Journal of Innovation & Knowledge 7 (2022) 100231

Journal of Innovation
& Knowledge
ht t p s: // w w w . j our na ls .e l se vi e r .c om /j ou r na l -o f - in no va t i on -a n d- kn owl e dg e

Adoption of green innovation technology to accelerate sustainable

development among manufacturing industry
Mohsin Shahzada, Ying Qua,*, Saif Ur Rehmanb, Abaid Ullah Zafarc
a
School of Economics and Management, Dalian University of Technology, Dalian, PR China
b
School of Professional Advancement, University of Management and Technology, Lahore, Pakistan
c
Shenzhen-Audencia Business School, Shenzhen University, Shenzhen, PR China

A R T I C L E I N F O A B S T R A C T

Article History: Recent advancements in green and innovative technologies have resulted in a number of innovations in
Received 7 May 2022 manufacturing operations to accelerate sustainable development (SD). Despite several benefits of green
Accepted 13 July 2022 innovation adoption (GIA), the adoption rate of these initiatives is still abysmal in manufacturing organisa-
Available online 29 July 2022
tions. To fill this gap, we have developed and validated the GIA model grounded on the unified theory of
acceptance and use of technology (UTAUT), which compels organisations to implement these novel technolo-
Keywords:
gies. Data was collected through a survey of 516 respondents from Pakistani manufacturing industries and
Green innovation adoption
analysed using structural equation modelling (SEM) and the artificial neural network (ANN) approach. The
Green behavioural intention
Technology acceptance
deliverables of SEM and ANN approaches demonstrated that all green integrated constructs of the research
Sustainable Development model, such as performance expectancy, effort expectancy, hedonic motivation, social influence, facilitating
Structural equation modelling conditions, and innovation cost, predict green behavioural intention (GBI). Besides, GBI was found to have a
strong direct and mediating effect among integrated constructs towards GIA. In addition, the moderation of
JEL codes: organisational size highlighted the differentiation among small, medium and large size enterprises. Addition-
Q01 ally, ANN specifies the robustness and relative importance of all integrated constructs, whereas green facili-
Q55 tating conditions have the highest relative importance value for GIA. The proposed integrated model offers
O14
novel insights for decision-makers and suggests various implications for adopting and implementing innova-
O32
tive green technologies to achieve SD objectives.
© 2022 The Authors. Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This
is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)

Introduction effectiveness (Jansson, 2011; Skare & Riberio Soriano, 2021). During
the last decade, sustainable development (SD) has gained substantial
From the beginning of the 21st century, the transition and expan- attention in the manufacturing industry due to increased awareness
sion of Information Technology (IT) have brought disruptive changes and perceived benefits for society of green technologies (Shahzad
to all aspects of human life; it has advanced the methods of inception, et al., 2020a).
production, and delivery of products and services (Guo et al., 2020; Innovation, being the most critical driver for growth, propels a
Hilkenmeier et al., 2021). The recent rise of the inculcation of novel business towards excellence and guarantees a competitive advan-
digital manufacturing technologies and precision equipment into tage; it also enhances environmental efficiency, thus gaining help in
these processes has opened new doors of innovation in the produc- raising the social capital necessary for future developments (Cillo
tion and delivery process (Guo et al., 2022). These new technologies et al., 2019). More and more organisations have adopted green inno-
have contributed to higher quality and increased value, reducing vation (GI) as a key component of their stratagem to mitigate the
time to development and market and facilitating green manufactur- negative consequences of traditional growth models (Guo et al.,
ing (Forcadell et al., 2021; Han & Chen, 2021). As green manufactur- 2020; Jahanshahi et al., 2020). For instance, the Chinese government
ing fully respects the environmental impact and resource efficiency has already integrated GI in Constitution 2018, laying the ground-
in production. The main features of green technologies are system- work to promote a green technology bank for supporting green tech-
atic, eco-prevention-focused, economic compliance, and enhanced nology adoption (Hansen et al., 2018). Several nations have recently
organised state-level financial institutions to promote SD and follow
the "Green Industry Plan," i.e., Japan and Canada (Guo et al., 2020).
* Corresponding authors. These institutions can leverage public-private partnerships (PPPs) to
E-mail address: quying@dlut.edu.cn (Y. Qu).

https://doi.org/10.1016/j.jik.2022.100231
2444-569X/© 2022 The Authors. Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
M. Shahzad, Y. Qu, S.U. Rehman et al. Journal of Innovation & Knowledge 7 (2022) 100231

facilitate green infrastructure and technological innovation (Yang affect green behavioural intention (GBI) (J. Lee et al., 2021; Wang
et al., 2016). Further, GI enables organisations to manufacture eco- et al., 2020). Although it can be argued that GIA is comparable to that
friendly products by minimising resource utilisation and wastage to of other technologies, several scholars endorse that the implementa-
accomplish SD (Khan et al., 2021; Shin et al., 2022). Scholars such as tion and conditions of different technologies will diverge signifi-
Fernando et al. (2019) and Shahzad et al. (2020b) have recommended cantly, resulting in variations in adoption factors depending on the
GI as a significant driver for SD by emphasising that green processes technology type (Song et al., 2019; Tseng et al., 2018). Therefore, for a
and green products provide similar value to consumers with minimal business to successfully introduce a green technological initiative
social and environmental impacts (Awan et al., 2020). After identify- into its operations, it must comprehend which factors will increase
ing the key benefits of GI, various stakeholders pressured for its adop- social acceptance among the stakeholders. Hence, there is a real need
tion and implementation (Shahzad et al., 2022; Shahzad et al., 2020a). to critically investigate the implementation issues and adoption con-
Though radical change is obligatory at the ecological, cultural, and cerns by analysing these GIA challenges. The problems stated above
social levels, organisations have to play their certain role in SD (Khan and the literature gap compelled this investigation to ask these
et al., 2021). However, green innovation adoption (GIA) faces signifi- research questions:
cant challenges in achieving SD in manufacturing organisations due
to various decision-making factors.  How do green decision-making factors impact green behavioural
With rising commodity prices and concerns regarding sustainable intention to adopt green innovation technologies?
sourcing, organisations may prefer to use the latest innovative and  Does green behavioural intention mediate the relations among
environment-friendly technologies to minimise waste and costs, integrated constructs and green innovation adoption?
which can be helpful in attaining competitive advantage (Ahn et al.,  How does the boundary factor of organisational size influence the
2016; Anser et al., 2020). However, there are no specific criteria for relations of proposed constructs?
categorising green technology adoption globally (Skare & Riberio Sor-
iano, 2021). There are still many concerns regarding adopting green This research aims to provide various contributions to the litera-
and novel technologies, e.g. financial barriers, environmental policies, ture. This is the first study that offers multiple constructs of the
market demand, knowledge, and awareness (Awan et al., 2020; For- UTAUT in the context of a green and sustainable environment. Previ-
cadell et al., 2021; Guo et al., 2020). From a budgetary perspective, ously no any study employed these constructs in the context of green
purchasing the necessary precision tools and expertise could signify technologies adoption. Second, to gain a holistic understanding of the
a large proportion of organisational expenditure; therefore, organisa- decision-making factors of UTAUT, the direct and indirect effects of
tions must be confident about the feasibility of such investments these factors on both GBI and GIA were validated through structural
(Guo et al., 2020). equation modelling (SEM) and the artificial neural network (ANN)
Green and environmental policies and initiatives are thought to approach. These factors were considered critical indicators for tech-
increase corporate success only if implemented across the board, nological adoption (Xie et al., 2022; Venkatesh et al., 2012; Ahn et al.,
with confirmation and support by all partners. Research has demon- 2016). Existing studies reported inconclusive outcomes; by employ-
strated that a lack of customer engagement and recognition will lead ing novel SEM and ANN approaches current study provides a compre-
to a loss of investment and resources (Li et al., 2020). More recently, hensive conclusion for GIA. Third, assessing the moderating role of
Ahmad et al. (2021) identified that overdependence on coal energy is organisational size was helpful in evaluating initial confidence in
the primary source of hazardous emissions; improved energy effi- green technology characteristics. Larger organisations are more
ciency can reduce it through green technological innovation and resourceful and have a greater probability of adopting green initia-
green initiatives. Therefore, it is critical for organisations to evaluate tives and integrating technological changes more quickly. Lastly, this
the social, economic and environmental aspects of green technolo- research provides several implications for a developing country such
gies (Anser et al., 2020; Shahzad et al., 2021). Being the seventh most as Pakistan due to its vulnerability to global environmental changes
susceptible nation to climate change, Pakistan should seek sustain- and less coverage in prior literature; it demonstrates GIA's relevance
able and green technological solutions (J. Lee et al., 2021); it is in routine manoeuvres and elucidates how organisations can advance
regarded as one of the least innovative countries with a poor ranking their SD. The review of related literature is described in the following
in Asia as well as in the world (Global Innovation Index, 2018). Due parts, followed by methodology, results, and conclusions, and finally,
to poor air quality, the famous industrial city Lahore was declared the the study is concluded with future recommendations.
most polluted in the world recently. To overcome these environmen-
tal problems and consider SD, the current leadership of Pakistan Literature review & hypotheses development
implemented stringent environmental laws to protect the environ-
mental deterioration and tried to facilitate the organisations to lessen Green innovation
their dependency on fossil fuels and exploit renewable energy
resources. As stringency in environmental strategies, environmental GI provides organisations with the chance to diminish their oper-
tax, and reduced hazardous emissions positively affects GI (Maa- ations' adverse effects on the environment and guarantees a competi-
soumi et al., 2020). However, resources of renewable energy are also tive advantage (Awan et al., 2020). It can facilitate the development
limited. Research on developing nations such as Pakistan may offer of new manufacturing processes and products that are less injurious
clearer views of how GIA policies might control environmental to the ecosystem and natural environment (Khan et al., 2021). GI is
destruction and transform eco-friendly goods that avoid environ- "the production, application or exploitation of a good, service, pro-
mental pollution and diminish industrial waste. cess, organisational structure or management or business method
Extant literature on the technology acceptance model (TAM) and that is novel to the firm and results in a reduction of environmental
unified theory of acceptance and use of technology (UTAUT) identi- risk" (Ma et al., 2018). The definition of GI has various forms, e.g.
fied various factors, e.g. performance and effort expectancy, facilitat- green technological innovation, which encompasses product and
ing conditions, and social influence, as the essential drivers process innovation, and green non-technological innovation com-
influencing technology adoption (see Appendix A) (Jun et al., 2021; prising management innovation and organisational structure (Chang
Venkatesh et al., 2003). After developing the UTAUT2 model, & Chen, 2013; Chen et al., 2006; Hilkenmeier et al., 2021). The former
researchers also recognised the importance of hedonic motivation aims to assimilate various advanced and novel technologies that can
and innovation cost for green technology adoption (Ahn et al., 2016; improve the existing process and products to reduce energy con-
Anser et al., 2020; Venkatesh et al., 2012). These attributes strongly sumption, prevent pollution and save natural resources (Fernando
2
M. Shahzad, Y. Qu, S.U. Rehman et al. Journal of Innovation & Knowledge 7 (2022) 100231

et al., 2019; Khan et al., 2021; Xie et al., 2022). It also alludes to pro- UTAUT model, and verified the rationality of their attributes (Venka-
cess and product innovation. The latter encompasses adopting/ tesh et al., 2003). To anticipate technology adoption intention and
restructuring firms' management strategies, i.e. environment, energy, usage of novel and innovative technologies, the UTAUT prolongs the
quality management, green supply chain, and green marketing to TAM, the theory of reasoned action (TRA), diffusion of innovation the-
minimise harmful environmental effects (Klein et al., 2021; Shu et al., ory, and a mirror of cognition theory (Zhao & Bacao, 2020). The
2016). UTAUT comprises four fundamental driving factors of intention and
Chen et al. (2006) describe GI as "hardware or software innovation usage: performance and effort expectancy, facilitating conditions,
that is related to green products or processes, including the innova- and social influence (Venkatesh et al., 2003). Various studies inte-
tion in technologies that are involved in energy saving, pollution pre- grated the UTAUT to explore behavioural intention to accept the lat-
vention, waste recycling, green product designs, or corporate est technologies, stimulating its generalizability (Anser et al., 2020;
environmental management." It is positioned as the main driver of Zhao & Bacao, 2020). The UTAUT framework is the foremost consoli-
long-term socio-economic progress. Several studies acknowledged dated model, comprehensively describing technology adoption (Al-
the key factors that affect GI adoption, e.g. concerned stakeholders' Saedi et al., 2020). This model was further studied by integrating
pressure, strategic orientation, organisational learning, knowledge others factors such as compatibleness expectancy, sustainable inno-
management, absorptive capacity, and consumers' demands (Awan vativeness, and environmentalism, in adopting green household
et al., 2020; Dangelico, 2017; Klein et al., 2021; Shahzad et al., 2020a; technology (Ahn et al., 2016). Accordingly, knowledge of green prod-
Song et al., 2020). Further, organisational innovation is a driving force ucts influences users' behaviour to care for the natural environment
in enhancing industrial export, environmental performance, and, following the UTAUT model as knowledge influences all phases of the
eventually, business excellence and SD (Li et al., 2020; Wu et al., purchasing decision process (Hsu et al., 2017).
2019). In brief, GI inclines to improve competitiveness by developing Despite these four fundamental variables, Venkatesh et al. (2012)
innovative goods, processes, materials, and institutional frameworks. underlined the need to integrate more relevant prognosticator varia-
bles to forecast behavioural intentions for the technology adoption
Green innovation adoption and UTAUT perspective by modifying the UTAUT to provide a new predicting
model, namely UTAUT2. This latest model has progressively been
With increased environmental deterioration and climate change implemented for investigating multiple queries such as self-service
envisaged by rising hazardous emissions and pollution, global sus- technology, adoption of mobile technology, mobile banking and com-
tainable economy is certainly constrained (Khan et al., 2021). Green merce, online education, and online healthcare services (Huang &
technologies and monitoring policies are imperative to regulate and Kao, 2015). Hedonic motivation and cost of innovation are considered
encourage GIA (Li et al., 2020). GIA requires innovative organisational more important factors of UTAUT2, which are further integrated into
strategies to switch their classical and traditional means of produc- the research framework of this study to emphasise efficacy and util-
tion to novel and sustainable operations (Anser et al., 2020). Never- ity. Moreover, Ma et al. (2017) established that, compared to non-
theless, transformation into sustainable operations remains difficult labelled items, the sustainable label reading behaviour of products
for organisations because multiple uncertainties and complexities increases the purchasing of sustainable and green products, while
are involved in the transformation procedure (Han & Chen, 2021). the increasing ecological cognisance among individuals (Chen, 2008)
Different sectors have accepted and transformed their operations suggests that they are eager to pay a greater value for eco-friendly
into green operations following SD indicators, e.g. environmental, goods. Since this study's primary aim is to discover the factors that
social, and economic performance (Jahanshahi et al., 2020). GIA in influence GIA, the UTAUT2 model can offer better insights; therefore,
businesses has also attracted experts' and researchers' attention (For- it is employed as the research framework, as shown in Fig. 1.
cadell et al., 2021; Han & Chen, 2021; Klein et al., 2021). Recently
scholars have identified different barriers and enablers for GIA in Green performance expectancy (GPE)
manufacturing enterprises (Han & Chen, 2021). From prior literature,
the adoption of green technology, or its acceptability, can be recapit- Performance expectancy is a key variable of the UTAUT model,
ulated as the extent of the possibility of an emerging novel technol- which influences behavioural intentions. It is "the degree to which an
ogy being authorised by groups or individuals (Awan et al., 2020; individual believes that using the system will help him or her to
Jahanshahi et al., 2020). attain gains in job performance" (Venkatesh et al., 2003). It comprises
Many scholars modelled the critical elements of technology adop- four fundamental measures that gauge performance: perceived use-
tion for better decision-making, which is further developed in the fulness, job fit, extrinsic motivation, and comparative edge (Huang &

Fig. 1. Research model.

3
M. Shahzad, Y. Qu, S.U. Rehman et al. Journal of Innovation & Knowledge 7 (2022) 100231

Kao, 2015). It is the most significant contributor to identify individual H3: Green hedonic motivation positively affects green behavioural
intention to accept new technology and satisfaction (Zhao & Bacao, intention.
2020). Prior studies have certified that performance expectancy posi-
tively and considerably affects adoption and continuing usage of the Green social influence (GSI)
latest technologies, i.e., mobile banking. In the setting of this study,
GPE may have a considerable impact on GBI, as various green factors Social influence means that social networks incline individuals'
such as supplier selection, procurement, industrial engineering and decisions since they frequently evaluate the ideas and opinions of
consumerism all have a considerable impact on green purchase others when deciding whether or not to espouse innovative technol-
intention (Anser et al., 2020). Recent studies specified that green ogies (Anser et al., 2020). It is described as "the degree to which an
product knowledge positively affects individual green behaviour (e.g. individual perceives that important others believe he or she should
Hsu et al., 2017). Thus, the subsequent hypothesis is proposed: use the new system" (Venkatesh et al., 2003). It denotes encompass-
ing the individual decision-making process to accept innovative tech-
H1: Green performance expectancy positively affects green behav- nology affected by others' opinions (Ashfaq et al., 2021; Dangelico,
ioural intention. 2017). Social influence is also considered a subjective norm in TAM
and social norms in TRA (Zhao & Bacao, 2020). Recent research identi-
fied that social influence significantly influences the adoption of
Green effort expectancy (GEP) innovative technologies and behavioural intention at all points in
time (Wang et al., 2020). In this research setting, prior studies identi-
Effort expectancy is one of the dominant constructs of UTAUT, fied that social influence related to environmental conservation is the
described as "the degree of ease of use associated with the usage of a most influential element in predicting and adopting green technol-
new technology or a technology product" (Huang & Kao, 2015). It is a ogy (Ahn et al., 2016). Moreover, it helps shape individual behaviour
comparable construct with ease or complexity of use (Zhao & Bacao, towards green intentions and sustainable purchase decisions, e.g.
2020); the latter are identified as the extent to which innovative purchasing unique biodegradable packaging and carrying bags (Choi
technology is complex or easy to use and comprehend. The complex- & Johnson, 2019). The greater the social influence of green technology
ity of innovative technology may harm its adoption (Dangelico, adoption, the greater the individual's persistence in using it. As a
2017). It is expected that the larger the ease of use of innovative tech- result, the following hypothesis is advanced:
nology, the lower the individual behavioural intention (Al-Saedi et al.,
2020). Some research studies found that effort expectancy harms H4: Green social influence positively affects green behavioural
using novel technologies, i.e., internet banking and shopping online intention.
(Chopdar and Sivakumar, 2019). The latest studies identified that
effort expectancy significantly affects innovative technologies' utilisa-
Green facilitating conditions (GFC)
tion and satisfaction by employing and validating the UTAUT model
(Anser et al., 2020; Shang & Wu, 2017). Further, for our study context,
Facilitating conditions are the final and central element of the
green product labelling enhances the individual green behaviour and
UTAUT model. Facilitating conditions are "the factors in an environ-
intention to utilise sustainable and green products compared to non-
ment that hinder or make an activity easier to perform for an individ-
labelled products (Ma et al., 2017). Accordingly, the subsequent
ual" (Venkatesh et al., 2003). Individual-level and group-level are the
hypothesis is proposed:
two forms of facilitating conditions. The former is about the individ-
ual insight into environmental support; the latter is about organisa-
H2: Green effort expectancy positively affects green behavioural
tional support available for groups (Ahn et al., 2016). Without a
intention.
comprehensive set of facilitating conditions, it is challenging to adopt
and use the latest technology. However, it is rational in a green con-
text since the facilitating conditions, e.g. training and guidance about
Green hedonic motivation (GHM)
innovative and green technology (software and hardware), persuade
usage and GBI (Tariq et al., 2016). The prospective barriers to use can
Hedonic motivation, known as perceived enjoyment, refers to
be eliminated or reduced significantly (Venkatesh et al., 2012). Prior
internal pleasure, fun, or satisfaction experienced using the latest
literature identified that organisations' employees would accept and
innovative technology and articulates a key role in contributing to
adopt new technology when they received support and facilitating
the UTAUT2 model (Tam et al., 2020). An individual with utilitarian
assistance (Tam et al., 2020). Nysveen and Pedersen (2016) identified
motivation focuses on instrumental values, while one with hedonic
that an individual with accession to a conducive series of facilitating
motivation focuses on fun and pleasure (Wang et al., 2020). It has
conditions is expected to adapt and accept new technology. Wong
been demonstrated to be a more fundamental driver than other
(2013) suggested that adoption of green technologies enables organi-
UTAUT components and a core estimator of behavioural intention
sations to reduce adverse ecological impact, facilitating SD outcomes.
(Venkatesh et al., 2012). Empirical research identified that hedonic
So, the following hypothesis is proposed:
motivation affects technology adoption both in individual and organ-
isational contexts (Ashfaq et al., 2021; Huang & Kao, 2015). In the
H5: Green facilitating conditions positively affect green behavioural
context of GHM, users' hedonic motivation captures a vital role in
intention.
predicting green buying behaviour (Choi & Johnson, 2019). Prior
studies acknowledge that individuals' thinking and green motivation
incite their urge to purchase eco-friendly and green products (Ali Green innovation cost (GIC)
et al., 2020). Motivation for adopting smart technologies is a perti-
nent factor that affects individuals' intentions to enhance their house- Innovation cost is another of the most critical variables in the
holds' sustainability and sustainable consumption behaviour (Ahn UTAUT2 model, as product cost significantly influences technology
et al., 2016). Furthermore, individuals' novelty-seeking behaviour adoption (Tam et al., 2020). The price value is conventionally speci-
and green consumerism also impact green purchase intention (Anser fied as arbitration between cost and benefit analysis. When the
et al., 2020; Choi & Johnson, 2019). Thus, subsequent to the above advantages of adopting new technology are superior to the financial
discussion, we posit the below hypothesis: costs, the innovation cost shows positive results and positively
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M. Shahzad, Y. Qu, S.U. Rehman et al. Journal of Innovation & Knowledge 7 (2022) 100231

influences adoption intention (Venkatesh et al., 2012). Besides, GI is et al., 2020; Choi & Johnson, 2019). Moreover, Casey and Wilson-
not free; however, it is lucrative for organisations in the long run Evered (2012) also emphasise the key mediating role of behavioural
(Zailani et al., 2015). Prior research acknowledged that environmen- intention among performance expectancy, effort expectancy, and
tal compliance is an extra financial burden and increases production trust in new technology. Therefore, based on the extant literature, we
costs instead of considering it an essential strategy to avert harmful propose that GBI plays a mediating role among integrated constructs
ecological effects (Liu et al., 2021). However, the number of environ- and GIA. Thus, the following hypotheses are proposed.
mentally conscious consumers is rising; they prefer to use eco- Green behavioural intention mediates the relation among green
friendly products (Chang & Chen, 2013). They desire innovative and performance expectancy (H8a), green effort expectancy (H8b), green
green products and are determined to pay a greater price for green hedonic motivation (H8c), green social influence (H8d), green facili-
items (Chen, 2008). Further, Wei et al. (2018) stated that less environ- tating conditions (H8e), and green innovation cost (H8f) to green
mental motivated consumers are likely to pay less for green products. innovation adoption.
However, highly environmental motivated consumers are likely to
pay high. Green processes and product innovation diminish adverse Moderating influence of organisational size
ecological impacts and enhance production efficiency and sustainable
financial performance through cost and waste minimisation (Zailani Generally, the number of employees at any particular geographi-
et al., 2015). Prior studies show contradictory arguments regarding cal location is known as organisational size. Several researchers have
this relationship, so re-investigation of this relationship is indispens- identified that organisational characteristics have a higher propensity
able. Thus, the subsequent hypothesis is proposed: for the behavioural intention to adopt innovative technologies
(Aibar-Guzma
n et al., 2022). Following previous studies, this research
H6: Green innovation cost positively affects green behavioural also considers organisational size as moderating variable (Ma et al.,
intention. 2018; Shu et al., 2016). Lin and Ho (2008) emphasised that organisa-
tional resources, including quality of resources and organisational
size, further influence the adoption of new green technology. Further,
Green behavioural intention (GBI)
Lin et al. (2020) highlighted that organisational resources signifi-
cantly affect green technology adoption. More resourceful and larger
Psychologists and social scientists acknowledge that behavioural
organisations have higher chances of adopting and integrating tech-
intentions always strongly affect actual behaviour (Straub, 2009;
nological changes into their operations, as this is a lengthy process
Zafar et al., 2020). Nevertheless, the prediction of actual behaviour is
and needs massive investment. Organisations can implement an
still challenging. Behavioural intention denotes "the degree to which
advanced environmental strategy by adopting green technologies;
a person has formulated conscious plans to perform or not perform
when the organisation has higher resources and greater size, the
some specified future behaviour(s)" (Huang & Kao, 2015). The prior
adoption capacity of innovative technologies is higher. So, the follow-
researcher, Venkatesh et al. (2012) identified that behavioural inten-
ing hypothesis is proposed:
tion regarding technology adoption plays a magnificent role in actual
technology adoption. Several researchers employ intention behav-
H9: Organisational size significantly moderates the aforementioned
iour as a surrogate of actual adoption behaviour (Karampournioti and
relations towards green behavioural intention and green innova-
Wiedmann, 2022; Zafar et al., 2020). GI is now growing a competitive
tion adoption in confounding ways.
strategy due to increasing environmental regulations and optimal
sustainability outcomes. Further, GIA is a long-run effort that necessi-
tates an organisation to create substantial developments in processes
Research methodology
and products, which inevitably invoke environmental risks (Jahan-
shahi et al., 2020; K. Lee et al., 2021). Larger organisations are ready
Measures
to assimilate innovative technologies, capabilities, and external and
internal environments; they are more likely to put potential risks
A questionnaire survey comprised of two portions was adopted to
beneath control (Albino et al., 2009). Thus, consistent with the under-
gather data. The first portion is associated with the demographic evi-
lying theory and research model, we expect that GBI will substan-
dence of respondents and organisations (see Table 1). The second
tially influence GIA. Hence, we propose the following hypothesis:
consists of different measures related to targeted variables. The
employed instrument is adopted from prior studies with multiple
H7: Green behavioural intention positively affects green innovation
validated and reliable items. All measurements were concluded fol-
adoption.
lowing the endorsements of a panel included of three professors and
professionals to ensure face validity. GPE, GEE, GSI, GFC, and GBI
Mediating influence of GBI were evaluated by four, four, three, five, and three items, respectively
(Venkatesh et al., 2003); GHM, GIC, and GIA were assessed using four,
This research seeks to determine whether GBI acts as a mediator four, and six items, respectively (Venkatesh et al., 2012). All items
among diverse decision-making factors of UTAUT and green innova- were answered on a seven-point Likert scale "1=strongly disagree" to
tion adoption. Behavioural intention focuses on the desire to actual "7=strongly agree". Before conducting the formal survey, pilot testing
usage or adoption; such desires can be dominant and irresistible; still, was undertaken to ensure content validity, but few modifications
it does not basically ascertain the actions. Several studies provided were necessary to certify data validity and reliability.
the theoretical background and critical role of behaviour intentions
for actual technology adoption (Ashfaq et al., 2021; Ifedayo et al., Data collection
2021; Venkatesh et al., 2012). Ifedayo et al. (2021) identified behav-
iour intentions as a prognosticator of podcast technology acceptance Analysis of this investigation was based on quantitative data col-
in Nigeria directly and indirectly as well. Further, J. Lee et al. (2021) lected via a questionnaire from various manufacturing industries in
also acknowledged that eco-friendly behavioural intentions signifi- one of the emerging markets, i.e., Pakistan, between November 2021
cantly influence the adoption decisions regarding electric vehicles. and March 2022. The current government establishes stringent envi-
The previous scholars have extensively conferred how green thinking ronmental regulations to reduce dependence on coal energy and shift
and motivation relate to green behaviour and adoption intention (Ali manufacturing to renewable energy sources. However, renewable
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M. Shahzad, Y. Qu, S.U. Rehman et al. Journal of Innovation & Knowledge 7 (2022) 100231

Table 1 10X rule for sample size as guided by Hair et al. (2017), which is
Demographic details. "10 times the largest number of structural paths directed at a particu-
Particulars (N=516) Frequency Percentage lar latent construct in a structural model". The sample size was
derived through G*Power software proposed by Prajapati et al.
Gender Male 310 0.60
(2010) to ensure the sample's adequacy for the research model. A set
Female 206 0.40
Age (years) 18 to 25 145 0.28 of power analyses revealed that our sample is suitable for further
26 to 35 224 0.43 investigation.
36 to 45 81 0.16
46 to 55 43 0.08
Above 55 23 0.04 Common-method bias variance
Experience (years) Less than 1 111 0.22
1 to 3 194 0.38
Common method bias (CMB) variance is specified as "variance
4 to 7 128 0.25
8 to 10 62 0.12 that is attributable to the measurement method rather than to the
Above 10 21 0.04 constructs the measures represent" (Cohen, 1988). It is argued to be a
Education Master’s 121 0.23 main concern in the questionnaire survey. Initially, CMB was esti-
Bachelor’s 178 0.34 mated using Harman's single factor, where the first factor has a cut-
Intermediate 101 0.20
Matric 91 0.18
off value of less than 50% (i.e., 31.15%) (Harman, 1976). Besides, a
Others 25 0.05 more rigorous method for testing CMB vis full collinearity evaluation
Job Position Office Executives 116 0.22 was also implemented (Kock, 2015). The resulting variance inflation
Supervisors 165 0.32 factor (VIF) values were less than 3.3 (Kock, 2015). These findings
Assistant Managers 103 0.20
imply that CMB is unlikely to cause severe concern.
Managers/ Sr. Manager 74 0.14
CEO /Directors 58 0.11
Org. Size (emp) Less than 100 80 0.15
Results
101 to 150 81 0.15
151 to 200 85 0.16
201 to 250 97 0.18 PLS-SEM and ANN were utilised for this study, and the data were
251 to 300 64 0.12 analysed by SmartPLS (ver. 3.2.8) and IBM SPSS statistics (ver. 25).
Above 300 109 0.21 PLS-SEM is highly recommended when an investigation is explor-
Org. Structure Line 30 0.06
Line and Staff 105 0.20
atory and intends to predict (Hair et al., 2017). Normal distribution is
Functional 135 0.26 not a precondition of PLS-SEM compared to other methodologies,
Divisional 52 0.10 and it can work with a small sample. PLS-SEM has the potential to
Matrix 49 0.09 measure all causal relationships concurrently and can test a complex
Org. Portfolio Single Product Local 70 0.14
model without the removal of any model variable. These conditions
Multi-Product Local 160 0.31
Single Product Global 248 0.48 are suitable for employing the PLS-SEM methodology (Hair et al.,
Multi-Product Global 38 0.07 2017). Besides, ANN is more robust and proficient in recognising
both linear and non-linear relations and outperform classical regres-
sion investigations, e.g. multiple regression analyses (Sim et al.,
energy resources are also limited. So, it needs to take some corrective 2014). Though, it suffers from the shortcoming of a "black box" oper-
measures to promote green initiatives in the current challenging ation algorithm and is therefore not appropriate for testing hypothe-
environment. Similarly, it faces various SD issues requiring vigorous ses. Thus, we employed PLS-SEM for hypotheses testing and ANN for
green product development and process innovation (Awan et al., evaluating the relative importance of variables. Model is measured
2020). Therefore, Pakistan has been identified as an appropriate con- according to Hair et al. (2017) in two steps: (outer) measurement and
text for evaluating our research hypotheses. The questionnaires were (inner) structural model.
distributed online using Google docs and WhatsApp and offline
through personal visits including a cover letter illustrating the aim of Analysis of measurement model
this research and assuring respondents' data confidentiality. Due to
the epidemic, we conveniently contacted upper, middle, and front- The construct reliability method ("Cronbach's alpha (CA), rho_A,
level staff members to obtain higher responses from different and composite reliability (CR)") and validity ("discriminant and con-
manufacturing industries, including textiles and clothing, petroleum vergent validity") was used to estimate the measurement model by
and chemicals, electronics and IT, food and beverages, metal following Hair et al. (2017). Referring to the results in Table 2, the CA
manufacturing, and leather products. score ranges from 0.741 to 0.841, whereas the figures of rho_A are in
To enhance the response rate, reminders and follow-ups were the range of 0.742 and 0.842, and the statistics of CR squeeze a range
sent to concerned respondents. These corporations were listed in the from 0.853 to 0.889. All statistics are greater than the threshold of
"Pakistan Stock Exchange (PSX)" and registered with the "Securities 0.70; subsequently, the construct reliability is established (Cohen,
and Exchange Commission of Pakistan (SECP)." 980 questionnaires 1988; Hair et al., 2017). The loading of factors and "Average Variance
were dispersed to 399 manufacturing units in Pakistan; we received Extracted (AVE)" were assessed to determine the convergent validity
516 functional responses − a response rate of 52%. These respondents (CV). These statistics were also larger than the threshold of 0.50, as
signify the organisation as a whole. Usually, in survey studies, schol- Hair et al. (2017) advised. The resulting statistics authorised the CV of
ars have a low response rate due to respondents' busy schedules and variables.
non-access to the internet (Hair et al., 2017). Due to the pandemic, Furthermore, the discriminant validity (DV) is affirmed using a
many employees were working from home and had easy access to traditional but vastly familiar approach (Fornell & Larcker, 1981) and
the internet, so we had a higher response rate than usual. Also, a large a recent and latest approach heterotrait-monotrait (HTMT) ratio
sample leads to more precise estimation and results (Asiamah et al., (Henseler et al., 2015). In the first approach, the square root of AVE
2017). The majority of respondents held supervisory positions, i.e., should be larger than the correlation among targeted components.
45%, responsible for executing organisational strategies and imple- The second HTMT approach acclaims a cut-off value of 0.85 (Sarstedt
menting policies; 60% were male, and the majority were aged et al., 2017). The findings in Tables 3 and 4 approve both criteria of
between 18 and 35. (see Table 1). The current research adopted a DV.
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M. Shahzad, Y. Qu, S.U. Rehman et al. Journal of Innovation & Knowledge 7 (2022) 100231

Table 2 Table 4
Reliability and validity. Discriminant validity (HTMT).

Constructs Factor Loadings CA rho_A CR AVE GEE GFC GBI GIA GHM GIC GPE GSI

Green Effort Expectancy (GEE) 0.741 0.744 0.838 0.564 GEE

GEE1 0.804 GFC 0.700
GEE2 0.735 GBI 0.702 0.712
GEE3 0.749 GIA 0.161 0.133 0.309
GEE4 0.713 GHM 0.404 0.395 0.660 0.396
Green Facilitating Conditions (GFC) 0.841 0.842 0.887 0.612 GIC 0.652 0.596 0.774 0.360 0.746
GFC1 0.748 GPE 0.554 0.555 0.727 0.332 0.581 0.664
GFC2 0.801 GSI 0.681 0.668 0.767 0.312 0.699 0.798 0.685
GFC3 0.813
GFC4 0.805
GFC5 0.741 Mediation analysis
Green Behavioural Intention (GBI) 0.742 0.742 0.853 0.659
GBI1 0.800
GBI2 0.814 The mediating impact of the GBI was evaluated by the series of
GBI3 0.821 steps (Nitzl et al., 2016). At first, this study inspected the indirect
Green Innovation Adoption (GIA) 0.808 0.826 0.865 0.563 effect of the GPE, GEE, GHM, GSI, GFC, and GIC to GIA through GBI;
GIA1 0.741 and found a significant effect of these variables with beta values
GIA2 0.749
GIA3 0.706
0.043, 0.033, 0.038, 0.031, 0.50, 0.037, respectively. In the next step,
GIA4 0.803 the direct effect of GPE, GEE, GHM, GSI, GFC, and GIC was measured
GIA5 0.751 without removing the mediator (GBI). A significant positive outcome
GIA6 0.762 of these variables with beta values 0.182, 0.138, 0.154, 0.123, 0.212,
Green Hedonic Motivation (GHM) 0.803 0.804 0.871 0.629
and 0.157 were found respectively. The results are specified in
GHM1 0.777
GHM2 0.802 Table 5, which leads to partial mediation. Besides, this study noticed
GHM3 0.828 the sign of indirect and direct effects and found positive and in the
GHM4 0.764 same direction; therefore, it might be determined that the GBI has
Green Innovation Cost (GIC) 0.755 0.758 0.845 0.576 complementary partial mediation (Hair et al., 2017). Hence, H8a to
GIC1 0.747
GIC2 0.780
H8f is fully supported.
GIC3 0.765
GIC4 0.744 Multi-group analysis for moderation
Green Performance Expectancy (GPE) 0.771 0.772 0.854 0.593
GPE1 0.792
GPE2 0.780
The moderation effects of organisational size were estimated
GPE3 0.752 through the multi-group analysis (MGA) technique. MGA assists in
GPE4 0.755 estimating the significant difference among various groups in data
Green Social Influence (GSI) 0.814 0.821 0.889 0.728 for an identical model; predominantly when a categorical moderator
GSI1 0.820
is involved (Hair et al., 2017). As the organisational size is a categori-
GSI2 0.878
GSI3 0.861 cal moderator, to assess its moderating effect, data were divided into
three groups according to the number of employees (Less than 150-
small, n=161), (151 to 250-medium, n=182), and (More than 250-
Analysis of structural model Large, n=173). Results of MGA in Table 6 revealed a significant differ-
ence in GIA levels observed among these three groups. In the case of
Following the validation of the outer model, the structural model smaller organisations, the effect of GEE, GSI, and GIC on GBI was
was evaluated in order to test the hypotheses. To determine the rele- insignificant. For medium-size organisations, the impact of GHM and
vance of the hypotheses, a bootstrapping approach was used (5000 GSI on GBI was insignificant, whereas for larger organisations, the
resample). The findings of the model disclosed a significant and posi- effect of GSI on GBI was insignificant only, still it is significant at 10%
tive effect of GPE (H1: beta value=0.182; p<0.001), GEE (H2: beta level of significance. These results suggested that the propensity for
value=0.138; p<0.002), GHM (H3: beta value=0.154; p<0.001), GSI GIA among these groups has discrepancies (smaller to larger). Smaller
(H4: beta value=0.123; p<0.033), GFC (H5: beta value=0.212; p<0.001), organisations have limited resources and portfolios, so they have a
and GIC (H6: beta value=0.157; p<0.001) on GBI which support the
hypotheses H1 to H6 respectively. Furthermore, hypothesis H7
revealed a significant and positive influence of GBI on GIA (H7: beta Table 5
value=0.247; p<0.000). The result of control variables revealed that Hypotheses testing.
these were insignificant. The overall outcomes of the hypotheses are
Key Relationship Paths b Values T- Values P-Values Decision
provided in Table 5.
H1 GPE -> GBI 0.182 4.131 0.000 Supported
H2 GEE -> GBI 0.138 3.126 0.002 Supported
Table 3
H3 GHM -> GBI 0.154 3.306 0.001 Supported
Discriminant validity (Fornell-Larcker Criterion).
H4 GSI -> GBI 0.123 2.127 0.033 Supported
H5 GFC -> GBI 0.212 3.314 0.001 Supported
GEE GFC GBI GIA GHM GIC GPE GSI
H6 GIC -> GBI 0.157 3.184 0.001 Supported
GEE 0.751 H7 GBI -> GIA 0.247 5.776 0.000 Supported
GFC 0.556 0.782 Mediation Analysis (Indirect Effects)
GBI 0.522 0.563 0.812 H8a GPE -> GBI -> GIA 0.043 3.049 0.002 Supported
GIA 0.113 0.107 0.247 0.750 H8b GEE -> GBI -> GIA 0.033 2.534 0.011 (Complementary
GHM 0.311 0.325 0.510 0.322 0.793 H8c GHM -> GBI -> GIA 0.038 2.838 0.005 Partial Mediation)
GIC 0.488 0.476 0.582 0.281 0.577 0.759 H8d GSI -> GBI -> GIA 0.031 1.978 0.040
GPE 0.418 0.450 0.552 0.269 0.458 0.503 0.770 H8e GFC -> GBI -> GIA 0.050 3.108 0.002
GSI 0.531 0.554 0.598 0.253 0.565 0.624 0.544 0.854 H8f GIC -> GBI -> GIA 0.037 2.493 0.013

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M. Shahzad, Y. Qu, S.U. Rehman et al. Journal of Innovation & Knowledge 7 (2022) 100231

Table 6 common and renowned networks, i.e., the "multilayer perceptron"

MGA for moderation. (MLP) (Zafar et al., 2021), to train the neural networks. ANNs usually
Relationship Paths b Values T-Value b Values T-Value b Values T-Value include one input, more or one hidden layer, and one output layer,
(Large) (Medium) (Small) with no single rule for selecting the best values. The value of hidden
layers is proportional to the problem's intricacy (Sheela & Deepa,
GPE -> GBI 0.164 2.193 0.172 2.592 0.201 2.329
GEE -> GBI 0.184 2.745 0.285 4.204 0.045 0.606 2013). The importance of predictors was assessed in two steps. First,
GHM -> GBI 0.156 1.964 0.109 1.621 0.222 2.665 we provide seven significant covariates as predicting variables (input
GSI -> GBI 0.180 1.890 0.087 0.973 0.034 0.316 layer), whereas GIA was applied as an output layer in the neural net-
GFC -> GBI 0.318 3.231 0.194 2.731 0.404 3.816
work. A sigmoid function was utilised to represent the activation
GIC -> GBI 0.239 2.046 0.207 3.429 0.091 1.028
GBI -> GIA 0.217 3.232 0.270 4.663 0.307 4.650
function of neurons in both the hidden and output layers. Following

bana-cabanillas et al.
prior researchers such as Zafar et al. (2021); Lie
(2017), the ANN model was verified by employing the number of hid-
den nodes from 1 to 10. To minimise over-fitting, we employed ten-
Table 7 fold cross-validation, with 70% of the data employed to train the net-
R2, Q2 and effect size. work model and 30% to test it.
Endogenous variables R2 Q2 Exogenous variables F2 The neural network prediction accuracy was estimated using the
Root Mean Square Error (RMSE). The findings revealed that the aver-
Green Behavioural 0.536 0.345 Green Performance Expectancy 0.044
age RMSE for GIA was 0.1337 for training data and 0.1323 for testing
Intention
Green Innovation 0.061 0.031 Green Effort Expectancy 0.024 data. The disparity in produced values is minor, indicating that the
Adoption model used provides high accuracy (Leong et al., 2018). Outcomes
Green Hedonic Motivation 0.030 are given in Table 8. A sensitivity analysis was executed to gauge the
Green Social Influence 0.014
importance and normalised importance of integrated covariates in
Green Facilitating Conditions 0.055
Green Innovation Cost 0.026
the ANN model. The importance of incorporated constructs was com-
Green Behavioural Intention 0.065 puted by averaging their generated values in ten networks for pre-
dicting the output. Further, the normalised importance represents
the ratio of each input variable to the highest, indicating that GFC
low GI adoption level, unlike medium and large-size organisations. was the most important predictor for GIA with a 0.223 importance
Hence, H9 is fully supported. value, followed by GPE, GHM, GSI, GEE, GIC, and GBI, i.e., 0.215, 0.208,
0.177, 0.162, 0.161, and 0.128 respectively (see Table 9). The graphi-
cal representation of the average and relative importance of each
Goodness of fit (GOF) indexes
construct were shown in Fig. 2. Some minor differences were
observed in the ranking of variables, but GFC and GPE ranking is com-
The model fit was established by a largely adequate method, i.e.
parable in both analyses. The non-linear and non-compensatory
"standardised root mean square residual" (SRMR), where the SRMR
design of ANN models and their higher level of prediction accuracy
value should be less than 0.08 (Hair et al., 2017). The outcomes
may explain these differences.
revealed the value of SRMR is 0.065, suggesting our model is quite
well. Secondly, we also calculated GOF using the formula (GOF=x
(AVE £ R2)) (Wetzels et al., 2009). In our model, the GOF is 0.429,
Discussion and research implications
demonstrating the model fulfils the large criteria. Besides R2 (coeffi-
cient of determinants), F2 (effect size) and Q2 (predictive relevance)
Discussion on key findings
were also analysed. The resultant values were in good range and pro-
vided in Table 7.
This study incorporates the UTAUT to advance the conceptual
framework for estimating the influence of specified decision-making
Robustness check through the artificial neural network (ANN) approach factors on GIA − a previously relatively unexplored area. The empiri-
cal findings confirmed that GPE and GEE positively affect GBI accept-
Following prior social scientists (Chavoshi & Hamidi, 2019; Zafar ing H1 and H2. These results support preceding studies of Ahn et al.
et al., 2021), this study also employed ANN to identify each variable's (2016); Anser et al. (2020) by emphasising the insinuation of adopt-
relative importance and reinforce SEM results. Though the ANN has ing sustainable and innovative technologies by UTAUT. The positive
many types, the present study has employed one of the most effects of these variables suggested that innovative green technology

Table 8
Validation of neural networks for training and testing data.

Training Data Testing Data

Neural Networks N Sum of Square Error Mean Square Error RMSE N Sum of Square Error Mean Square Error RMSE

1 357 6.215 0.0174 0.1319 159 2.813 0.0177 0.1330

2 348 6.240 0.0179 0.1339 168 2.752 0.0164 0.1280
3 378 7.184 0.0190 0.1379 138 2.355 0.0171 0.1306
4 341 6.045 0.0177 0.1331 175 3.143 0.0180 0.1340
5 379 6.793 0.0179 0.1339 137 2.353 0.0172 0.1311
6 363 6.215 0.0171 0.1308 153 2.874 0.0188 0.1371
7 353 6.605 0.0187 0.1368 163 2.474 0.0152 0.1232
8 385 7.174 0.0186 0.1365 131 2.139 0.0163 0.1278
9 343 5.915 0.0172 0.1313 173 3.181 0.0184 0.1356
10 352 6.007 0.0170 0.1306 164 3.335 0.0203 0.1426
Average 0.1337 0.1323
St. Dev. 0.0026 0.0055

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M. Shahzad, Y. Qu, S.U. Rehman et al. Journal of Innovation & Knowledge 7 (2022) 100231

Table 9
Sensitivity analysis.

Neural Networks GPE GEE GFC GSI GHM GIC GBI

1 0.234 0.650 0.231 0.096 0.134 0.167 0.073

2 0.590 0.111 0.189 0.205 0.130 0.210 0.095
3 0.133 0.169 0.073 0.192 0.148 0.090 0.195
4 0.245 0.118 0.311 0.126 0.132 0.028 0.049
5 0.099 0.094 0.244 0.194 0.065 0.190 0.114
6 0.146 0.110 0.306 0.051 0.101 0.225 0.067
7 0.153 0.105 0.231 0.105 0.122 0.169 0.114
8 0.217 0.121 0.182 0.037 0.470 0.208 0.189
9 0.150 0.111 0.198 0.203 0.750 0.157 0.107
10 0.180 0.032 0.263 0.560 0.026 0.169 0.274
Average Importance 0.215 0.162 0.223 0.177 0.208 0.161 0.128
Relative Importance 0.964 0.728 1.000 0.794 0.933 0.724 0.573
Normalized Importance 96.36 72.75 100.00 79.39 93.26 72.39 57.31

is easy to implement and enhances long-term performance in the with the results of Venkatesh et al. (2012). Several studies suggested
current challenging business environment. Further, GHM and GSI that behavioural intention can be used as a surrogate for actual tech-
positively impacted GBI, leading to our H3 and H4. Our results coin- nological adoption (Karampournioti & Wiedmann, 2022; Zafar et al.,
cided with Ali et al. (2020); Wang et al. (2020). Prior researchers rec- 2020). Thus, this study also predominantly evaluated the mediating
ommend that green thinking and social influence shape individuals' effect of GBI as it instigates GIA. Our two-step mediation results show
pleasure-seeking behaviour to purchase green products, ultimately that GBI complementary partially mediates the integrated relation-
conserving the environment. Ashfaq et al. (2021) also claim that ship towards GIA by accepting H8a to H8f. These results have also
social influence and hedonic motivation significantly influence inten- coincided with Ashfaq et al. (2021) and Ifedayo et al. (2021) in the
tion to use the latest technology. broader context for technology adoption.
GFC most significantly affects GBI accepting H5, showing the out- Lastly, this study also conducted MGA to evaluate the moderating
come is congruent with Tariq et al. (2016). The results of their role of organisational size among integrated relations towards GBI
research accentuated that guidance and edification about innovative and GIA. The findings are distinctive and captivating as organisational
and green technology induce usage and GBI. Further, as internal size moderated structural relationships differently by accepting H9.
stakeholders of the process, employees would only accept innovative Not every organisation can accept technological changes in produc-
technologies when they attained a particular level of technical sup- tion operations; it is a long process and requires immense invest-
port and assistance (Shahzad et al., 2020a). The GIC also positively ment/financial resources. Larger organisations can take advantage of
affected GBI, accepting H6. Our results contradict Tam et al. (2020). economies of scale to adopt GI by increasing production levels. In a
The probable cause for this deviation is perhaps that consumers are developing country like Pakistan, organisations do not have a special-
now more environmentally conscious, prefer to use eco-friendly ised product line; they have diversified product lines. If they happen
products, and are willing to pay higher values (Liu et al., 2021). to own a specialised product line, their GIA levels might be higher.
Adopting innovative technology does not personify additional costs These results provide adequate evidence that GIA is a long-run effort
for consumers; on the contrary, it can offer financial and non-finan- that obliges an organisation to create considerable development in
cial benefits. processes and products, inevitably invoking environmental risks.
Furthermore, the acceptance of H7 shows a substantial positive Finally, the ANN's overall findings support the relevance of integrated
effect of GBI on GIA as predicted by UTAUT and is broadly coherent components. Sensitivity analysis revealed that GFC and GPE have rel-
atively the highest importance towards GIA as predicted by SEM.
Thus, we should consider the significance of these variables for
achieving GIA outcomes.

Theoretical implications

This research serves mainstream literature in a variety of areas.

First, a technological adoption model based on UTAUT is validated,
providing a new correlate to address the scarcity in the prior litera-
ture in the field of GI. We believe this is the first research exploring
GIA through diverse decision-making factors with green attributes,
i.e., GPE, GEE, GHM, GSI, GFC, GIC, GBI in a novel way through SEM
and ANN in developing nations, i.e. Pakistan. Second, this study
divulged the direct impact of GPE, GEE, GHM, GSI, GFC, and GIC on
GBI and, further on, GIA − a novel phenomenon not operationalised
in green and sustainable innovation literature previously. Besides,
this study also underpinned the key mediating role of GBI and devel-
oped its complementary partial mediation, as behavioural intention
reinforces actual technology adoption. The proliferation of ICT and
digital manufacturing alters manufacturing processes and operations,
significantly impacting GIA (Awan et al., 2020). Thus, our results
demonstrate that the study of UTAUT for GIA is imperative in the cur-
Fig. 2. Importance of each construct. rent era of technology-based innovation.
9
M. Shahzad, Y. Qu, S.U. Rehman et al. Journal of Innovation & Knowledge 7 (2022) 100231

Third, this research measured the moderating role of organisa- these innovative technologies can hinder the adoption of GI. Solving
tional size that facilitates the adoption levels of GI. The significant these difficulties is essential and needs top management and govern-
moderating results established that larger organisations promptly mental support immediately. More investment should be allocated
realised the importance of GI and effectively embraced SD agendas. for the skill-building of employees regarding GIA, to help improve
This research evocatively contributes to the existing literature and operational performance and profitability. Regular assistance can be
offers a vital and comprehensive mechanism to promote green tech- offered through various means; technical consultants may offer
nology innovation. Finally, combining the two methodologies (i.e., ongoing product/service consultations to all stakeholders, and call
PLS-SEM and ANN) yields new intuitions and emphasises the rele- centre services can provide prompt solutions to any problems.
vance of all independent variables contributing to GIA independently.
The findings indicate that ANN appears to be a more capable predic- Conclusion
tive model, shown by the low RMSEs of all ANN models for testing
and training datasets (Leong et al., 2018; Lie
bana-cabanillas et al.,
The sustainable innovation debate is gaining momentum as
2017). numerous countries strive to achieve SD goals in the coming decade.
This research has produced distinct outcomes that can be considered
Practical implications significant contributions to the mainstream literature. A comprehen-
sive framework was presented in this research based on UTAUT
This study has several practical contributions which facilitate model for influencing GIA in today's challenging business environ-
managers and policymakers. First, the findings emphasised the rele- ments to improve SD. We used survey procedures to gather data
vance of diverse decision-making factors based on the UTAUT model from the manufacturing industries and employed SEM and ANN to
to enhance GIA, which educates practitioners and enables organisa- validate our hypotheses and the relative importance of each variable.
tions to achieve SD goals by promoting GI. Our work acknowledged GIA made a substantial contribution by illuminating the significant
that GIA is a helpful tool to persuade manufacturing organisations to relationships of GPE, GEE, GHM, GSI, GFC, and GIC to GBI and on GIA.
consider and integrate innovative and cleaner production technolo- Further, the illumination of the mediating role of GBI among these
gies into their operations to reduce the environmental burden while relations was also an imperative contribution. Besides, organisational
deciding their strategic initiatives (Awan et al., 2020). GIA stimulates size has a significant moderating effect on the ability to pursue GI dif-
organisations to offer a sustainable production and consumption ferently among small, medium, and large organisations. The findings
model to concerned stakeholders. of ANN unveiled robustness by highlighting the relative importance
Second, to reap the benefits of the SD plan 2030, developing econ- of all consequential constructs towards GIA. These findings demon-
omies such as Pakistan must undertake GI to compete with devel- strate deep insights to comprehend the role of critical green determi-
oped economies. By taking the example of China, they have achieved nants that influence GIA, which aids organisations in succeeding in
swift economic growth while undergoing severe resource exhaustion excellence and helping to achieve SD. Besides, the GI dream will
and ecological pollution (Zhu et al., 2010). There is growing pressure never come true without adopting green practices and the latest
to invest in green technologies in these countries, and organisations innovative technologies.
are already burdened by emergency measures to stop environmental This study suggests several areas to be researched in the future.
impact; one solution is adopting green technologies. For countries Due to a lack of resources, it used a cross-sectional technique; a longi-
like Pakistan who are in developing mode, there is a possibility to tudinal approach could provide better and more accurate results. This
learn from the practices of developed countries regarding environ- study was limited to a particular sector; in the future, scholars should
mental conservation. The government should promote and work broaden its scope to include other industries and geographies to
effectively on a green business climate, i.e., "Punjab Green Develop- ensure generalizability. Some machine learning techniques can also
ment Program," to assist organisations in reducing their dependence be used to forecast more accurate and reliable outcomes. Finally, this
on fossil fuels and maximising the use of renewable energy (World paradigm may be tested by including cultural and political factors;
Bank, 2018). That will increase ecological awareness among indus- however, the findings may vary in other regions.
tries and enhance economic growth. Besides, PPPs will also be helpful
in providing the solution of advanced and green technologies at a
Funding
lower cost.
Third, organisations should provide favourable working condi-
National Natural Science Foundation of China, Grant Number:
tions and encourage employees to acquire more advanced knowledge
for specialised business operations (including supply chain integra- 71974028
tion, innovation, and technology transfer) through education and
training. Encountering software and hardware difficulties while using Appendix A: Summary of literature review

Authors Methods and Theory Objectives Integrated Constructs

Han and Chen (2021) 800 Survey TRA To uncover the antecedents of eco-innovation adoption Customer demands, Environmental concerns and regula-
by SMEs in Myanmar tions, Rivalry pressures, Eco-innovation adoption, and
Firm innovation capabilities,
J. Lee et al. (2021) 432 Survey UTAUT To discover the factors that affect behavioural intentions Effort Expectancy, Performance expectancy, Social influ-
to purchase electronic vehicles ence, Facilitating conditions, Environmental concerns,
and Behavioural intention
Ashfaq et al. (2021) 293 Survey BRT To explore individual attitudes to Ant Forest mobile gam- Hedonic motivation, Social influence, Environmental ben-
ing and their continued usage intentions efits, Convenience, Attitude and User intention
Jun et al. (2021) 288 Survey and To highlight the core elements of green innovation adop- External partnership and cooperation, Government sup-
Conceptual Model tion in SMEs in Pakistan port, Market and customer factors, Rules and regulatory
factors, Organisational and human resources, and Tech-
nological factors

(continued)

10
M. Shahzad, Y. Qu, S.U. Rehman et al. Journal of Innovation & Knowledge 7 (2022) 100231

(Continued)

Authors Methods and Theory Objectives Integrated Constructs

Anser et al. (2020) Bibliometric Analysis To understand the key factors of the TAM model which Green supplier selection, Green industrial engineering,
TAM enhances firms' green investment decision Green consumerism, Green procurement, Green inno-
vation, Green product recovery, and Green purchase
decisions
Zhao and Bacao (2020) 532 Survey UTAUT To examine users' continuance intention of using food Effort expectancy, Performance expectancy, Social influ-
delivery apps ence, Trust, Satisfaction, Confirmation, Perceived task-
technology fit, and Continuance intention
Al-Saedi et al. (2020) 436 Survey and Meta- To observe the users' continuance intention to adopt and Perceived trust, Perceived risk, Self-efficacy, Perceived
Analysis UTAUT use M-payment technology cost, Effort expectancy, Performance expectancy, Social
influence, and Behavioural intention
Wu et al. (2019) 470 Survey TAM To recognise the public adoption of electric vehicles Green perceived usefulness, Perceived ease of use, Envi-
ronmental concern, and Behavioural intention
Hsu et al., (2017) 320 Survey TAM and What factors influence the adoption of green information Perceived usefulness, Attitudes, Perceived behavioural
TPB technology products for sustainable development control, Subjective norms, and Intention to purchase
Ma et al. (2017) 903 Surveys TAM To explore consumers' perceptions about sustainable Perceived ease of use, Perceived usefulness, Attitude, and
apparel products to determine purchase intentions Behavioural intention to use
Ahn et al. (2016) 592 Survey UTAUT To understand what factors drive the adoption of sustain- Performance expectancy, Compatibleness expectancy,
able household technology Effort expectancy, Hedonic expectancy, Sustainable
innovativeness, Social pressure, Environmentalism, and
Behavioural intention
Huang and Kao (2015) 30 professionals UTAUT To explore and predict the intentions to use, and use Social influence, Effort Expectancy, Performance expec-
behaviours of Phablets tancy, Facilitating conditions, Hedonic motivation, Price
value, Habit, and Behavioural intention
Venkatesh et al. (2012) 1512 Survey UTAUT To extend the scope of the Unified Theory of Acceptance Facilitating conditions, Performance expectancy, Effort
and Use of Technology in the consumer context Expectancy, Social influence, Hedonic motivation,
Habit, Price value, and Behavioural intention
TRA=Theory of Reasoned Action, BRT=Behavioural Reasoning Theory, TAM=Technology Acceptance Model, UTAUT=Unified Theory of Acceptance and Use of Technology,
TPB=Theory of Planned Behaviour

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