{"title":"创意产业创新要素的外部经济效应","authors":"N. Kuznetsova","doi":"10.15544/mts.2022.17","DOIUrl":null,"url":null,"abstract":"Abstract To assess the external economic effect of the innovation factor of creative industries, it is offered to differentiate incoming and outgoing flows for all 84 indicators of the Global Innovation Index by the degree of impact on soft innovations. The paper covers the basic theoretical aspects of the classification of creative industries, soft innovations and methods of their evaluation. The dual model ʽThe Analysis of the External Effect of Soft Innovation in Creative Industries – EESICIʼ is developed. Creative industries innovation process is proposed to be considered as a network structure – integration of 2 processes: ‘Inbound process of soft innovation production’ (IsPP index) and ‘Outbound process of soft innovation commercialization’ (OsPP index). The effectiveness of soft innovation is defined as the ratio of the two proposed indices. The proposed analysis sequence of the external effect of creative industries soft innovations consists of 7 stages. In the first stage, the representativeness of 20 soft innovation indices is determined by factor analysis. In stages 2–4, 2 models of inbound and outbound soft innovation flows are constructed using taxonomy method. In stage 5, a cluster analysis is used to classify 132 countries under study according to their level of soft innovations implementation. In stages 6–7, the level of soft innovations is calculated and a strategic matrix of countries positioning based on the use of soft innovations in creative industries is constructed. Scenarios of changes in the positioning of countries depending on the achieved level of economic effect of the creative industries innovation factor are considered.","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"External Economic Effect of the Innovation Factor of Creative Industries\",\"authors\":\"N. Kuznetsova\",\"doi\":\"10.15544/mts.2022.17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract To assess the external economic effect of the innovation factor of creative industries, it is offered to differentiate incoming and outgoing flows for all 84 indicators of the Global Innovation Index by the degree of impact on soft innovations. The paper covers the basic theoretical aspects of the classification of creative industries, soft innovations and methods of their evaluation. The dual model ʽThe Analysis of the External Effect of Soft Innovation in Creative Industries – EESICIʼ is developed. Creative industries innovation process is proposed to be considered as a network structure – integration of 2 processes: ‘Inbound process of soft innovation production’ (IsPP index) and ‘Outbound process of soft innovation commercialization’ (OsPP index). The effectiveness of soft innovation is defined as the ratio of the two proposed indices. The proposed analysis sequence of the external effect of creative industries soft innovations consists of 7 stages. In the first stage, the representativeness of 20 soft innovation indices is determined by factor analysis. In stages 2–4, 2 models of inbound and outbound soft innovation flows are constructed using taxonomy method. In stage 5, a cluster analysis is used to classify 132 countries under study according to their level of soft innovations implementation. In stages 6–7, the level of soft innovations is calculated and a strategic matrix of countries positioning based on the use of soft innovations in creative industries is constructed. Scenarios of changes in the positioning of countries depending on the achieved level of economic effect of the creative industries innovation factor are considered.\",\"PeriodicalId\":0,\"journal\":{\"name\":\"\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0,\"publicationDate\":\"2022-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15544/mts.2022.17\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15544/mts.2022.17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
External Economic Effect of the Innovation Factor of Creative Industries
Abstract To assess the external economic effect of the innovation factor of creative industries, it is offered to differentiate incoming and outgoing flows for all 84 indicators of the Global Innovation Index by the degree of impact on soft innovations. The paper covers the basic theoretical aspects of the classification of creative industries, soft innovations and methods of their evaluation. The dual model ʽThe Analysis of the External Effect of Soft Innovation in Creative Industries – EESICIʼ is developed. Creative industries innovation process is proposed to be considered as a network structure – integration of 2 processes: ‘Inbound process of soft innovation production’ (IsPP index) and ‘Outbound process of soft innovation commercialization’ (OsPP index). The effectiveness of soft innovation is defined as the ratio of the two proposed indices. The proposed analysis sequence of the external effect of creative industries soft innovations consists of 7 stages. In the first stage, the representativeness of 20 soft innovation indices is determined by factor analysis. In stages 2–4, 2 models of inbound and outbound soft innovation flows are constructed using taxonomy method. In stage 5, a cluster analysis is used to classify 132 countries under study according to their level of soft innovations implementation. In stages 6–7, the level of soft innovations is calculated and a strategic matrix of countries positioning based on the use of soft innovations in creative industries is constructed. Scenarios of changes in the positioning of countries depending on the achieved level of economic effect of the creative industries innovation factor are considered.