{"title":"什么样的城市品牌生态最能吸引人才?中国98个城市的灰色配置分析","authors":"Zhaohu Dong, Peng Jiang, Zongli Dai, Rui Chi","doi":"10.1108/gs-03-2024-0035","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>Talent is a key resource for urban development, and building and disseminating urban brands have an important impact on attracting talent. This paper explores what kind of urban brand ecology (UBE) can effectively enhance urban talent attraction (UTA). We explore this question using a novel grey quantitative configuration analysis (GQCA) model.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>To develop the GQCA model, grey clustering is combined with qualitative configuration analysis (QCA). We conducted comparative configuration analysis of UTA using fuzzy set QCA (fsQCA) and the proposed GQCA.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>We find that the empirical results of fsQCA may contradict the facts, and that the proposed GQCA effectively solves this problem.</p><!--/ Abstract__block -->\n<h3>Practical implications</h3>\n<p>Based on the theory of UBE, we identify bottleneck factors for improving UTA at different stages. Seven configuration paths are described for cities to enhance UTA. Theoretically, this study expands the application boundaries of UBE.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>The proposed GQCA effectively solves the problem of inconsistent analysis and facts caused by the use of a binary threshold by the fsQCA. In practical case studies, the GQCA significantly improves the reliability of configuration comparisons and the sensitivity of QCA to cases, demonstrating excellent research performance.</p><!--/ Abstract__block -->","PeriodicalId":48597,"journal":{"name":"Grey Systems-Theory and Application","volume":"22 1","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"What kind of urban brand ecology attracts talent best? Grey configuration analysis of 98 Chinese cities\",\"authors\":\"Zhaohu Dong, Peng Jiang, Zongli Dai, Rui Chi\",\"doi\":\"10.1108/gs-03-2024-0035\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3>Purpose</h3>\\n<p>Talent is a key resource for urban development, and building and disseminating urban brands have an important impact on attracting talent. This paper explores what kind of urban brand ecology (UBE) can effectively enhance urban talent attraction (UTA). We explore this question using a novel grey quantitative configuration analysis (GQCA) model.</p><!--/ Abstract__block -->\\n<h3>Design/methodology/approach</h3>\\n<p>To develop the GQCA model, grey clustering is combined with qualitative configuration analysis (QCA). We conducted comparative configuration analysis of UTA using fuzzy set QCA (fsQCA) and the proposed GQCA.</p><!--/ Abstract__block -->\\n<h3>Findings</h3>\\n<p>We find that the empirical results of fsQCA may contradict the facts, and that the proposed GQCA effectively solves this problem.</p><!--/ Abstract__block -->\\n<h3>Practical implications</h3>\\n<p>Based on the theory of UBE, we identify bottleneck factors for improving UTA at different stages. Seven configuration paths are described for cities to enhance UTA. Theoretically, this study expands the application boundaries of UBE.</p><!--/ Abstract__block -->\\n<h3>Originality/value</h3>\\n<p>The proposed GQCA effectively solves the problem of inconsistent analysis and facts caused by the use of a binary threshold by the fsQCA. In practical case studies, the GQCA significantly improves the reliability of configuration comparisons and the sensitivity of QCA to cases, demonstrating excellent research performance.</p><!--/ Abstract__block -->\",\"PeriodicalId\":48597,\"journal\":{\"name\":\"Grey Systems-Theory and Application\",\"volume\":\"22 1\",\"pages\":\"\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Grey Systems-Theory and Application\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1108/gs-03-2024-0035\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Grey Systems-Theory and Application","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1108/gs-03-2024-0035","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
What kind of urban brand ecology attracts talent best? Grey configuration analysis of 98 Chinese cities
Purpose
Talent is a key resource for urban development, and building and disseminating urban brands have an important impact on attracting talent. This paper explores what kind of urban brand ecology (UBE) can effectively enhance urban talent attraction (UTA). We explore this question using a novel grey quantitative configuration analysis (GQCA) model.
Design/methodology/approach
To develop the GQCA model, grey clustering is combined with qualitative configuration analysis (QCA). We conducted comparative configuration analysis of UTA using fuzzy set QCA (fsQCA) and the proposed GQCA.
Findings
We find that the empirical results of fsQCA may contradict the facts, and that the proposed GQCA effectively solves this problem.
Practical implications
Based on the theory of UBE, we identify bottleneck factors for improving UTA at different stages. Seven configuration paths are described for cities to enhance UTA. Theoretically, this study expands the application boundaries of UBE.
Originality/value
The proposed GQCA effectively solves the problem of inconsistent analysis and facts caused by the use of a binary threshold by the fsQCA. In practical case studies, the GQCA significantly improves the reliability of configuration comparisons and the sensitivity of QCA to cases, demonstrating excellent research performance.