Content uploaded by Oluwaseun Kolade
Author content
All content in this area was uploaded by Oluwaseun Kolade on Aug 11, 2022
Content may be subject to copyright.
Technological Forecasting & Social Change 183 (2022) 121954
0040-1625/© 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Technology acceptance and readiness of stakeholders for transitioning to a
circular plastic economy in Africa
Oluwaseun Kolade
a
,
*
, Victor Odumuyiwa
b
, Soroush Abolfathi
c
, Patrick Schr¨
oder
d
,
Kutoma Wakunuma
e
, Ifeoluwa Akanmu
f
, Timothy Whitehead
g
, Bosun Tijani
h
,
Muyiwa Oyinlola
i
a
Centre for Enterprise and Innovation, De Montfort University, Leicester LE 1 9BH, UK
b
Department of Computer Sciences, University of Lagos, Nigeria
c
School of Engineering, University of Warwick, Coventry CV4 7AL, UK
d
Chatham House, UK
e
Centre for Computing and Social Responsibility, De Montfort University, Leicester LE 1 9BH, UK
f
DITCh Plastic Network, Nigeria
g
School of Engineering and Applied Science, Aston University, Birmingham B4 7ET, UK
h
Co Creation Hub, Lagos, Nigeria
i
Institute of Energy and Sustainable Development, De Montfort University, Leicester LE 1 9BH, UK
ARTICLE INFO
Keywords:
Circular plastic economy
Digital innovations
Technology acceptance
Technology readiness
Technology lock-in
Circular economy
ABSTRACT
Scholars and practitioners have highlighted the importance of digital innovations in the drive towards a circular
plastic economy. Therefore this paper investigates the role of digital innovators and the public's response to
digital innovations on the African continent. The study draws from four focus groups, and cross-sectional surveys
of 33 digital innovators and 1475 community members across 20 low-middle income communities in ve African
countries. The results indicate that, while digital innovators are strongly optimistic and highly motivated, their
engagement and impact on the circular plastic economy ecosystem are limited by a range of institutional,
infrastructural and socio-cultural factors. Furthermore, results from the regression models of cross-sectional data
of community members show that understanding of the technologies and perceived ease of use have signicant
positive impacts on uptake of technological innovations for the circular plastic economy, and perceived ease of
use is also a signicant moderator of barriers to adoption. The ndings underline the need for a well-informed
and motivated cohort of digital innovators to promote diffusion of circular plastic innovations. It also emphasizes
the importance of a more collaborative, multistakeholder and multi-sectoral synergy to create a critical mass of
the consumer public needed to break the linear economy lock-in mechanisms and accelerate the transition to a
circular plastic economy in Africa.
1. Introduction
For a long time, the global economy has been locked in the linear
paradigm of take-make-dispose in production and consumption pat-
terns. Within the past decade, the entrenched habits of the linear
economy have increasingly exacerbated the waste problem, including
plastic waste. Between 2010 and 2020, the annual global production of
plastics increased from 270 million tonnes to 367 million tonnes (Sta-
tista, 2022). Only 9 % of these plastics are ever recycled and about 8
million tonnes of plastics, annually, end up in the world's oceans (United
Nations Environment Programme, 2022). At this rate, according to one
estimate, there will be more plastics than sh in the world's oceans by
2050 (United Nations Environment Programme, 2022). Plastics' detri-
mental environmental and health impacts, including waste, degradation
of natural systems, carbon emission, and toxic chemicals, have therefore
been the subject of increasing global concerns and discussions in recent
years (Schroeder et al., 2021). The urgency of addressing the plastics
production and use problems has also been heightened in recent years as
part of the global conversation about the consequences global carbon
emissions and climate change. Furthermore, the global covid-19
* Corresponding author.
E-mail address: seun.kolade@dmu.ac.uk (O. Kolade).
Contents lists available at ScienceDirect
Technological Forecasting & Social Change
journal homepage: www.elsevier.com/locate/techfore
https://doi.org/10.1016/j.techfore.2022.121954
Received 28 January 2022; Received in revised form 14 July 2022; Accepted 6 August 2022
Technological Forecasting & Social Change 183 (2022) 121954
2
pandemic has precipitated a signicant 300 % increase in single-use
plastics products (Economist, 2022). Therefore, a transition to circular
plastic economic (CPE) has been identied as an imperative, if the global
community is to address plastic pollution's wicked problem while
driving sustainable development and growth. CPE can ensure a signi-
cant reduction of plastic use and robust and responsible management of
plastics throughout their lifecycle. This requires a systemic change in
how manufacturers design and produce plastics and how households use
them through their lifecycle. In other words, a transition from a linear to
circular plastic economy requires a transformation of the entire supply
and value chains. This transformation requires a multi-stakeholder,
multi-sectoral buy-in into the circular paradigm of production and
consumption.
Digital innovations can play an essential role in facilitating the sys-
temic changes needed to transition towards a circular plastic economy
and resource efciency in the plastic sector (Barrie et al., 2022). To date,
the majority of CPE initiatives have focused on physical materials and
resources, which are often run as individual projects and not at the scale
to address the plastic pollution problem meaningfully. Digital in-
novations can act as a facilitator, accelerating the CPE solutions at a
global level and across industries. Coherent and inclusive digitalisation
efforts at the local, national, and international levels are paramount to
achieving the environmental, economic, and climate targets of CPE
(World Economic Forum, 2021). To achieve this, appropriate incentives
are needed from governments to enable technology innovators to
develop the digital backbone and disruptive digital technologies that
support CPE transition. Sharing data, open-source software, CPE digital
toolbox, and adopting the concept of ‘a global public good’ will reduce
the cost, time, and business risk for adopting these digital innovations
(World Economic Forum, 2021).
In the context of Africa, the circular plastic economy potentially of-
fers a great opportunity for skills development, employability, economic
progress, and value creation. There have been many examples of inno-
vative CPE projects across Africa, mainly focusing on social enterprise
for plastic repurposing, female entrepreneurship, and community
empowerment. African CPE players are contributing to green growth by
creating green jobs and green products (e.g. Berg et al., 2018). For
instance, converting plastic waste to artefacts and pavement blocks in
Ghana reduces waste, effectively enhances resources and creates sus-
tainable jobs and skills training (Debrah et al., 2021). The challenge is
that these otherwise important examples of CPE initiatives are not suf-
ciently at scale across the continent to make signicant aggregate
impact on the plastic waste problem in Africa. Besides, these notable
examples of circular plastic initiatives are typically undertaken by social
enterprise innovators and NGOs who often work in silos from govern-
ments, university and other industry and community stakeholders.
Digital innovations can play a key role in connecting stakeholders from a
whole spectrum of backgrounds and sectors, and thereby help to drive
scaling of innovative CPE solutions across the continent. Thus, in this
paper, we argue that the diffusion of these innovative solutions will
depend on the attributes and approach of the innovators, and the
characteristics of the technological innovations designed to accelerate
transition to a circular plastic economy in Africa. Our paper therefore
addresses two complementary research questions: what is the readiness
level of digital innovators to design and promote CPE innovations? And
what factors inuence the uptake of CPE innovations among consumers
and community members?
While the African continent has witnessed the rapid expansion and
growing impact of tech hubs (Atiase et al., 2020), there is a gap in un-
derstanding technology readiness and uptake to support and enhance
CPE initiatives across the continent. The diffusion and scaling of CPE
innovations is currently slowed and limited by, among others: (1) a lack
of awareness of digital innovations (2) the role they can play in specic
CPE projects, (3) lack of appropriate backbone digital infrastructures to
enable adoption of the innovations, (4) limited digital literacy and
technological know-how, (5) affordability and accessibility, and (6) the
lack of appropriate policy and nancial incentives. These factors are
even more important within the context of the need to break the linear
economy lock-in and the grave consequences of continuing in the linear
economy trajectory. By 2030, for example, plastic waste is expected to
double to 165 million tonnes per year particularly in countries such as
Egypt, Nigeria, South Africa, Algeria, Morocco, and Tunisia (Ellen
MacArthur Foundation, 2021). The Ellen MacArthur Foundation report
further indicates that this type of increase in plastic imports without
appropriate technological systems for end-of-life treatment of plastic
waste will inevitably contribute to a negative environmental, social and
economic impact. For example, mismanaged waste and plastic pollution
results in the spread of communicable diseases such as malaria or
diarrhea which impacts the most vulnerable in the communities
disproportionately (He et al., 2021; Meng et al., 2021).
A limited number of studies investigated the role of digital in-
novations in CPE across Africa (e.g. Berg et al., 2018; Cagno et al., 2021)
and concluded that signicant changes to the current regulatory struc-
tures are needed to enable robust adoption of digital innovations for
CPE. Also, facilitating investment in tech entrepreneurship and tech-
nology disruptors and improving technology backbone infrastructures
across Africa are critical enablers of CPE at scales. Liu et al. (2021)
investigated the trends of integration of the digital economy and circular
economy. They proposed an integrative framework approach that in-
cludes digital technology toolkits and an all-inclusive strategy across
lifecycle stages to generate sustainable impacts for CE and CPE projects.
This proposed integrative framework can be benecial to support the
use of digital innovations in CPE across Africa.
Therefore, this study adopts a Technology Acceptance Model (TAM)
approach to illuminate the factors inuencing the adoption of digital
technologies among ordinary consumers and the general public in Af-
rica. TAM provides a deeper understanding of how technology users
perceive, accept, and subsequently use new technologies. To achieve a
comprehensive understanding of stakeholders' readiness in adopting a
specic technology, TAM considers both the functionality of the tech-
nology and the broader parameters, including socioeconomical issues
such as education, gender, and accessibility.
Perceived Usefulness (PU), and Perceived Ease of Use (PEOU) mea-
sures are also essential metrics within TAM that can quantify how
functional a specic technology can be to a stakeholder (Davis, 1989).
Quantifying PU for a particular digital technology is a function of costs,
availability, and know-how for a specic stakeholder. Meanwhile, PEOU
describes the ease of use of a technology as enabler or barrier to adop-
tion of the technology by a stakeholder. Venkatesh and Davis (2000)
further developed the Technology Acceptance model (i.e. TAM2) to
incorporate and quantify the other variables inuencing user accep-
tance. These parameters include; ‘Social inuence processes (subjective
norm, voluntariness, and image), Cognitive instrumental processes (job
relevance, output quality, result demonstrability, and Perceived ease of
use) signicantly inuenced user acceptance’. Investigating the role of
these parameters for stakeholder readiness, particularly within the
context of CPE in Africa, is very important. For example, subjective
norm denotes a collectivist approach (Lee and Wan, 2010), and in the
case of CPE this can be translated to a collectivist approach to technol-
ogy, particularly concerning ‘how’ and ‘whether’ others associated with
a particular stakeholder can nd a technology useful in equal measure.
In addition to investigation of adoption behaviours of ordinary
community members, this paper also adopts the technology readiness
approach to examine the motivational attributes and attitudes that
shape the engagement of digital innovators in the circular economy
landscape. This enables us to explicate the critical role of these in-
novators and early adopters in the diffusion of innovation across the rest
of society. It also underlines the imperative of a multi-stakeholder,
multi-sectoral approach to accelerating transition to a circular plastic
economy in Africa. In other words, given that there are several different
types of stakeholders in CPE, this paper focuses on investigating tech-
nology readiness of digital innovators and start-ups, and technology
O. Kolade et al.
Technological Forecasting & Social Change 183 (2022) 121954
3
acceptance of community members. These three groups are of particular
importance, given that (i) digital innovators are in the forefront of
driving any technological innovations that will eventually become
mainstream and taken up by users; (ii) Startups want to be in the fore-
front using technology innovations to boost up their chances of success
and upscaling their business model; and (iii) community members are
front end users of technologies, and a key indicator of effective socio-
technical transition to the circular plastic economy. This multi-faceted
approach to data collection and analysis underlines the imperative of
a multi-stakeholder approach to transitioning to a circular plastic
economy on the African continent.
2. Theory and hypotheses
2.1. Technology acceptance and technology readiness
Innovation scholars have proposed several theoretical and analytical
frameworks to explain the factors that inuence the uptake of in-
novations or the likelihood that an individual will perform a behaviour.
For example, the theory of planned behaviour, drawing on the precursor
theory of reasoned action, identies motivational and volitional factors
as two key pillars in predicting behaviour, including but not limited to
the uptake of technological innovations (Ajzen, 1991; Kolade and
Harpham, 2014). In other words, a person's performance of a behaviour
is a function of their intention to perform the behaviour, on the one
hand, and the resources and capability they have to complete the
behaviour on the other. Without this volitional control or perception of
the same, motivation is not enough as an antecedent of behaviour. While
the theory of planned behaviour addresses the key limitation in the
original theory of reasoned action with its inclusion of volitional control,
it still does not adequately account for technology-specic factors that
explain how individuals respond to technological innovations and ulti-
mately adopt or reject them. Whereas the theory of planned behaviour
speaks to behaviour in a broad, generic sense, the technology acceptance
model explicitly addresses attitude to, and use of, technological in-
novations in particular.
Thus, the technology acceptance model (TAM) (Davis, 1989) builds
on Ajzen and Fishbein's theory of reasoned action (1980) and Bandura's
self-efcacy theory (Bandura, 1982) to provide a more focused analyt-
ical framework for predicting uptake of technological innovations. The
technology acceptance model highlights ve key dimensions or factors
that predict adoption of technological innovations: Perceived Usefulness
(PU), Perceived Ease Of Use (PEOU), Attitude (ATT), Behavioural
Intention (BI), and Actual Use (Davis, 1989; Rajak and Shaw, 2021).
Perceived usefulness is the degree to which an individual believes that
using a particular innovation would enhance performance. On the other
hand, perceived ease of use is how a person believes that using a specic
technological innovation will be free of effort. Perceived usefulness and
ease of use are the two principal antecedents in the technology accep-
tance model (Davis, 1989). The distinction and linkages between the
two are similar to the difference between outcome expectation and self-
efcacy in Bandura's framework (Bandura, 1982). Self-efcacy refers to
people's perception of their capability to organise and implement actions
required to achieve designated outcomes and performances (Bandura,
1986). While self-efcacy focuses on an individual's response capabil-
ities, outcome expectation refers to the imagined consequences of per-
forming particular behaviours. Outcome expectations can be physical,
social, or self-evaluative. Thus, concerning technology adoption,
perceived usefulness denes the attributes of technology about the belief
of individuals that the technology can facilitate desired outcomes. On
the other hand, perceived ease of use focuses on the amount of effort
required to use the technology and the individual's perception of voli-
tional control or capability to apply the effort.
In the present study, we draw from Hong et al. (2014) to highlight
the context specic variations of the technology acceptance model in the
study context of circular plastic economy in Africa. First, we identify
technical, nancial, political and socio-cultural barriers, in combination,
as a critical external factor that inuences uptake of circular plastic
economy innovations. In addition, we incorporate into our conceptual
framework the interactions between perceived usefulness and aggregate
barriers to uptake of innovations for the circular plastic economy. The
ensuing decomposition of the TAM model enable us to grapple with
contextual variations in two dimensions: rst in the context of a devel-
oping country with generally less developed institutional environment,
where innovators, adopters and other stakeholders have to grapple with
different challenges and new opportunities to promote uptake of
innovations.
The conceptual decomposition is also essential in a discussion of the
role of, and attitude to, digital innovations within the context of the
circular plastic economy. Firstly, the transition from linear to the cir-
cular plastic economy is challenged and complicated by lock-in mech-
anisms associated with “old” technologies, sunk investment, and
entrenched societal consumption habits (Geels, 2010; Oyinlola et al.,
2022). Technological lock-in arises from the self-organising market
process through which early adopters of a competing technology inu-
ence subsequent adopters to take up the same. The subsequent aggre-
gation of network externalities and increasing returns associated with
the technology induce an economy to lock itself in outcomes that are not
necessarily superior nor easily altered (Arthur, 1989). This is the case
with societal lock-in to the linear paradigm of take-make-dispose in
production and consumption patterns (Sopjani et al., 2020). Thus, the
emergence of new and superior technology is not enough to dissolve a
lock-in. Instead, a breakout from technologies requires a “perfect storm”
arising from the interlocking networks of markets and the attractiveness
of the new technologies for users (Dolfsma and Leydesdorff, 2009). In
effect, the transition to the circular plastic economy requires a critical
mass of users motivated by the ideals and vision of the circular economy
and is willing and able to use technology to achieve the outcome ex-
pectations. This combined force of motivational and volitional factors,
captured in the decomposed TAM model, is required to overcome col-
lective societal inertia to new technologies and thereafter create
network externalities needed to drive new, circular production systems
and consumption habits (Araujo Galv˜
ao et al., 2018).
The preceding discussion underlines the critical importance of in-
novators- the rst in line to develop or adopt innovations- in the diffu-
sion of technological innovations (Rogers, 1995). Digital innovators and
champions of new technologies need to possess the requisite technical
aptitude and innovativeness and heightened consciousness and moti-
vation concerning the latest technologies. In this study, we conceptu-
alise and interrogate these motivational attributes of innovators in terms
of technology readiness. Technology readiness is “an overall state of
mind resulting from a gestalt of mental enablers and inhibitors that
collectively determine a person's predisposition to use new technolo-
gies” (Parasuraman, 2000, p. 308). It reects the propensity of people to
create, embrace and use new technologies (Sun et al., 2020).The tech-
nology readiness index (TRI) comprise four key dimensions that can be
classed into two sub-categories: motivators (optimism and innovative-
ness); and inhibitors (discomfort and insecurity). Optimism entails a
positive view of technology concerning its various possibilities, while
innovativeness implies a technology pioneer tendency. On the other
hand, discomfort refers to a lack of control over technology, and inse-
curity implies distrust of the technology (Parasuraman and Colby,
2015). The technology readiness is therefore an effective framework to
map the motivational attributes and personal traits of the ve categories
of adopters originally outlined by Rogers (1995): innovators, early
adopters, early majority, late majority, and laggards. Thus, as other
scholars have reported, innovators are typically individuals with higher
TRI's scores on optimism and innovativeness. They also tend to be more
comfortable and more trusting of new technologies (Walczuch et al.,
2007).
Therefore, the technology readiness index (TRI) is a complementary
analytical framework to the technology acceptance model (TAM) in
O. Kolade et al.
Technological Forecasting & Social Change 183 (2022) 121954
4
analysing attitudes and responses to new technologies. While TAM fo-
cuses mainly on the cognitive dimensions of technology response, TRI
emphasises affective, motivational factors. In the present study, we aver
that these affective factors are especially important for assessing the
readiness of digital innovators to adopt, develop and promote digital
innovations. We also argue that both volitional and motivational factors
are important explanatory variables that can help explain the process
through which society embrace innovations in order to break free from
the lock-in mechanisms of the linear economy in the drive towards the
circular economy. The following section draws mainly on the technol-
ogy acceptance model to set out the hypotheses related to the key factors
that inuence the general public's attitude to new technologies for the
circular plastic economy. In a subsequent section, we focus attention on
the technology readiness of digital innovators and their role in the
diffusion of digital innovations for CPE.
2.2. Hypotheses development
In the context of competing technologies, such as the case with
technologies driving linear and circular economies, ordinary consumers
need a certain threshold of informational knowledge and functional
understanding of the new technologies to break the lock-in. For
example, a study of Industry 4.0 technologies adoption among SMEs
indicates that knowledge accumulation triggers opportunity recognition
among SMEs who, by gaining a deeper understanding of the benets of
the technologies, can deploy them in their operations (Ricci et al., 2021).
A similar study nds that consumers' adoption of battery swap tech-
nology (BST) for electric vehicles is a function of the extent to which
they know what the technology is and how it works (Adu-Gyam et al.,
2022). Other scholars have reported similar ndings that potential
adopters require a level of functional understanding, not just awareness,
about new technologies, especially those related to sustainability and
green solutions (Chang and Wu, 2015; Liu et al., 2019). Given the pre-
ceding, we propose that:
H1. Technology understanding is positively associated with the uptake
of digital innovations for the circular plastic economy (CPE) in Africa.
Following Davis (1989)'s seminal work on the technology acceptance
model, several studies have investigated the impact of perceived use-
fulness and perceived ease of use (see earlier denitions) in a wide range
of empirical contexts. These include healthcare (Alam et al., 2020; Kim
and Ho, 2021); manufacturing (Chin and Lin, 2015); construction (Man
et al., 2021); crowdfunding (Djimesah et al., 2021). However, while
there is a growing body of work investigating the role of new technol-
ogies as drivers of the circular economy, the circular plastic economy is
still a relatively under-researched area. This is especially the case for the
African context. Therefore, his study investigating the role of digital
innovations as drivers of transition to a circular plastic economy in Af-
rica aims to contribute to this emerging area of research. We make the
following propositions:
H2. Perceived usefulness is positively associated with uptake of digital
innovations for CPE in Africa.
H3. Perceived ease of use is positively associated with uptake of digital
innovations for CPE in Africa.
Technology and innovation studies also grapple with the negative
impacts of technical and non-technical barriers to uptake technological
innovations, including innovations for the circular economy. Technical
barriers include lack of or inadequate technical know-how, reverse lo-
gistics and standardisation issues (van Keulen and Kirchherr, 2021).
Non-technical barriers include nancial barriers such as limited in-
vestment in technology (de Jesus and Mendonça, 2018) and the high
cost of recycled materials (Hart et al., 2019). There are also political
barriers such as policy incoherence, public procurement issues and
inadequate regulatory and tax incentives (de Jesus and Mendonça,
2018; Kolade et al., 2014; van Keulen and Kirchherr, 2021). In addition,
uptake of innovations for the circular economy can also be hampered by
socio-cultural factors, including the social acceptability of circular
economy products (Barquet et al., 2020). The impact of these barriers on
uptake of circular economy innovations is often felt in combination
rather than in isolation. Thus, we propose that:
H4. Aggregate technical, nancial, political and socio-cultural barriers
are negatively associated with digital innovations' uptake.
In addition to the key variables identied in key analytical frame-
works such as TAM and TRI, innovation studies usually account for the
impact of socioeconomic and personal factors such as age, educational
level, income, and gender (Charef et al., 2021; Kim and Ho, 2021;
Sierzchula et al., 2014). These are incorporated as controls or additional
explanatory variables in various regression models. However, several
studies have identied gender as a signicant factor in studying circular
economy innovations. For example, in a study investigating users' atti-
tude and perception of end-of-life scenarios (EoLs) for electrical and
electronic appliances, it was found that women showed positive atti-
tudes to environment-friendly EoLs- re-use, re-manufacturing and
recycle (Atlason et al., 2017). They are, among others, more willing to
pay a premium price for environment-friendly e-products. Our empirical
context is also an important consideration: women are often seen at the
forefront of initiatives to promote sustainability and environment-
friendly innovations on the African continent. We, therefore, propose
that:
H5. Gender is positively associated with the uptake of digital in-
novations for CPE in Africa.
Our nal cluster of three hypotheses focuses on the moderating ef-
fects of two important personal attributes- income level and education
level- on (previously discussed aggregate) barriers to uptake of digital
innovations for the circular plastic economy. In addition, we also
hypothesise the moderating effect of perceived ease of use on barriers to
uptake of digital innovations. Education level is typically associated
with increased capacity to access and process new knowledge about CPE
innovations (Hazen et al., 2017). With increased knowledge and deeper
understanding of the CPE ideal, we propose that consumers are more
likely to be positively disposed towards CPE innovations and therefore
overcome barriers to adoption. Similarly, we propose that the higher the
income level of individuals, the more likely they are likely to overcome
barriers, including nancial barriers, to innovation. Finally, in line with
previous discussions about the importance of perceived ease of use
(PEOU), we propose that PEOU is not only important as a direct pre-
dictor of CPE innovation uptake, it is also important as a potential
moderator of barriers to uptake of innovations. Taken together, we,
therefore, propose that:
H6. Income level moderates the impact of barriers on uptake of digital
innovations for CPE in Africa.
H7. Education level moderates the impact of barriers on uptake of
digital innovations for CPE in Africa.
H8. Perceived ease of use moderates the impact of barriers on uptake
of digital innovations for CPE in Africa.
3. Methodology
3.1. Study instrument
This study focused on two main groups: (a) the stakeholders (waste
management organisations, civil society, academia, digital innovation
rm/startups working on plastic waste and policymakers) to determine
the technology readiness level of the stakeholders with major emphasis
on the Digital Innovators (DIs) who are potential developers/deployers
of technology for managing plastic waste and (b) the community, who
O. Kolade et al.
Technological Forecasting & Social Change 183 (2022) 121954
5
need to adopt the deployed technology. Quantitative data was collected
from both groups using electronic questionnaires. The survey sought to
understand the depth of knowledge and the level of engagement of the
two stakeholder groups in ten (10) frontier technologies shaping the
present and future of digital innovations. These technologies were
identied through engagement with stakeholders and literature.
The technologies of interest include Articial Intelligence (AI),
Geographic Information Systems (GIS) BlockChain, Internet of Things
(IoT), Robotics (Rob), 3D Printing (3DP), Serverless computing (SC), 5G,
Mobile apps and Augmented Reality/Virtual Reality (ARVR). These
technologies were identied as critical enablers for the transition to a
circular economy by stakeholders during the focus group discussion as
well as from the literature. For example, other scholars such Chidepatil
et al. (2020) (AI and blockchain technology), Singh (2019) (Remote
sensing and GIS), Mdukaza et al. (2018) (Internet of Things), Hoosain
et al. (2020), Kristoffersen et al. (2020) and Schot and Kanger (2018)
have also identied similar technologies. Oyinlola et al. (2022) have
presented a comprehensive list of how these technologies could
contribute to the circular plastic economy transition.
A list of organisations using digital innovations to manage plastic
waste in Africa was compiled from the literature and databases (see
Oyinlola et al. 2022). A link to an electronic survey was sent to 39 or-
ganisations, with 17 of them completing the survey. In addition, 16
other stakeholders who are not digital innovators also responded to the
questionnaire, making 33 responses for the rst group of respondents.
Field workers were hired and trained to administer electronic ques-
tionnaires to over 1500 households in 20 low-middle communities
across ve countries: Kenya, Namibia, Nigeria, Rwanda, Zambia. These
countries have a comprehensive representation of Africa; for example,
they are geographically diverse (Eastern, Western and Southern),
economically different (Nigeria with GDP: $375.8 billion to Rwanda
with GDP: $9.137 billion), vary in a population (190 million in Nigeria
to 2.5 million in Namibia) and have very clear differences in literacy
rates. A total of 1475 completed responses were analysed.
The following questions, using a series of Likert scale items, were
administered to both group of respondents except the sixth question
which was addressed only to the community:
1. How would you rate your understanding of these technologies?
2. To what extent do you think these technologies are useful for man-
aging plastic wastes?
3. To what extent do you think these technologies are easy to use?
4. Do you currently use any of the following technologies?
5. Do you have an intention to use any of the following technologies?
6. Please rank technical, economic, political and socio-cultural barrier
to adoption of the technologies, from most signicant to least
signicant.
As detailed in Oyinlola et al. (2022), engagement with other stake-
holders informed the weighting and categorisation.
3.2. Variables and measures
3.2.1. Dependent variable
The primary dependent variable investigated in this study is the
uptake of digital innovations. This is measured by computing the par-
ticipants' responses to the question: “have you ever used any of the
following technologies?” The question was followed by a list of 10
digital technologies: Articial Intelligence, Geographical Information
Systems (GIS), Blockchains, Internet of Things (IOT), Robotics, 3D
Printing, Serverless Computing (Faas), Augmented Reality, 5G, Mobiles.
The response options were a binary yes/no. These were computed as
aggregates of responses for the 10 technologies.
3.2.2. Independent variables
The independent variables considered in this study are: Perceived
understanding, perceived usefulness; perceived ease of use; and barriers.
Perceived understanding. Perceived understanding is dened as the
level of functional knowledge that a potential user has about the oper-
ational features of a particular technology. In this study, respondents
were asked to rate their understanding of the listed ten technologies on a
5-point Likert scale from 1 (never heard of it) to 5(excellent). The re-
sponses were computed using the “sum” function on SPSS vs 26.
Perceived usefulness. Perceived usefulness was dened in the semi-
nal work of Davis (1989) as “the degree to which a person believes that
using a particular system would enhance his or her job performance”
(pp.321). In this study, we dene perceived usefulness as the degree to
which a potential user believes that using technology would help them
undertake circular plastic activities: reduce, re-use, recycle, and recover.
This is operationalised with a question on a 5-point Likert scale (from
“not useful at all” to “very useful”) in which participants were asked: “to
what extent do you think these technologies are useful for managing
plastic wastes?”. Here again, as for “perceived understanding”, partici-
pants were asked to rate the ten technologies investigated in this study.
Perceived ease of use. In line with Davis (1989) we dene perceived
ease of use as the degree to which a person believes that using a digital
innovation would be free of effort. Thus, respondents were asked: “To
what extent do you think these technologies are easy to use?”. The re-
sponses to each of the ten technologies were provided on a 5-point Likert
scale, from 1 (very difcult) to 5 (very easy).
Barriers. This study also includes barriers as an important explana-
tory variable. To operationalise this, respondents were asked: “Please
rank these barriers to your adoption of digital tools/technology in
plastic waste management”. The barriers listed were: technical, eco-
nomic, political, and social-cultural barriers. Respondents were asked to
assign a rank score for each barrier from 1 (most signicant) to 5 (least
signicant). The responses were then reverse-coded such that the
highest score of 5 was assigned to “most signicant” and 1 to “least
signicant” and other scores accordingly on the spectrum.
3.2.3. Controls
Our model included four key socioeconomic variables as controls:
age, gender, education level, and income level. This reects their pu-
tative relevance as predictors of innovation uptake. Previous studies
have shown that education level and income level are positively asso-
ciated with the uptake of technological innovations and that gender
often plays a key role (Balta-Ozkan et al., 2021). Others have reported
that age is negatively associated with innovation uptake (Mugu-
maarhahama et al., 2021). Education level is operationalised as an
ordinal variable, ranked from primary school (1) to postgraduate (5).
Similarly, for income level, respondents self-reported from a scale of
low-income (1) to high-income (5). For age, participants chose from a
range of: 18–24; 25–34; 35–44; 45–54; 55–64; 65–74; 74+. For gender,
we used a binary measure of male/female.
3.2.4. Interaction terms
We also computed interaction terms to investigate the moderating
effects of each of income level, education level and perceived ease of use
on the aggregate barrier to uptake of digital innovations. To do this, we
rst computed the aggregates, by sum, for each of the four variables.
Next, we mean-centred these variables by subtracting each variable
mean from its aggregate score previously calculated. The mean-centred
variables were used to compute the interaction terms, e.g. C_INC*C_BAR
being the interaction term between centred income and centred barrier.
3.3. Data analysis
3.3.1. Computation of technology readiness index for digital innovators
The objective of this was to examine the motivation, interest, con-
dence, and depth of understanding of the digital innovators in of each
of the selected technologies. These provide an operational measure of
their readiness to create, deploy and promote those technologies for
O. Kolade et al.
Technological Forecasting & Social Change 183 (2022) 121954
6
plastic waste management and transitioning to CPE. Thus, in order to
achieve this we draw both on a quantitative assessment of the readiness
levels, complemented with a qualitative exploration, in focus group
discussions, of the motivational factors that explains the readiness of the
digital innovators.
In the rst, quantitative part, we carried out a cross-sectional survey
of start-ups and innovators. A total of 33 respondents completed the
survey. The survey instrument include items on: the rating of the re-
spondent's understanding of the technology, rating of the respondent's
perceived usefulness of the technology as it relates to plastic waste
management and CPE, rating of the respondent's perceived ease of use of
the technology, rating of the respondent's actual use of the technology
and rating of the respondent's intent to use the technology. For each of
the technologies, the stakeholders rated themselves on a 5 point Likert
scale going from 0.0 to 1.0 with 0.2 intervals i.e. each DI chose from one
of the elements in the array (0.0, 0.2, 0.4, 0.6, 0.8, 1.0) for each of the
question.
To determine the importance of each technology in developing dig-
ital innovations for transitioning to circular plastic economy, a mean of
the perceived usefulness rating for each technology was computed and
normalised to generate the weight of importance of each technology as
shown in Table 1. The perceived usefulness rating of stakeholders with
poor or no understanding of the technology was not included in the
mean calculation. From Table 2, it can be seen that the most useful
technologies based on the current reality for plastic waste management
and transitioning to CPE in Africa are GIS, Mobile app, IoT and AI.
The results from the surveys were analysed to determine the tech-
nology readiness level of the DIs.
The stakeholders rated ten technologies based on ve questions,
generating a 10 ×5 matrix. We attributed weights to each of the
question categories based on our judgement of what determines readi-
ness, as shown in Table 2. We assumed that understanding technology
and knowing its usefulness play a key role in determining the willingness
of a DI to deploy such technology. The two categories were given an
equal weight of 0.15 each. The same weight applies to the perception of
a DI on how easy it is to use the technology. However, we gave higher
weights to the actual use of the technology (0.35) and the intention to
use the technology (0.20). The rationale for this is that a DI already using
technology is more ready and would have practically experienced the
usefulness of such technology, its challenges, and its limitations. In the
same vein, we proposed that a DI having an intention to use a technology
can be considered more ready than those who understood the technol-
ogy, knew the usefulness and could estimate how easy it is to use it but
have no intention to use it.
To compute the readiness, two mathematical models were dened as
shown in Eqs. (1) and (2). Eq. (1) computes the readiness of a DI in a
given technology, while Eq. (2) computes the overall readiness of a DI
given the ten frontier technologies that we believe can facilitate the
transitioning to a circular plastic economy. Tables 3 and 4 present the
result for the 17 DIs.
where N is the total number of question categories, subscript r refers to
the rating from the survey, while subscript w refers to the weight for
each question category, as shown in Table 2.
OverallReadiness =∑I
i=1(ITRi*TUWi(2)
where I is the total number of the technologies considered, subscript i
stands for each technology, ITR is the tech readiness of the DI in each of
the given technology and TUW is the computed usefulness weight for
each of the technology as shown in Table 1.
3.3.2. Focus group discussions
Focus group interviews were carried out to explore the views of start-
ups and digital innovators across the African continent. A total of four
focus groups were conducted, with each focus group having between 3
and 5 participants who were drawn from the region where the focus
group was holding i.e. southern, eastern and western Africa. These focus
groups were held in Namibia, Rwanda, Nigeria, and Zambia. The focus
group participants were asked question to elicit their perspectives on the
prospects of digital innovations and the various challenges and oppor-
tunities relating to the drive towards the circular plastic economy on the
continent.
3.3.3. Regression model for consumers
The variables listed in Section 3.2 were incorporated into a series of
OLS regressions, with USE (uptake of digital innovations) as the
dependent variable for each model. In model 1, we specify only the
controls as independent variables. In model 2, four additional variables
were included. In model 3, we added three interaction terms to inves-
tigate the moderating effects of income level, education level, and ease
of use on barriers to uptake of technological innovations. The model
specications are summarised below:
where β is the regression coefcient for each variable and
ε
is the error
term. The ‘C’ prexes represent the mean-centred values for the inter-
action terms, for example, mean-centred value of barriers and income
(CBAR-CINC).
GEN =Gender; INC =income level; EDU =Education level; UND =
Understanding; USF =Perceived usefulness; EOU =Perceived ease of
use; and BAR =Aggregate barriers to uptake of digital innovation.
IndTechReadiness (ITR) = ((Ar*Aw) + (Br*Bw) + (Cr*Cw) + (Dr*Dw) + (Er*Ew) )/N(1)
USE =∝+β1GEN +β2AGE +β3INC +β4EDU +
ε
(Model 1)
USE =∝+β1GEN +β2AGE +β3INC +β4EDU +β5UND +β6USF +β7EOU +β8BAR +
ε
(Model 2)
USE =∝+β1GEN +β2AGE +β3INC +β4EDU +β5UND +β6USF +β7EOU +β8BAR +β9CBAR CINC +β10CBAR CEDL +β11CBAR CEOU +
ε
(Model 3)
O. Kolade et al.
Technological Forecasting & Social Change 183 (2022) 121954
7
4. Results
The results are presented under two categories: technology readiness
of innovators and consumers' acceptance of technology.
4.1. Technology readiness of digital innovators
4.1.1. Technology readiness index
Table 3 shows the computed readiness of the DIs for each of the
technologies expressed as a value between 0 and 1 based on Eq. (1).
Using a threshold of 0.70 to determine if a DI can be considered ready to
deploy a technology in their digital innovations, we observed that for
seven out of the ten technologies, <20 % of the DIs have what it takes to
deploy those technologies in their solutions (cf. Fig. 2). The result of the
overall tech readiness of the DIs computed using Eq. (2) is shown in
Table 4. Only one DI has an outstanding index of 0.94. Five DIs have an
index above 0.60. Setting the threshold at 0.6 implies that only 35.29 %
of the DIs are ready to deploy the frontier technologies in creating digital
solutions for transitioning to a CPE.
4.1.2. Insights from focus group discussions with digital innovators
Participants in the focus group discussions expressed strong con-
dence in the potential impact of technological innovations in the various
stages of the plastic waste management process. These begin with waste
collection, where the innovators highlighted the impact of route opti-
misation algorithms and automated optical sensors as examples of
technologies driving efciency in the plastic waste collection process:
We have an application for customers that gets the exact location of a
customer and helps connect them to a reliable, affordable and vetted
collector in the area…For our waste pickers, we made what we call a
route optimization algorithm that helps them navigate through a
neighborhood depending on the jobs and locations they have to visit
on that day
(Rwanda Innovators Focus Group, Kigali, November 2020)
Technologies actually have a very massive role in waste collection.
Now for normal municipal waste collections, you'll see that having
those automated optical sensors that can tell you when a bin is full or
half full will prevent waste from staying in one place over a long
period of time and allow collection to be done seamlessly
(Nigeria Innovators Focus Group, Lagos, October 2020)
Furthermore, participants indicated high optimism that digital
technologies can have a game-changing impact in the plastic value
chains, for example in appropriating the use value of plastic waste in
additive manufacturing:
When it comes to plastic in particular, we see digital technology and
particularly additive manufacturing or commonly known as 3D
printing, being one of those technologies that could emerge from the
use of digital technology to actually help reduce and hopefully
Table 3
Technology readiness of digital innovators.
DIs AI GIS BlockChain IoT Robotics 3D Serverless computing AR/VR 5G Mobile apps
1 0.62 0.36 0.30 0.53 0.53 0.50 0.12 0.39 0.59 0.94
2 0.59 1.00 1.00 1.00 0.94 0.97 0.94 0.94 1.00 1.00
3 0.85 0.56 0.56 0.56 0.56 0.56 0.44 1.03 0.59 0.97
4 0.70 0.47 0.44 0.65 0.76 0.56 0.50 0.39 0.18 0.56
5 0.62 0.91 0.53 0.88 0.53 0.56 0.53 0.62 0.53 0.91
6 0.30 0.53 0.18 0.56 0.30 0.62 0.12 0.56 0.53 0.94
7 0.12 0.91 0.23 0.94 0.41 0.44 0.67 0.29 0.29 0.97
8 0.62 0.94 0.44 0.94 0.50 0.27 0.88 0.62 0.44 0.97
9 0.62 0.94 0.30 0.50 0.47 0.44 0.44 0.56 0.44 0.85
10 0.62 0.44 0.21 0.38 0.41 0.32 0.38 0.18 0.53 0.94
11 0.59 0.62 0.59 1.00 0.36 0.59 0.56 0.39 0.56 0.97
12 0.82 0.88 0.47 0.94 0.50 0.50 0.35 0.44 0.56 1.00
13 0.59 0.47 0.47 0.47 0.47 0.82 0.47 0.30 0.47 0.85
14 0.18 0.27 0.15 0.18 0.38 0.41 0.15 0.15 0.15 0.88
15 0.56 0.03 0.09 0.06 0.06 0.15 0.03 0.03 0.09 0.47
16 0.24 0.47 0.47 0.21 0.50 0.30 0.24 0.47 0.44 0.88
17 0.62 0.91 0.30 0.33 0.33 0.36 0.30 0.42 0.33 0.94
Table 1
Computed usefulness weight of the technologies.
Technology Usefulness weight
AI 0.10471
GIS 0.116066
BC 0.091824
IoT 0.107466
Rob 0.098128
3DP 0.094079
SC 0.090439
ARVR 0.085295
5G 0.098234
Mobile app 0.113758
Table 2
Question categories and attributed weights in determining tech
readiness of digital innovators.
Question category Weight
Understand the technology (A) 0.15
Usefulness of the technology (B) 0.15
Ease of use of the technology (C) 0.15
Actual use of the technology (D) 0.35
Intention to use the technology (E) 0.20
Table 4
Overall readiness of digital innovators.
DIs Score
1 0.50
2 0.94
3 0.67
4 0.53
5 0.67
6 0.48
7 0.55
8 0.68
9 0.57
10 0.46
11 0.64
12 0.67
13 0.54
14 0.30
15 0.16
16 0.43
17 0.50
O. Kolade et al.
Technological Forecasting & Social Change 183 (2022) 121954
8
eliminate plastic(sic). The beauty with 3D printing is that you can
actually add 100 to 1000 times value to plastic, so we see that as a
huge opportunity for all the players in the value chain
(Zambia Innovators Focus Group, Lusaka, November 2020)
Digital innovators are also strongly motivated by what they consider
to be the enormous potential of digital innovations to tackle unem-
ployment problems through their applications for the circular plastic
economy:
First of all, we understand…and the Funny thing is the waste man-
agement sector is a lucrative business, If I could use that phrase, it is a
lucrative business in that there are very few people that have a space
in this sector. Well look, I mean. Just to give you some gure…
Zambia, has a population of about 18 million people and 35 % of that
population is the youth. And among the youth, there's a 16 % un-
employment rate. So now you wonder to say look, there's an industry
here in which resources are largely available. And then you, you
wonder to say OK, why isn't there public and private sector
engagement? Because this is actually an industry…if I can put it to
you honestly, that if we were to mobilize ourselves as a sector and the
players in charge, we could reduce unemployment rate signicantly
(Zambia Innovators Focus Group, Lusaka, November 2020)
These high levels of optimism and motivation among African digital
innovators are tempered by concerns about limited and inadequate in-
frastructures that can support innovations, including transportation and
logistics. There are also concerns about the institutional environment, in
terms of limited market opportunities and inadequate policy in-
terventions to incentivise innovators and promote new societal habits
along the lines of circular economy values and principles. Finally, in-
novators expressed concerns about nancial barriers, and how public
sector nance, in particular, can be difcult to access:
We can get funding if we have a very good business plan. But the
challenge is the size of the market for recyclable material. If you get
funds from a bank, You have to repay the loan, but if you don't have a
large market, it would be a problem to reimburse the loan
(Rwandan Innovators Focus Group, Kigali, November 2020)
It can be quite frustrating doing this business here in Nigeria. Even
the logistics of picking up waste was even more expensive than what
I was collecting
(Nigeria Innovators Focus Group, Lagos, October 2020 2020)
“Funds in the plastic recycling space? I have never seen anyone get it
before. I know it exists, but it's difcult to access that fund. The
opportunity is there, but access is very, very complicated
(Nigeria Innovators Focus Group, Lagos, October 2020)
In transportation we are seeing a lot of challenges in… Not only
collection of waste for nal disposal, but also recovery of materials
for use and recycling. One of the biggest problems is double
spending… If the transporters are more efcient, more waste can be
recovered, and so more waste can be sold to processors and so more
revenue can be generated in the market
(Zambia Start-up, Lusaka, November 2020)
4.2. Consumers' acceptance of technology
4.2.1. Prole of respondents
The community survey was carried out in various low to middle-
income communities in several African countries. A greater percentage
(29 %) of the respondents were based in Nigeria, with Kenya and
Rwanda following closely with 22 % and 23 % respectively, and Zambia
and Namibia accounting for about 19.5 % and 6 % of the total re-
spondents, respectively. The survey was administered to individuals
between the ages of 18–75+, with the largest portion of respondents (34
%) falling between 18 and 24 years of age. In terms of gender, there were
roughly equal numbers of male and female respondents as it was a close
50.5 % to 49.5 % respectively. Though a few of the respondents were
either single, widowed or divorced, a larger percentage of the re-
spondents (54 %) were married, and an even larger percentage of all
respondents (91 %) lived in households where they were either the head
of the home or lived with a member of their nuclear family i.e. wife/
husband/child/sibling. The majority of the respondents were educated
up to secondary and tertiary levels, and only about 2.5 % had no edu-
cation at all. Of all the respondents, the majority (41 %) self-reported to
be of low-income, while 33.15 % fell into the middle class and only
about 5 % claimed to be of high-income level. Table 5 shows the prole
of the community survey respondents.
4.2.2. Descriptive statistics and correlation matrix
The summary of the descriptive statistics and correlation matrix is
provided in Table 6. The correlation coefcients for the variables are
<0.5, except for the use of technology/Understanding and Ease of use
(EOU)/Usefulness (USF) which stand at 0.74 and 0.75, respectively.
Furthermore, the variance ination factor (VIF) for any of the nine
variables is well below the threshold of 10. These, taken together,
Table 5
Prole of respondents.
Freq Percent
Gender Male 745 50.51
Female 730 49.49
Total 1475 100
Region Lagos 429 29.08
Kenya 329 22.31
Rwanda 341 23.12
Namibia 88 5.97
Zambia 288 19.53
Total 1475 100
Age 18–24 504 34.17
25–34 350 23.73
35–44 348 23.59
45–54 191 12.95
55–64 61 4.14
65–74 18 1.22
75+3 0.20
Total 1475 100
Household relationship Head 492 33.36
Wife/husband 471 31.93
Son/daughter 324 21.97
Brother/sister 69 4.68
Friend/house mate 59 4.00
Son-in-law/daughter-in-law 18 1.22
Niece/nephew by blood 14 0.95
Grandchild 11 0.75
Adopted/foster/step child 5 0.34
Parent-in-law 5 0.34
Help 4 0.27
Niece/nephew by marriage 3 0.20
Total 1475 100
Income level Low income 618 41.90
Middle income 368 24.95
Lower middle income 295 20
Upper middle income 121 8.20
High income 73 4.95
Total 1475 100
Educational level Tertiary 453 35.77
Secondary 565 35.50
Primary 212 10.43
Vocational training 132 9.97
Post-graduate 67 5.76
None 46 2.56
Total 1475 100
Marital status Married 798 54.10
Single 624 42.31
Widow(er) 45 3.05
Divorced 8 0.54
Total 1475 100
O. Kolade et al.
Technological Forecasting & Social Change 183 (2022) 121954
9
indicate there is no signicant concern with multi-collinearity. Key
descriptive statistics- mean, standard deviation, minimum and mini-
mum values- were also provided in summary in Table 6.
4.2.3. Regression results
The result of the regression modelling is outlined in Table 7. We
specied four socioeconomic variables in model 1. These are gender,
age, income level and education level. The result indicated that all four
variables are signicantly associated with uptake of digital innovations,
at 1 % level of signicance. However, the impact of age on uptake of
innovations is negative, at a magnitude of −0.297. This implies, in ef-
fect, that the older respondents are less likely to take up digital
innovations.
In the second model, we specied understanding (UND), perceived
usefulness (USF), perceived ease of use (EOU) and barriers (BAR) as the
main independent variables. The four socioeconomic variables from
model 1 were incorporated as controls. In the second model, only age
and income level were signicant predictors of innovation uptake, with
age still negative at 5 % level of signicance. Gender is no longer a
signicant predictor of innovation uptake, as it was in model 1. All the
four leading independent variables were signicantly associated with
innovation uptake, with all but perceived usefulness signicant at 1 %
signicance level. Perceived usefulness is negatively signicant at 10 %
level of signicance. This is counter-intuitive, as usefulness is typically a
strong predictor of uptake innovation. However, this unexpected
outcome is set within the context of a strong impact of perceived ease of
you, which is a signicant positive predictor of innovation uptake at 1 %
level of signicance. At a strong 1 % level of signicance, the negative
impact of barriers is expected but noteworthy as a backdrop to the third
and nal model.
For the third model, we specied interactions between aggregate
barriers and income level, education level and perceived ease of use. The
objective was to examine if, and to what extent these three factors
moderate the negative impact of barriers on uptake of digital
innovations. The result indicates that income level exacerbates, rather
than moderate, the impact of barriers on uptake of digital innovations.
Compared with a magnitude of −0.032 for barriers in model 3, the
impact of the barrier-income-level interaction term is −0.037 at a
higher, 1 % level of signicance. Conversely, perceived usefulness is an
effective moderator of barriers, at a reduced but still negative magnitude
of −0.002, compared with −0.032 for barriers. Finally, education level
is an insignicant moderator of barriers.
5. Discussion
5.1. Technology readiness of digital innovators
The reason for computing the technology readiness of the DIs playing
in the CPE space in Africa is to identify the gaps and the level of pre-
paredness of the DIs in developing and deploying relevant digital in-
novations that can enhance transitioning to CPE in Africa. We intended
to determine the capability and preparedness of the DIs to use ten
frontier technologies mentioned in section – in their innovations. Our
results show that most of the DIs are not well equipped to use most
frontier technologies. Out of the ten technologies, the results show that
DIs in Africa are more familiar with 3 of the technologies (Mobile app,
GIS and IoT). Out of the three, only mobile apps seem to be the most
used as the record shows that 88 % of the DIs is already using this. Our
observation shows that most of DIs have deployed or are in the process
of developing mobile applications in managing plastic waste and tran-
sitioning to a CPE; 41.18 % are ready to deploy GIS and 35.29 % are
prepared for IoT deployment. This means that more empowerment is
needed for the DIs to scale their deployment of these 10 frontier tech-
nologies in the solutions they are creating. However, as popular as AI is,
it was observed that the DIs have not deployed AI even though some
professed to use AI in their innovations. This calls for investment in
capacity building for the DIs in Africa and a need to create a network of
experts and stakeholders for knowledge sharing and co-creation of
innovative digital solutions for enhancing CPE uptake in Africa.
The insights of participants in the focus group discussions show that
digital innovators are highly motivated and optimistic about the pros-
pects of a digitally enabled circular plastic economy in Africa. However,
they are also awake to the reality of institutional challenges, socio-
cultural factors and infrastructural limitations that are slowing down
the pace of progress on the African continent. In order to drive uptake of
CPE innovations across the wider population, digital innovators
implicitly recognise the need for greater synergy and collaboration with
other stakeholders in the ecosystem. For example, telecommunication
and other digital and physical infrastructures provided or facilitated by
national governments can have signicant impact on increasing uptake
of digital innovations by removing or mitigating barriers to uptake.
Similarly, better interactions between digital innovators and community
groups, local leaders and frontline NGOs can facilitate co-creation of
technological and digital solutions that are more relevant, more acces-
sible and more responsive to community user needs, experiences, pe-
culiarities and use-habits. In other words, a multi-stakeholder co-
production of digital CPE innovations can contribute to better attitudes
Table 6
Descriptive statistics and correlation matrix.
Variable Mean Std. Dev. Min Max VIF GEN AGE INC EDU UND USF EOU BAR USE
GEN 0.51 0.50 0 1 1.06 1.00
AGE 2.44 1.17 1 6 1.13 0.05 1.00
INC 2.14 1.19 1 5 1.53 0.14 0.26 1.00
EDU 2.69 1.19 1 5 1.45 0.13 0.00 0.43 1.00
UND 22.00 10.00 9 50 2.75 0.18 −0.07 0.35 0.45 1.00
USF 33.40 13.63 10 60 2.43 0.00 0.13 0.05 0.27 0.48 1.00
EOU 31.96 14.23 9 60 2.45 0.02 0.18 0.02 0.28 0.45 0.75 1.00
BAR 13.17 3.72 4 20 1.09 0.12 −0.05 0.21 0.13 0.15 0.01 0.07 1.00
USE 2.33 2.58 0 10 2.36 0.13 −0.06 0.38 0.38 0.74 0.36 0.37 0.17 1.00
Table 7
Perceived usefulness, ease of use and uptake of digital innovations for CPE.
Variables Model 1 Model 2 Model 3
GEN 0.320*** −0.068 −0.061
AGE −0.297*** −0.086** −0.088**
INC 0.657*** 0.327*** 0.305***
EDL 0.524*** −0.003 0.005
UND 0.170*** 0.172***
USF −0.010* −0.011**
EOU 0.019*** 0.02***
BAR −0.037*** −0.032**
CBAR_CINC −0.037***
CBAR_CEDL −0.011
CBAR_CEOU −0.002**
Model summary
Number of observations 1474 1474 1474
R-Square 0.222 0.575 0.583
Adjusted R-Square 0.220 0.573 0.580
* =P <0.10, ** =P <0.05, *** =P <0.01.
O. Kolade et al.
Technological Forecasting & Social Change 183 (2022) 121954
10
of community members and ordinary users to the innovations.
5.2. Community members' attitude to digital innovations
The result indicates, among others, that understanding is signi-
cantly and positively associated with consumers' uptake of circular
plastic economy (CPE) innovations. This supports hypothesis H1
,
in
consonance with previous innovation studies which identify active
knowledge search and understanding as predictors of innovation uptake
(Maina et al., 2021; Ricci et al., 2021). We argue that functional un-
derstanding of a new technology is especially important for the adoption
of CPE innovation in the light of the collective inertia associated with the
linear economy lock-in. Understanding CPE innovations' basic technical
features and practical value enables consumers to overcome initial
misgivings and bandwagon effects associated with linear economy
products and technologies. It can also enable them to embrace a long-
term orientation about the merits of CPE principles: re-use, re-manu-
facture, and recycle.
Perceived usefulness (PU) was signicantly but negatively associated
with uptake of CPE innovations. This rejection of hypothesis H2 appears
counter-intuitive, given that previous studies have reported the positive
impact of perceived usefulness on the adoption of innovations (Djimesah
et al., 2021; Man et al., 2021). However, this unexpected outcome needs
to be understood within the context of the signicant positive effect of
perceived ease of use on uptake of CPE innovations (support for H3). In
other words, it is not enough that CPE innovations are perceived to be
useful if, say, they require more effort to use. Again, the technology lock-
in associated with CPE can also partly explain this, as Dolfsma and
Leydesdorff (2009) observed that users' perception that technology is
useful or superior is not enough to break technology lock-in and inu-
ence adoption. Among a list of comparatively “useful” technologies,
users may ultimately choose the ones popular within their networks and
the wider society rather than the ones perceived to be more useful.
However, perceived ease of use can be a more decisive factor. This
stands to reason, because the fundamental psychology of technology
lock-in is that older, established technologies tend to be habit-forming.
Agents stick with them because it is more convenient and relatively
free of effort to do so (Khalil, 2013). Thus, if a newer technology is
perceived to be easier to use, consumers may be more incentivised to
break free from their habituation to older technologies. This is a perti-
nent consideration for inventors and innovators designing and pro-
moting circular economy innovations.
The result also supports hypothesis H4 that aggregate barriers
(comprising technical, nancial, political and socio-cultural barriers)
negatively inuence CPE innovations' uptake. This is expected, in line
with extant literature (Barquet et al., 2020; de Jesus and Mendonça,
2018; van Keulen and Kirchherr, 2021). Therefore, it is pertinent to
examine, as hypotheses H6 to H8 seek to do, the moderating impact of
some variables on these barriers. In the meantime, we note that gender
hypothesis, H5
,
is not supported in our third and nal model, although it
was supported in model 1. This implies that in the presence of other
factors such as understanding, PU, PEOU and barriers, gender is not a
signicant predictor of CPE innovation uptake. In other words, both men
and women may adopt CPE innovations not because of their gender, but
because the technologies are well understood and easier to use. On one
level, this appears contrary to the ndings of Atlason et al. (2017), who
reported a positive impact of gender on CPE products uptake. However,
Atlason et al. (2017) also found that perception of the attractiveness of
the CPE products plays as important a role as gender.
Finally, we turn attention to the three hypotheses (H6 to H8), looking
at the moderating effects of income level, education level, and perceived
ease of use on barriers to uptake of CPE innovations. The results do not
support either H6 or H7 on the impact of income level and education
level, respectively. Indeed, while the impact of education is found to be
insignicant, income level is found to exacerbate, rather than mitigate,
barriers to uptake of digital innovations. One explanation for this is that
education can be a driver of negative conrmation bias, shaping nega-
tive attitudes, to CPE innovations. The negative impact of income level is
also slightly unexpected but not entirely surprising.
Given that circular economy innovations are about waste reduction
and resource efciency, it is possible that higher-income individuals
may not see it as an immediate priority- in the face of technical, political,
and socio-cultural barriers. On the other hand, the study supports hy-
pothesis H8 on the positive signicant moderating effect of perceived
ease of use on barriers to uptake of CPE innovations. This aligns with the
signicant positive impact of perceived ease of use as a primary factor
(H3). In other words, when consumers and users are convinced that a
CPE innovation require minimal effort to use, they are more likely to
overcome the inertia due to technical, nancial, political and socio-
cultural barriers.
5.3. Policy implications
Based on the results on the study, there are several implications for
the role that policy has to play to facilitate and overcome barriers to the
transition to a CPE in African countries.
Digital innovations and the transitions to a CPE are closely linked.
Many governments in Africa have and are developing and implementing
digital policies to enable widespread digital access and/or the use of
digital technology. Examples of such larger strategies include Kenya's
National information and communications technology policy (2016), or
Rwanda's Smart Rwanda masterplan 2015–2020 (see World Economic
Forum, 2021; Schroeder and Barrie, 2022). These are important not only
for the CPE but also for industrialisation, education, skills, social in-
clusion, and sustainable development.
Governments will need to invest more into capacity and skills
building for the uptake and use of DIs for the CPE through stronger focus
on STEM education, especially for girls and women.
To overcome technology lock-in issues will require closer collabo-
ration across government departments and agencies, especially those
departments responsible for science and technology development,
environmental regulations, and industrial innovation policy and sup-
port. Furthermore, the support of technology hubs and innovation
centres, including community-based initiatives, is important (Floyd and
Adhikary, 2021).
The public sector needs to avoid disincentivising policies. In recent
years, stakeholders in the African innovation eco-system have expressed
concerns about hostile policies of national governments to technology
entrepreneurs. While there are strong arguments for the merits of reg-
ulations, policymakers should realise that these eco-systems thrive on
the ideals of open innovation. By adopting a principle of limited regu-
lation and minimal control, African governments can free up the space
for digital innovations to co-create new products and services that
address core societal needs and drive the transition to CPE.
Thus, in terms of policies for CPE, governments can prioritise the
facilitation of the use of recycled plastics in other forms of packaging. In
addition, DIs can help with traceability and product information about
the quality and composition of recycled contents and thereby support
plastic waste related policy implementation.
Financial incentives or taxation can help to drive new job creation in
the CPE. Improving access to nance, e.g. through government-backed
loans and technical assistance for SMEs and the informal sector, can
help them benet from the DIs and link them to larger industrial value
chains and potential trade opportunities.
6. Conclusion
Circular plastic economy is not only essential to stop the wicked and
ever-increasing plastic pollution problem, but it also offers robust social,
economic, and climate advantages including reduction to the volume of
the plastic entering water systems, reduction of greenhouse gas emis-
sions, as well as creating additional jobs in the local economies. This
O. Kolade et al.
Technological Forecasting & Social Change 183 (2022) 121954
11
study investigates the role of digital innovations to facilitate the tran-
sition to a circular plastic economy, focusing on the African landscape,
where data on the technology readiness and adoption of digital in-
novations is particularly scarce. For the rst time, a comprehensive
dataset was collected by cross-sectional engagement with 33 major
circular economy stakeholders and 1500 households across sub-Saharan
Africa to assess the level of technological readiness and the range of
digital tools adopted for accelerating the transition to a circular econ-
omy. A quantitative study was conducted on the survey data to develop a
technology readiness model to evaluate the readiness of digital in-
novators to develop and implement various digital tools for the circular
economy across Africa. The potential and likelihood of adopting the
range of digital innovations identied in this study were determined
using a range of statistical models and by analysing the data obtained
from the survey.
The survey conducted in this study identied ve key attributes to
assess the role of digital innovations in transitioning to CPE in Africa,
including understanding the technology, usefulness of the technology,
ease of use, actual use of the technology, and the intention to use the
technology. A variable weighting system was introduced to provide an
overall scoring system for the DIs and develop the technological readi-
ness model. The proposed model was implemented to conduct a
comparative study between different DIs, i.e. AI, GIS, BC, IoT, Rob, 3D,
SC, ARVR, 5G, and Mobile Apps (Fig. 1). This study also developed three
regression models to analyse the survey results further and by looking at
specic categories and variables inuencing the use of DIs in CPE. The
rst regression model (Model 1) investigated the four specic socio-
economic variables (i.e. gender, age, income, level of education). The
second model (Model 2) used the specic understanding, perceived
usefulness, ease of use and barriers as the main independent variables,
while the four socioeconomic variables were incorporated as controls.
Model 3 were designed to examine the interactions between aggregate
barriers and income levels, education level and perceived ease of use.
The results show that out of the ten frontier technologies investigated
in this study, only three DIs (i.e. Mobile app, GIS, and IoT) have
appropriate technology readiness to be implemented in CPE strategies.
Mobile apps are the most developed digital tool across Africa, with 88 %
of the digital innovators already using mobile apps, followed by GIS with
41.18 % of the DIs ready to implement the technology and the IoT where
35.29 % technological readiness was observed. The results indicate the
readiness of the digital innovators to develop and adopt the frontiers
technologies; however, this requires capital investment to the innova-
tion hubs across Africa and capacity building.
The study highlighted the need for new actors to emerge as the
Fig. 1. Technology acceptance model (adapted from Davis, 1989).
Fig. 2. Readiness index of the DIs in each of the ten frontier technologies.
O. Kolade et al.
Technological Forecasting & Social Change 183 (2022) 121954
12
circular plastic economy drivers. Our recommended approach to
addressing this is by fostering an enabling eco-system that synergises the
efforts of key players/actors. Such synergies will bring about technical
efciency. This, in turn, will create new opportunities to leverage digital
transformation to leapfrog some of the most critical sectors of the cir-
cular plastic economy in Africa. Examples include traceability using
blockchain and sorting with Articial intelligence. An enabling eco-
system will help create, transform, and communicate knowledge,
thereby nurturing local capacity for the circular plastic economy
innovations.
The survey results show the signicant role of the community
members and end users' engagement with the technology to facilitate
the CPE innovations and generate real impact from implementing these
digital tools. Detailed analysis of community-level behaviour and their
socioeconomic background is signicantly important for bridging the
barriers and uptake of the CPE innovation. It was found that digital
innovations and the transitions to a CPE are closely linked. Policy
analysis conducted in this study shows that governments across Africa
are developing and implementing digital policies to enable widespread
digital access and use of digital technologies. These policies are impor-
tant not only for the CPE but also for industrialisation, education, skills,
social inclusion, and sustainable development. A review of the existing
policies and their implications across Africa highlight the necessity of
supporting technology hubs and innovation centres, including
community-based initiatives, and avoiding disincentivising policies. The
government's investment into capacity and skills building for the uptake
and use of DIs for the CPE, e.g. through a stronger focus on STEM ed-
ucation, especially for girls and women, is essential. Financial incentives
and taxation can further help to facilitate CPE and create more jobs in
the local and regional economies.
For the rst time, this study provides a comprehensive, evidence-
based model to examine the role and technological readiness of digital
innovations for transitioning towards a circular plastic economy in Af-
rica. The methodological approach outlined in this study can be used for
evaluating technological readiness for CPE in other small and large-scale
studies.
CRediT authorship contribution statement
The authors declare that this manuscript is original, has not been
published before and is not currently being considered for publication
elsewhere.
We conrm that the manuscript has been read and approved by all
named authors and that there are no other persons who satised the
criteria for authorship but are not listed. We further conrm that the
order of authors listed in the manuscript has been approved by all of us.
We understand that the Corresponding Author is the sole contact for
the editorial process. He is responsible for communicating with the other
author about progress, submissions of revisions and nal approval of
proofs.
Data availability
Data will be made available on request.
Acknowledgement
This work was supported by the UKRI GCRF under Grant EP/
T029846/1.
References
Adu-Gyam, G., Song, H., Obuobi, B., Nketiah, E., Wang, H., Cudjoe, D., 2022. Who will
adopt? Investigating the adoption intention for battery swap technology for electric
vehicles. Renew. Sust. Energ. Rev. 156, 111979 https://doi.org/10.1016/j.
rser.2021.111979.
Ajzen, I., 1991. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50,
179–211. https://doi.org/10.1016/0749-5978(91)90020-T.
Alam, M.Z., Hu, W., Kaium, M.A., Hoque, M.R., Alam, M.M.D., 2020. Understanding the
determinants of mHealth apps adoption in Bangladesh: a SEM-neural network
approach. Technol. Soc. 61, 101255 https://doi.org/10.1016/j.
techsoc.2020.101255.
Araujo Galv˜
ao, G.D., de Nadae, J., Clemente, D.H., Chinen, G., de Carvalho, M.M., 2018.
Circular economy: overview of barriers. Procedia CIRP 73, 79–85. https://doi.org/
10.1016/j.procir.2018.04.011.
Arthur, W.B., 1989. Competing technologies, increasing returns, and lock-in by historical
events. Econ. J. 99, 116. https://doi.org/10.2307/2234208.
Atiase, V.Y., Kolade, O., Liedong, T.A., 2020. The emergence and strategy of tech hubs in
Africa: implications for knowledge production and value creation. Technol. Forecast.
Soc. Chang. 161 (December 2020), 120307. https://doi.org/10.1016/j.
techfore.2020.120307. Elsevier.
Atlason, R.S., Giacalone, D., Parajuly, K., 2017. Product design in the circular economy:
users’ perception of end-of-life scenarios for electrical and electronic appliances.
J. Clean. Prod. 168, 1059–1069. https://doi.org/10.1016/j.jclepro.2017.09.082.
Balta-Ozkan, N., Yildirim, J., Connor, P.M., Truckell, I., Hart, P., 2021. Energy transition
at local level: analyzing the role of peer effects and socio-economic factors on UK
solar photovoltaic deployment. Energy Policy 148, 112004. https://doi.org/
10.1016/j.enpol.2020.112004.
Bandura, A., 1982. Self-efcacy mechanism in human agency. Am. Psychol. 37, 122–147.
https://doi.org/10.1037/0003-066X.37.2.122.
Bandura, A., 1986. Social Foundations of Thoughts and Actions: A Social Cognitive
Theory. Prentice-Hall, Inc.
Barquet, K., J¨
arnberg, L., Rosemarin, A., Macura, B., 2020. Identifying barriers and
opportunities for a circular phosphorus economy in the Baltic Sea region. Water Res.
171 https://doi.org/10.1016/j.watres.2019.115433.
Barrie, J., Anantharaman, M., Oyinlola, M., Schr¨
oder, P., 2022. The circularity divide:
what is it? And how do we avoid it? Resour. Conserv. Recycl. 180, 106208 https://
doi.org/10.1016/J.RESCONREC.2022.106208.
Berg, A., Antikainen, R., Hartikainen, E., Kauppi, S., Kautto, P., Lazarevic, D., Piesik, S.,
Saikku, L., 2018. Circular Economy for Sustainable Development. Finnish
Environment Institute.
Cagno, E., Neri, A., Negri, M., Bassani, C.A., Lampertico, T., 2021. The role of digital
Technologies in Operationalizing the circular economy transition: a systematic
literature review. Appl. Sci. 11, 3328. https://doi.org/10.3390/app11083328.
Chang, M.C., Wu, C.C., 2015. The effect of message framing on pro-environmental
behavior intentions: an information processing view. Br. Food J. 117, 339–357.
https://doi.org/10.1108/BFJ-09-2013-0247/FULL/PDF.
Charef, R., Ganjian, E., Emmitt, S., 2021. Socio-economic and environmental barriers for
a holistic asset lifecycle approach to achieve circular economy: a pattern-matching
method. Technol. Forecast. Soc. Chang. 170, 120798 https://doi.org/10.1016/j.
techfore.2021.120798.
Chidepatil, A., Bindra, P., Kulkarni, D., Qazi, M., Kshirsagar, M., Sankaran, K., 2020.
From trash to cash: how blockchain and multi-sensor-driven articial intelligence
can transform circular economy of plastic waste? Adm. Sci. 10, 23.
Chin, J., Lin, S.-C., 2015. Investigating users’ perspectives in building energy
management system with an extension of technology acceptance model: a case study
in indonesian manufacturing companies. Procedia Comput. Sci. 72, 31–39. https://
doi.org/10.1016/j.procs.2015.12.102.
Davis, F.D., 1989. Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Q. 13 (3), 319–340. https://doi.org/10.2307/249008.
de Jesus, A., Mendonça, S., 2018. Lost in Transition? Drivers and barriers in the eco-
innovation road to the circular economy. Ecol. Econ. 145, 75–89. https://doi.org/
10.1016/j.ecolecon.2017.08.001.
Debrah, J.K., Vidal, D.G., Dinis, M.A.P., 2021. Innovative use of plastic for a clean and
sustainable environmental management: learning cases from Ghana, Africa. Urban
Sci. 5, 12. https://doi.org/10.3390/urbansci5010012.
Djimesah, I.E., Zhao, H., Okine, A.N.D., Li, Y., Duah, E., Kissi Mireku, K., 2021. Analyzing
the technology of acceptance model of ghanaian crowdfunding stakeholders.
Technol. Forecast. Soc. Chang., 121323 https://doi.org/10.1016/j.
techfore.2021.121323.
Dolfsma, W., Leydesdorff, L., 2009. Lock-in and break-out from technological
trajectories: modeling and policy implications. Technol. Forecast. Soc. Chang. 76,
932–941. https://doi.org/10.1016/j.techfore.2009.02.004.
Economist, 2022. Covid-19 has led to a pandemic of plastic pollution | The Economist
[WWW Document]. URL. Econ. https://www.economist.com/international/202
0/06/22/covid-19-has-led-to-a-pandemic-of-plastic-pollution. (Accessed 13 January
2022).
Ellen Macarthur Foundation, 2021. Circular economy in Africa: plastics, circular
economy. Available at: https://ellenmacarthurfoundation.org/circular-economy-in
-africa-plastics. (Accessed 8 August 2022).
Floyd, R., Adhikary, D., 2021. Industrial innovation: the next frontier for African
progress – ACET [WWW document]. URL. African Cent. Econ. Transform. https://ac
etforafrica.org/media/blogs/industrial-innovation-the-next-frontier-for-african-pro
gress/. (Accessed 28 January 2022).
Geels, F.W., 2010. Ontologies, socio-technical transitions (to sustainability), and the
multi-level perspective. Res. Policy 39 (4), 495–510. https://doi.org/10.1016/j.
respol.2010.01.022. Elsevier B.V.
Hart, J., Adams, K., Giesekam, J., Tingley, D.D., Pomponi, F., 2019. Barriers and drivers
in a circular economy: the case of the built environment. Procedia CIRP 80,
619–624. https://doi.org/10.1016/j.procir.2018.12.015.
O. Kolade et al.
Technological Forecasting & Social Change 183 (2022) 121954
13
Hazen, B.T., Mollenkopf, D.A., Wang, Y., 2017. Remanufacturing for the circular
economy: an examination of consumer switching behavior. Bus. Strateg. Environ. 26,
451–464. https://doi.org/10.1002/bse.1929.
He, D., Zhang, Y., Gao, W., 2021. Micro(nano)plastic contaminations from soils to plants:
human food risks. Curr. Opin. Food Sci. 41, 116–121. https://doi.org/10.1016/j.
cofs.2021.04.001. Elsevier Ltd.
Hong, W., Chan, F.K.Y., Thong, J.Y.L., Chasalow, Lewis, C., Dhillon, G., 2014. Theorizing
in information systems research. Inf. Syst. Res. 25, 111–136.
Hoosain, M.S., Paul, B.S., Ramakrishna, S., 2020. The impact of 4ir digital technologies
and circular thinking on the united nations sustainable development goals.
Sustainability 12, 10143.
Khalil, E.L., 2013. Lock-in institutions and efciency. J. Econ. Behav. Organ. 88, 27–36.
https://doi.org/10.1016/j.jebo.2011.10.017.
Kim, T.B., Ho, C.T.B., 2021. Validating the moderating role of age in multi-perspective
acceptance model of wearable healthcare technology. Telemat. Informatics 61,
101603. https://doi.org/10.1016/j.tele.2021.101603.
Kolade, O., Harpham, T., 2014. Impact of cooperative membership on farmers’ uptake of
technological innovations in Southwest Nigeria. Dev. Stud. Res. 1, 340–353. https://
doi.org/10.1080/21665095.2014.978981.
Kolade, O., Harpham, T., Kibreab, G., 2014. Institutional barriers to successful
innovations: perceptions of rural farmers and key stakeholders in Southwest Nigeria.
Afr. J. Sci. Technol. Innov. Dev. 6, 339–353. https://doi.org/10.1080/
20421338.2014.966039.
Kristoffersen, E., Blomsma, F., Mikalef, P., Li, J., 2020. The smart circular economy: a
digital-enabled circular strategies framework for manufacturing companies. J. Bus.
Res. 120, 241–261. https://doi.org/10.1016/J.JBUSRES.2020.07.044.
Lee, C., Wan, G., 2010. Including subjective norm and technology trust in the technology
acceptance model. ACM SIGMIS Database: DATABASE Adv. Inf. Syst. 41 (4), 40–51.
https://doi.org/10.1145/1899639.1899642.
Liu, Y., Ruiz-Menjivar, J., Zhang, L., Zhang, J., Swisher, M.E., 2019. Technical training
and rice farmers’ adoption of low-carbon management practices: the case of soil
testing and formulated fertilization technologies in Hubei, China. J. Clean. Prod.
226, 454–462. https://doi.org/10.1016/j.jclepro.2019.04.026.
Liu, Z., Liu, J., Osmani, M., 2021. Integration of digital economy and circular economy:
current status and future directions. Sustainability 13, 7217. https://doi.org/
10.3390/su13137217.
Maina, K.W., Ritho, C.N., Lukuyu, B.A., Rao, E.J.O., Mugwe, J.G., 2021. Knowledge,
attitudes, and practices of dairy farmers in the adoption of brachiaria grass in Kenya.
Sci. Afr. 13, e00874 https://doi.org/10.1016/j.sciaf.2021.e00874.
Man, S.S., Alabdulkarim, S., Chan, A.H.S., Zhang, T., 2021. The acceptance of personal
protective equipment among Hong Kong construction workers: an integration of
technology acceptance model and theory of planned behavior with risk perception
and safety climate. J. Saf. Res. 79, 329–340. https://doi.org/10.1016/j.
jsr.2021.09.014.
Mdukaza, S., Isong, B., Dladlu, N., Abu-Mahfouz, A.M., 2018. Analysis of IoT-enabled
solutions in smart waste management. In: IECON 2018 - 44th Annual Conference of
the IEEE Industrial Electronics Society, pp. 4639–4644. https://doi.org/10.1109/
IECON.2018.8591236.
Meng, J., et al., 2021. Plastic waste as the potential carriers of pathogens. Curr. Opin.
Food Sci. 41, 224–230. https://doi.org/10.1016/j.cofs.2021.04.016. Elsevier Ltd.
Mugumaarhahama, Y., Mondo, J.M., Cokola, M.C., Ndjadi, S.S., Mutwedu, V.B.,
Kazamwali, L.M., Cirezi, N.C., Chuma, G.B., Ndeko, A.B., Ayagirwe, R.B.B.,
Civava, R., Karume, K., Mushagalusa, G.N., 2021. Socio-economic drivers of
improved sweet potato varieties adoption among smallholder farmers in South-Kivu
Province, DR Congo. Sci. Afr. 12, e00818 https://doi.org/10.1016/j.sciaf.2021.
e00818.
Oyinlola, M., Schr¨
oder, P., Whitehead, T., Kolade, O., Wakunuma, K., Shari, S.,
Rawn, B., Odumuyiwa, V., Lendelvo, S., Brighty, G., Tijani, B., Jaiyeola, T.,
Lindunda, L., Mtonga, R., Abolfathi, S., 2022. Digital innovations for transitioning to
circular plastic value chains in Africa. Afr. J. Manag. 1–26. https://doi.org/10.1080/
23322373.2021.1999750.
Parasuraman, A., 2000. Technology readiness index (Tri): a multiple-item scale to
measure readiness to embrace new technologies. J. Serv. Res. 2, 307–320. https://
doi.org/10.1177/109467050024001.
Parasuraman, A., Colby, C.L., 2015. An updated and streamlined technology readiness
index: TRI 2.0. J. Serv. Res. 18, 59–74. https://doi.org/10.1177/
1094670514539730.
Rajak, M., Shaw, K., 2021. An extension of technology acceptance model for mHealth
user adoption. Technol. Soc. 67, 101800 https://doi.org/10.1016/j.
techsoc.2021.101800.
Ricci, R., Battaglia, D., Neirotti, P., 2021. External knowledge search, opportunity
recognition and industry 4.0 adoption in SMEs. Int. J. Prod. Econ. 240, 108234
https://doi.org/10.1016/j.ijpe.2021.108234.
Rogers, E.M., 1995. Diffusion of Innovations. The Free Press, New York.
Schot, J., Kanger, L., 2018. Deep transitions: emergence, acceleration, stabilization and
directionality. Res. Policy 47, 1045–1059.
Schr¨
oder, P., Barrie, J., 2022. A global roadmap for an inclusive circular economy.
Available at: https://circulareconomy.
earth/publications/a-global-roadmap-for-an-inclusive-circular-economy.
Schr¨
oder, P., MacEwen, M., Barrie, J., Wetterberg, K., Wallace, J., 2021. What is the
circular economy? Environ. Soc. Programme. https://www.chathamhouse.
org/2021/06/what-circular-economy.
Sierzchula, W., Bakker, S., Maat, K., Van Wee, B., 2014. The inuence of nancial
incentives and other socio-economic factors on electric vehicle adoption. Energy
Policy 68, 183–194. https://doi.org/10.1016/j.enpol.2014.01.043.
Singh, A., 2019. Remote sensing and GIS applications for municipal waste management.
J. Environ. Manag. 243, 22–29.
Sopjani, L., Arekrans, J., Laurenti, R., Ritz´
en, S., 2020. Unlocking the linear lock-in:
mapping research on barriers to transition. Sustainability 12, 1034. https://doi.org/
10.3390/su12031034.
Statista, 2022. Global plastic production 1950-2020 | Statista [WWW document]. Chem.
Resour. Plast. Rubber. URL. https://www.statista.com/statistics/282732/global
-production-of-plastics-since-1950/. (Accessed 28 January 2022).
Sun, S., Lee, P.C., Law, R., Hyun, S.S., 2020. An investigation of the moderating effects of
current job position level and hotel work experience between technology readiness
and technology acceptance. Int. J. Hosp. Manag. 90, 102633 https://doi.org/
10.1016/j.ijhm.2020.102633.
United Nations Environment Programme, 2022. #BeatPlasticPollution this world
environment day [WWW document]. Beat Plast. Pollut. URL. https://www.unep.
org/interactive/beat-plastic-pollution/. (Accessed 28 January 2022).
van Keulen, M., Kirchherr, J., 2021. The implementation of the circular economy:
barriers and enablers in the coffee value chain. J. Clean. Prod. 281, 125033 https://
doi.org/10.1016/j.jclepro.2020.125033.
Venkatesh, V., Davis, F.D., 2000. A theoretical extension of the technology acceptance
model: four longitudinal eld studies. Manag. Sci. 46 (2), 186–204. https://doi.org/
10.1287/mnsc.46.2.186.11926.
Walczuch, R., Lemmink, J., Streukens, S., 2007. The effect of service employees’
technology readiness on technology acceptance. Inf. Manag. 44, 206–215. https://
doi.org/10.1016/j.im.2006.12.005.
World Economic Forum, 2021. Digitalization is critical to creating a circular economy |
World Economic Forum, Circular Economy. Available at: https://www.weforum.or
g/agenda/2021/08/digitalization-critical-creating-global-circular-economy/.
(Accessed 8 August 2022).
Oluwaseun Kolade is an Associate Professor in Strategic Management at De Montfort
University, where he also leads the African Entrepreneurship Cluster. His research covers
the broad areas of transformative entrepreneuring, digital transformation, and SMEs
strategies in turbulent environments.
Victor Odumuyiwa is the acting Director of the NITDA ICT Hub, and a senior lecturer in
the Department of Computer Sciences, University of Lagos. His research focus includes
user information behaviour, collaboration systems and knowledge management.
Soroush Abolfathi is Head of Environmental Sustainability Theme of Global Research
Priorities at the University of Warwick. His research interests include pollution transport
in the environment as well as resilient infrastructures to natural hazards, climate change
and ooding.
Patrick Schr¨
oder is a senior research fellow at Chatham House, Royal Institute of Inter-
national Affairs. His research focuses on the issues of policy, nance, trade and technology
innovation in the circular economy.
Kutoma Wakunuma is Associate Professor Research and Teaching in Information Systems
at De Montfort University. Her research interests are in understanding the social and
ethical implications of ICTs and the role of emerging technologies in developed and
developing countries.
Ifeoluwa Akanmu is a research assistant with the DITCh Plastic Network. She holds a
degree in Computer Science from the University of Lagos, Nigeria. Before joining DITCh
Plastic Network, she was an associate consultant with Digital Encode limited, where she
performed vulnerability assessments and penetration tests of web applications and sites.
Timothy Whitehead is the Head of Design and Senior Lecturer in Product Design with a
research interest in developing tools and approaches to improve the design of products
distributed in low- income countries. Timothy has worked on a number of GCRF projects
which utilize design methods and new technology to improve the livelihood of those living
on less than US$1 a day.
Bosun Tijani is co-founder and CEO of Co-Creation Hub, a pan-African innovation enabler
that work at the forefront of accelerating the application of innovation and social capital
for a better society.
Muyiwa Oyinlola is an Associate Professor in Engineering for Sustainable Development.
One area of his research focuses on utilizing Digital Innovations to promote and accelerate
the transition to a Circular Plastic Economy in Africa.
O. Kolade et al.
