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Article

Research on the Generating Mechanism of Urban Talent Competitiveness Based QCA Method: A Configurational Analysis of 24 Chinese Cities

by
Sike Liu
1,2,* and
Wuyi Wang
1
1
Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao SAR 999078, China
2
Institute of Finance and Trade, Hubei Academy of Social Sciences, Wuhan 430077, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(7), 6120; https://doi.org/10.3390/su15076120
Submission received: 10 February 2023 / Revised: 22 March 2023 / Accepted: 30 March 2023 / Published: 1 April 2023
(This article belongs to the Special Issue Sustainability and International Business: Innovation, Talent Management, Business Model Innovation, Market Entry Strategies, Competitiveness)
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Review Reports Versions Notes

Abstract

:
Talent competition is the core of urban competition; urban development needs strong talent competitiveness. This study applies configuration thinking and the QCA method through the integration of six influencing conditions, namely talent scale, talent structure, talent innovation, talent development, talent efficiency and talent living, it selects 24 Chinese cities as research cases, explores the generation mechanism of urban talent competitiveness. The results demonstrate that: (1) Talent scale, talent innovation, talent development and talent living are the key conditions for generating urban talent competitiveness; (2) The quality improvement mode, innovation leading mode and resource competitive mode are the main modes of generating urban talent competitiveness; the resource competitive mode is the most common combination of strategies for generating urban talent competitiveness; (3) Talent development and talent living have a substitution relationship in the process of generating urban talent competitiveness; (4) The configuration that hinders the generation of competitiveness and promotes the generation of competitiveness is asymmetric.

1. Introduction

Urban competition is the competition for talents, and talent dividends are the future of a city [1,2]. The competitiveness of urban talents shows the competitive advantages of a city in talents. Urban talent competitiveness is a comprehensive, multi-level and systematic concept, covering many factors [3,4]. Nowadays, urban competition is becoming increasingly fierce; all kinds of high-quality resources quickly gather in highly competitive cities. Talent has become an important variable affecting urban competitiveness; cities with strong talent competitiveness are likely to stand out in urban competition [5,6,7,8]. Talent has become an important resource for the development of global cities. New York, London, Tokyo and other first-tier cities in the world have become international high-quality talent-gathering places, while cities in developing countries, such as Beijing and Shanghai, are also striving to create a high-quality talent development environment, and gradually become a new gathering place for international talent [9].
From a micro perspective, talent competitiveness is an important manifestation of urban development and also a source of power that affects urban development and innovation [10,11]. It is also the key to urban industrial transformation and upgrading, consumer market construction, the improvement of research and the development strength [12]. Which is of great significance to innovation, entrepreneurship and high-quality urban development. From the macro level, the coordinated development of talents and cities has effectively improved the comprehensive strength of the country, and has kept the country in a favorable position in the global competition, which is key to developing countries to “jump out of the middle-income trap” [13,14].
Meanwhile, because of the rapid development and changes of cities, the research on urban talent competitiveness also presents a larger scope of research. The research on the causes and mechanisms of urban talent competitiveness needs to be described subjectively from the perspective of talent attraction, talent cultivation and talent development [15,16,17]. Cerna and Mujtaba are the representatives of talent attraction [18,19]. They believe that cities should formulate more policies to attract talents, take attracting talents as the main direction of urban development and attract talents through the construction of livable urban environment, housing and other conditions [20,21,22]. Some scholars also believe that urban talent competitiveness is the result of urban development, and point out that it should not violate the law of urban development in order to attract talents [23,24,25]. Some scholars, who comment from the perspective of demand and supply, believe when cities can provide enough middle- and high-end jobs, cities will attract more high-quality talents [26,27]. Some scholars put forward more views on talent cultivation. They believed that cities need to build a talent cultivation system and cultivate local talents through innovative development and other ways, which can not only improve the competitiveness of cities, but also make local talents more stable [28,29,30]. Because there are many reasons for talent competitiveness, many experts have carried out systematic analysis around the evaluation indicators of talent experience, and have given a series of indicator systems, such as talent quality, talent innovation and talent environment [31,32]. The above research provides an important research foundation for the generation of urban talent competitiveness. At the same time, the existing research mostly focuses on the description of the reasons for urban talent competitiveness. However, it is worth asking the following: How are many of the reasons connected? What interaction rules are followed? What circumstances will urban talent competitiveness generate? These issues still need to be further clarified.
Therefore, this study focuses on the core issue of “how to generate urban talent competitiveness”, and based on the talent competitiveness index system developed by experts and scholars, adopts the qualitative comparative analysis research method, systematically analyzes the influencing factors of the generation of urban talent competitiveness, constructs the generation model of urban talent competitiveness, and reveals the internal logic of the generation of urban talent competitiveness. Additionally, this study provides a theoretical basis and practical reference for the cultivation of urban talent competitiveness.

2. Theoretical Basis and Research Framework

2.1. Theoretical Analysis on the Generation of Urban Talent Competitiveness

Urban talent competitiveness refers to the comprehensive effect of various factors such as the quantity, scale, quality, ability, structural level, output efficiency and growth environment of talent resources, and then, generates a comparative competitive advantage over other cities in terms of introducing, cultivating and stimulating talent potential [33,34]. The generation of urban talent competitiveness is a complex process, which is affected by multiple factors. To generate urban talent competitiveness, two aspects are involved: one is the talent attraction of the city, such as talent scale, quality structure, talent benefits and so on [35,36]; the other is the city’s ability to acquire, maintain and develop talents; this is reflected in the attraction talents of cities [32,37]. The widely recognized study of the World Competitiveness Yearbook published by IMD sets the complex factors of talent scale, talent structure, talent innovation, talent development, talent efficiency and talent living [38]. Then, the paper deeply analyzes the production mechanism of urban talent competitiveness in view of these six conditions.
Talent scale mainly refers to the talent reserve of cities. The permanent population and employees are both important driving forces towards the increase of talent competitiveness. Especially in modern cities, the increase in population flow has affected the stability of urban population size. Talents with technology and skills have the advantage of free flow; city managers also attract talents by formulating preferential policies such as housing and income [18]. The frequent flow of talents initially existed in international cities of developed countries; managers provided high-quality services to urban talents, which could maintain the talent number high. Then, with the increasing demand for talents in developing economies, more and more cities in developing nations began to build attractive talent policy systems, which could expand the scale of urban talents [39] Professor Glaeser of Harvard University pointed out in his book Triumph of the City that urban prosperity depends on population flow and talent gathering; cities provide a large number of jobs for the workforce, which creates a key platform for talent competition and makes employees from different industries settle down in the city [40]. The advantage of human resources gives the city strong competitiveness.
Talent structure is a form of talent system. The structure of personnel is composed of three levels: individual quality structure of talents, group structure of talents and social structure of talents. Education level is the basic standard of measuring talent structure; the urban talent system needs to be jointly constructed by talents with different academic qualifications [41]. Employment status is an important reflection of talent structure, as primary, secondary and tertiary urban industries all need sufficient employment talents [42]. The conditions of finance, technology and education guarantee a reasonable talent structure; first-class cities in the world often have these high-quality conditions [43]. The process of urban development and evolution need different types of talents. The Handbook of Regional and Urban Economics emphasizes the importance of talents’ work on scientific research, finance and other fields [44]. Additionally, high-knowledge talents can effectively promote urban economic growth [45]. So, talent structure is a significant factor to improve urban talent competitiveness.
Talent innovation is the process of discovering, cultivating and adding value to talents through effective methods. Enhancing talent innovation ability has become the common in urban competition [29]. Both developed and developing countries put no effort in urban talent innovation investment. Cities with strong innovation ability tend to have stronger competitiveness with talents. Innovative cities have invested large amounts of resources for talent development, which provide an advanced system and environment for stimulating talents’ innovative vitality, and it is more conducive to the formation of a talent gathering effect [17]. According to the Global Talent Flow: Trends and Prospects report by CCG, to a great extent, the performance of urban innovation determines the competitiveness of urban talents [46]. Therefore, the key role of talent innovation cannot be ignored in improving the competitiveness of urban talents.
Talent development has been a long-term issue in the relationship between knowledge level and work ability. Talent development is based on the combination of theory and practice [47]. Talent sustainable development requires continuous updating of knowledge, and quality cultural and educational resources are significant conditions for knowledge acquisition [48]. The role of education in the cultivation of urban competitiveness is crucial; educational conditions mainly include basic education, secondary education, vocational education and higher education; the quality of education provides knowledge and skills for talent growth and makes talents increase their employability in cities [4,15]. High-quality education conditions also make cities more attractive; in the process of talent migration, people prefer cities with richer educational resources [3]. Meanwhile, skills training should also not be ignored, especially when the manufacturing industry returns to the main economic battlefield. Cities in developing countries have increased their investment in vocational and technical education [13]. Toyota Talent: Developing Your People the Toyota Way underlines the great value of skills and practical talents for the modern manufacturing industry [49]. Urban competitiveness is inseparable from the comprehensive development of talents.
Talent efficiency is the performance of expected results or influence achieved by talent management. In the theory of human resource, talent efficiency demonstrates the effectiveness of the human resources system by calculating whether different human inputs have produced corresponding effects [50]. Investment and consumption are important cornerstones for improving urban competitiveness, and investment and consumption need to be activated by talents. Talent gathering increases high consumption demand and enhances urban purchasing power [12]. As explained in the Harvard Business Review (HBR), talent gathering makes cities obtain better economic and social benefits, and it is an important embodiment of the competitiveness of urban talents [51]. Young talents can more effectively promote urban competitiveness; the consumption demand of young people, such as college graduates, is stronger. Cities where young talents gather are also more likely to attract domestic and foreign capital investment [16].
Talent urban living is an experience that is comprehensive and impressive; it is a component of the urban soft environment. Based on the UNDP of the Human Development Report 2021–2022, talent gathering depends on the livability level. Talents’ living environment is one of most core competitiveness factors in urban transformation and development [52]. To some extent, the quality of life has become the primary guarantee of urban competitiveness; many talents choose to leave a city because of high housing prices, air pollution, traffic jams, lack of entertainment or insufficient medical resources [1,20,22]. In a study by WEF, the talent living environment is the potential competitiveness of cities, which determines whether talent vitality can be effectively released [53]. Thus, talent living is a crucial influencing factor for urban talent competitiveness.

2.2. Research Framework

Recent research has extensively discussed the competitiveness of urban talents, and it shows that the formation of urban talent competitiveness has affected multiple dimensions. Compared with the single-factor consideration or regression analysis of multiple factors, there seems to be room for expansion in the interactive research on the factors affecting the competitiveness of urban talents. Investigating the configuration effect among different influencing factors can help us uncover the black box of the generation of urban talent competitiveness, and compare and analyze the cases of different cities. Based on the scholars’ summary of the factors affecting the competitiveness of urban talent, this paper builds the research framework of “scale + structure + innovation + development + efficiency + living” (Figure 1). In this paper we discuss the generation mechanism of urban talent competitiveness from the perspective of configuration.

3. Methods, Data and Case Selection

3.1. Methods

This study analyses the interaction of factors such as talent scale, talent structure, talent innovation, talent development, talent efficiency, talent living and the common influence of these factors for generating urban talent competitiveness based on the QCA (qualitative comparative analysis) method. QCA is a qualitative comparative research method that uses Boolean algebra and set analysis to explore the “chemical reaction” of the combination of independent variables in order to find equivalent paths or solutions [54,55]. The QCA method selected in this paper is mainly based on the following reasons.
First, the generation of talent competitiveness is a system project; it is the unity of multiple aspects, multiple levels and integrity, which need to adopt a way of holistic thinking, explore the causal relationship between the combination of influencing factors and urban talent competitiveness. Additionally, the QCA method takes a general perspective; it uses configuration thinking, takes the research object as a cluster of practices with interrelated structures, rather than an independent single factor or a loosely structured entity [56]. Therefore, QCA prefers to analyze the complex relationship and mutual dependence between influencing factors for the generation of urban talent competitiveness.
Second, urban talent competitiveness is an important part of urban competitiveness, and it is closely related to urban development [48]. Understanding urban talent competitiveness should be combined with urban development, which is an analysis of the performance of talent competitiveness based on the development and changes of cities [57]. QCA precisely focuses on specific situations from the perspective of configuration through multi-case comparison to determine the number and characteristics of different causal models, so as to discover and explore complex multiple concurrent causal relationships.
Thirdly, the QCA method has the dual advantages of qualitative and quantitative analysis. It can not only reflect the uniqueness and depth of qualitative research, but it can also reflect the popularization of quantitative research [58]. On the one hand, it avoids the weakness of the poor external promotion effect of qualitative research; on the other hand, it also avoids the weak point of insufficient depth of quantitative research. So, the advantages of qualitative analysis and quantitative analysis are integrated in the QCA method, which achieve complementary advantages.
Additionally, according to different technical characteristics, the QCA method involves of crisp-set QCA (csQCA), fuzzy-set QCA (fsQCA) and multi-value QCA (mvQCA). This study selects csQCA, which has the widest scope of application [59]. The concise method of binary assignment of csQCA, which is beneficial to protecting the multiple connections between various influence factors of urban talent competitiveness and enhancing the diversity and credibility of the investigation results [60].

3.2. Case Selection

In this study, the cases that were selected follow these principles: (1) This study mainly aims at cities with large economic scale, and select cases from cities with GDP exceeding CNY 1 trillion in 2021. Talent growth is closely related to urban development, cities with certain economic scale could better reflect talent competitiveness; (2) Select cities cover the eastern, central and western regions of China, and make the research sample more representative. Therefore, according to the economics scale of Chinese mainland cities in 2021 and the availability of the relevant data, this study selects 24 representative cities, which are Beijing, Shanghai, Shenzhen, Guangzhou, Chongqing, Chengdu, Suzhou, Hangzhou, Nanjing, Tianjin, Changsha, Wuhan, Qingdao, Xi’an, Hefei, Ningbo, Dongguan, Jinan, Zhengzhou, Wuxi, Quanzhou, Foshan, Nantong and Fuzhou.

3.3. Indicators, Data and Encoding Process

The research data come from the ANALYSIS REPORT ON HUMAN RESOURCE MARKET IN CIHINA (2022) [61]. This report is the research achievement of the Chinese Academy of Personnel Science based on the statistical survey data of the National Bureau of Statistics, the Ministry of Human Resources and Social Security and other departments, as well as universities, scientific research institutions and human resources institutions, the report focused on the comprehensive sorting and index ranking of the talent scale structure, talent innovation, talent development, talent efficiency and talent living of major cities in China from 2020 to 2021 (Table 1). These indexes are obtained by comprehensive statics of multiple relevant indicators according to different weights, and the report ranks urban talent competitiveness based on the index. These talent indexes are widely adopted by government departments and scholars, which has important research and reference value. Compared with conditional measurement using single data, the comprehensive index can more systematically reflect the reality of urban talent competitiveness.
This study selects 6 conditional variables of talent scale (TC), talent structure (TS), talent innovation (TI), talent development (TD), talent efficiency (TE), talent living (TL), which, as mentioned above, observe the conditional variables of talent competitiveness in 24 major cities of China and the urban talent competitiveness (UTC) as a result variable. This study measures condition variables based on the correlation index provided by the ANALYSIS REPORT ON HUMAN RESOURCE MARKET IN CIHINA (2022).
We have tested the combined conditions for generating urban talent competitiveness. According to the dichotomy assignment method of csQCA, the 24 cities with the index of conditional variables ranking at or above the middle level (top 12) are assigned a code of 1, cities below the middle level as 0. Regarding the result variable, the report divides urban talent competitiveness into three levels, A, B and C (Table 2); Level A is the best, followed by level B. Level A and B cities are considered to have better performance of talent competitiveness, so code cities of Level A and B are assigned code 1, cities of Level C are assigned code 0 (Table 3).

3.4. Truth Table Analysis

After the variables are coded, the coded data need to be summarized and analyzed. The truth table shows the state whether the condition combinations produce the result or not. Talent scale (TC), talent structure (TS), talent innovation (TI), talent development (TD), talent efficiency (TE) and talent living (TL) are the condition variables in this study; urban talent competitiveness (UTC) is the result variable. According to Table 4, there are 19 combinations in the truth table.

4. Result

Based on the experience of scholars [62], first of all, the study should analyze the single variable of necessity in order to observe the relationship between the single condition variable and the specific result by analyzing the consistency. The study could check whether there is a single necessary variable for the occurrence of a specific result, and through analysis, the coverage, it could describe the extent to that a single cause variable can explain a specific result. According to the set principle, the consistency is the proportion of the intersection of X and Y in X (1); the coverage is the proportion of the intersection of X and Y in Y (2).
Consistency(Xi ≤ Yi) = ∑(min(Xi,Yi))/∑(X)i
Coverage(Xi ≤ Yi) = ∑(min(Xi,Yi))/∑(Y)i

4.1. Necessarity Analysis

In the analysis of QCA, through consistency and coverage, it could determine whether there is a sufficient and necessary relationship between the condition variable and the result variable [63]. This study uses the fs_QCA3.0 software to calculate the data and the necessity analysis results of the single condition variable for the result variable, as shown in Table 5.
According to Ragin’s research, when the consistency is at or above 0.9, the single variable could be the necessary condition of the result [64]. As seen in Table 4, is no variable could reach the point, which shows that the single condition variable could not generate high urban talent competitiveness. It is noteworthy that the consistency of talent scale, talent innovation and talent development are close to 0.9, which demonstrates that these three conditions are significant variables for generating urban talent competitiveness. Moreover, the consistencies of other conditions are all around 0.8; this shows the positive impact of these variables for promoting talent competitiveness. At the same time, it also validates the previous research. Meanwhile, because the consistency of all variables is lower than 0.9, it is necessary to make configuration analysis on the generation of urban talent competitiveness.

4.2. Configurations

This study uses the fs_QCA3.0 software to analyze the urban talent competitiveness of 24 Chinese cities, and find out the generating configuration of urban talent competitiveness. Based on the advice of Fliss [54], the set consistency threshold is 0.8; the case threshold is 1. QCA provides three kinds of solutions; they are complex solution, parsimonious solutions and intermediate solutions. The form of intermediate solution is generally simpler than that of complex solutions; compared with the parsimonious solution, the intermediate solution is more reliable because it only includes counterfactual assumptions. Therefore, Ragin reports the intermediate solutions with a consistency greater than 0.8 [56]. As shown in Table 6, the generation paths to promote urban talent competitiveness finally output five combinations, solution consistency and solution coverage are one; it demonstrates that these five configurations could explain all circumstances for generating urban talent competitiveness; solution consistency and solution coverage are higher than the QCA level of organization and management. In accordance with the structure of configurations, this study divides the configuration into three modes.
(1)
Innovation Leading Mode
Configuration H1: TC*TS*TI*TD. H1 is the combination of high talent scale, high talent structure, high talent innovation and high talent development. When the city has a large population and employees, the proportion of people employed in different industries is reasonable; scientific and technological R&D expenditure remains at a high level, and so does the adequate supply of cultural and educational resources, which could be promote the urban talent competitiveness. The total amount of urban talent reserves is large; talents generally have high academic qualifications. Finance, science, technology and other service industries are relatively prosperous, which provides a stimulating and competitive environment for talent innovation and development, lays a talent foundation for the improvement of urban innovation strength, and it is also conducive to attracting more external innovative talents. At the same time, long-term innovative development requires the participation of new forces and strong educational support, so cities should provide rich cultural and educational resources that are the basis of cultivating innovative talents. Shanghai, Shenzhen, Hangzhou, Tianjin and Xi’an are representative of this type of city.
(2)
Quality Improvement Mode
Configuration H2a: TS*TI*TD*TE*~TL and H2b: TC*TI*~TD*TE*TL. H2a is the combination of high talent scale, high talent innovation, high talent development, high talent efficiency and low talent living. If the city has more labor reserves, an attractive talent innovation environment, strong education support for talent development, high per capita performance in consumption, output value, deposits, etc., even if the urban living environment is not satisfactory, it still might be able to produce high urban talent competitiveness. H2b is the combination of high talent scale, high talent innovation, high talent efficiency, high talent living and low talent development; even with lack of educational and development resources, the configuration can still achieve the target of generating urban talent competitiveness. When the total number of urban talents is large and the expenditure on innovation and R&D is high, cities need to reflect the value of talent investment by strengthening the quality of talent development [36,65]. Additionally, positive ways to improve talent development quality is to create an attractive and competitive talent development environment, such as providing more jobs with growth potential, allowing talents have more learning opportunities. Due to benefits from the combined effect of talent innovation, talent development or talent living, the effective release of the talent scale effect, and the talent efficiency have improved significantly. Chengdu, Wuhan, Suzhou and Ningbo are representative of this type of city.
(3)
Resource Competitive Mode
Configuration H3a: TS*TI*TD*TL and H3b: TS*~TI*TD*TE*TL. H3a is the combination of high talent structure, high talent innovation, high talent development and high talent living. When the employment structure of urban talents is relatively optimized, providing strong support for talent innovation, talent development has sufficient cultural and educational resources, and the quality of urban life has been effectively guaranteed, cities can achieve the goal of generating talent competitiveness. H3b is the combination of high talent scale, high talent development, high talent efficiency, high talent living and low talent innovation. If the total number of urban talents is large, urban cultural and educational resources are abundant, and the high quality urban living environment, even if the talent innovation investment is insufficient, cities could still allow for a high level of performance of talent competitiveness. The configuration of 3a and 3b tends to take the excellent performance of urban resources as an important basis. Cultural and educational resources and living conditions being superior is conductive to attracting more talents and improving the cultural knowledge level of talents. Factors such as talent scale and talent structure could be improved too to some extent. Significantly, in the full supply of urban resources, increasing the total number of talents and improvement in per capita performance could make up for insufficient investment in talent innovation. This provides another way to effectively release urban talent dividends. Urban competition is talent competition in the final analysis; cities with talents could have a better future. Beijing, Guangzhou, Nanjing, Qingdao, Hefei, Chongqing, and Changsha are representative of this type of city.
Additionally, this study explores the configuration of cities without high urban talent competitiveness. The results still use an intermediate solution, and the generation paths to hinder urban talent competitiveness finally outputs seven combinations. The results are shown in Table 7.
In the combinations of N1–N7, the coverages of N1 and N2 are higher than other combinations, so these two combinations deserve more attention. N1: ~TC*~TI*~TD*~TE*TL is the combination of low talent scale, low talent innovation, low talent development, low efficiency and high talent living. N2: ~TC*~TS*~TD*~TE*TL is the combination of low talent scale, low talent structure, low talent development, low talent efficiency and high talent living. The combination of N1 and N2 are the typical paths for hindering talent competitiveness in cities, which cover half the cases. This shows that under the circumstances of insufficient population size and inadequate cultural and educational conditions, even if urban life is more convenient, cities still cannot achieve the strong talent competitiveness.
As seen in Table 7, the condition variable of talent efficiency does not have a clear positive incentive effect; the impact of generating urban talent competitiveness is not significant. This explains why talent efficiency may not be pursued excessively in urban development, especially in developing countries, where many cities are still in the stage of talent cultivation; if the city adopts too many incentive policies to release talent dividends, it may prematurely consume talents’ enthusiasm for innovation and development. Talent development and talent living occur six times in the configuration as edge conditions; except for configurations N4 and N5, these two conditions appear alternately in the other configurations; talent development and talent living cannot exist at the same time.
According to the three modes of generating urban talent competitiveness, the coverage of the resource competitive mode is 0.625; this mode explains nearly 63% of the result variables. Additionally, the coverage is higher than the other two modes. More cities generate talent competitiveness through the resource competitive mode. This clearly demonstrates that the combination of talent development and talent living could promote the generation of urban talent compositeness. Additionally, the existence of an innovation leading mode and quality improvement mode, which shows the generation of urban talent competitiveness, is affected by diversification. According to the configuration of hindering the generation of urban talent competitiveness, Configurations of N1 and N2 could explain half of the result variables; the coverage is higher in other combinations, which proves that these two ways have caused a more negative impact on the generation of urban talent competitiveness.
Additionally, the variable conditions of talent innovation, talent development and talent living show that the dual effects are more significant than the others. On the one hand, in configurations that promote the generation of urban talent competitiveness, whether high level or low level, these three conditions could promote urban talent competitiveness together with other conditions. For example, in the combinations of H1, H2a, H3a and H3b, high talent development and other conditions could jointly promote urban talent competitiveness; in the combination of H2b, low talent development and other conditions could also jointly promote urban talent competitiveness. Additionally, the conditions of talent scale, talent structure and talent efficiency tend to be high level in H1, H2a, H2b, H3a and H3b. On the other hand, in configurations that hinder the generation of urban talent competitiveness, these three conditions have obvious dual effects for the result too. In the combinations of N3, N4, N5 and N6, low talent living and other conditions could jointly hinder urban talent competitiveness; in contrast, in the N1, N2 and N7 combination, high talent living and other conditions could jointly hinder urban talent competitiveness. Therefore, the study highlights the dual effects of the three conditions of talent innovation, talent development and talent living.

4.3. Stability Analysis

To ensure the robustness of the results, and further explore the generating mechanism of urban talent competitiveness, this study improves the standard of result variables; set cities of Level A are 1, cities of Level B and C are 0, analyzing the generation configuration of talent competitiveness in Level A cities. See Table 8 for the analysis results.
There are three combinations that promote the generation of urban talent competitiveness at the highest standard, they are the paths of H4, H5 and H6. Comparing Table 8 and Table 6, the configurations of Level A cities still follows three modes: the resource competitive mode, innovation leading mode and quality improvement mode. Representative cities of H4 include Beijing, Shanghai, Shenzhen, Guangzhou, Chongqing and Hangzhou; the representative city of H5 is Chengdu; the representative city of H6 is Suzhou. This proves that the result of configuration analysis is stable and effective. Significantly, talent scale and talent efficiency play a more important role in generating configurations of urban talent competitiveness in Level A cities. This illustrates that the combination of talent scale and talent efficiency could produce higher performance of urban talent competitiveness.

5. Conclusions and Discussion

5.1. Conclusions

This study takes 24 Chinese cities as research cases, using the QCA method and configurative thinking, discussing the generation of urban talent competitiveness. The main conclusions are the following:
(1) Talent scale, talent innovation, talent development and talent living are significant conditions for the generation of urban talent competitiveness. In the process of producing urban talent compositeness, the conditions of talent scale, talent innovation, talent development and talent living essentially play leading roles, while the conditions of talent structure and talent efficiency produce an important supporting effect; (2) There are mainly three configuration modes for the generation of urban talent competitiveness: innovation leading mode, resource competitive mode and quality improvement mode. The three modes involve of five combinations, and each combination is the result of multiple conditions; no single condition can be the sufficiency condition for the generation of urban talent competitiveness. This finding reveals the generation mechanism of urban talent competitiveness from an general perspective; (3) The generating configuration with the highest level of performance of urban talent competitiveness is different from the results of the full sample. The reason for the difference is not a simple superimposition of influencing factors. For example, the H5 and H6 configurations add the condition of low talent structure on the basis of H2a and H2b; optimizing talent structure might not necessarily lead to stronger urban talent competitiveness in cities with a high level of performance of urban talent competitiveness. This further shows that the generation of talent competitiveness is diversified; different combinations of condition have different effects on the generation of urban talent competitiveness; (4) The configuration that hinders the generation of urban talent competitiveness is also composed of multiple elements, and the configuration that hinders the generation of urban talent competitiveness is not the reverse comparison of the generating configuration of competitiveness. For the generation of urban talent competitiveness, which is a complex system engineering, having high urban talent competitiveness or not having high urban talent competitiveness both follow a certain interactive logic.
The results of our configuration analysis are unique compared to the existing research on the generation mechanism of urban talent competitiveness. Scholars and the government have generally focused on various factors affecting urban talent competitiveness, such as talent scale, talent innovation, talent efficiency and so on, and urban managers invest lots of energy to attract talents and develop science, technology and culture. Researchers believes that the influence of the factors of talent scale, talent structure, talent development, talent efficiency and talent innovation, are effective for urban talent competitiveness; most studies focus on the correlation coefficient between talent factors and urban competitiveness [17,32,48]. However, research prefers to provide a universal solution; it hardly considers the change of situational factors, and an agreement on how to match these factors more effectively to promote urban talent competitiveness has not been reached [14,18,47]. It is difficult for this universal solution reveal the adequacy or necessity of the factors; the discussion on the effectiveness situation of influence factors still needs to be enriched. In focusing on the generation mechanism of urban talent competitiveness, this study analyzes the interactions between the influencing factors, which has found a reasonable “recipe” between the factors of urban talent competitiveness, which resonates with the findings of several studies. For example, the improvement of urban talent competitiveness not only involves talent innovation and talent efficiency, but also investment in talent development such as culture and education [47], which conforms to the configuration meaning of H2a in the quality improvement mode.
Furthermore, the impact of talent development on urban talent competitiveness improves when the impact of talent living declines; in contrast, the impact of talent living on urban competitiveness improves when the impact of talent development declines. This demonstrates the substitution effect between the two conditions. The configurations of H2a, H2b, H5 and H6 all reflect this substitution relationship. Meanwhile, in configurations H1 and H3a, talent scale and talent living also have a substitution relationship, i.e., when one of the two conditions are present, the other condition can be absent.
In addition, this study further specifies the boundary contexts in which higher level urban talent competitiveness combinations work, i.e., scale and efficiency conditions. For example, migration policy (i.e., providing jobs/increasing income/cultural recreation/environmental benefits) is beneficial in expanding the scale of talents and urban talent development [5]. This talent policy focuses on a dual function; one is the expansion of talent scale, the other is the stimulation of talent efficiency. The configurations of H4, H5 and H6 similarly found synergistic effects between the similar conditions of talent scale and talent efficiency. Additionally, talent scale and talent efficiency are used as the general conditions in urban talent competitiveness configurations of Level A cities, which corroborates the positive effect between talent scale, talent efficiency and talent competitiveness [18].

5.2. Theoretical and Practical Contributions

First, this study focuses on the analysis of the configuration effects from a holistic perspective, and finds the causal relationship between conditional configuration and results based on the case comparison. Previous studies focused on the “net effect” of each variable, analyzing urban talent competitiveness in an isolated way. This research logic may ignore the interdependence of variables and the overall effect. This study analyzes the mechanism of generating urban talent competitiveness based on the QCA method; it effectively makes up the isolation of factors caused by analytical thinking, and builds a more complete cognitive system for generating urban talent competitiveness.
Second, this study uses the QCA method to discover if there is a substitution relationship between the construction conditions of the generated configurations for urban talent competitiveness. Talent development and talent living have a substitution relationship. The educational function contained in talent development, which is closely related to the quality of talent living, makes these two conditions have a substitution relationship under certain circumstances. This finding strengthens the theoretical logic loop between “talent attraction-talent cultivation-talent development-talent retention-talent attraction”. It finds that there are two different ways to generate urban talent competitiveness for resource upgrading, which provides more paths for different cities.
Third, this paper uses the QCA method to analyze the diversity issue of the generation of urban talent competitiveness. Although previous studies have comprehensively analyzed the influencing factors of urban talent competitiveness, this universal solution does not reveal the adequacy or necessity of the factors; it also does not consider the change of situational factors. As a result, the generation theory of urban talent competitiveness cannot give specific and operational strategic suggestions, which has affected the application value of the theory. This study is based on configuration thinking and provides three configuration modes for generating urban talent competitiveness, clarifying the role of each condition in the configuration, and ensuring the effectiveness of each configuration.
Finally, cities should select the generation mode of talent competitiveness based on their own characteristics. Cities that lack educational resources and for the living environment to be improved could focus on innovation, increase investment in scientific research, attract talents through innovative development, and improve the performance of talent competitiveness. Cities with insufficient innovation strength could improve their urban living environment, enhance the quality of education and training and focus on the long-term cultivation and development of talents, gradually promoting the competitiveness of urban talents.

5.3. Research Prospects

This study has some limitations; future research needs to be explored in the following aspects: (1) Explore the generation mechanism of urban talent competitiveness according to different talent types, such as skilled talents, scientific talents and digital talents; (2) Collect more city cases, explore the multiple configurations of generating talent competitiveness in different cities.

Author Contributions

Conceptualization, S.L. and W.W.; Methodology, S.L.; Investigation, S.L. and W.W.; Writing—original draft, S.L.; Writing—review and editing, S.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Hubei Soft Science Research Project (grant number 2022EDA108).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data used during the study are available from the corresponding author by request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Lin, X.; Ren, T.; Wu, H.; Xiao, Y. Housing price, talent movement, and innovation output: Evidence from Chinese cities. Rev. Dev. Eco. 2021, 25, 76–103. [Google Scholar] [CrossRef]
  2. Papa, R.; Gargiulo, C.; Russo, L.; Franco, C. On the relationship between the promotion of environmental sustainability and the increase of territorial competitiveness: The Italian case. Urb. Reg. Sus. 2017, 4, 655–666. [Google Scholar] [CrossRef] [Green Version]
  3. Jin, C.; Li, B.; Jansen, S.J.; Boumeester, H.J.; Boelhouwer, P.J. What attracts young talents? Understanding the migration intention of university students to first-tier cities in China. Cities 2022, 128, 103802. [Google Scholar] [CrossRef]
  4. Green, A.; Hogarth, T. Attracting the best talent in the context of migration policy changes: The case of the UK. J. Ethn. Migr. Stud. 2017, 16, 2806–2824. [Google Scholar] [CrossRef] [Green Version]
  5. Sabrina, S.; Carmela, G. The evolution of urban competitiveness studies over the past 30 years. A bibliometric analysis. Cities 2022, 128, 103811. [Google Scholar]
  6. Boczy, T.; Cefalo, R.; Parma, A.; Nielsen, R.S. Positioning the urban in the global knowledge economy: Increasing competitiveness or inequality. Soc. Incl. 2020, 4, 194–207. [Google Scholar] [CrossRef]
  7. Dima, A.M.; Begu, L.; Vasilescu, M.D.; Maassen, M.A. The relationship between the knowledge economy and global competitiveness in the European Union. Sustainability 2018, 10, 1706. [Google Scholar] [CrossRef] [Green Version]
  8. Forleo, M.B.; Benedetto, G. Creative cities of gastronomy: Towards relationship between city and countryside. Int. J. Gastron Food. Sci. 2020, 22, 100247. [Google Scholar] [CrossRef]
  9. Shi, X.; Chen, Y.; Xia, M.; Zhang, Y. Effects of the Talent War on Urban Innovation in China: A Difference-in-Differences Analysis. Land 2022, 11, 1485. [Google Scholar] [CrossRef]
  10. Nasi, G.; Choi, H.; Cucciniello, M.; Christensen, R.K. A systematic literature review of city competitiveness: A 30-year assessment and future agenda for public administration. Public. Manag. Rev. 2022, 1–25. [Google Scholar] [CrossRef]
  11. Vida, G.; Dudás, G. Geographical context of the revealed competitiveness of urbanised areas in Hungary excluding the Budapest agglomeration. Geogr. Pannonica 2017, 21, 179–190. [Google Scholar] [CrossRef] [Green Version]
  12. MacLachlan, I.; Gong, Y. China’s new age floating population: Talent workers and drifting elders. Cities 2022, 131, 103960. [Google Scholar] [CrossRef]
  13. Shi, Q.; Liu, T.; Zhuo, Y.; Peng, R. People and Places in the 2020 Census: New Geographies of Population Growth in China? Pro. Geo. 2022, 27, 233–247. [Google Scholar] [CrossRef]
  14. Huang, Y. Evaluation of the Impact of the Introduction of Overseas High-Level Talents on the Development of Central Cities with Artificial Neural Network. Secur. Commun. Netw. 2022, 10, 3495179. [Google Scholar] [CrossRef]
  15. Kovács, A.; Zsigmond, T.; Machová, R. Comparison of Municipal Development Plansof Czech Cities. Hradec Econ. Days 2020, 11, 438–448. [Google Scholar]
  16. Lao, X.; Gu, H.; Yu, H.; Xiao, F. Exploring the spatially-varying effects of human capital on urban innovation in China. Appl. Spat. Anal. Polic. 2021, 14, 827–848. [Google Scholar] [CrossRef]
  17. Verginer, L.; Riccaboni, M. Talent goes to global cities: The world network of scientists’ mobility. Res. Policy 2021, 50, 104127. [Google Scholar] [CrossRef]
  18. Cerna, L.; Chou, M.H. Defining “talent”: Insights from management and migration literatures for policy design. Policy Stud. J. 2019, 47, 819–848. [Google Scholar] [CrossRef]
  19. Mujtaba, M.; Mubarik, M.S. Talent management and organizational sustainability: Role of sustainable behaviour. Int. J. Organ. Anal. 2022, 30, 389–407. [Google Scholar] [CrossRef]
  20. Filindra, A.; Goodman, S.W. Studying public policy through immigration policy: Advances in theory and measurement. Policy Stud. J. 2019, 47, 498–516. [Google Scholar] [CrossRef]
  21. Gong, Y.; MacLachlan, I. Housing allocation with Chinese characteristics: The case of talent workers in Shenzhen and Guangzhou. Eurasian Geogr. Econ. 2021, 62, 428–453. [Google Scholar] [CrossRef]
  22. Teo, S.S. Shared projects and symbiotic collaborations: Shenzhen and London in comparative conversation. Urban Stud. 2022, 59, 1694–1714. [Google Scholar] [CrossRef]
  23. Collings, D.G.; Mellahi, K.; Cascio, W.F. Global talent management and performance in multinational enterprises: A multilevel perspective. J. Manag. 2019, 45, 540–566. [Google Scholar] [CrossRef] [Green Version]
  24. Appau, M.W.; Attakora, A.E.; Yakubu, I. Innovation diffusion effects on student housing investment in Ghana. Prop. Manag. 2022, 6, 0043. [Google Scholar] [CrossRef]
  25. Larsson, J.P. Non-routine activities and the within-city geography of jobs. Urban Stud. 2017, 54, 1808–1833. [Google Scholar] [CrossRef]
  26. Chin, J.T. Location choice of new business establishments: Understanding the local context and neighborhood conditions in the United States. Sustainability 2020, 12, 501. [Google Scholar] [CrossRef] [Green Version]
  27. Abebe, G.; Caria, A.S.; Fafchamps, M.; Falco, P.; Franklin, S.; Quinn, S. Anonymity or distance? Job search and labour market exclusion in a growing African city. Rev. Econ. Stud. 2021, 88, 1279–1310. [Google Scholar] [CrossRef]
  28. Bayraktar, O.; Alayoglu, N. A Talent Management Model for Today’s Organizations: The Devshirme System. Amme. Idaresi Derg. 2018, 51, 89–119. [Google Scholar]
  29. Kaliannan, M.; Darmalinggam, D.; Dorasamy, M.; Abraham, M. Inclusive talent development as a key talent management approach: A systematic literature review. Hum. Resour. Manage. Rev. 2022, 5, 100926. [Google Scholar] [CrossRef]
  30. Al, T.A.; Awamleh, R.E.; Khlaifat, S.A. The impact of talent management on achieving strategic objectives: A study on the jordanian pharmaceutical industries. Int. J. Ebusiness Egovernment Stud. 2022, 14, 1–25. [Google Scholar]
  31. Magrizos, S.; Roumpi, D.; Rizomyliotis, I. Talent orchestration and boomerang talent: Seasonally employed chefs’ evaluation of talent management practices. Int. J. Contemp. Manag. 2023, 1, 17571049. [Google Scholar] [CrossRef]
  32. Chagas, J.F.; Fleith, D.S.; Prado, R.M. Development of talent according to Sinti and Calon Romani. Front. Psychol. 2022, 13, 964668. [Google Scholar] [CrossRef] [PubMed]
  33. Rezaei, F.; Beyerlein, M. Talent development: A systematic literature review of empirical studies. Eur. J. Train Dev. 2018, 42, 75–90. [Google Scholar] [CrossRef]
  34. Narayanan, A.; Rajithakumar, S.; Menon, M. Talent management and employee retention: An integrative research framework. Hum. Resour. Dev. Rev. 2019, 18, 228–247. [Google Scholar] [CrossRef]
  35. Jayaraman, S.; Talib, P.; Khan, A.F. Integrated talent management scale: Construction and initial validation. Sage Open 2018, 8, 2158244018780965. [Google Scholar] [CrossRef]
  36. Bonneton, D.; Schworm, S.K.; Festing, M.; Muratbekova, T.M. Do global talent management programs help to retain talent? A career-related framework. Int. J. Hum. Resour. Man. 2022, 33, 203–238. [Google Scholar] [CrossRef]
  37. Olszewski, K.P.; Subotnik, R.; Worrell, F. Domain-specific abilities and characteristics: Evolving central components of the talent development megamodel. High Abil. Stud. 2022, 30, 1–6. [Google Scholar] [CrossRef]
  38. International Institute for Management Development. IMD World Competitiveness Yearbook; International Institute for Management Development: Lausanne, Switzerland, 2021. [Google Scholar]
  39. Aljbour, A.; French, E.; Ali, M. An evidence-based multilevel framework of talent management: A systematic review. Int. J. Product. Perfor. 2022, 71, 3348–3376. [Google Scholar] [CrossRef]
  40. Edward, L.G. Triumph of the City; Penguin Press: London, UK, 2011. [Google Scholar]
  41. Mujtaba, M.; Mubarik, M.S.; Soomro, K.A. Measuring talent management: A proposed construct. Empl. Relat. 2022, 5, 1192–1215. [Google Scholar] [CrossRef]
  42. Swailes, S. Responsible talent management: Towards guiding principles. J. Organ. Eff. People Perform. 2020, 7, 221–236. [Google Scholar] [CrossRef]
  43. Ishiyama, N. The impact of the talent management mechanism and self-perceived talent status on work engagement: The case of Japan. Asia Pac. Bus. Rev. 2022, 28, 536–554. [Google Scholar] [CrossRef]
  44. Edwin, S.M. Handbook of Regional and Urban Economics: Urban Economics; Elsevier: Amsterdam, The Netherlands, 1987. [Google Scholar]
  45. Daghfous, A.; Belkhodja, O. Managing talent loss in the procurement function: Insights from the hospitality industry. Sustainability 2019, 23, 6800. [Google Scholar] [CrossRef] [Green Version]
  46. Center for China & Globalization. Global Talent Flow: Trends and Prospects. 2022. Available online: http://www.ccg.org.cn/ (accessed on 1 February 2023).
  47. Zhang, H.; Deng, T.; Wang, M.; Chen, X. Content analysis of talent policy on promoting sustainable development of talent: Taking Sichuan Province as an example. Sustainability 2019, 11, 2508. [Google Scholar] [CrossRef] [Green Version]
  48. Ye, X.; Fan, Y.; Miao, J.; He, Z. The Competitiveness of Regional Urban System in Hubei Province of China. Land 2022, 11, 879. [Google Scholar] [CrossRef]
  49. Jeffrey, L.; David, M. Toyota Talent: Developing Your People the Toyota Way; McGraw Hill: New York, NY, USA, 2007. [Google Scholar]
  50. Li, Y.; Wang, G.; Zuo, J. Assessing Green-Building Policies with Structural Consistency and Behavioral Coherence: A Framework of Effectiveness and Efficiency. J. Constr. Eng. Manag. 2021, 147, 04021149. [Google Scholar] [CrossRef]
  51. Harvard Business Review. The Harvard Business Review Entrepreneur’s Handbook; Harvard Business Review Press: Cambridge, MA, USA, 2018; pp. 80–91. [Google Scholar]
  52. United Nations Development Programme. Human Development Report 2021–2022. 2022, pp. 5–6. Available online: http://hdr.undp.org (accessed on 2 February 2023).
  53. World Economic Forum. The Global Competitiveness Report 2019. Available online: https://www.weforum.org/reports/how-to-end-a-decade-of-lost-productivity-growth?DAG=3&gclid=EAIaIQobChMIwZnGtayH_QIVyFBgCh06EAkXEAAYASAAEgI7TfD_BwE (accessed on 3 February 2023).
  54. Fiss, P.C. A set-theoretic approach to organizational configurations. Acad. Manage. Rev. 2007, 32, 1180–1198. [Google Scholar] [CrossRef] [Green Version]
  55. Fiss, P.C. Building better causal theories: A fuzzy set approach to typologies in organization research. Acad. Manage. J. 2011, 54, 393–420. [Google Scholar] [CrossRef] [Green Version]
  56. Ragin, C.C. From fuzzy sets to crisp truth tables. In Working Paper; University of Arizona: Tucson, AZ, USA, 2005; Available online: http://www.compasss.org/Raginfztt_April05.pdf (accessed on 10 January 2023).
  57. Bagheri, M.; Tuan, K.A.; Jaafar, M.; Adam, R. Integrating Social, Economic, and Environmental Factors to Evaluate How Competitive Urban Landscapes Are for the Development of Sustainable Cities: Penang Island in Malaysia as a Case Study. Land 2023, 12, 104. [Google Scholar] [CrossRef]
  58. Ragin, C.C. Set relations in social research: Evaluating their consistency and coverage. Polit. Anal. 2006, 14, 291–310. [Google Scholar] [CrossRef]
  59. Rihoux, B.; Ragin, C.C. Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques; Sage: Los Angeles, CA, USA, 2009; Volume 15, pp. 395–400. [Google Scholar]
  60. Rutten, R. Uncertainty, possibility, and causal power in QCA. Sociol. Method. Res. 2021, 27, 00491241211031268. [Google Scholar] [CrossRef]
  61. Yu, X.G. Analysis Report on Human Resource Market in China; Social Sciences Academic Press: Beijing, China, 2022. [Google Scholar]
  62. Mohammadi, M.S.; Sabbaghi, N.R.; Mohammadi, M. Innovative model for ternary QCA gates. IET Circ. Device. Syst. 2018, 2, 189–195. [Google Scholar] [CrossRef]
  63. Akbari, H.R.; Sabbaghi, N.R.; Tanhayi, M.R. New polarization and power calculations with error elimination in ternary QCA. Comput. Electr. Eng. 2021, 96, 107557. [Google Scholar] [CrossRef]
  64. Ragin, C.C. Redesigning Social Inquiry: Fuzzy Sets and Beyond; Wiley Online Library: Hoboken, NJ, USA, 2008. [Google Scholar]
  65. Wei, H.; Yuan, R.; Zhao, L. International talent inflow and R&D investment: Firm-level evidence from China. Econ. Model. 2020, 89, 32–42. [Google Scholar]
Sustainability 15 06120 g001 550
Figure 1. Research framework on the generation of urban talent competitiveness.
Figure 1. Research framework on the generation of urban talent competitiveness.
Sustainability 15 06120 g001
Table
Table 1. Correlation indicators and preliminary data of talent competitiveness in 24 cities.
Table 1. Correlation indicators and preliminary data of talent competitiveness in 24 cities.
NO.CityTalent
Scale		Talent
Structure		Talent
Innovation		Talent
Development		Talent
Efficiency		Talent
Living
1Beijing0.11970.05040.09050.16680.11740.1061
2Shanghai0.13390.02180.08850.13770.13760.1005
3Shenzhen0.13140.03300.10890.09300.09720.1008
4Guangzhou0.08720.04660.06590.09430.07490.0958
5Chongqing0.09490.01110.02140.11950.07350.0887
6Chengdu0.10320.00540.05230.07360.04390.0673
7Suzhou0.05890.00890.07460.04030.06530.0831
8Hangzhou0.05560.01330.05420.06300.04540.0903
9Nanjing0.02250.01340.05030.06180.04770.0938
10Tianjin0.05180.0150 0.02890.08350.03470.0674
11Changsha0.05140.00880.02640.05810.02220.1086
12Wuhan0.03320.01480.04930.08040.03980.0527
13Qingdao0.03780.0140 0.03340.0530 0.02280.0865
14Xi’an0.0590 0.01720.04270.05450.01930.0482
15Hefei0.03240.01220.05290.04710.01890.0694
16Ningbo0.03770.01040.04310.03340.02610.0798
17Dongguan0.03740.01690.02620.01660.0280 0.0897
18Jinan0.02990.01110.02360.03730.01360.0923
19Zhengzhou0.05210.0100 0.01920.05440.02190.0343
20Wuxi0.02940.0080 0.03470.01690.02080.0722
21Quanzhou0.02450.01560.01240.03260.0290 0.0620
22Foshan0.02240.00900.03290.01590.02790.0564
23Nantong0.02450.00390.02380.02950.01200.0667
24Fuzhou0.01520.01050.01790.04040.15200.0602
Table
Table 2. Cases of 24 cities in China mainland.
Table 2. Cases of 24 cities in China mainland.
A level Beijing; Shanghai; Shenzhen; Guangzhou; Chongqing; Chengdu; Suzhou; Hangzhou
B level Nanjing; Tianjin; Changsha; Wuhan; Qingdao; Xi’an; Hefei; Ningbo
C level Dongguan; Jinan; Zhengzhou; Wuxi; Quanzhou; Foshan; Nantong; Fuzhou
Table
Table 3. Variable selection and data coding.
Table 3. Variable selection and data coding.
Variable Name MeasurementAssignment Criteria
Talent scale
(TC)		Number of employee Rank at or above the middle level is 1; otherwise it is 0
permanent population
Employees in the secondary industry
Employees in the tertiary industry
Talent structure
(TS)		Proportion of employees in secondary and tertiary industriesRank at or above the middle level is 1; otherwise is 0
Finance, technology and R&D personnel
Number of college students
Talent innovation
(TI)		R&D fundingRank at or above the middle level is 1; otherwise is 0
Proportion of R&D expenditure in GDP
Per capita financial expenditure on science and technology
Patent authorization
Talent development
(TD)		Unemployment rateRank at or above the middle level is 1; otherwise is 0
Number of universities
Number of secondary vocational schools
Education expenditure per 10,000 people
Talent efficiency
(TE)		GDPRank at or above the middle level is 1; otherwise is 0
Per capita retail sales
Growth rate of fixed investment
Industrial output value
Balance of loans from financial institutions
Talent living
(TL)		Per capita incomeRank at or above the middle level is 1; otherwise is 0
Per capita consumption expenditure
Number of health institutions per 10,000 people
Number of buses per 10,000 people
Per capita green area
Public library collections per 10,000 people
Social security participation rate
Urban talent
competitiveness (UTC)		-Cities of Levels A and B is 1;
Level C is 0
Table
Table 4. Truth table.
Table 4. Truth table.
TCTSTITDTETLNumberUTC
11111151
11110021
10111011
11111011
01110111
11110111
10101111
11101111
10011111
11011111
01111111
00010010
01001010
00101010
10011010
00000110
01000110
00100110
11001110
Table
Table 5. Necessary condition test.
Table 5. Necessary condition test.
Conditional
Variable		High UTCNot High UTC
ConsistencyCoverageConsistencyCoverage
TC0.8750000.8750000.2500000.125000
~TC0.1250000.2500000.7500000.750000
TS0.8125000.8125000.3750000.187500
~TS0.1875000.3750000.6250000.625000
TI0.8750000.8750000.2500000.125000
~TI0.1250000.2500000.7500000.750000
TD0.8750000.8750000.2500000.125000
~TD0.1250000.2500000.7500000.750000
TE0.7500000.7500000.5000000.250000
~TE0.2500000.5000000.5000000.500000
TL0.7500000.7500000.5000000.250000
~TL0.2500000.5000000.5000000.500000
Note: The conditions and outcome with the symbol ‘~’ mean low, and the ones without ‘~’ mean high. For example, TC means high talent scale, ~TC means low talent scale.
Table
Table 6. Configurations of promoting the generation of urban talent competitiveness.
Table 6. Configurations of promoting the generation of urban talent competitiveness.
ConfigurationInnovation Leading
Mode		Quality Improvement
Mode		Resource Competitive
Mode
H1H2aH2bH3aH3b
TCSustainability 15 06120 i001Sustainability 15 06120 i001Sustainability 15 06120 i001 Sustainability 15 06120 i002
TSSustainability 15 06120 i002 Sustainability 15 06120 i002
TISustainability 15 06120 i001Sustainability 15 06120 i001Sustainability 15 06120 i001Sustainability 15 06120 i002Sustainability 15 06120 i004
TDSustainability 15 06120 i002Sustainability 15 06120 i002Sustainability 15 06120 i004Sustainability 15 06120 i001Sustainability 15 06120 i001
TE Sustainability 15 06120 i002Sustainability 15 06120 i002 Sustainability 15 06120 i002
TL Sustainability 15 06120 i004Sustainability 15 06120 i001Sustainability 15 06120 i002Sustainability 15 06120 i001
consistency11111
coverage0.56250.1250.1250.50.125
unique coverage0.1250.06250.1250.1250.125
solution coverage1
solution consistency1
Note: Sustainability 15 06120 i001 indicates the existence of core conditions; Sustainability 15 06120 i002 indicates the existence of edge conditions; Sustainability 15 06120 i004 indicates the absence of edge conditions; the space indicates that the condition can either appear or be absent, which is irrelevant.
Table
Table 7. Configurations of hindering the generation of urban talent competitiveness.
Table 7. Configurations of hindering the generation of urban talent competitiveness.
ConfigurationN1N2N3N4N5N6N7
TCSustainability 15 06120 i003Sustainability 15 06120 i003Sustainability 15 06120 i003Sustainability 15 06120 i003Sustainability 15 06120 i003Sustainability 15 06120 i002Sustainability 15 06120 i002
TS Sustainability 15 06120 i003Sustainability 15 06120 i003Sustainability 15 06120 i002Sustainability 15 06120 i003Sustainability 15 06120 i004Sustainability 15 06120 i002
TISustainability 15 06120 i003 Sustainability 15 06120 i003Sustainability 15 06120 i003Sustainability 15 06120 i002Sustainability 15 06120 i003Sustainability 15 06120 i003
TDSustainability 15 06120 i003Sustainability 15 06120 i003Sustainability 15 06120 i002Sustainability 15 06120 i003Sustainability 15 06120 i003Sustainability 15 06120 i002Sustainability 15 06120 i003
TESustainability 15 06120 i003Sustainability 15 06120 i004Sustainability 15 06120 i004Sustainability 15 06120 i002Sustainability 15 06120 i002Sustainability 15 06120 i002Sustainability 15 06120 i002
TLSustainability 15 06120 i002Sustainability 15 06120 i002Sustainability 15 06120 i003Sustainability 15 06120 i003Sustainability 15 06120 i004Sustainability 15 06120 i003Sustainability 15 06120 i002
consistency1111111
coverage0.250.250.1250.1250.1250.1250.125
unique coverage0.1250.1250.1250.1250.1250.1250.125
solution coverage1
solution consistency1
Note: Sustainability 15 06120 i002 indicates the existence of edge conditions; Sustainability 15 06120 i003 indicates the absence of core condition; Sustainability 15 06120 i004 indicates the absence of edge conditions; the space indicates that the condition can either appear or be absent, which is irrelevant.
Table
Table 8. Results of stability analysis.
Table 8. Results of stability analysis.
ConfigurationH4H5H6
TCSustainability 15 06120 i001Sustainability 15 06120 i001Sustainability 15 06120 i001
TSSustainability 15 06120 i001Sustainability 15 06120 i003Sustainability 15 06120 i003
TI Sustainability 15 06120 i001Sustainability 15 06120 i001
TDSustainability 15 06120 i001Sustainability 15 06120 i002Sustainability 15 06120 i004
TESustainability 15 06120 i001Sustainability 15 06120 i002Sustainability 15 06120 i002
TLSustainability 15 06120 i001Sustainability 15 06120 i004Sustainability 15 06120 i002
consistency111
coverage0.750.1250.125
unique coverage0.750.1250.125
solution coverage1
solution consistency1
Note: Sustainability 15 06120 i001 indicates the existence of core conditions; Sustainability 15 06120 i002 indicates the existence of edge conditions; Sustainability 15 06120 i003 indicates the absence of core condition; Sustainability 15 06120 i004 indicates the absence of edge conditions; the space indicates that the condition can either appear or be absent, which is irrelevant.
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Liu, S.; Wang, W. Research on the Generating Mechanism of Urban Talent Competitiveness Based QCA Method: A Configurational Analysis of 24 Chinese Cities. Sustainability 2023, 15, 6120. https://doi.org/10.3390/su15076120

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Liu S, Wang W. Research on the Generating Mechanism of Urban Talent Competitiveness Based QCA Method: A Configurational Analysis of 24 Chinese Cities. Sustainability. 2023; 15(7):6120. https://doi.org/10.3390/su15076120

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Liu, Sike, and Wuyi Wang. 2023. "Research on the Generating Mechanism of Urban Talent Competitiveness Based QCA Method: A Configurational Analysis of 24 Chinese Cities" Sustainability 15, no. 7: 6120. https://doi.org/10.3390/su15076120

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