Sustainable technology selection decision-making model for enterprise in supply chain: Based on a modified strategic balanced scorecard

Journal of Cleaner Production 141 (2017) 1337e1348

Contents lists available at ScienceDirect

Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Sustainable technology selection decision-making model for enterprise in supply chain: Based on a modified strategic balanced scorecard De Xia a, Qian Yu b, Qinglu Gao a, *, Guoping Cheng a a b

Management School, Wuhan University of Technology, Wuhan, 430070, China Economics School, Wuhan University of Technology, Wuhan, 430070, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 23 December 2015 Received in revised form 10 September 2016 Accepted 12 September 2016 Available online 13 September 2016

During technology adoption, assessment of its sustainable character is a difficult task due to limited insight and the various dimensions of sustainability, as well as its complicated application at the operational level. A proper decision-making framework and method may be conducive to bridging generic and macro-level sustainability with local-site and task-oriented technology selection, improving the application of sustainable technology. For this reason, a framework is explored to highlight the sustainable nature of relevant components during an operational decision-making process within the supply chain. With an eye to the triple bottom of sustainability, the product chain, value-added activities of supply chain and stakeholders are analyzed and embedded into the technology selection method. The dynamic relationships among these components (product chain, value-added activities and stakeholders) as carriers of technology are also discussed so as to investigate their sustainable features. Furthermore, to figure out the whole technology decision-making logic map, a modified strategic balanced scorecard is established and applied to evaluate technology candidates in terms of their features of sustainability. Next, a computing method is designed to produce a sustainable technology choice. The multilateral mechanisms among the three groups, as well as within each group, during the technology selection process are identified and elaborated completely. The framework of analysis and method presented in the paper add insight to sustainability theoretically, and guide its application in technology adoption. Managerial implications, as well as limitations of this work, are concluded at the end of this paper. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Sustainability Technology selection Decision-making Balanced scorecard

1. Introduction Over the past decades more and more enterprises have devoted themselves to greening technology for sustainability as set forth in the report of the Brundtland Commission. One of the significant challenges of sustainable practice is to make the Brundtland Report operational, namely, to use it to guide the technology adoption decision, because too much negative news has been raised about the unsuccessful adoption of technology (Neilson and Pritchard, 2007; Murphy et al., 1996). The work (Varey, 2011; Krewitt et al., 1998) has provided some assistance on this issue with the view of strategic balance that a diminished quality of life should not be

* Corresponding author. E-mail addresses: [email protected] (D. Xia), [email protected] (Q. Yu), [email protected] (Q. Gao), [email protected] (G. Cheng). http://dx.doi.org/10.1016/j.jclepro.2016.09.083 0959-6526/© 2016 Elsevier Ltd. All rights reserved.

caused by the design of industrial systems, due either to losses of future opportunities or to adverse impacts on social condition, human health and environment. Meanwhile, two researches (Windsor, 2006; Hauschild et al., 2005) put forward that if enterprises are inclined to embed the sustainable principles into technology adoption operationally, they should consider the sustainable performance of the corresponding supply chain as a whole rather than just considering those links which belong to their own sphere of legal responsibility. Other literature also shows that innovations in different operational sections of a supply chain influence the environmental performance in a complex manner, which deserves to be explored further (Kuijer, 2014; Lockton et al., 2010; Elkington, 2004). At the operational scale, striving for sustainability of technology selection is a multi-dimensional decision-making process involving raw material, half-finished products, end products as well as

1338

D. Xia et al. / Journal of Cleaner Production 141 (2017) 1337e1348

discarded ones, and their corresponding sub-supporting operational systems (Perrini and Tencati, 2006; Leach et al., 2012). Unfortunately, previous theories are not enough to guide corporations' assessment and balance among various components and requirements during technology selection in a complex situation/ case (Koplin et al., 2007; Jennings and Zandbergen, 1995) while the research of economics and sociology on sustainability is insufficient for sustainable business operations. Previous theories also cannot provide proper support for the local-site and micro decisionmaking process on technology innovation (Vergragt et al., 2014), which leaves practitioners confused at the crossroads of sustainability. Actually, the fractional sustainable achievements resulting from reasonable selection of technology in supply chains are always ignored while the operational technical applications and corresponding achievements are fragmented and far from the macro definition of sustainability (Wesselink et al., 2015). They show that what's concrete meaning and criterion of sustainability in technical operation is still a little vague. Operational dilemmas with which enterprises struggle are: how to identify and evaluate technology performance of different operational sections; and how to achieve a balance among short and long term demand as well as various involved stakeholders' requirements through proper technology application (Porter and van der Linde, 1995; Starik and Rands, 1995). Furthermore, the environmental and economic responsibilities of enterprises are not always consistent with each other (Holt, 2004) at micro level. In practice, sustainability is usually narrowed or replaced solely by economic or environmental aspects (Rodrigue et al., 2013). Empirical studies on enterprise technology and innovation also show the existence of conflicts among constituents in the decision-making processes (Garg et al., 2015). Meanwhile, experience has shown that the commitment to only one dimension during technology innovation does not serve the goal of sustainability (Kersten et al., 2015) and the enthusiastic pursuit of economic goals cannot lead to sustainability (Song and Zhou, 2016). Overvaluing environmental and social requirement is also unfeasible in practice (Loorbach, and Wijsman, 2013). Therefore, figuring out the relations among components involved in the decision-making process to fulfil the intersection of the triple bottom is the key task to make a dynamic system balance in technology adoption. To overcome the dilemma in technology selection, many literatures are focusing on establishing a framework or method of assessment and balance tactic for operationally sustainable decisions. The research by Schmidt et al. (2004) designs an analytical model which consists of social-, ecological-, and efficiency parameters to evaluate the economic, environmental and social performance operationally. Dreyer et al. (2005) establish a framework to identify the influence of products and services with a careful consideration of the basic needs, health and dignity of human beings. The work of Pettersen et al. (2013) emphasizes the role of extensive design in technology influence. These authors advocate an analytical structure which includes requirements of related stakeholders. Additional researches (Lozano, 2011; Schliephake et al., 2009) also show that the successful application of green technology in enterprises means a broad involvement of partners and their collaboration. That is to say, influence associated with certain technology adoption is beyond the perspective of a single player and assessment angle in the supply chain; and the concerns and benefits of such stakeholders as part providers, target outsourcers, customers as well as even authorities, should be included during the adoption process. The research (Waller and Fawcett, 2013) also indicates that the frameworks will only work properly with sufficient life cycle assessment (LCA) data inventory on the production and

consumption process. According to a study by Knight and Jenkins (2009), potential negative effects of and barriers to technology and innovative solutions on product, production and stakeholders have to be evaluated and dealt with for their proper function. Many factors may contribute to this awkward condition while the associated high cost and uncertainty encountered by enterprises are always deeply concerned in relevant technology application (Csutora, 2012; IBM, 2005; Daily, 2001). More specifically, O'Brien's research identifies the essential environmental factors needed to conduct environmental life cycle assessment (ELCA) from the social and policy aspect. Meanwhile, the research by Hunkeler (2006) shows that it is vital to apply regional data about a product's features within the evaluating process regarding the sustainable performance of each functional union. So to identify exactly stakeholders' requirements as well as the features of the three groups may help to make sense of sustainability and to tailor relevant technology for the successful application of sustainable technology. Despite the obvious differences among researches, their common ground is even more interesting and valuable. All of the researches directly or indirectly show that the success of a sustainability-oriented technology in practice is essentially determined by three factors: relevant product chain; production and value-added activities; and stakeholders who not only judge but also are involved in and contribute to the sustainable performance of technology through their practice. Therefore, the purpose of this paper is to explore a sustainability-oriented framework and method of technology selection for a sustainable operation in supply chain. The work strives to build the essential value of sustainability into an operational evaluation and selection process to guide the decision-making on technology adoption. The arrangement of this paper is as follows: firstly, the decisionmaking logic about technology selection is analyzed to improve the sustainability of technology application. Then in the second section, the essential factors and their elements in decision-making process as well as the inter-group and interior relation with sustainability are identified. Meanwhile, a correlation and reactive mechanism are also elaborated among these factors in a dynamic supply chain system based on the analysis of technology selection framework. In the third section, a decision-making path is designed for a sustainable technology selection in supply chain management. Then a more detailed decision framework and a computing method are put forward based on the theories of balanced scorecard and analytic network process (ANP). Next, a decision-making and selection model is applied in a numerical example which provides a technology option for enterprises to boost their sustainable performance. The results achieved are analyzed and discussed for the sustainable nature of the target technology. Lastly, the features and limitations of the decision-making framework and method of technology selection along with possible future research for the practitioners in supply chain are addressed. 2. A basic logic and strategy of sustainable technology selection Corporations engaged in sustainability have launched many kinds of technology programs with various sustainable features, which may partly meet the requirements of social, environmental and financial responsibilities (Song and Zheng, 2016; Closs et al., 2011). For example, the adoption of certain technical measures to reduce packing materials not only lowers the cost of raw materials but also reduces the negative influence on the environment. For a higher sustainability at the operational level in supply chains, many technology options may be explored, leading to changes in product and production design, and changes in stakeholders' behavior

D. Xia et al. / Journal of Cleaner Production 141 (2017) 1337e1348

(Vergragt et al., 2014). In order to focus on the main goal of this work, various technology programs are simplified into five groups, those being: reduction; recycling; substitution; transformation; and status quo according to the functional feature of the technical measure in sustainability (Baumann et al., 2002). Based on the results of previous researches (Vergragt et al., 2014; Abreu et al., 2011) the sustainability of technology selected in business is attributed to the participants (enterprises in the supply chain and their critical stakeholders) and carriers (product chains and value-added activities in certain supply chains). Therefore, determining their inter-dependency in sustainability is essential to effective selection of technology (Lozano, 2011). This requires exact annotation of the enterprise, features of its stakeholders, as well as their functions and roles in shaping sustainable product chain and production process through certain technology programs. For the reason above a systemic illustration of the sustainable nature of participants, products and productions key to the decision-making process. Many academicians' work (Pettersen et al., 2013; Dreyer et al., 2005; O'Brien and Clift, 1996) in the field of enterprise technology selection and operational evaluation helps us to build the view of strategic balance and LCA into the analytical logic about the sustainable effect of the three groups. Even though the research angles may be different, an obvious intersection is that all of them have explored the application of traditional LCA and taken efforts to introduce the triple bottom into the operational area of enterprises, especially into the sustainable management of product & production innovation and relevant stakeholders (Kuijer, 2014; Lockton et al., 2010; Elkington, 2004). To improve the applicability of the decision-making method in our research and the comparability of technology options, a set of general sustainable evaluation indices are introduced which are associated with different forms of sustainable performance (Sikdar, 2003; Bare et al., 2000). These indices include Benefit, Opportunity, Cost and Risk; they are based on many previous researches of technology middle and end influence (Dubey et al., 2016; Hutchins and Sutherland, 2008). In the following sections, potential technology performance in supply chains on local-site-based sustainability will be described and evaluated based on these four metrics/ measures. Therefore, an analytical framework of technology's functional features on sustainability in supply chain is designed as the triangle formed by the three groups: product chain, valueadded activities and stakeholders, as shown in Fig. 1. With regard to the different effects on product, production and stakeholders resulting from the adoption of the above mentioned five simplified functional features, a reasonable decision-making framework will be conducive to illustrate the dynamic system.

1339

3. Multi-dimension analysis of technology sustainability in supply chain According to the framework in Fig. 1, significant components of the decision-making process are presented, linking macro- and meso-levels with the micro operational scale. Three aspects which include enterprise, related close stakeholders' behaviours as well as product chain and value-added activities in supply chains, make the main contribution to the sustainability of technology selected in supply chain (Song et al., 2015a, b; Bocken et al., 2014). A CERES/ GRI (2015) theme/sub-theme framework is used to organize and select indicators of sustainable development, which may be developed and classified according to the assessment content of technology selection in a business operational scale (Tyl et al., 2015). Relevant indicators are shown in Table 1 and the local sitebased feature along with the corresponding performance will be illustrated with four essential metrics related to the sustainable features of technology. These metrics include benefit (B), opportunity (O), cost(C) and risk (R). In this way, the applicability of assessment with a detailed picture in terms of sustainability can be enhanced (Leach et al., 2012; Dow Jones Sustainability Indexes, 2015), indicating that various selections of technology will have a detectable effect on the sustainability change. 3.1. Sustainable performance of related stakeholders The stakeholders include various players whose critical active or passive roles help determine the selection of sustainability technology. These stakeholders include enterprises in supply chain, customers and authorities, For enterprises in supply chain, adopting a proper technology would contribute to the competitive margin and influence the benefit of employees and investors (Von Schomberg, 2013). Similarly, the consumption by and life style of customers lead and are also influenced by technical solution built into products (Vergragt et al., 2014), while the authorities' confidence and reputation will benefit from the prosperity of industry, ample job opportunities and taxation, which are ultimately determined by their successful policy and regulation of technology application in practice (Rodrigue et al., 2013). 3.2. Sustainable performance of product chain In this work, the product chain is defined as the physical parts at the end or working processes including end products, raw materials and resources, half-finished products as well as goods discarded during production and consumption. The innovation of each component in a product chain will trigger or influence others'

Fig. 1. A technology selection logic and framework for sustainability.

1340

D. Xia et al. / Journal of Cleaner Production 141 (2017) 1337e1348

Table 1 Theme/sub-theme framework for technical sustainability of Supply Chain (SC) based on CERES/GRI. Group

Factor framework

Indicator on B\O\C\R (effect)

Stakeholder (S)

Enterprises in SC (Et.)

Market share Competitive margin Operational cost Emissions Purchasing power Consumption experience/Access to new life style Use cost Nutritional and health status Tax base/Fiscal income Employment Biodiversity Reputation Consumption of raw materials Environmental feature Purchasing price Possibility of quality fluctuation Duration Possibility of Quality deficit Effluent and waste Health and safety feature Consumption of Energy, Water, Gas Access to new energy occupational health and safety Tool wear rate Re-usability Poisonousness Degradability Cooperative motivation of suppliers Number of qualified suppliers Supplier environmental performance Technical compatibility Occupational health and safety Ratio of equipment wear Consumption of energy and materials Effluent and waste Package cost Storage requirement Transportation fee Quality loss in logistic section Interchangeability of spare parts Reusability Number of complaint Responsiveness

Customer (Cu.)

Authority (Au.)

Product chain (P)

Components (P-comp.)

Core products (P-core.)

Supportive (production & consumption) product& facilities (P-sp.)

By products (P-bp.)

Value-added activities(V)

Procurement (V-pr.)

Production & manufacture (V-pm.)

Logistics (V-ls.)

Service after sale (V-sa.)

Resource: European Commission, 2015; Ceres, 2015; Shonnard et al., 2003.

modification and sustainability (European Commission, 2015; Von Schomberg, 2013). At the same time, the product chain displays the sustainable features of technology utilized in different forms. A technical adjustment towards greening end-products of the supply chain may mean a substitute of raw materials and parts as well as a wave of emission volume and energy consumption (Chitnis et al., 2013). Similar relations are generic to a product chain, which determines the sustainable nature of technology in business (Gaziulusoy et al., 2013).

et al., 2000). Similarly, innovation in production and equipment can also exert various effects on procurement, distribution and other after-sale supportive systems (Dendler, 2014), while the adoption of green technology and new sustainable code in distribution and after-sale service can produce important influence on previous value-added activates, which may force partners to apply a set of strict criteria on quality, environment and health (Kersten et al., 2015).

3.3. Sustainable performance of value-added activities

4. Correlation analysis of managerial sectors of sustainable technology selection in supply chain

Value-added activities in a supply chain, consisting of procurement, production, logistics, and after-sale service, not only support the product chain and demand in the end, but also demonstrate the sustainable performance of technology (Kimminget al., 2015; Yang et al., 2011). In the case of sustainability of value-added activities embedded with certain technology, correlation exists among continuous value-added phases (Kovacs, 2008). For example, a new technical approach introduced in procurement for sustainability, such as less emission and employee protection, would consequently influence the subsequent phases which include production, distribution and after-sale service (Song et al., 2015a, b; Carter

The above analysis shows that the sustainability of technology adopted is determined by three: critical stakeholders, product chain and value-added activities in supply chain. The technical features and characteristics of product chain and operational processes not only determine the sustainability of a supply chain as a whole but also influence the related stakeholders (Bocken et al., 2014). Meanwhile, stakeholders involved in technology selection also play an essential role in the decision making process and exert corresponding effects on product chain and production (Geels, 2010). For example, an electric engine design which substitutes for/supplants the gasoline engine, leads to changes in parts supply

D. Xia et al. / Journal of Cleaner Production 141 (2017) 1337e1348

and energy consumption within the product life cycle and production process, may drive partners to adjust their business models or drop out, influences customers' habits and life style and may extend the fiscal basis for government with a new industry. On the other hand, enactment by government of new code for renewable vehicles will trigger replacement of traditional engines by green engines as well as relevant parts and components, leading to change of the supporting production process. Meanwhile the changes not only influence business partners in the engine supply chain but also customers' benefits and habits. This illustrates that the relation among three groups regarding technology sustainability is an even more essential component of the whole story about decision-making processes (Kuijer, 2014; Tsoi, 2010). This inter-dependency and reactive mechanism among product chain, operational process and stakeholders make the selection of technology more complex (Seuring et al., 2008). To achieve the goal of fulfilling the intersections among economic, environmental and social responsibility, this interdependency supports the essential component of a decision-making model for technology selection in the supply chain. This model offers a strategic sustainable vision based on a modified balanced scorecard among the three groups, as shown in Fig. 2. 4.1. Bilateral sustainable influence of product chain and valueadded activities built with new technology Product chain and value-added activities – although composed of different components, both built with a technology option – not only play critical roles but also determine what demand will ultimately be fulfilled (Vergragt et al., 2014). Besides, interdependency between the two groups as well as correlation among the components within each group exist during the application of operational technology (Von Schomberg, 2013). The orientation of value-added activities is determined by the components of product chains while the sustainable nature of product chain is determined by the valueadded activities. The reactive relation between these two groups may be analyzed with LCA (Gaziulusoy et al., 2013; Klopffer, 2003). The sustainable features of an eco-designed product are seriously affected by the value-added activities occurring in each stage of the supply chain (Seuring, 2004). During procurement the environmental feature of suppliers' materials and production processes is

1341

the very factor influencing the sustainability of the end products. Furthermore, as a supportive system, the production process not only facilitates the realization of eco-design value, but also determines how resources are consumed and produces significant side-effects (Loorbach, 2010). In the phase of logistics and distribution, a perfect supportive and service system is the basis for good reputation and sustainable performance of a certain product and brand. After-sale service also plays a very important role in improving product's competitive edge through greening the operation model, such as facilitating reuse and other charitable activities in the whole life of the product. On the other hand, the product chain can also affect the sustainable performance of value-added activities (Schliephake et al., 2009). As mentioned above, the sustainable feature of procurement is determined by such aspects of end products as their structure, function and material characteristics (Pesonen, 2001). The feature of a product chain including resources, energy and byproducts determines not only the efficiency of production but also its sustainability as a whole. A similar interdependent relation also exists among the logistics, distribution and even the consumption stage at the end of the forward supply chain (Dendler, 2014). In terms of the operation in a closed supply chain, the product chain is the very factor and indicator that influences and illustrates the performance of the green business processes. Therefore, the decision regarding technology selection exerts influences not only on the product chain but also on the sustainable performance of valueadded activities resulting from the described interdependent and reactive mechanism. 4.2. Correlation among stakeholders, product chain and valueadded activities While the correlation between product chain and value-added activities plays a complex and essential role in technical sustainability, similar relations exist among stakeholders and two groups with respectively different forms (Gaziulusoy et al, 2013; Perrini and Tencati, 2006). They determine the sustainable performance of technology selection as a whole. As suppliers of raw materials, parts, equipment and technology, organizations of production and business as well as advertisers and promoters in the market (Blok et al., 2015), all enterprises in a supply chain, either inner or outside players, determine the

Fig. 2. Evaluation path among and within groups of technology selection based on balanced scorecard. Notes:Et.denterprises,Cu.dcustomer,Au.dauthority, (stakeholder); Pr.dpurchasing, Pm.dproduction and manufacturing, Ls.dlogistics, Sa.-service after sale, (value-added activities); Comp.dcomponent, coredcore product, Sp.dsupporting product, Bp.–by product, (product chain).

1342

D. Xia et al. / Journal of Cleaner Production 141 (2017) 1337e1348

sustainable performance of technology through their own work and behaviors (Perrini and Tencati, 2006). Meanwhile, the realization of green and eco-design in core products and value-added activities is dependent on the material and production features of suppliers (Schmidt et al., 2004). The partners downstream affect the sustainable ambitions of the core player through exploration of green markets and the cultivation of sustainable consumption & life style. Comparatively, in horizontal angle, providers of equipments built with sustainable technology determine the sustainable features of the production process (Rotmans, 2006), while a proper rule shaped by and complied with by competitors will facilitate the sustainability of business (Pesonen, 2001). The sustainable performance of two groups (product chain and value-added activities) embedded with certain technology is also influenced directly and intensively by customers (Dendler, 2014). Customers' increasing consciousness of sustainability not only imposes pressure on the authorities to formulate sustainable policies but also drives enterprises to select proper technologygreening products and production processes (Vergragt et al., 2014). Consumers' behaviors illustrate the tendency of sustainable technology and shape enterprises' orientation of technology development (Dendler, 2014). Moreover, enterprises are also motivated by these behaviours to make positive technology strategies including eco-product design, green production, and recycling process. In this way, a situation of sustainability can be created and maintained (Loorbach, 2010). Authorities play a very critical role in leading the product chain and value-added activities to the right track of pursuing economic, environmental and social goals through regulation and code (Xia et al., 2015; Leach et al., 2012). For example, Waste Electrical and Electronic Equipment (WEEE), launched by Euro Committee forces enterprises, including famous Chinese companies such as Lenovo and Haier to enact green strategy, meaning that the enterprises should adopt environmental procurement and manufacture new eco-designed product. In addition, the Chinese government also adopts carbon tax and green authentication to influence enterprises' behaviours in technology selection and balance economic development and environmental conservation (Leach et al., 2012). Meanwhile, banking institutions also provide favorable interest rates for implementing technological changes that support enterprises' green ambitions. Consequently, the stakeholders play an essential role in the sustainability of product-chain and value-added activity, and vice versa (Wesselink et al., 2015). Therefore, it is essential to build a reasonable relationship and management mechanism into the decision-making process regarding sustainable technology selection. In this way, the sustainability can be improved through effective collaboration among these managerial factors. (Von Schomberg, 2013). 5. Sustainable technology selection in the supply chain The sustainability of the three groups of components (product, value-added activities and stakeholders) associated with the various technical options is influenced by the relationship inside and outside each group. A reasonable assessment method built with the logical structure analyzed above will facilitate screening out a suitable sustainable technical option in the complex system. 5.1. Decision-making and computational method According to the above decision-making framework and analysis, an operational decision-making network is designed as shown in Fig. 3, which is built with relational logic among the three groups as well as among the factors within each group. The ultimate goal is

to improve the application of sustainable technology, which can be completed and illustrated within three dimensions. The relation is indicated by the dotted line G. Economic, environmental and social responsibility supported and affected by critical stakeholders, product chain and value-added activities are denoted by the dotted line H, J, S. Their sustainable performance can be illustrated in four criteria that reflect more details about the sustainability. The criteria include Benefit, Opportunity, Cost and Risk (BOCR) resulting from technology adoption. During the decision-making process, the correlation and reactive mechanism is explored and analyzed among groups and their factors, while the importance of each group and factor for sustainability is evaluated by focusing on the BOCR effects. This process is implemented along the path of each group, factor and its relevant relation using similar analysis but upon different subjects. Thus a whole picture of the selection mechanism for sustainable technology may be shown. The decision-making network in Fig. 3 illustrates all features of the technology selection mechanism, which is made of six decision-making units in supply chain operations. These units are: sustainable responsibility, stakeholders, product chain, valueadded activity and performance criteria as well as five types of candidate sustainable technologies for selection. At the same time, an essential and complex relation in the system is built into the paths, including unilateral supportive relations (dotted line) among different levels (G), cause and effect (H,J,S,K,L,M,N,O,P,Q,R), and bilateral relations (solid line) between groups (A, …,F) as well as internal circulation (double line) among the factors (T,U,V) in each group. Consequently the supportive and cause-and-effect relations run among the ultimate sustainability, responsible group, product chain and production processes. Comparatively, the bilateral relations exist among the stakeholder, product chain and valueadded activities, while the circulation ones exist among internal factors. In complex systems such as this, the theories of Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) developed by Saaty (1996) will support the decision-making process. These theories are applied here in our work to select a proper technology plan out of a field of candidate plans. Then sustainability may be achieved as much as possible in practice. 5.2. Measuring technical sustainability through establishment of judgment matrix After designing the computational paths described above, it is a critical task to evaluate the relative importance and relations among sustainable responsibility, stakeholders, product chain, value-added activities and candidate technology with focus on corresponding criteria. In practice, the evaluators may include practitioners as well as experts with various backgrounds. With focus on the sustainability and life cycle statistical data shown in Table 1, the importance is weighted based on the pair comparison among the groups and factors following the paths designed in Fig. 3. ANP and AHP are then applied to form a super decisionmaking matrix based on the pair-comparison of each decision unit. The ultimate purpose of this matrix is to embed technology sustainability into the control level of decision paths. The decision process is designed as follows. 5.2.1. Judgment matrix based on pair-wise comparison Firstly, the pair-wise comparison is carried out among managerial groups and their elements to produce for each relation X2 i {A,...,H,J,...,V}, a pair-wise comparison matrix Xm
m ; where i denotes the element of the influenced cluster in relation X and m the number of elements of the influencing cluster in the relationship. i From Xm
m , its eigenvector wi that donates the maximum eigenvalue is calculated. These eigenvectors are then used for all i to form

D. Xia et al. / Journal of Cleaner Production 141 (2017) 1337e1348

1343

Fig. 3. Decision-making process and paths for sustainable technology.

a weight sub-matrix Xm
k, where k refers to the number of elements in the influenced cluster in relation X. With respect to pair-wise comparison matrices, a table somewhat like a Likert scale with 9 grades is applied during the interview and questionnaire with related participants in GTS decisionmaking process. The value of each comparison stands for the degree of relative importance of the two elements being compared, as shown in the following Table 2. Generally, if the number of comparisons is large, an inconsistency in judgment can occur during an interview. For example, while comparing the relative importance of elements in a product chain (component, core product, supported product, and byproducts) and an element of value-added activities (i.e., a sustainable procurement), an interviewee may think that the component is 2 times as important as the core product and the core product is 3 times as important as the by-product. However, he or she may disagree with the conclusion that the component is 6 times as important as the by-product. Such inconsistency may result from various factors including personal experience, judgment competence, etc. However, producing somewhat consistent comparisons at this stage is a prerequisite to successfully applying AHP analysis. In practice if such consistency is very costly to produce, that is still Table 2 System of grades for comparison.

acceptable on condition that the result is not significantly different. In order to make sure that the inconsistency of result is controlled within a certain range, it is necessary to evaluate it properly. To this end, the notions of consistency ratio (CR), consistency index (CI) and random inconsistency (RI) were introduced by Saaty (1996) and they are defined as follows:

CIm ¼

limax  m m1

; and CRm ¼

CIm ; RIm

i where limax is the maximum eigenvalue of Xm
m , and the value of RIm can be taken from any of several relevant tables in textbooks. In practice if CRm < 0.1, then the inconsistency of the evaluation i process and the comparison matrix (Xm
m ) is acceptable. Otherwise the interviewee will be asked to make repeated judgements so that a new comparison matrix can be formed and the new

Table 4 Random in consistency. n

1

2

3

4

5

6

7

8

9

10

11

RI

0

0

0.58

0.90

1.12

1.24

1.32

1.41

1.45

1.49

1.51

Table 5 The layout of original judgment matrix.

Grade

Relative importance (the former vs. the latter)

1,3,5,7,9 2,4,6,8 1,1/2,1/3, …,1/9

Equal, slightly more, more, much more, extremely more between the two corresponding grades indicated above Equal, slightly less, less, much less, very much less

Table 3 Judgment matrix on factors' importance of product chain for sustainability based on pair-comparison. Purchasing

P-comp.

P-core.

P-su.

P-bp.

Weight

P-comp. P-core. P-su. P-bp.

1.000 1/6 1/3 1/8

6.000 1.000 2.000 1/2

3.000 1/2 1.000 1/4

8.000 2.000 4.000 1.000

0.447 0.073 0.151 0.044

SSC EES PCH VAA STH TMS PRO ALT

SSC

EES

PCH

VAA

STH

TMS

PRO

ALT

0 G3
1 0 0 0 0 0 0

0 0 H4
3 S4
3 J3
3 0 0 0

0 0 V4
4 B4
4 D3
4 P5
4 L4
4 0

0 0 A4
4 T4
4 F3
4 O5
4 M4
4 0

0 0 C4
3 E4
3 U3
3 N5
3 K4
3 0

0 0 0 0 0 0 0 Q3
5

0 0 0 0 0 0 0 R3
4

0 0 0 0 0 0 0 I

Notes: SSC stands for sustainability of technology application in SC; EES stands for Sub-sustainability in economy, environment and society; PCH stands for product chain in SC; VAA stands for value-added activities in SC; STH stands for stakeholders related with SC operation; TMS stands for generic technical measure; PRO stands for generic criteria of sustainable performance; ALT stands for candidates with combined measurements.

1344

D. Xia et al. / Journal of Cleaner Production 141 (2017) 1337e1348

Table 6 Stochastic weighted matrix.

SSC ECP ENP SOP P-comp P-core. P-sp. P-bp. V-pr. V-pm. V-ls. V-sa S-et. S-cu. S-au. T-D T-R T-S T-T T-S PRO-B PRO-O PRO-C PRO-R ALT1 ALT2 ALT3

SSC

ECP

ENP

SOP

P-comp.

P-core.

P-sp.

P-bp.

V-pr.

V-pm.

V-ls.

V-sa.

S-et.

0 0.10205 0.72583 0.17213 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0.01956 0.00536 0.05214 0.00695 0.07548 0.01071 0.03774 0.02283 0.1818 0.52442 0.06302 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0.00862 0.00497 0.04744 0.02297 0.06678 0.04171 0.00891 0.02935 0.54503 0.04641 0.17781 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0.01246 0.02496 0.00515 0.04143 0.02542 0.00818 0.04184 0.07132 0.12443 0.05226 0.59256 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0.01304 0.11943 0.04557 0.05561 0.02595 0.00488 0.00967 0.04207 0.00371 0.00884 0.08375 0.25011 0.14828 0.14828 0.04082 0 0 0 0 0 0 0

0 0 0 0 0.10354 0 0.05501 0.01949 0.05593 0.02773 0.00706 0.00538 0.0385 0.0046 0.01152 0.15568 0.04576 0.35737 0.08549 0.02695 0 0 0 0 0 0 0

0 0 0 0 0.04817 0.01517 0 0.11469 0.02472 0.05463 0.0047 0.01205 0.01262 0.03869 0.0033 0.05534 0.21294 0.29365 0.084 0.02532 0 0 0 0 0 0 0

0 0 0 0 0.04678 0.11726 0.014 0 0.01689 0.06418 0.01027 0.00476 0.01523 0.00393 0.03545 0.14686 0.38693 0.07488 0.03775 0.02483 0 0 0 0 0 0 0

0 0 0 0 0.11166 0.0152 0.0618 0.0096 0 0.07569 0.01244 0.03069 0.04031 0.00409 0.00994 0.30804 0.19227 0.06947 0.02787 0.03092 0 0 0 0 0 0 0

0 0 0 0 0.03772 0.00955 0.13274 0.01825 0.06768 0 0.03958 0.01157 0.03094 0.0181 0.00529 0.07248 0.27649 0.14536 0.09115 0.0431 0 0 0 0 0 0 0

0 0 0 0 0.11083 0.0206 0.05212 0.01471 0.0833 0.02292 0 0.01261 0.03522 0.01248 0.00663 0.39481 0.08989 0.07794 0.04226 0.02367 0 0 0 0 0 0 0

0 0 0 0 0.04379 0.01031 0.12505 0.01911 0.02647 0.00834 0.08403 0 0.0397 0.01024 0.0044 0.0575 0.31818 0.18933 0.03863 0.02493 0 0 0 0 0 0 0

0 0 0 0 0.03388 0.01135 0.00578 0.07805 0.05983 0.01845 0.03491 0.12219 0 0.04741 0.00593 0.12393 0.04166 0.326 0.07094 0.01969 0 0 0 0 0 0 0

corresponding maximum eigenvector may be produced. The k i maximum eigenvectors ðwi ¼ limax Þ of Xm
m form the corresponding weight sub-matrix{Xm
k} and k here represents the number of elements in the influenced cluster in the relation X,X2 {A,...,H,J,...,V}. 5.2.2. Converged weight matrix In the second step, the ANP is applied to the formation of a weight matrix W along the paths of the Green Technology Selection (GTS) sustainable decision-making system with weight submatrices {Xm
k}, X2{A,...,H,J,...,V}, as the input. Based on the result of the above decision-making process, the weight matrix W is then subjected to ANP by feeding W into Super Decision, a software tool based on the ANP, to provide a relevant converged matrix with desired information for decision makers. 5.3. Numerical example According to the computational method designed above an experiment is carried out to make it more straightforward. Firstly, Table 2 is filled by inviting information from experts and practitioners in relevant GTS management through questionnaires and interviews. The relative importance of elements in the product chain regarding the sustainability of elements' targets in valueadded activities is shown in this table. In terms of procurement, Table 3 below represents a matrix A14
4 of relative importance among the four elements of the product chain with respect to the first element of value-added activities (purchasing) together with the eigenvector (weight) vector. To ensure the consistency of the judgement process, it is necessary to check the value of CR4. Since

CI4 ¼

l1max  4 41

¼

4:0495  4 ¼ 0:012375; 3

CR4 ¼ CI4/RI4 ¼ 0.012375/0.90 ¼ 0.01375, where RI4 ¼ 0.90 can

be obtained from Table 4. Here, CR¼CI/RI ¼ 0.012375/1.12 ¼ 0.01105, The consistency ratio of A14
4 is less than 0.1, indicating the acceptability of the weight vector and of the priorities of the elements in the product chain for the procurement factor in valueadded activities. The priorities are given by 0.447 for components, 0.151 for supported products, 0.073 for core products and 0.044 for by-products. That is to say, in the case of the nice sustainable performance of procurement during the GTS adoption practice, most attention should be paid to components, which may be intensively tied up with the suppliers' environmental performance. What's more, greening supported products is very critical to the consumption of energy and resources. The application of green technology to the core product is also essential to the nature of supply activities while the related by-product is comparatively less important. For the remaining three types of value-added activities in SC, similar pair-comparison is carried out to form the matrix which leads the rest of the weight vectors (A24
4 ,A34
4 ,andA44
4 ) making up the weight sub-matrix A4
4 in Table 5 below (original judgement matrix). Subsequently, other weight sub-matrices for the corresponding relationships can be constructed as B4
4, C4
3, …H4
3,J3
3, …, V4
4. In consequence as many as 74 weight vectors (column) and one identity standard matrix containing the candidate technologychanges will be built into the super judgment matrix based on the decision framework and structure shown in Fig. 3. The accomplishment of the task leads to the establishment of the original judgment super matrix W, which consists of 21 subjudgment matrices, (A4
4, B4
4, C4
3, V4
4) as well as one 31
31 I identity matrix for the possible technology options. The original judgment matrix is shown in Table 5. According to the decision-making paths shown in Fig. 3, the columnar stochastic weight matrix Ws and limited converged matrix Wsc shown in Tables 6 and 7 are computed based on the

D. Xia et al. / Journal of Cleaner Production 141 (2017) 1337e1348

1345

S-cu.

S-au.

T-D

T-R

T-S

T-T

T-S

PRO-B

PRO-O

PRO-C

PRO-R

ALT1

ALT2

ALT3

0 0 0 0 0.01249 0.00502 0.07545 0.0361 0.01445 0.03145 0.07303 0.11646 0.04571 0 0.00762 0.05479 0.27169 0.16172 0.06408 0.02995 0 0 0 0 0 0 0

0 0 0 0 0.01782 0.0115 0.04399 0.05575 0.03574 0.02004 0.12316 0.05644 0.03556 0.01778 0 0.13741 0.31541 0.05975 0.04508 0.02458 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.23115 0.70852 0.06033

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.62501 0.1365 0.23849

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.12196 0.31962 0.55842

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.24987 0.09535 0.65479

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.309 0.58155 0.10945

0 0 0 0 0.06739 0.01991 0.0313 0.10125 0.02356 0.09275 0.0121 0.07249 0.30111 0.04384 0.1149 0 0 0 0 0 0 0 0 0 0.03056 0.00875 0.08009

0 0 0 0 0.05335 0.01321 0.02929 0.124 0.09388 0.01917 0.03216 0.05569 0.0387 0.32415 0.097 0 0 0 0 0 0 0 0 0 0.0125 0.07605 0.03084

0 0 0 0 0.06986 0.01203 0.11778 0.02017 0.10051 0.06914 0.0208 0.01046 0.29292 0.11877 0.04816 0 0 0 0 0 0 0 0 0 0.02518 0.08417 0.01005

0 0 0 0 0.0171 0.04871 0.03693 0.11711 0.05227 0.02093 0.01161 0.1161 0.03082 0.30405 0.12498 0 0 0 0 0 0 0 0 0 0.03169 0.00752 0.08019

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

original judgment matrix in Table 5 with the application of ANP. More details about the sustainable features of technology are provided by the results. In the I matrix, there are as many as 2n-1 technology candidates since any aggregate of the five operational managerial practices (reduction, transfer, reuse, substitution and status quo) is possible. Besides, only three candidates are presented in the I matrix without significant influence on the research purpose so as to simplify the computing process. Ultimately, the best choice is found in the converged matrix shown in Table 7, which details a proper technology with influence on the adoption of sustainable technology in the supply chain as a whole. 5.4. Analysis of the result In terms of comprehensive sustainable performance, one sees in Table 7 that the functional features of three options (ALTs) are illustrated in the SSC column, which are ALT1 (0.22028), ALT2 (0.20922) and ALT3 (0.21325) respectively. It shows that ALT1 is the best choice among options to achieve a reasonable sustainability in green technology application. What's more, more details about the combination of this tactic (ALT1) for technology are provided in the T,P,V and S cluster. With regard to the priority of elements in this group concerning sustainability, the greatest attention should be paid to “reuse” with the highest priority (0.07956) while “transferring” and “status quo” are the less preferred options with relatively low priority (0.0262 and 0.01057 respectively). This result indicates that “reuse” should be the first choice in practice, which means deployment of not only final products but also second-hand materials, parts, components, supporting-products and byproducts during production and consumption process. With the application of ALT1, it also means that “reuse” technology should be introduced into value-added activities including procurement, production, etc, for reduced input of resources such as energy and materials. For stakeholders, “reuse” should be the measure what enterprises and customers' priorities are, leading the change of

traditional consumption behavior. When it comes to the product chain's sustainable features, it is highlighted that greening the supportive products and components is the focus of technology adoption. The sustainable feature of byproducts is more important than that of the core product when decision on GTS is made for a sustainable performance. As the supporting system of a product chain, value-added activities also play a vital role in the comprehensive sustainable performance. As shown in Table 7, the adoption of a proper technology greening procurement and production plays a very important role in the realization of sustainability. Meanwhile it also demonstrates that after-sale service and logistics is a comparatively less important area during the decision-making process of GTS. When it comes to managerial implementation, the result shows the feature and efficiency of manufacturing equipment is critical. The priority of parts and components indicates that suppliers' role and motivation to cooperate are essential to sustainability. As a very critical group in the GTS decision-making method shown in Fig. 3, stakeholders play a role that should not be ignored. Essential information on this point is obtained from the results presented in the last table. In this particular analysis the enterprises are the main driving force pursuing sustainable operation through GTS, while customers still show little initiative. Other essential information on this group in Table 7 is that authority is not as important as it used to be, which reminds us that the change has taken place. That is to say, it is the enterprise rather than the customer or government that promotes the sustainable campaigns in green technology application. Occasionally, the result shows that at the beginning, traditionally it is government and some stakeholder, such as media and local community, who force enterprises to choose green technology. But eventually it is the intuitive motivation of the enterprises themselves, triggered by the market, that boosts them to achieve competitive advantage as well as sustainability through adoption of green technology, which is consistent with previous research. (Garrone et al., 2016; Gupta et al., 2013). The works show a significant tendency that

1346

Table 7 Limited converged matrix. SSC

ECP

ENP

SOP

P-Comp. P-Core. P-sp.

P-bp.

V-ps.

V-pm.

V-ls.

V-sa.

S-et.

S-cu.

S-au.

T-D T-R T-S T-T T-S PRO-B

PRO-O

PRO-C

PRO-R

ALT1 ALT2 ALT3

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0.01824 0.00871 0.02156 0.01496 0.01384 0.01292 0.00863 0.00936 0.01014 0.00571 0.00356 0.04893 0.07956 0.06437 0.0262 0.01057 0 0 0 0 0.22028 0.20922 0.21325

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

D. Xia et al. / Journal of Cleaner Production 141 (2017) 1337e1348

SSC ECP ENP SOP P-Comp. P-Core. P-sp. P-bp. V-ps. V-pm. V-ls. V-sa. S-et. S-cu. S-au. T-D T-R T-S T-T T-S PRO-B PRO-O PRO-C PRO-R ALT1 ALT2 ALT3

D. Xia et al. / Journal of Cleaner Production 141 (2017) 1337e1348

embedding sustainability into business operation through adoption of green technology is enterprises' consideration for competitive edge resulted from differentiation and first-mover advantage in the market (Pujari et al., 2003). 6. Summary and conclusion The establishment of the decision-making tool for the adoption of green technology at the micro-level is driven by intensive corporate environmental responsibility to meet the need for sustainable development. This has proved to be a goal that is very difficult to accomplish in a complex dynamic system. The framework and method described in this paper clarifies the multidimensional and multilateral mechanisms within supply chains and facilitates the decision-making process around sustainable technology in practice. The relationships among related factors and groups are built into the model with its dynamic features, including cause-effect, inter-activation and correlation. The new explanation of sustainability in GTS has been presented through a modified balance card and decision network, which contributes to converting the ambiguous concept of and strategy for sustainability into concerted technology adoption in supply chains at the goal, supporting and operational levels. The components of four critical groups including sustainable indicators, stakeholders, process and products, forming the structure of green technology decision-making process are identified and their influences on each other are analyzed. In this work the nature and mechanism of technology adoption for sustainability is analyzed and demonstrated with a decision-making model including product chain, value-added operation process and stakeholders in a supply chain. This work adds to an increasing discussion about efforts to improve sustainable performance through adoption of different technology in corporate managerial practice. It links the macroand meso-levels of sustainability with operational innovation activities taking place in supply chains. However, the limitations of our work still exist. Firstly, the data are mainly based on a subjective survey, so mass data mining can improve the objectivity with which the critical factors of technical features of sustainability are identified. Experiment and field studies may also avoid the distortion of information resulting from self-reporting. Secondly, the method in this work still cannot measure the cause-effect relation among decision-making factors exactly and quantitatively, which may be very important to identify and accurately locate the critical factor in supply chain systems for a proper solution in practice. Therefore, much work remains to be completed to enhance the innovation of sustainability, including sensitivity analysis of the functional factors in three groups, as well as the trigger mechanism among relevant variables. A better understanding of linkages among technical components and the business section is our basis to progress in achieving adoption of sustainable technology. The application of new evaluation technology, especially emerging Big Data Analysis, during the decision making process regarding greening technology is also important. It has the potential to generate information for many supply chain domains. Acknowledgments The authors would like to express their deep appreciation for the insightful comments by Professor Geert Duysters, Ann Scholten-Sampson, Nina Woodson and the anonymous referees. This work was supported in part by the National Social Science Fund of China (No. 11CGL030), and Opening Fund of Hubei Collaboration Innovation Centre & CCEM (No. JD2015051001).

1347

References Abreu, M.C.S., Castro, F., Soares, F.A., Filho, J.C., 2011. A comparative understanding of corporate social responsibility of textile firms in Brazil and China. J. Clean. Prod. 20 (1), 119e126. Bare, J.C., Hofstetter, P., Pennington, D.W., Udo de Haes, H.A., 2000. Life cycle impact assessment workshop summary: midpoints versus endpoints: the sacrifices and benefits. Int.. J. Life Cycle Assess. 5 (6), 319e326. Baumann, H., Boons, F., Bragd, A., 2002. Mapping the green product development field: engineering, policy and business perspectives. J. Clean. Prod. 10 (5), 409e425. Blok, V., Long, T.B., Idil Gaziulusoy, A., Ciliz, N., Lozano, R., Huisingh, D., Csutora, M., Boks, C., 2015. From best practices to bridges for a more sustainable future: advances and challenges in the transition to global sustainable production and consumption. J. Clean. Prod. 108, 19e30. Bocken, N.M.P., Short, S.W., Rana, P., Evans, S., 2014. A literature and practice review to develop sustainable business model archetypes. J. Clean. Prod. 65, 42e56. Carter, C.R., Kale, R., Grimm, C.M., 2000. Environmental purchasing and firm performance: an empirical investigation. Transp. Res. E Logist. Transp. Rev. 36 (3), 219e228. Ceres, 2015 (accessed 12.2015). www.ceres.org/resources/reports. Chitnis, M., Sorrell, S., Druckman, A., Firth, S.K., Jackson, T., 2013. Turning lights into flights: estimating direct and indirect rebound effects for UK households. Energy Policy 55, 234e250. Closs, D.J., Speier, C., Meacham, N., 2011. Sustainability to support end-to-end value chains: the role of supply chain management. J. Acad. Mark. Sci. 39 (1), 101e116. Csutora, M., 2012. One more awareness gap? the behaviour impact gap problem. J. Consum. Policy 35 (1), 145e163. Dendler, L., 2014. Sustainability meta labelling: an effective measure to facilitate more sustainable consumption and production? J. Clean. Prod. 63, 74e83. Dow Jones Sustainability Indexes, 2015. Available at: www.sustainability-indexes. com/(accessed 06.09.15). Dreyer, L.C., Hauschild, M.Z., Schierbeck, J., 2005. A framework for social life cycle impact assessment. Int.. J. Life Cycle Assess. 11 (2), 87e97. Dubey, R., Gunasekaran, A., Childe, S.J., Wamba, S.F., Papadopoulos, T., 2016. The impact of big data on world-class sustainable manufacturing. Int.. J. Adv. Manuf. Technol. 84, 631e645. Elkington, J., 2004. Enter the triple bottom line. In: Henriques, A., Richardson, J. (Eds.), The Triple Bottom Line: Does it All Add up? Earthscan, London, pp. 1e16. European Commission, 2015. Energy Efficient Products. European Energy Commission, Brussels, Belgium. Garg, A., Siu Lee Lam, J., Gao, L., 2015. Energy conservation in manufacturing operations: modelling the milling process by a new complexity-based evolutionary approach. J. Clean. Prod. 108, 34e45. Garrone, P., Groppi, A., Grilli, L., Marzano, R., 2016. Barriers and drivers in the adoption of advanced wastewater treatment technologies: a comparative analysis of Italian utilities. J. Clean. Prod. http://dx.doi.org/10.1016/j.jclepro.2016.02.018 (available online 12.02.16). Gaziulusoy, A.I., Boyle, C., McDowall, R., 2013. System innovation for sustainability: a systemic double-flow scenario method for companies. J. Clean. Prod. 45, 104e116. Geels, F.W., 2010. Ontologies, socio-technical transitions (to sustainability), and the multi-level perspective. Res. Policy 39 (4), 495e510. Gupta, S., Czinkota, M., Melewar, T.C., 2013. Embedding knowledge and value of a brand into sustainability for differentiation. J. World Bus. 48, 287e296. Hauschild, M., Jeswiet, J., Alting, L., 2005. From life cycle assessment to sustainable production: status and perspectives. CIRP Ann. 2, 535e555. Holt, D., 2004. Managing the interface between suppliers and organizations for environmental responsibility e an exploration of current practices in the UK. Corp. Soc. Responsib. Environ. Manag. 1 (2), 71e84. Hunkeler, D., 2006. Societal LCA methodology and case study. Int.. J. Life Cycle Assess. 11 (6), 371e382. Hutchins, M.J., Sutherland, J.W., 2008. An exploration of measures of social sustainability and their application to supply chain decisions. J. Clean. Prod. 16, 1688e1698. IBM, 2005. IBM's 2005 Innovations in Corporate Responsibility. IBM Corporation, Armonk, NY. Jennings, P.D., Zandbergen, P.A., 1995. Ecologically sustainable organizations: an institutional approach. Acad. Manag. Rev. 20 (4), 1015e1052. Kersten, W., Geelen, D.V., Meijer, S.A., Franken, V., Crul, M.R., 2015. Engaging beneficiaries of sustainable renovation - exploration of design-led participatory approaches. J. Clean. Prod. 106, 690e699. Kimming, M., Sundberg, C., Nordberg, Å., Baky, A., Bernesson, S., Hansson, P.A., 2015. Replacing fossil energy for organic milk production - potential biomass sources and GHG emission reductions. J. Clean. Prod. 106, 400e407. Klopffer, W., 2003. Life-cycle based methods for sustainable product development. Int.. J. Life Cycle Assess. 8 (3), 157e159. Koplin, J., Seuring, S., Mesterharm, M., 2007. Incorporating sustainability into supply management in the automotive industry: the case of the Volkswagen AG. J. Clean. Prod. 15 (11e12), 1053e1062. Kovacs, G., 2008. Corporate environmental responsibility in the supply chain. J. Clean. Prod. 16, 1571e1578. Krewitt, W., Mayerhofer, P., Trukenmuller, A., Friedrich, R., 1998. Application of the impact pathway analysis in the context of LCA: the long way from burden to

1348

D. Xia et al. / Journal of Cleaner Production 141 (2017) 1337e1348

impact. Int. J. Life Cycle Assess. 3 (2), 86e94. Kuijer, S.C., 2014. Implications of Social Practice Theory for Sustainable Design. Delft University of Technology, Delft, The Netherlands. €m, J., Raskin, P., Scoones, I., Stirling, A.C., Smith, A., Thompson, J., Leach, M., Rockstro Millstone, E., Ely, A., Arond, E., Folke, C., Olsson, P., 2012. Transforming innovation for sustainability. Ecol. Soc. 17 (2), 1239e1241. Lockton, D., Harrison, D., Stanton, N.A., 2010. The design with intent method: a design tool for influencing user behaviour. Appl. Ergon. 41, 382e392. Loorbach, D., 2010. Transition management for sustainable development: a prescriptive, complexity-based governance framework. Governance 23 (1), 161e183. Loorbach, D., Wijsman, K., 2013. Business transition management: exploring a new role for business in sustainability transitions. J. Clean. Prod. 45, 20e28. Lozano, R., 2011. Addressing stakeholders and better contributing to sustainability through Game theory. J. Corp. Citizensh. 43, 45e62. Murphy, P.R., Poist, R.F., Braunschwieg, C.D., 1996. Green logistics: comparative views of environmental progressives, moderates, and conservatives. J. Bus. Logist. 17 (1), 191e211. Neilson, J., Pritchard, B., 2007. Green coffee? the contradictions of global sustainability initiatives from an Indian perspective. Dev. Policy Rev. 25 (3), 311e331. O'Brien, M., Doig, A., Clift, R., 1996. Social and environmental life cycle assessment (SELCA): approach and methodological development. Int. J. Life Cycle Assess. 11 (2), 87e97. Perrini, F., Tencati, A., 2006. Sustainability and stakeholder management: the need for new corporate performance evaluation and reporting systems. Bus. Strategy Environ. 15, 296e308. Pesonen, H.L., 2001. Environmental management of value chains: promoting lifecycle thinking in industrial networks. Greener Manag. Int. 33, 45e57. Pettersen, I.N., Boks, C., Tukker, A., 2013. Framing the role of design in transformation of consumption practices: beyond the designer-product-user triad. Int.. J. Technol. Manag. 63, 70e103. Porter, M.E., van der Linde, C., 1995. Green and competitive: ending the state mate. Harv. Bus. Rev. 73 (5), 120e134. Pujari, D., Wright, G., Peattie, K., 2003. Green and competitive: influences on environmental new product development performance. J. Bus. Res. 56 (8), 657e671. Rodrigue, M., Magnan, M., Cho, C.H., 2013. Is environmental governance substantive or symbolic? an empirical investigation. J. Bus. Ethics 114, 107e129. Rotmans, J., 2006. Tools for integrated sustainability assessment: a two-track approach. Integr. Assess. Int.. J. 6 (4), 35e57. Saaty, T.L., 1996. The Analytic Network Process. RWS Publications, Pittsburgh. Schliephake, K., Stevens, G., Clay, S., 2009. Making resources work more efficiently: the importance of supply chain partnerships. J. Clean. Prod. 17 (14), 1257e1263. Schmidt, I., Meurer, M., Saling, P., Kicherer, A., Reuter, W., Gensch, C., 2004. SEE balance: managing sustainability of products and processes with the socio-ecoefficiency analysis by BASF. Greener Manag. Int. 45, 79e94. Seuring, S., Sarkis, J., Muller, M., Rao, P., 2008. Sustainability and supply chain management e an introduction to the special issue. J. Clean. Prod. 16 (15),

1545e1551. Seuring, S., 2004. Industrial ecology, life cycles, supply chains: differences and interrelations. Bus. Strategy Environ. 13, 306e319. Shonnard, D.R., Kicherer, A., Saling, P., 2003. Industrial applications using BASF ecoefficiency analysis: perspectives on green engineering principles. Environ. Sci. Technol. 37 (23), 5340e5348. Sikdar, S.K., 2003. Sustainable development and sustainability metrics. AICHE J. 49 (8), 1928e1932. Song, M., Zhou, Y., 2016. Quantitative analysis of foreign trade and environmental efficiency in China. Emerg. Mark. Finance Trade 52 (7), 1647e1660. Song, M., Zheng, W., 2016. Computational analysis of thermoelectric enterprises’environmental efficiency and Bayesian estimation of influence factors. Soc. Sci. J 53, 88e99. Song, M., Ai, H., Li, X., 2015a. Political connections, financing constraints, and the optimization of innovation efficiency among China's private enterprises. Technol. Forecast. Soc. Change 92, 290e299. Song, M., Wang, S., Cen, L., 2015b. Comprehensive efficiency evaluation of coal enterprises from production and pollution treatment process. J. Clean. Prod. 104, 374e379. Starik, M., Rands, G.P., 1995. Weaving an integrated web: multilevel and multisystem perspectives of ecologically sustainable organizations. Acad. Manag. Rev. 20 (4), 908e935. Tsoi, J., 2010. Stakeholders' perceptions and future scenarios to improve corporate social responsibility in Hong Kong and Mainland China. J. Bus. Ethics 91 (3), 391e404. Tyl, B., Vallet, F., Bocken, N.M.P., Real, M., 2015. The integration of a stakeholder perspective into the front end of eco-innovation: a practical approach. J. Clean. Prod. 108, 543e557. Varey, R.J., 2011. Marketing means and end for a sustainable society: a welfare agenda for transformative change. J. Macromarketing 30 (2), 112e126. Vergragt, P., Akenji, L., Dewick, P., 2014. Sustainable production, consumption, and livelihoods: global and regional research perspectives. J. Clean. Prod. 63, 1e12. Von Schomberg, R., 2013. A vision of responsible research and innovation. In: Owen, R., Bessant, J., Heintz, M. (Eds.), Responsible Innovation. Wiley, USA, pp. 51e74. Waller, M.A., Fawcett, S.E., 2013. Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J. Bus. Logist. 34 (2), 77e84. Wesselink, R., Blok, V., van Leur, S., Lans, T., Dentoni, D., 2015. Individual competencies for managers engaged in corporate sustainable management practices. J. Clean. Prod. 106, 497e506. Windsor, D., 2006. Corporate social responsibility: three key approaches. J. Manag. Stud. 43 (1), 93e114. Xia, D., Chen, B., Zheng, Z., 2015. Relationship among circumstance pressure, green technology selection and firm performance. J. Clean. Prod. 106, 487e496. Yang, M.G., Hong, P., Modi, S.B., 2011. Impact of lean manufacturing and environmental management on business performance: an empirical study of manufacturing firms. Int.. J. Prod. Econ. 129 (2), 251e261.