Resource management practice through eco-innovation toward sustainable development using qualitative information and quantitative data

Accepted Manuscript Resource management practice through eco-innovation toward sustainable development using qualitative information and quantitative data

Chia-Hao Lee, Kuo-Jui Wu, Ming-Lang Tseng PII:

S0959-6526(18)32392-8

DOI:

10.1016/j.jclepro.2018.08.058

Reference:

JCLP 13842

To appear in:

Journal of Cleaner Production

Received Date:

15 November 2017

Accepted Date:

06 August 2018

Please cite this article as: Chia-Hao Lee, Kuo-Jui Wu, Ming-Lang Tseng, Resource management practice through eco-innovation toward sustainable development using qualitative information and quantitative data, Journal of Cleaner Production (2018), doi: 10.1016/j.jclepro.2018.08.058

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ACCEPTED MANUSCRIPT Resource management practice through eco-innovation toward sustainable development using qualitative information and quantitative data

Chia-Hao Lee Department of Finance, Ming Dao University, Taiwan E-mail: [email protected]

Kuo-Jui Wu School of Business, Dalian University of Technology, China E-mail: [email protected]

Ming-Lang Tseng* (Corresponding Author) Institute for Innovation and Circular Economy, Asia University, Taiwan E-mail: [email protected]

1

ACCEPTED MANUSCRIPT Resource management practice through eco-innovation toward sustainable development using qualitative information and quantitative data

Abstract Many firms are aware that eco-innovation is an important issue in resource management practice due to its contribution to firms’ competencies and capabilities. Eco-innovation represents a differentiating attribute for sustainable business in fiercely competitive environments. However, prior studies have not examined the role of eco-innovation in sustainable development through hierarchical structural analysis of resource management practice to address firms’ capabilities and competencies; in particular, the attributes contain qualitative information and quantitative data. This study proposes a hybrid method comprising the fuzzy Delphi method, importance-performance analysis, and a decisionmaking trial and evaluation laboratory to rank the attributes and assist firms in decisionmaking. The result supports the use of advanced eco-friendly technologies and the selection of potential talent to support eco-innovation and foster changes in strategic vision, management structure, and administrative procedures. Clear policies and procedures, mutual trust and respect, and sales staff characteristics are needed to improve firm performance toward sustainable business.

Keywords: eco-innovation; resource management practice; fuzzy Delphi method; importance-performance analysis; decision-making trial and evaluation laboratory (DEMATEL)

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ACCEPTED MANUSCRIPT Resource management practice through eco-innovation toward sustainable development using qualitative information and quantitative data

1.

Introduction Firms consider environmental strategy as an instrument to simultaneously achieve

growth targets, competitiveness and profitability (Andersen, 2010; Porter and Van Der Linde, 1995). Achieving economic growth is highly reliant on innovation and generates vast environmental impacts. Consumer environmental awareness, social expectations and government pressure are forcing firms to pay more attention to social and environmental issues in new product development (Bocken et al., 2014; De Medeiros et al., 2014). Addressing these sustainable development issues becomes an involved and complex task that challenges time limitations, knowledge, finances, human resources, management styles and organizational structure (Lee, 2002; Jenkins, 2004). Green practices implemented to resolve these challenges need to involve all the management function’s activities, and all firms’ functions take responsibility for innovating the green practices. A set of attributes and environmental dynamism that explains the interrelationship between resource management practice (RMP) and eco-innovation is required to guide firms toward sustainable development goals in order to ensure that green practices are efficient and effective while addressing the associated challenges. There are eco-innovation activities involved in the implementation of interventions, subsidies and other instruments to achieve sustainable development goals, although they progressively isolate market signals. These activities might not be implemented in the appropriate time period to generate the desired control level. Hence, planning and resource utilization have become prescriptive and are promoted by regulations and policies (Robertson, 1993; Tseng and Bui, 2017). Prior studies have attempted to inspect the operational process for eco-innovation in qualitative information and quantitative data. 3

ACCEPTED MANUSCRIPT However, the attributes that determine whether these operations affect RMP are not well addressed (Sierzchula et al., 2012; Cai and Zhou, 2014). LesLEvidow et al. (2016) presented that eco-innovation consists of several innovative practices, including economic and ecological resource benefits, production process improvements to realize lower resource burdens, harmful material substitutions, water recycling and waste reduction. To achieve effective eco-innovation, RMP plays an important role as a source of creativity and intelligence that guarantees the quality and innovation of a firm’s sustainable development and newly developed green products. Firms maintain team motivation toward resolving challenges through a set of reliable and valid measures. RMP includes human, knowledge and information technology resources, which are divided into intangible and tangible resources (Sarkis et al., 2010; Doran and Ryan, 2012; Tseng and Bui, 2017). Though prior studies focused on sustainable business practices have generally concentrated on investigating eco-innovation development and performance in a specific area (such as product-service innovation, service innovation, technological innovation, or infrastructure and policy innovation), the linkage between eco-innovation and RMP has not been identified (Rehfeld et al., 2007; Shin et al., 2008; Tseng et al., 2013). In particular, the attribute measures contain qualitative information. Thus, this study addresses the following questions: 

What is a firm’s eco-innovation attributes in RMP?



What attributes improve RMP through eco-innovation with qualitative information and quantitative data? Firms encounter difficulties in balancing social, economic and environmental

performance than developed economies, and this balancing process needs to consider ecoinnovation in RMP integration. However, eco-innovation and RMP are lacking in prior studies, in which the launch of eco-innovation is considered a cost-prohibitive activity and the resources needed to guarantee strategy implementation are lacking (Wu et al., 2016; 4

ACCEPTED MANUSCRIPT Tseng and Bui, 2017). This study applies the fuzzy Delphi method (FDM) to address linguistic preferences and form the set of attributes. The decision-making trial and evaluation laboratory (DEMATEL) addresses the interrelationships among the measures and uses an importance-performance

analysis

(IPA)

to

perform

the

performance

measures.

Nevertheless, the qualitative information and quantitative data are present in the measures. Hence, there is a need to apply this hybrid method to study RMP through eco-innovation toward sustainable development. Few prior studies have enabled firms to overcome the complexity and uncertainty in eco-innovation assessments and toward sustainable development among business practices (Dong and Shi, 2014). Hence, this study not only concentrates on specific eco-innovation types for assessing performance but also identifies the RMP effects on a firm’s eco-innovation performance. The following section is a literature review that provides an extensive theoretical background of RMP and illustrates proper eco-innovation measurement collection and multi-attribute measurement development. Detailed information on hybrid methods is presented in section 3. Case and empirical results are presented in section 4, and implications and conclusions are discussed in section 5 and the final section.

2.

Literature review This section presents the proposed RMP attributes and reviews the extensive literature

focused on RMP through eco-innovation. The subsections review RMP, eco-innovation, proposed methods and measures from prior studies.

2.1 Resource Management Practice RMP provides a solid theoretical basis for deliberating on the contribution of resources and capabilities to eco-innovation performance (Menguc and Ozanne, 2005; Dangelico and Pujari, 2010). However, RMP may not be able to describe the optimal method of employing 5

ACCEPTED MANUSCRIPT resources to gain a competitive advantage under rapid external eco-innovation changes (DeSarbo et al., 2005; Hart, 1995). The capabilities and performance improved firms’ understanding of eco-innovation due to the interrelationships among multiresources. Under shifting government regulations, stakeholder pressure and rapid market movement in terms of eco-innovation, RMP must be able to identify methods for improving business performance through eco-innovation activities. In addition, traditional management functions ignore the constraints imposed by the natural environment, and the activities have unaddressed ecological impacts. Prior studies neglect the application of RMP to ecoinnovation, in which significant emerging resources provide a competitive advantage without sufficient support (Hart, 1995). Steven and Robert (1985) argued that RMP in an operation embodies a set of important choices about various attributes, such as equipment, process technologies, human skills and inventory. Somsuk et al. (2012) categorized resources into four types: organizational, technological, human and financial resources. These four types of resources had nearly the same function, which suggested that technological resources were utilized in place of physical

resources

(Barney

and

Hesterly,

1999).

Technological

resources are

intangible resources for intellectual property, accumulated skills and experience, software licenses and innovative patents. Borch et al. (1999) defined “technological resources” as a firm’s specific product and (physical) technology, equipment/laboratories, highly specialized skill sets and technological capabilities. Moreover, the human resources include the development team, management team and staff, which require unique aptitudes and abilities to lead a firm toward success. Löfsten (2010) presented financial resources as all types of financial support that a firm utilizes in all management activities. Organizational resources consider activity plans, controls, coordination, systems, routines and relationships within the entire firm (Barney, 1999; Tomer, 1987). Then, prior studies have emphasized that the attributes of innovative success are a firm’s resources and capabilities (Christensen 6

ACCEPTED MANUSCRIPT and Overdorf, 2000; Panne et al., 2003; Teece, 1988). RMP appears to be an appropriate approach for launching innovative development. Sirmon et al. (2007) provided extensive solid support than core competency theory for effectively investigating the eco-innovation process. Nonetheless, there is a need to provide RMP through eco-innovation. Hence, focusing on stakeholders enabled the establishment, generation and expansion of processes to support RMP in efficient and effective resource development. A reconfiguration process is used to address the transformation of assets by monitoring the current situation to identify gaps and manage future changes (Drnevich and Kriauciunas, 2011; Pavlou and El Sawy, 2011; Tseng and Bui, 2017). RMP has been attracted attention in the literature on thorough process discussions connecting precedent practices with ecoinnovation activities (Tseng and Bui. 2017; Wu et al., 2016; Wu et al., 2017). Many firms took RMP and eco-innovation into account when launching sustainable development initiatives. However, only a few studies have presented RMP through eco-innovation toward sustainable development. Several prior studies have emphasized aspects of these practices for assessing higher performance (Kindstrom et al., 2013; Tseng, 2011). This study integrated RMP and eco-innovation to provide guidelines for sustainable practices’ benefits and to generate lower revenue risk and reduce resource waste (Teece, 2007; Kindstrom et al., 2013; Michailova and Zhan, 2014).

2.2 Eco-innovation Eco-innovation is the development of products and processes that contribute to sustainable development and the application of this knowledge to elicit direct or indirect ecological improvements in external and internal boundaries. The external boundary contains all the firm’s external practices for green and sustainable practices and includes suppliers, regulations, and market demand (del Río et al., 2010; Lin et al., 2013). The internal boundary includes practices related to the efficient and effective management of the eco7

ACCEPTED MANUSCRIPT innovation process among firms, including the firm’s management, production process and new product design (Dangelico and Pontrandolfo, 2010; Lin et al., 2013; Wu et al., 2016). In addition, Alegre and Chiva (2008) stated that sustainable development requires the generation of continuous eco-innovation as one of the critical practices in this rapidly changing world. Carrillo-Hermosilla et al. (2010) utilized eco-innovation as an instrument and linked it with innovation systems and renovated the entire system through ecological and economic considerations. Eco-innovation needed to fulfill customer needs and social expectations in the initial step and then promote the investment of further eco-innovation through cost savings, efficient resource utilization, and regulation compliance (Kesidou and Demirel, 2012). Moreover, several studies have attempted to address eco-innovation from different perspectives, such as organizational culture, strategy and leadership, government policy, stakeholders and the features of eco-innovation (Porter-O’Grady and Malloch, 2010; Veugelers, 2012; Lin et al., 2013). However, Kemp and Arundel (1998) argued that eco-innovation should take technical, organizational and marketing innovations into account. The Organization for Economic Cooperation and Development (OECD) (2005) identified four distinct types of eco-innovation: product, process, organizational and marketing innovation. del Río et al. (2010) categorized eco-innovation into three innovation types — process/product, mature/immature and radical/incremental innovation — in the decisive design process to determine the environmental impacts of innovation. Horbach (2008) and Triguero et al. (2013) presented eco-process, eco-product, eco-organization and innovation practices as critical attributes of eco-innovation and discovered that eco-innovation activities encompass each main aspect of a firm that contains the relevant activities that pertain to different sorts of functions. These activities lead to the improvement of a firm’s process function, changes in existing products and the development of new products toward sustainable development. Several theories pertain to different aspects of the eco-innovation puzzle and cover 8

ACCEPTED MANUSCRIPT institutional theory, cognitive theories, transaction cost economics, sociotechnical approaches, market orientation and resource-based views; however, the relationship between eco-innovation and RMP is undefined (Barney, 1991; Borch et al., 1999; Cai and Zhou, 2014). Prior studies have attempted to demonstrate that eco-innovation is an effective RMP approach (Wu et al., 2015; Tseng and Bui, 2017). These firms’ activities are difficult to perceive, build, assess and implement in related attributes due to the complex interrelationships and uncertainties that occur in practice. Hence, the goal of this study is to establish a systematic assessment for identifying specific attributes from eco-innovation requirements (Carrillo-Hermosilla et al., 2010). To overcome such limitations, this study proposes a hybrid method for assessing eco-innovation performance and RMP together.

2.3 Proposed Method Prior studies have applied classical statistical methods to approach RMP (Barney, 1991; Robertson, 1993; Somsuk et al., 2012; Trainor et al., 2013). However, few studies have discussed RMP attributes through linguistic preferences. Hence, the FDM is proposed to filter unnecessary attributes based on experts’ judgment. An IPA is integrated with DEMATEL to assess the degree of performance and categorize the attributes into cause and effect groups. FDM has been implemented in management science for the prediction and analysis of public policy and project planning (Dalkey and Helmer, 1963). Tseng and Bui (2017) integrated triangular fuzzy numbers and FDM to enhance the accuracy of results and reduce the uncertainty of expert judgments. This proposed method enables the transfer of expert judgments from two terminal points into membership degrees, thereby avoiding the impacts of statistical bias and extreme values. The advantage of this method is the simple integration of all expert judgments and the screening out of unnecessary criteria (Javad et al., 2016; Tseng, 2009b). The IPA is applied more widely in analytical fields, is used to solve particular 9

ACCEPTED MANUSCRIPT management problems and provides an extremely transparent and replicable instrument for identifying the pros and cons of scenarios and determining areas for resource arrangement based on increasing the adaptive capacity to provide resource management in districts with complex governance (Tseng and Bui, 2017; Wu et al., 2016). However, the IPA cannot integrate performance with importance into a single number as a final score. This study integrates DEMATEL with IPA to determine the overall final score and achieve decisionmaking. A full understanding of cause-and-effect interrelationships is developed for RMP attributes. This study proposes a hybrid method that integrates the advantages of FDM, IPA and DEMATEL. The proposed method enables the assessment of the interactions among RMP criteria and explores the effects between RMP and eco-innovation. This study employs fuzzy set theory to transform the linguistic preferences from expert judgments into quantitative values due to human preferences containing high uncertainty and possessing qualitative features (Javad et al., 2016; Tseng, 2009a). These values must be transformed into precise values before acquiring the final results. Such linguistic terms are used to define rough perceptions based on fuzzy numbers to manage the uncertainty of assessing information and the vagueness of linguistic expression. Hence, this study applies a hybrid method approach to handle complex situations.

2.4 Proposed Measures Javad et al. (2016) proposed four critical concerns when exploring an optimal solution of inventory management issues within buyer and supplier partnerships. These concerns address physical constraints, shortages, discounts and demand variations. In the supply chain, discounts are used to motivate the buyer to purchase greater amounts of product; thus, offering price reductions affects order quantities. However, shortages occur, and the original orders cannot be fulfilled when the market demand or production is not satisfied on 10

ACCEPTED MANUSCRIPT time, which generates back-order costs. In addition, the features of demand variation possess high uncertainty, and generating accurate forecasts is difficult, which causes additional costs and insolvency. The bullwhip effect is a well-known phenomenon under demand variation, and it typically occurs if a firm makes overly optimistic predictions of market demand. Subsequently, the available space, budget, facilities and logistics are all restricted by physical constraints that generally occur in real world eco-innovation launches. Somsuk et al. (2012) reviewed the extensive business literature in terms of RMP aspects and then categorized resources into four types: technological, human, financial, and organizational resources. Technological resources concentrate on know-how, infrastructure and technologies/ideas (Hisrich and Smilor, 1988; Barney and Hesterly, 1999), and financial resources refer to all financial support and consulting, in-kind financial support and access to finance and capitalization (Hacket and Dilts, 2004; Lee and Osteryoung, 2004). Human resources include talented managers, expert organization, coaching and on-site business expertise (Hackett and Dilts, 2004; Sierzchula et al., 2012), and organizational resources refer to the capabilities connected with the selection process for potential talent, concise program milestones with clear policies and procedures, mutual trust and respect, technology transfer, and research and development (Hacket and Dilts, 2004; Lee and Osteryoung, 2004). Moreover, knowledge management is considered a crucial resource for developing a competitive advantage and creating value, and it needs to be considered a fundamental core competence. Knowledge management enables the generation of dynamic assets that firms maintain and guides the firm’s conduct (Massa and Testa, 2009; Tseng, 2011). The function of knowledge management is to bridge humans and computers when exploring meaningful knowledge resources (Lee et al., 2002; Feng et al., 2004; Holsapple and Joshi, 2004; Holsapple and Wu, 2011). Additionally, Massa and Testa (2009) indicated that knowledge creation/acquisition, knowledge storage, knowledge transfer and knowledge application should be included in knowledge management. Knowledge creation/acquisition relates to 11

ACCEPTED MANUSCRIPT the internal knowledge used to conduct current practices or the knowledge obtained from outside resources. Knowledge transfer helps firms distribute knowledge to those who need it. The integrative capability of a firm involves the ability to deliver unexpected resources/abilities when adapting to new routines/opportunities that occur during regular changes and adjustments (Liao et al., 2009). This capacity enhances the firm’s ability to gather, integrate and adopt all practices to launch eco-innovation; therefore, integrative capability can be used to forecast the performance of eco-innovation (Cai and Zhou, 2014; Wu et al., 2015). Wu et al. (2015) discussed eco-innovation in dynamic organizational capability by proposed product-service processes; collaboration with research institutes, agencies and universities; subsidies and fiscal incentives; new product development time; organizational performance adjustments; sales staff quality; and eco-organizational innovation motivations when assessing eco-innovation effectiveness and performance (Horbach, 2008; Horbach et al., 2012; Cai and Zhou, 2014; Hofstra and Huisingh, 2014). The initial set of proposed measures is shown in Table 1. Table 1. Initial set of proposed measures Aspects

AS1

Organizational resources

EcoAS2 managerial innovations Technological AS3 resources AS4

Financial resources

Criteria C1 C2 C3 C4 C5

Selection process for potential talent Concise program milestones with clear policies and procedures Mutual trust and respect Technology transfer and R&D

References Hacket and Dilts, 2004; Lee and Osteryoung, 2004;

C6

Administrative efforts toward renewing organizational routines Procedures

Cruz et al., 2006 Massa and Testa, 2008

C7 C8 C9

Technology/ideas Know-how Infrastructure

Hisrich 1988

C10 Access to financing and capitalization C11 Financial support and consulting 12

and

Smilor,

Hacket and Dilts, 2004; Lee and Osteryoung,

ACCEPTED MANUSCRIPT

AS5

Inventory management

Knowledge AS6 management

AS7

Human resources

Eco-product AS8 innovation

Eco-innovation effectiveness AS9 and performance

C12 In-kind financial support

2004

C13 C14 C15 C16

Javad et al., 2016

Discounts Shortages Demand variation Physical constraints

C17 Knowledge creation/acquisition C18 Knowledge transfer

Holsapple and Wu, 2011; Massa and Testa, 2009

C19 C20 C21 C22

Sierzchula et al., 2012; Hacket and Dilts, 2004;

Coaching Talented managers Expert organization On-site business expertise

C23 Shortening product life cycles C24 Advanced eco-friendly technologies C25 Increasing competition C26 Collaboration with research institutes, agencies and universities C27 Product-service process C28 Subsidies and fiscal incentives C29 New product development time C30 Motivation for eco-organizational innovation C31 Organizational performance adjustments

Carrilo-Hermosilla al., 2010

et

Horbach, 2008; Horbach et al., 2012; Cai and Zhou, 2014; Hofstra and Huisingh, 2014

C32 Quality of the sales staff

3.

Method

RMP attributes are often incorporated into an assessment model that contains qualitative and quantitative information. Data transformation and computation processes are described below. 3.1 Case Background The Taiwanese electronics firms increase RMP during eco-innovation processes. The case firm has attempted to improve human life through the design of a series of products and the provision of a variety of services. These products and services rely on the firms’ 13

ACCEPTED MANUSCRIPT know-how, management and eco-innovation practices, which were produced through longterm research and development, offer flexibility and competence for Taiwanese electronics firms and enable cooperation with partners in developed countries under the original equipment manufacturing mode. In addition, these firms guarantee the growth of economic scale and simultaneously satisfy consumers’ requirements for low costs, on-time delivery and consistent quality. The question to be answered is how causal decisive criteria affect eco-innovation and generate the appropriate dynamics to reinforce the firm’s competitiveness. The present case firm is to undergo a merger and acquisition by an international firm. This firm is one of the world’s largest electronic firms. However, there are a few failed strategies regarding RMP through eco-innovation. Hence, strategies and business models are important references for relevant firms to compete in the intense industry. To clarify the competition of the case firm and provide a better understanding of RMP and eco-innovation, this study takes incomplete information into account and proposes a hybrid method to identify the interrelationships among the attributes. Face-to-face interviews and online inquiries are adopted to consult with expert groups for the development of the effectiveness questionnaire. The expert group is composed of three executive managers, five senior managers, five senior engineers and three professors who have seven years of working experience related to the industry. The following analytical results can provide quantitative support for guiding electronics firms to enhance the competitiveness and improve the performance to compete in the market.

3.2 Quantitative Data Transformation Crisp values are identified from the assessment of performance, which contains numerous units and cannot be directly calculated. Standardizing these values requires 14

ACCEPTED MANUSCRIPT normalization of the crisp values. The normalization process of 𝐴𝑆𝑎𝑏 adopts the following equation (Tseng et al., 2009; Wu et al., 2016): 𝐴𝑆𝑎𝑏 =

(𝐴𝑆𝑎𝑏𝑖 ‒ 𝑚𝑖𝑛𝐴𝑆𝑎𝑏𝑖 ) (𝑚𝑎𝑥 𝐴𝑆𝑎𝑏𝑖 ‒ 𝑚𝑖𝑛𝐴𝑆𝑎𝑏𝑖 )

𝑖

(

1

(1)

, 𝑖 = 1,2,⋯𝑚

2

𝑖

)

(

𝑖

1

2

𝑖

)

where 𝑚𝑎𝑥 𝐴𝑆𝑎𝑏 = 𝑚𝑎𝑥 𝐴𝑆𝑎𝑏,𝐴𝑆𝑎𝑏,⋯𝐴𝑆𝑎𝑏 and 𝑚𝑖𝑛𝐴𝑆𝑎𝑏 = 𝑚𝑖𝑛 𝐴𝑆𝑎𝑏,𝐴𝑆𝑎𝑏,⋯𝐴𝑆𝑎𝑏 .

3.3 Fuzzy Delphi Method Ishikawa et al. (1993) proposed the integration of fuzzy set theory with the traditional Delphi method. In addition, Noorderhaben (1995) applied FDM to acquire a group decision to solve the fuzziness of expert judgments in order to improve the efficiency and quality of questionnaires. Suppose that the value of significance of a number 𝑏 element is assessed by a number 𝑎 expert as 𝒿 = (𝓁𝑎𝑏,𝒸𝑎𝑏,𝑟𝑎𝑏), 𝑎 = 1,2,3,⋯𝑛;𝑏 = 1,2,3,⋯𝑚; then, the weighting 𝒿𝑏

(

of the number 𝑏 element is 𝒿𝑏 = (𝓁𝑏,𝒸𝑏,𝑟𝑏), where 𝓁𝑏 = 𝑚𝑖𝑛 (𝓁𝑎𝑏), 𝒸𝑏 = ∏𝑛1𝒸𝑎𝑏

)

1𝑛

, and 𝑟𝑏 =

𝑚𝑎𝑥 (𝑟𝑎𝑏). Thus, the linguistic terms and triangular fuzzy numbers are transformed into linguistic values, as shown in Table 2. Table 2. Transformation table of linguistic terms Linguistic terms (performance/importance) Extreme Demonstrated Strong Moderate Equal

Corresponding

Important 1.0

triangular

fuzzy

numbers

Performance

(0.75, 1.0, 1.0) (0.5, 0.75, 1.0) (0.25, 0.5, 0.75) (0, 0.25, 0.5) (0, 0, 0.25)

0

0.25

0.50

0.75

1.0

Triangular fuzzy membership functions for performance/importance

To generate the convex combination value 𝐻𝑏, the following equations are proposed, which adopt an 𝛼 cut approach to generate the result (Wu et al., 2016): 15

ACCEPTED MANUSCRIPT 𝑢𝑏 = 𝑟𝑏 ‒ 𝛼(𝑟𝑏 ‒ 𝒸𝑏), 𝑙𝑏 = 𝓁𝑏 ‒ 𝛼(𝒸𝑏 ‒ 𝓁𝑏), 𝑏 = 1,2,3,⋯𝑚

(2)

Generally, 0.5 is used to represent 𝛼 under the common situation. This value can be adjusted based on whether the experts are optimistic or pessimistic adopters by setting it to 1 or 0. The precise value of 𝐻𝑏 can be generated as follows: 𝐻𝑏 = ∫(𝑢𝑏, 𝑙𝑏) = λ[𝑢𝑏 + (1 ‒ λ)𝑙𝑏]

(3)

where λ is utilized to express the degree of optimism for a decision maker and to balance the radical judgments from the expert group. Then, 𝛿 = ∑𝑛𝑎 = 1(𝐻𝑏 𝑛) is the threshold in filtering the necessary attributes. If 𝐻𝑏 ≥ 𝛿, the number 𝑏 criterion is accepted to assess the criteria. Otherwise, the criterion needs to be rejected.

3.4 Fuzzy Importance-Performance Analysis Integrated with DEMATEL Assuming that a fuzzy set 𝑇 in a universe of discourse 𝐷 is featured by the membership function 𝑓𝑇(𝐷), which represents a 0 to 1 membership function of 𝑇 (Tseng et al., 2008; Tseng, 2009a; Wu et al., 2016), the membership function can be expressed through the following equation to define the corresponding triangular fuzzy number (𝓁,𝒸,𝑟):

𝑓𝑇(𝐷)

{

0, 𝐷 < 𝓁

(𝐷 ‒ 𝓁 ) (𝑐 ‒ 𝓁 ) , 𝑐 ≥ 𝐷 ≥ 𝓁 (𝑟 ‒ 𝐷 ) (𝑟 ‒ 𝑐) , 𝑟 ≥ 𝐷 ≥ 𝑐

(4)

0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

These triangular fuzzy numbers depend on the three-value determination that covers the minimal number 𝓁, the mean number 𝒸 and the maximal number 𝑟. The criteria values are compared with linguistic terms before they are transformed into triangular fuzzy numbers. Table 2 displays the triangular fuzzy numbers that correspond to linguistic terms based on the proposed quick transformation. Set 𝐼𝑎𝑏 becomes the importance-weighted value of aspect 𝑎 and criterion 𝑏, whereas the membership function of the triangular fuzzy numbers 𝐼𝑎𝑏 ∈ 𝐸. Subsequently, 𝐽𝑎𝑏 16

ACCEPTED MANUSCRIPT becomes the performance value of aspect 𝑎 and criterion 𝑏 when the membership function of triangular fuzzy numbers 𝐽𝑎𝑏 ∈ 𝑄. 𝑘

(

𝑘

𝑘

𝑘

)

𝑘

𝑘

𝑘

𝑘

(5)

𝑘

(

𝑘

𝑘

𝑘

)

𝑘

𝑘

𝑘

𝑘

(6)

𝐼𝑎𝑏 = ∆𝓁𝑎𝑏,∆𝒸𝑎𝑏,∆𝑟𝑎𝑏 , 𝐼𝑎𝑏 ∈ 𝐸, where 0 ≤ ∆𝓁𝑎𝑏 ≤ ∆𝒸𝑎𝑏 ≤ ∆𝑟𝑎𝑏 ≤ 1 𝐽𝑎𝑏 = ∇𝓁𝑎𝑏,∇𝒸𝑎𝑏,∇𝑟𝑎𝑏 , 𝐼𝑎𝑏 ∈ 𝑄, where 0 ≤ ∇𝓁𝑎𝑏 ≤ ∇𝒸𝑎𝑏 ≤ ∇𝑟𝑎𝑏 ≤ 1 𝑘

where 𝐼𝑎𝑏 represents the assessed value from an expert’s judgement of aspects 𝑎 and criterion 𝑏. Because the judgments are expressed as fuzzy numbers, a defuzzification process is required to transform these numbers into crisp values. Subsequently, the center-of-area approach proposed by Lin et al. (2013) is adopted to obtain the best nonfuzzy performance 𝛽𝑎. Eqs. (7) and (8) generate the best nonfuzzy performance value from fuzzy weights. ∆ 𝛽𝑎 = ∇ 𝛽𝑎 =

[(∆𝑟𝑎𝑏𝑘 ‒ ∆𝓁𝑎𝑏𝑘 ) + (∆𝒸𝑎𝑏𝑘 ‒ ∆𝓁𝑎𝑏𝑘 )] 3

[(∇𝑟𝑎𝑏𝑘 ‒ ∇𝓁𝑎𝑏𝑘 ) + (∇𝒸𝑎𝑏𝑘 ‒ ∇𝓁𝑎𝑏𝑘 )] 3

𝑘

(7)

𝑘

(8)

+ ∆𝓁𝑎𝑏, ∀𝑖 + ∆𝓁𝑎𝑏, ∀𝑖

The final score 𝜏 is obtained from Eqs. (7) and (8): 𝜏=

(∑𝛽∆𝑎 × 𝛽∇𝑎)

(9)

𝜇

where 𝜇 represents the amount of aspects or criteria.

( ∇)

( ∆)

Finally, a cause-and-effect diagram is produced through mapping 𝛽𝑎 and 𝛽𝑎 as

( ∇)

horizontal and vertical axes. If 𝛽𝑎 has a better performance, the aspects or criteria are

( ∆)

located on the right-hand side. 𝛽𝑎 , which denotes the importance of aspects or criteria, is located on the upper side, representing its higher importance. Moreover, the cause-andeffect diagram can be divided into four quadrants to identify the effects.

3.5 Proposed Analytical Procedures This study attempts to assess the importance of criteria to provide precise guidelines for firms in managing the RMP. The proposed method addresses the gaps in prior studies. To 17

ACCEPTED MANUSCRIPT enhance the validity and reliability of the measures, the following systematic analytical procedures are applied. 1. Possible attributes are collected from the literature. Then, using face-to-face interviews or online inquiries, these proposed attributes are finalized with the experts. 2. To enhance the consistency and accuracy of a proposed measure, FDM is used to filter the necessary attributes by applying Eqs. (1) - (3). The questionnaire is reproduced and the experts performed an additional assessment based on these valid and reliable attributes. 3. In the second round of assessment, the respondents follow Eqs. (4) - (8) to acquire the best nonfuzzy performance. Eq. (9) is subsequently adopted to generate the final score.

( ∇)

( ∆)

4. The attributes are mapped into the cause-and-effect diagram taking 𝛽𝑎 and 𝛽𝑎 as the horizontal and vertical axes. This diagram is divided into four quadrants: driving area (I), indicating higher importance and better performance; core problem area (II), showing higher importance but lower performance; independent area (III), meaning lower importance and lower performance; and volunteering area (IV), presenting higher performance and lower importance.

4.

Results This section provides the background for the Taiwanese electronics industry and the

analytical results. The results provide quantitative support for firms managing RMP and ecoinnovation.

1. The relevant information from recent literature is collected, and the expert group is consulted to structure the initial set of proposed measures, as shown in Table 1. 2. The thirty-two proposed criteria in Table 1 are assessed by the expert group based 18

ACCEPTED MANUSCRIPT on experience and judgment. After the assessment, these linguistic terms are transformed into corresponding triangular fuzzy numbers by adopting Table 2. Then, Eq. (1) is used to normalize different units into comparable values. Through these comparable values, Eqs. (2) and (3) are applied to filter out the appropriate attributes, which are presented in Tables 3 and 4. The final attributes are subsequently renamed, as shown in Table 5. Table 3. FDM filtering for aspects Initial Set

Renamed

𝐻𝑏

Decision

AS1 AS2 AS3 AS4 AS5 AS6 AS7 AS8 AS9

A1

0.78 0.47 0.80 0.77 0.59 0.53 0.71 0.75 0.77

Accepted Rejected Accepted Accepted Rejected Rejected Accepted Accepted Accepted

𝛿

0.69

A2 A3

A4 A5 A6

Table 4. FDM filtering for criteria Initial Set

Renamed

𝓁𝑎𝑏

𝒸𝑎𝑏

𝑟𝑎𝑏

𝓁𝑏

𝑢𝑏

𝐻𝑏

Decision

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13

D1 D2 D3 D4

0.25 0.25 0.50 0.25 0.00 0.00 0.25 0.00 0.00 0.00 0.00 0.25 0.00

0.72 0.84 0.96 0.59 0.62 0.00 0.73 0.59 0.00 0.00 0.00 0.69 0.59

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

0.02 -0.04 0.27 0.08 -0.31 0.00 0.01 -0.30 0.00 0.00 0.00 0.03 -0.30

0.86 0.92 0.98 0.80 0.81 0.50 0.86 0.80 0.50 0.50 0.50 0.84 0.80

0.43 0.45 0.56 0.42 0.33 0.25 0.43 0.32 0.25 0.25 0.25 0.43 0.32

Accepted Accepted Accepted Accepted Rejected Rejected Accepted Rejected Rejected Rejected Rejected Accepted Rejected

D5

D6

19

ACCEPTED MANUSCRIPT C14 C15 C16 C17 C18 C19 C20 C21 C22 C23 C24 C25 C26 C27 C28 C29 C30 C31 C32

D7 D8

D9

D10

D11 D12

0.00 0.00 0.00 0.00 0.00 0.00 0.25 0.25 0.00 0.00 0.25 0.00 0.00 0.25 0.00 0.00 0.00 0.25 0.25

0.00 0.00 0.00 0.79 0.63 0.59 0.72 0.73 0.62 0.00 0.64 0.59 0.00 0.80 0.59 0.00 0.00 0.79 0.84

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

0.00 0.00 0.00 -0.40 -0.31 -0.30 0.02 0.01 -0.31 0.00 0.06 -0.30 0.00 -0.03 -0.30 0.00 0.00 -0.02 -0.04

0.50 0.50 0.50 0.90 0.81 0.80 0.86 0.86 0.81 0.50 0.82 0.80 0.50 0.90 0.80 0.50 0.50 0.90 0.92

0.25 0.25 0.25 0.35 0.33 0.32 0.43 0.43 0.33 0.25 0.42 0.32 0.25 0.44 0.32 0.25 0.25 0.44 0.45

Rejected Rejected Rejected Rejected Rejected Rejected Accepted Accepted Rejected Rejected Accepted Rejected Rejected Accepted Rejected Rejected Rejected Accepted Accepted

0.36

𝛿

Table 5. Final attributes after renaming Aspects

Criteria D1

A1

Organizational resources

Selection process for potential talent

D3 D4

Concise program milestones with clear policies and procedures Mutual trust and respect Technology transfer and R&D

D2

A2

Technological resources

D5

Technology/ideas

A3

Financial resources

D6

In-kind financial support

A4

Human resources

D7 D8

Talented managers Expert organization

A5

Eco-product innovation

D9

Advanced eco-friendly technologies

A6

Eco-innovation effectiveness and performance

D10 D11 D12

Product-service process Organizational performance adjustments Quality of the sales staff

20

ACCEPTED MANUSCRIPT 3. Eqs. (4) – (9) are used to calculate the scores of importance and performance for the

( ∇)

aspects, as stated in Table 6. In addition, Figure 1 places performance 𝛽𝑎 on the

( ∆)

horizontal axis and importance 𝛽𝑎 on the vertical axis to map the aspects into a diagram. A1, A2, and A5 are located in quadrant I; no aspects fall into quadrant II; A4 is positioned in quadrant III; and A3 and A6 are located in quadrant IV.

( ∆)

( ∇)

Table 6. Importance 𝛽𝑎 and performance 𝛽𝑎 of the aspects 𝑘

𝑘

𝐼𝑎𝑏 𝑘

A1 A2 A3 A4 A5 A6

𝐽𝑎𝑏

𝑘

𝑘

𝑘

𝑘

∆

𝛽𝑎

0.897 0.770 0.746 0.598 0.806 0.703

0.725 0.707 0.726 0.569 0.731 0.750

𝑘

∆𝓁𝑎𝑏

∆𝒸𝑎𝑏

∆𝑟𝑎𝑏

∇𝓁𝑎𝑏

∇𝒸𝑎𝑏

∇𝑟𝑎𝑏

0.720 0.545 0.505 0.348 0.595 0.470

0.970 0.795 0.755 0.598 0.845 0.720

1.000 0.970 0.978 0.848 0.978 0.918

0.498 0.490 0.495 0.335 0.515 0.550

0.748 0.740 0.745 0.585 0.765 0.800

0.930 0.890 0.938 0.788 0.913 0.900

A1

0.890

I

II 0.840

Performance

A5

0.790

A2 0.56

0.58

0.6

0.62

0.64

0.66

0.740 0.68 0.7

0.72A3

0.640

A4

Importance

0.590

Figure 1. Aspects cause-and-effect diagram 21

0.74

A6 IV

0.690

III

∇

𝛽𝑎

0.76

ACCEPTED MANUSCRIPT

4. The last step is repeated to calculate the performance and importance values for the criteria, as presented in Table 7. In Figure 2, these values are mapped into a diagram, and the effects are identified. Quadrant I includes D1 and D9; quadrant II includes D2, D3 and D12; quadrant III includes D4 and D10; and quadrant IV includes D5, D6, D7, D8, and D11.

( ∆)

( ∇)

Table 7. Importance 𝛽𝑎 and performance 𝛽𝑎 of the criteria Performance

Importance

0.678 0.595 0.565 0.538 0.663 0.635

0.643 0.687 0.638 0.617 0.629 0.615

D1 D2 D3 D4 D5 D6

Importance

0.660 0.653 0.713 0.608 0.683 0.585

0.546 0.500 0.690 0.588 0.560 0.821

D7 D8 D9 D10 D11 D12 0.85

II

Performance

Importance I

D12 0.8 0.75 0.7

D2 Performance D3 0.53

D4

0.55

0.57

D9

0.65 0.59

0.61

D5 0.67

0.63 0.65 D6 0.6

D1 0.69

0.71

0.73

D10

0.55

III

D7

D11

D8

0.5

IV

Figure 2. Criteria cause-and-effect diagram 5.

Implications This section provides theoretical and managerial implications of the analysis performed 22

ACCEPTED MANUSCRIPT to identify significant insights.

5.1 Theoretical Implications The results indicated that when implementing RMP, organizational resources (A1) must be prioritized. Organizational resources provide a fundamental competence for the control and coordination, which has been emphasized by Tomer (1987) and Barney (1999). In addition, the resources reinforce the understanding of RMP and play an important role in managing resources and leading firms toward sustainable development. Eco-innovation generates the appropriate dynamics for firms to manage rapid changes both internally and externally. The organizational resources motivate continuous organizational learning, which promotes eco-innovation effectiveness and improves performance (Wu et al., 2015). This evidence reveals the firms’ desire to acquire internal stability for competitive advantage development. Eco-product innovation (A5) plays a role in linking RMP development with the emergence of specific capabilities for creating, exploring and exploiting opportunities to compete with rivals (Zahra et al., 2006). Once firms perceive an eco-product innovation opportunity and launch new product development, production processes and service improvements (Teece, 2007; ; Tseng and Bui, 2017), the potential revenue obtained by complying with environmental regulations and preventing negative impacts through the launched products can be explored. Evidence reveals that eco-production innovation is a necessity; however, it also requires academicians and practitioners to achieve an in-depth understanding of relevant regulations and possess the ability to realize environmental changes. These results offer significant data that fulfil the knowledge gaps and establish a link with eco-innovation to explore RMP through a multidimensional assessment. The assessment considers prior studies and reflects real situations that enhance the ability of 23

ACCEPTED MANUSCRIPT organizational resources to seize and profit from opportunities in a rapidly changing environment. The results also demonstrate that a specific RMP can generate dynamics by applying eco-innovation and show that RMP and eco-solutions are critical instruments for constructing the required capabilities. Consequently, these capabilities enable the generation of the dynamics required to achieve a competitive advantage.

5.2 Managerial Implications Advanced eco-friendly technologies (D9) enable a firm to benefit from decreased costs of products and services by improving the efficiency of resource utilization (Bodhani, 2012). Advanced technologies include social media that promotes eco-friendly content, new technologies that improve traditional points of sales, reduced product and service costs to attract potential customers, new systems to search for alternative materials or components to reduce environmental impacts. These technologies not only improve human life but also change current business models and management styles. The electronics industry considers technology the basis for providing e-commerce. Cloud computing represents a recent technology that reduces cost, strengthens security and improves information storage and access. Therefore, applying advanced eco-friendly technologies is a critical practice in ecoproduct innovation and is connected to RMP. Selection processes for potential talent (D1) are an essential practice for successful incubators (Hackett and Dilts, 2004). Selection processes differ between incubators and other types of firms. However, developing a functional selection process requires precise threshold standards. These standards possess the function of screening out potential talent and assessing suitable talent. In addition, several firms concentrate only on head hunting for acquiring talent and are not interested in exploring potential talent. Occasionally, the requirements of the customer may necessitate hiring qualified talent to design or improve the process for satisfying environmental standards. The results reveal 24

ACCEPTED MANUSCRIPT that more customers are active in pursuing eco-innovation products or processes and acting as inventors and designers (Hienerth, 2006). Accordingly, the selection processes for potential talent become a decisive practice for eco-innovation. If firms can develop a precise standard for choosing potential talent, then the organizational resources are enhanced by strengthening the firms’ competencies and capabilities. Resource limitations cause firms to invest resources in critical areas to obtain maximal improvement. The criteria included advanced eco-friendly technologies (D9) and selection processes for potential talent (D1), which are located in the area of the diagram that indicates better performance and higher importance. Therefore, these two criteria have received sufficient resources for performance, although they are also important for firms launching eco-innovation because they can improve RMP for developing a competitive advantage. Nevertheless, three criteria require concise program milestones: clear policies and procedures (D2), mutual trust and respect (D3) and quality of the sales staff (D12). These criteria fall into the core problem area, which indicates higher importance but lower performance. The criteria performance must be improved because the ranked criteria possessed a higher importance for adopting eco-innovation.

6.

Conclusions The electronic firm has encountered difficulty in launching eco-innovation, which is

considered a cost-prohibitive activity that cannot guarantee profit generation. Most firms applied RMP to develop competencies and capabilities to face the challenges of intense rivals. This study proposes nine aspects and thirty-two criteria as initial measures based on an extensive literature review to provide a greater understanding of the linkage between RMP and eco-innovation. The hybrid method filters out unnecessary attributes in advance, and the attributes are reduced to six aspects and twelve criteria. In addition, the hybrid method offers a visual analysis to assist in the identification of an improvement method 25

ACCEPTED MANUSCRIPT under resource limitations and enhances the accuracy and effectiveness in decision-making for strengthening competences and capabilities. This study presents three main theoretical, managerial and methodological contributions. For the theoretical contribution, this study attempts to develop a framework that is valid and reliable through eco-innovation and then enhance the understanding of RMP through eco-innovation. For the managerial contribution, the analytical results reveal the decisive attributes that provide precise guidelines for assisting firms in adjusting resource utilization more efficiency and effectively. For the methodological contribution, this study integrates fuzzy set theory to eliminate linguistic uncertainties and adopts the FDM to screen out less important attributes, which makes the assessment more concentrated and saves time while maintaining measurement consistency. The proposed hybrid method maps the attributes onto a diagram to offer a straightforward visual analysis. The findings verified that eco-innovation offers fundamental support to RMP and implied that organizational resources and eco-product innovation are the top two attributes for managing resources and generating the dynamics required to reinforce a competitive advantage. Organizational resources should be focused on the process of selecting potential talent, and eco-product innovation needs to focus on applying advanced eco-friendly technologies. The two criteria assisted firms in developing the competencies and capabilities through RMP. Moreover, the three criteria that presented higher importance and lower performance were concise program milestones with clear policies and procedures, mutual trust and respect, and quality of the sales staff. Urgent improvements are required, and firms should shift a portion of the current resources from criteria that exhibited better performance to the three criteria to achieve a sustainable business. Several limitations are encountered in this study. Though the eco-innovation and RMP literature was reviewed to identify the proposed measures, the series of attributes may not be extensive. In addition, experts were chosen from the Taiwanese electronics firm only, 26

ACCEPTED MANUSCRIPT which allowed us to control the contextual and operational attributes. However, these findings present limitations in generalizability. Future studies should extend this work to diverse industries to overcome the limitations. Additional cases need to be investigated to reveal the unidentified attributes in order to deepen our understanding of RMP through ecoinnovation. The interrelationship between RMP and eco-innovation is an assumption of the proposed method; however, further studies are required to determine the interrelationships of these attributes, and comparisons should be performed to identify any differences in the proposed attributes.

Acknowledgments This study was supported by the Young Scientists Fund of the National Natural Science Foundation of China (No. 71701029) and the Dalian University of Technology Fundamental Research Fund [DUT16RC(3)038].

References 1. Alegre, J., and Chiva, R. (2008). Assessing the impact of organizational learning capability on product innovation performance: An empirical test. Technovation, 28(6), 315-326 2. 3.

4. 5. 6.

Amit R., Shoemaker P. (1993). Strategic assets and organizational rent, Strategic Management Journal, 14 (1) 33–46. Andersen, M.M., 2010. On the faces and phases of eco-innovation - on the Dynamics of the Greening of the Economy. Imperial College London Business School, London, United Kingdom. Aschhoff, B., and Sofka, W. (2009). Innovation on demand—Can public procurement drive market success of innovations? Research policy, 38(8), 1235-1247. Bansal, P., and Roth, K. (2000). Why firms go green: A model of ecological responsiveness. Academy of management journal, 43(4), 717-736 Barney J.B. (1991) .Firm resources and sustained competitive advantage. Journal Management, 17 (1) 99–120.

27

ACCEPTED MANUSCRIPT 7.

Barney, J.B., Hesterly, W. Organizational economics: understanding the relationship between organizations and economic analysis, in: R. Clegg, C. Hardy (Eds.), Studying Organization, Theory & Method, Sage Publications, London, 1999.

8.

Bocken, N.M.P., Farracho, M., Bosworth, R., Kemp, R., 2014.Thefront-endofecoinnovation force innovative small and medium sized firms. Journal of Engineering Technology Management 31, 43–57.

9.

Borch O.J., Huse M., Senneseth K. (1999) . Resource configurations, competitive strategies, and corporate entrepreneurship: an empirical examination of small firms. Enterprise Theory Practice, 24 ,49–70.

10. Cai, W.G, & Zhou, X.l, (2014). On the drivers of eco-innovation: empirical evidence from China. Journal of Cleaner Production, 79, 239–248. 11. Carrillo-Hermosilla, J., Del Río, P., & Könnölä, T. (2010). Diversity of Eco-Innovations: Reflections from Selected Case Studies. Journal of Cleaner Production, 18(10), 10731083 12. Christensen, C. M., and Overdorf, M. (2000). Meeting the challenge of disruptive change. Harvard business review, 78(2), 66-77. 13. Cruz. L, Pedrozo. E., de Fátima Barros Estivalete. V., (2006). Towards sustainable development strategies: A complex view following the contribution of Edgar Morin. Management Decision. 44, 871-891. 14. Dangelico, R. M., and Pontrandolfo, P. (2010). From green product definitions and classifications to the Green Option Matrix. Journal of Cleaner Production,18(16), 16081628. 15. Dangelico, R. M., and Pujari, D. (2010). Mainstreaming green product innovation: Why and how firms integrate environmental sustainability. Journal of Business Ethics, 95(3), 471-486. 16. De Medeiros, J. F., Ribeiro, J. L. D., and Cortimiglia, M. N. (2014). Success attributes for environmentally sustainable product innovation: a systematic literature review. Journal of Cleaner Production, 65, 76-86. 17. DeSarbo, W. S., Anthony Di Benedetto, C., Song, M., and Sinha, I. (2005). Revisiting the Miles and Snow strategic framework: uncovering interrelationships between strategic types, capabilities, environmental uncertainty, and firm performance. Strategic Management Journal, 26(1), 47-74. 18. Dong, Y.,Wang,X.,Jin,J.,Qiao,Y.,Shi,L.,2014.Effectsofeco-innovation typology on its performance: Empirical evidence from Chinese enterprises. Journal of Engineering Technology Management 19. Doran, J. and Ryan, G. (2012). Regulation and Firm Perception, Eco-Innovation and Firm Performance. European Journal of Innovation Management, 15(4), 421-441

28

ACCEPTED MANUSCRIPT 20. Doran, J., and Ryan, G. (2012). Regulation and firm perception, eco-innovation and firm performance. European Journal of Innovation Management, 15(4), 421-441. 21. Drnevich, P. L., and Kriauciunas, A. P. (2011). Clarifying the conditions and limits of the contributions of ordinary and dynamic capabilities to relative firm performance. Strategic Management Journal, 32(3), 254-279. 22. Hackett, S. M. & Dilts, D. M. (2004). A Systematic Review of Business Incubation Research. The Journal of Technology Transfer, 29(1), pp. 55-82 23. Hart, O. (1995). Firms, contracts, and financial structure. Clarendon Press, United Kingdom 24. Hisrich, R. and Smilor, R. (1988). The university and business incubation: technology transfer through entrepreneurial development, Technology Transfer (fall), 14-19. 25. Hofstra, N. & Huisingh, D. (2014). Eco-innovations characterized: A taxonomic classification of relationships between humans and nature. Journal of Cleaner Production, 66, 459–468 26. Holsapple, C. W. & Joshi, K. D. (2004). A knowledge management ontology. In Handbook on Knowledge Management 1 (pp. 89-124). Springer Berlin Heidelberg, Germany. 27. Holsapple, C. W. & Wu, J. (2011). An elusive antecedent of superior firm performance: The knowledge management factor. Decision Support Systems,52(1), 271-283. 28. Horbach, J. (2008). Determinants of environmental innovation—new evidence from German panel data sources. Resources Policy, 37 (1), 163-173. 29. Horbach, J., Rammer, C., Rennings, K. (2012). Determinants of eco-innovations by type of environmental impact- The role of regulatory push/pull, technology push and market pull. Ecological Economics, 78, 11-122. 30. Ishikawa, A., Amagasa, M., Shiga, T., Tomizawa, G., Tatsuta, R., & Mieno, H. (1993). The max–min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy Sets and Systems, 55, 241–253. 31. Javad S., Seyed M. M. , Seyed T. A.N. (2016). Optimizing an inventory model with fuzzy demand, backordering, and discount using a hybrid imperialist competitive algorithm. Applied Mathematical Modelling, 39 (6), 1-18. 32. Jenkins, H. M. (2004). Corporate social responsibility-engaging SMEs in the debate. New Academy Review, 3, 76-95. 33. Kammerer, D. (2009). The effects of customer benefit and regulation on environmental product innovation. Empirical evidence from appliance manufacturers in Germany. Ecological Economics, 68(8), 2285-2295. 34. Kesidou, E., and Demirel, P. (2012). On the drivers of eco-innovations: Empirical evidence from the UK. Research Policy, 41(5), 862-870

29

ACCEPTED MANUSCRIPT 35. Kindstrom, D., Kowalkowski, C., and Sandberg, E. (2013). Enabling service innovation: A dynamic capabilities approach. Journal of business research,66(8), 1063-1073. 36. Lee, J., Lee, K., Rho, S. (2002).An evolutionary perspective on strategic group emergence: a genetic algorithm-based model. Strategic Management Journal, 23(8), 727–746. 37. Lee, S. S., & Osteryoung, J. S. (2004). A comparison of critical success factors for effective operations of university business incubators in the United States and Korea. Journal of Small Business Management, 42(4), 418-426. 38. Li, X., Dai, Y., Ma, Y., Han, S., & Huang, B. (2014). Graphene/gC 3 N 4 bilayer: considerable band gap opening and effective band structure engineering. Physical Chemistry Chemical Physics, 16(9), 4230-4235 39. Liao, J. J., Kickul, J. R., & Ma, H. (2009). Organizational dynamic capability and innovation: An empirical examination of internet firms. Journal of Small Business Management, 47(3), 263-286. 40. Löfsten H. (2010) . Critical incubator dimensions for small firm performance — a study of new technology-based firms localised in 16 incubators. Internal Journal Business Innovation Research, 4 (3) 256–279. 41. Malloch, K., and Porter-O'Grady, T. (2010). Introduction to evidence-based practice in nursing and health care. Jones & Bartlett Learning Management 34, 78–98 42. Massa, S. and Testa, S. (2008). Innovation and SMEs: Misaligned perspectives and goals among entrepreneurs, academics, and policy makers. Technovation 28 (7), 393-407 43. Massa, S. and Testa, S. (2009). A knowledge management approach to organizational competitive advantage: Evidence from the food sector. European Management Journal 27 (2), 129-14 44. Maxwell, D., van der Vorst, R., 2003. Developing sustainable product and services. Journal of Cleaner Production 11, 883–895. 45. Menguc, B.,and Ozanne, L. K. (2005). Challenges of the “green imperative”: A natural resource-based approach to the environmental orientation–business performance relationship. Journal of Business Research, 58(4), 430-438. 46. Michailova, S., and Zhan, W. (2015). Dynamic capabilities and innovation in MNC subsidiaries. Journal of World Business, 50(3), 576-583. 47. Moore, S. B., & Ausley, L. W. (2004). Systems thinking and green chemistry in the textile industry: concepts, technologies and benefits. Journal of Cleaner Production, 12(6), 585-601. 48. Noorderhaben, N. (1995). Strategic decision making. Addison-Wesley, UK. 49. Opatha, H.H.D.N.P., Arulrajah, A.,(2014). Green human resource management: simplified general reflections. International Business Research, 7, 101-112.

30

ACCEPTED MANUSCRIPT 50. Organisation for Economic Co-operation and Development(OECD), Eurostat, 2005. Oslo Manual Guidelines for Collecting and Interpreting Innovation Data, third ed. OECD (Organization of Economic Cooperation and Development) and EUROSTAT (European Statistical Office), Paris, Luxemburg. 51. Pavlou, P. A., and El Sawy, O. A. (2011). Understanding the elusive black box of dynamic capabilities. Decision Sciences, 42(1), 239-273. 52. Perez-Sanchez, D., Barton, J. R., & Bower, D. (2003). Implementing environmental management in SMEs. Corporate Social Responsibility and Environmental Management, 10(2), 67-77. 53. Porter, M. E., and Van der Linde, C. (1995). Toward a new conception of the environment-competitiveness relationship. The journal of economic perspectives, 9(4), 97-118. 54. Pujari, D. (2006). Eco-innovation and new product development: understanding the influences on market performance. Technovation, 26(1), 76-85 55. Rehfeld K., Rennings, K. and Ziegler, A. (2007), Determinants of Environmental Product Innovations and the Role of Integrated Product Policy – An Empirical Analysis, Ecological Economics, 61, 91-100 56. Rehfeld, K. M., Rennings, K., & Ziegler, A. (2007). Integrated product policy and environmental product innovations: An empirical analysis. Ecological Economics, 61(1), 91-100. 57. Robertson, W.A. (1993). New Zealand's new legislation for sustainable resource management The resource management Act 1991.Land Use Policy, 10(4), 303-311 58. Sarkis, J., Gonzalez-Torre, P., and Adenso-Diaz, B. (2010). Stakeholder pressure and the adoption of environmental practices: The mediating effect of training. Journal of Operations Management, 28(2), 163-176. 59. Sarkis, J., Helms, M.M., Hervani, A.A. (2010). Reverse logistics and social sustainability. Corporate Social Responsibility and Environmental Management, 17 (6), 337-354 60. Sierzchula, W., Bakker, S., Maat, K., and Van Wee, B. (2012). Technological diversity of emerging eco-innovations: a case study of the automobile industry. Journal of Cleaner Production, 37, 211-220. 61. Somsuk N., Wonglimpiyarat J., Laosirihongthong T. (2012) . Technology business incubators and industrial development: resource-based view, Industrial Management Data Systems, 112 (2) ,245–267. 62. Teece, D.J. (2007). Manager, Markets and dynamic capability. In dynamic capabilities: understanding strategic changes in organization. Blackwell: Oxford 19-29, United Kingdom 63. Teece, D.J. Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy, in: D.J. Teece (Ed.), The Competitive 31

ACCEPTED MANUSCRIPT Challenge. Strategies for Industrial Innovation and Renewal, Harper & Row, New York, 1987, pp. 185 – 219, USA 64. Tomer, J.F. (1987). Organizational Capital: The Path to Higher Productivity and Well-Being, Praeger Publishers, New York. United states 65. Trainor, K.J., Andzulis, J., Rapp, A., Agnihotri R. (2014). Social media technology usage and customer relationship performance: A capabilities-based examination of social CRM. Journal of Business Research 67, 1201–1208 66. Tseng M.L. (Jan. 2011). Using a hybrid MCDM method to evaluate firm environmental knowledge management in uncertainty. Applied Soft Computing 11(1), 1340-1352 67. Tseng, M.L. (2009a). Using extension of DEMATEL to integrate hotel service quality perceptions into a cause-effect model in uncertainty. Expert systems with applications36(5), 9015-9023 68. Tseng, M.L. (2009b). A causal and effect decision-making model of service quality expectation using grey-fuzzy DEMATEL approach. Expert systems with applications 36(4), 7738-7748 69. Tseng, M.L., Chiu, A.S.F., Tan, R.R., Manalang, A.B. (2013). Sustainable consumption and production for Asia: Sustainability through green design and practice. Journal of Cleaner Production40, 1-5 70. Tseng, ML., Bui, TD. (2017) Identifying eco-innovation in industrial symbiosis under linguistic preferences: a novel hierarchical approach. Journal of Cleaner Production 140 (3), 1376-1389 71. Veugelers, R. (2012). Which policy instruments to induce clean innovating? Research policy 41 (10), 1770-1778 72. Wu, K.J., Liao, C.J., Tseng, M L., Chen, C.C., Lin, Y.H, Tsai, C.F.M. (2016). Exploring ecoinnovation in dynamic organizational capability under incomplete information in the Taiwanese lighting industry, International Journal of Production Economics, 181(B), 419-440 73. Wu, K.J., Liao, C.J., Tseng, M.L., Lim,M.K., Hu, J. (2017). Toward Sustainability: Using Big Data to Explore Decisive Supply Chain Risk Factors Under Uncertainty, Journal of Cleaner Production, 142(2), 663-676

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ACCEPTED MANUSCRIPT

Teece DJ. 1988. Technological change and the nature of the enterprise. In Technical Change and Eco-

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ACCEPTED MANUSCRIPT

nomic Theory, Dosi G, Freeman C, Nelson RR, Silverberg G, Soete, L (eds). Pinter: London; 256–281

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