Review and proposed eco-process innovation performance framework

ABSTRACT

Rapid growth of industrial activities throughout the world has resulted in an ever-worsening environmental degradation. Manufacturers must be proactive on environmental issues and link their operations beyond the economic rewards, to consider environmental and social impacts in their processes. In the manufacturing sector, eco-innovation has been recognised as an essential tool for addressing the growing economic, environmental and social concerns and at the same time supporting the achievement of sustainable development target. Eco-process innovation (a type of technical eco-innovation) tools and techniques should be implemented to change the perception that manufacturers are contributing to the negative environmental impacts to the belief of improving society’s standard of living and environmental quality. This paper presents the results of an extensive systematic literature review on 45 empirical researches relating to eco-process innovation published from 2006 to July 2017. They were analysed in terms of research patterns, measurement aspects, approach and performance indicators employed by previous researchers. The analysis indicate a compelling reason to develop and propose an instrument able to measure operational economic, environmental and social performance of eco-process innovation. This review resulted in a conceptual framework for measuring eco-process innovation performance in manufacturing firms which serves as an essential fundamental prior to conducting studies in developing an improved instrument for assessing eco-process innovation performance. Future research is in the throes of conducting a case study for verifying this eco-innovation framework that will be useful to manufacturers.

KEYWORDS:

• Eco-process innovation
• performance indicator
• manufacturing firm
• systematic literature review

Introduction

Rapid growth of industrial activities throughout the world has resulted in an ever-worsening environmental degradation. At the same time, there is pressure from various governmental institutions on manufacturers to be proactive and more concerned on environmental issues. Manufacturers must link their operations beyond the economic rewards, and to consider the environmental and social impacts in their processes. An area recognised as ecological innovation (also known as eco-innovation) is showing increased importance amongst manufacturers particularly on their significant energy consumption and other non-renewable resources (Organisation for Economic Cooperation and Development (OECD) 2009a), and their potential contribution to pollutions (Aja et al. 2016; Organisation for Economic Cooperation and Development (OECD) 2009a). In this sense, Dewberry and De Barros (2009) urged businesses to think sustainability with significant improvement for optimising resources productivity mindset.

In the context of manufacturing sector, eco-innovation has been recognised as an essential tool for addressing the growing economic, environmental and social concerns, and at the same time supports the achievement of sustainable development target (Organisation for Economic Cooperation and Development (OECD) 2014; Rizos et al., 2015; Fernando, Wah, and Shaharudin 2016). The Organisation for Economic Cooperation and Development (OECD) (2008) has always suggested that manufacturing firms aimed for more efficient resource utilisation and producing less waste through the improvement of their production processes. Eco-process innovation tools and techniques should be implemented to change the perception that manufacturers are contributing to the negative environmental impacts to the belief of improving society’s standard of living and environmental quality.

In order for these to occur, measuring eco-innovation performance is essential to determine whether the practices are at par with other manufacturers and the improvement necessary to achieve economic, environmental and social goals (Eco-Innovation Observatory (EIO) 2013). Researchers and practitioners have tried to develop an instrument for measuring eco-process innovation performance in manufacturing firms (Cheng, Yang, and Sheu 2014). However, literature that focuses on the assessment of eco-innovation performance is relatively limited. Efforts for producing a comprehensive assessment of different types of eco-innovation performance is still scarce (Dong et al. 2014). At the same time, knowledge on eco-process innovation performance measure is seen to be scattered with different versions of indicators proposed to reflect firm-level eco-process innovation performance. The development of better instrument would not be possible without an adequate understanding of how eco-process innovation practice has been developed by previous scholars.

This research is partly an attempt to investigate the many unanswered questions in eco-process innovation measurement instrument development. The main research question concerns about the gap and the promising research directions currently emerging in the field of eco-process innovation measure. From the main research question, it is then important to further explore the secondary concerns relating to how previous research has developed the instrument at firm level, how they evaluate eco-process innovation, and to be able to define a relevant conceptual framework for eco-process innovation performance measurement. This paper has performed a systematic literature review on the measuring aspects and approaches used by previous scholars to identify research directions in measuring the performance of eco-process innovation. This is followed by identifying relevant assessment indicators and to map a conceptual framework based on the analyses.

This paper offers a small contribution to the literature on assessing eco-process innovation performance. First, it provides an overview of growth in eco-process innovation study over time, the leading countries which published the most relevant studies in the field, and the studied sector or industry. Second, it analyses the measurement aspects and approaches used by previous researchers in assessing eco-process innovation performance. Third, this study identifies main indicators used by previous researchers to indicate the performance of eco-process innovation and a conceptual framework is developed based on the results. Finally, this paper concludes with highlights on future research agenda in which further investigations are needed. Subsequent sections are organised into: a brief theoretical background underpinning the study, followed by a discussion on the method employed in this study.  The next section describes the main results, and the conceptual framework for measuring eco-process innovation performance.  Finally, the main conclusions and future research are given.

Theoretical background

Definition and nature of eco-innovation

The concept of eco-innovation has been expanding ever since it was introduced by Fussler and James (1996) in their book entitled Driving Eco-innovation: A Breakthrough Discipline for Innovation and Sustainability. Today, various versions of eco-innovation definitions have emerged. Many authors have suggested generic definition of eco-innovation. Fussler and James (1996), Hemmelskamp (1997) and Rennings (2000) define eco-innovation as any innovation which resulted in reducing the negative impacts of their operations on environment regardless of whether it is intended or not. At the same time, their notion of innovation has been limited to only product and process innovations.

Kemp and Arundel (1998), and Rennings (2000) suggested two different kinds of changes with regard to innovation. One is things which are new to the world (i.e. developed internally by the firm) and the other are those being new to the firm (i.e. application of innovation developed by other firm). Two different approaches which are new to the firm innovation are termed as adaptation and adoption. Adaptation refers to the modification of readily available environmental technologies while adoption involves the use of on-the-shelf technologies without any modification. The inclusion of the word ‘adoption’ in definitions (like in Kemp and Pearson 2007; Oltra, Kemp, and Vries 2009; Mazzanti and Montini 2009) suggests that innovation could comprise of anything that has been introduced by other firm. The concept is in line with the definition given in the Oslo Manual. For example, a company can eco-innovate their process by acquiring a more energy-efficient equipment from a supplier for application in its production line (Arundel and Kemp 2009). In short, any ecological improvement which is new to the company even though it is common to other companies can be considered as eco-innovation.

On the motivation for practicing eco-innovation, almost all scholars (includes Hellstrom 2007; Reid and Miedzinski 2008; Organisation for Economic Cooperation and Development (OECD) 2009a; Carrillo-Hermosilla, Pablo, and Totti 2010) believe that eco-innovation are innovations which reduce the unfavourable environmental impact. In unintended eco-innovation, the main intentions can be economical (competitively priced products), social (safe product) and institutional (green product designs) aspects. Environmental benefits can be unexpectedly gained as an outcome of the innovation initiative together with other achievements which have been set as the main target. Innovation with intended environmental effects, on the other hand, are strategically executed and placing the environmental aspect as the main concern in decision-making at each stage of the innovation process. In this case, ecological benefits are expected to be gained throughout the technologies’ life cycle. In terms of innovation benefits, the new perspective demonstrated broader expectations, beyond reducing environmental impacts but also addressing economical aspect through efficient use of resources. Horbach (2005) highlighted that by coupling environmental and economic improvements, a firm could also enjoy advantages of improved social and institutional performance.

Frondel, Horbach, and Rennings (2007), Organisation for Economic Cooperation and Development (OECD) (2009b) and ÖZER (2012) categorised eco-innovation into technical eco-innovation and non-technical eco-innovation. Technical eco-innovations take place in products and processes and involve technologies aimed at improving the environmental performance of products (eco-product innovations) and processes (eco-process innovations). Whereas non-technical eco-innovations refer to the improvements related to marketing (such as introduction of more environmental-oriented product design, pricing, packaging, promotional and distribution programmes) (Abu Seman et al. 2014), organisational (related to the use of green system of management like environmental management system) (Cheng, Yang, and Sheu 2014), institutional (which involve the transformation of behaviour, culture, norms and values) (Organisation for Economic Cooperation and Development (OECD) 2009a) and system (systemic level transformation among inter-connected companies that require improvements in product, process, marketing, organisational and institutional) (Eco-Innovation Observatory (EIO) 2011).

This paper adopted eco-innovation definition as

the strategic implementation of any form of technical and non-technical changes that are either new to the world or new to the firm, with the intention of achieving a balanced priority of economic returns, environmental preservation and society well-being resulting in sustainable economic, environmental, social and institutional benefits. (Dahan, Yusof, and Taib 2017)

The operational definition includes both types of eco-innovations: environmentally motivated innovations and environmentally resulted innovations. In short, it is not limited to innovation activities conducted intentionally for reducing the harmful environmental impacts, but also comprises of the unintended eco-effects innovations.

With regard to the nature of eco-innovation, this paper addresses a specific type of the technical eco-innovation carried out in manufacturing processes, known as eco-process innovation. Eco-process innovation can be seen as an attempt to incorporate the eco-innovation initiatives into the manufacturing processes. This includes the introduction of new production processes, technologies or methods, or the improvement of current processes so as to prevent and minimise the negative environmental impacts (Cheng, Yang, and Sheu 2014). The initiatives relate closely to the manufacturing operation activities which could cause the cost and waste reduction through more efficient production processes and better resources utilisation (Cheng, Yang, and Sheu 2014; Dong et al. 2014). In agreement to Dahan, Yusof, and Taib (2017), this paper refers to eco-process innovation as ‘any eco-innovation activities performed on production processes which aimed at or resulted in the reduction of unfavourable environmental impacts’.

Necessity for measuring eco-innovation performance

Measuring eco-innovation performance is important for various reasons. The measurement results indicate the progress of eco-innovation practice and at what stage the firm is performing in supporting the achievement of sustainability development agenda. The information on current performance would help firm to identify drivers and barriers of the different types of innovative activities and to develop improvement plan so that it could contribute in a better way of addressing environmental challenges (Oltra, Kemp, and Vries 2009; Organisation for Economic Cooperation and Development (OECD) 2009a; Sarkar, 2013; United Nations Environmental Programme (UNEP) 2014). Knowledge on current firm’s performance is essential to guide on resource consumption in order to elevate return on eco-innovation investment to a higher level (Esders 2008; United Nations Environmental Programme (UNEP) 2014). It helps firms to have a clear picture of their strengths and weaknesses in implementing eco-innovations and strategise for better resource distribution to enjoy the greatest rewards together with improved eco-innovation performance.

Performance assessment should also be done for benchmarking purpose (Esders 2008; Oltra, Kemp, and Vries 2009; Organisation for Economic Cooperation and Development (OECD) 2009c; Sarkar 2013). Firms are equipped with information concerning their current performance compared to their competitors, partners or industrial standard across industry, region or even country through the benchmarking analysis. The performance result enables the assessor to benchmark towards the overall trend of eco-innovation activity (Arundel and Kemp 2009) and identify the best-practice innovator in the particular field which then set as point of reference in setting firm’s targets and developing suitable action plan to achieve them (Esders 2008). Evaluation of the impact of eco-innovation initiatives could provide stakeholders with information on firms’ actual eco-innovation performance (Esders 2008; Organisation for Economic Cooperation and Development (OECD) 2009a) which may be at micro-, meso- or macro-level. Consequently, signals are delivered to the decision and policy-makers on the need for necessary strategies and effective course of actions to meet the sustainable development targets. In short, due to the aforementioned reasons, measuring the performance of eco-innovation implementation is crucial for manufacturers to gain privileged position in the competitive markets.

Research methods

In an attempt to understand how eco-process innovation is being measured, a systematic literature review process was undertaken. Systematic literature review involves systematic and strict protocols of literature searching and evaluation so that the research allows for analytical objectivity (Hallinger 2013) and reproducible (Tranfield, Denyer, and Smart 2003). This study has adopted the systematic review protocols and steps practiced by Bossle et al. (2016) (outlined in Figure 1) and Xavier et al. (2017).

Step 1: research question formulation

According to Xavier et al. (2017), a well-defined and answerable question should be formulated as a basis for a good systematic review. The primary and secondary research questions were introduced in the introduction section of this paper.

Figure 1. Systematic review: design of the research protocol.

Step 2: research location

Determination of keywords and search strings are indispensable prior to the literature search, which was done from the scoping study (Tranfield, Denyer, and Smart 2003). Scoping study or review is a preparatory and rapid process conducted to identify the extent and nature of research area by mapping it to visualise the possible range of materials (Dijkers 2015). The scoping study revealed that previous research has applied few terms to describe ‘eco’, which comprised of terminologies ‘green’, ‘environmental’ and ‘sustainable’. This paper agreed to several reputed review paper (including Schiederig, Tietze, and Herstatt 2012; Karakaya, Hidalgo, and Nuur 2014; Díaz-García, González-Moreno, and Sáez-Martínez 2015; Bossle et al. 2016; Xavier et al. 2017; Tariq et al. 2017) which assume the four terminologies as similar and interchangeable. In other words, the terms ‘green innovation’, ‘environmental innovation’ and ‘sustainable innovation’ have been applied as synonyms for ‘eco-innovation’. Boolean functions such as OR and AND were also used to form relationships between terms and words to form search strings and further refine the search output.

Since the focus of this study is on the eco-process innovation implementation in manufacturing sector, then the predetermined keywords used to locate the relevant article were: ‘eco-process innovation’ OR ‘process eco-innovation’ OR ‘green process innovation’ OR ‘environmental process innovation’ OR ‘sustainable process innovation’ OR ‘green process improvement’ AND ‘manufacturing’. The search was performed through topic, title, keywords and abstract of the article, which limited to materials published from 2006 to July 2017 because literature showed that the first relevant study of measuring eco-process innovation was done by Chen, Lai, and Wen (2006), whose research appeared in ISI Web of Science (WoS) and Google Scholar (GS) search with 218 times cited (in WoS Core Collection) and 586 times cited count in GS, thus considered as prominent research.

The literature were searched, filtered and extracted from the WoS, Scopus and GS databases (done by Zubeltzu-Jaka, Erauskin-Tolosa, and Heras-Saizarbitoria 2018) in June until July 2017. WoS database was chosen because within the social science field of study, it is the main source of information for extensive bibliometric research (Liu et al. 2015; Franceschini, Faria, and Jurowetzki 2015; Bossle et al. 2016). However, since the stricter methodological criteria for database coverage of WoS have limited the number of search result (Schiederig, Tietze, and Herstatt 2012; Díaz-García, González-Moreno, and Sáez-Martínez 2015), then Scopus (such as done by Díaz-García, González-Moreno, and Sáez-Martínez (2015) andGarcia-Granero, Piedra-Munoz, and Galdeano-Gomez (2018)) and GS (for instance in Schiederig, Tietze, and Herstatt (2012); Karakaya, Hidalgo, and Nuur (2014) andTariq et al. (2017)) databases were also used to get access to significantly broader data coverage.

Step 3: research assessment and selection

Predetermined selection criteria were applied to evaluate the relevancy of each traced study to the research question. Studies were included and excluded from this critical analysis according to the following criteria:

• Inclusion criteria: only journal and conference article; only English; key study focus of measuring eco-process innovation using predetermined indicators; empirical study; micro-level; manufacturing industry.

• Exclusion criteria: editor materials, article in press, patents and citations; non-English; not focus in applying indicators of eco-process innovation; theoretical/conceptual study; macro-/meso-level; non-manufacturing industry, small- and medium-sized enterprise (SME).

The search in the databases resulted in 518 articles to choose a shortlist for inclusion. At this stage, the abstract was screened for relevance and full article was consulted if it was not clear that it met all the inclusion or exclusion criteria. One hundred and twenty-eight articles were shortlisted and 390 articles were excluded from analysis resultant from the inclusion and exclusion criteria screening, because among them were found addressing other issues related to eco-innovation or did not use any indicators of eco-process innovation, undertaken the theoretical studies, performed a macro/meso-level analysis, studied eco-innovation in service industry and SMEs. Eighty-three articles were also excluded from the list due to duplication (similar study appeared several times in a database or similar study found in more than one databases) and full articles that were unavailable, leaving 45 articles remained as the relevant studies for review. The search results are shown in Table 1.

Table 1. Search results.

Database	Hits	Extracted articles
Web of Science	31	12
Scopus	28	11
Google Scholar	459	22
TOTAL	518	45

Step 4: synthesis and analysis

The analysis and synthesis of the extracted studies is performed to separate the individual study into distinct elements, identify how the different elements are connected to each other and finally develop relationships between the elements (Xavier et al. 2017). According to Tranfield, Denyer, and Smart (2003), by using a data extraction form, a synthesis should be able to produce a new knowledge which is unobvious if the individual studies are reviewed in isolation. In this study, Mendeley software was used to manage and organise the extracted articles to ensure the unification and consolidation of the bibliographic details. In addition, a review template was created to organise, synthesise, integrate and visualise the extracted information of the different articles included in the review, in which the thematic categories were developed based on the primary and secondary questions posed. The template consists of the following categories: (1) details of article source (authors, publication year, journal, country); (2) studied sector; (3) eco-process measuring aspects and measuring approaches; (4) analysis methods; (5) indicators of eco-process innovation. The collected information was then deeply analysed to reach insights, to recognise important trends and theories, and finally to come up with valid conclusions on previous studies’ findings related to the measure of eco-process innovation performance.

Step 5: reporting

The main purpose of the literature review is to make people understand the implications of presented research findings so that they can make informed and practical decisions (Xavier et al. 2017). In this sense, the main results of this study and the proposed conceptual framework are discussed in Sections 4 and 5 of this article, consecutively, to identify the knowledge gaps and future directions of research in measure of eco-process innovation performance.

Results

Overview of eco-process innovation study

The distribution of the extracted articles over the research period of 2006 to July 2017 is depicted in Figure 2. Results indicate that eco-process innovation is a relatively new field of study, but its relevance within academia is growing as shown by the growing number of articles published over the period. There were very little studies carried out in the period of 2006–2011. However, since 2012, there is an upsurge in publications, as evidenced by 82% of the 45 reviewed studies were published in the last 5 years (2012–July 2017). Particularly, systematic literature review by Tariq et al. (2017) noted that there was a rapid increase of studies in the domain of eco-process innovation over the past few years.

Figure 2. Distribution of extracted articles by year of publication.

On the main authors’ affiliation country, the countries where there have been the most dominant authors were Taiwan (20%), China (16%) and followed by Malaysia (13%). However, when looking at the regional coverage, most studies were concentrated in Europe (31%). The distribution of articles by country is shown in Figure 3.

Figure 3. Distribution of extracted articles by country.

The next area which was looked at was the sector and segments of the economy where the proposed indicators of eco-process innovation have been tested. Most studies were conducted in manufacturing firms with 26 studies, followed by the Electrical & Electronic and Information & Communication Technology) sector with 10 studies. The detailed information is illustrated in Figure 4.

Figure 4. Distribution of extracted articles by sector.

Aspects and approaches of eco-process innovation measure

Another interesting area is the aspects on which the eco-process innovation is assessed. Huppes et al. (2008) argued that the assessment should concern the impacts resulting from the improvement process since it is the process of enhancing sustainability. Three distinct categories of eco-process innovation measurement were established in the literature: economic, environmental and social as shown in Table 2. Calik and Bardudeen (2016), Scarpellini et al. (2016) and Ganapathy et al. (2014) agreed that the measure of eco-innovation should cover the three aspects of sustainability: economic, environmental and social performance, corresponding to the definition of eco-innovation. In agreement with the view, Flores et al. (2008) suggested that the improved economic performance of a firm such as its competitive advantage will in turn help to enhance the firm’s environmental and social performance as well. However, the vast majority of studies have only addressed the evaluation of two pillars of sustainability, namely economic and environmental aspect without social performance. Less than 10% of the reviewed studies (by Hauschild, Dreyer, and Jørgensen (2008), Gadenne et al. (2012), Sezen and Çankaya (2013) and Iranmanesh et al. (2015)) have tried to evaluate the social performance such as worker’s satisfaction, and safety and healthcare (Fleiss 1971). Gadenne et al. (2012) have studied the connection between firms’ sustainability performance and their sustainability management practices. They suggested that environmental and social-oriented firms showed better performance in respect to economic, environmental and social aspects.

Table 2. Studies on eco-process innovation measure at firm level.

		Measure Approach		Measure Aspect
No	Author	Perceptual	Operational	Economic	Environmental	Social	Analysis Method	[Country] Sector
1.	Chen, Lai, and Wen (2006)	√			√		Statistical	[Taiwan] Information & electronics
2.	Hauschild, Dreyer, and Jørgensen (2008)		√			√	Social LCA	[Denmark] Manufacturing
3.	Chen (2008)	√			√		Statistical	[Taiwan] Information & electronics
4.	Solding, Petku, and Mardan (2009)		√		√		DES	[Sweden] Manufacturing
5.	Ren (2009)	√		√	√		Statistical	[Netherlands] Petrochemicals production
6.	Kara, Bogdanski, and Li (2011)		√	√	√		Statistical	[Australia] Manufacturing
7.	Chiou et al. (2011)	√			√		Statistical	[Taiwan] Manufacturing & service
8.	Chang (2011)	√			√		Statistical	[Taiwan] Manufacturing
9.	Grobecker, Wolf, and Germain (2012)	√		√	√		Statistical	[Russia] Manufacturing
10.	Gadenne et al. (2012)	√		√	√	√	Statistical	[Australia] Manufacturing
11. 12. 13. 14.	Cheng and Shiu (2012) Horbach, Rammer, and Rennings (2012) Antonioli, Mancinelli, and Mazzanti (2013) Van Den Berg, Labuschagne, and Van Den Berg (2013)	√ √ √ √		√	√ √ √ √		Statistical Statistical Statistical Statistical	[Taiwan] Information Technology (IT), electronics, telecommunication & others [German] Manufacturing & service [Italy] Manufacturing [Africa] Manufacturing & service
15.	Chen and Chang (2013)	√			√		Statistical	[Taiwan] Information & electronics
16.	Tseng et al. (2013)	√			√		Fuzzy set theory, Analytical Network Process (ANP) & entropy weight	[Taiwan] Electrical & Electronic (E&E)
17.	Bi, Bao, and Feng (2013)	√			√		Statistical	[China] Manufacturing
18.	Aguado, Alvarez, and Domingo (2013)		√	√	√		LCA	[Spain] Metal working
19.	Diaz-Elsayed et al. (2013)		√	√	√		DES	[USA] Automotive
20.	Sezen and Çankaya (2013)	√		√	√	√	Statistical	[Turkey] Automotive, chemistry & electronic
21.	Wong (2013)	√			√		Statistical	[China] E&E
22.	Muscio, Nardone, and Stasi (2013)	√			√		Statistical	[Ireland] Manufacturing & service
23.	Dornfeld (2014)		√		√		Statistical	[USA] Manufacturing
24.	Doran and Ryan (2014)	√			√		Statistical	[Ireland] Manufacturing & service
25.	Rexhäuser and Rammer (2014)	√			√		Statistical	[German] Manufacturing & service
26.	Lin et al. (2014)	√			√		Statistical	[China] Manufacturing
27.	Cheng, Yang, and Sheu (2014)	√		√	√		Statistical	[Taiwan] IT, electronics, telecommunication & others
28.	Dong et al. (2014)	√		√	√		Statistical	[China] Manufacturing
29.	Chassagnon and Haned (2015)	√			√		Statistical	[France] Manufacturing
30.	Huang & Li (2015)	√		√	√		Statistical	[Taiwan] Information & Communication Technology (ICT)
31.	Masoumik, Abdul-Rashid, and Olugu (2015)	√		√	√		Statistical	[Malaysia] Manufacturing
32.	Iranmanesh et al. (2015)	√			√	√	Statistical	[Malaysia] E&E
33.	Abdullah et al. (2015)	√		√			Statistical	[Malaysia] Food & beverage, rubber & plastics, chemical, E&E & metal working
34.	Segarra-Oña, Peiró-Signes, and Mondéjar-Jiménez (2016)	√		√	√		Statistical	[Spain] Manufacturing & service
35.	de Oliveira Brasil et al. (2016)	√		√	√		Statistical	[Brazil] Textile
36.	Kawai, Strange, and Zucchella (2016)	√			√		Statistical	[Europe] Manufacturing
37.	Peng and Liu (2016)	√			√		Statistical	[China] Manufacturing
38. 39. 40.	Kong, Feng, and Ye (2016) Arenhardt, Battistella, and Grohmann (2016) Abduaziz et al. (2016)	√ √	√	√	√ √ √		Statistical Statistical DES	[China] Manufacturing [Brazil] E&E [Malaysia] Automotive
41.	Hojnik and Ruzzier (2016)	√		√	√		Statistical	[Slovenia] Manufacturing & service
42.	Xie et al. (2016)	√		√	√		Statistical	[China] Manufacturing
43.	Hojnik, Ruzzier, and Antončič (2017)	√			√		Statistical	[Slovenia] Manufacturing & service
44.	Keshminder and Chandran (2017)	√			√		Statistical	[Malaysia] Chemical
45.	Fernando and Wen (2017)	√			√		Statistical	[Malaysia] Green technology

Meanwhile, Sezen and Çankaya (2013) investigated the impact of eco-innovation and green manufacturing on firm performance of economic, environmental and social. Their regression analysis showed that the three firms’ performances are significantly impacted by their eco-process innovation practices. Iranmanesh et al. (2015), on the other hand, investigated on the extent to which eco-process and eco-product innovation influence job satisfaction through job intensity. Their investigation did not address the measure of economic performance. They discovered that both eco-process and eco-product innovations have positive direct influence on job intensity and negative indirect influence on job satisfaction through job intensity. They believed that manufacturers need to consider the risk of negative effect of eco-process innovation on staffs’ job satisfaction since it diminishes firm’s performance. Besides the above-said studies, analysis of the literature demonstrated that previous scholars have not investigated extensively the measure of social performance of eco-process innovation practice and therefore deserves special attention from researchers to develop a more comprehensive measuring instrument.

When looking at the approach employed by previous researchers to measure eco-process innovation, the analysis and synthesis permits the classification of the 45 research into two methods: perceptual and operational. Perceptual approach means survey questionnaire-based instrument is used to collect data from respondents. The measurement is performed according to the developed items and perceptual scales, whereby responses are self-declared based on respondents’ perception of eco-process innovation implementation in their firm. Operational approach, on the other hand, refers to the application of instrument which is able to compute the real operational and technical production data. The instrument is used to quantify the actual objective data of production process in assessing its innovation performance. The list of the studies by type of assessment method is given in Table 2. Analysis indicates that perceptual approach was commonly used by scholars for testing indicators of eco-process innovation that they developed. In these cases, various statistical analyses were performed to interpret the data, find patterns and draw conclusions. Cheng and Shiu (2012), for instance, carried out interviews and questionnaire survey of senior managers to collect data and validate eco-process implementation scale in their research. The analysis of survey response from Chinese companies by Dong et al. (2014) revealed that firms’ environmental performance and competitiveness were significantly influenced by different types of eco-innovation and firm size. Later, Hojnik and Ruzzier (2016) empirically validated the hypothesised relationships between process eco-innovation, several determinants and company performance. All these studies developed and tested perceptual types of instrument in which the indicators measure the respondents’ perception of eco-process innovation implementation in their firm.

There were limited attempts (by Solding, Petku, and Mardan (2009), Kara, Bogdanski, and Li (2011), Aguado, Alvarez, and Domingo (2013), Diaz-Elsayed et al. (2013), Dornfeld (2014) and Abduaziz et al. (2016)) in using the operational approach to quantify and analyse the objective data of production process in assessing innovation performance. Solding, Petku, and Mardan (2009) have conducted operational research for optimising the energy usage in the Swedish foundry industry which considered as an electricity intensive industry. They have introduced a specialised software which combines electrical energy optimisation and discrete event simulation (DES) to allow for more structured direct measurement and management of data from the production operation. The simulations and analyses of four case studies’ production and energy system demonstrated that electrical energy consumption, peak power loads and the associated costs could be reduced.

With the aim of monitoring the electrical energy consumption and identifying the improvement areas, Kara, Bogdanski, and Li (2011) developed a new energy monitoring system in the case study company to measure and monitor the electrical consumption. The use of power metre devices which were connected to supervisory control and data acquisition system enabled the management of energy usage in real time, and expected to help the company to save about 30% of the energy cost. Dornfeld (2014) suggested the systematic approach of measuring processes and resources effectiveness which includes the assessment of consumables, water, energy, waste and air emissions.

In their investigation of metal working company, Aguado, Alvarez, and Domingo (2013) constructed and validated a model which combined the practice of lean system and eco-process innovation. The life cycle analysisrevealed that the company enjoyed great improvements of economic, environmental and social performance. Meanwhile, Diaz-Elsayed et al. (2013), who applied DES method to perform cost-time profile analysis discovered hybrid lean and eco-process innovation implementation that led to reduction in operational cost of an automotive manufacturer. Their manufacturing system model was tested using various sources of data, namely, historical data, expert consultants’ interview and shop floor measurement. Abduaziz et al. (2016) used similar method in their study of energy efficiency of a vehicle assembly line. They found out that eco-process innovation resulted in cost and energy saving. Aguado, Alvarez, and Domingo (2013), Diaz-Elsayed et al. (2013) and Abduaziz et al. (2016) have employed operational approach in their studies; however, their instrument only covered the measurement of environmental and economic performance of eco-process innovation. This clearly shows that there has not been sufficient research in developing a comprehensive instrument capable of assessing the quantifiable measures of eco-process innovation performance, hence require immediate clarification from scholars.

Indicators of eco-process innovation performance

Numerous approaches have been introduced to measure eco-innovation performance. Quantitative approach of measuring eco-innovation initiatives is one of the most important ways to better understand eco-innovation. It would be useful to supply essential inputs such as trends illustrations, awareness promotion and clearer picture of benefits gained from its implementation to the firms, policy-makers and other stakeholders for informed decision-making (Organisation for Economic Cooperation and Development (OECD) 2009a). The data should be indicated by a simple unit of measurement, named indicators, to allow for timely decision-making (Olsthoorn et al. 2001). Indicators help decision-makers to visualise necessary actions and demonstrate patterns of different scenarios. Indicators are helpful in objectives settings, baseline and current performance explanations and evaluation of actions effectiveness (Organisation for Economic Cooperation and Development (OECD) 2009a). In this study, indicators are used as unit of measurement to represent the performance of eco-process innovation. The synthesis of the eco-process indicators in the context of manufacturing firms was done through an intensive review of the extracted literature, and attention was focused on indicators that were applied by other scholars in their research such as presented in Table 3. Three main measured performances of eco-process innovation are considered as important measurement aspects. The measuring aspects are represented by indicators and number of occurrence indicates the number of previous studies which have used the indicator.

Table 3. Indicators used to measure eco-process innovation at micro-level in reviewed studies.

Aspect	Indicator	Author	Number of occurrence
Economic	Sales	Gadenne et al. (2012); Cheng and Shiu (2012); Diaz-Elsaye et al. (2013); Sezen and Çankaya (2013); Cheng (2014); Huang and Yong-Hui (2015); de Oliveira Brasil et al. (2016)	7
	Profit	Cheng and Shiu (2012); Grobecker, Wolf, and Germain (2012); Cheng, Yang, and Sheu (2014); de Oliveira Brasil et al. (2016)	4
	Profit growth	Grobecker, Wolf, and Germain (2012); Huang and Yong-Hui (2015)	2
	Cash flow	Huang and Yong-Hui (2015)	1
	Market share	Cheng and Shiu (2012); Cheng, Yang, and Sheu (2014); Dong et al. (); Huang and Yong-Hui (2015); Masoumik, Abdul-Rashid, and Olugu (2015); de Oliveira Brasil et al. (2016)	6
	Production efficiency	Aguado, Alvarez, and Domingo (2013); Diaz-Elsayed et al. (2013); Masoumik, Abdul-Rashid, and Olugu (2015); Huang and Yong-Hui (2015)	4
	Production flexibility & capacity	Segarra-Oña, Peiró-Signes, and Mondéjar-Jiménez (2016)	1
	R&D investment	Dong et al. (2014)	1
	Return on Investment (ROI)	Cheng and Shiu (2012); Gadenne et al. (2012); Grobecker, Wolf, and Germain (2012); Sezen and Çankaya (2013); Cheng, Yang, and Sheu (2014); de Oliveira Brasil et al. (2016)	6
	Return on asset (ROA)	Xie et al. (2016)	1
	Material cost	Aguado, Alvarez, and Domingo (2013); Sezen and Çankaya (2013); Diaz-Elsayed et al. (2013); Masoumik, Abdul-Rashid, and Olugu (2015)	4
	Energy cost	Ren (2009); Kara, Bogdanski, and Li (2011); Sezen and Çankaya (2013); Aguado, Alvarez, and Domingo (2013); Diaz-Elsayed et al. (2013); Masoumik, Abdul-Rashid, and Olugu (2015); Abduaziz et al. (2016)	7
	Water cost	Masoumik, Abdul-Rashid, and Olugu (2015)	1
	Waste treatment cost	Sezen and Çankaya (2013)	1
	Labour cost	Segarra-Oña, Peiró-Signes, and Mondéjar-Jiménez (2016)	1
	Pollution control expenditure	Dong et al. (2014)	1
Environmental	Material consumption	Chen, Lai, and Wen (2006); Chen (2008); Chang (2011); Grobecker, Wolf, and Germain (2012); Horbach, Rammer, and Rennings (2012); Bi, Bao, and Feng (2013); Wong (2013); Aguado, Alvarez, and Domingo (2013); Chen and Chang (2013); Muscio, Nardone, and Stasi (2013); Antonioli, Mancinelli, and Mazzanti (2013); Dornfeld (2014); Dong et al. (2014); Rexhäuser and Rammer (2014); Doran and Ryan (2014); Masoumik, Abdul-Rashid, and Olugu (2015); Abdullah et al. (2015); Iranmanesh et al. (2015); Chassagnon and Haned (2015); Segarra-Oña, Peiró-Signes, and Mondéjar-Jiménez (2016); Kawai, Strange, and Zucchella (2016); Hojnik and Ruzzier (2016); Arenhardt, Battistella, and Grohmann (2016); Kong, Feng, and Ye (2016); Hojnik, Ruzzier, and Antončič (2017)	25
	Energy consumption	Chen, Lai, and Wen (2006); Chen (2008); Solding, Petku, and Mardan (2009); Ren (2009); Kara, Bogdanski, and Li (2011); Chiou et al. (2011); Chang (2011); Cheng and Shiu (2012); Grobecker, Wolf, and Germain (2012); Horbach, Rammer, and Rennings (2012); Bi, Bao, and Feng (2013); Wong (2013); Aguado, Alvarez, and Domingo (2013); Diaz-Elsayed et al. (2013); Van Den Berg, Labuschagne, and Van Den Berg (2013); Chen and Chang (2013); Tseng et al. (2013); Muscio, Nardone, and Stasi (2013); Antonioli, Mancinelli, and Mazzanti (2013); Dornfeld (2014); Cheng, Yang, and Sheu (2014); Dong et al. (); Rexhäuser and Rammer (2014); Doran and Ryan (2014); Masoumik, Abdul-Rashid, and Olugu (2015); Abdullah et al. (2015); Iranmanesh et al. (2015); Huang and Yong-Hui (2015); Chassagnon and Haned (2015); Segarra-Oña, Peiró-Signes, and Mondéjar-Jiménez (2016); Kawai, Strange, and Zucchella (2016); Hojnik and Ruzzier (2016); Abduaziz et al. (2016); Arenhardt, Battistella, and Grohmann (2016); Kong, Feng, and Ye (2016); Peng and Liu (2016); de Oliveira Brasil et al. (2016); Hojnik, Ruzzier, and Antončič (2017)	38
	Water consumption	Chen, Lai, and Wen (2006); Chen (2008); Chiou et al. (2011); Chang (2011); Gadenne et al. (2012); Horbach, Rammer, and Rennings (2012); Grobecker, Wolf, and Germain (2012); Wong (2013); Muscio, Nardone, and Stasi (2013); Van Den Berg, Labuschagne, and Van Den Berg (2013); Chen and Chang (2013); Tseng et al. (2013); Dornfeld (2014); Dong et al. (2014); Rexhäuser and Rammer (2014); Masoumik, Abdul-Rashid, and Olugu (2015), Iranmanesh et al. (2015); Abdullah et al. (2015); Huang and Yong-Hui (2015); Kawai, Strange, and Zucchella (2016); Hojnik and Ruzzier (2016); Arenhardt, Battistella, and Grohmann (2016); Hojnik, Ruzzier, and Antončič (2017)	23
	Waste, emission & pollution	Chen, Lai, and Wen (2006); Chen (2008); Chiou et al. (2011); Chang (2011); Cheng and Shiu (2012); Gadenne et al. (2012); Grobecker, Wolf, and Germain (2012); Horbach, Rammer, and Rennings (2012); Sezen and Çankaya (2013); Van Den Berg, Labuschagne, and Van Den Berg (2013); Chen and Chang (2013); Tseng et al. (2013); Muscio, Nardone, and Stasi (2013); Bi, Bao, and Feng (2013); Wong (2013); Aguado, Alvarez, and Domingo (2013); Diaz-Elsayed et al. (2013); Antonioli, Mancinelli, and Mazzanti (2013); Dornfeld (2014); Dong et al. (2014); Rexhäuser and Rammer (2014); Doran and Ryan (2014); Masoumik, Abdul-Rashid, and Olugu (2015); Abdullah et al. (2015); Iranmanesh et al. (2015); Huang and Yong-Hui (2015); Chassagnon and Haned (2015); Kong, Feng, and Ye (2016); de Oliveira Brasil et al. (2016); Peng and Liu (2016); Xie et al. (2016); Kawai, Strange, and Zucchella (2016); Hojnik and Ruzzier (2016); Keshminder and Chandran (2017); Hojnik, Ruzzier, and Antončič (2017); Fernando and Wen (2017)	36
	Material recycling and reuse	Chen, Lai, and Wen (2006); Chen (2008); Chiou et al. (2011); Cheng and Shiu (2012); Bi, Bao, and Feng (2013); Wong (2013); Aguado, Alvarez, and Domingo (2013); Chen and Chang (2013); Tseng et al. (2013); Rexhäuser and Rammer (2014); Doran and Ryan (2014); Masoumik, Abdul-Rashid, and Olugu (2015); Abdullah et al. (2015); Iranmanesh et al. (2015); Huang and Yong-Hui (2015); Chassagnon and Haned (2015); Arenhardt, Battistella, and Grohmann (2016); de Oliveira Brasil et al. (2016); Kawai, Strange, and Zucchella (2016); Hojnik and Ruzzier (2016); Hojnik, Ruzzier, and Antončič (2017); Keshminder and Chandran (2017); Fernando and Wen (2017)	23
	Hazardous materials reduction	Sezen and Çankaya (2013); Masoumik, Abdul-Rashid, and Olugu (2015); Huang and Yong-Hui (2015); Hojnik and Ruzzier (2016)	4
	Hazardous materials replacement	Horbach, Rammer, and Rennings (2012); Dong et al. (); Rexhäuser and Rammer (2014); Doran and Ryan (2014); Chassagnon and Haned (2015); Hojnik, Ruzzier, and Antončič (2017)	6
	Non-renewable energy replacement	Dong et al. (); Masoumik, Abdul-Rashid, and Olugu (2015)	2
	End-of-life management	Cheng, Yang, and Sheu (2014)	1
	ISO 14000 series certification	Lin et al. (2014)	1
	Environmental law compliant	Cheng and Shiu (2012); Van Den Berg, Labuschagne, and Van Den Berg (2013); Wong (2013); Aguado, Alvarez, and Domingo (2013); Cheng, Yang, and Sheu (2014); Peng and Liu (2016); de Oliveira Brasil et al. (2016); Fernando and Wen (2017)	8
	Environmental accidents	Sezen and Çankaya (2013); Huang and Yong-Hui (2015)	2
Social	Health & safety	Hauschild, Dreyer, and Jørgensen (2008); Gadenne et al. (2012); Sezen and Çankaya (2013)	3
	Training and education	Hauschild, Dreyer, and Jørgensen (2008); Sezen and Çankaya (2013)	2
	Human right	Sezen and Çankaya (2013)	1
	Workers’ absenteeism	Gadenne et al. (2012)	1

Economic indicators

At micro-level, economic performance is defined as a firm’s influences on its stakeholders’ economic condition and on the condition of economic systems of local, national and global level (Global Reporting Initiative (GRI) 2014). Adoption of eco-process innovation, with no doubt, has a significant positive impact on a firm’s costs (Xie et al. 2016; Sezen and Çankaya 2013). According to the findings, three indicators appear as predominant to indicate economic performance, namely, 1) sales, (2) market share, and (3) return on investment (ROI) and energy cost. In addition, few other indicators were also found to be relevant to represent the economic aspect of eco-process innovation, such as profit, production efficiency and material cost. The literature revealed that all these indicators are interconnected to each other. Ecological improvements made on manufacturing processes focus on upgrading the process and technology, which resulted in improved production efficiency and cost savings (Dong et al. 2014; Aguado, Alvarez, and Domingo 2013).

Production efficiency is the outcome of standardised, balanced processes and job procedures, shorter production cycle time and lead time, more effective use of resources (Gadenne et al. 2012), reduced inventory level and space required, and better process flexibility (Aguado, Alvarez, and Domingo 2013). The shorter production lead time, for example, allows manufacturing to hold fewer inventory, thus resulting in the lower inventory costs (Diaz-Elsayed et al. 2013). The other cost savings stem from the decrease in the cost of material, energy and other resources consumed in the production processes, reduction and elimination of emission and waste produced by the processes, and the cost of treating the waste (Sezen and Çankaya 2013). Both production efficiency and cost savings would contribute to the positive impact on the economic gains through improved sales, market share and ROI due to the improved products’ value, corporate image and competitive advantages.

Environmental indicators

According to Chen, Lai, and Wen (2006), eco-process innovation performance relates to the prevention of pollution, reduction of energy usage, zero toxicity and recycling of wastes. Environmental performance was also referred as the extent to which firm’s operations affect the natural systems (living or non-living) such as water, land, air and others. Hence, environmental indicators indicate the performance of the systems’ inputs (such as water, energy and material) and outputs (such as waste, emissions and effluents) (Global Reporting Initiative (GRI) 2014). Many studies have shown that eco-process innovation resulted in the improvement of a firm’s environmental performance (Chiou et al. 2011; Sezen and Çankaya 2013; Li 2014).

From the review, it was evident that five indicators were used widely by previous scholars in representing environmental performance of eco-process innovation: energy consumption; waste, emission and pollution; material consumption; material recycling and reuse; and water consumption. Similar views of eco-process innovation performance indicators were found by Chen, Lai, and Wen (2006) and indicated in ISO 14031 standards. They proposed four items for assessing the performance of eco-process innovation: (1) the manufacturing process of the firm effectively reduces the emission of hazardous substances or waste; (2) the manufacturing process of the firm recycles waste and emission that allow them to be treated and re-used; (3) the manufacturing process of the firm reduces the consumption of water, electricity, coal or oil; (4) the manufacturing process of the firm reduces the use of raw materials. Environmental pollution emerges because of inefficient resource consumption in production process (Porter and van der Linde 1995; Abdul Rashid et al., 2008). Adoption of eco-process innovation approach is associated with the improved resources productivity (Huang and Yong-Hui 2015), use of fewer materials and energy (Sezen and Çankaya 2013), as environmental efficiencies are achieved (Masoumik, Abdul-Rashid, and Olugu 2015). This will generate less waste and minimal environmental emission and pollution (Sezen and Çankaya 2013; Li 2014; Hojnik, Ruzzier, and Antončič 2017).

Social indicators

Social performance addresses the firm’s influence on the social systems within which it operates (Global Reporting Initiative (GRI) 2014). Studies concerning the measurement of social performance of sustainable manufacturing operation are limited in the literature compared to economic and environmental performance. Most of the indicators are subjective and qualitative in nature (Hutchins and Sutherland 2008). A study by Sezen and Çankaya (2013) showed that implementation of eco-process innovation can improve the social performance of the firm. The social aspect addresses the manufacturers’ responsibilities to fulfil the needs of current and future generations which include worker’s safety and local community benefits (Lee et al. 2012; Zhang, Calvo-amodio, and Haapala 2013). Four key aspects of social performance which includes ethical sourcing, employment practices, social impact of product and community relations, has been proposed by Ranganathan (1998).

In term of employment practices, Ranganathan (1998) emphasised that they relate to the employer’s ability to provide job and financial security, opportunity for career development, safe working environment and free from any form of discrimination. Consistent with his view, the result of this review demonstrates that health and safety is the indicator commonly adapted to measure social performance of eco-innovation taking place in the manufacturing processes as can be seen in Table 3. Since workers are the most important resource of manufacturing, carrying out production activities and supporting the eco-process innovation practice, a sense of their well-being should be among the key concern of firms. Access to the productive manpower allows firm to minimise the occurrence of negative environmental impacts, given more effective use of resources, resulting in improved environmental and economic performances (Ferreira, Moulang, and Hendro 2010). Hence, continuous improvement should be done on production process to ensure that it reduces occupational injury, diseases and fatalities as worker’s health and safety is critical aspect during production (Calik and Bardudeen 2016). It is believed that the high rate of illness and injury incidents indicates the unsafe production environment, thus affecting the production lines’ productivity, and their ability to keep the resources usage, waste and pollution generation, and incurring costs at the minimum level.

Conceptual framework of eco-process innovation performance measure

Having completed a detailed review and analysis of previous studies, a conceptual framework for measuring eco-process innovation performance in manufacturing firm is developed, and illustrated in Figure 5. Although there are several other indicators proposed by other scholars, the range of indicators emphasised in this conceptual framework are the ones found relevant to the phenomenon being measured (i.e. production process) and relationship to each other, the most frequently used, those measurable and quantifiable such as that performed by Davidescu et al. (2015). The adopted definition indicates that eco-innovation practices support the three pillars of sustainability performance, namely, economic, environmental and society. Hence, in the framework proposed, eco-process innovation practice is measured in terms of economic, environmental and social performance, demonstrating a total picture of the sustainability performance. The performance indicators are measured by sustainable improvement brought by innovations introduced in the production processes. The indicators for the economic, environmental and social performance were derived from the results of analysis done on indicators that were used by previous researchers in their study.

Figure 5. Conceptual framework for measuring eco-process innovation performance.

Economic performance is defined as a firm’s influences on its stakeholders’ economic condition and on the condition of economic systems of local, national and global level (Global Reporting Initiative (GRI) 2014). Three indicators were selected to indicate economic performance, namely energy cost, material cost and production efficiency. Ecological improvements made on manufacturing processes focus on upgrading the process and technology, resulted in improved production efficiency and cost savings (Dong et al. 2014; Aguado, Alvarez, and Domingo 2013). The improved production process allows the use of resources productively such as material and energy at lower rate, which causes the cost of those resources to reduce. The energy which consumed to power numerous types of manufacturing processes might include electricity, petroleum fuels and natural gas. Material refers to the natural or synthetic substances that are used to manufacture products. In relation to production efficiency, this paper suggests the measure of production cycle time performance to represent the production efficiency. In the same vine as Marsudi and Shafeek (2014), production cycle time is referred as the cumulative time used to carry out processes involved in producing a product.

Environmental performance has been noted as the extent to which firm’s operations effect the natural systems (living or non-living) such as water, land, air, etc. Hence, environmental indicators reflect the performance of the systems’ inputs (such as water, energy and material) and outputs (such as waste, emissions and effluents) (Global Reporting Initiative (GRI) 2014). The resources inflow and produces outflow of production process were both seen as the source of environmental degradation, and also stand out as firm primary focus of mitigation strategy. In this study, the indicators, namely, energy consumption; material consumption; and waste, emission and pollution indicators were considered to represent the environmental performance of eco-process innovation. Eco-process innovation may help to improve the efficiency of the process, reduce resource utilisation and minimise waste, emissions and pollution (Calik and Bardudeen 2016). Environmental pollution emerges because of inefficient resource consumption in production process (Porter and van der Linde 1995). Adoption of eco-process innovation approach is associated with the improved resources productivity (Huang and Yong-Hui 2015), and the use of fewer materials and energy (Sezen and Çankaya 2013). Therefore, as environmental efficiencies are achieved (Masoumik, Abdul-Rashid, and Olugu 2015), less waste and minimal environmental emission and pollution are generated (Sezen and Çankaya 2013; Li 2014; Hojnik, Ruzzier, and Antončič 2017).

Social performance addresses the firm’s influence on the social systems within which it operates (Global Reporting Initiative (GRI) 2014). The social aspect addresses the manufacturers’ responsibilities to fulfil the needs of current and future generations which include worker’s safety and local community benefits (Lee et al. 2012; Zhang, Calvo-amodio, and Haapala 2013). Worker’s health and safety was chosen in this study to indicate the social performance of eco-process innovation practiced in manufacturing firm. The access to the productive manpower allows firm to minimise the occurrence of negative environmental impacts, given more effective use of resources, resulting in improved environmental and economical performances (Ferreira, Moulang, and Hendro 2010). Hence, continuous improvement should be done on production process to ensure that it reduces occupational injury, diseases and fatalities as worker’s health and safety is critical aspect during production (Gadenne et al. 2012; Calik and Bardudeen 2016). It is believed that the high rate of illness and injury incidents indicates the unsafe production environment, thus affecting the production lines’ productivity, and their ability to keep the resources usage, waste and pollution generation, and incurred costs at minimum level. In fact, workers are the most important resource of manufacturing, who carry out the production activities and support the eco-process innovation practice, a sense of their well-being, thereby should be among the key concern of firms.

This paper suggests that the proposed performance indicators of eco-process innovation be measured using the operational approach as the review revealed that the analysis of actual operational data of production process has not been fully addressed in previous studies. Operational data of real-life case study of a Malaysian electronic component manufacturer will be employed to validate the indicators set using DES approach. Arena simulation software will be utilised to model and simulate the production line in which eco-process innovations have taken place, to measure the improvement in the proposed indicators, brought by the implementation of eco-innovative process changes. For the purpose, two separate models of production line will be constructed to represent the lines state before and after the eco-process innovation, to enable the measure of improvement in the performance indicators. The analysis of actual operational data will validate the appropriateness of the proposed indicators to indicate the eco-process innovation performance.

Conclusions and future research

Studies on eco-innovation have evolved rapidly over the last two decades due to the extensive environmental awareness amongst industrialists. However, the literature on measuring eco-process innovation performance is rather limited. Numerous concerns relating to the eco-process innovation evaluation, the measuring aspects, approaches and indicators are not much understood. This study is an attempt to address some of the issues through a systematic analysis of the 45 relevant previous researches. Using bibliographic evidence, this paper has identified the growth patterns in eco-process innovation study over time, the leading countries which published the most relevant studies in the field, and the sector studied. It has been proved that there is a need to construct a much more comprehensive instrument for measuring the economic, environmental and social performance of eco-process innovation as most previous empirical research highlighted the measure of economic and/or environmental performance with the exclusion of social aspect.

Grounded on a thorough review of the literature, a conceptual framework was developed for measuring eco-process innovation at micro-level. The framework supports the argument that eco-innovation assessment should entail these aspects, in accordance with the adopted eco-innovation definition. Seven performance indicators were identified: energy cost; material cost; production efficiency; energy consumption; material consumption; waste, emission and pollution; and worker’s health and safety. In terms of limitation, it is important to acknowledge that results were dependent on the databases where the searching took place, and the inclusion and exclusion criteria applied for the article selection, and there might be other eco-process innovation performance measure research not covered in this study. Additionally, the conceptual framework suggests the measure of performance improvements resulted from the implementation of eco-innovation which is focused to the manufacturing process stage only and does not address the other phases of product’s life cycle such as the supply of materials from suppliers, products delivery to the users and the product’s recycling.

The next stage of this research involves conducting a case study in an electrical component manufacturer in Malaysia to test the proposed performance indicators (shown in Figure 5) through actual operational data. Several production processes which have undergone significant improvements will be investigated. Production process simulations and relevant statistical analyses will be conducted to evaluate the data and test the developed set of eco-process innovation performance indicators. Apart from that, the study of eco-process innovation performance measure can also be conducted through a meta-analysis, with a broader scope, including other sectors such as services and small and medium enterprises (SME). It is believed that these eco-process innovation performance indicators would be critical information for firm’s decision-making process to ensure continual organisational sustainability.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Suziyana Mat Dahan

Suziyana Mat Dahan is a PhD candidate in the Razak Faculty of Technology and Informatics at Universiti Teknologi Malaysia, Malaysia.  She received her MSc in Engineering Management from the University of Northumbria and BA in Management (Technology) from the Universiti Teknologi Malaysia.  Her current research focuses on developing indicators of eco-process innovation performance in manufacturing firm.  Her research interests include innovation management, technology management and operational management.  She is currently a Senior Lecturer at the University Malaysia Pahang (UMP).

Sha’ri Mohd Yusof

Sha’ri Mohd Yusof obtained his degree in Industrial Engineering from University of Miami, a Master of Science in Integrated Quality Systems from University of Birmingham and Doctor of Philosophy with a focus on Total Quality Management for Small manufacturing business from the University of Birmingham.  He is Professor of Quality Engineering and Management at the Razak Faculty of Technology and Informatics of the Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia.  Having graduated from the USA, he spent one-year training in Body Assembling in Mitsubishi Motors Corporation, Japan and worked with Proton for over five years. He has been in academia since 1990 and his main research interests are in Quality Engineering, Robust Engineering, Lean Manufacturing and Sustainability.