” OR “Artificial
Intelligence” OR “data analytics” OR “predective analytics” OR “machine
learning” OR “ automat* robots” OR “smart robots” OR “smart data” OR “
digital manufacturing”)

Data-driven business model (data OR “data collection” OR “data gathering” OR “data analysis” OR “data
analytics” OR “data mining” )

Table 1: Keywords used in the search settings

Journal of Business Models (2023), Vol. 11, No. 1, pp. 78-88

8181

After removing duplicates, the screening process
was continued by reading titles, keywords, and ab-
stracts. To ensure the final sample, the articles
should 1) include the concepts of circular BMs and
circular value creation, 2) address the digital tech-
nologies and CE, 3) address the utilisation of data
in circular BMs by digital technologies. Accordingly,
the articles that did not meet these criteria were ex-
cluded. Furthermore, the author read the full articles
for a more accurate decision, specifically the results
sections. The literature search process is shown in
Figure 2, based on which 47 articles were selected
for full article screening. After reading the full text
of each article carefully, the irrelevant articles were
excluded and resulted in 22 selected articles for a
systematic review regarding the theoretical, con-
ceptual and empirical contribution to answering the
RQs of this study.

Furthermore, the relevant data was collected man-
ually and documented systematically in an Excel
sheet. The aspects of the articles related to the role
of data in circular BMs and value creation by digi-
tal technologies were assessed and identified. The
main terms of circular BMs, the definition of the
BMs, the role of data in circular BMs and the descrip-
tion of how digital technologies enable data for cir-
cularity in these BMs were identified and coded after
the data analysis. This way, the contents of articles
were classified and compared to form a systematic
finding.

Results of the literature review
The role of digital technologies as an enabler of cir-
cular strategies, ReSOLVE strategies, and circular
BMs has been discussed in various research and
studies since 2017 (Alcayaga et al., 2019; Blomsma et
al., 2020). However, according to the results of this
study, the vital role of data, how data creates value

in circular BMs through digital technologies, has only
been argued by limited researchers since 2018. The
results from the systematic literature review indi-
cate the increasing trend in research on this topic,
with three publications in 2018, two publications in
2019, three publications in 2020, seven publications
in 2021, and seven publications by the Spring of 2022
(Figure 3).

Moreover, the journals with the highest publications
are respectively as follows: Sustainability open ac-
cess Journal with six publications, four publications
in Business Strategy and the Environment Journal,
two publications in Journal of Cleaner Production
and two publications in Journal of Resources, con-
servation and Recycling, and the rest were pub-
lished in various Journals (Figure 4).

According to the results, as illustrated in Figure 5,
IoT is the most discussed digital technologies in dif-
ferent research due to the capability of IoT sensors
and links in connecting physical products and online
services. Therefore, it can enable tracking, tracing
and transferring of real-time data, which results
in saving resources, optimisation of processes,
transportation and material flows, as well as mini-
mizing unnecessary expenses on material extrac-
tion throughout the entire network of supply chains
(Ivanov et al., 2022; Chauhan et al., 2022; Ranta et
al., 2021; Ingemarsdotter et al., 2020).

Furthermore, Data-based services are a rising trend
aiming at increasing transparency and creating new
value from supply chain data. According to the re-
sults, 13 publications discussed the “Product-service
System (PSS)” BM in the concept of CE and highlight-
ed the significant role of digital technologies, spe-
cifically IoT. Servitisation and PSS model provides
services and performance instead of products,

Search the
databases with

the strings

Elimination of
Duplicates

Reading titles,
keywords,
abstracts

Reading the full
articles

Export of meta
data

470 Excluded: 31
Selected: 439

Excluded: 392
Selected: 47

Excluded: 25
Selected: 22

Figure 2: Research design for the literature review.

Journal of Business Models (2023), Vol. 11, No. 1, pp. 78-88

8282

7 7

N
um

be
r o

f P
ub

lic
at

io
n

Year of publication

2018 2019 2020 2021 2022

Figure 3

Figure 3:

Figure 4:

0 1 2 3 4 5 6 7

Sustainability

Business strategy and the environment

Journal of Cleaner Publication

Journal of Resources, conservation and recycling

Number of publications

Journals

Figure 4

Journal of Business Models (2023), Vol. 11, No. 1, pp. 78-88

8383

which increases resource and material optimisa-
tion. Various authors emphasized the role of data in
extending the life of products for creating value and
developing service-based BMs. In this regard, data
enables repair, reuse, maintenance services and re-
cycling while helping companies to optimize product
design. The second vital circular BM highlighted by
the authors was “Blockchain-based supply chain”,
with the important role of Blockchain technology in
creating a transparent and trustable data transac-
tion throughout the entire supply chain. Many au-
thors mentioned that the tight connections between
Blockchain and IoT sensors, create a trustworthy
environment for different actors within the sup-
ply chains and enable safe and visible transactions
without the need for a third party. “Sharing Econo-
my”, “Digital remanufacturing”, “Digital Recycling
Ecosystem”, and “Pull demand-driven model” were
respectively the most discussed circular BMs, high-
lighting the role of digital platforms and cloud-based
technologies. Table 2 below includes an overview of

the data-driven BMs in CE identified through the lit-
erature review.

Discussions and Conclusions
The findings of this paper offer a greater understand-
ing of the role of data in CE and why data is crucial
in developing circular BMs. The existing data-driven
BMs have been identified and why data is important
in circular BMs has been discussed through this re-
search. Data is the source of value in various deci-
sion-making processes in CE and enables material
and process optimisation. Precise and accurate data
supports the best choices and decisions in changing
supply chains, ecosystems, and networks dynami-
cally. The results from the systematic review show
the increasing trends in this topic and the increas-
ing potential for more emperical studies in future.
There is huge potential for research in identifying
the benefits of data by utilizing digital technologies

Data-driven circular
business model

Supply-chain-as-a-
service (blockchain-
based supply chain)

Product-service
systems (PSS)

Sharing economy

Pull demand driven
model

Digital recycling
ecosystem

Digital
remanufacturing

(Robot control-as-
a-service)

Digital technologies

IoT

Blockchain

Big data

Cloud
computing

Digital capabilities

Tracking products and assets
through embedded sensors

Remote monitoring & controlling

Smart robots with intelligent
sensors

Accessibility and exchange of
reliable data

Data integration

Digital platforms

Connecting objects

Sensors transferring real-time data

Automated production

Predictive analytics

Traceability and Transparency

Source of data in CE

Data on lifecycle of
products

Data on waste stream

Data on consumer
behavior and demands

Data on location,
availability and status

of products

Data on use of
products

Data on infrastructures

Data on purity of
recycled material

Data on feedstock

Data on product
design and prototyping

Data on material flow

Data on connected
products

Data on recycling
partners

Data on payments

Data on recycled
materials and test

process

Figure 5. Data-driven BMs, source of data and digital technologies

Journal of Business Models (2023), Vol. 11, No. 1, pp. 78-88

8484

Table 2.

Data-driven
Circular BM

Definition of circular BM Role of Data Industry 4.0 Technologies Reference
examples

Supply-chain-
as-a-service
(cloud-based
enabled supply
chain) , Block-
chain-based
supply chain

Supply chain-as-a-service
enables major principles of
resilience and viability. Main
resilience strategies such as
multi-sourcing, collabora-
tion, visibility, and flexible
re-routing. Viability is the
ability of a supply chain to
survive in a changing environ-
ment through a redesign of
structures and replanning of
performance with long-term
impacts.

Data on each stage of
product's life enhances
transparency and visibil-
ity in the supply chains
which is highly important
for efficiency, resilience
and sustainability while
tracing performances.
Data from connected
products, plants and sys-
tems enables operation
optimization and create
better quality products.

Tightly connected IoT
sensors and platforms
to Blockchain technol-
ogy allow contracting in
the platform context and
creating improvements
in performance through
transferring real-time
data, visibility and trust.
Blockchain registers each
transactions of products
and materials throughout
the value chain, thus ena-
bling access and exchang-
ing of reliable data without
the need for third party
operators. AI and big
data analytics can enable
visibility and outsourcing
in pricing and revenue
decisions.

Ivanov et al.,
2022, Huynh
2021

Product-ser-
vice systems
(PSS)

The PSS business model
offers products entirely as a
service or supportive services
in addition to products such
as maintenance contracts.
Support services that can
improve and extend lifecycle
of the products through reuse,
recycling, and remanufactur-
ing operations. PSS enables
resource efficiency.

Data on lifecycle of prod-
ucts helps in prolonging
life of products. data on
waste stream, data on
consumer behavior. Data
on location, availability
and status of products.
Data on product facili-
tates decision making.

IoT enables tracking of the
products during and after
use, enables durability
in products, connects
objects and enables
service-based model. Uti-
lizing data enables remote
monitoring and control-
ling of products. Big data,
and cloud computing
enable digital platforms
that manage operational
activities and services.
Blockchain enhances the
sorting process. Cloud
technologies integrate
and show data to the
company and consumer,
enabling the potential for
offering context-specific
maintenance services.

Chauhan et al.,
2022, Huynh
2021, langley
2022, Subramo-
niam et al., 2021,
Cetin et al., 2021,
Okorie 2020,
Ranta et al.,
2021., Ingemars-
dotter et al.,
2020., Rossi et
al., 2020., Lieder
et al., 2020,
Garcia et al 2018,
Bressanell et al.,
2018, Lindström
et al 2018

Journal of Business Models (2023), Vol. 11, No. 1, pp. 78-88

8585

Table 2.

Data-driven
Circular BM

Definition of circular BM Role of Data Industry 4.0 Technologies Reference
examples

Sharing
Economy (SE)

Sharing economy business
model aims to optimize re-
source consumption through
collaborative consumption
(sharing, exchangin, and rent-
ing resources leads to reduc-
tion in resource and energy
usage). Improving operation
mangement. Enables sharing
access to assets and resourc-
es instead of owning assests.

Data on entire lifecyle
of product, data on
consumer behavior, data
on use of products, data
on products and systems
demand, data on infra-
structures.

Big data platforms, Em-
bedded sensors and IoT
enabling data collection
for products and services.
Installing sensors on as-
sets enables tracking and
monitoring the condition
of products, allowing
predictive maintenance.
Artificial intelligence
enables new product
development, preventive
maintenance services .

Vecchio et al.,
2021, Massaro
eta l., 2020

Digital recy-
cling ecosys-
tem (extended
blockchain ser-
vice)

Reducing waste and carbon
footprint across the supply
chain.Creating a closed-loop
supply chain by innovating
products from post-consumer
materials which are fully certi-
fied through a traceable and
transparent supply chain.

Data on purity of recycled
material for customers'
trust. Accurate data on
payments for waste col-
lectors and other part-
ners. Data on recycling
partners' capacities. Data
for choosing the right
feedstock and how to use
it for which end product.
Data for handling types of
feedstock. Data for test
process of the content of
recycled materials.

Blockchain secures
transparent process and
cost of the entire value
chain. Blockchain system
enables tracing post con-
sumer recycled materials
to their source. Private
Blockchain with a custom-
ized token system enables
setting transparent rules
as well as a tokenizer
reward system.

Chaudhuri et al.,
2022

Pull demand-
driven model

Facilitating a radical shift
in the entire production-
consumption paradigm of
supply and demand as well
as upstream/downstream
businesses. The pull demand-
driven business model reforms
the linear model to a more
collaborative and integrative
circular process. This model
increases the speed from
design to delivery, producing
more personalized products
and more flexible for small-
scale production.

Real-time data helps to
solve two main problems:
overproduction and
underuse. Data on design
and prototyping help in
prodcut development
and production phases,
making involve all the
stakeholders from the
first stage.

Digital plaforms enable
communication and inter-
action between end-users
and designers and busi-
ness partners. AI enables
automated production
which reduces labour
costs while increasing
higher acuracy in produc-
tion.

Huynh 2021

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8686

Table 2.

Data-driven
Circular BM

Definition of circular BM Role of Data Industry 4.0 Technologies Reference
examples

Digital re-
manufacturing
business model
(Power-by-the-
Hour, Robot
control-as-a-
service)

Provides remanufacturing
companies the capacity to
gain access to the customer
base and to enable rapid
respond to the changes in
demands, reducing resource
consumption while increases
competitiveness. Integration
of digital remanufacturing is
crucial for product develop-
ment, process development,
production, and after sales in
CE. Improves real-time inven-
tory management.

Data on different parts'
condition enables high
quality remanufacturing.
Historical data enhances
decision-making to
qualify or separate the
returned products.
Data on product design
(design for disassembly,
design for repair, design
for upgrade…) enhances
taking consideration
parameters for bet-
ter remanufacturing.
Data on material flow
and returns improves
design processes, lack of
information results in en-
hanced processing of the
product. Data on custom-
ers' behavior and demand
reduces response time to
drive changes.

IoT enables tracking the
parts to ensure availabil-
ity of replacement parts.
Sensors enables tracking
part performances and
facilitating predictive ana-
lytics such as predictive
maintenance. Transfer-
ring real-time data on
returned product defects
and demands helps plant
managers to schedule
operations. A cloud-based
service supports the
development of distribu-
tion process planning in
decentralized dynamic
remanufacturing envi-
ronment. Smart robotic
remanufacturing using
intelligent sensing and
real-time adaptation.

Subramoniam et
al., 2021, Kerin
and Pham 2019

in supply chains, ecosystems and various value crea-
tion strategies in CE with the focus on different in-
dustries such as textile and fashion which have more
complex supply chain.

This research is limited to understanding the role of
data and digital technologies in circular BMs. Hence,
the study only examined the papers that included
the term “data” and excluded articles that only fo-
cused on digital circular BMs and not mentioned
data utilization. Moreover, the concepts “circular
BMs” and “digital technologies” are showing strong
and fast development recently, especially from the

data-driven perspectives. Future research will ben-
efit from a comprehensive study on the role of data
on different CE strategies, process and product
designs. Moreover, a deeper understanding of the
role of data as a driver of circular BM innovation and
configuration, the role of data in enhancing collabo-
rative ecosystems, and the role of data in creating
and capturing value in the circular supply chain are
required through studies of different cases. Finally,
although the role of IoT has been identified consid-
erable as an enabler of data-driven BMs in CE, there
is still potential to explore the capabilities of AI, ana-
lytics and blockchain technologies in this field.

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