{"title":"基于DEA方法的人工智能企业创新效率研究","authors":"Xia Gao, Z. Yang, Zhao-yan Sun","doi":"10.2991/aebmr.k.200402.001","DOIUrl":null,"url":null,"abstract":"This paper uses DEA method to evaluate innovation efficiency of 40 typical artificial intelligence enterprise in our country, which inputs element as human (research and development personnel accounted for the ratio of the total number of employees) and capital (the ratio of R&D and business revenue), and which output elements as technology (patent number and the sum of the number of software copyright) and economic (operating profit margin). Combining with the evaluation results, the paper analyzed comprehensive efficiency, pure technical efficiency and scale efficiency, return to scale and input redundancy of 40 artificial intelligence enterprise representative in our country. The results show that comprehensive efficiency is low, scale efficiency and pure technical efficiency are not high, and some enterprises have factor redundancy. It is innovative in industry research for selecting 40 enterprises and collecting relevant data of 40 enterprises by using enterprise statements, statistical yearbook, government bulletin and peer, and to conduct research","PeriodicalId":231543,"journal":{"name":"Proceedings of the 3rd International Conference on Advances in Management Science and Engineering (IC-AMSE 2020)","volume":"412 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Research on the Innovation Efficiency of Artificial Intelligence Enterprise Based on DEA Method\",\"authors\":\"Xia Gao, Z. Yang, Zhao-yan Sun\",\"doi\":\"10.2991/aebmr.k.200402.001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper uses DEA method to evaluate innovation efficiency of 40 typical artificial intelligence enterprise in our country, which inputs element as human (research and development personnel accounted for the ratio of the total number of employees) and capital (the ratio of R&D and business revenue), and which output elements as technology (patent number and the sum of the number of software copyright) and economic (operating profit margin). Combining with the evaluation results, the paper analyzed comprehensive efficiency, pure technical efficiency and scale efficiency, return to scale and input redundancy of 40 artificial intelligence enterprise representative in our country. The results show that comprehensive efficiency is low, scale efficiency and pure technical efficiency are not high, and some enterprises have factor redundancy. It is innovative in industry research for selecting 40 enterprises and collecting relevant data of 40 enterprises by using enterprise statements, statistical yearbook, government bulletin and peer, and to conduct research\",\"PeriodicalId\":231543,\"journal\":{\"name\":\"Proceedings of the 3rd International Conference on Advances in Management Science and Engineering (IC-AMSE 2020)\",\"volume\":\"412 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-04-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 3rd International Conference on Advances in Management Science and Engineering (IC-AMSE 2020)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2991/aebmr.k.200402.001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 3rd International Conference on Advances in Management Science and Engineering (IC-AMSE 2020)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2991/aebmr.k.200402.001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on the Innovation Efficiency of Artificial Intelligence Enterprise Based on DEA Method
This paper uses DEA method to evaluate innovation efficiency of 40 typical artificial intelligence enterprise in our country, which inputs element as human (research and development personnel accounted for the ratio of the total number of employees) and capital (the ratio of R&D and business revenue), and which output elements as technology (patent number and the sum of the number of software copyright) and economic (operating profit margin). Combining with the evaluation results, the paper analyzed comprehensive efficiency, pure technical efficiency and scale efficiency, return to scale and input redundancy of 40 artificial intelligence enterprise representative in our country. The results show that comprehensive efficiency is low, scale efficiency and pure technical efficiency are not high, and some enterprises have factor redundancy. It is innovative in industry research for selecting 40 enterprises and collecting relevant data of 40 enterprises by using enterprise statements, statistical yearbook, government bulletin and peer, and to conduct research
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