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Digital technologies (DTs), such as the Internet of Things (IoT), big data, and data analytics, are considered essential enablers of the circular economy (CE). However, as both CE and DTs are emerging fields, there exists little systematic guidance on how DTs can be applied to capture the full potential of circular strategies for improving resource efficiency and productivity. Furthermore, there is little insight into the supporting business analytics (BA) capabilities required to accomplish this. To address this gap, this paper conducts a theory- and practice-based review, resulting in the Smart CE framework that supports translating the circular strategies central to the goals of manufacturing companies in contributing the United Nation’s (UN) 12th Sustainable Development Goal, that is, “sustainable consumption and production,” into the BA requirements of DTs. Both scholars and practitioners may find the framework useful to (1) create a common language for aligning activities across the boundaries of disciplines such as information systems and the CE body of knowledge, and (2) identify the gap between the current and entailed BA requirements and identify the strategic initiatives needed to close it. Additionally, the framework is used to organize a database of case examples to identify some best practices related to specific smart circular strategies.
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Journal of Business Research
journal homepage: www.elsevier.com/locate/jbusres
The smart circular economy: A digital-enabled circular strategies framework
for manufacturing companies
Eivind Kristoffersen
a,
, Fenna Blomsma
b
, Patrick Mikalef
a
, Jingyue Li
a
a
Norwegian University of Science and Technology, NO-7491 Trondheim, Norway
b
University of Hamburg, 20146 Hamburg, Germany
ARTICLE INFO
Keywords:
Digital circular economy
Digitalization
Circular economy
Sustainability
Industry 4.0
Big data analytics
ABSTRACT
Digital technologies (DTs), such as the Internet of Things (IoT), big data, and data analytics, are considered essential
enablers of the circular economy (CE). However, as both CE and DTs are emerging fields, there exists little systematic
guidance on how DTs can be applied to capture the full potential of circular strategies for improving resource efficiency
and productivity. Furthermore, there is little insight into the supporting business analytics (BA) capabilities required to
accomplish this. To address this gap, this paper conducts a theory- and practice-based review, resulting in the Smart CE
framework that supports translating the circular strategies central to the goals of manufacturing companies in con-
tributing the United Nation’s (UN) 12th Sustainable Development Goal, that is, “sustainable consumption and pro-
duction,” into the BA requirements of DTs. Both scholars and practitioners may find the framework useful to (1) create
a common language for aligning activities across the boundaries of disciplines such as information systems and the CE
body of knowledge, and (2) identify the gap between the current and entailed BA requirements and identify the
strategic initiatives needed to close it. Additionally, the framework is used to organize a database of case examples to
identify some best practices related to specific smart circular strategies.
1. Introduction
The concept of circular economy (CE) has gained momentum among
businesses, policymakers, and researchers by virtue of its potential to con-
tribute to sustainable development (Geissdoerfer, Savaget, Bocken, &
Hultink, 2017; Ghisellini, Cialani, & Ulgiati, 2016) through a range of ef-
ficiency- and productivity-enhancing activities collectively known as cir-
cular strategies (EMF, 2013). For instance, consider circular strategies such
as reduce, reuse, repair, recycle, restore, and industrial symbiosis.
For two reasons, the CE holds potential to contribute to multiple UN
Sustainable Development Goals (SDGs) (Schroeder, Anggraeni, &
Weber, 2019). First, the CE proposes that negating or reducing struc-
tural waste decreases the demand for virgin finite material. That is,
through the application of circular strategies, the otherwise underused
capacity of resources
1
can be applied to deliver value (EMF, 2015,
2015). Second, the CE promotes moving away from using the natural
environment as a “sink” to dump used resources (Irani & Sharif, 2018).
The CE is attributed with the ability to avoid, reduce, and negate value
loss and destruction through, for instance, lower emissions, reduced
pollution levels, and loss of biodiversity and habitats associated with
resource extraction (EMF, 2013; Kumar & Putnam, 2008).
For these reasons, CE practices are strongly linked to SDG 12 (re-
sponsible consumption and production) and can have an additional bene-
ficial impact on related goals, such as SDG 6 (clean water and sanitation),
SDG 7 (affordable and clean energy), and SDG 15 (life on land) (Schroeder
et al., 2019). Given the strong link with SDG 12 and the importance of
manufacturing companies for this SDG, our study focuses on the manu-
facturing industry and the reduction of structural waste through improved
resource management. At present, the adoption of circular strategies in
industry is somewhat modest (Circle Economy, 2020; Haas, Krausmann,
Wiedenhofer, & Heinz, 2015; Planing, 2015; Sousa-Zomer, Magalhães,
Zancul, & Cauchick-Miguel, 2018). Moreover, this also holds true for
manufacturing firms; although they play a vital role in the creation of value,
there are few improvements to decouple from the linear consumption of
finite resources (Sousa-Zomer et al., 2018). There are multiple reasons for
this. First, the CE is an emergent concept, implying lack of tools for con-
ducting CE-oriented innovation, or circular-oriented innovation (COI)
(Blomsma & Brennan, 2017; Brown, Bocken, & Balkenende, 2019). Second,
the link between CE and possible enabling digital technologies (DTs) is not
yet well established (Alcayaga, Wiener, & Hansen, 2019; Jabbour, de Sousa
https://doi.org/10.1016/j.jbusres.2020.07.044
Received 30 August 2019; Received in revised form 24 July 2020; Accepted 25 July 2020
Corresponding author.
E-mail addresses: eivind.kristoffersen@ntnu.no (E. Kristoffersen), fenna.blomsma@uni-hamburg.de (F. Blomsma), patrick.mikalef@ntnu.no (P. Mikalef),
jingyue.li@ntnu.no (J. Li).
1
Here, we refer to physical resources such as materials, components, and products.
Journal of Business Research 120 (2020) 241–261
Available online 25 August 2020
0148-2963/ © 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/BY/4.0/).
T
Jabbour, Sarkis, & Godinho Filho, 2019; Jawahir & Bradley, 2016; Nobre &
Tavares, 2017; Okorie et al., 2018).
Digital technologies could be critical enablers of CE by tracking the flow
of products, components, and materials and making the resultant data
available for improved resource management and decision making across
different stages of the industry life cycle (Antikainen, Uusitalo, & Kivikytö-
Reponen, 2018; Bressanelli, Adrodegari, Perona, & Saccani, 2018b; EMF,
2019, 2016; European Commission, 2020a, 2020b; Lacy, Long, & Spindler,
2020; Nobre & Tavares, 2017; Pagoropoulos, Pigosso, & McAloone, 2017).
As such, DTs can play an important role in positioning information flows that
enable resource flows to become more circular. For instance, the Internet of
Things (IoT) can enable automated location tracking and monitoring of
natural capital (EMF, 2016). Big data facilitates several aspects of circular
strategies, such as improving waste-to-resource matching in industrial sym-
biosis systems via real-time gathering and processing of input-output flows
(Bin et al., 2015; Low et al., 2018). Moreover, data analytics (simply known
as analytics) can serve as a tool to predict product health and wear, reduce
production downtime, schedule maintenance, order spare parts, and opti-
mize energy consumption (Conboy, Mikalef, Dennehy, & Krogstie, 2020;
Lacy et al., 2020; Porter & Heppelmann, 2014; Shrouf, Ordieres, &
Miragliotta, 2014). These examples illustrate that DTs’ contribution to the CE
include a range of circular strategies and business processes: from recycling
to reuse, and designing new offerings to managing maintenance.
Although there are real and theorized examples of information flows
enabling circularity, there remains a gap between the expected, and largely
unrealized, potential to use DTs to leverage circular strategies (Nobre &
Tavares, 2019; Pagoropoulos et al., 2017; Rosa, Sassanelli, Urbinati,
Chiaroni, & Terzi, 2020). So far, the answers to questions such as in what
areas and in which ways, DTs support for implementing circular strategies for
manufacturing companies have been insufficiently systematized. However,
there is lack of support for improving the existing and new ways in which
DTs can support the CE through smart circular strategies (Kristoffersen,
Aremu, Blomsma, Mikalef, & Li, 2019; Kristoffersen et al., 2020). A Gartner
survey of 1374 supply chain leaders supports this premise. The results show
that 70% of the respondents are planning to invest in the CE; however, only
12% have so far linked their digital and circular strategies (Gartner, 2020b).
In other words, there is lack of guidance on how to leverage DTs to max-
imize resource efficiency and productivity for a specific circular strategy.
This paper addresses this gap by linking the two emerging fields of DTs
and the CE and developing the Smart CE framework, which establishes a link
between DTs and resource management through an integrative model based
on maturity thinking. The framework provides detailed understanding of
the relationship between DTs and the CE through technical mechanisms and
business analytics (BA) capabilities. It allows assessment of the current and
future smart circular strategies with their associated and target level of
maturity, and provides guidance on how to leverage DTs to maximize re-
source efficiency and productivity for a specific circular strategy. This will
enable practitioners and academics to develop and implement roadmaps
through BA gap analysis, find new opportunities for innovation through
examples of best practices, and align people across the boundaries of dis-
ciplines. Existing digital CE frameworks present techniques to understand
these two fields, mainly by summarizing high-level integrative strategies,
enablers, and barriers. However, none provide the necessary support to
systematically search, analyze, and advance such strategies, as presented
within the Smart CE framework.
The rest of the paper is organized as follows. Section 2 details the
gaps identified in applying DTs in the CE. Section 3 explains the study
design, that is, the conduct of literature and practice reviews central to
this research. Section 4 presents the proposed Smart CE framework and
real-world examples collected from the practice review. Next, Section 5
discusses the practical implications and limitations of the research.
Section 6 summarizes and presents the conclusive remarks.
2. Background
This section presents the definitions of the key constituents of DTs;
next, we highlight the difficulties in leveraging them for manufacturing,
focusing on their role in CE. Lastly, we articulate the scope of this paper
and the associated research objectives.
2.1. Digital technologies in manufacturing
The term digital technologies encompasses several related technological
trends such as IoT, big data, and data analytics. Furthermore, DTs, also
known as Industry 4.0 (Kagermann, Helbig, Hellinger, & Wahlster, 2013;
Lasi, Fettke, Kemper, Feld, & Hoffmann, 2014; Liao, Deschamps, Loures, &
Ramos, 2017), are transforming operations management in fields such as
automation and industrial manufacturing, supply chain management, agile
and lean production, and total quality management (Agrifoglio, Cannavale,
Laurenza, & Metallo, 2017). For instance, DTs have the ability to give
production systems the capacity to use historical data to improve quality by
detecting abnormal behavior and adjusting performance thresholds ac-
cordingly (Aruväli, Maass, & Otto, 2014). Furthermore, the improved
sharing of information throughout the value chain helps to control and
make real-time adjustments of operations according to varying demand
(Moeuf, Pellerin, Lamouri, Tamayo-Giraldo, & Barbaray, 2018). This in-
creases operational efficiency and provides insights into the potential for
new products, services, and business models (Kagermann et al., 2013). For
the remainder of the paper, however, we focus leveraging circular strate-
gies, as opposed to finding new offerings and business models.
Digital technologies are still an emerging field (Van den Bossche,
2016), lacking support for effective implementation for manufacturing
at scale (Brettel, Friederichsen, Keller, & Rosenberg, 2014, 2018, 2019,
2016, 2017, 2016). A possible explanation for this is that ambiguous
definitions without clear descriptions of the key constituent elements
(i.e., IoT, big data, and data analytics) (Moeuf et al., 2018) are ham-
pering the field. In Table 1, we illustrate the breadth of DT definitions in
the extant literature and clarify our use of these terms in this paper.
In addition, a study of 161 manufacturing firms has identified three
key barriers to using DTs to facilitate circular strategies: lack of inter-
face design (e.g., challenges with compatibility, interfacing, and net-
working), difficulties in upgrading technology (e.g., bringing data
analytics and IoT implementation to (near) state-of-the-art), and out-
dated automated synergy models (e.g., collaborative models, process
digitalization, and automation) (Rajput & Singh, 2019). In this study,
we limit our scope of DTs to focus on the upgrade of existing technol-
ogies and adoption of new tools, that is, IoT, big data, and data ana-
lytics, for exploring BA requirements central to circular strategies.
2.2. Difficulties in leveraging digital technologies for the circular economy
When confronted with the need to support the leveraging of a circular
strategy—such as tracking stocks of natural capital, supporting industrial
symbiosis matchmaking, and monitoring and managing product
health—BA capabilities required to satisfy the need must be established.
For any data-driven business, and within the CE, this entails leveraging
the full strategic potential of information flows by assembling, integrating,
and deploying analytics-related resources (Shuradze & Wagner, 2016). This
includes both tangible and intangible organizational resources such as data
governance, existence of a data-driven culture, presence of suitable man-
agerial and technical skills, and processes for data-driven organizational
learning (Mikalef, Pappas, Krogstie, & Giannakos, 2018).
To date, efforts supporting information systems research primarily fo-
cused on explaining the mechanisms through which BA leads to competitive
performance, for example, through the mediating role of dynamic and op-
erational capabilities (Mikalef, Krogstie, Pappas, & Pavlou, 2019). As such,
unpacking how the application of analytics unfolds within an organization
to generate new or improved sources of value remains an underexplored
area of research (George, Haas, & Pentland, 2014). Specifically, how
DTs—through strategies of BA—lead to enhanced resource management,
consistent with the CE, remain to be detailed.
Acknowledging the potential of DTs for the CE, various sources have
E. Kristoffersen, et al. Journal of Business Research 120 (2020) 241–261
242
reported the need for work that links DTs and the CE. For instance
(Chauhan, Sharma, & Singh, 2019; EMF, 2019, 2016; European
Commission, 2020b; European Policy Centre, 2020; Okorie et al., 2018;
Rosa et al., 2020), aim to raise awareness on DTs’ potential for the CE and
support further development through research and innovation. Other au-
thors have investigated how DTs relate to servitized business models and CE
value drivers (Alcayaga et al., 2019; Bressanelli, Adrodegari, Perona, &
Saccani, 2018a; Pham et al., 2019) and the type of DTs needed within the
various categories of well-known CE frameworks, such as the ReSOLVE
(regenerate, share, optimize, loop, virtualize, exchange) framework (de
Sousa Jabbour, Jabbour, Godinho Filho, & Roubaud, 2018b; Jabbour et al.,
2019; Nobre & Tavares, 2019). Policy initiatives are also underway, such as
the Circular Economy Action Plan, which includes a call for the creation of
an architectural and governance infrastructure in the form of a dataspace
for smart circular applications (European Commission, 2020a).
However, there is a gap between theory and practice (Rosa et al., 2020):
research is presently in a pre-paradigmatic stage, as frameworks that sup-
port linking DTs and the CE have started to appear only recently, and no
dominant framework has emerged as yet (Askoxylakis, 2018; Bianchini,
Pellegrini, Rossi, & Saccani, 2018; Ingemarsdotter, Jamsin, Kortuem, &
Balkenende, 2019; Rosa et al., 2020; Ünal, Urbinati, & Chiaroni, 2018).
Although such frameworks may include a range of circular strategies, none
systematically cover circular strategies that are relevant for manufacturing
companies, and none detail the BA requirements needed to implement and
improve them. That is, such frameworks do not allow for unpacking tech-
nical architectures, integrations, or implementations in terms of the prin-
ciples of information and communications technology (ICT) or according to
their different potential to contribute toward improving resource pro-
ductivity and efficiency. As such, existing frameworks do not support
bridging the gap between an organization’s CE objectives and the opera-
tional alignment required to achieve them. This alignment is an essential
step in COI (Brown et al., 2019) and the continuous improvement processes
within manufacturing companies.
This research gap can be understood by drawing on a simplified
version of the VMOST (vision, mission, objectives, strategy, tactics)
framework (Sondhi, 1999); see Fig. 1. This framework illustrates how
high-level goals can be made increasingly more concrete by moving
from Vision to Mission to Objectives to Strategy, and eventually, op-
erational Tactics. Here, we are concerned with the three last compo-
nents of translating CE objectives into digital tactics.
As part of this COI and continuous improvement, it is necessary to have
the ability to systematically search, analyze, and advance smart circular
strategies to the highest possible levels of resource productivity and effi-
ciency (EMF, 2016, 2019; Nobre & Tavares, 2017). For this reason, this
Table 1
Overview of definitions in extant literature and those adapted for this study. (See below-mentioned references for further information.)
E. Kristoffersen, et al.
Journal of Business Research 120 (2020) 241–261
243
paper focuses on the development of such a systematic approach to
breaking down high-level circular business objectives into subsequent re-
quirements for operational digital tactics.
3. Research methodology
3.1. Research scope and objectives
As already mentioned, we focus only (supporting) on leveraging
circular strategies in the context of technological upgrades (e.g., data
analytics and IoT development challenges) (Rajput & Singh, 2019).
Thus, we do not answer why a CE strategy may be of importance to the
business. Based on this scope, we outline two research objectives (ROs):
RO1 Develop a framework that supports the systematic identification of BA
requirements needed to advance different smart circular strategies.
RO2 Consolidate and further advance the framework through the de-
velopment of a knowledge base that can be used for BA gap
analysis and the creation of roadmaps for the application of smart
circular strategies within organizations.
3.2. Research design
Given the emerging and burgeoning characteristic of the domain,
our study investigated not only academic sources but also practice case
study examples and “grey literature” (i.e., published material that has
not been subject to a peer review process; Adams, Smart, & Huff
(2017)). We followed the methodology used by Bocken, Short, Rana,
and Evans (2014), who detail three iterative phases for a practice and
literature review: (1) identification of themes and categorizations by
literature review, (2) synthesis by developing an integrative framework,
and (3) identification and mapping of examples from practice to vali-
date and further develop the framework. In addition, we adhered to the
guidelines for reviewing academic literature by Kitchenham and
Charters (2007) and those for grey literature by Adams et al. (2017).
3.2.1. Phase 1 - Literature review
In phase 1, we built on previous evaluation and review of existing
CE frameworks, conducted in (Blomsma et al., 2019). This work created
the Circular Strategies Scanner, which organizes circular strategies re-
levant to manufacturing companies.
In addition, we performed two systematic literature reviews fol-
lowing the guidelines of Kitchenham and Charters (2007). The litera-
ture review comprised two parts: (a) existing digital CE frameworks,
and (b) digital frameworks to address RO1. For part (a), we sought
frameworks that detail the connection between DTs and the CE. For
part (b), we sought organizing principles that provide complementary
insights into how different DTs relate to one another.
Two databases, Scopus and Web of Science, were selected for the
reviews based on their broad coverage of journals relevant for both DTs
and CE. See Fig. 2 for the search strings generated for RO1 and the steps
involved. Additionally, see Appendix C for the full search string and
synonyms used. Papers were limited to English peer-reviewed articles in
conferences and journals. Articles were extracted from the databases on
March 27, 2020. The database search included articles published over
the past ten years, due to the burgeoning characteristic of the field. See
Section 4.1 for an overview of the results of phase 1 of the review.
For part (a), we established inclusion criteria comprising only papers
that illustrate a structured relationship between one or more DTs and cir-
cular strategies relevant to manufacturing. As such, articles that were too
narrow in scope and focused on a specific circular strategy (e.g., supply
chain management) or business model proposal (e.g., product-service
system) were excluded, as they did not provide a range of circular strategies
(e.g., only providing value drivers or enablers/barriers), were not scoped for
manufacturing (e.g., targeting cities, economies, and countries at large), and
did not give a clear description of a framework, organizing principles, or
mechanisms. Furthermore, manual additions were prepared to complement
the searches. This resulted in ten included papers, with six were from the
database search. Following the criterion development process by Blomsma
et al. (2019), existing frameworks were used to develop framework criteria
to guide development in the synthesis phase. The criteria were iterated until
they represented four precise requirements that the new digital CE frame-
work should address.
For part (b), to extend the frameworks identified in part (a), inclusion
criteria were set to include only papers that provided insights into how DTs
relate to one another through common ICT architectures and taxonomies.
As such, we excluded articles as they were too narrow in scope, did not
define or give a detailed explanation of the DTs, or did not provide a clear
description of a framework, organizing principles, or mechanisms. To
complement these searches, manual additions were based on the re-
searchers’ general reading. This resulted in 32 included papers, with 22
from database search. Relevant information on approaches and principles
underpinning the relationship between different DTs was extracted from the
papers and aggregated in a spreadsheet.
3.2.2. Phase 2 - Synthesis: developing a smart CE framework
In phase 2, the resulting organizing principles, frameworks, and
development criteria of phase 1 were used to synthesize and develop a
detailed understanding of how DTs relate to the CE. First, development
criteria were used to rate existing digital CE frameworks, highlight
gaps, and guide the synthesis via the choice of organizing principles.
Second, existing digital frameworks and principles were presented in
tabular form using spreadsheets and analyzed for commonalities and
theoretical underpinnings that allowed for connecting DTs to CE re-
source management. Next, approaches and principles that converged or
correlated were combined, creating a more robust foundation to the
underlying logic and organizing principles used. At this point, it became
evident that operational maturity could be linked to both an increase in
the level of resource productivity and unburdening of human decision
makers. See Section 4.2 for a description of the results of the synthesis.
3.2.3. Phase 3 - State-of-the-practice review
In phase 3, to address RO2, we performed a broader systematic search
of “DTs & CE strategies” in the literature, supplemented by a practice review
aimed at uncovering examples (real or theorized) where DTs support or
enable specific circular strategies related to manufacturing. Although the
same methodology was followed for systematic literature reviews
(Kitchenham & Charters, 2007), broader search terms and inclusion criteria
were used to generate a larger set of sources likely to contain relevant ex-
amples. See Appendix C for the full search string and synonyms used.
To combine the Smart CE framework and the Circular Strategies
Scanner (Blomsma et al., 2019) (detailed in Section 4.1), a matrix or grid
was created, with the hierarchical dimensions from the Smart CE frame-
work on the y-axis, and the CE strategy categories from the Scanner on the
x-axis (see Fig. 3 for illustration). Examples that provide insights into how
DTs can support circular strategies at different levels of operational maturity
were sought. The examples collected in phase 3 were mapped onto this
matrix and served as a validation of the Smart CE framework. If these
Fig. 1. Research scope.
E. Kristoffersen, et al. Journal of Business Research 120 (2020) 241–261
244
Fig. 2. Schematic illustration of the research approach that was followed to develop the Smart CE framework and matrix.
E. Kristoffersen, et al. Journal of Business Research 120 (2020) 241–261
245
examples were not assigned a place, it would indicate an inadequate re-
lationship between DTs and the CE.
Relatively few cells could be populated through this review, there-
fore, we decided to extend this part of the assessment with a practice
review and include grey literature, consistent with (Bocken et al.,
2014). The Circle Lab’s knowledge hub, which (at that time) contained
1583 case studies, was the main source, thus making it the largest
global open access innovation platform for CE case studies and ex-
amples (CircleLab, 2020). The result is a matrix that contains relatively
few examples drawn from the academic literature, and more from the
practice review. This may reflect that practice can be ahead of aca-
demia as both DTs and the CE represent emerging fields.
This resulted in 98 included papers and case studies for RO2 (with
65 added from the literature and 33 from practice). See Section 4.3 for
an overview of the included examples. After complementing with cases
from grey literature, 94% of the cells (46 out of 49) are detailed.
4. Research results
4.1. Results of Phase 1 - Literature review
4.1.1. CE frameworks
Building on previous evaluation and review of CE frameworks, the
Circular Strategies Scanner was selected (Blomsma et al., 2019). The
Scanner (shown in Fig. 3) presents a taxonomy of circular strategies based
on business processes typically found in the manufacturing context.
Drawing from both academic and practitioner perspectives, the framework
provides circular strategies ranging from incremental to transformative, or
from operational to strategic. Operational strategies include reducing, re-
storing, and avoiding impact in areas such as sourcing, manufacturing,
product use, and logistics, as well as the recirculation of products, compo-
nents, and materials into new or existing use cycles. Strategic applications
include rethinking and reconfiguring value-generating architectures and
reinventing the “paradigm” for radical decoupling. In other words, the
Scanner provides comprehensive support for manufacturing companies
engaging in COI processes. Compared to other CE frameworks (Bocken, De
Pauw, Bakker, & van der Grinten, 2016; Nußholz, 2017; Potting, Hekkert,
Worrell, & Hanemaaijer, 2017), the framework has an improved capacity to
(i) create a comprehensive understanding of circular strategies, (ii) map
current strategies applied, and (iii) identify opportunities for improved
circularity for different business processes (Blomsma et al., 2019).
4.1.2. Digital CE frameworks
Based on the stated research gap and frameworks identified from the
review, insights and theoretical underpinnings were used to develop four
framework criteria to guide the development and synthesis of the frame-
work.
Criterion (1) draws on the needs in the COI process, where it is im-
portant to align understanding, mindsets, and disciplines and represent a
complex phenomenon in an easily comprehensible manner to inspire and
motivate people (Blomsma et al., 2019; Brown et al., 2019). Criterion (2)
addresses the suitability of the framework in a CE manufacturing setting. As
there are different types of businesses, the framework should include a
comprehensive set of circular strategies and facilitate the alignment of as-
sociated business processes (Blomsma et al., 2019; Potting et al., 2017). In a
survey of Industry 4.0 implementation patterns in manufacturing compa-
nies, advanced adopters were leading all underlying DTs and not any spe-
cific technology (Frank, Dalenogare, & Ayala, 2019). A Gartner survey
corroborates this and claims that a synthesis of DTs will enable companies
to transition toward the CE (Gartner, 2020b). Building on this, criterion (3)
establishes the need for the framework to represent multiple DTs and to be
logically sound in terms of its relation to common ICT architectures and
taxonomies. The former survey also indicates variance in the adoption of
DTs related to varying organizational maturity (Frank et al., 2019). Hence,
criterion (4) addresses the applicability of frameworks in an industrial set-
ting to support adoption at various levels of maturity, BA gap analysis, and
optimization of circular outcomes detailed here as resource efficiency and
productivity.
The ten frameworks identified (Askoxylakis, 2018; Bianchini et al.,
2018; de Sousa Jabbour et al., 2018b; EMF, 2016; Jabbour et al., 2019;
Ingemarsdotter et al., 2019; Nobre & Tavares, 2019; Okorie et al., 2018;
Rosa et al., 2020; Ünal et al., 2018) were compared and rated based on
the above criteria (as in Table 2). Overall, the frameworks provide
novel insights into the value of leveraging DTs for CE and different
perspectives on understanding the digital CE through distinct theore-
tical assessments. Moreover, contributions varied from adaptation of
the technical life cycle (Okorie et al., 2018) and product life cycle
(Askoxylakis, 2018; Bianchini et al., 2018) to extensions of the Re-
SOLVE framework (de Sousa Jabbour et al., 2018b; Jabbour et al.,
2019; Nobre & Tavares, 2019) and mappings of value-generating me-
chanisms (EMF, 2016; Ünal et al., 2018). Two frameworks presented
new innovative models (Ingemarsdotter et al., 2019; Rosa et al., 2020).
Although a few frameworks addressed some of the criteria effec-
tively, such as Rosa et al. (2020, 2019, 2019, 2018b), they were unable
to satisfactorily address the majority of the criteria, in particular, cri-
teria (3) and (4). Overall, the inability of existing frameworks to fa-
cilitate BA gap analysis, support companies at various stages of im-
plementation or maturity, and effectively optimize resource efficiency
and productivity of strategies support the emergent state of the field
and justify the framework development.
4.1.3. Digital frameworks
Given the fact that digital CE frameworks do not support criteria (3)
and (4), additional ICT principles and technical mechanisms were
sought in a review of digital frameworks.
Of the 32 papers included, five papers used the Open Systems
Interconnection (OSI) model as the underlying logic (Akhbar, Chang, Yao, &
Muñoz, 2016; Da Xu, He, & Li, 2014; Jin, Gubbi, Marusic, & Palaniswami,
2014; Marjani et al., 2017; Tsai, Lai, & Vasilakos, 2014). Four papers pre-
sented a pyramid as a central element in the framework (Ardolino et al.,
2018; Li, Yu, et al., 2017; Mishra, Lin, & Chang, 2015; Siow, Tiropanis, &
Hall, 2018), and two of these used the Data-Information-Knowledge-
Wisdom (DIKW) pyramid (Ardolino et al., 2018; Siow et al., 2018).
Fourteen papers encompassed all three DTs: IoT, big data, and
analytics (Addo-Tenkorang & Helo, 2016, 2018, Queiroz, Wamba,
Machado, & Telles, 2020, 2019, 2018, 2020, 2019, 2015, 2017, 2017,
2015, 2018, 2019, 2018).
Fifteen papers mentioned one or more workflows synonymous with
data collection, data integration, data storage, data processing, and data
analysis (Addo-Tenkorang & Helo, 2016; Babar & Arif, 2017; Da Xu
et al., 2014; Dai, Wang, Xu, Wan, & Imran, 2019; Darwish & Bakar,
2018; Fatorachian & Kazemi, 2020; Jin et al., 2014; Li, Yu, et al., 2017;
Marjani et al., 2017; Merezeanu & Florea, 2017; Mishra et al., 2015;
Fig. 3. The Circular Strategies Scanner.
E. Kristoffersen, et al. Journal of Business Research 120 (2020) 241–261
246
Siow et al., 2018; Tsai et al., 2014; ur Rehman et al., 2018; Wu et al.,
2014). However, only one paper included different levels of data ana-
lytics and contrasted these with the DIKW pyramid (Siow et al., 2018).
In summary, most papers built on well-known ICT principles en-
abled the development of three separate organizing principles, or
technical mechanisms. First, Software-Oriented Architecture, the DIKW
pyramid, and OSI models were integrated under different data trans-
formation levels. Second, workflows, such as data collection, integration,
and analysis, were connected under data flow processes, along with the
corresponding DTs. Finally, data analytics levels, such as descriptive
and predictive analytics, were arranged under analytics capabilities
(Siow et al., 2018).
4.2. Results of Phase 2 - Developing a smart CE framework
Guided by the above criteria, the proposed Smart CE framework
addresses the shortcomings of existing digital CE frameworks. A de-
tailed overview of the improvements for each criterion is presented in
Table 4 in Section 5.1.
The framework consists of three main elements: data transformation
levels (blue triangle), resource optimization capabilities (green triangle),
and a layer linking these elements together, data flow processes (grey
background), as seen in Fig. 4. The different elements were combined
by using a hierarchy as the main organizing principle where each in-
dividual level relies on the previous ones. That is, for the data trans-
formation levels, resources must be connected by an IoT sensor in order
to generate data. This can then be turned into information by in-
tegrating it with other data sources and providing the context, and so
on all the way up to wisdom.
Likewise, for resource optimization capabilities, diagnostic analytics
provide insights into why something happened and build upon de-
scriptive insights of what actually transpired. Similarly, in the data flow
processes, data is first collected and integrated to facilitate data ana-
lysis. The remainder of this section explains the three elements, illus-
trates their compatibility in a single framework, and details the various
levels of adoption through maturity thinking.
4.2.1. Data transformation levels
The data transformation levels draw on the DIKW pyramid, a widely
recognized model in the information and knowledge literature in-
troduced by Ackoff (1989). The DIKW hierarchy presents the terms
data, information, knowledge, and wisdom to illustrate the computer
processes involved in transforming raw data into insights (Rowley,
2007). Inspired by the physical layer in the OSI model, we modified the
traditional DIKW model to include a fifth layer at the bottom named
“connected resources.” Each of the five layers are detailed below:
Connected resources are products, components, and materials con-
nected through, for instance, an IoT device. This enables to collect data
across different stages of the resources’ industrial life cycle.
Data are merely raw, elementary symbols based on the observation
of objects, events, and/or their environment (Ackoff, 1989; Rowley,
2007). On their own, data lack interpretation and need con-
textualization to offer direct value or usability.
Information is inferred or transformed from data through techni-
ques such as aggregation, interpretation, selection, and sorting. As
such, information is contained within descriptions and provides
answers to questions raised by words such as who, what, where, and
when (Ackoff, 1989; Rowley, 2007).
Knowledge represents the transformation of information into action-
able instructions, knowhow, and valuable insights, and answers ques-
tions such as how and why (Ackoff, 1989; Rowley, 2007). As such,
knowledge can be considered as the refinement of information with
inference rules and increased understanding (Jankowski & Skowron,
2007).
Wisdom connects actionable instructions of knowledge to autono-
mous decisions and actions. Wisdom combines knowledge with in-
teractive processes and adaptive judgment. Interactive processes are
the sequence of actions and reactions, while adaptive judgment is
the actual decision made based on the evaluation of interactive
processes and their current status (Jankowski & Skowron, 2007).
For instance, consider an IoT device for measuring temperature in a
machine with the objective of extending its life cycle. Then, the raw
Table 2
Comparison of frameworks identified from the review based on development criteria.
Criteria of the new framework: Rosa et al.
(2020)
Nobre and
Tavares
(2019)
Ingemarsdott-
er et al. (2019)
Okorie
et al.
(2018)
de Sousa
Jabbour
et al.
(2018b)
Jabbour
et al.
(2019)
Askoxylakis
(2018)
EMF
(2016)
Ünal et al.
(2018)
Bianchini et al.
(2018)
(1) A tool for inspiring, motivating
and aligning people across
disciplines
+ + ++ 0 + + + + 0 +
(2a) Include a broad range of
circular strategies (from
strategic to operational)
+ ++ + + + + 0 + 0 +
(2b) Support the translation of
circular strategies to business
processes relevant for
manufacturing
++ 0 ++ + 0 0 + ++ 0 +
(3a) Include a broad range of DTs ++ ++ 0 + ++ 0 + 0 + +
(3b) Provide an overview of the
underlying technical
mechanisms of how the DTs
relate
0 0 ++ + 0 + ++ + 0 0
(4a) Facilitate (self) assessment
and BA gap analysis
+ + 0 0 ++ 0 0 0 0 0
(4b) Include digital maturity levels
of adoption
0 0 0 0 + 0 0 0 0 0
(4c) Include resource optimization
levels for maximizing resource
efficiency and productivity
0 0 0 0 0 0 + 0 0 0
+++ = framework satisfies criterion very strongly, ++ = framework satisfies criterion strongly, + = framework satisfies criterion moderately, 0 = framework
does not meet criterion or only marginally.
E. Kristoffersen, et al. Journal of Business Research 120 (2020) 241–261
247
temperature readings form the data. Thus, information is interpretation of
this temperature represented by an average over the operating hours, or a
description of the machine overheating. This could be an indication of an
impending failure of the machine, for which a reactive maintenance scheme
is created. Perhaps, knowledge can identify the possible reasons for the
machine’s abnormal temperature readings. Known as condition-based
maintenance, this could give insights into the machine’s actual condition
and schedule maintenance. Finally, wisdom could then identify a specific
trend in the temperature readings and project this across future operational
planning and provide an optimal service window to correct the problem
based on these predictions, known as predictive maintenance.
4.2.2. Resource optimization capabilities
Building on the analytics capabilities and generic interpretations by
Siow et al. (2018), we provide the analytic capabilities, resource-spe-
cific interpretations, and supplementary questions to conform to COI
and CE resource management. The resulting resource optimization
capabilities present five levels of descriptive, diagnostic, discovery, pre-
dictive, and prescriptive analytics:
Descriptive is the preliminary step that answers the question “what
happened” or “what is happening.” As such, it can be considered as
the process of describing, aggregating, and adding context to raw
data from an IoT device, thus transforming it into information.
Diagnostic builds on the information obtained from the descriptive
level to understand “why something happened.” It tries to unravel
the cause and effect of events and behaviors and augments knowl-
edge to the information. As a bridge to business models and in-
telligence, both descriptive and diagnostic levels provide hindsight
value of what happened and why.
Discovery addresses the acute problem of high volumes in the IoT
and big data. It employs inference, reasoning, and detection of non-
trivial knowledge from information and data. It attempts to build a
deeper understanding of why something happened by discovering
additional trends and clusters, or something novel. As such, dis-
covery provides oversight value.
Predictive provides foresight value by identifying future prob-
abilities and trends to determine “what is likely to happen.”
Predictive methods convert past knowledge to forecast events and
behaviors, thereby obtaining wisdom.
Prescriptive draws actions and judgments from the forecasts pro-
vided by the predictive level, allowing for investigation of future
opportunities or issues, and provides the best course of action. As
such, the prescriptive level considers the inherent uncertainty of
predicting the future and combines this with advanced optimization
to answer the question “what if.”
These capabilities can, for example, be observed in organizations
adopting three levels of analytics: aspirational, experienced, and trans-
formed. Aspirational organizations use analytics in hindsight as a justifica-
tion of actions. Experienced organizations apply analytics to gain insights to
guide decisions, while transformed organizations can achieve foresight and
prescribe actions in advance of decision making (Siow et al., 2018). Like-
wise, for the CE, these capabilities represent organizational potential to
increase resource efficiency and productivity.
4.2.3. Data flow processes
Similarly, data flow processes represent a hierarchical structure.
Nonetheless, this is not necessarily always the case in practice. For in-
stance, all three processes of data collection, integration, and analytics
may be employed to perform a descriptive analysis of what has hap-
pened. However, the rationale underlying this structure is emphasizing
where the different DTs typically interconnect.
Data collection is the process of generating and gathering data
from various heterogeneous sources such as the IoT, wireless sensor
networks, and embedded systems.
Data integration is the process of contextualizing and curating these
disparate data sources for analysis by preprocessing and aggregation. It
relies on interoperability and context-awareness, which are typically
included by big data, cloud computing, and fog computing.
Data analysis is the process of understanding the data for under-
pinning or deriving actionable decisions. It includes deployment and
application of data with associated insights and foresight, facilitated by
techniques such as artificial intelligence, machine learning, and deep
learning.
Furthermore, storage and computing are abstract processes involved
in each of the above steps. Overall, data can be piped from one step to
another, and thus do not necessarily require physical storage in
Fig. 4. The Smart CE framework.
E. Kristoffersen, et al. Journal of Business Research 120 (2020) 241–261
248
separate locations. Similarly, computation can be done on a physical
device or in transit (e.g., fog computing), and a separate computing
component is not required (Siow et al., 2018).
4.2.4. Maturity levels
The hierarchical structure presented in the framework serves both
as an organizing and adoption principle. Building on maturity thinking,
the upper levels represent a greater potential of strategies to support or
unburden human decision makers (blue arrow) and increase the effi-
ciency and productivity (green arrow) of the systemic resource. In other
words, the structure illustrates different levels of operational maturity
in implementing DTs for decoupling value creation from the con-
sumption of finite resources, building on extant research that considers
the adoption of Industry 4.0 (Dalenogare, Benitez, Ayala, & Frank,
2018; de Sousa Jabbour et al., 2018b; Frank et al., 2019; Qu, Ming, Liu,
Zhang, & Hou, 2019).
Moreover, the hierarchical structure of increasing maturity also
indicates the aggregation of DTs as “Lego” blocks (Frank et al., 2019)
for the application of autonomous functions (Qu et al., 2019). Hence,
when a company matures and implements more advanced DTs (IoT,
cloud computing, big data, and analytics, respectively), it can leverage
self-sensing, self-adaptive, self-organizing, and self-deciding functions.
Based on this, we theorize a correlation between increasing in-
dustrial automation and expanding systemic resource efficiency and
productivity in a CE. Support for this can be seen in the automatic
production processes of smart manufacturing, enabling improved
quality, productivity, and flexibility of large-scale production for
sustainable resource consumption (Dalenogare et al., 2018; de Sousa
Jabbour, Jabbour, Foropon, & Godinho Filho, 2018a).
4.3. Results of Phase 3 - State-of-the-practice review
The literature review on smart circular strategies resulted in 65 included
papers (27 from the database search). The practice review of case studies
from the Circle Lab’s knowledge hub was filtered using the label
“Incorporate digital technology,” resulting in 207 results. Both the case
descriptions in this database and company website(s) illustrating the cases
were consulted (in line with Adams et al. (2017)), resulting in 33 examples
added for a total of 98 real-world and theoretical case examples.
The Circular Strategies Scanner and the Smart CE framework enabled
mapping of strategies into a matrix (represented by Figs. A.1–A.3 in
Appendix A). The Scanner, representing the x-axis or the columns, covers a
range of circular strategies relevant for manufacturing companies. The
Smart CE framework, representing the y-axis or rows, covers DTs and dif-
ferent maturity levels of adoption. Strategies were then placed in a cell
corresponding to the category, DTs, and maturity of the application. See
Table 3 for a summary of the examples mapped or Appendix A for the
detailed matrix and complementary case descriptions. The cases represent a
mix of theorized applications and real-world examples (see Appendix B for
reference number and theoretical/real-world labeling).
The results show that both theorized and real-world examples embody
all the circular strategy categories. Moreover, up to and including the pre-
scriptive level, the matrix has good coverage for all the categories, except
the recirculation of parts, products, and materials. To address this issue and
Table 3
Summary of results where {} are real world cases and [] are theoretical cases.
E. Kristoffersen, et al.
Journal of Business Research 120 (2020) 241–261
249
outline avenues for future research, the authors propose examples of future
strategies, where both literature and practice are incomplete. However, the
overall satisfactory coverage of circular strategies supports the validity of
the Smart CE framework. The final mapping outlined 100 theorized and
real-world smart circular applications (including strategies from literature,
practice, and the authors).
In the following subsections, we explain how DTs can leverage
various circular strategies, from operational processes to corporate
strategies, along with excerpts from the example cases. However, for
the purpose of this study, the focus is on operational strategies.
4.3.1. Digital technologies supporting circular strategies in operational
processes
The first category of circular strategies discussed is Restore, Reduce,
and Avoid. These strategies apply to raw materials and sourcing (e.g.,
use of recyclable materials and sourcing of waste), manufacturing (e.g.,
reworking and cascading by industrial symbiosis), logistics and energy
(e.g., optimized routing and renewable energy), and product use and
operations (e.g., product longevity and use of idle product capacity). In
addition, end-of-use and end-of-life processes can be found in the
strategies of Recirculation, both for parts and products (e.g., reuse and
remanufacturing) and for materials (e.g., recycling and composting).
To facilitate our discussion, we use an illustrative example (see Fig. 5)
from each of these categories. The strategies are taken from Figs. A.1–A.3 in
Appendix A and highlight examples of industrial symbiosis, maintenance,
and recycling. In Fig. 5, we expand the examples with digital and human
requirements for each level to illustrate the increasing ability of DTs to
support or unburden human decision makers (providing increased quality,
productivity, and flexibility). One way to understand this is that the digital
and human elements together represent all the decisions needed to co-
ordinate resource flow for a specific strategy. Hence, when the number of
decisions made by DTs increases, the decisions made by humans decrease or
shift, providing flexibility for pursuing increased resource productivity. Note
that we are not detailing the ideal digital and human requirements for
implementation, but rather a proposed structure for explanatory purposes.
Restore, Reduce, and Avoid
In this category, the strategies target the prevention of excessive re-
source use and improve the inherent efficiency and circularity potential in
the manufacturing process. For instance, industrial symbiosis, where the
outgoing flow from one manufacturing facility is used by another, reduces
and, in some cases, replaces a company’s reliance on virgin raw materials.
The descriptive level of DTs can support this strategy by describing and
monitoring the type, quantity, and timing of input for current material flows
(Bin et al., 2015; EMF, 2016; Pagoropoulos et al., 2017). This requires, for
instance, IoT sensors for accurate collection and measurement of flow and/
or aggregated information from internal sourcing, inventory, and logistics
databases. When integrated with analytics, this may allow the discovery of
new and alternative waste-to-resource matches and potential eco-networks
for their application (if linked with information from other manufacturing
facilities) (Bin et al., 2015; Low et al., 2018; Song, Yeo, Kohls, & Herrmann,
2017). Ultimately, on a prescriptive level, self-optimizing algorithms may be
capable of automatically prescribing and arranging the exchange of flows
through self-adapting sourcing plans (Srai et al., 2016).
Similar solutions, with analytics capabilities ranging from descriptive to
prescriptive, can be envisioned for other strategies, including agriculture
(EMF, 2016; Smart Bin, 2020) and natural resource conservation
(Aquabyte, 2020; CreateView, 2020), manufacturing (Airfaas, 2020; Fisher,
Watson, Escrig, & Gomes, 2019; KemConnect, 2020), product use and op-
erations (Bressanelli et al., 2018b; Pham et al., 2019; Rymaszewska, Helo, &
Gunasekaran, 2017), logistics (12Return, Liebig, Piatkowski, Bockermann,
& Morik, 2020; Liebig et al., 2014), and energy (Shrouf et al., 2014; Tomra,
2020). For instance, examples include optimized vehicle and fleet usage
(Sensoneo, 2020), reverse logistics planning (Cirmar, 2020), and opera-
tional scheduling based on the availability of renewable energy (Qayyum
et al., 2015) (see Figs. A.1 and A.2 in Appendix A for further examples and
details).
Recirculation of Parts and Products
In this category, strategies recirculate parts and products by extending
existing use cycles and introducing new ones. Strategies extending the ex-
isting use cycle typically fall under the subcategories of upgrade, repair, and
maintenance. Strategies extending the new use cycle fall under the reuse,
refurbish, remanufacture, and repurpose subcategories. An example of DTs
leveraging such processes can be seen in various levels of data-driven
maintenance. First, on the descriptive level, DTs can trigger a request for
repair based on sudden product failure, for instance, through a reactive
maintenance scheme (Bressanelli et al., 2018a; Caterpillar, 2020;
Rymaszewska et al., 2017). Furthermore, the information obtained from the
descriptive strategy can be used to explore and discover new patterns or
potential for alternative life cycle-extending operations, for instance,
through a condition-based maintenance scheme (Baines & Lightfoot, 2014;
Bressanelli et al., 2018b; Rymaszewska et al., 2017). Ultimately, a pre-
scriptive maintenance scheme may be employed to autonomously de-
termine the need for, and scheduling of, maintenance and replacement of
parts (Rajala, Hakanen, Mattila, Seppälä, & Westerlund, 2018). This re-
quires more advanced algorithms, for instance, deep learning methods such
as artificial neural networks and operational data paired with maintenance
logs and failure data for improved fault diagnosis and decision support (Li,
Wang, & Wang, 2017) (see Fig. A.3 in Appendix A for further examples and
details).
Recirculation of Materials
In this category, strategies recirculate materials via the effective
application of end-of-life strategies, with the purpose of capturing (re-
sidual) value or reducing value loss through the continued use of ma-
terials. Moreover, these strategies can be further categorized into re-
cycling, cascading, and recovery.
An example of DTs supporting such strategies can be observed with
smart bins (Bin-e, 2020; GreenSpin, 2018; Sensoneo, 2020), which increase
the traceability of materials location and quantity to correctly select an end-
of-life strategy (Nasiri, Tura, & Ojanen, 2017), or in the incentivization of
increased recycling based on pay-as-you-throw models (WasteIQ, 2020). If
paired with material grades, this information can, in turn, be used to dis-
cover new and more effective material cascades, for instance, through di-
gital material marketplaces (Cirmar, 2020; Excess Materials Exchange,
2020) utilizing data mining methods on open access material databases.
Finally, data on materials quantity, composition, and quality can be used by
self-optimizing algorithms (e.g., swarm intelligence or long short-term
memory networks) to perform automatic cost-benefit analysis and optimal
selection of end-of-life strategies (see Fig. A.3 in Appendix A for further
examples and details).
4.3.2. Digital technologies supporting circular strategies related to corporate
strategy
Reinvent the Paradigm
Reinvent, or refuse, strategies strive to fully decouple value creation
from the consumption of finite resources. This may be achieved by
making physical products redundant through offering the same func-
tion, or combined functions, in other products/services. The prominent
technical mechanism in this category is virtualization. The virtual
contrasts with the real or physical, and implies having the essence, or
effect, without a real-life appearance or form. As such, virtualization
has an inherent use for reinvention and refusal.
Virtualization removes fundamental constraints concerning loca-
tion, time, and human observation (Verdouw, Beulens, & Van Der
Vorst, 2013). This is a fundamental element, or building block, of DTs’
contribution to the CE as it allows to gather information across different
stages of the industrial life cycle. Furthermore, virtualization enables
the design of more modular, repairable products that can be easily
(digitally) updated (Antikainen et al., 2018), and the simulation of new
and alternative CE approaches (Lieder, Asif, & Rashid, 2020).
Industrial examples are digital twins (Kuehn, 2018; Pham et al.,
2019), virtual supply chains (Liebig et al., 2014), and digital manu-
facturing (Jeschke, Brecher, Meisen, Özdemir, & Eschert, 2017; Qu
E. Kristoffersen, et al. Journal of Business Research 120 (2020) 241–261
250
et al., 2019). An example of digital twins, virtual representation of
products is combined with analytics for better decision making in
complex manufacturing scenarios. For instance, by simulating future
production plans or operational modes, digital twins can be used to test-
drive various circular strategies in a virtual environment before a de-
cision is applied to the real-world system (Kuehn, 2018). This enables
organizations to reinvent and explore strategies before their application
(see Fig. A.1 in Appendix A for further examples and details).
Rethink & Reconfigure Value Chain Creation Architecture
Rethink, or reconfigure, strategies focus on new ways of delivering a
function and/or value proposition through circular business model in-
novations, such as usage and performance-based models (Bundles,
2020; Klickrent, 2020; WasteIQ, 2020). Broadly, the design of most
physical products does not change radically with time. However, with
the recent digitalization efforts, many products are now embedded with
software and analytics (or digital materiality) that do change. This
opens for new smart product-service systems and business model con-
figurations (Alcayaga et al., 2019).
Integrating DTs to rethink and reconfigure value creation mechan-
isms requires companies to strengthen their BA capabilities and become
data-driven. A data-driven organization entails that decision makers
base their actions on data and insights generated from analytics rather
than instinct. Studies evidence that companies that embrace a data-
driven approach experienced noticeable gains in business development,
Fig. 5. Illustrative examples with representative requirements (see Figs. A.1–A.3 in Appendix A for further examples and details).
E. Kristoffersen, et al. Journal of Business Research 120 (2020) 241–261
251
productivity, and profitability (McAfee, Brynjolfsson, Davenport, Patil,
& Barton, 2012; Waller & Fawcett, 2013), suggesting that similar gains
in sustainable development and the CE could be found. For instance,
Romero and Noran (2017) introduce the concept of “green sensing
virtual enterprises,” whose predictive and agile capabilities enable
better self-environmental awareness and intelligence for the CE (see
Fig. A.1 in Appendix A for further examples and details).
5. Discussion
5.1. Research implications
This work presents a digital-enabled circular strategies framework
and extends the existing body of knowledge on how to leverage DTs for
CE adoption. To the best of the authors’ knowledge, the paper con-
tributes by proposing a novel framework and database to align several
calls for action within sustainable development and the CE, such as that
by the European Commission (2020a). As such, it provides a concrete
framework that can be used as a point of reference for using DTs in
supporting and enabling CE adoption and the enactment of circular
strategies. While much of the business-related literature is grounded on
corresponding theoretical perspectives that explain the value-gen-
erating mechanisms of different strategies, the same cannot be stated in
the context of circular strategies. As such, the proposed framework can
be used as a basis upon which researchers can examine the impact that
different technologies, applied in different contexts, have on the en-
ablement of circular strategies and corresponding SDGs.
Our framework is scoped to address DTs’ lack of support for COI in
manufacturing and significantly improve on the existing digital CE
frameworks; see Table 4. The main difference between related frame-
works and our framework is that existing frameworks summarize high-
level strategies, possibilities, and/or capabilities, while our model ex-
tends this with a detailed structure to systematically support practi-
tioners in searching, analyzing, and advancing smart circular strategies.
Our framework makes the following contributions: (1) a detailed un-
derstanding of the relationship between the technical mechanisms of
DTs and the strategic and operational strategies of the CE, (2) the ability
to map strategies with their associated and target level of maturity, (3)
the ability to accommodate multiple circular strategies and find new
opportunities for innovation through example best practices, (4) the
ability to derive digital requirements and BA capabilities for im-
plementing circular strategies, and (5) guidance on how to leverage DTs
to maximize resource efficiency and productivity for a given context.
In addition, our framework complements previous contributions by
allowing both researchers and practitioners to communicate better
across the boundaries of disciplines. It highlights key technical me-
chanisms needed for a more data-driven mode of CE business opera-
tions. By extension, our framework provides the basis for further ex-
ploration of the BA resources and capabilities central to the adoption of
circular strategies. From a research standpoint, our framework high-
lights the role of novel DTs in shaping the information value chain
within the context of the CE. Thereby, it differentiates between strategic
and operational circular strategies, decomposing them into specific
attainable approaches and the corresponding DT resources required to
foster them. Therefore, it introduces a structured approach in bridging
the technical, operational, and strategic aspects of circular strategies.
5.2. Practical implications
The example strategies presented in the matrix form a knowledge
base that, when organized using the Smart CE framework, may be used
by organizations for BA gap analysis and to create roadmaps toward CE
Table 4
Overview of the improvements the new framework makes in relation to the development criteria.
Criteria of the new framework: Smart CE
framework
Summary of improvements
(1) A tool for inspiring, motivating and aligning people
across disciplines
+++ The Smart CE has an improved capacity to serve as a hub, or gateway, where stakeholders
can easily connect through a combined set of intuitive framework elements and inspiring
examples.
(2a) Include a broad range of circular strategies +++ Drawing from the Circular Strategies Scanner, the Smart CE encompasses a broad range of
strategies, from incremental (e.g., restore, reduce, and avoid) to transformative (e.g., rethink
and reconfigure).
(2b) Support the translation of circular strategies to business
processes relevant for manufacturing
++ Building on the categories from the Circular Strategies Scanner, the Smart CE organizes
circular strategies into business processes they are applicable. For instance, rethink and
reconfigure applies to strategic initiatives and business model innovation while the rest apply
to operational processes such as material sourcing and product use and operations.
(3a) Include a broad range of DTs ++ The Smart CE combines three system-level DTs of IoT, Big Data, and Data Analytics—each
integrating several base-level DTs (e.g., embedded systems and machine learning). The
respective DTs have been comprehensively evaluated and defined for the purpose of the
framework.
(3b) Provide an overview of the underlying technical
mechanisms of how the DTs relate
+++ The three elements of data transformation levels, resource optimization capabilities, and
data flow processes provide a comprehensive structure (based on well-known ICT
architectures and theoretical underpinnings) to understand, detail, and integrate DTs.
(4a) Facilitate (self) assessment and BA gap analysis +++ The Smart CE can be directly used as a tool for mapping strategies that are currently applied,
explore new ones, and how they can be improved through digital and/or human
interventions.
(4b) Include digital maturity levels of adoption +++ The hierarchical structure of the Smart CE builds on maturity thinking and Industry 4.0
adoption by representing a structure gradually increasing in complexity through the
aggregation of DTs and autonomous functions.
(4c) Include resource optimization levels for maximizing
resource efficiency and productivity
++ The Smart CE unites levels of digital maturity with resource optimization and provides
guidance on how to leverage DTs to maximize resource efficiency and productivity for a
specific circular strategy.
+++ = framework satisfies criterion very strongly, ++ = framework satisfies criterion strongly, + = framework satisfies criterion moderately,
0 = framework does not meet criterion or only marginally.
E. Kristoffersen, et al. Journal of Business Research 120 (2020) 241–261
252
adoption. A primary requirement for effectively leveraging smart cir-
cular strategies and tactics is the alignment of BA development with the
business model. Hence, managers, in particular, may find both the
framework and the knowledge base useful for effectively aligning DT
implementation with COI and business model development by (1)
identifying which smart circular strategies are primarily important to
the company, (2) mapping the current level of digital maturity and CE
adoption, (3) establishing the required level of digital maturity neces-
sary to implement a desired smart circular strategy, and (4) deriving BA
factors essential for its successful adoption.
To demonstrate this, Fig. 1 illustrates how parts of such a mapping
could be done by first identifying which current circular strategies and
DTs have been implemented. Second, the framework can be used to
gauge the target maturity level or smart circular strategy that is of
strategic importance. This serves as a benchmark upon which managers
can allocate necessary resources and deploy the corresponding tech-
nologies to attain the targeted level of maturity. Finally, by developing
a roadmap for implementation using BA gap analysis, it is possible to
compare the current and desired BA capabilities. This is a particularly
useful tool for practitioners, who typically have very few practical
guidelines to proceed with digitally enabling circular strategies. The
framework can, therefore, be used to not only identify the target ob-
jectives but also to provide support in realizing these strategies. It also
complements existing methods that are more focused on leveraging
data artefacts, or that consider such strategies from a broader industry
perspective (van de Wetering, Mikalef, & Helms, 2017). Some empirical
studies have worked in this direction, such as that of Kristoffersen et al.
(2019), who provide a custom data science process and analytic support
for the CE.
5.3. Limitations and avenues for future research
This paper is a first step in detailing the mechanisms and strategies
of a Smart CE. The work seeks to balance both comprehensiveness and
relevance. However, the work possesses certain limitations and further
investigation and alignment between researchers and practitioners can
help to build the research stream and ensure merit.
First, as the paper presents theoretical groundings, it advocates
further empirical research on the Smart CE research stream, for in-
stance, in the form of expert interviews and surveys to investigate the
organizational aspects that are decisive when adopting DTs for the CE.
Specifically, researchers could study the key BA factors (i.e., organi-
zational resources and capabilities) needed to effectively leverage cir-
cular strategies, for example, through the lens of the resource-based
view. Furthermore, this should be extended with practical implications
and lessons for managers, explicitly addressing their role in effectively
organizing firm resources for Smart CE adoption.
Second, given its theoretical development process, the proposed
framework should be empirically validated with a set of companies to
(1) determine the clarity of the framework elements and strategies
presented, (2) detail a process for self-assessment and BA gap analysis,
and (3) identify how it can be further improved to better support COI in
manufacturing, related industries, and extended with a broader range
of DTs (e.g., blockchain and 3D printing). It is also noted that the de-
finitions, organizing principles, and frameworks were evaluated
through a subjective interpretive process. However, the theoretical
validation process, by mapping strategies, offers justification.
Third, alignment with data science and BA process methodologies
should be explored in greater depth, as done by Kristoffersen et al.
(2019). This could take the form of in-depth case studies of specific
smart circular strategies, such as predictive maintenance, to provide an
in-depth understanding of the implementation practices and process
methodologies.
Building on the rich underpinnings of the strategies described and
the comprehensive theory covered in this study, the authors anticipate
that these issues may hold merit in contributing to future studies. The
theoretical and real-world applications mapped clearly outline the pre-
paradigmatic nature of this subject and the need to strengthen em-
pirical research through in-depth case studies, action research, and
quantitative surveys to investigate the cause-and-effect relationship
between DTs and the CE.
6. Conclusion
Motivated by the role of DTs and the CE in achieving SDG 12 of
“sustainable consumption and production,” by reducing the need for
extraction of finite and virgin resources, this paper proposes a theore-
tically grounded framework and database of examples of the Smart CE.
It supports the identification of new and alternative manufacturing
strategies that can provide additional value propositions to customers,
while negating or reducing structural waste. Through a review of extant
research and frameworks, organizing principles and synthesis were
given by the Smart CE framework on how to understand the relation-
ship between DTs and the CE through common technical mechanisms.
To validate and elaborate the framework, several examples of dif-
ferent circular strategies relevant to manufacturing companies were
collected from both academic literature and real-world case databases.
The examples were aggregated in a matrix by combining the Smart CE
framework and the Circular Strategies Scanner. The placement of these
cases within the framework confirms that DTs and associated BA cap-
abilities indeed hold different potential with regards to optimizing re-
source efficiency and productivity. These examples illustrate how dif-
ferent DTs and their associated BA capabilities support capturing
different levels of resource efficiency and productivity. Using the fra-
mework and matrix as a guide, (self) assessments can be conducted to
evaluate the DTs and BA capabilities companies presently have and
those needed to capitalize on the desired value creation and capture
capacities of circular strategies. As such, the Smart CE framework and
associated knowledge base of theorized and real-world cases serves as a
novel contribution in this emerging research field.
This work has contributed to the body of knowledge for the suc-
cessful implementation of the CE by appropriately leveraging data from
intelligent resources. Both practitioners and researchers may find this
work useful to (1) create roadmaps, prioritize strategic initiatives, set
targets, and facilitate gap analysis between BA requirements and cap-
abilities to achieve new or improved smart circular strategies, and (2)
create a common language for aligning activities across the boundaries
of disciplines (e.g., information systems and CE fields). Accordingly,
this paper establishes a much needed, and underexplored, link between
two emerging fields. The Smart CE shows how DTs can support in be-
coming more resource-efficient. Specifically, for businesses, this work
shows the BA capabilities required for accomplishing this.
The smart use of resources in the CE can be supported by the
creation, extraction, processing, and sharing of data from DTs.
Effectively using this digital transformation will be pivotal for organi-
zations in transitioning to, and leveraging, the CE at scale.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influ-
ence the work reported in this paper.
E. Kristoffersen, et al. Journal of Business Research 120 (2020) 241–261
253
Acknowledgment
The authors would like to thank the reviewers of this paper. The
authors acknowledge that this work was conducted as part of the re-
search project CIRCit (Circular Economy Integration in the Nordic
Industry for Enhanced Sustainability and Competitiveness), which is
part of the Nordic Green Growth Research and Innovation Programme
(Grant No.: 83144), and funded by NordForsk, Nordic Energy Research,
and Nordic Innovation.
Appendix A. The smart CE matrix
Figs. A.1–A.3.
Fig. A.1. The Smart CE matrix I/III.
E. Kristoffersen, et al. Journal of Business Research 120 (2020) 241–261
254
Fig. A.2. The Smart CE matrix II/III.
E. Kristoffersen, et al. Journal of Business Research 120 (2020) 241–261
255
Appendix B. Reference coding of the smart circular strategies
T: theoretical case (40 cases in total), R: real world case (58 cases in total)
1. (Govindan, Soleimani, & Kannan, 2015) - T
2. (Pagoropoulos et al., 2017) - T
3. (Bressanelli et al., 2018b) - R
4. (Antikainen et al., 2018) - T
5. (Bin et al., 2015) - T
6. (Nechifor, Petrescu, Damian, Puiu, & Târnaucă, 2014) - T
7. (Rajala et al., 2018) - T
8. (Romero & Noran, 2017) - T
9. (Spring & Araujo, 2017) - T
10. (EMF, 2016) - T
11. (Zhou, Cai, Xiao, Chen, & Zeng, 2018) - R
12. (Nobre & Tavares, 2017) - T
13. (Jabbour et al., 2019) - T
14. (Baines & Lightfoot, 2014) - T
15. (Jayaraman, Ross, & Agarwal, 2008) - R
16. (Lenka, Parida, & Wincent, 2017) - R
17. (Parida, Sjödin, Wincent, & Kohtamäki, 2014) - R
18. (Reuter, 2016) - T
19. (Reim, Parida, & Örtqvist, 2015) - T
20. (Rymaszewska et al., 2017) - R
Fig. A.3. The Smart CE matrix III/III.
E. Kristoffersen, et al. Journal of Business Research 120 (2020) 241–261
256
21. (Porter & Heppelmann, 2014) - T
22. (Bressanelli et al., 2018a) - R
23. (Nasiri et al., 2017) - T
24. (Vargheese & Dahir, 2014) - T
25. (Gupta, Chen, Hazen, Kaur, & Gonzalez, 2019) - T
26. (Low et al., 2018) - R
27. (Molka-Danielsen, Engelseth, & Wang, 2018) - R
28. (Salminen, Ruohomaa, & Kantola, 2017) - T
29. (Ge & Jackson, 2014) - T
30. (Lieder & Rashid, 2016) - T
31. (Srai et al., 2016) - T
32. (Allmendinger & Lombreglia, 2005) - T
33. (EMF, 2013) - T
34. (Liebig et al., 2014) - R
35. (Verdouw et al., 2013) - R
36. (He, Yan, & Da Xu, 2014) - T
37. (Ploennigs, Schumann, & Lécué, 2014) - R
38. (Hofmann, 2017) - T
39. (Odero, Ochara, & Quenum, 2017) - T
40. (Qayyum et al., 2015) - T
41. (Yoo, Boland, Lyytinen, & Majchrzak, 2009) - T
42. (Normann, 2001) - T
43. (Michel, Vargo, & Lusch, 2008) - T
44. (Marshall, 2018) - R
45. (Tomra, 2020) - R
46. (Phillips, 2020) - R
47. (Shrouf et al., 2014) - T
48. (WasteIQ, 2020) - T
49. (Akanbi, Oyedele, Omoteso, et al., 2019) - R
50. (Torsæter, 2019) - R
51. (Song et al., 2017) - R
52. (Akinade & Oyedele, 2019) - R
53. (Yang, Aravind Raghavendra, Kaminski, & Pepin, 2018) - R
54. (Akanbi, Oyedele, Davila Delgado, et al., 2019) - R
55. (Gligoric et al., 2019) - R
56. (Aquabyte, 2020) - R
57. (CreateView, 2020) - R
58. (Smith, 2013) - R
59. (Airfaas, 2020) - R
60. (KemConnect, 2020) - R
61. (Excess Materials Exchange, 2020) - R
62. (DOZR, 2020) - R
63. (The Internet of Clothes, 2020) - R
64. (The Economist, 2017a) - R
65. (Style Lend, 2020) - R
66. (Los Angeles Times, 2013) - R
67. (Smart Bin, 2020) - R
68. (GreenSpin, 2018) - R
69. (Cirmar, 2020) - R
70. (Bundles, 2020) - R
71. (The Economist, 2017b) - R
72. (Too Good To Go, 2020) - R
73. (MIND Mobility, 2020) - R
74. (FIBERSORT, 2020) - R
75. (TimAnn-Box, 2020) - R
76. (Caterpillar, 2020) - R
77. (Peters, 2016) - R
78. (Madaster, 2020) - R
79. (Aurora, 2019) - R
80. (Klickrent, 2020) - R
81. (12Return, 2020) - R
82. (Aiir Innovations, 2020) - R
83. (Clancy, 2017) - R
84. (Kuehn, 2018) - R
85. (Qu et al., 2019) - T
86. (Sensoneo, 2020) - R
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257
87. (Jabbour et al., 2020) - R
88. (Dev, Shankar, & Qaiser, 2020) - T
89. (Monostori et al., 2016) - T
90. (Bin-e, 2020) - R
91. (Pham et al., 2019) - R
92. (Fisher et al., 2019) - T
93. (Lin, Yu, & Chen, 2019) - T
94. (Turner et al., 2019) - T
95. (Lieder et al., 2020) - R
96. (Jeschke et al., 2017) - T
97. (Charnley et al., 2019) - R
98. (Li, Wang, et al., 2017) - R
Appendix C. Literature review search strings
Table C.1
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Literature review search strings.
Keyword Keyword set
Internet of Things IoT, internet of things, pervasive computing, ubiquitous computing, intelligent assets, industrial internet, web of things
Big Data big data, cloud computing, and fog computing
Data Analytics machine learning, artificial intelligence, deep learning, analytics
DTs IoT, internet of things, pervasive computing, ubiquitous computing, ubicom, ambient intelligence, intelligent asset*, internet of everything, smart device*,
connected device*, connected object*, smart product*, connected product*, industrial internet, industry 4.0, machine to machine, m2m, device to device,
d2d, web of things, domotics, second internet, digiti*ation, disruptive technologies, technical asset*, smart sensor*, smart city, smart home, cyber-physical
system*, cyber physical system*, machine learning, artificial intelligence, deep learning, big data, cloud computing, fog computing and analytics
CE circular econom*, circl* econom*, cycl* econom*, closed loop econo*, closed loop * chain*, clos* material loop*, and circulation economics
Framework framework, model, architecture, and conceptuali*ation
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Eivind Kristoffersen is a PhD candidate at the Department of Computer Science of the
Norwegian University of Science and Technology and a Research Scientist in SINTEF
Digital. His research focus on bridging the digital and sustainable through the strategic
use of information systems and IT-business value for circular economy, known as the
smart circular economy. In the past, Eivind has worked in industry as an IT solutions
architect and received his M.Sc. in Engineering and ICT from the Norwegian University of
Science and Technology.
Fenna Blomsma is a Junior Professor at Hamburg University, holding the chair in
Circular Economy and Systems Innovation, as part of the School of Business, Economics
and Social Science of the University of Hamburg. Her work focuses on unpacking the
complexity involved in circular oriented innovation, how to design and implement sus-
tainable circular business models, and on the design and development of circular value
chains. Previously, Fenna worked in the Department of Mechanical Engineering at the
Technical University of Denmark (DTU). Before, she did a doctoral research at the Centre
for Environmental Policy (Imperial College London), and she also holds degrees from
Cranfield University and Delft University of Technology.
Patrick Mikalef is an Associate Professor in Data Science and Information Systems at the
Department of Computer Science and a research scientist in SINTEF Digital. In the past, he
has been a Marie Skłodowska-Curie post-doctoral research fellow working on the research
project “Competitive Advantage for the Data-driven Enterprise” (CADENT). He received
his B.Sc. in Informatics from the Ionian University, his M.Sc. in Business Informatics for
Utrecht University, and his Ph.D. in IT Strategy from the Ionian University. His research
interests focus on the strategic use of information systems and IT-business value in tur-
bulent environments. He has published over 100 papers in international conferences and
peer-reviewed journals including the Journal of Business Research, European Journal of
Information Systems, British Journal of Management and Information and Management.
Jingyue Li is an associate professor at the Department of Computer Science at the
Norwegian University of Science and Technology in the area of Software Engineering and
Artificial Intelligence.
E. Kristoffersen, et al. Journal of Business Research 120 (2020) 241–261
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... Then, given the observed value of in the state, the maximum likelihood estimates of the probability of being transferred to state j at time is given by: = (16) By weighting the transition probabilities within the study range of all observed values, the probability of transition from initial state to state is obtained as follows [78]: ...
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Optimal resource utilization and sustainability are gaining importance in the last decades, raising awareness about the circular economy principles. The transition toward the circular economy demands appropriate culture, environment and technology. The developments in information and communication technologies could form the base for these requirements. Our study targets identifying factors that affect the implementation of circular economy principles. In addition, the role of information technologies in their implementation is targeted. A structured literature review was conducted to define these factors. These factors are categorized into four categories: cultural, automation, sharing, and measurement. The importance of these factors is ranked based on a questionnaire. The results show that the found factors are considered success factors in implementing circular economy practices. With respect to categories, the highest impact was noticed by the cultural category, emphasizing the impact of human factor, relations, and communication on the success of circular economy policies. In addition, factors related to appropriate infrastructure and data collection support the transition toward circular economy.
... d) This combination of operations and technology leads to adaptation of the integration strategies between supply chain layers and interconnects them with products, services, stakeholders, and consumers in real-time. Such a statement is also supported by [103]. The interconnection is achieved through data transformation levels, resource optimization capabilities and data flow processes. ...
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The research interest in Digital Circular Economy models is constantly growing, especially by studying the impact and implications of circular principles and Internet of Things technologies in modern society. Up until now, Industry 4.0 has been recognized as a vital enabler of circular approaches, building the first step towards sustainable Industry 5.0 solutions, while creating new growth opportunities. To fully understand digital Circular Economy each field needs to be investigated. We achieve that by conducting a systematic review with a thorough analysis on the Internet of Things, Digital Circular Economy, and their collaborative relationship independently, by studying business models, architectures, applications, and their respective features.