DOI 10.1007/s10661-010-1537-x
Using hybrid method to evaluate the green performance
in uncertainty
Ming-Lang Tseng· Lawrence W. Lan · Ray Wang· Anthony Chiu · Hui-Ping Cheng
Received: 3 October 2009 / Accepted: 26 May 2010 / Published online: 23 June 2010 © Springer Science+Business Media B.V. 2010
Abstract Green performance measure is vital for enterprises in making continuous improvements to maintain sustainable competitive advantages. Evaluation of green performance, however, is a challenging task due to the dependence com-plexity of the aspects, criteria, and the linguistic vagueness of some qualitative information and quantitative data together. To deal with this issue, this study proposes a novel approach to evalu-ate the dependence aspects and criteria of firm’s green performance. The rationale of the pro-posed approach, namely green network balanced scorecard, is using balanced scorecard to combine fuzzy set theory with analytical network process (ANP) and importance-performance analysis (IPA) methods, wherein fuzzy set theory accounts for the linguistic vagueness of qualitative criteria and ANP converts the relations among the de-pendence aspects and criteria into an intelligible structural modeling used IPA. For the empirical case study, four dependence aspects and 34 green
M.-L. Tseng (
B
)· L. W. Lan · R. Wang · A. Chiu · H.-P. ChengDepartment of Business Innovation & Development, College of Management, MingDao University, Changhua, Taiwan
e-mail: ml.tseng@msa.hinet.net
performance criteria for PCB firms in Taiwan were evaluated. The managerial implications are discussed.
Keywords Analytic network process· Importance-performance analysis· Fuzzy set theory· Balanced scorecard · Green performance
Introduction
Facing constantly fluctuating environments, orig-inal equipment manufacturing of printed circuit board (PCB) firms require maintaining environ-mental and managerial responses to changing en-vironments and sustain competitive advantage in Taiwan. Such responses require PCB firms to indicate the green performance on overhauling operation process to achieve firm’s goal of waste elimination and reduce the impact of environment to ensure corporate survival and sustainable de-velopment (Tseng 2008,2009a,b; Tseng and Lin 2008). In addition, the firm’s green performance evaluation is dependent on wider knowledge inte-gration to achieve the goal due to the mandated environmental order from European Union such as Waste Electrical and Electronic Equip-ment (WEEE) and Restriction of Hazardous
Substances (ROHS) Directives. Hence, the per-formance evaluation is an on-going process that Requires continuous monitoring to maintain high level of internal process evaluation across a num-ber of aspects in organization. The evaluation is important to help firm making environmental continuous improvement in the competitive and sustainable market.
Hence, the evaluation of green performance is an on-going process across a set of aspects and criteria. In terms of multi-aspects evaluation, the balanced scorecard (BSC) is well recognized in the literatures that performance measurement should incorporate both financial and non-financial measures; it captures not only a firm’s current performance, but also the drivers of its fu-ture performance (Banker and Datar1989; Dyson 2000). However, there are fewer studies of the development and implementation of the BSC in measuring the performance of green activities. Nevertheless, the BSC is a model for the analy-sis of performance measurement for all types of organization developed by Kaplan and Norton in 1992. The BSC is an important activity that helps organizations to make continuous improvements, due to a great emphasis on performance evalua-tion. However, BSC engenders multi-dimensional difficulties that involve numerous organizational functions and resource integration among various departments (Tseng et al.2008; Tseng2008). Tra-ditionally, BSC categorized evaluation measures into four aspects: financial, customer, internal business process, and learning and growth aspect. With regards to BSC, the firms can evaluate their management in terms of their effectiveness in creating value for customers, developing inter-nal capabilities, and investing in the people and systems that are necessary to improve their future performance. In reality, there are dependence re-lations existed in the BSC aspects and criteria. Therefore, the traditional statistical approach is no longer suited to evaluate proposed network balanced scorecard (NBSC), due to the traditional approach assumed that the aspects are always in-dependent. Moreover, the evaluation-related ac-tivities have inherent and highly uncertainty and imprecision, and difficult to assess accurately with qualitative information. This study proposes to utilize the analytical network process (ANP)
tech-nique to analyze the proposed NBSC. ANP de-veloped by Saaty (1996) that this technique takes into account both the relations of feedback and dependence. In addition to these, merits of ANP provides a more generalized model in decision-making without decision-making assumptions about the independence relations among the aspects and criteria.
In literatures, though, there are fewer studies of the development and implementation of the BSC in measuring the green performance of firm’s activities. However, there are studies on other industries such as banking, textile, pharmaceutical etc. (Bremser and Barsky2004; Cebeci2009; Wu et al.2009). The BSC conceptual framework has been widely accepted in the business community, the proper method of implementing the frame-work remains an issue. For instance, Leung et al. (2006) incorporating a wider set of non-financial attributes into the measurement system of a firm by using the analytic hierarchy process (AHP) and its variant, the analytic network process, to facilitate the implementation of the BSC. Yuan and Chiu (2009) used BSC design and proposed a three-level feature weights design to enhance case-based reasoning inference performance. BSC also minimizes the effect of subjective factors that has to be carefully dealt in performance evalu-ation process. Cebeci (2009) proposed decision support system integrated with strategic manage-ment by using BSC may be an alternative to some methods for ERP selection. There are even fewer studies dealing with the dependence relations of the BSC with measures in qualitative and quanti-tative approaches to carry out these activities and implement in a firm.
In addition, some of the qualitative criteria mea-sured in linguistic expression are vague and uncer-tain in nature, and the quantitative data should be comparable to the qualitative information, which makes this evaluation more challenging. In view of qualitative and quantitative measures, the crisp values of criteria have varying values that can-not be compared. The crisp number must be converted to comparable form among all criteria (Karsak2002; Tseng2009a). In view of qualitative measures, the fuzzy set theory can address situa-tions that lack well-defined boundaries of activity or observation sets (Zadeh1965,1975). In reality,
the human perceptions are uncertain and qualita-tively descriptive, not easy to assign exact numer-ical values to precisely describe the preferences. Linguistic terms have been used for approximate reasoning within the framework of fuzzy set the-ory to handle the ambiguity of evaluating data and the vagueness of linguistic expression (Al-Najjar and Alsyouf 2003; Tseng and Lin 2008; Tseng et al.2009c). A linguistic preference is a variable whose values (namely linguistic values) have the form of phrases or sentences in a natural language (Von Altrock1996). Especially, linguistic prefer-ences are used to evaluate the aspects or criteria whose values are not numbers but linguistic term. In practice, linguistic values can be represented by triangular fuzzy numbers (TFN). This study adopts fuzzy set theory to assess NBSC in the assessment of green performance measurement. This study addresses two important and related aspects in the implementation of NBSC: the han-dling of dependency among aspects and criteria— especially those of qualitative nature and trans-form the crisp values to compare with the other measures.
This study attempts to develop a green perfor-mance hierarchical framework that is sufficiently general in NBSC concept. To date, few stud-ies have adopted such a rigorous methodology. This study presents a BSC multi-criteria hier-archical analytical framework that can be ap-plied under various study settings (Tseng 2008; Tseng et al.2009c). The firms’ evaluation in this assessment method can help firms to measure the weighted aspects and criteria. Consequently, resolving problems in evaluating firm is funda-mentally important to both researchers and prac-titioners. The unique point of this study was involved in qualitative and quantitative measures in linguistic preferences presented by triangular fuzzy numbers and defuzzified into a crisp value for analyses in dependence relations among as-pects and criteria, and apply ANP to acquire the weights of aspects and criteria and thereafter uses importance-performance analysis (IPA) to draw and identify the relative importance of the aspects and criteria associated with green performance while at the same time indicating the degree of performance in quadrants (Martilla and James 1977).
Evaluations of NBSC on the IPA four quad-rants then are combined into a matrix that allows a firm to identify key drivers of satisfaction, to formulate improvement priorities, and to find ar-eas of possible overkill and arar-eas of “acceptable” disadvantages. Tonge and Moore (2007) used IPA and gap analysis to evaluate the sensed quality of visitors to a Marine-Park to conduct more effec-tive management with environmental protection. In practice, IPA is considered a simple but effec-tive tool (Hansen and Bush1999). It is very helpful in deciding how to best allocate scarce resources in order to maximize satisfaction (Eskildsen and Kristensen2006; Daniels and Marion2006; Shieh and Wu2007). Hansen and Bush (1999) pointed out that IPA is a simple and effective technique that can assist practitioners in identifying im-provement priorities for customer attributes and direct quality-based marketing strategies. Numer-ous practitioners and researchers have applied IPA to identify the critical performance factors in customer satisfaction survey data for products and services (Enright and Newton 2004; O’Neill and Palmer2004; Zhang and Chow2004).
The contribution is that this study is the first to combine the two concepts into a single study criteria framework to build a visual map and to evaluate NBSC successfully which few can sys-tematically evaluate and which model contained complex dependence relations among aspects and criteria in uncertainty. Furthermore, the sector can apply this approach to evaluate and determine the aspects and criteria weights and to compose the visual map to reduce the management risks. In conclusion, this study contributes to, in particular, the literatures by: (1) proposing a green perfor-mance hierarchical framework that relates aspects and criteria in NBSC and (2) developing valid and reliable measures for the green performance based on expert’s qualitative preferences together with quantitative data.
The study begins with a brief introduction of the green performance and study objectives. “Hierarchical structure” follows a discussion of the green performance hierarchical struc-ture and related literastruc-tures. “Research method” presents the proposed method, especially; the aspects and criteria with dependence relations in linguistic preferences. “Results” presents the
empirical study result. A study framework sug-gests providing a context for applying the pro-posed methods. The measures are provided with measurement guidance. “Managerial implica-tions” and “Concluding remarks” concludes with a summary of the study managerial implications of the study as well as concluding remarks for its further development and practical application.
Hierarchical structure
The proposed structure presents the evaluation aspects and criteria for this approach. The eval-uation framework consists of four aspects with 34 criteria, which are determined from extensive literature review and expert suggestions. In this study, four primary dependence aspects of BSC are identified and to be evaluated: financial as-pect; student asas-pect; internal operations asas-pect; and learning and growth aspect. The hierarchi-cal structure is referred from Kaplan and Norton (1992), Kaplan and Atkinson (1998), and Leung et al. (2006). Kaplan and Norton (1996) also em-phasizes that the BSC is only a template and must be customized for the specific elements of an organization or industry. The BSC presented the knowledge, skills, and systems that the em-ployees will need (learning and growth aspect) to innovate and build the right strategic capabilities and efficiencies (internal operations aspect) that deliver specific value to the high school student population (student aspect) which will eventually lead to higher shareholder value (financial as-pect). This is even presented that there are de-pendence relations existed in evaluation process, in nature.
Financial aspect (AS1): financial objectives serve as the focus for the objectives and measures of the other criteria. Every mea-sure should be part of interdependence rela-tionships culminating in long-term, sustainable financial performance. The measures are sales, cost of sales, profitability, prosperity, growth, new green products/services, and industrial lead-ership. Customer aspect (AS2): financial
suc-cess is closely linked to customer satisfaction. Satisfied customers mean repeat business, refer-rals and new green business, customer retention, customer acquisition, customer complain, cus-tomer profitability, and cuscus-tomer lead-time, and thereby contribute to the financial results of the firm. Internal operations aspect (AS3): customer satisfaction is directly achieved through the oper-ational activities of the firm. The objectives and measures thus enable a firm to focus on maintain-ing and improvmaintain-ing the performance of processes that deliver the established objectives that are keys to satisfying customers, which in turn satisfy shareholders. The criteria are service processing time, cost of service quality comparison, low cost green provider, reduce service cost, facilities uti-lization rate, and safety incident index. Learning and growth aspect (AS4): the ability, flexibility and motivation of staff support all of the financial results, customer satisfaction, and operational ac-tivities measured in the other quadrants of the NBSC. The AS4 criteria are innovation of green product measures, breakeven time, rate of new green products introduction per quarter, number of new green products with successful introduc-tion to public, annual increase in number of new green products, employee capabilities, employee satisfaction survey, employee retention, employee productivity, salaries compared to the norm in the local industry, percentage of competency deploy-ment matrix filled, number of promotions from within, and absenteeism rate (Fig.1).
The NBSC shows how the firms’ overall strate-gic green objectives are translated into the green performance measures that the firm has identified as critical success factors (criteria). The green per-formance drivers are translated into more tangible measures that allow the firm to quantify the per-formance measures. It should be noted to consider
Goals Aspects
dependence dependence Criteria
the collective evaluation results, thus the informa-tion of one aspect may be submerged by that of another aspect. Table 1 presents the evaluation aspects and criteria for firms’ NBSC, narrated in details as follows.
Figure 2 presented the dependence relations of proposed framework. A two-way arrow among different levels of criteria may graphically
repre-sent the dependencies in an ANP model. If de-pendencies are present within the same level of analysis, a “looped arc” may be used to represent such interdependence. In order to solve the com-plexity relations, the following section proposed the hybrid methods (fuzzy set theory, analyti-cal network process, and importance-performance analysis) for this approach.
Table 1 Evaluation aspects and criteria
Aspects Criteria
Financial aspect (AS1) Sales: annual growth in sales (C11) (last 3 years data)
Cost of sales: extent that it remains flat or decreases each year (C12) (last 3 years data) Profitability: economic value added (EVA) or return on total capital employed (C13)
(last 3 years data)
Prosperity: cash flows (C14) (last 3 years data)
Company growth versus industry growth (C15) (last 3 years data) Ratio of international sales to total sales (C16)
New green product: gross profit/growth from green products (C17) Industry leadership: market share (C18)
Customer aspect (AS2) Market share for target customer segment (C21)
Customer retention/percentage of growth with existing customers (C22)
Customer acquisition: number of new customers/total sales to new customers/actual new customers divided by prospective inquiries (C23)
Customer satisfaction in green products (via satisfaction surveys) (C24) Customer profitability (via accounting analyses) (C25)
Customer lead time (on-time delivery) (C26)
Service quality: customer complain rates, reworks, percentage of returns (C27) Internal business aspect (AS3) Shorten the service cycle processing time (C31)
Cost of service quality comparison (Other firms) (C32)
Low cost green provider: unit cost versus competitors’ unit cost (C33) Reduce service costs: service costs as percentage of sales (C34) Service output per hour/facilities utilization (C35) (last 3 years data) Safety incident index (C36) (last 3 years data)
Learning and growth Innovation of green products measures (C41) (last 3 years data)
aspect (AS4) Breakeven time: the time from the beginning of green products development work till the green products been introduced (C42)
Rate of new green products introduced per quarter (C43)
Number of new green products with successful introduction to public (C44) Annual increase in number of new green products (C45)
Employee capabilities in green efforts (C46)
Employee satisfaction survey in green performance (C47) Employee retention: percentage of key staff turnover (C48) Employee productivity: revenue per employee (C49) Salaries compared to the norm in the local industry (C410)
Percentage of competency deployment matrix filled (C411) (last 3 years data) Number of promotions from within (C412) (last 3 years data)
Absenteeism rate (C413) (last 3 years data) The aspects and criteria are with interdependent and self-feedback relationships
Fig. 2 A triangular fuzzy number ˜N
Research method
To determine the green performance, the eval-uation aspects and criteria are multiple and fre-quently structured into hierarchical structure. Hence, the first phase is to define the decision objective. After defining the decision objective, it is required to generate and establish evaluation aspects used BSC approach. As discussed in the previous section, four aspects of BSC are to be considered. Moreover, the criteria cluster has to dependence. Overall BSC evaluation can be ob-tained by (1) assigning weights to four aspects (AS1, AS2, AS3, and AS4) and their associated xijcriteria (xij, i = 1,2,3,4; j = 1,2,. . . , xj) and (2)
as-sessing the performance rating of each aspect and its associated criteria. This section is to introduce the fuzzy set theory, ANP technique, and IPA method and followed by the proposed analytical procedures.
Determining the quantitative data
The quantitative (crisp) numbers of criteria (last 3 years data, see Table1) have varying value that cannot be compared. The crisp value number must be normalized. The crisp number is normalized to achieve criteria values that are unit-free and com-parable among all criteria. The normalized crisp
values of Wij are calculated as expressed in the
follow in equation (Karsak2002; Tseng2009a).
Wijcrisp= W k ij− min Wijk maxWk ij− min Wkij , Wijcrisp ∈ [0, 1] ; k = 1, 2, ...n (1) Where max Wk ij= max W1 ij, Wij2, ...Wijn and minWijk= min Wij1, Wij2, ...Wijn
Fuzzy set theory (qualitative data)
To determine the qualitative measures (linguistic preferences), fuzzy set theory can express and handle vague or imprecise judgments mathemat-ically. In fuzzy set theory, each number between 0 and 1 indicates a partial truth, whereas crisp sets correspond to binary logic [0, 1]. In particular, to tackle the ambiguities involved in the process of linguistic estimation, it is a beneficial way to convert these linguistic terms into TFNs. This study builds on some important definitions and notations of fuzzy set theory from Chen (1996) and Cheng and Lin (2002). Some definitions as follows:
Definition 1 A TFN ˜N can be defined as a triplet (l, m, u), and the membership function μ ∼
N(x) is defined as: μ ∼ N(x) ⎧ ⎪ ⎪ ⎨ ⎪ ⎪ ⎩ 0, x≺ l (x−l)(m − l), l ≤ x ≤ m (u−x)(u−m), m ≤ x ≤ u 0, x u (2)
Where l, m, and u are real numbers and l≤ m ≤ u. See Fig.1.
Definition 2 Let ˜N1 = (l1, m1, u1) and ˜N2= (l2, m2, u2) be two TFNs. The multiplication of ˜N1 and ˜N2denoted by ˜N1⊗ ˜N2. Two positive TFNs,
˜N1⊗ ˜N2approximates a TFNs as follows:
The criteria consist of four aspects and 34 measures, the criteria are determined from ex-tensive literatures and expert team. The trian-gular fuzzy membership functions (Table 2) can accommodate the qualitative data while the evalu-ators process the evaluation in linguistic informa-tion. This proposed framework allows experts to identify options using linguistic expressions. The unique point of this study was involved in qual-itative measures in linguistic terms presented by TFNs and defuzzified into a crisp value for analyze in ANP.
Furthermore, in achieving a favorable solution, the group decision-making is usually important to any organizations. This is because the process of arriving at a consensus should be based upon the reaction of multiple individuals, whereby an acceptable judgment may be obtained. To deal with the problems in uncertainty, an effective fuzzy aggregation method is required. Any fuzzy aggregation method always needs to contain a defuzzification method because the results of hu-man judgments with fuzzy linguistic variables are fuzzy numbers. The defuzzification refers to the selection of a specific crisp element based on the output fuzzy set, which convert fuzzy numbers into crisp may score. This study is applying the converting fuzzy data into crisp scores developed by Opricovic and Tzeng (2003), the main proce-dure of determining the left and right scores by fuzzy minimum and maximum, the total score is determined as a weighted average according to the membership functions.
Assume ˜X to be an arbitrary convex and bounded fuzzy number. The assessed values of qualitative criteria metrics for NBSC, ˜X= Lxij,mxij,Rxij , i = 1,2,3,4 and j = 1,2,3. . . ,7 in this study. ˜X= Lxij,mxij,Rxij is TFNs, and xij
Table 2 Linguistic scales for the importance weight Linguistic preference Linguistic values Extreme importance (EI) 0.75, 1.0, 1.0 Demonstrated importance (DI) 0.5, 0.75, 1.0 Strong importance (SI) 0.25, 0.5, 0.75 Moderate importance (MI) 0, 0.25, 0.5 Equal importance (EI) 0, 0, 0.25 This table is the linguistic scales and their corresponding TFNs defined by Wang et al. (2008)
presents at the left, middle, and right positions,
Lxkij,mxkij,Rxkij, represent overall average ratings of
aspect ith, criteria jth over kth evaluators, and xijp, p = 1, 2,. . . k, is fuzzy numbers for each evaluator. The normalization of TFNs as follows: ⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ zLxpij= Lxkij− minLxkij /δmax min zmxpij= mxkij− minmxkij /δmax min; where δmax
min = maxRxkij− minLxkij
zRxkij= Rxkij− minRxkij /δmax min (4)
Compute the left(ls) and right (rs) normalized value ⎧ ⎪ ⎨ ⎪ ⎩ zlsijp= zmxkij 1+mxkij−Lxkij zrsijp= zRxijk 1+Rxkij−mxkij (5)
Compute total normalized crisp value
ykij= zlsijp 1− zlsijp + zrsk ijzrskij 1− zlsijp+ zrsijp (6)
Compute crisp values:
wkij= minLxkij+ ykijδmaxmin (7) To integrate the different opinions of evalua-tors, this study adopts the synthetic value notation to aggregate the subjective judgment for k evalu-ators, given by ˜w = 1 k ˜w1 ij+ ˜w 2 ij+ ˜w 3 ij+ ... + ˜w k ij (8) ANP
The ANP is a generalization of the analytical hierarchical process (Saaty1996). While the AHP represents a framework with a unidirectional hi-erarchical AHP relationship, the ANP allows for complex interrelationships among decision lev-els and criteria. The ANP feedback approach replaces hierarchies with networks in which the relationships between levels are not easily repre-sented as higher or lower, dominant or subordi-nate. Hence, given the problems encountered in reality, a dependent and feedback relationship is
usually generated among the evaluation criteria and such dependent relations usually becomes more complex with the change in scope and depth of the decision-making problems.
ANP uses supermatrix to deal with the relations of feedback and dependence among the criteria. If no interdependent relationship exists among the criteria, then the pairwise comparison value would be 0. If an interdependent and feedback relationship exists among the criteria, then such value would no longer be 0 and an unweighted supermatrix M will be obtained. If the matrix does not conform to the principle of column stochastic, the decision maker can provide the weights to adjust it into a supermatrix that conforms to the principle of column stochastic, and it will become a weighted supermatrix M. Then get the limited weighted supermatrix M* based on Eq. 9 and allow for gradual convergence of the interdepen-dence relationship to obtain the accurate relative weights among the criteria: The following descrip-tions are the equadescrip-tions applied in this study. M∗ = lim
k→∞M
k (9)
In testing for the consistency of judgment ma-trix, the matrix result to be acceptable, consistency index (C.I.) and consistency ratio (C.R.) values should be less than 0.1, the C.I. of a judgment matrix can be obtained as follows:
C.I. = λmax− n
n− 1 (10)
When λmax = 0, complete consistency exists within judgment procedures. Whenλmax= n, the C.R. of C.I. to the mean random consistency index R.I. is expressed as C.R. The equation as follows
C.R. =C.I.
R.I. (11)
Moreover, the ANP is the mathematical theory that can deal with all kinds of dependence system-atically. The ANP has been successfully applied in many fields (Shang et al.2004; Yurdakul2004). Messey (2008) studies on multi-objective resource allocation of shared resources by group
decision-making can combine analytic and qualitative mod-eling, the subsequent phases of the qualitative and the analytic solution of a multi-objective cooper-ative resource allocation problem can be applied within the group decision-making framework of defense requirements capability-based planning. The merits of ANP in group decision-making are as follows (Dyer and Forman 1992; Tseng et al. 2008): (1) both tangibles and intangibles, indi-vidual values, and shared values can be included in the decision process; (2) the discussion in a group can be focused on objectives rather than on alternatives; (3) the discussion can be structured so that every factor relevant to the decision is con-sidered; and (4) in a structured analysis, the dis-cussion continues until relevant information from each individual member in the group is considered and a consensus is achieved.
Importance-performance analysis
An IPA is to draw implications for managing green performance aspects and criteria. It iden-tifies the relative importance of the aspects and criteria associated with a service or product while at the same time indicating the degree of perfor-mance (Martilla and James1977). The results are plotted graphically on a two-dimensional grid, in which the importance of the criteria is displayed on the vertical axis while the satisfaction level is displayed on the horizontal axis. Yavas and Shemwell (2001) integrated relative importance as a weighted index to replace the vertical-axis importance and employed relative performance to compare and calculate the difference between organization performance and the competition’s performance regarding each quality characteris-tic. The resulting four quadrants are labeled as: Concentrate here, Keep up the good work, Low priority, and Possible overkill (Fig.3).
1. In the “Concentrate here” quadrant, the crite-ria are perceived to be very important, but the performance levels are seen by the experts as below average. This implies that improvement efforts should be concentrated here.
2. Criteria situated in the “Keep up the good work” quadrant are perceived to be very
Fig. 3 Importance-performance analysis evaluation grid
important to the experts and the firm per-forms highly in these activities.
3. In the “Low priority” quadrant, the attributes are performed lowly by the firm but they are of low importance to the customers. Limited resources should be expended on these cri-teria as experts do not care too much about them in evaluating the green performance. 4. The “Possible overkill” quadrant contains
low importance criteria with relatively high performance. The evaluators are satisfied with the firm’s green performance; however, present efforts on the criteria are over-utilized and managers should consider resources allo-cated elsewhere.
Using simple visual analysis, the IPA evaluation grid reveals strengths and weaknesses of the as-pects and criteria under consideration and so draws managerial implications for resource alloca-tion. The firm’s current competitive positions are identified, and further improvement strategies are discussed.
Proposed approach
1. Identifying decision goal—gathering the rel-evant information from the literature review and expert’s opinions. It is necessary to con-sult a group of experts to confirm reliable information of the criteria influences and di-rections and moreover develop the evaluation of BSC aspects and criteria and survey
instru-ment (pairwise comparison in linguistic pref-erences). It is important to establish a set of aspects and criteria for evaluation with quali-tative and quantiquali-tative information. However, the aspects and criteria have the nature of complicated relations within the cluster of as-pects and criteria. To deal with the problem of dependence, the ANP is appropriate to be applied.
2. The crisp value number must be normalized to achieve criteria values that are comparable among all criteria using Eq. 1. In addition, interpret the linguistic preferences into fuzzy linguistic scale, shown in Table 2, and uses linguistic preferences to convert TFNs into crisp value, the fuzzy assessments using the definition in Eqs.2and3and applies the Eqs. 4–8are defuzzified and aggregated as a crisp value (˜w).
3. Analyze the proposed method in decision goal and the crisp values are to compose the unweighted supermatrix. The result can ob-tain the normalized unweighted supermatrix from the multiplied result and raises to lim-iting powers to calculate the overall priority weights, using Eq.9.
4. Using visual analysis IPA evaluation grid to draw the four quadrants for the proposed as-pects and criteria, the evaluation grid reveals strengths and weaknesses of the aspects and criteria of green performance.
Results
This section aims to operationalize evaluation methodology of the aspects and criteria, which is relatively important to the PCB sector. There are reasons, first, the case firms continue to improve green manufacturing processes and face challenge to how they manage the green performance in the competitive market. Second, case firms have to sustain reform of the green performance in the sector in order to deal with market competi-tion and customer green requirements. The expert perceptions are obtained from the industrial and academic expert group with extensive experience consulting in this study.
Study problem
Under the prosperous and booming electronic consumption products and network market, Tai-wan plant are built for I.C. substrates and en-tering I.C. packing field meeting the customer demand in related product in 1998. Now, PCB manufacturing sector needs the most advanced equipment in the entire production line and the most convenient services to at the disposal stage. The sector is with different types of PCB such as a single-side, double-side, multi-layer, ceramic, Teflon, aluminum substrate, flexible PCB, or for other special processes.
There are five largest PCB firms in Taiwan, and ranked as largest firms worldwide. To offer the best service for electronic manufacturer, the firms are continuing to develop new green genera-tion technology, enhance environmental compet-itiveness, and fully satisfy the green market and customer demands. The firms insisting on the principle of “ISO 14000”, have and continue to spend a lot of effort on improving produc-tion processes, developing in green performance and set the fully green quality system to meet customer environmental requirements. Due to electronic product replacement, rapid and new technologies are explored; the capability of devel-oping of and researches in new green technology are global competition resources, which can meet green product demands from customer and ex-plore new green product in market. When facing intense global competition, this study is not only devoted to satisfy the green market and customer demands. The green performance is relatively im-portant for the sector to sustain in such compet-itive market. Therefore, the drawing of strategic map (using IPA) is to act as a strategic decision to develop a total approach solution.
The results
This study follows the four proposed steps to an-alyze the data from the experts. The data analysis and the results are addressed in this section. 1. The study objectives are to explore the
rel-ative importance of aspects and criteria that
influence the green performance and to eval-uate the competitiveness of the largest PCB firms in the world. It is necessary to con-sult a group of experts to confirm reliable information of the criteria influences and di-rections. To carry out the study objective, survey data are collected and four BSC as-pects, namely financial, customer, internal business, and learning and growth aspects, are defined. Table3presents the sample of pair-comparison in linguistic preferences.
2. The crisp value number must be normalized to achieve criteria values that are compara-ble among all criteria using Eq. 1. In addi-tion, interpret the linguistic preferences into fuzzy linguistic scale, shown in Table 2, and uses linguistic preferences to convert TFNs into crisp value, the fuzzy assessments using the definition in Eqs. 2 and 3 and applies the Eqs. 4–8are defuzzified and aggregated as a crisp value (˜w). Table 4 presents the pairwise comparison of four aspects after de-fuzzification and uses Matlab 6.5 to decom-pose the matrix into Eigen vector (E vector). Using Eqs.10and 11, the CR should be less than 0.1. It indicates that the consistency level of the pairwise comparison matrix is accept-able. When CR is greater than 0.1, it indicates that the results of the decision process are not consistent, suggesting that the decision maker needs to perform the pairwise comparison again.
Table5is presented the notification of submatrix for supermatrix composition. To compose the su-permatrix, analyses in decision goal and the crisp values are to compose the unweighted superma-trix, shown in Table6.
3. The result can obtain the normalized un-weighted supermatrix from the multiplied re-sult and raises to limiting powers to calculate the overall priority weights, using Eq.9. The unweighted supermatrix contains the weights derived from the pairwise comparisons of the aspects and criteria. In an unweighted super-matrix, its columns may not be column sto-chastic. To obtain a stochastic matrix (i.e., each column sums to one), multiply the blocks of the unweighted supermatrix by the
cor-Table 3 An illustration of financial p airwise comparison w ith o ther aspects Aspect E xtreme Demonstrated Strong Moderate Equal M oderate Strong Demonstrated Extreme Aspect unimportance unimportance unimportance unimportance importance importance importance importance importance Financial v Customer v Internal business v Learning and g rowth
Table 4 An illustration of aspect matrix (Firm 1)
Goal AS1 AS2 AS3 AS4 E-vector Weights AS1 1.00 0.14 0.14 1.52 0.719 0.563 AS2 6.95 1.00 0.17 1.46 0.219 0.172 AS3 0.77 6.00 1.00 0.16 0.253 0.198 AS4 0.66 0.69 6.32 1.00 0.085 0.067
λmax = 8.245, C.I. = 0.084, C.R. = 0.075
responding cluster weight. The supermatrix must satisfy the principle of column stochastic, which means every column should add up to 1. Based on the rule of ANP (Saaty1996), the decision makers believe that if the column-stochastic is not conformed, then the matrix weights of the column are 0.5 and the remain-ing matrix weights would add up to 0.5. The columns in Table 6 have to multiple 0.5 in order to satisfy the principles.
Table 7 presents the converged supermatrix of firm 1, the result showed that the weights are (goal, AS1, AS2, AS3, AS4, C11, C12, C13, C14, C15, C16, C17, C18, C21, C22, C23, C24, C25, C26, C27, C31, C32, C33, C34, C35, C36, C41, C42, C43, C44, C45, C46, C47, C48, C49, C410, C411, C412, and C413)= (0.0321, 0.1257, 0.1358, 0.1487, 0.0985, 0.1721, 0.1487, 0.1600, 0.1709, 0.1970, 0.1196, 0.1201, 0.1560, 0.0832, 0.0682, 0.0770, 0.0986, 0.0806, 0.1001, 0.0989, 0.1732, 0.1505, 0.1174, 0.1397, 0.1716, 0.1078, 0.0566, 0.0432, 0.0410, 0.0426, 0.0471, 0.0309, 0.0444, 0.0382, 0.0360, 0.0351, 0.0369, 0.0401, and 0.0294). Hence, the most considered top five criteria of firm 1 are company growth versus industry growth (C15-0.1970), shorten the service cycle processing time (C31-0.1732), service output per hour/facilities utilization (C35-0.1716), sales: annual growth in sales (C11-0.1721), and prosperity: cash flows (C14-0.1709). The numerical numbers of firms 1– 5 were repeated the proposed analytical process.
Table 5 Submatrix notation for supermatrix composition
Goal Aspects Criteria
Goal A
Aspects B C
Table 6 An illustration of column stochastic supermatrix (Firm 1) Goal AS1 AS2 AS3 AS4 C11 C12 C13 C14 C15 C16 C17 C18 C21 C22 C23 C24 C 25 C26 C27 C31 C32 C33 C34 C35 C36 C41 C42 C43 C44 C45 C 4 6 C47 C48 C49 C410 C411 C412 C413 Goal 1.000 0.000 0.000 0.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1. 000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 AS1 0.563 0.269 0.119 0.366 0.121 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 AS2 0.172 0.167 0.336 0.092 0.257 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 AS3 0.198 0.461 0.347 0.086 0.298 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 AS4 0.067 0.104 0.199 0.456 0.324 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C11 0.000 0.141 0.000 0.000 0.000 0.015 0.141 0.095 0.128 0.152 0.118 0.070 0.205 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 .000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 .000 0.000 0.000 0.000 0.000 0.000 C12 0.000 0.078 0.000 0.000 0.000 0.113 0.098 0.157 0.107 0.173 0.118 0.224 0.156 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 .000 0.000 0.000 0.000 0.000 0.000 C13 0.000 0.241 0.000 0.000 0.000 0.154 0.146 0.185 0.095 0.158 0.182 0.180 0.058 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 .000 0.000 0.000 0.000 0.000 0.000 C14 0.000 0.135 0.000 0.000 0.000 0.091 0.223 0.098 0.078 0.121 0.072 0.107 0.176 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 .000 0.000 0.000 0.000 0.000 0.000 C15 0.000 0.164 0.000 0.000 0.000 0.123 0.078 0.182 0.153 0.095 0.140 0.154 0.085 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0. 000 0.000 0.000 0.000 0.000 0.000 C16 0.000 0.049 0.000 0.000 0.000 0.111 0.185 0.175 0.149 0.109 0.056 0.093 0.105 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 .000 0.000 0.000 0.000 0.000 0.000 C17 0.000 0.062 0.000 0.000 0.000 0.178 0.099 0.055 0.185 0.102 0.132 0.116 0.101 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 .000 0.000 0.000 0.000 0.000 0.000 C18 0.000 0.130 0.000 0.000 0.000 0.215 0.030 0.053 0.105 0.090 0.182 0.057 0.114 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 .000 0.000 0.000 0.000 0.000 0.000 C21 0.000 0.000 0.090 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.154 0.240 0.172 0.144 0.229 0.216 0.152 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C22 0.000 0.000 0.102 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.228 0.052 0.153 0.052 0.112 0.109 0.154 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C23 0.000 0.000 0.096 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.105 0.183 0.183 0.197 0.180 0.085 0.182 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C24 0.000 0.000 0.153 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.213 0.130 0.171 0.253 0.105 0.204 0.110 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C25 0.000 0.000 0.129 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.101 0.109 0.184 0.103 0.101 0.153 0.195 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C26 0.000 0.000 0.215 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.104 0.201 0.095 0.124 0.052 0.103 0.135 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C27 0.000 0.000 0.215 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.095 0.085 0.042 0.127 0.221 0.130 0.072 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C31 0.000 0.000 0.000 0.213 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.1 21 0.203 0.217 0.237 0.158 0.240 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C32 0.000 0.000 0.000 0.156 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.2 29 0.168 0.190 0.211 0.179 0.179 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C33 0.000 0.000 0.000 0.126 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.1 99 0.073 0.226 0.111 0.195 0.052 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C34 0.000 0.000 0.000 0.173 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.1 05 0.196 0.161 0.146 0.118 0.215 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C35 0.000 0.000 0.000 0.211 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.2 45 0.204 0.099 0.183 0.183 0.185 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C36 0.000 0.000 0.000 0.121 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.1 01 0.156 0.107 0.112 0.167 0.128 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C41 0.000 0.000 0.000 0.000 0.121 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.098 0.103 0.050 0.135 0.114 0.091 0.083 0.047 0.118 0.152 0.050 0.081 0.135 C42 0.000 0.000 0.000 0.000 0.094 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.108 0.120 0.049 0.049 0.046 0.085 0.045 0.089 0.045 0.046 0.103 0.047 0.095
C43 0.000 0.000 0.000 0.000 0.068 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.156 0.097 0.072 0.085 0.039 0.087 0.059 0.041 0.107 0.103 0.082 0.107 0.118 C44 0.000 0.000 0.000 0.000 0.098 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.051 0.049 0.049 0.091 0.103 0.050 0.052 0.053 0.094 0.054 0.054 0.095 0.049 C45 0.000 0.000 0.000 0.000 0.109 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.052 0.095 0.085 0.076 0.045 0.046 0.085 0.099 0.076 0.043 0.095 0.086 0.044 C46 0.000 0.000 0.000 0.000 0.055 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.081 0.042 0.121 0.041 0.077 0.036 0.039 0.040 0.047 0.095 0.041 0.103 0.046 C47 0.000 0.000 0.000 0.000 0.097 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.075 0.054 0.057 0.099 0.055 0.118 0.081 0.073 0.059 0.118 0.059 0.056 0.057 C48 0.000 0.000 0.000 0.000 0.075 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.052 0.079 0.052 0.056 0.068 0.103 0.081 0.186 0.046 0.039 0.048 0.085 0.037 C49 0.000 0.000 0.000 0.000 0.062 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.038 0.075 0.108 0.107 0.107 0.049 0.059 0.045 0.118 0.040 0.107 0.044 0.103 C410 0.000 0.000 0.000 0.000 0.056 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0. 000 0.000 0.000 0.000 0.000 0.000 0.047 0.095 0.099 0.051 0.081 0.096 0.048 0.133 0.055 0.095 0.081 0.107 0.048 C411 0.000 0.000 0.000 0.000 0.064 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0. 000 0.000 0.000 0.000 0.000 0.000 0.045 0.089 0.083 0.047 0.095 0.081 0.107 0.047 0.103 0.078 0.091 0.046 0.118 C412 0.000 0.000 0.000 0.000 0.078 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0. 000 0.000 0.000 0.000 0.000 0.000 0.079 0.052 0.081 0.078 0.051 0.107 0.085 0.051 0.047 0.081 0.085 0.095 0.107 C413 0.000 0.000 0.000 0.000 0.023 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0. 000 0.000 0.000 0.000 0.000 0.000 0.117 0.051 0.095 0.085 0.118 0.050 0.176 0.095 0.085 0.056 0.103 0.049 0.042
Table 8 presented the completed picture of the weights for aspects and criteria.
4. Using visual analysis IPA evaluation grid to draw the four quadrants for the proposed as-pects and criteria, the evaluation grid reveals strengths and weaknesses of the aspects and criteria of green performance.
Table8presents the top five PCB firms in Taiwan, and also ranked as largest firms on worldwide (firms 1–5). The industrial professionals and aca-demicians evaluated the column of PCB industry that is the weights after converged of supermatrix. The top-five weighted aspects and criteria of in-dustrial sector are internal business aspect (AS3-2.6870), learning and growth aspect (AS4-2.6850), profitability: economic value added (EVA) or re-turn on total capital employed (C13-2.6740), in-novation of green products measures (C41-2.569), and breakeven time: the time from the beginning of green products development work until the green products has been introduced (C42-2.56). The top-five weighted average aspects and criteria are financial aspect (AS1-0.8694), customer as-pect (AS2-0.8051), internal business asas-pect (AS3-0.9332), new green product: gross profit/growth from green products (C17-0.7825), and industry leadership: market share (C18-0.7430).
The IPA evaluation is displayed in Fig. 4. It composed by the industrial average as x-axis and expert’s perception on PCB sector as y-axis. There were 13 criteria (goal, C13, C42, C22, C24, C411, C21, and C32) loaded in the “Concentrate here” quadrant. Three of the eight criteria were re-lated to the customer aspect and the study goal is located in the quadrant, means the goal should be concentrated. In addition, 12 criteria were lo-cated in the “Low priority” quadrant which are C44, C410, C14, C25, C23, C27, C48, C413, C46, C49, C16, and C33. Although the ten criteria pre-formed below the average level, they were con-sidered not very important to green performance. As the respondents were putting less attention to these criteria, the firm may not need to invest much to improve its performance in the areas. Ten criteria were identified in the “Possible overkill” quadrant. They are AS1, C17, C26, C412, C15, C47, C35, C31, C36, and C18. Finally, the remain-ing eight criteria were in the “Keep up the good
Table 7 An illustration of converged supermatrix (Firm 1) Goal AS1 AS2 AS3 AS4 C11 C12 C13 C14 C15 C16 C17 C18 C21 C22 C23 C24 C 25 C26 C27 C31 C32 C33 C34 C35 C36 C41 C42 C43 C44 C45 C 4 6 C47 C48 C49 C410 C411 C412 C413 Goal 1.000 0.000 0.000 0.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1. 000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 AS1 0.563 0.269 0.119 0.366 0.121 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 AS2 0.172 0.167 0.336 0.092 0.257 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 AS3 0.198 0.461 0.347 0.086 0.298 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 AS4 0.067 0.104 0.199 0.456 0.324 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C11 0.000 0.141 0.000 0.000 0.000 0.015 0.141 0.095 0.128 0.152 0.118 0.070 0.205 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 .000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 .000 0.000 0.000 0.000 0.000 0.000 C12 0.000 0.078 0.000 0.000 0.000 0.113 0.098 0.157 0.107 0.173 0.118 0.224 0.156 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 .000 0.000 0.000 0.000 0.000 0.000 C13 0.000 0.241 0.000 0.000 0.000 0.154 0.146 0.185 0.095 0.158 0.182 0.180 0.058 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 .000 0.000 0.000 0.000 0.000 0.000 C14 0.000 0.135 0.000 0.000 0.000 0.091 0.223 0.098 0.078 0.121 0.072 0.107 0.176 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 .000 0.000 0.000 0.000 0.000 0.000 C15 0.000 0.164 0.000 0.000 0.000 0.123 0.078 0.182 0.153 0.095 0.140 0.154 0.085 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0. 000 0.000 0.000 0.000 0.000 0.000 C16 0.000 0.049 0.000 0.000 0.000 0.111 0.185 0.175 0.149 0.109 0.056 0.093 0.105 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 .000 0.000 0.000 0.000 0.000 0.000 C17 0.000 0.062 0.000 0.000 0.000 0.178 0.099 0.055 0.185 0.102 0.132 0.116 0.101 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 .000 0.000 0.000 0.000 0.000 0.000 C18 0.000 0.130 0.000 0.000 0.000 0.215 0.030 0.053 0.105 0.090 0.182 0.057 0.114 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0 .000 0.000 0.000 0.000 0.000 0.000 C21 0.000 0.000 0.090 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.154 0.240 0.172 0.144 0.229 0.216 0.152 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C22 0.000 0.000 0.102 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.228 0.052 0.153 0.052 0.112 0.109 0.154 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C23 0.000 0.000 0.096 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.105 0.183 0.183 0.197 0.180 0.085 0.182 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C24 0.000 0.000 0.153 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.213 0.130 0.171 0.253 0.105 0.204 0.110 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C25 0.000 0.000 0.129 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.101 0.109 0.184 0.103 0.101 0.153 0.195 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C26 0.000 0.000 0.215 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.104 0.201 0.095 0.124 0.052 0.103 0.135 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C27 0.000 0.000 0.215 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.095 0.085 0.042 0.127 0.221 0.130 0.072 0.0 00 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C31 0.000 0.000 0.000 0.213 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.1 21 0.203 0.217 0.237 0.158 0.240 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C32 0.000 0.000 0.000 0.156 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.2 29 0.168 0.190 0.211 0.179 0.179 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C33 0.000 0.000 0.000 0.126 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.1 99 0.073 0.226 0.111 0.195 0.052 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C34 0.000 0.000 0.000 0.173 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.1 05 0.196 0.161 0.146 0.118 0.215 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C35 0.000 0.000 0.000 0.211 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.2 45 0.204 0.099 0.183 0.183 0.185 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 C36 0.000 0.000 0.000 0.121 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.1 01 0.156 0.107 0.112 0.167 0.128 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
C43 0.000 0.000 0.000 0.000 0.068 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.156 0.097 0.072 0.085 0.039 0.087 0.059 0.041 0.107 0.103 0.082 0.107 0.118 C44 0.000 0.000 0.000 0.000 0.098 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.051 0.049 0.049 0.091 0.103 0.050 0.052 0.053 0.094 0.054 0.054 0.095 0.049 C45 0.000 0.000 0.000 0.000 0.109 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.052 0.095 0.085 0.076 0.045 0.046 0.085 0.099 0.076 0.043 0.095 0.086 0.044 C46 0.000 0.000 0.000 0.000 0.055 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.081 0.042 0.121 0.041 0.077 0.036 0.039 0.040 0.047 0.095 0.041 0.103 0.046 C47 0.000 0.000 0.000 0.000 0.097 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.075 0.054 0.057 0.099 0.055 0.118 0.081 0.073 0.059 0.118 0.059 0.056 0.057 C48 0.000 0.000 0.000 0.000 0.075 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.052 0.079 0.052 0.056 0.068 0.103 0.081 0.186 0.046 0.039 0.048 0.085 0.037 C49 0.000 0.000 0.000 0.000 0.062 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.038 0.075 0.108 0.107 0.107 0.049 0.059 0.045 0.118 0.040 0.107 0.044 0.103 C410 0.000 0.000 0.000 0.000 0.056 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0. 000 0.000 0.000 0.000 0.000 0.000 0.047 0.095 0.099 0.051 0.081 0.096 0.048 0.133 0.055 0.095 0.081 0.107 0.048 C411 0.000 0.000 0.000 0.000 0.064 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0. 000 0.000 0.000 0.000 0.000 0.000 0.045 0.089 0.083 0.047 0.095 0.081 0.107 0.047 0.103 0.078 0.091 0.046 0.118 C412 0.000 0.000 0.000 0.000 0.078 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0. 000 0.000 0.000 0.000 0.000 0.000 0.079 0.052 0.081 0.078 0.051 0.107 0.085 0.051 0.047 0.081 0.085 0.095 0.107 C413 0.000 0.000 0.000 0.000 0.023 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0. 000 0.000 0.000 0.000 0.000 0.000 0.117 0.051 0.095 0.085 0.118 0.050 0.176 0.095 0.085 0.056 0.103 0.049 0.042 C41 0.000 0.000 0.000 0.000 0.121 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.098 0.103 0.050 0.135 0.114 0.091 0.083 0.047 0.118 0.152 0.050 0.081 0.135 C42 0.000 0.000 0.000 0.000 0.094 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.0 00 0.000 0.000 0.000 0.000 0.000 0.108 0.120 0.049 0.049 0.046 0.085 0.045 0.089 0.045 0.046 0.103 0.047 0.095
work” quadrant, of which internal business aspect (AS3) received the highest importance, and learn-ing and growth aspect (AS4) are most satisfactory by the expert group, indicating that the firm had performed particularly well in this area. The ANP analysis result showed that the “internal business aspect” is the most satisfactory and important aspect. In addition, the “cost of sales” is the most satisfactory criteria and the “profitability” is the most important criteria.
Managerial implications
Managing and controlling green performance us-ing BSC provides a number of important insights for the management. It reveals the importance and application of BSC four aspects in green performance (financial, customer, internal busi-ness process, and learning and growth aspect). The green performance derived from the BSC aspects is critical to their firms’ financial and non-financial goals. Specifically, the development and usage of green concepts of customer aspects that motivated the internal business and learning and growth aspects to direct their business activities and processes in a coordinated manner to attain firms’ goals. This is particularly crucial for those firms that focus on the BSC and satisfy their customers’ needs to improve firm’s green perfor-mance. The key managerial implications of this study emphasize some issues.
First, as the result, there is a need to coordinate firm’s activities with various related departments. The improvement plan of case firms might derive from “Concentrate here” and “Keep up the good work” quadrant. The quadrants indicated the in-ternal business aspect received highest impor-tance and learning and growth aspect gives most satisfactory result. The importance of learning and growth aspect provided support from past learn-ing experience, the experience are indicated two critical points (1) the innovation of green products measures and (2) the breakeven time from the beginning of green products development work until the green products has been introduced. This result provided the reference to decision-makers to focus on (1) green product innovation and (2) green product development.
Table 8 Expert
evaluation of importance and satisfaction of PCB firms’ green performance
Industrial Firm 1 Firm 2 Firm 3 Firm 4 Firm 5 Average evaluation Goal 1.9850 0.3210 0.2971 0.2985 0.3315 0.2988 0.3094 AS1 0.8690 1.2570 0.6510 0.7572 0.8274 0.8546 0.8694 AS2 1.5890 1.3580 0.9610 0.9627 0.7439 0.0000 0.8051 AS3 2.6870 1.4870 0.9040 0.7616 1.0278 0.4856 0.9332 AS4 2.6850 0.9850 0.8180 0.7193 0.7323 0.0000 0.6509 C11 1.8590 1.7205 0.1250 0.0970 0.1060 0.8754 0.5848 C12 1.4600 1.4870 0.7250 0.0837 0.0914 1.3250 0.7424 C13 2.6740 1.6001 0.1260 0.1468 0.1604 0.1870 0.4441 C14 0.8950 1.7093 0.1860 0.0972 0.1062 0.1709 0.4539 C15 0.5540 1.9695 0.1200 0.1105 0.1207 0.9560 0.6553 C16 0.3560 1.1958 0.0580 0.0676 0.0739 0.2145 0.3220 C17 0.8910 1.2009 1.0670 0.0671 0.0733 1.5042 0.7825 C18 0.1580 1.5601 0.7500 0.0874 0.0955 1.2221 0.7430 C21 1.2120 0.8317 0.1270 0.1271 0.0982 0.8145 0.3997 C22 1.7560 0.6824 0.1130 0.1130 0.0873 0.5561 0.3104 C23 0.7850 0.7701 0.1220 0.1224 0.0946 0.7652 0.3748 C24 1.7500 0.9864 0.1560 0.1563 0.1208 0.8540 0.4547 C25 1.0500 0.8061 0.1280 0.1279 0.0989 0.7456 0.3813 C26 0.8600 1.0012 1.0590 0.1591 0.1229 0.9542 0.6593 C27 0.5600 0.9894 0.1570 0.1570 0.1213 0.8756 0.4600 C31 0.1700 1.7316 0.1820 0.1533 0.2069 0.5682 0.5684 C32 1.2250 1.5049 0.1580 0.1332 0.1798 0.1759 0.4304 C33 0.2790 1.1743 0.1230 0.1040 0.1403 0.1856 0.3455 C34 1.5260 1.3966 0.1470 0.1237 0.1669 1.1120 0.5892 C35 0.3590 1.7159 0.1820 0.1519 0.2050 0.5522 0.5614 C36 0.2150 1.0784 0.0890 0.9550 0.1289 0.5246 0.5552 C41 2.5690 0.5661 0.1680 0.7810 0.7952 0.4562 0.5533 C42 2.5600 0.4318 0.1520 0.5950 0.6060 0.3586 0.4287 C43 1.8560 0.4103 0.8840 0.5660 0.5160 0.4445 0.5642 C44 1.1112 0.4264 0.0690 0.5880 0.5990 0.3951 0.4155 C45 1.2730 0.4713 0.0740 0.6500 0.6620 0.8597 0.5434 C46 0.2563 0.3092 0.0710 0.4260 0.4340 1.0256 0.4532 C47 0.5890 0.4435 0.1205 0.6120 0.6230 1.2050 0.6008 C48 0.5510 0.3821 0.1520 0.5270 0.5260 0.5124 0.4199 C49 0.3670 0.3602 0.1792 0.4970 0.5060 0.2356 0.3556 C410 1.1456 0.3511 0.1555 0.4840 0.4930 0.4580 0.3883 C411 1.5830 0.3691 0.1625 0.5090 0.5180 0.3691 0.3855 C412 0.5680 0.4007 0.1235 0.5520 0.5620 1.3250 0.5926 C413 0.5620 0.2945 0.2540 0.4050 0.4130 0.1524 0.3038 Mean 1.1641 0.9676 0.3102 0.3598 0.3465 0.6301 0.5228
Second, the hierarchical structure contains of a set of criteria that can lay as the foundation for effective measures in green performance. The green performance act as opportunities in turbu-lent and competitive green markets. To empha-size on green performance is preeminent over other issues due to the evaluation that enables PCB firms to maintain long-term competitive ad-vantage. Moreover, through the establishment of
green routinely develops that encourage the green concepts creating better business process, allows a firm to offer new green products, and to improve their business process.
Third, this analytical result allows the develop-ment of new green knowledge in firms through identifying the IPA quadrants (Concentrate here quadrant, Low priority quadrant, Possible overkill quadrant, and Keep up the good work quadrant).
Fig. 4 IPA evaluation grid map (average vs. industrial) AS3 AS4 C13 C41 C42 C43 C11 AS2 C12 C34 C45 Goal C22 C24 C411 C36 C31 C18 C35 C46 C33 C16 C49 C413 C48 C27 C23 C14 C412 C47 C15 C26 C17 AS1 C410 C25 C44 C21 C32 0.0000 0.5000 1.0000 1.5000 2.0000 2.5000 3.0000 0.0000 0.2000 0.4000 0.6000 0.8000 1.0000
These four quadrants constitute a single firm’s evaluation. That is to say, the aspects and criteria are either to be concentrated or improved, but they are continually with dependence relations in interacting. Moreover, not only the firm’s aspects and criteria are determined in quadrants, but also the industrial sector perceptions on BSC aspects and criteria are determined.
Fourth, with the aim of evaluated each case firm in green performance, sustainability of case firms should be awarded. The top-five weighted aspects and criteria are financial aspect, customer aspect, internal business aspect, new green prod-uct: gross profit/growth from green products, and industry leadership: market share. This implied that the management should be awarded with a total solution approach, which are analyzing the effects of business internal process, to be in contact with customers about green products, the industry leadership all around the industrial sector will encourage all firms to be conscious on green products and more challenges in green market.
Fifth, the results also indicate that possible over kills quadrant is a necessity but not a sufficient condition for the maintaining of competitive ad-vantage. The important managerial implications are that financial aspect may help firms to direct their efforts to attain a competitive advantage and
can help firms in a first stage, but the competitive environment obliges firms to attain more on green development to respond to corporate social re-sponsibility than comprehensive financial details.
Sixth, this hybrid approach based on industry experts’ evaluation; the result showed that inter-nal business aspect is the most satisfaction and importance aspects to competitiveness. The other important role of keeping up the good work are followed by customer aspect (AS2), learning and growth aspect (AS4), sales (C11), cost of sales (C12), reduce service costs (C34), innovation of green products measures (C41), rate of new green products introduction per quarter (C43), and an-nual increase in number of new green products (C45).
Finally, this hierarchical BSC aspects and cri-teria are externally or internally generated that could be converted to result into strategic im-provement plan, or may help to reduce the management costs. In any case, failure in the iden-tification, measures, and evaluation of the worth of green performance leads to the decisions that may not attain competitive advantage in the long term. In addition, this study emphasizes the net-work relations among the aspects and criteria as an important organizational interacting rule for evaluation. In practice, customer aspect should
be always with all the relevant knowledge to in-ternal business process and therefore this study proposed study framework as network balanced scorecard. Green performance evaluation is criti-cal to PCB firms due to the environmental direc-tives (WEEE and ROHS). The measures support a plan that can give the management easier assess-ment to the result. Consequently, the firms should aim to become success in the sustainable business.
Concluding remarks
The proposed model incorporates hierarchical as-pects and criteria structure, fuzzy set theory, ANP, and IPA, and comprise an effective weighting of firms from subjective. This method is also useful for evaluating final decision making of the firms. This proposed approach can easily and effectively accommodate that aspects and criteria are not independent. This result is involving the depen-dence relations and uncertainty. In particular, the IPA draws the strategic map from the evaluation of industry and firm’s experts. The PCB manufac-turing is gaining an increasingly large portion of world electronic market, which has to satisfy the environmental directives (ROHS and WEEE). This analysis is consistent not only with the case firms along, but also with the industry sector. These observations point to case firm is industrial focus. This analysis reveals the potential aspects and criteria for its success in the competitive global market.
From the experiences of the world market, use-ful lessons and managerial implications may be drawn from this study for performing a higher level of green performance in the four aspects as well as developing a green customer-oriented business plans. The study has also raised two fu-ture studies. First, uncovering the potential cri-teria behind the evaluation discrepancy between the industry and firm experts remained an inter-esting area of future research. Second, it will be worthwhile to study the perception gap between industry and firm experts. The effective and ap-propriate analysis for industry and firm experts acquired by applying the proposed approach thus enables business managers to achieve a competi-tive advantage.
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