Innovative Intellectual Property in the Marine Seed

Description

Our research employs the Group Eigenvalue Method (GEM) to identify key indicators, aiming to enhance the accuracy of value assessment. The principal advantage of GEM lies in its ability to effectively overcome the inconsistency issues common in the judgement matrices of the Analytic Hierarchy Process (AHP). Moreover, it offers simplicity in data processing (as used in method analysis). This method is applicable not only to quantitative issues but also to the analysis of qualitative problems. For the research problem addressed in this paper, staff from companies and research institutions in the marine seed industry technology innovation field were selected as experts to score the preliminary screening indicators. The ten invited experts hail from prestigious institutions including Shandong Marine Limited Company, Shandong New Sunshine Seed Industry Science and Technology Limited Company, Tongji University, Xiamen University, the First Institute of Natural Resources Ministry, and the Chinese Academy of Sciences' Institute of Oceanography. The expert consultation process was conducted in two phases: the first round involved distributing questionnaires to the experts who assessed each indicator based on their knowledge and experience to collect baseline data. The scoring was designed according to the Likert 7-point scale method, as shown in Table 2. The second round involved detailed processing of the collected data; through further consultation with the experts, we discussed the threshold setting standards for each sub-indicator system and sought expert advice on retaining, eliminating, or merging indicators. Although the GEM has been applied to critically screen the indicators for the valuation of intellectual property in marine seed industry innovation, the selected indicators may still exhibit some degree of correlation. According to the principle of diversity in the design of the assessment indicator system, excessive correlation among indicators can reduce the precision and practicality of the evaluation results. Therefore, maintaining an appropriate level of independence among indicators is crucial for constructing a scientifically sound and rational assessment system. By employing Pearson correlation analysis, it is possible to effectively identify highly correlated indicators, which often provide overlapping information. Consequently, merging or eliminating these highly correlated indicators from the assessment system is an essential step in ensuring the effectiveness and accuracy of the indicator system. Through this approach, we ensure the independence and scientific integrity of the assessment indicator system, enhancing the reliability and utility of the overall evaluation model.

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