Journal of Environmental Management 332 (2023) 117437
Contents lists available at ScienceDirect
Journal of Environmental Management
journal homepage: www.elsevier.com/locate/jenvman
Research article
Barriers to employing digital technologies for a circular economy: A
multi-level perspective
Adriana Hofmann Trevisan a, *, Ana Lobo a, Daniel Guzzo b,
Leonardo Augusto de Vasconcelos Gomes c, Janaina Mascarenhas a
a
São Carlos School of Engineering, University of São Paulo, Department of Production Engineering, Av. Trabalhador São Carlense, 400, São Carlos, 13566-590, SP,
Brazil
Insper Institute of Education and Research, Rua Quatá 300, Vila Olímpia, São Paulo, SP 04546-042, Brazil
c
School of Economics, Business, Administration and Accounting, University of São Paulo, 908 Prof. Luciano Gualberto Avenue, São Paulo, SP 05508-010, Brazil
b
A R T I C L E I N F O
A B S T R A C T
Keywords:
Circular economy
Industry 4.0
Digitalization
Implementation barriers
Brazil
Industry 4.0 and digital technologies might significantly impact resource optimization in a smart circular
economy. However, adopting digital technologies is not easy due to barriers that may arise during this process.
While prior literature offers initial insights into barriers at the firm level, these studies pay less attention to these
barriers’ multi-level nature. Focusing only on one particular level while ignoring others may not unleash the full
potential of DTs in a circular economy. To overcome barriers, it’s necessary to have a systemic understanding of
the phenomenon, which is missing in previous literature. By combining a systematic literature review and
multiple case studies of nine firms, this study aims to unpack the multi-level nature of barriers to a smart circular
economy. The primary contribution of this study is a new theoretical framework composed of eight dimensions of
barriers. Each dimension provides unique insights related to the multi-level nature of the smart circular economy
transition. In total, 45 barriers were identified and categorized into the following dimensions: 1. Knowledge
management (five barriers), 2. Financial (three barriers), 3. Process management & Governance (eight barriers),
4. Technological (ten barriers), 5. Product & Material (three barriers), 6. Reverse logistic infrastructure (four
barriers), 7. Social behaviour (seven barriers), and 8. Policy & Regulatory (five barriers). This study examines
how each dimension and multi-level barrier affects the transitions toward a smart circular economy. An effective
transition copes with complex, multidimensional, multi-level barriers, which might require mobilization beyond
a single firm. Government actions need to be more effective and correlated with sustainable initiatives. Policies
also should focus on mitigating barriers. Overall, the study contributes to smart circular economy literature by
increasing theoretical and empirical understanding of digital transformation barriers towards circularity.
1. Introduction
A Smart circular economy (SCE) is “an industrial system that uses
Digital Technologies (DTs) to provide intelligent functions for implementing
value-added circular strategies” (Lobo et al., 2021, p. 1). Nowadays, it’s
unlikely to transition to a circular economy (CE) without the adoption of
DTs (e.g. Internet of Things [IoT], simulation, blockchain) since these
technologies unleash new alternatives for optimizing resources,
increasing productivity (Kristoffersen et al., 2020), and enhancing sup­
ply chain agility (Oliveira-Dias et al., 2022). However, some companies
face significant barriers to adopting DTs in their business which might
affect the CE transition (Lobo et al., 2021). In fact, recent studies suggest
that the transition to CE is in its early stages (Stucki et al., 2023), which
may be due to barriers faced during the journey. For instance, Zhang
et al. (2019) presented twelve critical barriers to smart waste manage­
ment implementation. Ingemarsdotter et al. (2021) identified nineteen
challenges related to the maintenance field. Kumar et al. (2021)
addressed seventeen barriers to using big data analytics (BDA) for sus­
tainable manufacturing operations. Also, Liu et al. (2021) discussed
econouncmic, institutional, social, and technological challenges in smart
water management.
Despite the growing number of studies on barriers to an SCE
(Cezarino et al., 2019; Lobo et al., 2021), the literature presents some
critical gaps. First, many studies remain engaged only in theory (e.g.
Trevisan et al., 2021), while others look exclusively from the perspective
* Corresponding author.
E-mail addresses: adrianatrevisan@usp.br (A.H. Trevisan), jana.mascarenhas@usp.br (J. Mascarenhas).
https://doi.org/10.1016/j.jenvman.2023.117437
Received 8 December 2022; Received in revised form 19 January 2023; Accepted 31 January 2023
Available online 15 February 2023
0301-4797/© 2023 Elsevier Ltd. All rights reserved.
A.H. Trevisan et al.
Journal of Environmental Management 332 (2023) 117437
Technological, 5. Product & Material, 6. Reverse logistic infrastructure,
7. Social behaviour, and 8. Policy & Regulatory. The results show that
while some dimensions impact the micro-level of a single company,
others demand a meso and macro perspective. In addition, practical
recommendations for all levels were formulated, showing how aligning
activities among the levels is crucial to overcoming the barriers. Finally,
coherent with Cezarino et al. (2019), the paper advances the SCE field in
emerging countries. Emerging economies generally improve environ­
mental aspects slowly (e.g., energy efficiency) (He et al., 2022)
compared to countries already at the forefront of technological devel­
opment. Thus, this study indicates that developing economies may
present barriers that differ from developed nations.
In the next section, an overview of the methodological procedure
applied in this study is provided (Section 2). Subsequently, the results
are presented, and the multi-level nature of barriers is unpacked (Sec­
tion 3). Next, the article’s theoretical and practical contributions are
highlighted. Recommendations for policymakers and managers are
provided (Section 4). The last section describes the conclusion, limita­
tions, and directions for further research (Section 5).
Nomenclature
AI
BDA
CE
DTs
ESG
GDPL
IoT
RL
SCE
SLR
Artificial Intelligence
Big Data Analytics
Circular Economy
Digital Technologies
Environmental, Social, and Corporate Governance
General Data Protection Law
Internet of Things
Reverse Logistics
Smart Circular Economy
Systematic Literature Review
of a single company (e.g. Ingemarsdotter et al., 2020), ignoring that a CE
is a broad concept. Indeed, while a growing research stream has focused
on developing more eco-friendly technologies (e.g., green synthesis)
(Mahdi et al., 2022; Yousefi et al., 2021), more research is needed to
understand the use of DTs for CE by combining empirical and theoretical
research methods. Second, prior research has focused on barriers related
to a specific technology (IoT, BDA, Blockchain) (Cui et al., 2021; Okorie
and Russell, 2022), industrial sector (Abdul-Hamid et al., 2020), or
business model (Chiappetta Jabbour et al., 2020a). In this vein, there is a
lack of studies on barriers to SCE that consider a broader, multi-level
perspective.
This is even more critical when several scholars recognize that CE
can be implemented at different levels. Micro-level refers to an indi­
vidual company (Ghisellini et al., 2016); meso-level corresponds to in­
dustrial parks and ecosystems (Antikainen and Valkokari, 2016;
Ghisellini et al., 2016), and macro-level encompasses cities and nations
(Kirchherr et al., 2017). Dąbrowska et al. (2022) argue that digital
transformation also occurs across multiple lenses. For instance, DTs can
support the CE at the national level (Khatami et al., 2023). Thus, a
multi-level approach is essential to understand this phenomenon.
However, current research generally focuses on the micro level associ­
ated with a single company (Dąbrowska et al., 2022), ignoring the fact
that both CE and DT transitions require multiple lenses. In order to
achieve a CE, many levels should be considered, and companies must
overcome barriers collectively. A systemic understanding of the phe­
nomenon is required. It is necessary to unpack the multi-level perspec­
tive of barriers and increase the knowledge of the different levels that
are missing in the previous literature.
The following research question attempts to address these gaps: “At
what levels (micro, meso and macro) do barriers to a smart circular economy
emerge and how do they systemic influence the transition?” A multimethod
approach was adopted (Hunter and Brewer, 2015) that combined a
systematic literature review (SLR) and multiple case studies of nine
Brazilian circular startups. Different methods were adopted to contrast
theoretical advances with the insights brought by the rapid digital
transformation of companies. Combining the SLR procedure and mul­
tiple cases provides a reliable picture of the phenomenon. Thus, startup
companies represent a suitable setting for the research because they are
on the front line of data analytics services and digital platform models
(Hahn, 2019). Startups are surrounded by different actors (e.g. in­
vestors, universities, and governments) with different perspectives.
This paper complements the emerging literature on SCE by intro­
ducing the multi-level nature of barrier emergence. The major contri­
bution of this study is a new theoretical framework composed of eight
dimensions of barriers to an SCE. The findings enable the actors involved
in the digital transformation of a CE to create realistic expectations and
to be able to develop strategies that reduce or avoid barriers, being more
prepared for the journey. Thus, the study provides a set of 45 barriers
categorized into eight representative dimensions: 1. Knowledge man­
agement, 2. Financial, 3. Process management & Governance, 4.
2. Methodology
This study employs multimethod qualitative research. Multimethod
research brings more reliable and accurate results, allowing for a greater
generalization of findings (Hunter and Brewer, 2015). The multimethod
approach also allows for complementarity, where each method com­
plements the other, strengthening the research (Reis et al., 2019). The
study was divided into a systematic literature review and a multiple case
study. First, the extensive scientific literature was evaluated, and the
most-cited barriers to SCE were found. Then, this analysis was com­
plemented by carrying out a multiple case study. Nine Brazilian circular
startups were investigated, which allowed for theory refinement (Lee
et al., 1999). This methodology enabled us to identify barriers not
revealed in the literature and to bring new insights regarding the bar­
riers at micro-, meso- and macro-levels. The methodological steps are
detailed in the following subsections. Fig. 1 summarizes the
methodology.
2.1. Data collection
Data collection followed specific guidelines for each of the methods
applied. For the SLR, the databases, search terms and sample exclusion
criteria were defined (Briner and Denyer, 2012). For the case study,
theoretical sampling criteria were also defined (Eisenhardt, 1989), and
semi-structured interviews were conducted. The methodological pro­
cedure is described below.
2.1.1. Systematic literature review
The search for publications was carried out in the Scopus and Web of
Science databases. These databases were chosen because they are widely
renowned (Rosa et al., 2020) and cover the most relevant articles. Fig. 2
presents the research string employed, all the exclusion criteria, and the
number of remaining publications at each step of the selection. Papers
published before November 2021 were considered.
As exclusion criteria, it was decided to disregard papers before 2012
because, after this, the Ellen MacArthur Foundation started to propagate
the CE concept (Ellen Macarthur Foundation, 2013). Only journal and
conference papers published in English were considered to reduce
publication bias (Briner and Denyer, 2012). After the full-text reading,
only papers addressing barriers to SCE were selected for analysis.
2.1.2. Multiple case study
As mentioned earlier, this paper employed theoretical sampling to
select the cases (Eisenhardt, 1989). Following that, four well-defined
criteria were established. First, firms should be considered startups.
This type of organization, recognized for exploring disruptive DTs, is at
2
A.H. Trevisan et al.
Journal of Environmental Management 332 (2023) 117437
Fig. 1. Research methodological approach.
specific target as it is the primary source for observing more recent
technological nature phenomena.
Second, the startups should be considered circular. According to
Henry et al. (2020, p. 2), circular startups are “new, independent and
active companies pursuing a circular business model”. Among the circular
business models, those involving reverse logistics (RL) are the basis for
developing an economy based on the recirculation of materials (Ellen
Macarthur Foundation, 2013). Thus, the RL sector in Brazil was selected.
The report of the Distrito (2020) was used, presenting a set of successful
potential Brazilian startups in this context. Also, other startups from the
authors’ network and media publications were included.
Third, the startup should employ emerging DTs in its business model.
Like other CE case studies (Ranta et al., 2021), the case selection was not
restricted to a particular technology (e.g. IoT, BDA). The sample re­
striction to one specific DT could weaken the understanding of the
phenomenon. Finally, the fourth eligibility criterion is that the startup
should be a rich data source on the phenomenon studied. The startups
must be able to provide detailed data and information so that the data
analysis process can be conducted. After applying all these criteria, the
sample consists of nine startups located in different Brazilian states
(Table 1).
After sample selection, semi-structured interviews were conducted.
The interview protocol included open-ended questions and was
Fig. 2. Methodological procedure of the SLR.
the forefront of technological innovation because it can adapt in a more
agile and flexible way (Ayesa, 2019). Startups have explored business
opportunities that traditional large companies have not due to the
technological risks involved. Therefore, the sample focused on this
Table 1
Description of the 9 cases studied.
Case
Location (Brazilian
City)
Description
Main DTs
Foundation
year
Interviews
Case
1#
Case
2#
Case
3#
Case
4#
Case
5#
Case
6#
Case
7#
Case
8#
Case
9#
São Paulo
Startup focused on issuing recycling credit certificates for environmental
compensation
Platform-based startup for waste management and commercialization
(Marketplace)
Platform-based startup for disclosure environmental, social and corporate
governance (ESG) data, including reverse logistics data.
Platform-based startup for waste management
Blockchain
Cloud; BDA; AI
Blockchain
Cloud; BDA; AI
BDA; Blockchain
2016
CTO
2017
2019
Environmental
Engineer
CEO
IoT; Cloud
2018
CEO
IoT; BDA
2018
CEO
Fortaleza
Startup focused on collection, and disposal of materials, based on recycling
credit certificates
Platform-based startup for waste management
2017
CEO
Cotia
Platform-based startup for waste management
IoT; Blockchain;
Cloud; AI
IoT; Cloud; BDA; AI
2005
Technical Director
São Carlos
Platform-based startup for collecting and disposing e-waste
Cloud
2018
CEO
Porto Alegre
Platform-based startup for tracking raw material and textile products
Blockchain; BDA; AI
2019
CEO
Belo Horizonte
Florianópolis
Vitória
Porto Alegre
3
A.H. Trevisan et al.
Journal of Environmental Management 332 (2023) 117437
designed based on the results gathered from the SLR. Overall, the CEOs
and CTOs of the startup firms were contacted through the LinkedIn
platform, and the first two authors conducted the interviews. In order to
stimulate the participants to speak openly, anonymity was given to them
(as in Ott and Eisenhardt, 2020). The interviews lasted between 30min
and 1h15min. They were recorded with the participant’s permission and
were later transcribed. In order to supplement the interviews and allow
for more reliable analysis, different sources of data were used: (i) web
pages; (ii) reports and media files released by the firms (including videos
and podcasts); (iii) reports published by the press; and (iv) emails and
informal conversations between firms and researchers. All data were
triangulated to increase the study’s accuracy (Yin, 2011).
transformation (see Dąbrowska et al., 2022). During the data analysis
process, the authors also sought to understand where barriers were
emerging and which levels were experiencing consequences. This pro­
cess allowed the authors to synthesize the data and understand more
deeply the nature of the phenomenon under investigation.
3. Results and discussions
Based on the analysis that combined the results of the case studies
with the SLR, 45 barriers regarding the use of DTs for CE emerged
(Table 2). These barriers were categorized into eight dimensions: 1.
Knowledge management, 2. Financial, 3. Process Management &
Governance, 4. Technological, 5. Product & Material, 6. Reverse Logis­
tics Infrastructure, 7. Social Behaviour, and 8. Policy & Regulatory.
Some dimensions are more related to the CE domain (e.g., Reverse Lo­
gistics Infrastructure), while others are to DTs (e.g. Technological). All
literature references for the barriers can be found in the supplementary
material.
Fig. 4 demonstrates how each barrier was located within the eight
dimensions and across micro, meso, and macro-levels of CE and DTs
transitions. Some dimensions are more related to the micro and mesolevel, while others present barriers emerging from a macro lens. In the
following paragraphs, each dimension is explored, and their multi-level
natures are unpacked.
2.2. Data analysis
The selected articles (from SLR) and interview transcripts were
scrutinized following the guidelines of Gioia et al. (2012) and Miles et al.
(2014) for an open coding process. The inductive content analysis
method (Elo and Kyngäs, 2008) was employed, allowing patterns to
emerge without the presence of confirmation biases of pre-established
codes or templates. The MAXQDA® software supported the content
analysis and assisted in performing a line-by-line microanalysis of each
paragraph.
The coding process followed two analysis cycles as recommended by
Miles et al. (2014). In the first cycle, codes were generated through
descriptive open coding that synthesizes an idea in a paragraph or
sentence. In the second cycle, the codes initially generated were cate­
gorized into theoretical dimensions representing the patterns that
emerged in the first cycle. Fig. 3 presents an example of the first-order
and second-order of the code structure tree concerning the barriers.
The results obtained with SLR and case study were compared to assess
differences between theory and reality. This process was iterative and
allowed us to refine the categories as the authors evolved in under­
standing the data. Two authors performed this iterative process over
several months. At the end of the second cycle, eight aggregated cate­
gories emerged that were reviewed and agreed upon by all authors.
At the end of the data analysis, the categories were contrasted with
the multi-levels of CE application (see Ghisellini et al., 2016) and digital
3.1. Understanding knowledge management barriers
Barriers in the knowledge management dimension concern the
perception and understanding of technical and environmental knowl­
edge fundamental to an SCE. When these types of barriers arise, they
limit organizational development and sustainable technological evolu­
tion. All barriers in this category (B1, B2, B3, B4, and B5) were observed
in the literature and the cases under investigation.
The literature suggests that companies face difficulties in finding
professionals who have combined Information Technology and CE
knowledge (B1) (Halstenberg et al., 2021). Organizations, for example,
lack workforces with the necessary skills to use technologies to optimize
resources (N. Kumar et al., 2021), making it difficult to solve problems in
Fig. 3. Example of data-structure tree concerning the “Knowledge management” dimension.
4
A.H. Trevisan et al.
Journal of Environmental Management 332 (2023) 117437
Table 2
Barriers to implementing an SCE.
Table 2 (continued )
Dimension
Barrier
Description
Knowledge
management
B01. Lack of knowledge
about technology
application and
sustainability
B02. Lack of knowledge
about good environmental
practices
Many managers are unaware
of the opportunities brought
by DTs or lack the knowledge
to implement them.
Many companies are
unfamiliar with CE principles
and do not master initiatives
for circularity.
In circular operations,
employees must equate data
development and analysis
skills with sustainable
management skills.
Many stakeholders involved in
the digital transformation
process lack eco-awareness.
Many companies fail to see the
benefits of using smart
technologies for CE.
Some organizations cannot
make the necessary
investments due to budgetary
limitations, lack of interest
and lack of financing lines.
Initial and operating costs can
be prohibitive for some
companies, particularly those
working under budget
constraints or in uncertain
markets.
Many companies still see
sustainability as a financial
burden, and the investments
in DTs within the sector are
even smaller.
Many internal initiatives fail
due to a lack of leadership and
management support.
Technology provider
companies and their
customers need a responsible
sector to guide the transition.
Many companies find it
challenging to adapt their
processes to an SCE.
Lack of trust, stakeholder
integration, and coordination
tools prevent collaboration on
SCE.
Redefining the business model
and structured processes can
be difficult in rigid companies
with limited knowledge.
Low communication and
alignment between the
different areas of the company
prevent the structuring of
processes and the
implementation of
innovations.
Since digital and sustainable
investments may not pay off
immediately, companies that
do not invest for long-term
returns are more reluctant to
adopt these strategies.
Since SCE is a recent approach
involving considerable
investments in technological
innovation, many managers
still maintain a fearful
behaviour.
Small and medium-sized
companies may find it
B03. Lack of skilled labor
Financial
B04. Lack of
environmental awareness
and education
B05. Lack of perception of
environmental and
economic gains
B06. Lack of financial
resources
B07. High implementation
and running costs
B08. Lack of investment in
digitalization for
sustainability
Process
Management &
Governance
B09. Lack of leadership and
management support
B10. Lack of a responsible
sector within the client
B11. Difficulties in process
adaptation
B12. Lack of cooperation
and coordination between
business partner
B13. Lack of innovation
capacity
B14. Lack of integration of
company areas
B15. Lack of long-term
planning
B16. Lack of confidence in
investment and risk
aversion
Technological
B17. Lack of infrastructure
for GDPL application
Dimension
Barrier
B18. Difficulty in
supporting and
maintaining systems
B19. Difficulties in data
collection and storage
B20. Difficulties in data
analysis and model
building
B21. Technological failures
and limitations
B22. Lack of
interoperability and
integration
B23. Data Security and
Privacy issues
B24. Lack of standards and
protocols
B25. Lack of models and
tools
B26. Lack of adequate IT
infrastructure
Product &
Material
B27. Difficulties in
technology and product
development
B28. Low added value of
certain materials
B29. Low quality of
material collected
Reverse logistic
infrastructure
B30. Lack of infrastructure
for waste pickers’
cooperatives
B31. Low logistics
infrastructure
B32. Informality of waste
pickers’ cooperatives
Description
challenging to follow general
data protection laws that
demand high technological
infrastructure.
As DTs constantly improve,
system changes and updates
can prevent mobile devices/
applications from working.
Incorrect collections, lowquality data, and data in
incompatible formats, among
other problems, are common.
It is common for analyzes and
models to fail to provide
adequate support for decisionmaking.
Applications may not be
feasible due to technological
limitations of the technologies
available on the market and
the frequency of failures.
Integrating different DTs and
data from various partners is
challenging due to the lack of
standards and protocols.
Companies are vulnerable to
data loss due to cyber-attacks
and blackmailers.
The lack of standards and
protocols in several areas
(product development, data
sharing, among others)
undermines SCE.
Many managers complain
about the lack of models that
guide the implementation of
DTs, especially humanmachine interaction, and tools
that help measure circularity
maturity.
Applying new DTs requires an
update of the companies’
hardware and software, which
implies costs and can generate
resistance.
Circular and digital strategies
may require developing and
adapting products and
technologies.
Smart RL solutions are
hampered by unbalanced
product collection. Collectors
prioritize materials with
higher added value to the
detriment of low economic
valuable materials.
Dirty, wet, mixed with nonrecyclable materials cannot be
tracked using invoices and end
up in landfills.
Many cooperatives do not
have the physical
infrastructure to sort more
complex materials and
provide quality data and
material.
Digital solutions based on
waste management cannot
operate efficiently in places
with low logistics
infrastructure.
The use of data technologies
for tracking waste is hampered
by the informality of
collectors or the absence of
data.
(continued on next page)
5
A.H. Trevisan et al.
Journal of Environmental Management 332 (2023) 117437
2020a), which limits innovation processes within companies. According
to Bag et al. (2021), it is challenging to find talent with analytical ca­
pacity. Educational fragility (Cezarino et al., 2019) and lack of human
training and qualification (Narwane et al., 2021) also add to the equa­
tion. Although the literature highlights the difficulty in finding a
workforce, the cases indicate that keeping good professionals within the
firm is also extremely difficult. Companies compete for qualified pro­
fessionals since the demand is very high. As the interviewee in Case #4
said, “The pandemic has accelerated this crisis even more because companies
from outside the country started stealing our talents, besides local and na­
tional companies”.
The lack of environmental awareness and education (B4) (Chiap­
petta Jabbour et al., 2020b) is another barrier. The cases show that low
awareness occurs not only within the company’s domain but also among
other actors involved in the SCE. The interviewee from Case #2 stressed:
“They (stakeholders in RL) do not broaden one’s horizons to implement
technologies to improve the process”. Furthermore, many managers still
lack an understanding of the implications of I4.0 for sustainability
(Narwane et al., 2021). They are unaware of the benefits of using DTs (P.
Kumar et al., 2021). This problem stems from a lack of perception of
environmental and economic gains (B5) (Narwane et al., 2021).
Empirical data indicate that companies need to change their mindset
regarding “costs”. It is essential to expand the understanding of costs to
the potential subsequent cost reductions and additional revenues by
optimizing processes.
Unpacking the multi-level nature of knowledge management barriers.
This research suggests that knowledge management barriers permeate
all three levels (micro, meso and macro). While some barriers emerge
and affect one more specific level, such as lack of knowledge about
technology application and sustainability (B1) and lack of skilled labor
(B3), other barriers are related to the ecosystem and the macroeconomic system. For example, the lack of knowledge about good
environmental practices (B2) goes beyond the boundaries of a single
organization. While the literature highlights the lack of knowledge
within companies (Liu et al., 2021), the cases reveal that the ecosystem
needs to have knowledge aligned about an SCE. At the company level,
managers may not understand relevant technologies (Liu et al., 2021) or
be familiar with terms such as I4.0 and CE (S. Kumar et al., 2021). At the
ecosystem level, key actors may not understand what ESG environ­
mental practices are.
Regarding the lack of environmental awareness and education (B4),
companies must evolve their sustainable thinking and social awareness.
For Väisänen et al. (2019), the lack of widespread awareness can be a
barrier to circular software-based solutions. In addition, if consumers
are not environmentally educated about which locations are suitable for
the correct disposal of material, other actors in the ecosystem can be
affected. The interviewee in Case #8 said, “[ …] (consumers) put e-waste
in the trash bins, thinking they are doing a good job. Very few waste pickers’
cooperatives are qualified to work with e-waste because it is considered
hazardous waste [ …]. This even exposes cooperative pickers to contami­
nation because they do not have the necessary infrastructure”. Thus, edu­
cation and management knowledge require dissemination at all levels.
Table 2 (continued )
Dimension
Social Behaviour
Barrier
Description
B33. Low investment in
selective collection
Lack of selective collection
initiatives diminishes the
quality of materials collected.
Concerns about personal data
confidentiality and
anonymity.
Depredation of smart devices/
products (e.g., smart bins)
shared by the population.
The lack of sustainability
culture and the fear of new
technologies can lead the
consumer not to collaborate
with the company’s strategies.
The efficiency and reliability
of technologies can prevent
malicious companies and
corrupt city halls from
automating processes.
Digitalization causes
disruptions in the labor
market, reducing the number
of available jobs and
hindering unskilled labor
reallocation.
The absence of competitors
investing in digital and
circular strategies, conscious
consumers, and specific
legislation, among other, is a
disincentive to innovation.
An inadequate culture of the
digital reality that is not very
receptive to innovations
generates a lack of
engagement among
stakeholders.
Lack of tax and economic
incentive policies hamper
digital and circular
development.
Companies may feel
unmotivated due to high taxes
and bureaucracy to enable
sustainable business
operations.
SCE may not be implemented,
especially in emerging
nations, due to the high cost of
importing technologies from
developed countries.
Government fails to create,
enforce and update
regulations.
Setting low targets both
internally and externally to
the company discourages the
use of technologies for CE.
B34. Data sharing concerns
B35. Lack of care for public
property
B36. Difficulties related to
consumer behaviour
B37. Corruption and lack of
transparency in waste
management
B38. Fear of structural
unemployment
B39. Lack of market
pressures and demands
B40. Resistance to change
Policy &
Regulatory
B41. Lack of government
incentives
B42. Excessive
bureaucracy and taxation
in the country
B43. High cost of importing
material and technologies
B44. Low government
inspection and control
B45. Low environmental
targets
an industry 4.0 (Ozkan-Ozen et al., 2020). The interviewee from Case #7
evidenced: “The developer understands programming and system analysis
but does not understand waste management”. While previous studies
highlight that professionals face difficulty choosing which technologies
will be most suitable for achieving business goals (Bag et al., 2021), the
empirical evidence suggests that this problem may stem from the rapid
technological evolution observed nowadays. As pointed out by Case #3,
it is necessary to constantly study the technology to know which
emergent technological strategy is the most consistent with the com­
pany’s purpose.
The absence of knowledge about good environmental practices (B2)
(S. Kumar et al., 2021) hampers the efficient management of resources
(Kayikci et al., 2021). In addition, there is a lack of skilled labour in the
market (B3) (Abdul-Hamid et al., 2020; Chiappetta Jabbour et al.,
3.2. Understanding financial barriers
Financial barriers prevent technological advancement toward
circularity due to economic issues and are frequently reported in the
literature (Kim and Wu, 2021; Okorie and Russell, 2022) as hindering
the development of innovative solutions. Three barriers within this
category were identified (B6, B7, and B8), evidenced by scholars and
reinforced by empirical data.
Several companies have difficulties with self-financing (Cezarino
et al., 2019) and lack of financial resources (B6) (Kayikci et al., 2021;
Kumar et al., 2020). The literature highlights that companies can
struggle in investing in new business ideas (Antikainen et al., 2018), and
this barrier mainly affects small and medium-sized ones with budget
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Data
Fig. 4. Theoretical framework: the dimensions and multi-level nature of barriers.
constraints (Kim and Wu, 2021). An interviewee from Case #2 empha­
sized: “because we are a startup, we cannot do many things, the budget limits
us”.
Previous studies suggest that the high implementation and running
costs (B7) (Bag et al., 2021) can make the adoption of DTs prohibitive.
Consequently, the use of DTs is not prioritized among other business
aspects. According to Halstenberg et al. (2021), the costs become even
higher when companies consider integrating circular strategy imple­
mentation with systems development. On the other hand, empirical
findings show that a large volume and customer demand are necessary
for the investment to be viable. Therefore, it is a great challenge when
many customers cannot visualize the added value of using DTs. Finally,
the cases indicate that investments in digitalization for sustainability are
still minimal (B8). In Brazil, many companies see sustainability as an
expense, and a cultural change of mindset is necessary to overcome this
challenge.
Unpacking the multi-level nature of financial barriers. This study in­
dicates that financial barriers impact the micro- and meso-levels. In
particular, small companies are significantly affected, as they are still
pivoting their business. However, the high cost of digitalization per­
vades other ecosystems’ actors. For example, waste picker cooperatives
may refuse to implement DTs that facilitate waste management. Client
companies also perceive the monetary cost and often choose not to add
certain digital functions and features that make the acquired solution
more expensive. The interviewee of Case #8 said, “as long as we are not
sure that there will be a client hiring and paying for the solution, we do not
develop it because it is expensive”. Regarding investments, the environ­
mental engineer in Case #2 highlighted the need for more active
participation of large industries. The findings suggest that, in order to
overcome financial barriers, there needs to be mobilization and invest­
ment of the entire ecosystem.
3.3. Understanding process management & governance barriers
The results indicate that companies may face eight barriers associ­
ated with process management and governance (B9–B16), which
represent organizational and collaborative obstacles to an SCE. The first
barrier within this category is the lack of leadership and management
support (B9) (Bag et al., 2021; Kayikci et al., 2021). Leaders can be
resilient in adopting smart technologies (Kumar et al., 2020) to support
sustainable operations (P. Kumar et al., 2021). Without the approval of
top managers, financial resources will hardly be directed to the imple­
mentation of DTs (Narwane et al., 2021), and research and development
activities can be harmed (Ozkan-Ozen et al., 2020). Committed leader­
ship is essential for business process redesign (Zhang et al., 2019), as an
SCE demands significant change. In this sense, another barrier is the
difficulty in adapting processes (B11) (Kim and Wu, 2021).
While research on SCE highlights the leader’s critical role in the
transition to CE within organizations (e.g. Abdul-Hamid et al., 2020; Bag
et al., 2021), this study advances the literature by showing that lead­
ership within the customer is also fundamental. In B2B companies, the
lack of a responsible sector that dialogues with technology providers can
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hinder a successful transition (B10). The interviewee in Case #9 re­
ported that many companies still do not know which sector is in charge
of the CE-DTs initiatives. Consequently, communication among the
partners is compromised since it is essential to have leadership for
digital transformation on both sides of the dyad. However, in the real
context, it is not an easy task to find and coordinate business partners
(B12) that are aligned to enable the smart recirculation of resources (Cui
et al., 2021).
Another problem is the lack of innovation capacity (B13) (Abdul-­
Hamid et al., 2020; Okorie and Russell, 2022), which is exemplified by
three main factors: First, the difficulty in redefining business models and
converting ideas into successful solutions (Rajput and Singh, 2019a).
Second, it is not easy to find leaders who have a flourishing culture of
innovation (Zhang et al., 2019). And third, difficulty in creating mis­
sions and goals capable of being accomplished (Abdul-Hamid et al.,
2020). In this way, the involvement of different areas within the orga­
nization may be fundamental (Chiappetta Jabbour et al., 2020a). The
analysis revealed that the lack of integration of company areas (B14) is
also a barrier that compromises progress (Liu et al., 2021) and the
management of user requirements (Ingemarsdotter et al., 2021).
Finally, scholars suggest that lack of confidence in investment and
risk aversion (B16) significantly impacts the SCE implementation (N.
Kumar et al., 2021; P. Kumar et al., 2021). The lack of confidence stems
from: 1. Unclear economic benefits (Abdul-Hamid et al., 2020; Bag et al.,
2021), 2. Insufficient technical workforce (Chauhan et al., 2019); 3. An
increase in organizational expenses (Kouhizadeh et al., 2020); and 4.
The complexity of circular flows which further maximizes investment
risk (Ozkan-Ozen et al., 2020). This problem is amplified when the
company does not have long-term planning (B15) (P. Kumar et al.,
2021). According to Zhang et al. (2019, p. 6), companies will be unlikely
to adopt waste management technologies when they “pursue short-term
profitability instead of long-term sustainability.” Those barriers (B15 and
B16) were not reported in the cases, primarily because small companies
have low-risk aversion (Kouhizadeh et al., 2020). They plan to scale
their business in the medium and long term instead of the instant market
response.
Unpacking the multi-level nature of process management & governance
barriers. The findings indicate that the barriers related to process man­
agement and governance affect the micro and meso-levels. The barriers
located at the micro-level demonstrate the organization per se needs to
seek resources and strategies to overcome obstacles along the transition.
The organization must provide an ideation environment where em­
ployees have creative freedom (Bag et al., 2021) and training programs
to maximize their capabilities (N. Kumar et al., 2021). On the other
hand, the barriers located at the meso-level demand greater collabora­
tion between actors and depend on third parties to be overcome. The
existence of players engaged in digital transformation that go beyond
the company’s boundaries is essential. Consistent with our empirical
data, if the link between the players is weak, successful participant co­
ordination is unlikely to occur. The respondent of Case #7 told us: “It is
necessary to gather and sieve a lot of players to be able to structure an
ecosystem that really works.” The collaboration and alignment of activ­
ities have to be continuous.
and in data analysis and model building (B20) are barriers that permeate
different business structures and sizes. This study captured six important
aspects regarding data collection and analysis. First, companies must
deal with a vast amount of data from different sources with different
formats (Bag et al., 2021). Second, they may not know what types of
data they will collect if they do not have a structured process (Ingem­
arsdotter et al., 2021). Third, the quality of the data collected is not
always good (Cui et al., 2021), and it may not be stored may not be in an
appropriate format (Fisher et al., 2020). Fourth, data management
within companies can be poor, resulting in a lack of data, for instance, on
the performance of previous products (Ingemarsdotter et al., 2020).
Fifth, the source of the dataset collected may not be clear (Ingem­
arsdotter et al., 2020). Finally, as there is a lack of data analysis abilities,
the result is not always accurate and reliable (Liu et al., 2021).
While research on SCE highlights the difficulties in collecting and
storing data, the empirical findings reveal that it is extremely chal­
lenging to audit the collected data. Due to informality in RL associated
with the fear of sharing data, companies must put more effort into
performing an audit. Generally, the information collected is outdated
and unreliable. This problem does not only occur with primary data;
according to the CEO of Case #5, “There is a lack of public data to compare
and analyze. Secondary data is also awful”.
Concerning technological failures and limitations (B21), the litera­
ture points out some factors for the emergence of this barrier, among
them: insecure connectivity that impairs communication between
companies (Abdul-Hamid et al., 2020); limited internet coverage and
speed (N. Kumar et al., 2021); hardware limitation (Bressanelli et al.,
2018); high computational capacity demanded (Okorie and Russell,
2022); and high energy consumption for using technologies such as
blockchain (Damianou et al., 2019). The lack of interoperability and
integration is also a barrier to an SCE (Abdul-Hamid et al., 2020). The
CEO of Case #9 explains that “Excel in the industry is the boss” to
emphasize that it is still the most-used control system. In this way,
platforms and procedures need to communicate with Excel if the com­
pany wants to leverage its business.
Another barrier that is well recognized by scholars is data security
and privacy issues (B23). This barrier demands great attention from the
industry to reduce vulnerabilities and cyber security threats (Abdul-­
Hamid et al., 2020). Thus, protocols, frameworks, and technological
security methods must be established (Narwane et al., 2021). In this
sense, the lack of standards and protocols (B24) is also a barrier found in
the literature. The current scholarship explains that numerous aspects
need to be standardized in the industry, such as CE performance
assessment tools (Kayikci et al., 2021), product and component design
standardization (Despeisse et al., 2021), information and data re­
quirements (Kim and Wu, 2021), material data repositories (Kovacic
et al., 2020), regulations (S. Kumar et al., 2021), waste treatment re­
quirements (Zhang et al., 2019), and data sharing protocols and infra­
structure (Narwane et al., 2021), among others. For example, the lack of
standardization of product labels prevents more efficient data collection
and process automation (Despeisse et al., 2021).
The lack of models and tools (B25) is another barrier. The use of DTs
demands the existence of collaborative models to ensure work safety
between humans and robots (Abdul-Hamid et al., 2020). Models for
measuring the effectiveness and performance of technology investments
still need to be improved (N. Kumar et al., 2021). Easy-to-use tools are
required in order to analyze data (Ingemarsdotter et al., 2021) and
manage risk (Bag et al., 2021). Despite all the advances in CE studies,
according to Halstenberg et al. (2021), there is still a demand for sys­
temic circular strategy design tools. These tools should help the early
stages of product and service development. Finally, if there is no
adequate IT infrastructure (B26), it is not feasible for companies to
implement DTs (Bag et al., 2021). The empirical data reveal that the IoT
communication network is still very precarious in Brazil, and internet
coverage is only suitable in places with high demand. The Case #4
respondent said, “Our city doesn’t have IoT coverage in most places. So, it
3.4. Understanding technological barriers
The technological dimension corresponds to the technical infra­
structure and data management barriers. This dimension contains most
of the obstacles identified in the paper (B17–B26). Two barriers within
this dimension were found only in the case studies. Those barriers are:
lack of infrastructure for Brazil’s General Data Protection Law (GDPL)
application (B17) (Brasil, 2018) and difficulty in supporting and main­
taining systems (B18). These barriers emerge due to the nature of the
interviewed companies, which are small and suffer more regarding the
lack of infrastructure.
On the other hand, difficulties in data collection and storage (B19)
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was impossible to validate our product, [and] we gave up”.
Unpacking the multi-level nature of technological barriers. Most barriers
within the technological dimension are located at the micro- and mesolevel. This study suggests that it is unlikely that only the firm will face
obstacles that demand big data integration from multiple stakeholders.
All barriers related to the data flow process go beyond the organizational
boundary, as data come from different sources and are commonly shared
and used by ecosystem actors. However, IT infrastructure and stan­
dardization barriers permeate all three levels. Models, tools, and stan­
dards should be established internally, cross-industrially and globally (e.
g. regulations). The macro-level also needs to provide adequate infra­
structure for all other levels. This aspect is essential to reduce techno­
logical connectivity failures that hamper communication between
companies and devices.
recycle materials far from developed locales (e.g., large urban areas in
the southeast Brazilian region). The informants in Cases #1, #2, and #5
explained that it is complicated to find regulated companies to dispose of
and transport waste in the countryside. In many cases. it is necessary to
transport materials over long distances (more than 2000 km) to make
recycling feasible. This logistical difficulty impacts platform-based
business models that connect waste buyers and sellers.
The lack of regularization and informality of waste picker co­
operatives (B32) is another barrier that directly interferes with data
collection and analysis through invoices. The CTO of Case #1 was
emphatic: “I would say that the difficulty of applying technology, whether in
any market, is informality and lack of data.” Many waste collectors and
cooperatives cannot provide sales invoices to prove material trans­
actions due to non-regulation before the law. It is almost impossible to
correctly measure the percentage of recyclables nationally without the
invoices to track them. The absence of regularization also facilitates the
irregular work of collectors without personal protective equipment since
the Brazilian reality of collecting and sorting materials is still not
automated. Most of the barriers related to RL infrastructure stem from
the low investment in the selective collection (B33). This demonstrates
that the circulation of technical nutrients is carried out successfully only
in developed places.
Unpacking the multi-level nature of reverse logistic infrastructure barriers.
The analysis shows that RL infrastructure barriers need to be overcome
at the meso- and macro-levels. Although the evidence acquired from the
cases partially represents the reality of waste management in Brazil, it
offers us some important insights into emerging countries. The data
indicate that it is not only the company that uses DTs for sustainability
that should have the necessary physical and economic infrastructure.
The ecosystem around it also needs capabilities to perform activities that
guarantee circularity. It is important to direct attention to social aspects
because data analysis aimed at recycling can be hampered without ini­
tiatives to regularize informal workers. To do so, macro-level in­
vestments are required to make the micro- and meso-level efforts
worthwhile.
3.5. Understanding product & material barriers
Barriers within this dimension are related to the circular flow of
products and materials. The three barriers identified (B27, B28, B29)
generally emerge because an SCE demands adaptations in product
design and take-back systems. The barrier of technology and product
development (B27) stem from some factors. First, companies need to
rethink the way products are designed, including interoperability and
upgradeability (Ingemarsdotter et al., 2020). Second, aspects such as
design for remanufacturing, regeneration and restoration need to be
considered in circular business models (S. Kumar et al., 2021). Third, it
is not an easy task to include recycled materials in products and extend
their lifespan (Chiappetta Jabbour et al., 2020a) while maintaining
durability and quality (Rajput and Singh, 2019b). Finally, assets con­
taining digital passports include the further challenge of updating the
information throughout the product’s life cycle (Walden et al., 2021).
Regarding product collection, this study advances the literature by
showing that there are barriers associated with the low added value of
certain materials (B28). For example, glass is a heavy material that takes
up a significant amount of space, and its market value is not as attractive
as aluminium. Consequently, many recyclable waste pickers choose not
to collect it, which increases the rate of recyclable waste sent to landfills.
Furthermore, recyclables collected with non-recyclables end up being
unusable after the collection due to their low quality (B29). In this sense,
as much as the literature on SCE points out initiatives for the digitali­
zation of waste management (for example, smart bins), it is necessary
that, in practice, companies carry out prior work on social awareness
and restructuring the dynamics of recycling. Otherwise, technological
advances for circularity will be minimal.
Unpacking the multi-level nature of product & material barriers. Barriers
related to product design impact and emerge at the micro-level. How­
ever, obstacles associated with the collection of post-use products have
more significant interference from the ecosystem and the macroeco­
nomic system. The data allow us to affirm that material collection and
recycling systems should adopt a different logic to stimulate waste
collection and become economically viable. To improve the quality and
homogeneity of collected material, actors in the ecosystem and society
should be aligned to encourage more selective collection.
3.7. Understanding social behaviour barriers
The barriers in the social behaviour dimension concern the actors’
attitudes involved in the digital transformation. Seven barriers were
found (B34–B40), where some of them were identified only in the cases,
while others were previously mentioned in the literature.
Overall, data-sharing concerns (B34) were pointed out by both cases
and scholars. Previous studies show that entrepreneurs may feel
apprehensive about sharing sensitive business data, especially when it
involves a network of competitors who may have access to it (Antikainen
et al., 2018). Consumers may also be reluctant due to increased concerns
about personal data privacy (Liu et al., 2021). Empirical results reveal
that besides preventing adequate data collection, this barrier makes it
challenging to structure ecosystems based on platforms. For example,
the informant in Case #9 highlighted, “We have an interface within our
platform that is not used, called company networking, because companies do
not want to provide data and have a public profile there”.
The empirical data indicated that negative human behaviours are
also responsible for the emergence of barriers such as lack of care for
public property (B35) and lack of transparency in waste management
(B37). The CEO of Case #4 explained that installing sensors in public
trash bins is complex. According to him, “there is a great chance of
replacing the sensor, of people destroying the device, breaking it, and blowing
up the trash bin”. Instead, the company decided to use smart bins only
inside the private companies’ facilities or in gated communities with
guaranteed security. Public waste management is also hampered by the
corruption present in the sector. The findings suggest that initiatives to
make waste management more efficient through DTs are not welcome
since it is preferable to maintain established practices due to the
financial benefits received. “We know that solid waste management is one
3.6. Understanding reverse logistic infrastructure barriers
This category arose due to the characteristics of investigated cases.
All barriers within this dimension were reported exclusively by the
multiple cases (B30, B31, B32, B33), reflecting the fragility of the cur­
rent recycling system structured in Brazil. The first barrier refers to the
lack of infrastructure for waste pickers’ cooperatives (B30) responsible
for sorting materials. According to Case #1, “Some packages have greater
complexity in separating the materials present in their composition, and many
sorting centres do not have the necessary infrastructure to reuse them.” In this
sense, these materials cannot be traced after use.
Additionally, a low logistics infrastructure (B31) makes it difficult to
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of the easiest paths to take advantages, bribes, and dishonest operations”
(highlighted Case #7).
The analysis also revealed difficulties associated with consumer
behaviour (B36). This barrier mainly originates from factors such as lack
of confidence in the quality of the products (Chauhan et al., 2019), lack
of understanding of I4.0 technologies’ benefits, sustainable products,
and acceptance of circular business models (Chiappetta Jabbour et al.,
2020a), lack of perception of the value of digital features embedded in
products (e.g. optimized maintenance) (Ingemarsdotter et al., 2021),
and misconception that circular products have high prices (Kayikci
et al., 2021). These problems corroborate the low market pressure and
demand for innovative solutions (B38). The demand for a circular
product needs to be balanced with the investments made by companies
to be worthwhile (Kayikci et al., 2021).
The fear of structural unemployment (B39) is another barrier
regarding social behaviour. The literature suggests that many employees
are afraid of losing their jobs due to the disruptive changes caused by
digitalization (Kumar et al., 2020). This fear is also associated with
resistance to change and acceptance of innovations that come with DTs.
Indeed, the resistance to change (B40) is a barrier strongly supported by
the study’s empirical and theoretical data. Previous publications suggest
that resistance comes from organizations with hardened cultures
(Okorie and Russell, 2022), employees (P. Kumar et al., 2021), and
customers who are less adept at modernization (S. Kumar et al., 2021).
This study advances that literature by revealing that resistance to
change can also be caused by a perceived increase in operational
complexity. In other words, implementing DTs within collectors’ co­
operatives - formed by vulnerable people with a low educational level
and less digital skills - represents an increase in complexity in the work
environment.
Unpacking the multi-level nature of social behaviour barriers. Barriers
within the social behaviours category affect all levels. However, more
significant effort is demanded from the mesos and macro-levels to
overcome them. Ozkan-Ozen et al. (2020) pointed out that data security
concerns, for example, refer to all actors engaged in the digital trans­
formation and not just those internal to the company. The lack of care
for public property and the corruption involved in the waste manage­
ment process need to be addressed at the macro-sociocultural level
based on public educational policies. The reduction of structural un­
employment also starts with educational initiatives that open up op­
portunities for more skilled jobs. Finally, resistance to change emerges at
all three levels involving human beings. Despite the literature’s focus on
organizational resistance (i.e. within the company) (see Narwane et al.,
2021; Okorie and Russell, 2022), the findings show that this problem is
also rooted in society throughout different geographic locations. At the
firm level, there needs to be engagement and trust-building among
employees and leaders. At the meso-level, consumers and business
partners need to see the long-term benefits to accept the changes. And at
the macro-level, the innovation culture should be fostered in the whole
country, reducing regional differences in accepting an SCE. As the
interviewee in Case #6 explains, “Entrepreneurs from the southeast and
south of Brazil are more up-to-date with innovation and ESG issues compared
to entrepreneurs in the north and northeast.” Therefore, the culture of an
SCE should be propagated, not limited to a region.
come from the government and sectoral associations composed of
multiple companies. Therefore, the complexity surrounding an SCE
demands greater support from the public and private decision-making
institutions.
The excessive bureaucracy and taxation in the country prevent an
SCE (B42). The data show that it is highly bureaucratic for companies to
move forward with innovation when there are several hierarchical in­
stitutions (e.g. municipal, state and federal) that do not communicate
easily. The informant in Case #4 told us: “By the time we can open all these
doors, our innovation is gone”. Each local government has specific legis­
lation in Brazil, and taxation on the waste market is hugely complex. The
CEO of Case #5 explained that “the legislation changes a lot. In São Paulo
(southeast region), we have to register in 15 different systems; in the north­
east, there is not even a system to register”. A further barrier faced, espe­
cially in emerging countries, is the high cost of importing materials and
technology (B43). According to the respondent in Case #4, “today, the
components for the development of our sensors are mostly imported; they are
not manufactured in Brazil. Due to import tax, the value of what we bought
abroad is doubled when it arrives here”.
Low government inspection and control (B44) is also a barrier.
Several scholars have recognized that without regulatory pressure and
control, companies may not be encouraged to invest in an SCE (Kayikci
et al., 2021; P. Kumar et al., 2021). The existence of robust legislation is
not enough if there is no practical application. As observed in the cases,
“Brazil’s legislation is based on the first world, but the culture and inspection
are not”, said the respondent in Case #6. This lack of inspection also
hinders reliable data sharing in waste management. In developing
countries like Brazil, which has gigantic continental dimensions, it is
even more challenging to guarantee the control and application of the
law. Finally, low environmental targets (B45) also discourage the
improvement and use of DTs. The findings captured a direct relationship
between low targets set by the government and the companies them­
selves with the lack of inspection and regulatory pressure. A quote from
Case #8 exemplifies this: “as they [waste generators] are not sure if there
will be a charge, how [the government] will control and oversight the targets,
they have a certain comfort in what they do”.
Unpacking the multi-level nature of policy & regulatory barriers. Most of
the barriers are attributed to the macro-level. This implies that the
government needs to stipulate initiatives more assertively to encourage
an SCE. Despite the cases recognizing that Brazil’s legislation is
advanced, the lack of control in practice causes inadequate and irregular
operations. Consequently, there is unfair competition between actors
operating regularly (meso level) and an increase in greenwashing
practices (micro level). Low national environmental targets at the
macro-level also imply a slower movement of companies to create their
own targets, triggering slow progress in terms of digitalization.
4. Implications for theory and practice
4.1. Theoretical implications
This paper contributes to the emergent SCE literature by proposing a
new theoretical framework: the dimensions and multi-level nature of
barriers (Fig. 4). A multimethod was adopted by combining literature
review and multiple case studies into nine firms. This framework iden­
tifies and maps the different barriers at multiple dimensions and levels
concerning the transition toward SCE. Although the current scholarship
offers insightful discussions about barriers to adopting DTs, these studies
tend to focus on a specific technology (Cui et al., 2021; Okorie and
Russell, 2022), industrial sector (Abdul-Hamid et al., 2020) or business
model (Ingemarsdotter et al., 2020). Our framework shows that the
transition to SCE is a multi-level phenomenon which demands a more
holistic, dynamic understanding of barriers. Without a systemic under­
standing, the SCE field might fail to provide theoretical guidance to
overcome barriers. Thus, the developed framework identifies unique,
non-documented barriers in relation to prior scholarship.
3.8. Understanding policy & regulatory barriers
Five barriers within the policy and regulatory dimension were
identified (B41–B45). The first barrier is the lack of government in­
centives (B41) (Cui et al., 2021). Incentives need to come not only from
the federal government but also from local institutions to drive educa­
tion and awareness programs (Kim and Wu, 2021). Research on SCE
suggests that governments of developed economies should also provide
financial support for I4.0 (Narwane et al., 2021). In line with this
literature, the empirical data show that investments should be made
through subsidies and tax reductions. In addition, investments must
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Prior studies recognize that knowledge management barriers play a
crucial role in SCE (Abdul-Hamid et al., 2020). However, these studies
tend to focus on micro-level barriers, focusing on what individuals and
firms know about SCE, ignoring that these barriers also entail meso and
macro aspects. Knowledge has to be widely disseminated and not just
remain within organizational boundaries. The developed framework
theorizes that effective transitions also require building practices
(regarding knowledge management) and sharing knowledge among
suppliers, clients, governments, and actors.
A growing research stream also recognizes the role of financial
barriers (Kim and Wu, 2021), paying less attention that these financial
barriers have an interdependence nature in SCE transitions. For
example, the full potential of DTs might not be unleashed due to a lack of
certain firms’ capacity (e.g., waste pickers’ cooperatives) to afford these
technologies. Thus, financial limitations permeate the different actors
involved in the transition. The framework also reveals that process
management and governance barriers involve a key feature: the align­
ment between different actors. Complementing prior studies (e.g.
Abdul-Hamid et al., 2020; Bag et al., 2021), the findings of the study
explain that technological barriers related to data management require
the involvement of multiple actors for dealing with standards to collect,
integrate, and share data at the micro-level (e.g., appropriate data
format), at the meso-level (e.g., aligning standards at ecosystem) and at
the macro-level (e.g. laws, infrastructure).
Barriers concerning the product and material represent a step further
concerning product design for circularity (S. Kumar et al., 2021). The
barriers also involve waste-picking processes and the necessity of
deploying solutions to improve transportation and storage management.
Less attention to barriers related to post-consumption (reverse logistics
infrastructure barriers) was reported previously by literature. The
developed framework adds a geographical dimension to barriers:
different regions might present additional capabilities, infrastructure,
and resources related to DTs. In this case, the government should bal­
ance the distribution of resources throughout the country’s territory.
While prior literature provides a rich portrait of resistance to
adopting DTs at the firm level (P. Kumar et al., 2021; Narwane et al.,
2021), this study indicates such resistances also manifest at the
meso-level, involving suppliers, clients, and other actors. Resistance to
change can also present different nuances according to the region and
knowledge of the actors. Therefore, the geographical aspect of barriers
and knowledge management is critical. The findings also provide an
initial link between corruption and the adoption of an SCE. DTs might
increase the transparency and visibility of actors and flows. Previous
work highlights the importance of disclosing environmental information
to increase transparency (Dagestani et al., 2022; Dagestani and Qing,
2022), an activity that can definitely be supported by DTs. The frame­
work also indicates that scholars should move from a binary approach
(having or lacking a regulatory framework) to a more complex notion;
different goals and incentives are important to deal with heterogeneous
players.
Table 3
Implications for practice.
Microlevel
General recommendations
Description
1. Encouraging educational
partnerships with technical
schools and universities
Educational partnerships support
recruiting students and interns with
programming skills and knowledge
about the CE.
Training and workshops can help to
spread knowledge and reduce
organization inertia.
The offer of remote work opens the
possibility of hiring professionals
regardless of location, reducing
local demand for workforce.
The investment’s cost and risk are
reduced when the company chooses
to lease or share equipment/
infrastructure.
Companies must rethink their
product design to facilitate end-oflife strategies (e.g. recycling,
remanufacturing).
Access control mechanisms such as
passwords, limiting access to a few
employees, and security software to
detect malicious activity increases
data security.
Shared visions and implementation
pathways developed by companies
and specialists can guide the
implementation of DTs and circular
strategies.
Incubators and accelerators can
stimulate innovation by startups in
the market.
Collaborative platforms connect
companies to share and reuse
resources, waste and energy.
Automation allows higher-quality
materials to be sorted and sent for
recycling.
Formalization helps qualify workers
and raises the amount of material
sent for reprocessing.
Industrial communication
standards can enhance the flow of
information between actors and
guarantee data interoperability.
The development of norms and
standards can guide the different
actions in the ecosystem for
implementing DTs for CE.
Use intermediate B2B companies to
integrate actors in the value chain
via support for digital
transformation, data protection,
communication, and consultancy.
New educational curriculum and
teaching models can help train
skilled labor and disseminate
environmental education.
Government financial incentives
and standards can potentialize the
SCE transition.
Products composed of easy-torecycle materials must be
encouraged to the detriment of
difficult-to-recycle materials that
need to be reduced or banned.
A functioning innovation market
retains qualified professionals and
prevents a brain drain.
2. Conducting workshops and
training programs
3. Opening opportunities for
remote work
4. Sharing equipment and
infrastructure
5. Stimulating Design for the
Environment
6. Creating data access
mechanisms and controls
Mesolevel
7. Developing roadmap for
digital and circular transition
8. Stimulating the development
of startups
9. Collaborating on platforms for
industrial symbiosis
10. Automating waste collection
and separation
11. Formalizing recycling agents
12. Developing industrial
communication standard
13. Working with the
government to develop
regulations
14. Pursuing integration via
business-to-business companies
Macrolevel
4.2. Practical implications
15. Fostering new educational
programs
16. Encouraging innovative
solutions financially
Based on the SLR, interviews with CEOs and specialists from the
multiple cases, and discussions among the authors, eighteen potential
recommendations to be applied at all levels were proposed. Table 3
provides the implications for practice.
17. Encouraging the use of
materials that are easy to
reprocess.
5. Conclusions
18. Leveraging and investing in
science and technology
This study relied on an SLR and multiple case studies to investigate
the barriers to an SCE. In total, 56 published papers were analyzed. Also,
nine case studies located in Brazil were explored. The primary contri­
bution of this paper is a holistic framework that systematizes knowledge,
allowing us to classify barriers according to dimension and level. A set of
45 barriers categorized into eight dimensions was identified: Knowledge
management, Financial, Process management & Governance, Techno­
logical, Product & Material, Reverse logistic infrastructure, Social
behaviour, and Policy & Regulatory. Coupling theoretical and empirical
investigations revealed unnoticed barriers not previously evidenced by
11
A.H. Trevisan et al.
Journal of Environmental Management 332 (2023) 117437
literature.
Second, the developed framework captures the multi-level nature of
barriers and breaks down the idea that only companies should overcome
them. Companies are not solely responsible for overcoming all barriers.
Only in-house efforts are not enough to reach an SCE. The authors argue
that barriers affect and emerge at different levels, and the levels must be
synchronized to leverage digital transformation. Furthermore, the study
advances the literature on SCE in emerging countries. This paper iden­
tified barriers within the Brazilian context that might be relevant to
other developing economies.
The paper also set some recommendations for all levels. At the micro
level, companies should seek to develop circular products, encourage
educational programs, and continually provide training to reduce
organizational inertia. At the meso level, actors must foster collabora­
tion with heterogeneous players, including the government, to enhance
the circularity of resources. Finally, policies must be formulated at the
macro level to encourage economically and environmentally innovative
solutions in favour of an SCE. The government can develop policies to
avoid or mitigate barriers relevant to the macro-geopolitical context. For
example, government environmental authorities may create policies
encouraging companies to use DTs in their sustainable initiatives. On the
social side, policies must be formulated to boost educational programs
that qualify professionals in the technology domain. Local governments
can also co-create value with companies through mechanisms that
support innovation ecosystems. In any case, the identified barriers and
practical recommendations must be taken in the face of the geographic
context and sectoral limitations.
Similar to other qualitative studies, this research has limitations.
First, although this study reached saturation in the data analysis, a larger
sample of actors would improve the findings. Second, even though the
authors have selected companies that play different roles in RL activ­
ities, future work may investigate different industries and companies’
sizes. This study does not deeply analyze the impacts of the Covid-19
pandemic on the emergence and prevalence of barriers. Future work
may focus on barriers that arise from crises and abrupt events. Finally,
further research is necessary to refine the nature of each barrier and
provide tools and models to minimize their occurrence. An interesting
avenue of research is the social behaviour in the digital transformation
oriented toward circularity. Future work can also explore the root cause
of barriers, as some seem to cause others. A causal map to prioritize
those with the most significant consequences would be a welcome
contribution.
Acknowledgments
The authors would like to acknowledge the São Paulo Research
Foundation (FAPESP) – under the processes 2019/23655–9 and 2020/
14462–0 – for supporting this research. The opinions, hypotheses,
conclusions, and recommendations expressed in this material are the
responsibility of the authors and do not necessarily reflect the views of
FAPESP.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.jenvman.2023.117437.
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Credit author statement
Adriana Hofmann Trevisan: Conceptualization, Methodology,
Data acquisition and curation, Investigation, Formal analysis, Writing –
original draft. Ana Lobo: Methodology, Data acquisition and curation,
Investigation, Formal analysis, Writing – original draft. Daniel Guzzo:
Writing - Review & Editing, Supervision. Leonardo Augusto de Vas­
concelos Gomes: Writing - Review & Editing, Supervision. Janaina
Mascarenhas: Writing – Formal analysis, Review & Editing,
Supervision.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
Data will be made available on request.
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