O R I G I N A L R E S E A R C H
Evidence for the Mediating Effects of Eco-Innovation
and the Impact of Driving Factors on Sustainable Business Growth of Agribusiness
Dhekra Ben Amara1•Hong Chen1
Received: 15 January 2021 / Accepted: 31 May 2021 / Published online: 3 July 2021 Global Institute of Flexible Systems Management 2021
Abstract The examination of the drivers of eco-innovation at the micro-level has been the topic of widespread dis- cussion in recent years. However, several issues about these drivers in developing countries with low-tech sectors remain unaddressed. The current paper underlines the impacts of the drivers of eco-innovation on sustainable business growth in the agri-food sector. This study tests a set of hypothesized relationships that focus on a sample of 306 Tunisian enterprises. We harness structural equation modeling to examine the relationship between the driving factors (i.e., regulatory pressure, competitive pressure, customer demand, technological competence, and effi- ciency) and enterprises’ sustainable business growth by analyzing the mediating effects of eco-innovation strategy.
The findings reveal that: (1) regulatory pressure is the most influential driver of eco-innovation strategy, followed by competitive pressure and technological competence, (2) there is a positive relationship between eco-innovation strategy and enterprises’ sustainable business growth, and (3) eco-innovation strategy fully mediates the relationship between the drivers and sustainable business growth. These results are consistent with the ‘Porter Hypothesis’ and have great potential for contributing to the achievement of sustainable development objectives.
Keywords AgribusinessCompetitive pressure Driving forces Eco-innovation strategy Regulatory pressureSustainable growth
Introduction
Environmental sustainability and innovation became key concepts, and both must be well integrated into enterprises’
management, strategies, and activities. The growth of environmental government pressure and consumer awareness pushes enterprises to include corporate envi- ronmental strategies (Gunarathne et al., 2021; Gupta and Gupta2021), thereby leading entrepreneurs to pay more attention to environmental issues investing in new products (Musona et al.,2021). It is within this context that the eco-innovation approach developed. An eco-innovation concept is an approach of designing new processes, prod- ucts, and services while creating value for the green con- sumers and the enterprises, alleviating environmental harm, and optimizing resources (Fussler & James, 1996;
Kemp & Pearson, 2007; Bahrani and Khamseh2020).
Eco-innovation practices achieve an efficient transition toward a sustainable low-carbon economy (Costantini et al., 2017). As a result, the eco-innovation strategy has direct and indirect effects on the decrease of ecological pressures. Several researchers also suppose that eco-inno- vation achieves both environmental and economic gain (Astuti et al., 2019). Therefore, its adoption may ensure a win–win situation to promote competition and sustainable development (Bossle et al., 2016; Trollman and Col- will 2020; Khamseh2021).
Recently, as eco-innovation is a significant component in winning the fight against environmental damage, several studies in the field of innovation, management, resource-
& Hong Chen
nefuchenhong@yeah.net Dhekra Ben Amara ben.amara@nefu.edu.cn
1 School of Agricultural Economics and Management, Northeast Forestry University, Harbin, P.R. China https://doi.org/10.1007/s40171-021-00274-w
based view, stakeholder theory, environmental economics, and institutional theory have examined its main driving forces. It appears that empirical and theoretical research from the area of innovation revealed in general that drivers of eco-innovation have mostly expanded from technology push (supply side) to market pull (demand side) (Horbach, 2008). Recent studies of management focus on the importance of organizational capabilities and efficiency in promoting eco-innovation adoption (Wagner,2008). This is particularly true for the environmental management systems (EMS). EMS supports the adoption of eco-inno- vation as long as it stimulates the enterprise’s environ- mental awareness and raises its operating efficiency. Many studies have recognized the importance of the resource- based view (RBV) in explaining the adoption of environ- mental practices. The RBV argued that an enterprise’s resources must be rare, inimitable, valuable, and non-sub- stitutable to maintain a competitive advantage (Green et al.,1994). These resources, such as technological com- petence (Sarkis et al., 2010), play an essential role in producing innovation (Baumol, 2002; Gupta and Gupta 2019). Current stakeholder theoretical research demon- strates that stakeholder pressure is a crucial determinant of the adoption of eco-innovation. From the micro-level, several pieces of research emerging from the theory of environmental economics examine the effect of govern- ment environmental regulation (e.g., subsidies, permits, and emissions charges) on adopting eco-innovation (Frondel et al.,2007; Kammerer,2009). Porter and van der Linde (1995) suggested what we call the ‘Porter Hypoth- esis.’ They presumed that regulatory pressures could offer enterprises attractive opportunities to create innovative businesses and further achieve enhanced performance (Porter & Van der Linde,1995).
Furthermore, many studies applying institutional theory assume that process of institutional isomorphism fosters the proliferation of organizational innovations (Zhu & Geng, 2013; Li, 2014). Institutional theory points out that eco- innovation is affected by three types of external pressures.
The first one is coercive, related to government environ- mental regulations (comes from government). The second one is normative, related to the enterprise’s needs for boosting capabilities to satisfy the customers and suppliers.
The third one is mimetic, referring to the necessity to imitate the strategies of business leaders (comes from competitors) (Cai & Li,2018). Although many researchers have investigated the driving forces of eco-innovation, many issues are remaining unaddressed. For instance, it is still indefinite whether these driving forces, directly or indirectly, impact an enterprise’s eco-innovation strategy and business growth.
On the other side, an increasing number of inquiries examine the relationship between environmental
innovation or eco-innovation and firm performance (Porter
& Van der Linde,1995; Frondel et al.,2007). Palmer et al.
(1995) argued that environmental improvement has adversely affected the performance of polluting firms because of the external or compliance cost (Palmer et al., 1995). In contrast, the famous ‘Porter Hypothesis’ pre- sumes that environmental regulations encourage enter- prises to innovate and ensure performance without constraining the environmental objectives (Porter & Van der Linde, 1995) and may result in enhanced sustainable business growth at a later date. Therefore, environmental pressure induces many advantages, such as reducing cost and raising revenues and profitability (Porter & Van der Linde,1995; Nidumolu et al.,2009).
In line with the academic literature, the entrepreneur’s decision to adopt an eco-innovation strategy is influenced by driving factors that are internal and external to the enterprise (Del Rı´o et al.,2016). Meanwhile, grasping the drivers of eco-innovation could help decision-makers implement suitable economic/political tools that would stimulate the adoption of the eco-innovation strategy.
However, most of the studies conducted on the developed countries and their valuable outcomes cannot be grossed up to developing economies. Therefore, providing relevant evidence regarding the driving force of eco-innovation in a developing country as Tunisia has been one of the current paper’s main objectives. Tunisia is characterized by a low rate of economic growth (Elbargathi & Al-Assaf, 2019).
Tunisia’s choice as a case of study is inspired by the social and political revolution of the Arab Spring itself. Since 2011, Tunisia has searched to implement better environ- mental management and eco-innovation strategy to mini- mize environmental harm (Trape,2017). Previous studies emphasized that Tunisia is one of the few nations in the developing world that has implemented a proactive policy to advance energy efficiency and renewable energy (Saidi
& Fnaiech, 2014). Other studies highlighted the positive influence of eco-management on innovation in the Tunisian context (Hamdoun et al., 2018). However, Tunisia’s eco- nomic policy is not environmentally sustainable and does not integrate environmental factors (ITES, 2017). Conse- quently, profound studies must be carried out to understand better the influence of driving forces of eco-innovation adoption in protecting the environment and achieving economic performance and sustainable development goals.
Furthermore, even though the agri-food sector is char- acterized, more than any other sector, by a substantial dependence on natural resources (Nazzaro et al.,2020) and by a substantial amount of pollution emission, such as carbon emission (Balsalobre-Lorente et al., 2019), few investigations have been conducted on small- and medium- sized agricultural holdings and agri-food enterprises, which are typically depicted as low-tech (Triguero et al., 2018).
The current study’s main contribution is to fill these gaps mentioned above in the literature. Thus, our research identifies the influential drivers that impact adopting the eco-innovation strategy to achieve the enterprise’s sus- tainable business growth in Tunisia, a developing country.
This study is the first research conducted in Tunisia.
In the current paper, we purpose, therefore, to expand our understanding of eco-innovation strategy and to develop a new framework, which involves a particular combination of several external drivers (regulatory push (RP), competitive pressures (COP), and customer pressures (CP)) and internal drivers (technological competence (TC) and efficiency (EF)). We also aim to identify the motives or drivers that encourage enterprises to adopt an eco-innova- tion strategy (EIS) and explore the mediating role of EIS between the relevant eco-innovation drivers (RP, COP, CP, TC, and EF) and the enterprise’ sustainable business growth (ESBG) in Tunisian agri-food sectors. The main questions discussed in the current study are as follows: Do RP, COP, CP, TC, and EF, operate as the drivers that spur the adoption of eco-innovation strategy? If yes, which driver is the most effective or influential for encouraging enterprises to adopt EIS? Can the eco-innovation strategy achieve ESBG? Can the EIS really mediate the relationship between those drivers and ESGB?
In answering these questions, we provide three principal contributions to the current literature in the field of eco-in- novation. First, we advance a new theoretical framework that involves a unique mix of the research field and extends our understanding of the causal relationship between environ- mental/stakeholders’ pressures and enterprises’ innovation.
Second, the results of this study reveal that the most com- pelling motives are demonstrated as a reaction of the enter- prises to adopt eco-innovation and instilling environmental behavior in agribusiness. Third, we support Porter’s Hypoth- esis and add to the body of knowledge on how eco-innovation strategy mediates the relationship between the relevant drivers/
pressures and enterprises’ sustainable business growth.
The remainder of this study is organized as follows:
Section two provides more details on the theoretical liter- ature of eco-innovation (internal and external drivers) and the research hypotheses. Section three explains how the data and variables are applied. Section four depicts the main statistical findings. Section five discusses the finding of the research. Finally, section six presents the conclusion and provides implications for practice and further research.
Theoretical Framework and Research Hypotheses
To maintain an economic and sustainable balance, enter- prises have to cope with ecological issues to evade envi- ronmental damage and promote a green reputation. These
environmental issues will be depending on the enterprise’s activity (external factors) and the strategy (internal fac- tors). Consequently, entrepreneurs or managers need to integrate environmental strategy in an enterprise’s external and internal decisions. In this regard, adopting an eco-in- novation strategy is the most appropriate way to exploit business opportunities and green market changes. There- fore, it is crucial to identify the significant internal and external drivers for the adoption of eco-innovation.
According to the previous research, we develop a con- ceptual framework to examine mechanisms that potentially explain the eco-innovation’s direct and indirect relation- ship (see Fig.1). In this section, we will define specific hypotheses about the relationships conceptualized in the following framework.
Eco-Innovation Strategy and External Drivers
The chronological analysis of Bossle et al. (2016) demonstrated that regulatory pressure is the main external driving force of eco-innovation adoption and is related to compliance with environmental regulations and laws (Bossle et al.,2016). Furthermore, to face fierce competi- tion in the dynamic market, enterprises have to raise their investment in eco-innovations to increase market differ- entiation, create greater competitiveness and reduce pro- duction costs. Therefore, competitive pressure is an important external factor that triggers an eco-innovation strategy. Additionally, enterprises have to eco-innovate to respond to consumers’ demands and preferences. Con- sumers now prefer enterprises that offer eco-innovation products and services. In this regard, the inculcation of environmental ideas into innovation strategies is empha- sized in adopting the EIS. Consequently, consumers have an important influence on integrating environmental strat- egy and adopting eco-innovation strategy (Keshminder &
del Rı´o, 2019). Therefore, consumers’ pressure is a rele- vant external driving factor that stimulates eco-innovation adoption. Overall, we assumed to investigate regulatory pressure, competitive pressure, and consumer pressure as external factors (Li & Ye,2011).
Regulatory Pressure
Regulatory pressures (RP) are related to external factors and significantly influence eco-innovation (Bitencourt et al., 2020). Many researchers demonstrated that EIS might push entrepreneurs to meet the requirements of protecting the environment and avert complaints imposed by RP (Chen, 2008; Zhu et al., 2008; Chang, 2011). The famous ‘Porter Hypothesis’ assumed that environmental regulations have led enterprises to innovate while
addressing environmental issues without restraining enter- prises’ performance (Porter & Van der Linde,1995).
Several researchers suggested that strict environmental regulation boosts eco-innovation while offsetting the cost of complying (Porter, 1991; Bergquist & So¨derholm, 2011). Strict regulation can also contribute to creating a leading market in eco-innovation (Hojnik & Ruzzier, 2016). Moreover, many studies demonstrated that flexible regulations boost enterprises’ innovation (Jaffe & Palmer, 1997; Yuan & Zhang, 2020). In this regard, if this regu- lation is successfully applied, it may provide financial return via a different path such as decreasing production cost (water, energy, and raw material) and creating a cor- porate brand image by integrating environmental values that attract new customers.
Furthermore, some authors have confirmed that future regulations’ expectation leads to the prevention of adverse environmental problems (Christmann,2000) and promot- ing eco-innovation (Horbach et al., 2012). Besides, the expectation of regulation is the typical innovative behavior of entrepreneurs. Therefore, entrepreneurs adopt eco-in- novation to adhere to and comply with current regulations and anticipate future regulations. It means not only able to avoid the additional cost (higher taxes) and punishment (Triguero et al., 2013) but also to beneficiate from the achievement of competitive advantage (Doran & Ryan, 2016). In this case, the best environmental performance could help the entrepreneurs to anticipate future regulations and match environmental values and economic opportuni- ties to implement more eco-innovation practices. Thus, RP is a significant driving factor for adopting EIS to attain a win–win condition for the enterprise and society. There- fore, the following hypothesis is posited.
H1aRP has a significant positive impact on the adoption of EIS.
Competitive Pressure
Competitive pressure (COP) that generates enhanced pro- duct quality and environmentally sustainable performance has an increasingly vital role in the rising demand for eco- innovation practices (Cai & Li, 2018). Therefore, enter- prises have to develop their businesses with a good repu- tation among their stakeholders for improved quality and green competitiveness (Chen, 2008). More obviously, when enterprises face more ferocious market competition, they are more likely to be ‘greener’ than their competitors by establishing new environmental management strategies or developing new green products. This will also lead to a competitive advantage for enterprises by creating better conditions to respond to the challenges in the market (Lin et al., 2014). Additionally, EIS can generate a differentia- tion strategy based on improving the niche green market (Rabada´n et al., 2019). Thus, environmental products are acquiring greater prominence worldwide. At the same time, entrepreneurs look for profitable ways to adopt eco-inno- vation to increase market share, establish a green image, and achieve competitive advantage and sustainable per- formance. Therefore, external COP qualified as an essential driver for eco-innovation to spur enterprises’ market position (Bansal & Roth, 2000; Hojnik et al., 2018).
Therefore, the following hypothesis is posited:
H1b COP has a significant positive impact on the adoption of EIS.
H1a H1b
H1c
H3 H2a
H2b
External drivers
Internal drivers
Eco-innovation strategy (EIS)
Enterprise’s sustainable business growth (ESBG) Efficiency (EF)
Technological competence (TC) Consumer pressure (CP) Competitive pressure (CoP)
Regulatory pressure (RP)
Fig. 1 Conceptual framework for the effect of eco-innovation drivers on sustainable business growth
Consumer Pressure
Customer pressure (CP) is qualified as normative pressure (Cai & Li,2018). Satisfying green customers’ demand is a vital stimulus for entrepreneurs to adopt EIS (Horbach, 2008; Wagner, 2008). Triguero et al. (2018) demonstrated that CP promotes eco-innovation adoption while focusing more on food safety requirements (Triguero et al.,2018).
Recently, CP is crucial to building a dynamic business environment (Chiou et al., 2011). Therefore, customer demands and preferences influence entrepreneurs’ deci- sions to engage with eco-innovations (Kesidou & Demirel, 2012; Rabada´n et al., 2019). Besides, consumers are increasingly aware of how their purchasing choices affect the environment; for that reason, they put more pressure on enterprises to make them work harder to mitigate their operations’ adverse effects (Kemp & Foxon, 2007).
Therefore, customer pressure can provide a significant number of sustainable and business opportunities for enterprises to decrease the adverse impact of technology and production processes on the environment and push them to compete and reward efficiency, innovation, and quality. Thereby, the following hypothesis is posited:
H1cCP has a significant positive impact on the adoption of EIS.
Technological Competence
In this section, we referred to the work of Mazzanti and Zoboli (2006). They supposed that technological compe- tence, including relationship/networking and research and development (R&D), is a significant driver of eco-inno- vation (Mazzanti & Zoboli,2006). Several research pieces demonstrated that network and cooperation with universi- ties and research institutes are crucial to driving all eco- innovation types (Carrillo-Hermosilla et al.,2010). Enter- prises must learn to produce much more cleanly and use resources more responsibly—in a way that does not worsen our climate or the quality of our air. Previous studies point out that the entrepreneur who collaborates with different stakeholders (customers, suppliers, universities) is more active and motivated to apply all eco-innovation types (Triguero et al.,2013; Kobarg et al., 2020). Additionally, networks provide various opportunities to learn about the adoption of EIS (Taddeo, 2016). Therefore, relationships and networking are qualified as driving factors for EIS (Green et al.,1994).
Furthermore, Horbach (2008) argued that internal R&D and improvements in enterprises’ innovation capability are significant eco-innovation drivers (Horbach, 2008). The R&D’s primary role in the sustainable environmental context is to generate new or improve products and pro- cesses by mitigating the production impact on the
environment, offering cleaner production and consumption (Demirel & Kesidou,2011), and facilitating technological adaptations to develop clean technologies (Carrillo-Her- mosilla et al., 2009; Bitencourt et al., 2020). Environ- mental R&D includes enterprises’ technological capabilities, enhances the absorptive capacity of the enterprise in environmental matters (Cohen and Levinthal 1990), and covers both product and process innovation.
Therefore, R&D has a positive impact on EIS (van Kemenade, 2015). Thereby, the following hypothesis is posited:
H2aTC has a significant positive impact on the adoption of EIS.
Efficiency
Cost savings were designed as the principal motivation for adopting EIS (Horbach et al., 2012) and an efficient organizational capability (Weng & Lin,2011). Cost saving focuses on the most efficient processes and production methods, which, in return, motivate and encourage enter- prises to invest and adopt EIS (Hitchens et al., 2003).
However, many enterprises revealed that environmental protection activities appear to be a more costly investment for them. Palmer et al. (1995) argue that cost savings resulting from eco-innovations are lower than the envi- ronmental compliance costs (Palmer et al., 1995). There- fore, they presume that cost savings are unlikely to provide sufficient incentives to stimulate environmental innova- tions. In contrast, Porter and Van der Linde (1995b) pre- sumed that environmental regulations push enterprises to eco-innovate.
All in all, the possible increase in costs would be offset by cost saving through the more efficient and rational use of resources (Porter & Van der Linde, 1995). That can similarly lead to improve competitive advantage and an enterprise’s performance. Moreover, several authors reported that eco-innovation raises the enterprises’ oppor- tunity for cost saving in several ways, such as improving the enterprises’ production effectiveness and decreasing their consumption of raw materials and energy (Cleff &
Rennings, 1999; Rabada´n et al., 2019).
Furthermore, EMS generates significant efficient orga- nizational capabilities and practices in environmental pro- tection, such as green product design, recycling, source reduction, and pollution prevention. It helps the enterprises boost operating efficiency to improve environmental quality (Cai & Li,2018). EMS’s principal aim is to ensure continuous improvement in integrating environmental performance while complying with the existing govern- ment regulations to decrease hazardous emissions and waste (Kolln & Prakash,2002). EMS is considered a nor- mative pressure that affects environmental and
organizational capabilities by boosting environmental awareness and organizational learning (Melnyk et al., 2003). EMS can be crucial to surmount incomplete infor- mation within an enterprise, particularly regarding the new access to cost-saving cleaner technologies (Horbach et al.
2012). EMS is also used as a tool to determine the dearth of information when firms have difficulties in trying to iden- tify the potential cost savings for EIS, such as for energy or resource savings (Fernando & Xin, 2015). Studies demonstrate that EMS certification is an efficient drive for adopting EIS (Bitencourt et al.,2020; Rehfeld et al.,2007).
Therefore, the following hypothesis is posited:
H2bEF has a significant positive impact on the adoption of EIS.
Eco-Innovation and Enterprise’ Sustainable Business Growth
The environmental strategy has been the object of diver- gence. This strategy contradicts the targets of competi- tiveness, profitability, and growth of the enterprises (Porter
& Van der Linde,1995). On the other hand, enterprises can minimize their production impact on the environment and mitigate the risk of global warming while raising new business and sustainable opportunities and further attaining enhanced financial, economic, and environmental perfor- mance (Guenther & Hoppe,2014) and achieving sustain- able business growth (Ben Amara and Chen 2020). Eco- innovation is qualified as a win–win strategy in a strategic, systemic, and conscious way (Cainelli et al.,2012). In this regard, EIS provides significant benefits to enterprises that boost competitiveness (Musaad et al., 2020) and sustain- able business performance and growth (Zhu et al.,2012). In Chinese enterprises, Zhang et al. (2019) reported that eco- innovation has a positive relationship between eco-inno- vation and enterprise’ performance, as assessed by growth sales and net profitability (Zhang et al.,2019).
The thinking behind the suggested relationship between EIS and ESBG is based on several factors. First, Eco-in- novation is designed as a new or improved product, pro- cess, service, or business strategy that mitigates pollution, waste, and environmental damage (Arundel & Kemp, 2009). Thus, an EIS promotes the efficient use of resources and the elimination of pollution. Porter and van der Linde (1995a) argue that an enterprise’s process innovation, product innovation, and resource productivity are primor- dial to achieve a competitive advantage overcomes their competitors by offering differentiated products and lower production costs. Besides, EIS can generate a differentia- tion strategy based on improving the green product for a niche market (Rabada´n et al.,2019). Second, EIS assists
entrepreneurs in meeting the requirements of protecting the environment and averting complaints (compliance cost) imposed by regulatory pressure (Chen, 2008; Zhu et al., 2008; Chang, 2011). Third, EIS may help enterprises explore new ways of transforming waste into saleable products that generate more revenues and extra profitability (Porter & Van der Linde, 1995). Fourth, EIS helps enter- prises perfect their environmental strategy and practices regarding their green reputation compared to competitors.
In this respect, several researchers brought to light that EIS may raise sales, attract a new market, promote competitive advantage, create product differentiation, and accelerate business growth at a later date (Gunarathne,2019; Rabada´n et al., 2019). Thus, entrepreneurs are more motivated to adopt an EIS because such action makes a substantial contribution to their ESBG. Therefore, the following hypothesis is posited:
H4EIS has a significant positive impact on ESBG.
The Mediating Role of Eco-Innovation Strategy
Referring to the famous Porter Hypothesis, RP, which drives EIS, can compensate for the costs of complying with environmental regulation. EIS exerts a positive impact on enterprises’ business performance (which is also the case of ESBG). More notably, the Porter Hypothesis proposes that this driving force affects enterprise business performance indirectly by encouraging enterprises to adopt an EIS. This indirect effect may be translated into a mediating effect.
Therefore, we propose that the Porter Hypothesis can be examined as a mediation hypothesis where the EIS turns into the mediator (Eiadat et al., 2008). According to the hypotheses mentioned above, each proposed pressure or driving force has a direct and specific effect on EIS. In turn, EIS mediates these relevant driving forces on ESBG. A primary objective of the current study is to investigate whether or not an EIS accomplishes the role of mediator.
That is, whether RP, COP, CP, TC, and EF drive the adoption of an EIS, and this EIS generates a significant positive impact on ESBG.
We believe that enterprises that are subject to RP are highly aware of the need to comply with environmental regulations to face fierce competition and increased demand for green operations. They are conscious of the importance of improving TC and implementing the most efficient operations. These enterprises are quite eager to adopt an EIC because grasping this strategy will greatly assist their business growth. Thereby, we believed that an EIS mediates the relationship between driving factors of eco-innovation and ESBG.
Therefore, the following hypotheses are posited:
H4 EIS mediates the relationship between driving fac- tors of eco-innovation and ESBG.
H4a EIS mediates the relationship between RP and ESBG.
H4b EIS mediates the relationship between COP and ESBG.
H4c EIS mediates the relationship between CP and ESBG.
H4d EIS mediates the relationship between TC and ESBG.
H4e EIS mediates the relationship between EF and ESBG.
Eco-Innovation Strategy and Internal Drivers
Internal factors are essential for enterprises that seek to adopt an eco-innovation strategy (Sumrin et al.,2021). In this regard, enterprises have to invest and allocate their resources efficiently in promoting internal factors to inte- grate more value for the enterprise (Chen et al., 2012).
These factors are important to adopt eco-innovation. The RBV argued that an enterprise’s resources must be rare, inimitable, valuable, and non-substitutable to maintain a competitive advantage (Green et al.,1994). These resour- ces, such as technological competence (Sarkis et al.,2010), are significant determinants of EIS adoption. Technological competence fulfills an essential role in creating innovation (Baumol, 2002). These competences involve knowledge, experiences, and technologies dedicated to developing environmentally friendly products and processes. There- fore, an enterprise with great technological competence can take full advantage of knowledge to strengthen its net- working and relationship, acquire more expertise from other organizations, and promote its research and devel- opment to advance its eco-innovation ability and attract long-term sustainable business growth.
Additionally, Arnold and Hockerts (2011) investigated internal enterprise factors that trigger and promote the adoption of the EIS. Internal factors motivate enterprises to estimate better risks, costs, and profits intricate in adopting eco-innovations (Arnold & Hockerts, 2011). This means that enterprises seek to improve the effectiveness while mitigating environmental damage (Tseng et al., 2013).
Consequently, enterprises must develop cost savings (Horbach et al., 2012) and Environmental Management systems (Demirel & Kesidou, 2011) because they are considered the most relevant efficient organizational capabilities that trigger eco-innovation adoption.
To sum up, it is assumed that internal factors are asso- ciated with technological competence, involving relation- ship/networking and research and development (R&D) (Mazzanti & Zoboli,2006); and efficiency, including cost saving and SEM (certifications) (Green et al.,1994).
Methodology Research Approach
The present paper proposes to apply a quantitative methodology to handle the research objectives and test the hypotheses mentioned above. We note that our sample is taken from the list of enterprises that are members of Technical Center of Organic Agriculture (TCOA), Agency responsible for the Promotion of Agricultural Investment (APAI), Technical Center for Agri-Food (TCAF), and Europages (https://www.europages.fr).
The sample includes farmers, food enterprises; drink enterprises; and food transformation and services (bread and pastries, meat and fresh poultry, feed for animals, edible oils and fats, dairy products, dietary and organic foods, fish and fruit preserves, cookies, dry cakes, cookies, and chocolate) in Tunisia. This sample involves 80.3%
micro, 14.7% small, and 5% medium enterprises.
Our population totaled 7,400 organic agricultural and agri-food enterprises (Chebbi et al., 2019). We designated an email with a link to the web survey as an instrument for distributing the questionnaire. A cover letter has been attached to the survey to describe the aim of this study and express our great estimation for participating and guaran- teeing the confidentiality of shared information. The survey was conducted in 2018.
In the current study, we chose the Cochran sampling technique. This instrument makes sampling more effective (Cochran,1977). It contributes to select the sample at the lowest possible cost, as data are obtained from only a small fraction of the total population; thus, the costs are the smallest. The data can be collected more rapidly with a sample than with the whole population. Thus, according to this sampling technique, our sample size is 365. However, we limited the survey to 306 participants.
Measurement Development
This study adopts 28 items, which consist of ESBG, EIS, and driving factors of eco-innovation.
Five items were used to measure ESBG; new scales were developed and adapted from the previous studies (Delmar et al., 2003; Love & Roper, 2015), while eight- items were selected to measure EIS and were adapted from the work of Eiadat et al. (2018). Besides, we measured the driving factors of eco-innovation with 15 items and involved:
• External factors include three constructs: CP (two items), COP (five items), and RP (3 items). We have developed new scales from the research of Li (2014) to
compute CP and COP. While we have measured RP, referring to the research of Horbach (2008).
• External factors include two variables: EF (two items) and TC (three items). We have developed new scales from the research of Bossle et al., (2016) and Cai &
Li, (2018) to evaluate these relevant factors.
A 6-point Likert scale was used to measure the items.
The variables were calculated from ‘strongly disagree’ (1) to ‘strongly agree’ (6) (Ben Amara2019).
Data Analysis
This paper tested the appropriateness of the structural equation model (SEM) and analyzed the hypothesized relationships. IBM SPSS statistics software (version 25) and IBM SPSS AMOS (version 23) were used to analyze the data gathered from the questionnaire survey. The software also helped in proving the research hypotheses.
We harnessed the two-step method recommended by Anderson and Gerbing. We analyzed the measurement and structural equation models in distinct steps (Anderson &
Gerbing,1988). First, we used exploratory factor analysis and confirmatory factors analysis and tested the hypothe- sized factor structure’s suitability for variables. Harman’s one-factor test was applied on all items to investigate the impendence of common method bias. Seven distinct factors were extracted from Harman’s one-factor test, with eigenvalues greater than 1.0 (Podsakoff et al.,2003). The un-rotated principal components factor analysis outcomes show that the first factor only accounted for 49.38% of the variance, and no particular factor described the majority of the variance (Podsakoff et al., 2003). Second, we used SEM as the most suitable statistical technique to simulate the multiple liked independent (external and internal driving factors) and dependent relationships (EIS and ESBG) and took into consideration measurement error (estimates) in the assessment process (Hair et al.,1998).
SEM is an extremely flexible technique since it deals with correlation, factor analysis, single simple or multiple linear regression (the case of our study), and regression equations (Eid,2000). SEM provides numerous opportu- nities to improve analyses involving useful and useless matters. Nevertheless, this method is often interpreted as complex and hard to grasp. Additionally, SEM examines and evaluates latent variables and their relationships, pro- viding the possibility to investigate the dependencies of the relevant constructs of our research deprived of measure- ment errors. But a similar impact effect may be achieved with investigating and analyzing solely observed con- structs. Its principal characteristic is to make a comparison between the assumed models and empirical data. This comparison of the model to fit data is, namely ‘fit-
statistics.’ If the fit is tolerable, the proposed relationships between latent and observed variables (the first step is the measurement models and fits) and the supposed depen- dencies between the different latent variables (the second step is the structural model estimation) are supported by the data (Nachtigall et al., 2003). Therefore, the proposed model or hypothesis is accepted. SEM software such as AMOS SPSS using the Maximum Likelihood Method (ML) to assess parameters and evaluate model fit. How- ever, ML requires multivariate normally distributed con- tinuous variables. Furthermore, to harness SEM, a suitable sample size is needed, and the data often must satisfy distributional assumptions. More precisely, Kline (2015) argued the minimum sample size must be 200 observations (Kline, 2015). Therefore, this condition is satisfied because the sample size of our research is 306 participants. Overall, the disadvantages and difficulties of SEM are also involved in the danger of creating models post hoc, ignoring substantive contextual, and high data requirements (Nachtigall et al., 2003).
Furthermore, other techniques could be used in this study: PLS-PM (SmartPLS3) and multiple regression analysis. Still, SEM is the best choice for this methodology because, on the one hand, for testing the measurement model, CFA using SEM or CB-SEM (AMOS) is the best approach. The PLS-PM approach (SmartPLS3) requires small sample sizes (Chin & Newsted, 1999), while SEM needs a bigger sample size. In addition, PLS is suitable for prediction-based research, while SEM is more appropriate for model fit. In other words, PLS-PM is preferred when the researcher tends to develop a new theory (exploratory research). In contrast, SEM or CB-SEM is preferred when the researcher wants to examine a theory (confirmatory research), which is the case of the present study.
On the other hand, for testing the mediation model, multiple regression analysis is the most generally used technique. However, SEM provides less inconsistent results than multiple regression analysis in terms of detecting mediating effects. The multiple regression anal- ysis chosen for the theoretical model had only one mediator and is created typically with observed variables. While SEM was more effective in assessing the effects of mul- tiple mediators presented in the investigative model (Li, 2011), which is the case of the present study. Therefore, SEM is the best choice for this methodology.
Results
Measurement Model Results
Under the development of the measurement scale, the psychometric properties (reliability, internal consistency,
convergent validity, and discriminant validity) were evaluated.
The loading factors of the included items are extended from 0.763 to 0.896. Barclay et al. (1995) argued that better
loadings’ values vary from 0.5 to 0.6, which might be considered acceptable (Barclay et al., 1995). In compar- ison, Hair et al. (2016) suggested that it is preferable to drop all loading factors lower than 0.7 (Hair Jr et al.,2016).
Table 1 Measurement items, factor loadings
Constructs Items Loading AVE CR Cronbach’s
alpha Enterprise’ sustainable
business growth (ESBG)
SG (Sale growth): The enterprise’s sale develops; 0.793 0.676 0.893 0.817 MS1 (Market share): The market value of your firm increases; 0.816
MS2: Our sales developed compared to our competitors (not included); – MS3: The number of potential customers has increased; 0.838
GRIN: Return on green investment; 0.842
Eco-innovation (EIS) EI1: Containment of waste (not included); – 0.663 0.921 0.951
EI2: Invest in environmental science and technology (not included); – EI3: Environmental Certifications are very important to adopt ecological
strategies;
0.759 EI4: Adopt changes toward pollution prevention; 0.875 EI5: Use the raw materials that produce the least quantities of pollution; 0.838 EI6: Is a response to economic pressure (market); 0.820 EI7: Is a response to non-economic pressure (environmental protection,
ethical value);
0.819
EI8: Improves consumer satisfaction 0.771
Regulatory pressure (RP) R. FLEX (Flexible legislation and policy): Flexible legislation and policies improve the development of eco-innovation;
0.857 0.730 0.890 0.925 R. EXIS (Existence of environmental regulation): Environmental regulation
support eco-innovation in order to promote environmental protection and enhance ethical behavior;
0.896
R.EXP (Expected law and regulation): Predict law and regulations in order to conform with our future strategies;
0.808 Competitive pressure
(CoP)
C.MSC1: Look for the environmental label in order to open a new market (not included);
– 0.712 0.908 0.941 C.MSC2: Environmentally friendly characteristic products or services have a
comparative advantage;
0.814 C.MSC3: Eco-innovation creates differentiation and makes a real change; 0.830 C.MSC4: Struggle to keep old customers and get new ones; 0.875 C.MSC5: Incorporate environmental and ethical messages in marketing and
packaging;
0.856 Consumer pressure
(CP)
CP1: Develop web pages and enterprise magazines to enhance customer awareness toward environmental and ethical behavior;
0.891 0.783 0.878 0.885 CP2: Customer comportment and preference is significant pressure on the
market;
0.879 Technological
competence (TC)
RD1 (Research and development activities): I am always seeking new technology to allocate adequate resources;
0.852 0.617 0.859 0.803 RD2: I am always seeking to invest in a new idea for a more environmentally
friendly context;
0.841 RN (Relationships and networking): I am trying to maintain relationships and
networking with customers and suppliers to develop eco-innovative ideas;
0.763 Efficiency (EF) EMS (Environmental management system): Eco-innovation enhances the
performance of environmental management in order to satisfy the environmental protection requirement
0.905 0.808 0.893 0.893
CS (Cost saving) Eco-innovation strategies make me able to recognize the potential of cost reduction (material or energy saving);
0.893
Given the debate above, and since several scales in this research are freshly created, we dropped all items with loadings lower than 0.7. Therefore, Table 1 shows that MS2, C.MSC1, EI1, and EI2 were excluded to improve the reliability of our model.
The internal consistency is measured by calculating the composite reliability (CR) and Cronbach’s alpha. In exploratory research, the values of CR between 0.6 and 0.7 may be deemed acceptable (Hair et al.,2013). The CR of each construct extended from 0.859 to 0.921. Similarly, Cronbach’s Alpha of each construct ranged from 0.803 to 0.951, which is more significant than the recommended value of 0.7 (Hair Jr et al., 2010). Therefore, our model reveals that all included items have strong internal con- sistency (see Table1).
The values of the average variance extracted (AVE) extended from 0.671 to 0.808, which exceeded the rec- ommended value of 0.50 (Wong, 2016) (see Table 1).
Thus, our model indicates high convergent validity.
All of the correlation coefficients are not equal to one and lower than the AVE value’s square root for each construct (Fornell & Larcker,1981). Discriminant validity is supported (Table2).
The global fit indices of our model show a good model fit (v2= 631.658, df = 218; v2 /df = 2.89; CFI = 0.94;
TLI = 0.93; RMSEA = 0.078).
Structural Model Estimation
The second step in the assessment was run to test the structural model to prove the mediating role of EIS. Model 1, our guideline model, represents a full mediating model.
We specified paths from RP, COP, CP, TC, and EF to EIS and from EIS to ESBG. The guideline model had a good fit (v2= 644.293, df = 222; v2/df = 2.9; CFI = 0.94; TLI = 0.93; RMSEA = 0.079). Thus, Model 1 was accepted.
Model 2 was similar to the guideline model, except for inserting a direct path from RP to ESBG. Model 3 was
similar to Model 1, except for the inclusion of two direct paths from RP and COP to ESBG. In Model 4, we added three direct paths from RP, COP, and CP to ESBG. In Model 5, we inserted four direct paths from RP, COP, CP, and TC to ESBG. In Model 6, we inserted five direct paths from RP, COP, CP, TC, and EF to ESBG; the guideline model is consequently nested within Models 2, 3, 4, 5, and 6.
As indicated in Table3, the path coefficient from RP to EIS is 0.439 (t = 11.561, p = 0.000) reported a significant positive relationship between the two variables. Therefore, H1a was approved. The path coefficient from COP to EIS is 0.354 (t = 10.202, p = 0.000) reported a significant positive relationship between the two variables. Therefore, H1b was accepted. Most importantly, the standardized coefficients of both drivers RP and COP are quite high, demonstrating the most important impact in stimulating EIS adoption.
The path coefficient from CP to EIS is 0.073 (t = 2.892, p = 0.004) reported a significant positive relationship between the two constructs. Therefore, H1c was supported.
The path coefficient from TC to EIS is 0.164 (t = 4.150, p = 0.000), revealed a significant positive relationship between the two constructs. Thus, H2a was accepted. EF’s path coefficient to EIS is 0.051 (t = 2.753, p = 0.006) indicated a significant positive relationship between the two variables. Thus, H2b was supported. The path coeffi- cient from EIS to ESBG is 0.435 (t = 5.982, p = 0.000), reported a significant positive relationship between the two constructs. Hence, H3 was accepted. Therefore, these results extend our understanding of innovation and man- agement literature by highlighting that these internal and external driving forces play a primordial role in spurring EIS adoption.
The differences between Chi-squared values (Dv2)were not significant for Model 1 in comparison with Models 2, 3, 4, 5, or 6 (Table4). Therefore, the statistical results showed that our partial mediation model with all direct and indirect
Table 2 Correlation between constructs and the square root of the AVE
Research construct ESBG EIS RP COP CP TC EF
ESBG 0.822
EIS 0.113** 0.814
RP 0.332** 0.629** 0.854
COP 0.304** 0.682** 0.679** 0.834
CP 0.210** 0.572** 0.457** 0.494** 0.884
TC 0.492** 0.563** 0.458** 0.436** 0.283** 0.819
EF 0.283** 0.557** 0.445** 0.449** 0.443** 0.403** 0.898
Diagonal is the square root of the AVE
**Correlation is significant at the 0.01 level (2-tailed)
paths fits our data better than the full mediation model (guideline model). We assumed that EIS mediates the relationship between driving factors (RP, COP, CP, TC, and EF) and ESBG. Thus, we supported H4a, H4b, H4c, H4d, and H4e. Therefore, EIS is an important mediator between external/internal drivers and ESBG;
Discussions
Based on the literature, we used SEM to test the suggested hypothesized relationships between several drivers/ pres- sures, EIS, and ESBG and explore the mediating role of EIS. The insights and findings referring to the direct and indirect relationship that drive EIS are as follows:
This research demonstrates a significant positive impact of RP on EIS, offering empirical support to the research of Bitencourt et al. (2020). They highlighted that RP signifi- cantly influences EIS; this is particularly important for eco- innovation investment (Bitencourt et al.,2020). This result supports the Porter Hypothesis that assumed RP triggers EIS’s adoption while improving the efficiency of produc- tion processes and offsetting the innovation and compli- ance cost. Besides, the PR construct’s standardized coefficient is quite high, spotlighting its relevance for EIS’s adoption. Therefore, being in line with current literature (Bossle et al., 2016), we concluded that RP is the most significant and frequent trigger driver. In other words, the
enterprise’s motivation to develop EIS remains directed toward assuring compliance with standards, more than achieving the environmentally sustainable target. This finding highlighted the high need for sustainable education for managers, customers, and stakeholders to realize the efficiencies and opportunities intrinsic to EIS adoption.
Furthermore, the empirical finding demonstrates that COP has a positive significative impact on EIS, supporting the findings of Li (2014). We also concluded that COP is the second most influential driver of EIS. Recently, enter- prises seek new ways to offer green products and services, create and incorporate a green image, and differentiate themselves from their competitor through EIS to create a competitive advantage. Thus, EIS is viewed as a means for coping with fierce competition (Chassagnon & Haned, 2015). Therefore, we conclude that competitors’ pressure and ferocious market competition are a substantial driver of EIS. COP pushes enterprises toward adopting EIS to gain market share by efficiently using energy and resources and, in turn, improving competitiveness and sustaining the green image.
Our empirical outcomes also reported that CP applied a significant positive impact on EIS, consistent with other studies (Kesidou & Demirel, 2012). Today’s Tunisian green consumer awareness is more powerful than before, and their willingness to pay a high price to get green products and services. On the other hand, consumers who consider the enterprise’s environmental impact closely are Table 3 Model parameters and t-values
Relationship Estimate t-values p-value Hypothesis Results
RP?EIS 0.439** 11.561 0.000 H1a Supported
COP?EIS 0.354** 10.202 0.000 H1b Supported
CP?EIS 0.073** 2.892 0.004 H1c Supported
TC?EIS 0.164** 4.150 0.000 H2a Supported
EF?EIS 0.051** 2.753 0.006 H2b Supported
EIS?ESBG 0.435** 5.982 0.000 H3 Supported
**Correlation is significant at the 0.01 level (2-tailed)
Table 4 Result of structural model comparisons
Model v2 df Dv2 RMSEA TLI CFI
Model 1 634.457 222 – 0.079 0.93 0.94
Model 2 632.618 221 1.839 0.078 0.93 0.94
Model 3 631.045 220 2.412 0.078 0.93 0.94
Model 4 631.708 219 2.749 0.078 0.93 0.94
Model 5 631.668 218 2.769 0.078 0.93 0.94
Model 6 631.658 218 2.799 0.078 0.93 0.94
‘
more environmentally conscious regarding their purchasing decisions. This implies that consumers demand more green products and want to be better informed of those products’
nature and quality, which further considerably influence the enterprise’s decision to adopt EIS. Additionally, we con- clude also that the quest for sustainability is reflected in customers’ behaviors, which consecrate more in-depth attention to the environmental considerations of enter- prises’ production processes.
Moreover, we concluded that TC had a significant positive impact on EIS and was the third most important driver for adopting EIS based on Tunisian enterprises’
sample, consistent with the previous research (Cai & Li, 2018). Encouraging investment in R&D is essential to spur TC to find a valuable solution to use resources more responsibly and provide cleaner products and services.
Building up cooperation and interrelationship between partners, suppliers, distributors, customers, and research institutions is substantial to improve TC to raise the opportunities to adopt EIS (Bossle et al.,2016).
The finding also reveals that EF exerts a significant positive effect on EIS, consistent with the previous research (Tseng et al., 2013). Enterprises need to adapt their selves to environmental changes and operating effi- ciently to maintain sustainable development. Cost saving focuses on the most efficient processes and production methods, which, in return, motivate and encourage enter- prises to invest and adopt EIS (Hitchens et al.,2003). EMS improves the operational efficiency to surmount incom- plete information within an enterprise, particularly regarding the potential cost savings for EIS, such as for energy or resource savings (Fernando & Wah,2017). EMS as well performs coordinated tasks to increase compliance and reduce waste and may adopt EIS. Thus, we can support that efficiency is a business strategy that can help the enterprise to addressee both environmental issues and spur competencies to access new markets and provide the means to increase productivity and profitability.
Further on, our result reports that it is worthwhile to adopt EIS. We found that EIS had a positive significative impact on ESBG. This finding is consistent with the research of Porter and van der Linde (1995a). In this respect, enterprises identified the potential of EIS and learned how to size the business (Ben Amara et al.,2019) and sustainable opportunities (Ben Amara et al., 2020).
Therefore, our results revealed that enterprises that adopt EIS could reap several benefits from doing so, such as a differentiation strategy based on improving the green pro- duct for a niche market (Rabada´n et al.,2019), a benefit in competitive advantage and savor enhanced profitability and increased growth at a later date (Gunarathne,2019). Hence, based on this study, we can conclude that EIS does pay off and benefits the ESBG.
Finally, we found that EIS mediates the relationship between all driving forces (RP, COP, TC, CP, and EF) and ESBG. Our result is supported by the suggestion of Eiadat et al. (2008). They proposed that the Porter Hypothesis is considered a mediation hypothesis where the EIS is the mediator variable (Eiadat et al., 2008). This finding is an important implication of the current study. Enterprises need, furthermore, to focus their strategy, operation, and activities on EIS rather than worrying about the achieved degrees of environmental performance to result in enhanced ESBG at a future date.
Conclusions
According to the previous literature, we developed and tested the suggested hypothesized relationships between several drivers/ pressures (RP, COP, TC, CP, and EF), EIS, and ESBG using a new dataset of 306 Tunisian enterprises in the agri-food sector. Moreover, our examination depicts interesting new insights. First, RP, COP, TC, CP, and EF play an important role as the determinant drivers that trigger EIS; RP is the primary driver in adopting EIS, followed by COP, TC, CP, and EF. In this regard, the prior studies deeply heightened the relevance of RP to adopt EIS. Regulations are required to push enterprises to respect the ecological standards or compensate for the cost of innovation. Besides, today’s competition is guided by environmental requirements; enterprises seek sustainable and business opportunities to adopt EIS to differentiate themselves positively from competitors in the market and gain a competitive advantage.
Moreover, eco-innovation TC provides enterprises with a favorable environment that increases investment in R&D and provides effective guidance to maintain and build up a strong relationship and cooperation between the different stakeholders. Furthermore, the increasing pressure from green customers pushes enterprises to act proactively and adopt environmental sustainability strategies in the inno- vation process. Further, improving EF is needed by focusing on efficient environmental organization capabili- ties (cost saving, EMS). It is essential to promote cost saving and EMS (certification), such as ISO14001, to spur the adoption of EIS; for this reason, those drivers use efficient ways and production processes to reduce envi- ronmental impact. Second, our finding demonstrates the positive relationship between EIS and ESBG. This rela- tionship harnesses eco-innovation features to match envi- ronmental behavior with economic opportunities to achieve sustainable growth. Third, consistent with Porter’s Hypotheses, we identified empirical evidence of full mediation, which suggests that EIS fully mediates the effect of driving factors on ESBG. In line with earlier
publications and the findings of the current paper, we supported that ecological matter and eco-innovation prac- tices cannot be ignored in the enterprise’s business model to accelerate sustainable growth and achieve sustainable development objectives.
Limitations
Some limitations of the current research deserve consid- eration. First, the sample was taken only from Tunisia; the present study’s findings might not be used in other coun- tries. However, in the light of these outcomes, eco-inno- vation research on Tunisia can be an opportunity to compare the eco-innovation level of this nation and other developing countries with a similar cultural, economic, social, environmental aspects such as the Arab Maghreb nations (Algeria, Libya, Mauritania and, Morocco).
Therefore, further study is suggested to elaborate the pro- cess and techniques in assessing these developing coun- tries’ economic, social, and environmental values. We argue that the countries of Arab Maghreb can pursue a sustainable path. Still, we cannot guarantee that all these countries have the same influential eco-innovation drivers and respect for the environment as we do in Tunisia.
Second, this research is focused on a specific sector, ‘‘the agri-food sector,’’ acknowledged by a higher accent on dropping uncertainty and responding quickly to new mar- ket demands. Third, participants were apparently affected by emotions once the form was completed. Lastly, this research may be limited by the restricted selection of dri- vers of eco-innovation. Therefore, future research would focus on getting more drivers, detecting moderating effects, and investigating more explicit mediations. Addi- tionally, future research would be better to carry out tests on samples of enterprises from different countries and various sectors for a worldwide analysis and perform a comparison between samples. This would provide an extremely interesting study to extract a more accurate outcome.
Implications
The above-mentioned empirical findings provide several implications for policymakers and entrepreneurs.
First, policymakers should consider their important role in encouraging eco-innovation strategies to create more sustainable business opportunities. They have to be effi- cient in shaping the dynamics of eco-innovation by designing an adequate strategy integrating different policy instruments and tools such as exemptions, exclusively for enterprises that are adopting substantial eco-innovations operations and mitigating the production impact. Policy- makers have to implement specific types of public grants to
attract private investment in renewable energy and other clean technologies. These grants will offer benefits to the enterprise, the environment, society, and the economy.
Second, the finding of the current paper reveals that EIS is motivated by RP, COP, TC, CP, and EF, which can be designed to address sustainable development. A combina- tion of those drivers and pressure guided by market demand and the aim of sustainable development would both spur the capacity of enterprises to adopt EIS and accelerate sustainable growth. Third, entrepreneurs or managers have an important role in stimulating EIS adoption. They must invest in training and additional education associated with the environmental matter. It would create greater awareness about the need for and relevance of the adoption of eco-innovation. Besides, they have to foster knowledge transfer and create strong col- laboration and networking between stakeholders (govern- ment, suppliers, consumers, partners, universities, and research institutes) to enhance their capabilities to imple- ment or develop eco-innovation. Finally, policymakers and enterprises should include a greater emphasis on efficient operations and generate new effective organization capa- bilities to spur the adoption and the development of EIS instead of in the evolvement of the existing production systems.
Funding We received no specific funding for this work.
Declarations
Conflict of interest The authors declare that they have no conflict of interest.
References
Anderson, J. C., & Gerbing, D. W. (1988). ’Structural equation modeling in practice: A review and recommended two-step approach.Psychological Bulletin, 103, 411.
Arnold, M. G., & Hockerts, K. (2011). The greening dutchman:
Philips’ process of green flagging to drive sustainable innova- tions.Business Strategy and the Environment, 20, 394–407.
Arundel, A., and Kemp, R., (2009) ’Measuring eco-innovation. UNU- MERIT Working Papers ISSN 1871–9872.
Astuti, W. T., Sudiro, A., & Hadiwidjojo, D. (2019). ‘‘Is Product Innovation always Beneficial for Small and Medium Enter- prises?’’ In1st Aceh Global Conference (AGC 2018). Atlantis Press, 292, 687–694.
Balsalobre-Lorente, D., Driha, O. M., Bekun, F. V., & Osundina, O.
A. (2019). Do agricultural activities induce carbon emissions?
The BRICS Experience.Environmental Science and Pollution Research,26, 25218–25234.
Bansal, P., & Roth, K. (2000). Why companies go green: A model of ecological responsiveness.Academy of Management Journal, 43, 717–736.
