Decision making is a recursive process and usually involves multiple decision criteria. However, such multiple criteria decision making may have a problem in which partial decision criteria may conflict with each other. An information technology, such as
People are making decision all the time. Typically, decision making is a recursive process in which decision maker may repeatedly move back and forth among multiple decision steps, such as objective clarification, decision criteria identification, alternative rank, and selection. For decision maker, the primary concern is to pick up an appropriate choice from a group of candidate alternatives [
However, unfortunately, most people are much poorer at decision making than they think. For illustration, there is a misconception that the decision maker thought they do not have enough information to make good decision [
An information technology, as known as decision support system (DSS), emerges to assist decision maker to accelerate the convergence of decision-making process. DSS is interactive computer-based information system which helps decision-makers utilize knowledge base and models to solve ill-structured problems [
In this research, we proposed a hybrid GDSS architecture, named HDMSM, integrated four decision approaches (Delphi, DEMATEL, ANP, and MDS) to help decision maker with alternative rank and selection issue. HDMSM consists of four steps. In Step
The priorities of decision criteria imply the preference of domain expert and therefore, to make better decision, and decision maker can make their choice according to the alternative rank via HDMSM. Also, the visual abilities of HDMSM enable decision maker to compare all available alternative form perspective and then improve decision making quality. Finally, we provide a system demonstration section to illustrate that how HDMSM aggregated the opinion from a group of domain experts. How to appropriately integrate a variety of MCDM approaches is an important issue in decision science [
The remainder of this paper is organized as follows. Section
The HDMSM proposed in this study integrates four decision approaches, namely, Delphi, DEMATEL, ANP, and MDS, to help decision maker with criteria selection and alternative ranking when facing a decision problem. Four methodologies are briefly introduced below.
Delphi method relies mainly on a panel of experts’ experiences, intuition, and value judgment. The experts participate in multiple rounds of questionnaire interviews and are given ways to understand one another’s viewpoints on the same question. The experts are encouraged to revise their previous opinions, so that the experts as a group can finally reach a consensus on the goal of decision making [
Determine the goal of decision making and list relevant evaluation criteria for the decision making, choose experts in the related field to form an expert group, and invite the experts to answer in a Delphi expert questionnaire interview for multiple rounds. The experts must judge the importance and suitableness for each evaluation criteria and give each criterion a score between 0 and 100.
To enable expert group to gradually reach a general agreement of opinion, a consensus deviation index (CDI) for each evaluation criteria is calculated as a round of the Delphi expert questionnaire finished. A smaller CDI indicates a higher consensus among the experts. In general, a CDI threshold is set to 0.05. That is, when the last round of Delphi expert questionnaire is completed and the CDI for all of the evaluation criteria is smaller than 0.05, this indicates a consensus of experts has been reached [
The CDI calculated based on the last round of Delphi expert questionnaire has to be normalized to derive the relative weight
The evaluation criteria involved in decision making problem can be identified via Delphi. Then, DEMATEL is adopted to analyze the direct/indirect effects among these evaluation criteria detailed below.
Decision making trial and evaluation laboratory (DEMATEL) was originated from the Geneva of the Battelle Memorial Institute in 1973. It effectively observes the level of mutual influence among different factors and understands the complicated cause-and-effect relationship in the decision problem [
List the factors that may affect the decision-making problem through literature review or brainstorming and interview with the domain experts to determine the correlation between any two factors.
As the decision problem has
In order to understand whether two evaluation criteria relate to each other indirectly, formula (
If
ANP is a decision-making analytical method that uses network and nonlinear structure to represent a decision problem. ANP is developed in response to the fact that many decision problems in the realistic environment could not be presented with the structured hierarchy. The main objective of ANP is to correct the traditional AHP, with which the problems of dependence and feedback might occur between the criteria or the layers [
ANP can decompose a decision problem into multiple types of dimensions, and each dimension can include multiple criteria. The dimensions and the criteria are correlated with one another to form a network structure of the evaluation framework, and arrows are used to indicate their mutual influence.
Through the pairwise comparison of among each two criteria, ANP is calculated to acquire the eigenvectors of criteria and to form a Supermatrix, as shown in
Through normalization of the Supermatrix and complex matrix multiplication, a limit supermatrix containing the weights of the evaluation criteria can be obtained. According to these weights, the decision maker can figure out the priority of evaluation criterion for decision problem solving.
Multidimensional scaling (MDS) is a data reduction method, which uses the distance or similarity between data points to locate the spatial coordinates and the relative positions of several given data in the low-dimensional space [
MDS computes the Euclidean distance between each two factors and shows all factors in perceptual map which has two dimensions. If the similarity between two factors is more stronger, the configuration of two factors would be more close in the map. As a result, through the illustration of perceptual map, the spatial relation among factors can be visualized more clearly. The classification results of factors can be achieved via spatial difference analysis that helps decision maker to easily grasp the concept of factors.
To obtain the perceptual map, the Euclidean distance (
For decision making, decision maker must locate important decision criteria and evaluate the fitness of all possible alternatives. To increase decision process, decision maker needs to compare these alternatives as soon as possible. Via MDS, the visualization of candidate alternative enables decision maker to quickly grasp the similarities and dissimilarities among the alternatives.
This study proposes a GDSS architecture named hybrid decision-making support model (HDMSM) as shown in Figure
Hybrid decision-making support model (HDMSM).
To make MCDM, some appropriate evaluation dimensions and criteria are selected as candidate decision criteria. Then, a domain expert group is organized via Delphi for conducting multiple rounds of questionnaire interviews. To finally establish a consensus score for each of the criteria, the experts group gradually reaches a consensus in their opinions. Based on the consensus scores, the top
To understand the correlation among the selected evaluation criteria in Step
To show the direct relation among these evaluation criteria, HDMSM plots a network structure of the evaluation framework. And then, an ANP expert questionnaire is designed based on the plotted evaluation framework. The domain experts fulfilled the ANP questionnaire and the collected questionnaires are further analyzed via ANP method to derive the absolute weight. Thereafter, for each decision criterion, it cross-multiplies the absolute weight by the consensus score (in Step
To evaluate the feasibility for each alternative, multiple decision criteria are usually taken into consideration simultaneously. Therefore, in this step, according to the analysis results in Step
For particular complex case, appropriate alternative might be a combination of several alternatives. Therefore, the decision maker must understand the effect of each of the evaluation criteria. Additionally, they need to analyze the structural similarity among different alternatives. Therefore, in Step
HDMSM adopts four methodologies that complement each other. Delphi summarizes the opinion from expert group and then generates appropriate evaluation factors for a multicriteria decision making. DEMATEL reveals the correlation among these decision factors. ANP implements pairwise comparison of these factors and derives the important weights for all evaluation factors. By importance ranking, ANP provides decision maker with the insight into the decision problem. Based on the analysis results, MDS generates the perceptual map to improve the representation of alternative analysis. The visualization representation enables decision maker to quick-grasp the similarity and dissimilarity among alternatives and increase decision making process.
A case study is implemented based on the proposed HDMSM. In this case study, we intend to find out the important indexes of corporate social responsibility (CSR) from multiple perspectives. CSR involves the conduct of a business so that it is economically profitable, law abiding, ethical, and socially supportive. To be socially responsible, profitability and obedience to the law are foremost conditions to discuss the firm’s ethics and the extent to which it supports the society in which it exists with contributions of money, time, and talent. Thus, CSR is composed of four dimensions: economic, legal, ethical, and philanthropic [
To make complicate multicriteria decision, like evaluation factors of CSR, it is necessary to select dimensions, criteria, and available alternatives as evaluation factors. Table
CSR dimensions and criteria selection.
Dimensions | Evaluation criteria | |
---|---|---|
Economic responsibilities | E1 | Reasonable product prices |
E2 | Transparent business operations | |
E3 | Avoid price collusion with any competitor | |
E4 | Stimulate local economic development | |
E5 | Increase employment opportunities | |
E6 | Maximum possible profits for the organization | |
E7 | Honour agreed-upon contracts | |
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Legal responsibilities | L1 | Implement the consumer protection act |
L2 | Ensure good and safe working environment for employees | |
L3 | Provide employees with the newest information on pertinent laws | |
L4 | Provide occupational injury compensation and health insurance systems | |
L5 | Keep customer information confidential and protect it against illegal use | |
L6 | Provide proper waste disposal and reduce pollutant emissions | |
L7 | Actively inspect and certify products | |
|
||
Ethical responsibilities | M1 | Provide consumers with customer complaint service and thorough follow-up service |
M2 | Provide employees with ways of improving their working conditions | |
M3 | Eschew exaggerated or false advertisements | |
M4 | Pay salaries and wages on time | |
M5 | Provide employees with a good working environment | |
M6 | Provide employees with fair selection, promotion, termination, and retirement systems | |
M7 | Provide employees with reasonable welfare and protection | |
M8 | Provide employees with educational training and living-related assistance | |
M9 | Cooperate with the government in energy-saving and carbon-reduction policy | |
M10 | Ensure transparent production processes | |
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Philanthropic responsibilities | P1 | Protect vulnerable social groups |
P2 | Engage in charitable activities | |
P3 | Provide for community welfare | |
P4 | Use company resources efficiently |
To implement the case study, five domain experts were invited to form a decision-making team and fulfill a Delphi expert questionnaire that consists of 28 CSR evaluation criteria. The expert gave each criterion a score between 0 and 100 points. Then, for two discussion rounds, the experts gradually reached a consensus on the target decision problem (i.e., CDI < 0.05). According to the average score of decision criteria (the threshold is set 60-points), some criteria are deleted from the candidate pool of decision criteria. For the remained criteria, to uniform evaluation scale, the normalization process was conducted to obtain the rating weight for each CSR evaluation criteria as shown in Table
CDI and rating weights of CSR criteria.
(D) | (C) | Score of final round |
|
CDI | Means | Rating weight by normalization | ||||
---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | ||||||
Economic | E1 | 90 | 90 | 80 | 85 | 90 | 4.47 | 0.050 | 87.0 |
|
E2 | 90 | 95 | 85 | 85 | 90 | 4.18 | 0.047 | 89.0 |
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|
E3 | 75 | 70 | 75 | 70 | 80 | 4.18 | 0.047 | 74.0 |
|
|
E4 | 75 | 70 | 70 | 75 | 75 | 2.74 | 0.031 | 73.0 |
|
|
E5 | 85 | 80 | 80 | 80 | 75 | 3.54 | 0.040 | 80.0 |
|
|
E6 | 55 | 60 | 60 | 55 | 50 | 4.18 | 0.047 | 56.0 | (deleted) | |
E7 | 55 | 60 | 60 | 55 | 55 | 2.74 | 0.031 | 57.0 | (deleted) | |
|
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Sum of nondeleted mean: | 403.0 | — | ||||||||
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Legal | L1 | 85 | 80 | 85 | 90 | 85 | 3.54 | 0.038 | 85.0 |
|
L2 | 75 | 85 | 80 | 80 | 77 | 3.78 | 0.041 | 79.4 |
|
|
L3 | 75 | 78 | 70 | 80 | 80 | 4.22 | 0.046 | 76.6 |
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|
L4 | 70 | 75 | 72 | 78 | 75 | 3.08 | 0.034 | 74.0 |
|
|
L5 | 90 | 95 | 90 | 90 | 95 | 2.74 | 0.030 | 92.0 |
|
|
L6 | 85 | 87 | 90 | 93 | 95 | 4.12 | 0.045 | 90.0 |
|
|
L7 | 75 | 70 | 80 | 76 | 73 | 3.70 | 0.040 | 74.8 |
|
|
|
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Sum of nondeleted mean: | 571.8 | — | ||||||||
|
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Ethical | M1 | 85 | 80 | 83 | 88 | 90 | 3.96 | 0.044 | 85.2 |
|
M2 | 61 | 62 | 55 | 54 | 55 | 3.78 | 0.042 | 57.4 | (deleted) | |
M3 | 55 | 54 | 50 | 45 | 53 | 4.04 | 0.045 | 55.0 | (deleted) | |
M4 | 70 | 65 | 68 | 73 | 70 | 2.95 | 0.033 | 69.2 |
|
|
M5 | 45 | 53 | 52 | 50 | 47 | 3.36 | 0.037 | 49.4 | (deleted) | |
M6 | 70 | 77 | 75 | 73 | 80 | 3.81 | 0.042 | 75.0 |
|
|
M7 | 65 | 63 | 60 | 70 | 66 | 3.70 | 0.041 | 64.8 |
|
|
M8 | 45 | 48 | 45 | 52 | 50 | 3.08 | 0.034 | 48.0 | (deleted) | |
M9 | 90 | 93 | 85 | 88 | 95 | 3.96 | 0.044 | 90.2 |
|
|
M10 | 85 | 88 | 90 | 93 | 90 | 2.95 | 0.033 | 89.2 |
|
|
|
||||||||||
Sum of nondeleted mean: | 473.6 | — | ||||||||
|
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Philanthropic | P1 | 98 | 90 | 95 | 95 | 100 | 3.78 | 0.040 | 95.6 |
|
P2 | 95 | 90 | 92 | 96 | 95 | 2.51 | 0.026 | 93.6 |
|
|
P3 | 55 | 60 | 56 | 58 | 55 | 2.17 | 0.023 | 56.8 | (deleted) | |
P4 | 61 | 63 | 55 | 54 | 55 | 4.10 | 0.043 | 57.6 | (deleted) | |
|
||||||||||
Sum of nondeleted mean: | 189.2 |
|
D denotes dimension, C denotes criterion,
After the CSR evaluation criteria were identified via Delphi method in Step
Direct relation matrix.
Criteria | E1 | E2 | E3 | E4 | E5 | L1 | L2 | L3 | L4 | L5 | L6 | L7 | M1 | M4 | M6 | M7 | M9 | M10 | P1 | P2 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
E1 | 0 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
E2 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 3 | 0 | 0 | 2 | 0 | 0 | 3 | 0 | 0 |
E3 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
E4 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
E5 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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L1 | 3 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 |
L2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
L3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 0 | 0 | 0 | 0 |
L4 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 1 | 0 |
L5 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
L6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
L7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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M1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
M4 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
M6 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
M7 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 3 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 |
M9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
M10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 |
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P1 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
P2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 |
After obtaining the direct relation matrix, formula (
Direct/indirect relation matrix.
Criteria | E1 | E2 | E3 | E4 | E5 | L1 | L2 | L3 | L4 | L5 | L6 | L7 | M1 | M4 | M6 | M7 | M9 | M10 | P1 | P2 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
E1 | 0.00 | 0.01 | 0.21 | 0.14 | 0.03 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.02 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
E2 | 0.07 | 0.01 | 0.22 | 0.01 | 0.01 | 0.03 | 0.02 | 0.00 | 0.00 | 0.07 | 0.18 | 0.25 | 0.02 | 0.00 | 0.14 | 0.00 | 0.04 | 0.20 | 0.00 | 0.00 |
E3 | 0.02 | 0.07 | 0.03 | 0.00 | 0.00 | 0.07 | 0.00 | 0.00 | 0.00 | 0.02 | 0.01 | 0.03 | 0.08 | 0.00 | 0.01 | 0.00 | 0.00 | 0.02 | 0.00 | 0.00 |
E4 | 0.00 | 0.00 | 0.00 | 0.01 | 0.20 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
E5 | 0.00 | 0.00 | 0.00 | 0.07 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
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L1 | 0.21 | 0.01 | 0.19 | 0.03 | 0.01 | 0.05 | 0.00 | 0.00 | 0.00 | 0.21 | 0.02 | 0.23 | 0.15 | 0.00 | 0.00 | 0.00 | 0.01 | 0.07 | 0.00 | 0.00 |
L2 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.04 | 0.01 | 0.14 | 0.00 | 0.07 | 0.00 | 0.00 | 0.00 | 0.00 | 0.03 | 0.00 | 0.00 | 0.01 | 0.00 |
L3 | 0.00 | 0.00 | 0.00 | 0.00 | 0.02 | 0.00 | 0.02 | 0.06 | 0.02 | 0.00 | 0.00 | 0.00 | 0.00 | 0.22 | 0.01 | 0.07 | 0.00 | 0.00 | 0.00 | 0.00 |
L4 | 0.00 | 0.00 | 0.00 | 0.01 | 0.02 | 0.00 | 0.26 | 0.05 | 0.08 | 0.00 | 0.02 | 0.00 | 0.00 | 0.02 | 0.03 | 0.22 | 0.00 | 0.00 | 0.07 | 0.01 |
L5 | 0.04 | 0.00 | 0.04 | 0.01 | 0.00 | 0.21 | 0.00 | 0.00 | 0.00 | 0.04 | 0.00 | 0.05 | 0.03 | 0.00 | 0.00 | 0.00 | 0.00 | 0.01 | 0.00 | 0.00 |
L6 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
L7 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
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M1 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
M4 | 0.00 | 0.00 | 0.00 | 0.00 | 0.07 | 0.00 | 0.00 | 0.21 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.04 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 |
M6 | 0.00 | 0.00 | 0.00 | 0.00 | 0.07 | 0.00 | 0.14 | 0.00 | 0.02 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
M7 | 0.00 | 0.00 | 0.00 | 0.00 | 0.02 | 0.00 | 0.28 | 0.24 | 0.25 | 0.00 | 0.02 | 0.00 | 0.00 | 0.12 | 0.14 | 0.07 | 0.00 | 0.00 | 0.02 | 0.00 |
M9 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 |
M10 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.20 | 0.20 | 0.00 | 0.00 | 0.00 | 0.00 | 0.20 | 0.00 | 0.00 | 0.00 |
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P1 | 0.00 | 0.00 | 0.00 | 0.08 | 0.22 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.03 | 0.14 |
P2 | 0.00 | 0.00 | 0.00 | 0.02 | 0.04 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.21 | 0.03 |
According to the direct/indirect relation matrix in Table
Prominence of CSR evaluation criteria.
CSR evaluation criteria |
|
|
|
|
---|---|---|---|---|
E1 | Reasonable product prices | 0.35 | 0.43 | 0.78 |
E2 | Transparent business operations | 0.11 | 1.30 | 1.41 |
E3 | Avoid price collusion with any competitor | 0.58 | 0.36 | 0.94 |
E4 | Stimulate local economic development | 0.39 | 0.22 | 0.61 |
E5 | Increase employment opportunities | 0.82 | 0.08 | 0.90 |
|
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L1 | Implement the consumer protection act | 0.38 | 1.03 | 1.41 |
L2 | Ensure good and safe working environment for employees | 0.85 | 0.31 | 1.16 |
L3 | Provide employees with the newest information on pertinent laws | 0.56 | 0.42 | 0.98 |
L4 | Provide occupational injury compensation and health insurance systems | 0.53 | 0.80 | 1.32 |
L5 | Keep customer information confidential and protect it against illegal use | 0.35 | 0.41 | 0.76 |
L6 | Provide proper waste disposal and reduce pollutant emissions | 0.53 | 0.00 | 0.53 |
L7 | Actively inspect and certify products | 0.67 | 0.00 | 0.67 |
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M1 | Provide consumers with customer complaint service and thorough follow-up service | 0.29 | 0.00 | 0.29 |
M4 | Pay salaries and wages on time | 0.41 | 0.36 | 0.76 |
M6 | Provide employees with fair selection, promotion, termination, and retirement systems | 0.34 | 0.41 | 0.74 |
M7 | Provide employees with reasonable welfare and protection | 0.41 | 1.18 | 1.59 |
M9 | Cooperate with the government in energy-saving and carbon-reduction policy | 0.26 | 0.00 | 0.26 |
M10 | Ensure transparent production processes | 0.31 | 0.60 | 0.91 |
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P1 | Protect vulnerable social groups | 0.34 | 0.47 | 0.81 |
P2 | Engage in charitable activities | 0.18 | 0.29 | 0.47 |
In the previous step, DEMATEL generated the direct relation among the CSR evaluation criteria, as shown in Table
Network structure of the evaluation framework.
Next, an ANP expert questionnaire was designed based on the network structure of evaluation framework. Total 15 interviewers finished the ANP questionnaires. The 15 interviewers included 3 middle-high level managers from different companies. Taking economic responsibility dimension as an example, the pairwise comparisons for the criteria E1–E5 were conducted, and the 9-point paired-comparison scaling was used for rating [
Sample answer in pairwise comparison (based on the dimension of economic).
Absolutely important |
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Scaling | 9 : 1 | 7 : 1 | 5 : 1 | 3 : 1 | 1 : 1 | 1 : 3 | 1 : 5 | 1 : 7 | 1 : 9 | Scaling |
E1 | V | E2 | ||||||||
E1 | V | E3 | ||||||||
E1 | V | E4 | ||||||||
E1 | V | E5 | ||||||||
E2 | V | E3 | ||||||||
E2 | V | E4 | ||||||||
E2 | V | E5 | ||||||||
E3 | V | E4 | ||||||||
E3 | V | E5 | ||||||||
E4 | V | E5 |
The collected questionnaires were further analyzed and processed using ANP to create an unweighted super matrix as shown in Table
Unweighted super matrix.
Criteria | E1 | E2 | E3 | E4 | E5 | L1 | L2 | L3 | L4 | L5 | L6 | L7 | M1 | M4 | M6 | M7 | M9 | M10 | P1 | P2 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
E1 | 0.374 | 0.053 | 0.374 | 0.125 | 0.075 | 0.077 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
E2 | 0.304 | 0.043 | 0.391 | 0.217 | 0.043 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
E3 | 0.378 | 0.042 | 0.378 | 0.126 | 0.076 | 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 |
E4 | 0.366 | 0.024 | 0.366 | 0.122 | 0.122 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.125 | 0.000 |
E5 | 0.385 | 0.077 | 0.385 | 0.077 | 0.077 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.750 | 0.833 | 0.000 | 0.000 | 0.000 | 0.875 | 0.000 |
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L1 | 0.000 | 0.000 |
|
0.000 | 0.000 | 0.026 | 0.179 | 0.128 | 0.179 | 0.026 | 0.231 | 0.231 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
L2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.011 | 0.075 | 0.075 | 0.225 | 0.015 | 0.375 | 0.225 | 0.000 | 0.000 | 0.167 | 0.185 | 0.000 | 0.000 | 0.000 | 0.000 |
L3 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.011 | 0.057 | 0.057 | 0.285 | 0.019 | 0.285 | 0.285 | 0.000 | 0.250 | 0.000 | 0.659 | 0.000 | 0.000 | 0.000 | 0.000 |
L4 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.025 | 0.057 | 0.034 | 0.172 | 0.025 | 0.172 | 0.516 | 0.000 | 0.000 | 0.000 | 0.156 | 0.000 | 0.000 | 0.000 | 0.000 |
L5 | 0.000 | 0.146 | 0.000 | 0.000 | 0.000 | 0.032 | 0.161 | 0.097 | 0.226 | 0.032 | 0.226 | 0.226 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
L6 | 0.000 | 0.068 | 0.000 | 0.000 | 0.000 | 0.030 | 0.055 | 0.055 | 0.274 | 0.039 | 0.274 | 0.274 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.833 | 0.000 | 0.000 |
L7 | 0.000 | 0.167 | 0.000 | 0.000 | 0.000 | 0.036 | 0.107 | 0.064 | 0.107 | 0.046 | 0.320 | 0.320 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.167 | 0.000 | 0.000 |
|
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M1 | 0.000 | 0.000 |
|
0.000 | 0.000 | 0.247 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.131 | 0.654 | 0.044 | 0.131 | 0.015 | 0.026 | 0.000 | 0.000 |
M4 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.750 | 0.000 | 0.000 | 0.000 | 0.000 | 0.102 | 0.511 | 0.102 | 0.170 | 0.057 | 0.057 | 0.000 | 0.000 |
M6 | 0.000 | 0.586 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.198 | 0.330 | 0.066 | 0.330 | 0.009 | 0.066 | 0.000 | 0.000 |
M7 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.250 | 0.750 | 0.000 | 0.000 | 0.000 | 0.180 | 0.541 | 0.036 | 0.180 | 0.026 | 0.036 | 0.000 | 0.000 |
M9 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.265 | 0.265 | 0.206 | 0.206 | 0.029 | 0.029 | 0.000 | 0.000 |
M10 | 0.000 | 0.032 | 0.000 | 0.000 | 0.000 | 0.612 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.227 | 0.409 | 0.045 | 0.227 | 0.045 | 0.045 | 0.000 | 0.000 |
|
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P1 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.250 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.833 | 0.167 |
P2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.833 | 0.167 |
We cross-multiplied the values in each row of the unweighted super matrix by the weight of the dimension, respectively, and then a weighted super matrix can be obtained as shown in Table
Weighted super matrix.
Criteria | E1 | E2 | E3 | E4 | E5 | L1 | L2 | L3 | L4 | L5 | L6 | L7 | M1 | M4 | M6 | M7 | M9 | M10 | P1 | P2 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
E1 | 0.093 | 0.013 | 0.093 | 0.031 | 0.019 | 0.019 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
E2 | 0.076 | 0.011 | 0.098 | 0.054 | 0.011 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
E3 | 0.095 | 0.011 | 0.095 | 0.032 | 0.019 | 0.016 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
E4 | 0.091 | 0.006 | 0.091 | 0.030 | 0.030 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.031 | 0.000 |
E5 | 0.096 | 0.019 | 0.096 | 0.019 | 0.019 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.188 | 0.208 | 0.000 | 0.000 | 0.000 | 0.219 | 0.000 |
|
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L1 | 0.000 | 0.000 | 0.300 | 0.000 | 0.000 | 0.010 | 0.072 | 0.051 | 0.072 | 0.010 | 0.092 | 0.092 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
L2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.004 | 0.030 | 0.030 | 0.090 | 0.006 | 0.150 | 0.090 | 0.000 | 0.000 | 0.067 | 0.074 | 0.000 | 0.000 | 0.000 | 0.000 |
L3 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.005 | 0.023 | 0.023 | 0.114 | 0.008 | 0.114 | 0.114 | 0.000 | 0.100 | 0.000 | 0.263 | 0.000 | 0.000 | 0.000 | 0.000 |
L4 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.010 | 0.023 | 0.014 | 0.069 | 0.010 | 0.069 | 0.206 | 0.000 | 0.000 | 0.000 | 0.062 | 0.000 | 0.000 | 0.000 | 0.000 |
L5 | 0.000 | 0.059 | 0.000 | 0.000 | 0.000 | 0.013 | 0.065 | 0.039 | 0.090 | 0.013 | 0.090 | 0.090 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
L6 | 0.000 | 0.027 | 0.000 | 0.000 | 0.000 | 0.012 | 0.022 | 0.022 | 0.109 | 0.016 | 0.109 | 0.109 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.333 | 0.000 | 0.000 |
L7 | 0.000 | 0.067 | 0.000 | 0.000 | 0.000 | 0.014 | 0.043 | 0.026 | 0.043 | 0.018 | 0.128 | 0.128 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.067 | 0.000 | 0.000 |
|
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M1 | 0.000 | 0.000 | 0.050 | 0.000 | 0.000 | 0.049 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.026 | 0.131 | 0.009 | 0.026 | 0.003 | 0.005 | 0.000 | 0.000 |
M4 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.150 | 0.000 | 0.000 | 0.000 | 0.000 | 0.020 | 0.102 | 0.020 | 0.034 | 0.011 | 0.011 | 0.000 | 0.000 |
M6 | 0.000 | 0.117 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.040 | 0.066 | 0.013 | 0.066 | 0.002 | 0.013 | 0.000 | 0.000 |
M7 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.050 | 0.150 | 0.000 | 0.000 | 0.000 | 0.036 | 0.108 | 0.007 | 0.036 | 0.005 | 0.007 | 0.000 | 0.000 |
M9 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.053 | 0.053 | 0.041 | 0.041 | 0.006 | 0.006 | 0.000 | 0.000 |
M10 | 0.000 | 0.006 | 0.000 | 0.000 | 0.000 | 0.122 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.045 | 0.082 | 0.009 | 0.045 | 0.009 | 0.009 | 0.000 | 0.000 |
|
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P1 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.038 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.125 | 0.025 |
P2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.125 | 0.025 |
To get a uniform weighted scale of super matrix, the normalization is implemented. A limit super matrix is shown in Table
Limit super matrix.
Criteria | E1 | E2 | E3 | E4 | E5 | L1 | L2 | L3 | L4 | L5 | L6 | L7 | M1 | M4 | M6 | M7 | M9 | M10 | P1 | P2 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
E1 | 0.037 | 0.037 | 0.037 | 0.037 | 0.037 | 0.037 | 0.037 | 0.037 | 0.037 | 0.037 | 0.037 | 0.037 | 0.037 | 0.037 | 0.037 | 0.037 | 0.037 | 0.037 | 0.037 | 0.037 |
E2 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 | 0.031 |
E3 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 | 0.036 |
E4 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 | 0.038 |
E5 | 0.060 | 0.060 | 0.060 | 0.060 | 0.060 | 0.060 | 0.060 | 0.060 | 0.060 | 0.060 | 0.060 | 0.060 | 0.060 | 0.060 | 0.060 | 0.060 | 0.060 | 0.060 | 0.060 | 0.060 |
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L1 | 0.082 | 0.082 | 0.082 | 0.082 | 0.082 | 0.082 | 0.082 | 0.082 | 0.082 | 0.082 | 0.082 | 0.082 | 0.082 | 0.082 | 0.082 | 0.082 | 0.082 | 0.082 | 0.082 | 0.082 |
L2 | 0.068 | 0.068 | 0.068 | 0.068 | 0.068 | 0.068 | 0.068 | 0.068 | 0.068 | 0.068 | 0.068 | 0.068 | 0.068 | 0.068 | 0.068 | 0.068 | 0.068 | 0.068 | 0.068 | 0.068 |
L3 | 0.078 | 0.078 | 0.078 | 0.078 | 0.078 | 0.078 | 0.078 | 0.078 | 0.078 | 0.078 | 0.078 | 0.078 | 0.078 | 0.078 | 0.078 | 0.078 | 0.078 | 0.078 | 0.078 | 0.078 |
L4 | 0.061 | 0.061 | 0.061 | 0.061 | 0.061 | 0.061 | 0.061 | 0.061 | 0.061 | 0.061 | 0.061 | 0.061 | 0.061 | 0.061 | 0.061 | 0.061 | 0.061 | 0.061 | 0.061 | 0.061 |
L5 | 0.074 | 0.074 | 0.074 | 0.074 | 0.074 | 0.074 | 0.074 | 0.074 | 0.074 | 0.074 | 0.074 | 0.074 | 0.074 | 0.074 | 0.074 | 0.074 | 0.074 | 0.074 | 0.074 | 0.074 |
L6 | 0.109 | 0.109 | 0.109 | 0.109 | 0.109 | 0.109 | 0.109 | 0.109 | 0.109 | 0.109 | 0.109 | 0.109 | 0.109 | 0.109 | 0.109 | 0.109 | 0.109 | 0.109 | 0.109 | 0.109 |
L7 | 0.086 | 0.086 | 0.086 | 0.086 | 0.086 | 0.086 | 0.086 | 0.086 | 0.086 | 0.086 | 0.086 | 0.086 | 0.086 | 0.086 | 0.086 | 0.086 | 0.086 | 0.086 | 0.086 | 0.086 |
|
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M1 | 0.033 | 0.033 | 0.033 | 0.033 | 0.033 | 0.033 | 0.033 | 0.033 | 0.033 | 0.033 | 0.033 | 0.033 | 0.033 | 0.033 | 0.033 | 0.033 | 0.033 | 0.033 | 0.033 | 0.033 |
M4 | 0.050 | 0.050 | 0.050 | 0.050 | 0.050 | 0.050 | 0.050 | 0.050 | 0.050 | 0.050 | 0.050 | 0.050 | 0.050 | 0.050 | 0.050 | 0.050 | 0.050 | 0.050 | 0.050 | 0.050 |
M6 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 | 0.028 |
M7 | 0.040 | 0.040 | 0.040 | 0.040 | 0.040 | 0.040 | 0.040 | 0.040 | 0.040 | 0.040 | 0.040 | 0.040 | 0.040 | 0.040 | 0.040 | 0.040 | 0.040 | 0.040 | 0.040 | 0.040 |
M9 | 0.021 | 0.021 | 0.021 | 0.021 | 0.021 | 0.021 | 0.021 | 0.021 | 0.021 | 0.021 | 0.021 | 0.021 | 0.021 | 0.021 | 0.021 | 0.021 | 0.021 | 0.021 | 0.021 | 0.021 |
M10 | 0.059 | 0.059 | 0.059 | 0.059 | 0.059 | 0.059 | 0.059 | 0.059 | 0.059 | 0.059 | 0.059 | 0.059 | 0.059 | 0.059 | 0.059 | 0.059 | 0.059 | 0.059 | 0.059 | 0.059 |
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P1 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 | 0.006 |
P2 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 | 0.003 |
According to the above MCDM method, the importance weights or scores for three groups of the CSR evaluation criteria are derived. However, the use of the results from one single decision-making analytic method to determine the importance level of each of the evaluation criteria to the overall decision problem seems to be too arbitrary and less persuasive and might even cause bias in determining the target decision, which obviously deviates from the initial purpose of employing different decision-making analytic methods. To avoid the above condition and to upgrade the accuracy of results from decision-making analysis, this study further multiplied the importance weights of the criteria derived with Delphi and ANP by the prominence scores of the criteria found by DEMATEL. However, since some criteria have a prominence score less than 1 (i.e.,
Composite important weight and priority ranking of criteria.
Criterion | DEMETAL prominence score | Delphi weight | ANP weight | Composite weight | Normalization of composite weight | General ranking |
---|---|---|---|---|---|---|
E1 | 0.782 | 0.053 | 0.037 | 0.0035 | 0.038 | 13 |
E2 | 1.407 | 0.054 | 0.031 | 0.0040 | 0.043 | 11 |
E3 | 0.938 | 0.045 | 0.036 | 0.0032 | 0.034 | 14 |
E4 | 0.606 | 0.045 | 0.038 | 0.0027 | 0.029 | 15 |
E5 | 0.898 | 0.049 | 0.060 | 0.0055 | 0.059 | 9 |
|
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L1 | 1.407 | 0.052 | 0.082 | 0.0102 | 0.109 | 1 |
L2 | 1.161 | 0.048 | 0.068 | 0.0071 | 0.076 | 5 |
L3 | 0.980 | 0.047 | 0.078 | 0.0072 | 0.077 | 4 |
L4 | 1.325 | 0.045 | 0.061 | 0.0064 | 0.068 | 7 |
L5 | 0.755 | 0.056 | 0.074 | 0.0073 | 0.078 | 3 |
L6 | 0.534 | 0.055 | 0.109 | 0.0092 | 0.098 | 2 |
L7 | 0.667 | 0.046 | 0.086 | 0.0066 | 0.070 | 6 |
|
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M1 | 0.289 | 0.052 | 0.033 | 0.0022 | 0.024 | 17 |
M4 | 0.762 | 0.042 | 0.050 | 0.0037 | 0.040 | 12 |
M6 | 0.741 | 0.046 | 0.028 | 0.0023 | 0.024 | 16 |
M7 | 1.591 | 0.040 | 0.040 | 0.0041 | 0.044 | 10 |
M9 | 0.263 | 0.055 | 0.021 | 0.0015 | 0.016 | 18 |
M10 | 0.913 | 0.054 | 0.059 | 0.0061 | 0.065 | 8 |
|
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P1 | 0.807 | 0.058 | 0.006 | 0.0006 | 0.007 | 19 |
P2 | 0.472 | 0.057 | 0.003 | 0.0002 | 0.003 | 20 |
|
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Total | — | 1.000 | 1.000 | — | 1.000 | — |
As shown in Table
In general, there are more than one alternative for decision maker to choose for problem solving. For each alternative, they may have levels of effect on different evaluation criteria. Therefore, according to described analytic results, in the study, the importance weight threshold is set to 0.05 for top 10 CSR critical criteria selection. And then, a second pairwise comparison is conducted for the five interested clusters (i.e., the alternatives used in this study). To facilitate the subsequent alternative similarity analysis, the 9-point paired-comparison scaling was also used for measurement. Finally, the relative weights of the interested clusters under the selected 10 critical criteria were obtained as shown in Table
Relative weights of alternatives under CSR criteria.
Interested parties | L1 | L6 | L5 | L3 | L2 | L7 | L4 | M10 | E5 | M7 | Sum of weight |
---|---|---|---|---|---|---|---|---|---|---|---|
A1 suppliers | 0.049 | 0.042 | 0.047 | 0.252 | 0.049 | 0.047 | 0.529 | 0.270 | 0.051 | 0.061 | 1.396 |
A2 shareholders | 0.052 | 0.165 | 0.125 | 0.122 | 0.212 | 0.252 | 0.151 | 0.096 | 0.222 | 0.514 | 1.912 |
A3 employees | 0.147 | 0.217 | 0.214 | 0.036 | 0.537 | 0.529 | 0.047 | 0.046 | 0.563 | 0.210 | 2.547 |
A4 customers | 0.571 | 0.052 | 0.042 | 0.520 | 0.041 | 0.066 | 0.220 | 0.522 | 0.048 | 0.171 | 2.254 |
A5 general public | 0.181 | 0.523 | 0.572 | 0.070 | 0.162 | 0.106 | 0.053 | 0.065 | 0.116 | 0.043 | 1.891 |
|
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Total | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | — |
Based on Table
Euclidean distance matrix for alternatives.
Interested parties | A1 |
A2 |
A3 |
A4 |
A5 |
---|---|---|---|---|---|
A1 suppliers | 0.000 | ||||
A2 shareholders | 0.257 | 0.000 | |||
A3 employees | 0.576 | 0.216 | 0.000 | ||
A4 customers | 0.258 | 0.423 | 0.726 | 0.000 | |
A5 general public | 0.423 | 0.307 | 0.385 | 0.565 | 0.000 |
From the perceptual map shown in Figure
Perceptual map of alternative allocation.
MCDM has been an important issue and many researches are devoted to help people make better decision. Particularly, some decision-making analytic methodologies (such as ANP and DEMTEAL) and information technologies (such as DSS and GDSS) can help decision makers to analyze the decision problem, collect information, indicate the available alternatives, and so on. However, the MCDM involves multiple decision criteria and, the worst, these criteria might mutually influence one another to lead to a complicated situation. Decision making is a sophisticated art and decision makers indeed require some help.
This study proposed a hybrid decision-making support model (HDMSM) that is an integrated GDSS architecture consisting of five steps, namely, criteria identification, criteria correlation, criteria evaluation, criteria selection, and alternative rank and comparison. Further, HDMSM appropriately integrates four systemic decision approaches (Delphi, DEMATEL, ANP, and MDS) to help the decision maker with alternative rank and selection issue. HDMSM consists of five steps. In Step
A case study was implemented based on the proposed HDMSM. The case study intends to find out the important indexes of corporate social responsibility (CSR) from multiple perspectives. The case study results showed some interest findings. We found that “implementing the consumer protection act,” “providing proper waste disposal and reduce pollutant emission,” “keeping customer information confidential and protecting it against illegal use,” “providing employees with the newest information of related laws,” and “ensuring good and safe working environment for employees” are top five critical criteria for enterprises to implement their corporate social responsibility (CSR). Further, according to the similarity analysis for interested clusters, three groups of interest clusters can be formed, including the employees and shareholder as the first one group, the shareholders and suppliers as the second one, and the suppliers and customers as the last one group. The members of the same group got closed viewpoint toward the CSR evaluation criteria. Therefore, to maximize the CSR policies effect, the reference to the analyzed results can help the enterprises to establish and develop their CSR strategies for particular interested cluster.
The proposed HDMSM can enable a group of decision makers to implement the MCDM effectively and help them to analyze the relation and degree of mutual influence among different evaluation factors. As the case study demonstrated, the HDMSM can locate the evaluation factors with relatively significant and deep influence and conduct a cross analysis for different alternatives of the decision problem. Therefore, according to the analysis result, decision maker can choose the optimal alternative for decision problem solving. In the future, the HDMSM can be applied to various domains of decision problem, such as system introducing and enterprise resource planning. Also, the proposed HDMSM can be further combined with other decision-making analytic methods, such as association rules analysis, TOPSIS, or VIKOR, to upgrade the accuracy and effectiveness of the HDMSM in handling decision problems.
The authors declare that there is no conflict of interests regarding the publication of this paper.