Evaluating the criteria for human resource for science and technology (HRST) based on an integrated fuzzy AHP and fuzzy DEMATEL approach
Highlights
► For human resource for science and technology (HRST), improving Infrastructure might be a better choice for the long period of time. ► Education, R&D Expenses and Immediate output are more important second-tier criteria. ► The improvement should be started with Infrastructure, particularly on identification of the Education, R&D Expenses and Immediate output.
Introduction
To build a sustainable national competitive advantage based on science and technology and the skilled workers. Although traditional analysis of national competitiveness can display a country's overall competitive advantages, it is unable to highlight the national competitive advantage derived from the technology application. The science and technology are competition advantage that derives from human talent. The human resource for science and technology (HRST) are the crucial survival and growth factor for economics. The human resource competitiveness is the most important factor in achieving economic competitiveness. Therefore, evaluating the performance of HRST in each country is the critical research topic. This study intends to use a combination of fuzzy Analytic Hierarchy Process (AHP) and fuzzy Decision-making Trial and Evaluation Laboratory (DEMATEL) method in human resource for science and technology (HRST). Specifically, this study uses AHP to evaluate the weighting for each criterion and then use DEMATEL method to establish contextual relationships among those criteria.
The reminder of this paper is organized as follows. Sections 2 Fuzzy Analytic Hierarchy Process (FAHP) method, 3 The fuzzy DEMATEL method present how we adopt the methodology, fuzzy AHP and fuzzy DEMATEL in real world. Section 4 displays our empirical results along with some discussions relating to managerial implications. Finally conclusions and remarks are then given in Section 5.
Section snippets
Fuzzy Analytic Hierarchy Process (FAHP) method
Analytic Hierarchy Process (AHP) is a powerful method to solve complex decision problems. Any complex problem can be decomposed into several sub-problems using AHP in terms of hierarchical levels where each level represents a set of criteria or attributes relative to each sub-problem. The AHP method is a multi-criteria method of analysis based on an additive weighting process, in which several relevant attributes are represented through their relative importance. Through AHP, the importance of
The fuzzy DEMATEL method
The DEMATEL method was developed to study the structural relations in the complex system [14]. The mathematics concept borrowed from Liou et al. [14] and Wu [20]. The DEMATEL model constructing process is described below:
Step 1: Selecting the committee of experts who have experienced about this research issue.
We should set the decision goal and set up a committee.
Step 2: Developing the criteria and designing the fuzzy linguistic scale.
The committee followed our proposed method with the steps.
Empirical study and discussion
This study intends to use a combination of Fuzzy Analytic Hierarchy Process (AHP) and fuzzy Decision-making Trial and Evaluation Laboratory (DEMATEL) method in Employment service outreach program. Specifically, this study first uses FAHP to evaluate the weighting for each criterion and then use FDEMATEL method to establish contextual relationships among those criteria. In this section, an empirical study is presented to illustrate the application of fuzzy AHP and fuzzy DEMATEL methods [22].
Step
Conclusion
This study applies AHP and DEMATEL method to evaluate the criteria of human resource for science and technology (HRST). The results provided by AHP can be used for outreach personnel to improve performance from a short time period. For human resource for science and technology (HRST), improving Infrastructure might be a better choice for the long period of time. Moreover, Education, R&D Expenses and Immediate output are more important second-tier criteria than Value, Cooperation, Labor Market,
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2022, Applied Soft ComputingCitation Excerpt :Chou et al. [193] split the fuzzy numbers into three crisp numbers to find the multiplicative inverse of fuzzy matrix in fuzzy DEMATEL. But Pandey and Kumar [194] disclosed the elements of this inverse matrix may not be triangular fuzzy numbers and suggested method by Chou et al. [193] is incorrect. Dytczak and Ginda [195] warned that fuzzy DEMATEL requires more intricate calculations but is not better than crisp DEMATEL in terms of quality of results.