科研之友
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摘要
Technological innovation is critically important for high-tech firms in laying the groundwork for corporate growth and competitive advantages. It is hoped that evaluating risk accurately and effectively will contribute to assign appropriate priorities of technological innovation projects and to enhance the success rate for the whole project. In considering the issue of risk evaluation, the essential question that arises concerns strong fuzziness, ambiguity, and inexactness during the process of assessing risks. However, comparatively little research has focused on accommodating higher degrees of uncertainty in evaluating risks associated with technological innovation projects. Because the existing risk evaluation methods (such as models using net present values, internal rates of return, fuzzy logic, real options, and stochastic simulation) have certain limitations, this paper takes the powerfulness of interval-valued Pythagorean fuzzy (IVPF) sets into account to handle imprecise and ambiguous information and to characterize complex uncertainty in practical risk evaluation problems. This paper aims to propose an IVPF inferior ratio(IR)-based assignment model and to establish a novel risk evaluation method of technological innovation for the purpose of appropriately tackling highly uncertain information in intricate and varied circumstances. The proposed method provides an effective way to fuse complicated information about risk assessments and to identify an aggregate ranking of technological innovation projects that has the closest agreement with all indicator-wise precedence ranks. The feasibility and applicability of the IVPF IR-based assignment model are illustrated via a real-world case concerning the risk evaluation of technological innovation in high-tech enterprises. Furthermore, the practicality and effectiveness of the developed methodology are verified through a comparative analysis with other relevant approaches.
- 出版日期2018-4-1
- 单位长春大学
