Accepted Manuscript

Green Innovation Adoption in Automotive Supply Chain: The Malaysian case

Suhaiza Zailani, Kannan Govindan, Mohammad Iranmanesh, Mohd RizaimyShaharudin

PII: S0959-6526(15)00768-4

DOI: 10.1016/j.jclepro.2015.06.039

Reference: JCLP 5694

To appear in: Journal of Cleaner Production

Received Date: 5 July 2014

Revised Date: 27 April 2015

Accepted Date: 9 June 2015

Please cite this article as: Zailani S, Govindan K, Iranmanesh M, Shaharudin MR, Green InnovationAdoption in Automotive Supply Chain: The Malaysian case, Journal of Cleaner Production (2015), doi:10.1016/j.jclepro.2015.06.039.

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ACCEPTED MANUSCRIPTGreen Innovation Adoption in Automotive Supply Chain: The Malaysian Case

Suhaiza Zailani

Faculty Business and Accountancy

University Malaya

Kuala Lumpur, 50203,

Malaysia

shmz@um.edu.my

Kannan Govindan

Department of Business & Economics,

University of Southern Denmark,

Odense, Denmark.

gov@sam.sdu.dk

Mohammad Iranmanesh

School of Management

Universiti Sains Malaysia

11800 Pulau Pinang,

Malaysia

iranmanesh.mohammad@gmail.com

Mohd Rizaimy Shaharudin

Graduate School of Business

Universiti Sains Malaysia

11800 Pulau Pinang,

Malaysia

rizaimy@kedah.uitm.edu.my

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Green Innovation Adoption in Automotive Supply Chain: The Malaysian case

Abstract

Green innovation has currently been receiving a great deal of international attention because

of the growing concern of consumers, governments, and the community as a whole with

regard to the degradation of natural resources and environmental pollution. The automotive

sector is one of the leading generators of industrial waste that affect the quality of the natural

environment. This study aims to investigate the determinants of green innovation adoption

and its effect on firm performance. Data were gathered by surveying 153 firms in the

Malaysian automotive supply chain industry. Data were analyzed using the partial least

squares technique. Environmental regulations, market demand, and firm internal initiatives

have a positive effect on green innovation initiatives (GII), while GIIs have a positive effect

on the three categories of sustainable performance (i.e., environmental, social, and

economic). These results have important implications for designing strategic plans for the

Malaysian automotive industry.

Keywords: Green Innovation Initiative; Automotive Supply Chain; Sustainable

Performance; Malaysia

1. Introduction The automotive industry, with its supporting supply chain, is the largest industry in

the world and employs more than 10% of the total workforce. With growing concerns about

global warming and social issues, the automotive supply chain industry is confronted with

increased pressure from stakeholders and regulatory agencies to achieve sustainability. As a

developing nation, Malaysia increasingly emphasizes economic development while

maintaining environmental and social protection. These pressures have created a challenge

for companies to balance economic, environmental, and social performances (Shultz and

Holbrook, 1999). Consequently, the Malaysian automotive industry and its supply chains

seek to balance its economic, environmental, and social performances. For any company to

survive these challenges, innovative strategies should be devised to ensure a sustainable

competitive edge while satisfying all requirements from stakeholders and regulatory agencies

(Olugo et al., 2011). Therefore, the automotive industry in developing countries, along with

its supporting supply chain, has moved to adopt green innovations.

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The Economist (2008) highlighted mass production, the Toyota production system,

lean production, just-in-time, and modular consortium as important innovations from the

production system perspective in the automotive industry over the last 30 years. A strong

focus on these innovations has decreased costs, which has reduced prices and diluted market

saturation and predatory price competition with low profit margins. Nevertheless, automotive

companies cannot rely on these benefits alone as the rules of competition have changed

(Katayama and Bennett, 1996), and the pressure to adopt environmentally friendly processes

and green products is increasing. Therefore, green innovation is essential. However, the

effect of green innovation initiatives (GII) on sustainable performance as core competencies

of firms has not been paid much attention to and is rarely focused on in the automotive

industry. This study investigates the effect of GII on sustainable performance (i.e.,

environmental, social, and economic) to fill this gap. In addition, several studies pertaining to

the determinants of GII have been conducted in various industries, including logistics (Lin

and Ho, 2008), manufacturing (Guoyou et al., 2013; Zhu et al., 2012), and IT (Wu, 2013).

Nevertheless, the understanding of the determinants of GII that automotive companies should

adopt remains a high-priority research issue (Chen et al., 2006), particularly in developing

countries. To the best of our knowledge, market demand is the only factor that affects GII,

according to previous studies conducted on the automotive industry (Huang et al., 2014).

This study aims to explore the determinants of GII for firms in the automotive supply chain.

These determinants should be fully understood to ensure that automotive managers could

enhance green innovation adoption. Success in GII provides new opportunities for

competition and “win-win” situations by lessening the environmental effect of automotive

supply chain companies while improving their economic position.

2. Model conceptualization and hypothesis development

Kemp and Pearson (2008) defined green innovation as the production, assimilation, or

exploitation of a product, production process, service, or management/business methods that

is novel to the organization that develops or adopts them; it significantly reduces

environmental risks, pollution, and other negative effects on resource use (including energy

use) throughout its life cycle. Similarly, Wong et al. (2013) suggest that green innovation

facilitates the reduction of the environmental impact of firms, which allows them to achieve

eco-targets and incorporate environmental benefits. Green innovation aims to systematically

align and implement this strategy throughout the supply chain from new product and service

development to consumption (Jone et al., 2008).

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Green innovation is classified into three main categories: green product innovation,

green process innovation, and green management innovation (Chen et al., 2006; Chen, 2008).

We examine green product and process innovation in the current study. Chen et al. (2006)

identified green product innovation as the introduction of new or significantly improved

products in response to environmental concerns (e.g., non-toxic raw materials, green design,

energy savings, pollution prevention, waste recycling, and waste minimization). Green

process innovation refers to modifying manufacturing processes and systems to produce

environmentally friendly products that meet eco-targets (e.g., energy savings, pollution

prevention, and waste recycling) (Meeus and Edquist, 2006; Kammerer, 2009).

The automotive industry has made remarkable contributions to the world economy

and people’s mobility, but its products and processes are significant sources of environmental

impact (Nunes and Bennett, 2010). Given that automobiles cause major environmental

impacts upon use, the environmental impacts of the automobile manufacturing process

should be explored (Keoleian et al., 1997; Graedel and Allenby, 1997). The application of

environmental pressures reduces emissions and waste throughout vehicle production, use,

and end-of-life. These pressures come in the form of stringent, complex, and costly

regulations and demands from a growing number of stakeholders for improved environmental

performance (Geffen and Rothenberg, 2000). Continuous innovation is vital to overcoming

pressures from customers, competitors, and regulators (Porter and Van der Linde, 1995). In

the past, most automotive companies considered environmental compliance an additional

production cost instead of considering it a fundamental process to prevent negative

environmental impacts. However, strict environmental regulations and environmentalists

have changed the competitive rules and patterns for companies. The increasing costs of

traditional modes of compliance and advances in material and process technologies have

driven some automotive supply chain companies to adopt green innovative approaches to

overcome environmental challenges (Richards and Pearson, 1998).

Several studies have been conducted on the determinants of GII. A summary of 25

studies on key determinants of GII is provided in Appendix I. The occurrences and

percentages for each type of green innovation key determinants (Appendix I) are as follows:

regulations (68%), market demand (40%), firm internal initiatives (68%), technological

capability (8%), competitive advantage (8%), and customer benefit (4%). Regulations are the

most studied, followed by firm internal initiatives and market demand. Therefore, these three

factors are identified as the key determinants of GII and examined in the present study.

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Numerous empirical findings confirm a positive relationship between green

innovation and firm performance (Lau et al., 2010). Green innovation is closely associated

with corporate environmental management and eco-target achievement; therefore, green

innovation is widely believed to stimulate environmental performance (Chen et al., 2006;

Kammerer, 2009). Green product and process innovation not only reduce negative

environmental impact, but they also increase the economic and social performance of a

company through waste and cost reduction (Kleindorfer et al., 2005). Companies implement

green process innovation in the manufacturing process to shorten production time and reduce

costs (Lambertini and Mantovani, 2009). In addition, a good product innovation improves

market position, affirms brand name, leapfrogs competition, creates breakthroughs, and

attracts new customers (Chandy and Tellis, 2000; Mu et al., 2009). Therefore, three

performance types (i.e., environmental, social, and economic) are considered the benefits and

outcomes of GII.

Figure 1 presents the proposed theoretical model to investigate the effect of the three

identified key determinants of GII on Malaysian automotive supply chain companies. This

study also investigates the effect of GII on environmental, economic, and social

performances. The control variables selected for this framework are ownership, size, and ISO

certification.

Figure 1. Proposed Theoretical Model

Environmental Regulations

Market Demand

Firm Internal Initiatives

Green Innovation Initiatives

• Product innovation • Process innovation

Environmental Performances

Social Performances

Economy Performances

Control Variables: • Firm Size • Ownership of the Firm • ISO Certification

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2.1. Environmental regulations

Environmental regulations include stringency, flexibility, realistic objectives or

adequate timeframes for implementing innovative solutions, clear requirement specifications,

and appropriateness to a country’s circumstances (Eidatet al., 2008). Firms that belong to

more regulated industries will tend to include more environmental issues in their

management strategies than less regulated firms (López–Gamero et al., 2010). Porter and van

der Linde (1995) claimed that strict environmental regulations and the associated compliance

costs could force industries to adopt innovative measures to increase resource efficiency and

enhance productivity. Government environmental regulations could overcome organizational

inertia and cause organizations to accept new ideas, stimulate creative thinking, upgrade

outdated facilities, and invest in technological improvements (Eiadat et al., 2008). According

to other studies, environmental regulations significantly encourage firms to invest in non-

polluting technologies and to consider environmental concepts for their products and

processes (Fergusson and Langford, 2006; Blayse and Manley, 2004; Qi et al., 2010). Two

recent studies that analyzed the effect of regulatory stringency on green innovation in

Switzerland and Germany presented contradictory results: Although regulatory stringency

positively affects green innovation in the chemical and pharmaceutical industry (Seijas–

Nogareda, 2007), it has no such effect in the food and beverage industry (Engels, 2008).

These conflicting findings may be due to industry characteristics, which makes investigating

the effect of environmental regulations in various industries important. Therefore, the present

study hypothesizes that

H1: Environmental regulations have a positive effect on GII in automotive supply chain

companies.

2.2. Market demand

Market demand is a key to business success. For automotive companies to

continuously respond to customer requirements and changing expectations, market

orientation is essential (Kirca et al., 2005; Zhou et al., 2009). Zhou et al. (2005) stated that

customers become sensitive and selective over time. Hence, firms should understand their

target customers and anticipate their changing preferences to promptly meet market demand

and gain competitive advantage (Desarbo et al., 2001; Zhou et al., 2009). Several studies

conducted on the automotive industry observe an increasing trend in environmental concern

among customers (Oltra and Jean, 2009; Lin et al., 2013) and emphasize the emergence of

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“green customers.” Therefore, a green market forces automotive firms to produce and adopt

innovative products and processes, respectively.

Green market demand is important in the spread of green innovation (Halila and

Rundquist, 2011; Triebswetter and Wackerbauer, 2008). Chiou et al. (2011) identified an

increasing number of environmentally conscious customers who prefer fuel-efficient and

environmentally friendly products. Environmentally conscious customers demand innovative

green products and are willing to pay high prices for green products (Chen et al., 2008).

Consequently, green product and process innovation is profitable for automotive supply chain

companies. Thus, the following hypothesis is developed:

H2: Market demand has a positive effect on GII in automotive supply chain companies.

2.3. Firms’ internal initiatives

According to a resource-based viewpoint, the internal characteristics of firms (e.g.,

strategy, structure, and core capabilities) are important determinants of innovation (Fagerberg

et al., 2005). Resources are classified into tangible (e.g., financial reserves), intangible (e.g.,

reputation), and personnel (e.g., culture, training) (Barney, 1991). With regard to the

innovation aspect, the most important asset of firms is its general commitment to innovation.

R&D units are tools for solving organizational problems. R&D expenditure is a common

proxy for and closely related to firm innovation activity (Sánchez, 1997). Rehfeld et al.

(2006) indicated that R&D activities positively influence green product innovation.

Technology accumulation, organizational encouragement, human resource quality, and top

management support also influence the adoption of green innovation ( Lin and Ho, 2008;

Bansal, 2003). A company with high human resource quality (e.g., superior education or

training) has high innovation ability. Top management is important in organizational support

(Zailani et al., 2014). Therefore, top management is expected to develop the environmental

innovation strategy of firms (Eiadat et al., 2008). Consequently, firm internal initiatives

influence the adoption of green innovation. Hence, the following hypothesis is developed:

H3: Firm internal initiatives have a positive effect on GII in automotive supply chain

companies.

2.4. Sustainable performance

Sustainability is important to organizations in the twenty-first century (Presley et al.,

2007). Robins (2006) categorized sustainability into three primary components, which are

often referred to as the triple bottom line: economic, social, and environmental. Organizations

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are pressured by multiple stakeholders to achieve sustainable performance (McMullen,

2001). Organizations are becoming increasingly aware that choices made with regard to

products and processes affect environmental and social performance (Sarkis, 2001). Thus,

organizational decision makers are overwhelmed by a plethora of stakeholder issues,

environmental agency pressures, and increased social consciousness from workers,

consumers, and communities; these conditions should be balanced to ensure a reasonable

return on investment and long-term enterprise viability for organizational stakeholders

(Presley et al., 2007).

Pollution results from inefficient use of resources by automotive suppliers, and they

can increase their productivity through green innovation to shoulder environmental costs

(Chen et al., 2006; Porter and van der Linde, 1995). Porter and van der Linde (1995) argued

that companies that pioneer green innovation have the first-mover advantage, which allows

them to charge relatively high prices for their green products and obtain competitive

advantages. Moreover, firms that apply environmental management not only avoid the hassle

of protests or penalties regarding environmental protection, but also improve their corporate

image, develop new markets, and increase their competitive advantages (Presley et al., 2007).

Moreover, companies could innovatively embody the green concept in their products and

processes to differentiate their products from competitors (Chen et al., 2006; Hart, 1995;

Porter and van der Linde, 1995). Hence, the following hypotheses are developed:

H4: GII has a positive effect on economic performances of automotive supply chain

companies.

H5: GII has a positive effect on environmental performances of automotive supply chain

companies.

H6: GII has a positive effect on social performances of automotive supply chain companies.

2.5. Control variables

This study considers the following variables, which may affect the results of the

study: firm ownership, number of employees (firm size), and ISO certification status. These

variables are chosen because they have significant effects. Large firms are more innovative

than small firms because the former has more resources and capital than the latter (Lee and

Ging, 2007; Damanpour, 1996). Firm ownership (local or foreign) also influences innovation

but not for small and medium enterprises (Lee and Ging, 2007). Firms with ISO certification

are more involved in green initiatives than firms without ISO certification because of the

required certification standards.

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3. Research methodology

3.1. Measure of constructs

This study employed a quantitative survey with a structured questionnaire. The

questionnaire had five sections with a total of 53 items: company basic information, GII

(green product and process innovation), determinants of GII (regulations, marketing demand,

and firm internal initiatives), sustainable performance (environmental, economic, and social

performances), and respondents’ personal information. Except for basic company information

and respondents’ personal information, the items were measured by using the 5-point Likert

scales anchored by “strongly disagree” and “strongly agree.” The items were adapted from

previous studies to ensure content validity. The scale for environmental regulation was

adapted from Eiadat et al. (2008), Eltayeb et al. (2010), Lin and Ho (2008), and Zhu et al.

(2007); the scale for market demand and firm internal initiatives was adapted from Horbach

(2008), Michel et al. (2001), Kammerer (2009), Lin and Ho (2008), and Eidat et al. (2008);

the scales for green product and process innovation were measured using items based on

Chen et al. (2006); and the scale for economic, environmental, and social performance was

adapted from Rao (2002), Chen et al. (2006), Zhu et al. (2007), Kassinis and Soteriou (2003),

and Carter (2005).

3.2. Data collection and the sample

The sampling frame consists of all firms in the Malaysian automotive supply chain

industry, which includes firms that produce raw materials, components, vehicles, and spare

parts. This study focuses on the Malaysian automotive industry for the following reasons: (1)

the automotive industry with its supporting supply chain is one of the most important,

strategic, and fast-growing industries in Malaysia. The huge potential effect of GII on

sustainable performance of automotive companies makes investigating the determinants of

GII necessary; (2) the automotive industry is one of the main pollution sources in Asia; and

(3) several automotive companies worldwide take advantage of component manufacturers in

Asia to benefit from low production costs. The successful implementation of GII allows

component manufacturers to attract additional international automotive companies.

The sampling list is obtained from the website of the Malaysian Automotive

Association. Malaysia currently has four passenger and commercial vehicle manufacturers,

namely, Proton, Perodua, Naza, and Modenas. Moreover, the country has nine motor vehicle

assemblers and 343 components or parts manufacturers. The total number of firms on the list

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is 356. Given our small sampling frame and low response rate from the mail survey (Sekaran,

2003), the survey questionnaire was sent to all firms.

This study targeted engineers, manufacturing managers, operation managers,

production managers, quality managers, executive directors, and managing directors of

Malaysian automotive companies; they were chosen as our respondents because they are

directly involved in the process, which makes them knowledgeable and experienced in all

operations and activities in their respective companies. The survey was conducted using a

structured mail questionnaire directed to the corresponding respondent in each firm. A total

of 153 usable responses were received out of the 356 distributed questionnaires.

3.3. Analysis

The casual relationships between constructs were analyzed through structural

equation modeling (SEM). SEM analysis was chosen over regression analysis, because SEM

can analyze all paths in a single analysis (Gefen et al., 2000; Hair et al., 2013). Partial least

squares (PLS) approach was selected because of its small size requirements and the

exploratory nature of the research (Hair et al., 2011). SmartPLS M3 Version 2.0 (Ringle et

al., 2005) was used for the analysis. The sample size of 153 exceeded the minimum sample

requirements recommended by Wixom and Waston (2001). According to the procedure of

Hulland (1999), PLS is analyzed and interpreted in two stages. In the first stage, the

measurement model has to be tested by performing validity and reliability analyses on each

measure to ensure that only reliable and valid construct measures are used prior to making

conclusions about the nature of construct relationships (Hulland, 1999). In the second stage,

the structural model is tested by estimating the paths between model constructs to determine

their significance and the predictive ability of the model.

4. Results

4.1. The sample

The discriminant analysis shows that 41.2% of the respondent firms are locally

owned, 36.6% are foreign based, and 22.2% are joint ventures (local and foreign). For the

ISO certification status, 92.2% of the respondent firms are ISO certified, and 7.8% are not

ISO certified. Common ISO certifications in the Malaysian automotive supply chain are

ISO/TS16949, ISO14000, and ISO9001. An examination of the number of employees

indicates that respondents are mostly large companies with more than 200 employees

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(75.2%), and more than half of the firms have annual sales turnovers of more than RM200

million (65.4%).

4.2 Common method variance

According to Podsakoff and Organ (1986), common method bias is problematic when

a single latent variable accounts for the majority of the explained variance. The unrotated

factor analysis results indicate that the first normalized linear combination explains only

28.55% of the total 82.62% variance, which proves that common method bias is not a serious

problem in the study.

4.3. Measurement Model Results

The reliability and validity of the reflective constructs were assessed. Composite

reliability (CR) is assessed in connection with internal reliability, which is similar to

Cronbach’s alpha. Table 1 shows that CR of all constructs was above 0.7, which satisfies the

rule of thumb in Hair et al. (2013). Hair et al. (2010) suggested accepting items with loadings

of at least 0.6. Given that the loadings associated with each scale were all greater than 0.6,

individual item reliability was reasonably judged. Convergent validity was evaluated using

average variance extracted (AVE). The AVE of all constructs was above 0.5, which signifies

a satisfactory degree of convergent validity (Fornell and Larcker, 1981).

Table 1

Measurement Model Evaluation.

Constructs Items Factor Loadings CR AVE

Environmental Regulations (ER) 5 0.675 - 0.840 0.890 0.619

Marketing Demand (MD) 5 0.655 - 0.937 0.904 0.659

Firms’ Internal Initiatives (FII) 6 0.765 – 0.967 0.936 0.712

Green Product Innovation (GPI) 4 0.847 – 0.926 0.937 0.790

Green Process Innovation (GPOI) 4 0.841 – 0.925 0.925 0.756

Economic Performance (ECP) 5 0.776 – 0.911 0.932 0.733

Environmental Performance (ENP) 6 0.801 - 0.956 0.944 0.737

Social Performance (SP) 6 0.774 – 0.954 0.930 0.689

CR= Composite Reliability; AVE= Average Variance Extracted

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Two approaches were used to assess the discriminant validity of constructs. First, the

cross loadings of indicators were examined. No indicator loads were higher than an opposing

construct (Hair et al., 2012). Second, according to the Fornell and Larcker (1981) criterion,

the square root of AVE for each construct should exceed the intercorrelations of the construct

with other model constructs (Table 2). Both analyses confirmed the discriminant validity of

all constructs.

Table 2 shows that although Malaysian automotive supply chain companies have

experienced great pressure from market demand (mean = 3.90), a lack of pressure has been

observed from environmental regulation (mean = 3.24). Automotive companies have good

internal initiatives, including environmental management system, top management support,

and R&D budget allocation (mean = 4.00), which support the development of green product

and process innovation. Although the green product innovation rate is satisfactory (mean =

3.75), the green process innovation rate lags (mean = 3.35). Finally, all sustainable

performances are high among automotive companies

Table 2

Discriminant Validity Coefficients.

Mean SD ER MD FII GPI GPOI ECP ENP SP ER 3.239 0.759 0.787 MD 3.898 0.613 0.235 0.812 FII 3.995 0.755 0.024 0.049 0.833 GPI 3.748 0.863 0.479 0.299 0.409 0.889 GPOI 3.357 0.789 0.280 0.386 0.277 0.096 0.869 ECP 3.644 0.833 0.260 0.166 0.310 0.458 0.323 0.856 ENP 3.833 0.866 0.362 0.293 0.127 0.242 0.569 0.171 0.858 SP 3.857 0.699 0.069 0.436 0.224 0.441 0.264 0.287 0.382 0.830

The reflective-reflective second-order construct of GII is characterized by green

product and process innovation. We assign all indicators from first-order to second-order

constructs using the repeated indicators approach to establish the second-order constructs.

Table 3 shows that the same measurement model evaluation criteria is applied to the GII

construct, and validity and reliability are well established.

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Table 3

Second-order Construct Evaluation.

Factor Loadings CR AVE

Green Product Innovation 0.773 0.708 0.548

Green Process Innovation 0.706

4.4. Assessment of the structural model

The measurement model had satisfactory results, and the structural model was

evaluated subsequently. The predictive accuracy of the model was evaluated in terms of the

explained variance portion. The results suggest that the model is capable of explaining 60.6%

of the variance in GII, 28.6% in economic performance, 28.6% in environmental

performance, and 23.4% in social performance. Aside from estimating the magnitude of R2,

predictive relevance developed by Stone (1974) and Geisser (1975) is included as additional

model fit assessments. This technique indicates the ability of the model to predict the

manifest indicators of each latent construct. Stone–Geisser Q2 (cross-validated redundancy)

was computed to examine predictive relevance using a blindfolding procedure in PLS.

Following the guidelines of Chin (2010), Q2 value greater than zero implies that the model

has predictive relevance. The present study obtained 0.213 for average cross-validated

redundancy (for all endogenous variables), which is far greater than zero. Thus, the model

exhibited acceptable fit and high predictive relevance. Nonparametric bootstrapping was

applied (Wetzels et al., 2009) with 5,000 replications to test the structural model. Table 4

presents the structural model that resulted from PLS analysis. All paths were significant, and

the hypotheses were supported. The strength of the effect of determinants on GII was

investigated by the effect size (f2) (Hair et al., 2013). A notable detail is that the internal

initiatives of firms have the highest effect on GII (f2 = 0.465), followed by environmental

regulations (f2 = 0.414), and marketing demand (f2 = 0.218).

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Table 4

Path coefficient and hypothesis testing.

Relationships Path Coefficient

Std. deviation

t-value Decision

ER -> GII 0.448 0.097 4.612*** Supported

MD -> GII 0.314 0.080 3.938*** Supported

FII -> GII 0.455 0.097 4.697*** Supported

GII -> ECP 0.535 0.100 5.332*** Supported

GII -> ENP 0.535 0.101 5.325*** Supported

GII -> SP 0.484 0.077 6.268*** Supported

Control Variables

Firm Size -> GII -0.027 0.068 0.395 -

ISO -> GII -0.142 0.078 1.816* -

Ownership -> GII 0.011 0.074 0.147 -

t values are computed through bootstrapping procedure with 153 cases and 5000 samples *p<0.05, **p<0.01 (one tail)

5. Discussion and implications

According to a literature review, the Malaysian automotive supply chain is still in its

developing stages and has significant negative environmental impacts. Therefore, GII should

be pursued aggressively to accommodate the current environmental situation. Green

innovation is a good approach for the automotive supply chain to adopt. Understanding the

determinants of GII is essential to automotive supply chain firms. Some studies have

examined the motivation and driving forces of GII but do not focus on the automotive supply

chain industry. Thus, this study investigates the determinants of GII. The benefits of adopting

GII in manufacturing firms have been explained to some extent. However, some managers

remain unconvinced in adopting green innovation. Thus, this study determines the potential

positive effects of GII on simultaneous firm economic, social, and environmental

performances. Several motivations drive automotive supply chain companies to adopt green

innovation. Environmental regulations, marketing demands, and firm internal initiatives are

the major factors that positively affect the adoption of green innovation by Malaysian

automotive supply chain firms.

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Our findings on the relationship between environmental regulation and GII are

consistent with those of Fergusson and Langford (2006) and Qi et al. (2010), who found that

stringent environmental regulations may promote green innovation. The Malaysian national

automotive policy has not taken action with regard to the environmental impact of the

automotive industry development. Directives or legislations on end-of-life vehicles for the

automotive industry have yet to be established (Amelia et al., 2009). Therefore, the

Malaysian government should set environmental regulations with stringent standards for the

automotive industry. Market demand is also an important factor for green innovation

adoption, a finding that agrees with that of Lin et al. (2014); a positive relationship exists

between market demand and GII for automotive companies in Taiwan. Malaysian customers’

concern about environmental issues has increased dramatically. In addition, with the

increasing petrol prices in Malaysia, petrol consumption will play an important role in the

selection of a new vehicle. Automotive companies that align these requirements with

innovative green products and processes meet customer demand well and outperform their

competitors. The internal initiatives of firms are also significantly related to GII. The

successful implementation of GII is assured with adequate technology, human capital,

environmental management systems, practices, and tools to support green innovation.

The results also showed that firms support the principles of institutional theory and

respond to external influences, such as regulations and market demand, in their adoption of

GII, although they do so in different manners. Overall, the study found that coercive pressure

(regulations) influences, more than normative (market demand) ones, suggest that more

stringent environmental regulations improve the adoption of green innovation initiatives.

Generally speaking, this finding is consistent with a previous study on the importance of

environmental regulation (especially monitoring and enforcement) in GII (Qi et al., 2010;

Kammerer, 2008, 2009; Eiadt et al., 2008). Among the three determinants, the internal

initiative of firms has the greatest effect on GII. Internal resources significantly induce firms

to initiate green innovation and gain advantage over other firms. Market demand and

environmental regulations do not promote GII without adequate internal initiatives. A

company with high-quality professional development, such as better education or training,

will be more capable of green innovation (Lin and Ho, 2008).

The literature highlights several benefits that can arise from integrating green

innovation into product development and business processes: increased efficiency in the use

of resources, return on investment, increased sales, development of new markets, improved

corporate image, product differentiation, and enhanced competitive advantage (Fraj–Andre et

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al., 2008; Pujari et al., 2003; York, 2009; Dangelico and Pujari, 2010). Chen et al. (2006)

found that the performance of both green product and green process innovation is positively

correlated to competitive advantage. The present study observed that GII significantly affects

the environmental, economic, and social performance of automotive companies. This finding

indicates that the adoption of green product and process innovation allows Malaysian

automotive supply chain companies to improve their economic, social, and environmental

performance. The results suggest that GII may lead to a win-win situation so that the

environmental, social, and economic performances of firms improve simultaneously.

Companies should design green innovative products and adopt green innovative processes to

achieve sustainable development. The implication of this finding for automotive firms’

managerial groups is the discovery that the practice of green innovation in the firms’

environment can obtain an economic advantage for the firms. This condition will help to

improve the competitive advantage of the firms in the market and at the same time contribute

to their environmental and social performance, which will further help to improve the image

and reputation of firms.

In terms of theoretical contribution, this study is the first to investigate the

determinants of GII in the Malaysian automotive supply chain industry. This sector is

particularly important because it is a fast-growing industry with a significant effect on the

environment. Although a few studies have explored the effect of GII on environmental and

economic performances of automotive companies, the effect of GII on social performance

has rarely been investigated. Social image is essential to companies to improve their market

position, affirm the reputation of their brand, and attract customers.

This study provides several implications for policymakers and managers in the

automotive supply chain industry. Recognizing the determinants of GII will help

policymakers to understand the factors that lead to green innovation adoption in the

automotive supply chain industry. Consequently, they could adjust their strategies and

policies to motivate automotive company managers to adopt green innovation. Moreover, this

understanding can help managers successfully promote GII in their companies. This study

also identifies the significance of the effect of GII on sustainable performance. Thus, the

study advances knowledge on the benefits of GII adoption for managers of automotive

companies.

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6. Limitations and future studies

This study has certain limitations that should be taken into account prior to

generalizing its results. First, the study tested and verified the hypotheses using a

questionnaire survey but provided only a cross-section of the study in nature. Therefore, this

approach limits the ability to imply causality in the relationships among variables. Thus, the

survey results are affected by the fact that this study cannot observe the dynamic changes of

green innovation in the development process of the automotive logistic industry in Malaysia.

A longitudinal study that examines the relationships for an extended period of time should be

performed to provide precise results. Furthermore, the sample population is limited to the

automotive supply chain. Other industries that wish to apply the findings of this study should

be conscious of the effect of determinants and performances, which may vary with the types

of industries. This study also used a survey sample limited to the Malaysian automotive

supply chain. However, the maturity of a certain technology and decision process for

adopting a technology may vary between countries. Future research should test the research

model in different industries and countries. The study should also be conducted in different

regions of the country for data comparison and to obtain additional information.

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Appendix I

Summary of literature on determinants for Green Innovation Initiatives.

No Author Year Determinants of Green Innovation RG MD FII TC CA CT CB

1 Qi et al. 2010 √ √ 2 Kammerer 2009 √ √ √ 3 Oltra and Saint Jean 2009 √ √ √ 4 Chen 2008 √ 5 Kammerer 2008 √ √ √ √ 6 Eiadat et al. 2008 √ √ 7 Horbach 2008 √ √ √ √ 8 Ibrahim et al. 2008 √ √ √ 9 Lin and Ho 2008 √ √ 10 Triebswetter and

Wackerbauer 2008 √ √ √ √ √

11 Beerepoot and Beerepoot 2007 √ 12 Frondel et al. 2007 √ √ √ 13 Bernauer et al. 2007 √ √ √ 14 Chen et al. 2006 √ 15 Kuik 2006 √ √ √ 16 Lee et al. 2006 √ 17 Rehfeld et al. 2006 √ 18 Fagerberg et al. 2005 √ 19 Roediger-Schluga 2004 √ 20 Del Brı´o and Junquera 2003 √ 21 Scupola 2003 √ 22 Jaffe et al. 2002 √ 23 Dyllick and Hamschmidt 2000 √ 24 Kemp et al. 2000 √ 25 Azzone and Noci 1998 √ Number of Occurrences 17 10 11 5 2 1 2 Overall Occurrences Percentage (%) 68 40 44 20 8 4 8 Note: “√” indicates the study have mentioned directly or indirectly the usage of Green Innovation Initiatives. Determinants: RG=Regulations; MD=Market Demand; FII=Firm Internal Initiative; TC=Technology Capabilities; CA=Competitive Advantage; CB=Customer Benefit