The Influential Factors on the Attraction of Outstanding Scientific and Technological Talents in Developed Cities in China

Abstract

With the rapid development of science and technology, the demand for talents in developed countries and regions has been focused on the outstanding scientific and technological talents. How to reflect the city’s attraction and effectively implement related policies to attract, cultivate, and retain outstanding scientific and technological talents has become one of the essential development strategies. This paper investigates the factors attracting outstanding scientific and technological talents in developed cities in China with an evaluation system composed by 8 sub-criteria indicators and 25 index indicators. The results show that the urban ecological attraction level is much higher than that of human resource ecological development. A good public service system and a standardized management platform are important factors influencing the concentration of top talents. In addition, it is important to provide related policies to support innovative companies to create a better environment that can tolerate failures.

1. Introduction

In the knowledge-based economy and information technology era, talent is the key to solve the development bottleneck, facilitate urban development, and enhance international competitiveness. The whole world is competing to get top talent. Major city governments recruit the best talent by directly purchasing the global services of executive search firms. For example, London spends £2.2 million per year on Hays, a top global HR management firm headquartered in London that focuses on the recruitment and management of global talent. By the end of 2020, India has issued more than 15 million Overseas Indian Citizenship Certificates and Indian Origin Cards, which have greatly facilitated the return and circulation of Indian talent from abroad. In recent years, China’s developed cities, such as Beijing, Shanghai, Shenzhen, and Guangzhou, have had the advantage in attracting talent. The developed market economy, convenient business environment, efficient government services, good innovation industry chain, and suitable living environment have significantly increased the growth rate of outstanding scientific and technological talents. Hence, top local talents keep emerging in these areas.

Scholars generally emphasize the following standards for talent: intelligence, physical strength, ability, education and professional qualifications titles, etc. According to the National Medium and Long-term Talent Development Planning Outline (2010–2020) in China, talent refers to a person with professional knowledge or expertise who performs innovative work and contributes to society. Talents are competent and qualified workers in human resources. Outstanding scientific and technological talents are those with higher education backgrounds, richer professional knowledge and skills, better innovative thinking and creative ability, or those who have scientific and technological expertise. They are engaged in scientific and technological industries, and can make great contributions to the progress of science and technology and the development of the economy and society [1].

Attracting, developing, and retaining talent is one of the most critical challenges today [2]. Extensive studies on how to promote the attraction and make or adjust corresponding policies of talents have been conducted, such as the development of CSR (corporate social responsibility) practices by companies located in technology parks [3], and adapting employer-driven and merit-based systems in immigration policy [4].

With the rapid development of science and technology, the demand for talents in the country and region has gradually changed to an innovative development mode that relies on excellent scientific and technological talents. The innovation and progress of scientific and technological development have become critical factors in urban development. How to reflect the city’s attraction to outstanding scientific and technological talents has become an important link in today’s socio-economic development, despite existing studies on technology and talent [5,6,7,8,9]. How to effectively implement dedicated policies to attract, cultivate, and retain talents has become one of the essential development strategies for every country and region to effectively realize the gathering and cultivation of excellent scientific and technological talents. Hence, this paper investigates the factors attracting scientific and technological talents to developed cities, which could provide references for developed cities to improve their attraction level to outstanding talents.

Many factors affect the attraction of developed cities to scientific and technological talents. The level of technological development, cultural atmosphere, scientific research conditions, infrastructure, and medical security are all key elements affecting the flow of scientific and technological talents [10,11,12,13,14,15]. In the quantitative study of measuring cities’ attractiveness of talent factors, scholars often use methods such as analytic hierarchy process (hereinafter AHP), factor analysis, grey correlation degree, entropy value, and regression model, etc. [16,17,18,19].

Mobley constructed the analysis model of talent flow influence factors and laid the theoretical foundation [20]. Henderson and Jacques-François believed that the key to attract talent is to meet work and life goals in the area where talent is concentrated [21]. Guo and Zhu built a new talent attraction evaluation system based on talent input, urban environment, and scientific research achievements. The main components of this system include 18 three-level indicators such as GDP per capita, number of patents, number of scientific and technological practitioners, residents’ consumption level, and urban green space construction [22].

Based on the theory of talent attraction and talent flow, Chen and Song analyzed the evaluation index system of talent attraction in western China via an expert questionnaire and AHP [23]. He and Yang constructed the evaluation index system of the city’s comprehensive attraction to the scientific and technological innovation talents with five first-level indexes, including natural ecological environment, economic development level, social living environment, public service level, and scientific and educational innovation environment [17]. The 2019 Global Urban Talent Competitiveness Index includes critical indexes such as the city’s endowment conditions, attractiveness, growth, and globalization. According to the International Talent Attraction Index jointly released by the Shanghai Academy of Sciences “Overseas Talent Research” team and the Shanghai Pudong Science and Technology Innovation Promotion Center, the international attraction of regional innovation centers is evaluated in terms of urban governance, economic development, regional positioning, scientific and technological innovation, talent status and living environment.

Though there are extensive studies on the factors that can influence urban talent attraction, there are few studies on the attraction of scientific and technological talents. Additionally, the indicators selected by scholars in the evaluation model of the factors influencing the attractiveness of urban talents tend to be universal and are not well-targeted on the attraction of urban scientific and technological talents, especially outstanding ones. Therefore, it is of theoretical and practical significance to investigate the factors influencing the attractiveness of excellent scientific and technological talents in developed cities, so that developed cities can accurately grasp the trend of talent flow, establish corresponding attraction evaluation systems, and formulate effective talent attraction policies.

2. An Evaluation Model of the Attraction of Developed Cities to Outstanding Scientific and Technological Talents

In terms of the factors affecting outstanding scientific and technological talents, Maslow’s hierarchy of needs emphasizes that the physical, safety, social, respect and self-actualization needs of outstanding technological talents should be effectively addressed to provide a reliable basis for their protection. Talent’s self-value and innovation can be effectively played and displayed [24,25]. Some researchers think that the flow of scientific and technological talents is affected by economic, political, cultural, and social factors, among which the economic factor is the primary one [26,27]. Cui et al. think the city’s economic level, living environment, social security, and other factors impact talent attractiveness [28].

Hence, this paper establishes a target-level index of the attraction of developed cities to outstanding scientific and technological talents. The urban ecological attraction and the talented person ecological development attraction are two-criteria layer indexes. With references to above-mentioned literature, 8 secondary rule layer indicators and 25 indicator-level indicators are constructed. The secondary rule layer indicators consist of economic development, living environment quality, public medical service, basic social security, science and technology development, talent value realization, educational innovation carrier, and humanistic ecological environment. There are 25 indicators, including the proportion of the primary sector of the economy output value, the total energy consumption per unit area, the rate of excellent and good environmental air quality, and the number of doctors per 10,000 people. Finally, the evaluation system of influencing factors for attracting outstanding scientific and technological talents in developed cities is constructed, which is described in

Table 1. Evaluation system of the attractiveness of developed cities to outstanding scientific and technological talents.

Target Layer	Criterion Layer	Secondary Rule Layer	Index Layer
The attraction of developed cities to outstanding scientific and technological talents (V)	Urban ecological attraction (S1)	Level of economic development (T1)	Primary sector of the economy (F1)
			Secondary sector of the economy (F2)
			Tertiary sector of the economy (F3)
			Total energy consumption per unit area (F4)
			Output value per unit of electricity consumption (F5)
			GDP per capita (F6)
			GDP growth rate (F7)
		Quality of living environment (T2)	Good rate of ambient air quality (F8)
			Per capita daily water consumption (F9)
			Per capita area of park green space (F10)
		Public health services (T3)	There are doctors for every 10,000 people (F11)
			The number of beds per 10,000 people (F12)
		Basic Social Security (T4)	Per capita area of urban residents (F13)
			Average selling price of commercial housing (F14)
			Participation rate of endowment insurance (F15)
			Unemployment insurance participation rate (F16)
			Participation rate of urban medical insurance (F17)
	The attraction of ecological development of talents (S2)	Level of scientific and technological development (T5)	R&D internal expenditure as a percentage of GDP (F18)
			Number of research and development institutions (F19)
		The realization of the value of talents (T6)	Number of patents granted per 10,000 population (F20)
			The number of books per capita (F21)
		Carrier of educational innovation (T7)	Number of institutions of higher learning (F22)
			Teacher Resources in institutions of higher learning (F23)
		Human ecological environment (T8)	Proportion of cultural and fiscal expenditure (F24)
			Proportion of urban residents’ expenditure on culture and entertainment (F25)

.

In the process of constructing the attraction model of developed cities to outstanding scientific and technological talents, the accuracy of the indexes’ weight plays a decisive role in the evaluation results. To establish a scientific and reasonable evaluation system of talent attraction, this paper applies AHP to determine the weight of each index and construct the target layer (V), criterion layer (S), secondary rule layer (T), and index layer (F) in the system hierarchical grouping. The level indicates the attractiveness of developed cities to outstanding scientific and technological talents are shown in Table 1.

Based on the above factors, the attractiveness of developed cities to outstanding scientific and technological talents is shown, and 25 indicators of the index layer are selected. Relevant public data as the original data for the evaluation model are collected from the statistical yearbooks of Beijing, Shanghai, Shenzhen, Guangzhou, and other developed cities in China between 2016 and 2020. The evaluation system of the attractiveness of cities to excellent scientific and technological talents is designed. The original data is screened and processed, and linear functions normalize the indicators to ensure the scientific comparison and analysis of the indicators.

Considering that the dimensions of the original data collected are not uniform, it is normalized to facilitate further processing and analysis. The formula of linear function normalization is as

$$X_i^{\prime }=\frac{X_i-X_{min}}{X_{max}-X_{min}}$$

Among them, $X_{max}$ is the maximum value in the sample under the metric, $X_{min}$ is the minimum value in the sample under the metric, $X_i$ is the original value of the sample, $X_i^{\prime }$ is the normalized value.

After the normalization process, the index influences zero value. The above-mentioned linear function normalization formula is simplified, and $X_i^{\prime }=\frac{X_i}{X_{max}}$ is used as the normalized formula for raw data processing. Hence, an evaluation system of attracting factors for outstanding scientific and technological talents is constructed.

The analytic hierarchy process (AHP) is a decision-making tool that helps to prioritize and make complex decisions by breaking them down into smaller, more manageable components. It is widely used in business, engineering, and social sciences as a reliable method for decision making [29,30]. In this paper, the sum-product method of the AHP is used to calculate the weights of the elements associated with the index layer. The index weight of each element in this level is obtained, reflecting the relative importance of these interconnected elements. Note that $s_{ij}$ is the index element. The concrete steps of calculating the index weight of each element in the index layer by the sum-product method are as follows.

The first step is to normalize the columns in the index layer under the sub-criteria layer and get $\stackrel{-}{s_{ij}}=\frac{s_{ij}}{\sum _{k=1}^n{s}_{kj}},i,j=\mathrm{1,2},\dots ,n$. The second step is to add the index layer weights obtained by normalizing each column in rows and get $\stackrel{-}{W_i}=\sum _{j=1}^n\stackrel{-}{s_{ij}}(i,j=\mathrm{1,2},\dots ,n)$. Finally, the vector $\stackrel{-}{W}=\begin{array}{ccc}[& W_1& \begin{array}{ccc}{W}_2& \dots & W_n\end{array}\end{array}{]}^T$ is normalized as $W=\frac{\stackrel{-}{W_i}}{\sum _{j=1}^n\stackrel{-}{W_j}}(i,j=\mathrm{1,2},\dots ,n)$. Then, the element weight is obtained.

Based on the determination and calculation of the indexes and weights of each element in the index layer, the index weight of the sub-criterion level and the index weight of the criterion level in the evaluation system of the attractiveness of the outstanding scientific and technological talents in developed cities are mainly assigned by the method of gradient order. The gradient order of the sub-criterion layer is “Level of economic development (0.15) > Level of scientific and technological development (0.14) > The realization of the value of talents (0.13) > Quality of living environment (0.12) = Basic Social Security (0.12) = Human ecological environment (0.12) > Public health services (0.11) = Carrier of educational innovation (0.11)”. The gradient order of the criterion layer is “The attraction of ecological development of talents (0.60) > Urban ecological attraction (0.40)”.

Therefore, in the calculation of the sub-criteria layer and criteria layer evaluation value, the evaluation value of the index layer is multiplied by the weight of the index layer and summed to obtain the evaluation value of the sub-criteria layer. The evaluation value of the sub-criteria layer is multiplied by the weight of the sub-criteria layer and summed to obtain the evaluation value of the criteria layer. The evaluation value of the criterion layer is multiplied by the weight of the criterion layer and summed to obtain the final evaluation value of the target layer. The specific formula is $S_i=\sum T_{ij}\times w_{ij}^T$, $V=\sum S_{ij}\times w_{ij}^S$, where $T_{ij}$ is the evaluation value of the index layer; $S_{ij}$ is the evaluation value of the sub-criteria layer. i denotes the ith indicator, and j denotes the jth indicator of the ith indicator.

3. Analysis and Comparison of the Attraction of Developed Cities to Outstanding Scientific and Technological Talents

The statistical yearbook is a tool that provides a comprehensive, systematic, and continuous record of annual economic, social, and other developments through highly dense statistical data, mainly in the form of statistical charts and analytical descriptions. The commonly used statistical yearbooks in China are the China Statistical Yearbook and the provincial (municipal) statistical yearbooks. Hence, the raw data is extracted from the statistical yearbook and related public information of the above-mentioned developed cities represented by Beijing, Shanghai, Shenzhen, and Guangzhou in China during the year between 2016 and 2020 [31,32,33,34]. We choose Shenzhen as a representative case for modeling and research. The related data of Shenzhen from 2016 to 2020 is extracted and processed in the index level. Specific results are shown in

Table 2. Evaluation of Shenzhen’s attractiveness to outstanding scientific and technological talents.

Index Layer	2016	2017	2018	2019	2020
Primary sector of the economy (F1)	0.1783	0.2189	0.1999	0.2010	0.2019
Secondary sector of the economy (F2)	0.1944	0.1895	0.2042	0.1989	0.2131
Tertiary sector of the economy (F3)	0.1960	0.1871	0.1975	0.2042	0.2152
Total energy consumption per unit area (F4)	0.1794	0.1818	0.2015	0.2114	0.2259
Output value per unit of electricity consumption (F5)	0.2180	0.1883	0.1923	0.1965	0.2050
GDP per capita (F6)	0.1770	0.1812	0.2008	0.2106	0.2304
GDP growth rate (F7)	0.2812	0.2798	0.2046	0.1670	0.0674
Good rate of ambient air quality (F8)	0.1981	0.1966	0.2013	0.1979	0.2061
Per capita daily water consumption (F9)	0.1940	0.1986	0.1997	0.2031	0.2046
Per capita area of park green space (F10)	0.2084	0.2050	0.1990	0.1989	0.1887
There are doctors for every 10,000 people (F11)	0.1989	0.2027	0.2026	0.2046	0.1911
The number of beds per 10,000 people (F12)	0.2012	0.1970	0.1971	0.1949	0.2098
Per capita area of urban residents (F13)	0.1895	0.1891	0.2055	0.2085	0.2074
Average selling price of commercial housing (F14)	0.2002	0.2050	0.2001	0.2000	0.1947
Participation rate of endowment insurance (F15)	0.2029	0.2015	0.1979	0.1979	0.1998
Unemployment insurance participation rate (F16)	0.2039	0.2025	0.1988	0.1962	0.1985
Participation rate of urban medical insurance (F17)	0.2030	0.2015	0.1979	0.1977	0.1999
R&D internal expenditure as a percentage of GDP (F18)	0.2232	0.2072	0.1946	0.1923	0.1827
Number of research and development institutions (F19)	0.1720	0.1913	0.2065	0.2094	0.2209
Number of patents granted per 10,000 population (F20)	0.1562	0.1676	0.2077	0.2209	0.2476
The number of books per capita (F21)	0.2297	0.2219	0.1948	0.1858	0.1678
Number of institutions of higher learning (F22)	0.2213	0.2086	0.1963	0.1904	0.1835
Teacher Resources in institutions of higher learning (F23)	0.1744	0.1897	0.2045	0.2115	0.2198
Proportion of cultural and fiscal expenditure (F24)	0.1658	0.1690	0.1906	0.1875	0.2872
Proportion of urban residents’ expenditure on culture and entertainment (F25)	0.2251	0.2228	0.2069	0.2092	0.1359

.

From Table 2, public medical service, basic social security, science and technology development level, talent value realization, educational innovation carrier, and humanistic ecological environment all show that Shenzhen maintains a robust level of quality of the living environment. The overall level of economic development shows a slight downward trend. Generally, the special administration’s economic policies and the government’s talent attraction strategy have boosted the rapid and efficient accumulation of outstanding scientific and technological talents in Shenzhen in recent years. As the “innovation capital” and the national economic city center, Shenzhen has been vigorously promoting the implementation of the talent strategy. The evaluation value of the sub-criteria layer on the attraction of outstanding scientific and technological talents in Shenzhen is detailed in

Table 3. The evaluation value of the sub-criteria for attracting outstanding scientific and technological talents in Shenzhen.

Secondary Rule Layer	2016	2017	2018	2019	2020
Level of economic development (T1)	1.3398	1.3827	1.2581	1.2392	1.1850
Quality of living environment (T2)	0.5967	0.5822	0.5718	0.5596	0.5754
Public health services (T3)	0.3253	0.3276	0.3406	0.3648	0.4009
Basic Social Security (T4)	0.9464	0.9534	0.9702	0.9914	0.9957
Level of scientific and technological development (T5)	0.2924	0.3051	0.3445	0.3543	0.4036
The realization of the value of talents (T6)	0.2605	0.2830	0.3185	0.3487	0.4154
Carrier of educational innovation (T7)	0.2880	0.3022	0.3474	0.3589	0.4033
Human ecological environment (T8)	0.3149	0.2942	0.3177	0.3093	0.3747

.

On the same basis, this paper further analyzes and obtains the evaluation value of the attractiveness of Beijing, Shanghai, and Guangzhou to outstanding scientific and technological talents, mainly for the city ecology attraction and the talented person ecology development attraction. The attraction of developed cities to outstanding scientific and technological talents is a target level index; details can be found in

Table 4. Evaluation of the attractiveness of developed cities to outstanding scientific and technological talents.

Criterion Layer	City	2016	2017	2018	2019	2020
Urban ecological attraction (S1)	Shenzhen	0.4219	0.4277	0.4112	0.4121	0.4104
	Shanghai	0.4015	0.4069	0.4174	0.4106	0.4120
	Beijing	0.4092	0.4190	0.4297	0.4192	0.4215
	Guangzhou	0.4112	0.4026	0.3844	0.4223	0.3921
The attraction of ecological development of talents (S2)	Shenzhen	0.1443	0.1481	0.1660	0.1715	0.1998
	Shanghai	0.1684	0.1730	0.1750	0.1845	0.1918
	Beijing	0.1675	0.1686	0.1738	0.1917	0.1897
	Guangzhou	0.1647	0.1675	0.1765	0.1841	0.1911

and

Table 5. Target evaluation of the attractiveness of developed cities to outstanding scientific and technological talents.

Target Layer	City	2016	2017	2018	2019	2020
The attraction of developed cities to outstanding scientific and technological talents (V)	Shenzhen	0.2553	0.2599	0.2641	0.2678	0.2841
	Shanghai	0.2617	0.2666	0.2720	0.2749	0.2799
	Beijing	0.2642	0.2687	0.2761	0.2827	0.2824
	Guangzhou	0.2633	0.2615	0.2596	0.2794	0.2715

.

In Table 4, in terms of the overall evaluation value of the criteria layer, the evaluation level of urban ecological attraction is much higher than that of human resource ecological development, which shows that the economic level and living environment are the basic guarantees to attract and retain talents. In the aspect of urban ecological attraction, Shenzhen and Beijing maintain a relatively high level of valuation, which shows that Shenzhen and Beijing attach great importance to the urban economic level, the living environment, and basic social security. However, in the talent ecology development attraction aspect, Shanghai maintains the highest valuation level. In fact, Shanghai has indeed attracted a lot of talent in recent years by lowering the threshold for settling in the city. In addition, because Shanghai has a more open and inclusive environment, it is easier to travel and connect with other developed cities, which also increases its attractiveness to talents.

As shown in Table 4 and Table 5, Shenzhen possesses the attraction for outstanding scientific and technological talents but does not have sufficient advantages and competitiveness in retaining talents. In recent years, Shenzhen has adopted a series of development strategies and policies to attract talent while maintaining a high level of ecological attraction. It also quickly raises the level of ecological development attraction, which leads to a leading position in evaluating the attractiveness of developed cities to outstanding scientific and technological talents by 2020 for Shenzhen. Although Beijing does not have an absolute advantage in the attraction of urban ecology and the attraction of talent ecology development, it dominates in the comprehensive evaluation value among the four cities. The results also show that Beijing has the advantages of attracting and retaining talents in urban construction and talent self-realization. Compared with other developed cities, Beijing, as the capital of China, is in a leading position among many cities. In addition, because of the advantageous political environment, big technology companies tend to set up their headquarters in Beijing, such as Xiaomi, Baidu, Byte Jump and so on, which has also contributed to the gathering of talents.

4. Discussion and Conclusions

Outstanding scientific and technological talents are crucial to the development of the city. This paper mainly investigates the attraction factors of developed cities in China to outstanding science and technology talents. An evaluation system that includes one target-level indicator (the attraction of developed cities to outstanding scientific and technological talents) and two criteria-level indicators (urban ecological attraction, the attraction of ecological development of talents) is constructed via AHP. According to the data collected and processed from Beijing, Shanghai, Shenzhen, Guangzhou and other cities, we proposed 8 sub-criteria indicators such as level of economic development, quality of living environment, public health services, basic social security, level of scientific and technological development, the realization of the value of talents, carrier of educational innovation, human ecological environment, and 25 index indicators such as the proportion of the primary sector of the economy output value, the total energy consumption per unit area, the rate of excellent and good environmental air quality, and the number of doctors per 10,000 people. The conclusions are as follows:

(1)

A good public service system is an important factor that affects the concentration of science and technology talents. In terms of the overall evaluation value of the criteria layer, the evaluation level of urban ecological attraction is much higher than that of human resource ecological development, which shows that the economic level and living environment are the basic guarantees to attract and retain talents. Hence, it is quite necessary to make a personalized public service plan according to the demands of different types of scientific and technological talents, which may effectively promote the introduction work to be more efficient and professional. High-end talents from home and abroad need a good scientific research environment, an innovative scientific atmosphere, and the realization of their value.

In addition, a standardized management platform which encourages the scientific and technological talents to expand their knowledge reserves and grow together is needed, but for the young and outstanding scientific and technological talents who have just graduated, financial support is the most powerful means to attract them. Therefore, it is necessary to combine the needs of the top talents of all ages and build market-oriented and efficient life service facilities and security systems to enhance the cities’ attractive advantages.

(2)

It is worth mentioning that in addition to the political environment provided by the government, the culturally inclusive environment of companies is also a very important factor in attracting talents. Creating a good environment in enterprises that encourage innovation and tolerate failures can open new paths for cultivating outstanding scientific and technological talents. Hence, it is important to provide related policies to support innovative companies to create a better environment that can tolerate failures. In terms of policy implementation, it is necessary to strengthen the support for failed enterprises, promote the effective implementation of relevant policies, and truly encourage companies to develop innovatively and accelerate the breakthrough of talent innovation. Thus, the goal of effectively enhancing the attraction of the city to outstanding scientific and technological talents can be realized.

The study discloses the factors influencing the attractiveness of developed cities for outstanding talents. The analysis of our results reveals that creating a comfortable environment is one of the important factors in attracting talents, in addition to the inclusive environment of companies. In practice, differences in compensation packages are one of the important reasons affecting the transfer of talent, which is also well discussed in Harris and Todaro’s literature [35]. Moreover, differences in gender or countries may also play an important role in the flow of top talents around the world. In future, the analysis of the impact of salary packages on talent attractiveness in developed cities is worth studying using data collected via interview or questionnaires of outstanding scientific and technological talents. A cluster analysis or factor analysis can be explored related to the major driving factors on the flow of outstanding talents around the world.