The Scene Logic of Innovative Talent Agglomeration: An Empirical Study Based on 54 Cities in China

Abstract

In recent years, China has been steadily implementing its innovation-driven development strategy, underscoring the vital importance of attracting innovative talents to cities. Major cities have come to realize that securing such talent is essential for maintaining sustainable urban competitiveness in the future. This article takes a novel perspective by focusing on the role of urban scenes, with a particular emphasis on the cultural, lifestyle, and quality-of-life factors that are crucial for attracting and retaining innovative talent, which is essential for sustainable urban growth. Utilizing ridge regression analysis, this study scrutinizes the scores across various sub-dimensions of urban ambiance and the location quotient of innovative talent in 54 cities nationwide. We report several findings. Firstly, urban scenes play a pivotal role in talent agglomeration, a critical factor for sustainable development. Secondly, both rational and transgressive scenes positively impact the gathering of scientific and financial talents, with transgressive scenes having a more pronounced effect. Thirdly, self-expressive scenes may counterintuitively impede the clustering of scientific and cultural talents, a finding that contrasts with international research outcomes. In conclusion, this study sheds light on how urban scenes drive the sustainable concentration of innovative talents, thus contributing to the enrichment of theoretical understanding of sustainable talent development and practical insights for policymakers aiming to create urban environments that foster innovation and sustainability.

1. Introduction

Technological innovation serves as a pivotal catalyst for fostering urban economic development, with sustainability at its core. To enhance their innovative prowess and sustainable competitiveness, cities worldwide are actively endeavoring to draw in and nurture creative minds [1]. In China, over 240 cities have enthusiastically joined the race to attract talent [2]. These cities offer a range of incentives, such as streamlined settling processes, entrepreneurial funding opportunities, and housing benefits. However, what remains underemphasized is the consideration of the quality of life and lifestyle preferences of these innovative talents. However, the challenge lies not only in attracting talent but, more importantly, in retaining it through a sustainable approach that considers the quality of life and lifestyle preferences of these innovative talents.

The study of talent agglomeration, which originally stemmed from labor agglomeration, has primarily been approached from a production-oriented standpoint. This perspective has centered on the influence of economic factors, such as economic opportunities [3], industrial agglomeration [4], and housing expenditures [5] on talent concentration. While research from the consumption perspective has started to consider human needs, it has thus far been confined to public services and amenities [6], failing to offer a comprehensive explanation for talent agglomeration. Essentially, the aggregation of innovative talents is driven by local quality, which includes not only economic opportunities but also the environmental, social, and cultural aspects that contribute to a sustainable lifestyle. Innovative talents often place greater emphasis on the quality of life and are willing to choose their place of residence based on local quality, as well as pay more for a better local environment. When innovative talents arrive in cities with higher local quality, they bring new ideas, thereby promoting the economic development of the region. This perspective can be succinctly summarized as “local quality drives sustainable urban development”. Cities can promote the aggregation of innovative talents by enhancing environmental attractiveness; the local quality, which includes both amenities and socio-cultural aspects, is becoming increasingly important for the economic vitality of a city. The creation of a local quality preferred by innovative talents and the resulting aggregation are key factors in driving urban development. Research conducted from the perspective of urban scenes precisely starts from the standpoint of local quality, giving prominence to the interplay and amalgamation of communities, amenities, social interactions, crowds, and shared values [7,8,9]. This approach underscores the holistic outlook, focusing on lifestyle, quality of life, and cultural significance [10].

Silver, Clark, and collaborators introduced the theory of scenes subsequent to their comprehensive investigation into amenities across 38 international metropolises [7], including prominent cities like New York, Chicago, London, and Tokyo, as well as an extensive survey of over 1200 small and medium-sized cities in North America. The concept of scenes establishes an intrinsic link between innovative talents and urban development, pinpointing the internal sources of urban growth in the knowledge economy era. This theory of scenes aligns with the diversified trends in urban research, not only delving into consumption as a standalone activity but also delving into the social structures that give consumption meaningful societal contexts [7]. In particular, the theory of scenes distills the distinctive cultural and aesthetic qualities of cities, unearthing the aspects of a city that people seek and elucidating the relationship between scenes and urban advancement [11].

From a theoretical perspective, this paper attempts to provide a new viewpoint for the study of the dynamics of innovative talent aggregation. Based on the driving mechanisms of aggregation, the study divides the perspectives on innovative talent aggregation into production, consumption, and scenes. Among them, the production perspective emphasizes the role of economic factors, focusing on the aggregation of general labor rather than the study of innovative talents. The consumption perspective focuses on the consumption choices of innovative talents for public facilities and spaces but pays insufficient attention to the culture and values within the urban social space. The theory of Scenes links physical space with social culture and systematically operationalizes local quality, offering a framework for understanding how the cultural and aesthetic qualities of cities can support sustainable urban growth.

From a practical standpoint, this paper offers new directions for thought, theoretical support, and empirical data for cities to formulate sustainable policies for innovative talents. From a methodological perspective, the use of consensus assessment techniques to score amenities overcomes, to some extent, the subjectivity of single-expert scoring and links physical space with social culture. Based on the aforementioned data, calculating the scene dimension scores of major Chinese cities according to the weights operationalizes local quality and promotes the application of the theory of scenes in China. Furthermore, this paper employs a ridge regression model, which allows for the inclusion of more variables, enhancing the model’s explanatory power. At the same time, it avoids potential high multicollinearity and the resulting variance inflation of estimates, making the regression coefficients and empirical results more stable and robust.

This study focuses on a dataset comprising 54 cities and employs scores within each sub-dimension along with the location quotient of innovative talent to investigate the impact and underlying mechanisms of urban scenes on the concentration of innovative talent. The primary goal is to assess the applicability of international findings on innovative talent agglomeration within the Chinese context. This research seeks to enhance our understanding of population mobility and urban development theories and offer fresh insights and theoretical foundations to assist cities in crafting policies for attracting and retaining innovative talent in a manner that supports long-term sustainability. This paper is organized in the following manner: Section 2 reviews the current state of the field related to the study of innovative talents, particularly focusing on the literature from the perspectives of production, consumption, and scenes. Section 3 explains the research design of this study, including key variables, sample selection, and methodology. Section 4 presents the main research findings based on regression models. Section 5 discusses the conclusions and implications of the research findings.

2. Three Research Perspectives of Innovative Talent Agglomeration

Research on innovation talent agglomeration mechanisms is relatively scarce in China [12]. Since talent agglomeration is a special form of talent flow, this paper reviews the traditional factors and research of innovative talent agglomeration from the perspective of talent flow.

2.1. Perspective of Production: How Economic Factors Affect the Agglomeration of Innovative Talents

Firstly, research on the flow of talent mainly focuses on economic opportunities, which explain the impact of economic factors on talent inflows. Early economists found that labor would choose to cluster in areas with more job opportunities and higher wage levels [13]. The “Push-Pull Theory” summarizes the relevant factors of population migration through push and pull, emphasizing the role of economic factors such as employment opportunities and income in population mobility [14,15]. In China, employment opportunities, especially wage differences between regions, are considered the dominant factor in attracting skilled labor mobility [16,17]. On the other hand, industrial clusters and talent gathering complement each other. The industrial cluster effect will promote talent to move closer to industrial clusters [18], and talent aggregation will also strengthen industrial clusters. Chinese scholars have found that service industry agglomeration is beneficial for promoting the agglomeration of innovative talents [4,19]. Secondly, a higher housing price-to-income ratio will also have a certain impact on talent aggregation [5]. Overall, there is a negative correlation between housing prices and the agglomeration of innovative talents, but it also varies due to differences in talent types: empirical research in the United States shows that rent does not significantly affect the agglomeration of innovative talents in cultural and sports occupations [7]. In China, the rise in regional relative housing prices will suppress the inflow of low-skilled labor and promote the agglomeration of high-skilled labor [20]. Against the backdrop of urban development and the improvement of innovative talent quality, studying talent agglomeration solely from a production perspective has the drawback of simplifying talent as input factors and neglecting people’s living needs. Therefore, they can not systematically and completely interpret the factors of talent agglomeration [21].

2.2. Perspective of Consumption: Can Amenities Explain the Agglomeration of Innovative Talents?

In the context of industrial development, cities are gradually shifting from production-oriented to consumption-oriented, and production-oriented activities are being replaced by consumption-oriented activities. Cities used to focus on providing policy incentives for production enterprises, but now they are focusing on providing comfortable public goods for the public [6]. Urban development increasingly relies on technology, innovation, and cultural consumption [10]. In this regard, Tib proposed the “Voting with Feet” theory, which expresses a public preference for services through movement [22].

In recent years, amenities have also begun to be valued from a consumer perspective and are seen as important variables in urban development. Amenities contain natural amenities, cultural amenities, and social amenities [10]. In China, amenities have become an important variable affecting urban development, the migration of high-end human capital into cities, and urban vitality [6]. The theory of amenities emphasizes analyzing the impact of amenities on human capital migration from a consumption perspective, effectively compensating for the shortcomings of traditional economic perspectives in explaining the agglomeration of innovative talents and urban development. However, these studies still focus on the category and quantity of the amenities, with little attention paid to the issue of “quality” [10]. The spatial economics model driven by spatial quality takes consumer needs into account and the decisive role of spatial quality in talent’s location selection [23] but fails to pay attention to the combination of amenities, environment, consumer goods, and the cultural significance behind them, and lacks discussions on a place’s cultural style, talent’s lifestyle, and quality of life. It cannot fully meet the needs of innovative talents and related research.

2.3. Scenes as a New Perspective: Based on Quality of Local Life

Scenes are the cultural style of a place, an innovative method for evaluating and measuring the quality and lifestyle of a place, and a set of conceptual tools that can be applied to multiple backgrounds [24]. The theory of scenes is a supplement to the theory of amenities [10], establishing a link between amenities, cultural style, and lifestyle. It not only includes urban culture and values but also provides a research system for “local quality of life”. It can be said that the scenes perspective is the deepening and development of the consumption perspective, which effectively compensates for the limitations of the amenity perspective in terms of “integrity” and “quality” [10] and solves the problems that cannot be solved and explained by public service, product, and amenities. The scenes view urban space as a mixture of social and cultural symbols, transforming social culture into amenities, which is a tangible and measurable indicator that can be perceived, building a bridge between innovative talents and urban development (see Figure 1).

The New Chicago School urban research team reviewed the literature, including the work from Hegel [25,26], Wagner [27], Weber [28], Levi Strauss [29], and others [27,30,31,32,33,34,35,36,37]. Drawing on and integrating a large amount of research on urban cultural experience and relevant surveys on basic value dimensions, they ultimately synthesized 15 sub-dimensions to form an indicator system for evaluating local social culture in order to operationalize the quality of life (see

Table 1. Symbolic dimensions of scenes.

Sub-Dimension	Definition	Example
Authenticity: sources of genuine identity supported or rejected by a scene
Local	Typical local characteristics that are uncontaminated by foreign customs	Savouring local fruit and meat at a farmers’ market
Ethnic	Deeply ingrained and inescapable, not influenced by a homogenized, isolated, and abstract global monoculture	Recognizing the twang of Appalachia in the Stanley Brothers’ Voices
State	Environments or behaviors with a profound national identity imprint	Visiting the Gettysburg Battlefield or the White House
Corporate	Standardization of group brands, logos, and culture	Enjoying the real thing that is Coke or prizing Gucci bags over knock-offs
Rational	Spontaneous thinking unaffected by commercial and political influences	Celebrating the power of human reason at a planetarium or before a university library or laboratory
Theatricality: styles of appearance supported or rejected by a scene
Glamourous	A dazzling, shimmering, mysterious yet enticing charm that is intrinsic yet transcends itself	Standing on the red carpet at Cannes gazing at the stars going by
Neighborly	High affinity within a community, where warm and caring members unite friends and partners with camaraderie, showing closeness and friendship among neighbors	Attending a performance by the community orchestra
Transgressive	A strong subcultural presence that breaks away from traditional and mainstream presentation styles	Watching a performance artist pierce his skin
Formal	Highly ritualized, with standardized etiquette for attire, speech styles, and manner of appearance	Going to the opera in a gown or white tie and tails
Exhibitionistic	A flamboyant personality that brings private life into the public eye, making the individual an object of attention and admiration	Watching weightlifters at Muscle Beach
Legitimacy: bases of moral authority supported or rejected by a scene
Traditional	Connected to the past, seeking the historical reasons behind people’s actions in the present	Hearing Mozart performed in the Vienna State Opera or church bells call one to worship
Charismatic	Rule through the outstanding character and achievements of great figures, creating legitimacy that links people with attractive heroes and encourages them to follow	Crowding to be near Michael Jordan
Utilitarian	Instrumentalizing the current situation, its legitimacy does not stem from tradition or prophecy, but from interests	Savouring the value of efficient production at a museum of industry
Egalitarian	Respect for human equality, where everyone should be treated fairly and equally, regardless of distance or status	Enjoying the democratic implications of a crafts fair or fair trade coffee
Self-expressive	Providing ample opportunities for self-expression and mutual display; enabling individuals to showcase their unique lifestyles, personal styles, and perspectives on the world through their behavior, acting in a manner that reflects their individual way of life and style	Hearing a jazz musician improvise a solo

) [24].

From scene studies in North America, it can be seen that, while controlling for other variables, transgressive, self-expressive, and neighborly scenes in the United States can all promote the aggregation of innovative talents [7]. In other words, although the scenes do have an impact on the agglomeration of innovative talents, not all sub-dimensions will have a significant impact. Based on past research and experience, this article will elaborate on the theoretical and empirical research results of rational, neighborly, transgressive, and self-expressive scenes on the agglomeration of innovative talents.

Overall assumption: The improvement of scores in the main sub-dimensions can significantly affect the agglomeration of innovative talents.

Innovative talents are different from general human capital. They exhibit both instrumental rationality and value rationality in their choice of mobility. They hope to realize their own value, achieve achievements, and gain social respect [38,39]. From the scenes perspective, rationalism is defined as spontaneous rational thinking that is not influenced by politics, business, etc. [7]. The sub-dimension comes from Weber’s value rationality, and actions based on value rationality are determined by conscious belief of self-value in certain specific ethical, aesthetic, religious, or any other form, independent of achieving successful goals and solely determined by their beliefs [40]. Rationalism can encourage talents to act wholeheartedly for innovation. Therefore, an increase in the score of the rational scenes may promote the gathering of innovative talents.

Therefore, we propose the hypothesis:

Hypothesis 1. 

An increase in the score of the rational scenes can significantly promote the agglomeration of innovative talents.

Neighborly refers to the use of friendship by enthusiastic and caring community members to unite friends and partners, showcasing the intimacy and friendship between neighbors, emphasizing harmony, intimacy, personal networks, and face-to-face intimacy [7]. The daily life patterns formed by the expansion of modern cities are vastly different from those prevalent in previous societies. Daniel Bell [41] believes that the traditional social support system (small towns, churches, and families) is disintegrating, and new organizations, especially companies, are taking over their position. The collapse of traditional lifestyles and the resulting sense of drift and confusion are the root causes of social anomalies in sociologists’ words. Building neighborliness in cities can attract and retain talent, especially for those who aspire to escape fierce competition. Empirical research based in the United States has found that neighborly scenes can promote the gathering of innovative talents [7].

Therefore, the Hypothesis 2:

Hypothesis 2. 

An increase in the score of the neighborly scenes can significantly promote the agglomeration of innovative talents.

From the scenes perspective, transgressive is defined as a way of presenting that breaks tradition and mainstream, undermines people’s normal expectations for etiquette, dress, and behavior, and abandons mainstream sensitivity in social culture [7]. Empirical research in North America shows that transgressive scenes promote the gathering of innovative talents and stimulate economic development [7]. There are similar cases in China, where the transgressive scenes of the 798 Art District in Beijing have attracted more potential cultural participants of the same type, successfully being transformed from an electronics factory to an annual space that attracts artists from all over the world [42].

Therefore, the Hypothesis 3:

Hypothesis 3. 

An increase in the score of the transgressive scenes can significantly promote the agglomeration of innovative talents.

From the scene perspective, self-expressive is defined as encouraging people to showcase their unique lifestyle, personal style, and ways of observing the world in their behavior [7]. The social culture and lifestyle consumption patterns that encourage self-expression can attract creative talents, nurture the creative spirit, and promote regional innovation and development [43]. Empirical research in the United States has found that self-expressive scenes significantly promote the gathering of innovative talents [24]. Chinese scholars have found that self-expressive scenes can promote the development of urban creativity [44].

Therefore, the Hypothesis 4:

Hypothesis 4. 

An increase in the score of self-expressive scenes can significantly promote the agglomeration of innovative talents.

3. Scenes of Chinese Cities: Exploring Scenes and Talent Aggregation

3.1. Sample Cities

The agglomeration of innovative talents is a manifestation of the trend of a postindustrial or knowledge-based society. When selecting sample cities, it is necessary to consider some social phenomena as prerequisites, such as relatively strong commercial resources, accessible transportation networks, and diverse lifestyles. Therefore, starting from the level of economic development, this article first refers to the “2021 City Commercial Charm Ranking” released by First Financial based on commercial resource agglomeration, urban hub, urban activity, lifestyle diversity, and future plasticity, and includes 49 cities in the sample. Secondly, considering the size of the cities, the above-mentioned cities already include all the super mega cities in the ‘Seventh national census’. Finally, considering China’s national conditions and the role of the administrative level in the agglomeration of innovative talents shown in the literature [45,46], five cities, including Haikou, Hohhot, Urumqi, Xining, and Yinchuan were added to the above cities, resulting in a total of 54 sample cities, including all municipalities directly under the central government, sub-provincial cities, planned cities, and all provincial capitals or capital cities except for Lhasa (see

Table 2. Sample cities.

City Type	Main Cities	Other Cities
Cities	Beijing, Changzhou, Chengdu, Chongqing, Dalian, Dongguan, Foshan, Fuzhou, Guangzhou, Guiyang, Harbin, Hangzhou, Hefei, Huizhou, Jinan, Jiaxing, Jinhua, Kunming, Lanzhou, Linyi, Nanchang, Nanjing, Nanning, Nantong, Ningbo, Qingdao, Quanzhou, Xiamen, Shanghai, Shaoxing, Shenzhen, Shenyang, Shijiazhuang, Suzhou, Taizhou, Taiyuan, Tianjin, Weifang, Wenzhou, Wuxi, Wuhan, Xi’an, Xuzhou, Yantai, Changchun, Changsha, Zhengzhou, Zhongshan, Zhuhai	Haikou, Hohhot, Urumqi, Xining, and Yinchuan
N	49	5

).

3.2. Dependent Variable—Location Quotient of Various Innovative Talents

This article refers to the industry classification of the national standard “Industrial Classification of the National Economy” (GB/T4754-2017) [47] and limits the scope of “innovative talents” to four categories of employed personnel: “information transmission, computer services and software industry” (referred to as information), “scientific research, technical services and geological exploration industry” (scientific), “cultural, sports and entertainment industry” (cultural), and “financial industry” (financial). The dependent variables represent the number of employees in four types of innovative talents. These data are sourced from databases released by the Chinese government, with a primary focus on the statistical yearbooks issued by the statistical bureaus of different cities. Given that some city statistical bureaus have not updated their data in a timely fashion, the statistical yearbook published by the National Bureau of Statistics of China is utilized as a secondary data source. The “location quotient” of various innovative talents is the dependent variable of this article. As a cluster identification method and analysis indicator, “location quotient” was initially used to measure industrial agglomeration to reflect the relative concentration and specialization level of industry [48], but now it is also used in empirical research related to innovation talent agglomeration [49,50,51]. The location quotient reflects the comparison of the relative advantages of various cities’ resources, which can exclude the impact of urban population base and geographical resource differences on the analysis results. The calculation method of location quotient for various innovative talents is shown in the following formula:

$${LQ}_i={\displaystyle \frac{{ct}_i/{ep}_i}{\sum _1^{n}ct/\sum _1^{n}ep}}$$

(1)

Among them, ${LQ}_i$ is the location quotient of sample city i, ${ct}_i$ is the number of innovative talent clusters in the city, ${ep}_i$ is the sum of all employed personnel in the city’s industries. $\sum _1^{n}ct$ is the sum of various innovative talents in cities at or above the prefecture level in China, $\sum _1^{n}ep$ is the sum of all industry employees in cities at or above the prefecture level in China. When included in the regression model, the location quotient will be treated as a natural logarithm.

3.3. Explanatory Variable—Sub-Dimension Score of Scenes

The explanatory variable is the score of 54 cities on 15 sub-dimensions of scenes. Based on the previous empirical research, this article refers to the national standard “Classification of National Economic Industries” (GB/T4754-2017) [47] and the classification method of POI in Baidu Map data. In addition, amenities are determined based on typical and general Chinese scenes. In order to conduct a more comprehensive assessment of urban scenes in China, the Chinese Scene Academy has convened ten scholars from various professional fields. These scholars have been deeply engaged in scene research in China for a long time and contribute annually with scholarly publications in the field of scene studies, which is their primary area of research. They possess a profound understanding of both the theory of scenes and the practical conditions of Chinese cities. Hailing from different cities, they exhibit diversity in geography, culture, and academic backgrounds. During the evaluation process, all scholars use the same scoring criteria [7], and their scores have been rigorously verified. The database is accessible upon reasonable request from the corresponding author.

Firstly, ensure that the participating experts have a clear and accurate understanding of the scoring criteria. Based on this, the first round of scoring is conducted, where the experts assign scores to each amenity in fifteen sub-dimensions. The score range is 1–5 points, indicating the degree of exclusion or compatibility between the amenity, activities, and the sub-dimension: 3 points represent neutrality; A score below 3 represents mutual exclusion between amenities and this sub-dimension; A score = above 3 represents amenities enhancement in this sub-dimension. After obtaining the scoring results, a descriptive statistical analysis is performed for the scores of each amenity, including calculating the mean, median, standard deviation, etc. The coefficient of variation (CV) is used to assess the consistency among different participants; those with a coefficient of variation greater than 0.2 are subject to individual interviews and calibration. A scoring table is then compiled and sent to the experts for a second round of scoring or confirmation. Based on the revised scores and related feedback, the data is validated and organized, ultimately determining the scores of the amenities on each sub-dimension (see Appendix A) [52].

The final calculated scores for each sub-dimension of the scenes are shown in Figure 2. It can be observed that the sub-dimensions with the highest scores in Chinese cities are Neighborly, Self-Expressive, Corporate, and Utilitarian. This indicates that Chinese major cities have retained traditional cultural elements during their development towards modernization, and there is a tendency among urban residents for warm and intimate interactions.

3.4. Control Variable

This article will incorporate 8 control variables into the model. Firstly, referring to previous studies, the per capita GDP (yuan), the proportion of the tertiary industry to GDP (%), the average price of residential housing (yuan/square meter) (referred to as housing prices), the wages of nonprivate urban employees (yuan) (referred to as income), the proportion of urban higher education population per 100,000 people (proportion of urban higher education population), and the size of cities (super, megacities/other cities) are calculated, Administrative level (municipalities directly under the central government, sub-provincial cities/other cities), city location (eastern/midwestern) as the control variable, data sourced from the China City Statistical Yearbook, supplemented by the statistical yearbooks of each city or province. Among them, city size, administrative level, and city location are binary variables, while other variables are fixed distance variables. Before incorporating the model, the distance variables are log-transformed.

3.5. Model

This article uses R language and ridge regression to verify the hypothesis and overcome the problem of a small number of urban samples. Ridge regression is mainly used as a biased estimation regression method when there is collinearity between variables, and there are few data points. This method has been improved on the basis of the least squares estimation method, discarding the unbiased nature of the least squares method making the regression coefficients more stable. The formula for calculating the regression coefficient is:

$$\widehat{w}={\left(X^{T}X+kI\right)}^{-1}{X}^{T}y$$

(2)

4. How Do the Scenes Promote the Gathering of Innovative Talents?

4.1. Concentration of Innovative Talents

From the results of the agglomeration level of innovative talents in different cities, the standard deviation of the location quotient for financial talents is the smallest, with a value of 0.511. The standard deviation of the location quotient for information talents is the highest, with a value of 0.899. From this, it can be seen that among the innovative talent agglomeration levels in the sample cities, the difference in the agglomeration level of information talents is the largest, while the difference in the agglomeration level of financial talents is the smallest. In addition, the maximum value of the location quotient for information talents is 5.397, while the minimum value is only 0.078, indicating a significant difference. The location quotient is divided by 1. If it is less than 1, it indicates that the region does not have an advantage in attracting innovative talent agglomeration, and if it reaches 2, it can be considered as having an absolute advantage [53]. Overall, regardless of the type of innovative talent, the minimum value of location quotient is less than 0.3, while the maximum value is higher than 3, indicating that among the 54 major cities in China, there have been significant differences in the level of innovative talent agglomeration among each city (see

Table 3. Concentration level of innovative talents in various cities.

Location Quotient	Minimum	Maximum	Mean	Standard Deviation
Information talents	0.078	5.397	0.756	0.899
Scientific talents	0.090	4.893	0.816	0.735
Cultural talents	0.181	4.388	0.905	0.677
Financial talents	0.267	3.359	0.913	0.511
N	54

).

In addition, the location quotients of various innovative talents were log-transformed, and a systematic clustering of innovation talent aggregation levels was conducted in 54 cities. Finally, the agglomeration level of innovative talents in 54 cities was classified into four levels: “Extremely high-high-medium-low”.

The first level of “extremely high innovation talent gathering” only includes Beijing; The second level of “high innovation talent gathering” includes 7 cities, including Shanghai, Shenzhen, Guangzhou, Chengdu, Hangzhou, Nanjing, and Zhuhai. Except for Zhuhai, a city in the Greater Bay Area adjacent to Macau, all other cities are sub-provincial or above-level cities. The third level of “medium innovation talent gathering” includes 36 cities, including Chongqing, Wuhan, Dongguan, Xi’an, etc., including some municipalities directly under the central government, provincial capital cities, sub-provincial cities, as well as some eastern prefecture-level cities; The fourth level of “low innovation talent gathering” includes 10 cities, including Nantong, Quanzhou, Wenzhou, Jinhua, Taizhou, Shaoxing, Xuzhou, Zhongshan, Weifang, and Linyi, all of which are prefecture-level cities (see

Table 4. Systematic Clustering Results of the Clustering Level of Innovative Talents in Sample Cities.

Group Average of Location Quotient (Logarithm)	Extremely High	High	Medium	Low
Information talents	1.686	0.704	−0.748	−2.097
Scientific talents	1.588	0.339	−0.369	−1.873
Cultural talents	1.479	0.392	−0.252	−1.143
Financial talents	1.212	0.055	−0.137	−0.893
Cities	Beijing	Shanghai, Shenzhen, Guangzhou, Chengdu, Hangzhou, Nanjing, Zhuhai	Chongqing, Tianjin, Wuhan, Dongguan, Xi’an, Foshan, Shenyang, Qingdao, Jinan, Changsha, Harbin, Zhengzhou, Kunming, Dalian, Changchun, Shijiazhuang, Taiyuan, Hefei, Fuzhou, Nanchang, Haikou, Guiyang, Lanzhou, Xining, Xiamen, Ningbo, Hohhot, Urumqi, Yinchuan, Nanning, Suzhou, Wuxi, Jiaxing, Changzhou, Huizhou, Yantai	Nantong, Quanzhou, Wenzhou, Jinhua, Taizhou, Shaoxing, Xuzhou, Zhongshan, Weifang, Linyi
N	1	7	36	10

).

4.2. The Impact of Scenes on the Agglomeration of Innovative Talents

4.2.1. How Traditional Factors Affect the Gathering of Innovative Talents

The results show that the model fits well. Under the control of other variables, the proportion of the urban higher education population significantly promotes the agglomeration of innovative talents. In addition, income can significantly promote the agglomeration of scientific and financial talents, while per capita GDP has no significant promoting effect on the agglomeration of innovative talents. The proportion of the tertiary industry will significantly promote the agglomeration of information, scientific, and cultural talents, while housing prices can significantly promote the agglomeration of information talents. Eastern cities will significantly inhibit the agglomeration of scientific talents, while super and megacities will significantly promote the agglomeration of scientific talents. Therefore, it can be seen that City size rather than location will have a positive impact on the agglomeration of innovative talents (see

Table 5. A Control Variable Model for the Aggregation of Innovative Talents.

Variable	Model 1 Dependent Variable: Location Quotient of Information Talents (Logarithmic)		Model 2 Dependent Variable: Location Quotient of Scientific Talents (Logarithmic)		Model 3 Dependent Variable: Location Quotient of Cultural Talents (Logarithmic)		Model 4 Dependent Variable: Financial Talent Location Quotient (Logarithmic)
	B	Beta	B	Beta	B	Beta	B	Beta
	(Standard Error)		(Standard Error)		(Standard Error)		(Standard Error)
Income (logarithmic)	0.361	0.063	0.573 *	0.117	0.470	0.127	0.771 *	0.247
	(0.383)		(0.297)		(0.305)		(0.354)
Per capita GDP (logarithmic)	0.261	0.090	0.227	0.091	0.137	0.072	−0.132	−0.083
	(0.176)		(0.136)		(0.140)		(0.162)
The proportion of the tertiary industry (logarithmic)	1.153 **	0.195	0.965 **	0.190	0.981 **	0.256	0.427	0.132
	(0.357)		(0.277)		(0.285)		(0.330)
Housing price (logarithmic)	0.406 **	0.189	0.200	0.108	0.184	0.132	0.022	0.019
	(0.141)		(0.109)		(0.112)		(0.130)
Proportion of urban higher education population (logarithmic)	1.143 ***	0.408	1.072 ***	0.445	0.720 ***	0.396	0.539 **	0.352
	(0.179)		(0.139)		(0.143)		(0.166)
Eastern cities	−0.108	−0.053	−0.337 **	−0.193	−0.227	−0.172	0.035	0.032
	(0.122)		(0.094)		(0.097)		(0.112)
Super\Megacities	0.143	0.072	0.185 *	0.108	−0.075	−0.058	−0.030	−0.028
	(0.122)		(0.095)		(0.097)		(0.113)
Directly administered municipality or sub provincial city	0.196	0.096	−0.059	−0.034	−0.037	−0.028	0.114	0.102
	(0.128)		(0.100)		(0.102)		(0.119)
Constant	−27.820 ***		−25.977 ***		−19.932 ***		−14.909 ***
	(3.796)		(2.945)		(3.024)		(3.508)
R2	0.828		0.860		0.740		0.509
F	27.115 ***		34.499 ***		16.039 ***		5.830 ***
N	54		54		54		54

Note: * represents p < 0.05, ** represents p < 0.01, *** represents p < 0.001.

).

4.2.2. How Scenes Affect the Gathering of Innovative Talents

From the results, the model fits well. Compared to the ridge regression model that only includes traditional economic factors, it has improved, indicating that the addition of scenes enhances the explanatory power of the model.

Under the control of other variables, the rational scenes significantly promote the gathering of information, scientific, and financial talents, while the transgressive scenes significantly promote the gathering of scientific, cultural, and financial talents. The improvement of the self-expressive scenes significantly suppresses the gathering of scientific and cultural talents. Overall, the rational and transgressive scenes have a promoting effect on the agglomeration of scientific and financial talents, and the role of transgressive scenes is stronger. However, neighborly scenes do not have a significant effect on the innovative talents. The self-expressive scenes have an inhibitory effect on the agglomeration of scientific and cultural talents.

4.3. The Differences between Scenes and Traditional Factors in Influencing Talent Aggregation

In terms of traditional factors, overall, the effects of variables on the agglomeration of different types of innovative talents are similar. Under the control of other variables, the proportion of the urban higher education population and the proportion of the tertiary industry have a promoting effect on the agglomeration of three types of talents, except for the financial. In addition, income significantly promotes the agglomeration of scientific and financial talents, and per capita GDP significantly promotes the agglomeration of scientific talents. The difference from the model that only includes traditional variables is that the per capita GDP did not have a significant effect on the agglomeration of innovative talents, but after adding the scenes, the per capita GDP significantly promotes the agglomeration of scientific talents.

The active effect of scenes on the agglomeration of innovative talents is already higher than some traditional factors. In terms of information talents, the importance of rationale scenes is already higher than the proportion of the tertiary industry, housing prices, and the proportion of the urban higher education population. In terms of scientific talents, the importance of transgressive scenes is the highest, which is already higher than income, per capita GDP, the proportion of the tertiary industry, and the proportion of urban higher education population, and also higher than rational and self-expressive scenes; For cultural talents, the importance of transgressive scenes is higher than the proportion of the tertiary industry and the proportion of urban higher education population; For financial talents, the importance of transgressive scenes is highest, already higher than income and rational scenes (see

Table 6. Ridge Regression Results of the Impact of Scenes on Innovative Talent Aggregation.

Variable	Model 5 Dependent Variable: Location Quotient of Information Talents (Logarithmic)		Model 6 Dependent Variable: Location Quotient of Scientific Talents (Logarithmic)		Model 7 Dependent Variable: Location Quotient of Cultural Talents (Logarithmic)		Model 8 Dependent Variable: Financial Talent Location Quotient (Logarithmic)
	B	Beta	B	Beta	B	Beta	B	Beta
	(Stand Error)		(Stand Error)		(Stand Error)		(Stand Error)
Income (logarithmic)	0.379	0.066	0.662 *	0.135	0.502	0.136	0.984 **	0.316
	(0.399)		(0.286)		(0.301)		(0.327)
Per capita GDP (logarithmic)	0.333	0.114	0.282 *	0.113	0.153	0.081	−0.106	−0.067
	(0.185)		(0.132)		(0.139)		(0.151)
The proportion of the tertiary industry (logarithmic)	0.876 *	0.148	0.774 **	0.152	0.990 **	0.258	0.284	0.088
	(0.390)		(0.279)		(0.294)		(0.319)
Housing price (logarithmic)	0.514 **	0.239	0.205	0.111	0.104	0.074	0.013	0.011
	(0.149)		(0.106)		(0.112)		(0.122)
Proportion of urban higher education population (logarithmic)	0.948 ***	0.338	0.802 ***	0.333	0.577 ***	0.317	0.271	0.177
	(0.190)		(0.136)		(0.143)		(0.156)
Eastern cities	−0.045	−0.022	−0.175	−0.100	−0.092	−0.070	0.191	0.172
	(0.133)		(0.095)		(0.100)		(0.108)
Super\Megacities	0.172	0.086	0.146	0.085	−0.138	−0.107	−0.099	−0.091
	(0.129)		(0.093)		(0.098)		(0.106)
Directly administered municipality or sub provincial city	0.141	0.069	−0.111	−0.063	−0.041	−0.031	0.052	0.047
	(0.133)		(0.096)		(0.101)		(0.109)
Rational Scenes Score (logarithmic)	12.821 *	0.138	10.645 *	0.133	−0.400	−0.007	13.944 **	0.275
	(6.272)		(4.496)		(4.725)		(5.131)
Neighborly Scenes Score (logarithmic)	−3.615	−0.028	5.459	0.049	8.696	0.103	4.343	0.061
	(7.643)		(5.479)		(5.758)		(6.252)
Transgressive Scenes Score (logarithmic)	3.979	0.029	18.922 **	0.159	17.506 **	0.196	22.178 **	0.294
	(8.515)		(6.104)		(6.414)		(6.966)
Self-expressive Scenes Score (logarithmic)	−7.747	−0.051	−18.468 *	−0.140	−17.573 *	−0.177	−12.641	−0.151
	(10.219)		(7.326)		(7.699)		(8.360)
Constant	−38.964		−55.036 **		−42.501 *		−44.735 *
	(22.511)		(16.138)		(16.959)		(18.417)
R2	0.840		0.889		0.784		0.642
F	16.184 ***		24.573 ***		11.181 ***		5.523 ***
N	54		54		54		54

Note: * represents p < 0.05, ** represents p < 0.01, *** represents p < 0.001.

).

4.4. The Mechanism of Scenes Influencing the Agglomeration of Innovative Talents

4.4.1. Based on the Proportion of Population with Higher Education

The “knowledge society” is the foundation of the scenes perspective; although the increase in the proportion of the university-educated population in various cities does not equate to the arrival of a “knowledge society”, it does indicate this trend to some extent. From this perspective, the more a city exhibits the trend of a “knowledge society”, the higher the level of innovative talent aggregation, and this influence is not only applicable to a specific type of innovative talent but is effective for the aggregation of all types of innovative talents. The proportion of the population with higher education is an accelerator, also creating a virtuous cycle for the aggregation of innovative talents. Universities in cities or groups with higher education are not isolated social spaces and actors; they coexist organically with other social spaces and groups. In the United States, specialized research and development centers are often linked with manufacturing industries and nearby university facilities. A typical example of this is Zhongguancun in China. Zhongguancun is an area with a dense concentration of national research institutions, colleges, universities, and talents, and its aggregation of innovative talents has been driven by the “knowledge society”.

4.4.2. Respect for Innovation

What kinds of amenities are connected to value rationality from the scene perspective? We find that research institutes, universities, public libraries, primary and secondary schools, and legal services are crucial for enhancing the sub-dimension of rationality. The rationality behind these amenities is manifested in two aspects: a commitment to scientific innovation as the core objective, rather than being subservient to other aims, and a motivation for rational labor that is typically rooted in religious beliefs or is highly valued by society due to the nature of the work. Individuals within these settings who drive innovation are respected by society, their innovative spirit is recognized and encouraged, and they are willing to actively engage in and contribute to this environment.

4.4.3. Tolerance for Individuality

Transgressive scenes serve as a “safe haven” for embracing individuality, but historically, in China, deviating from the norm might have posed certain challenges. If a member of society were to act outside the established rules, they could face social interference, sanctions, or even be stripped of the opportunity to continue their existence within that society. This is because Confucianism, a deeply rooted belief in China, does not encourage breaking the status quo. Confucian scholars are expected not only to live in the world but also to belong to it harmoniously. Thus, their objectivity and rationality are significantly constrained by traditionalism.

However, with the continuous improvement of urbanization and the increasing proportion of the population with higher education in China, the situation may have changed. Urban scenes that break the mold and embrace individuality have begun to emerge in the country. It can be observed that the impetus for initiating transgressive scenes does not mean constructing more “tattoo and piercing” shops, which might only symbolically represent the breaking of norms but still remain at the level of physical space. The essence of breaking conventions is not about relying on crime, drug use, or informal economic activities. If we look for commonalities among tattoo parlors, party venues, and arcades—what is truly needed is an inclusiveness of “differences”—a local quality that embraces individuality within the scenes.

4.4.4. Cultural Heterogeneity

Clearly, the role of scenes in the aggregation of innovative talents can vary under different national and social contexts. From the results presented earlier, it can be observed that an increase in the level of self-expressive scenes does not significantly promote the aggregation of various types of innovative talents; instead, it inhibits the aggregation of “scientific talents” and “cultural talents”. However, in previous empirical studies based on other cities, an enhancement of the self-expressive scenes has been shown to significantly promote the aggregation of innovative talents, particularly in the fields of sports, arts, and entertainment [7]. This paper attempts to provide an explanation based on cultural heterogeneity for this outcome regarding the self-expressive scenes. Western culture typically places greater emphasis on individualism and self-expression, whereas in Chinese culture, collectivism and harmony are often valued more. As a result, self-expression may not be as highly regarded in China, although this situation is gradually changing.

5. Conclusions and Discussion

Innovative talents are an indispensable “engine” for cities, and attracting and accepting talents is a key concern for sustainable urban growth. Although existing research recognizes this, it is limited by the development of the times and differences in research perspectives and cannot fully interpret the logic of cities attracting talent aggregation. From a production perspective, talents are seen as a simple element of development, and their lifestyle demands are neglected. With the increasing desire of people to have a better life, the driving force of consumption in the process of talent gathering is becoming increasingly apparent. However, the consumption perspective still stays at the shallow level of consumption—focusing on the quantity of public services or amenities or the quality of space, but does not pay attention to the quality, combination, and cultural style behind a place’s amenities, as well as the lifestyle and quality of life.

On the basis of considering all these perspectives, this study introduces a scene perspective, focusing on the cultural style, aesthetic characteristics, values, and lifestyles contained in the place, which attract innovative talents in a sustainable manner. Research has found that the enhancement of rational, neighborly, transgressive, and self-expressive scenes has different effects on innovative talents. Overall, rational and transgressive scenes promote the agglomeration of innovative talents, while neighborly scenes have no significant effect on innovative talents. The self-expressive scenes inhibit the agglomeration of innovative talents. This indicates that in order to attract innovative talents, cities not only need to provide high-quality public services and a comfortable material environment but also shape a rational and creative cultural atmosphere and lifestyle that supports sustainability.

The logic of promoting the agglomeration of urban innovative talents from the scene perspective is based on the proportion of the urban higher education population. The “knowledge society” is the foundation of the scene perspective, and the proportion of the urban higher education population can reflect the knowledge level of cities. The proportion of the urban higher education population is not only an accelerator for the gathering of innovative talents but also promotes the formation of key scenes such as rational and transgressive. The rational scenes create an atmosphere of innovation, while the transgressive scenes promote inclusiveness. In contrast, the effects of neighborly and self-expressive scenes on the agglomeration of innovative talents vary among different countries and cultures; the results of this article show that they are not significant in China. Therefore, the perspective of the scene is of great significance for both cities and innovative talents. For cities, the perspective of the scene is a booster for creating sustainable local quality. For innovative talents, the perspective of the scene is a rational choice to gather in specific cities, considering material conditions and cultural atmosphere. Bridges the sociocultural aspects with the physical comforts of space, amenities like education, and business services can command greater respect for communities within the scenes. This fosters a local quality imbued with a strong sense of rationality, which is conducive to innovation and aligns with the principles of sustainable development. Emphasizing personalized and experiential cultural and entertainment spaces for amenities helps to drive social inclusiveness of individuality and elevate the level of transgressive scene settings. This contributes to the creation of a positive local quality and, in turn, promotes the aggregation of innovative talents in urban areas.

The scene perspective is an urban research and analysis tool from a social science perspective. Compared to the production and consumption perspectives, it is more flexible and adaptable to the diverse urban social environment in the context of the “knowledge society”. Scenes are the new driving force for innovative talent gathering, which emphasizes the needs of individuals. The scenes not only help to gather innovative talents but also promote the updating of urban development paths towards sustainability. From an industrial society to a knowledge-based society, cities have shifted from being “vassals” of industrial production to being “dominant” in terms of quality of life. Scenes that showcase individuality, culture, and lifestyle have become the key to the transformation of urban development dynamics. The scene precisely illustrates the transformation of urban development logic. The perspective of the scenes does not exclude the production and consumption of material space. Instead, it aims to create a driving force for urban development through scenes, from production to consumption, and then to good quality of place, that is, from production-oriented urban development to local quality of life-oriented urban development.

It should be emphasized that studying the aggregation of innovative talents from the scene perspective does not follow a cultural determinism approach but rather links physical space with social culture to form a dynamic of scenes that supports sustainable development. This viewpoint is supported by two aspects. First, from a theoretical standpoint, amenities are used as the basis of physical space and scene dimensions, and the cultural overview reflects the urban scenes, creating a favorable local quality for sustainability. Second, methodologically, the scenes perspective encompasses both the empirical research tradition of sociology and the cultural interpretive tradition. Similarly, the dynamics of scenes are not meant to illustrate that a specific social culture is the driving force for the aggregation of innovative talents. Instead, it conveys the idea that only by breaking the binary opposition between economy and society, and physical space and social culture, and under a systematic operational framework, can we obtain data through empirical methods and combine cultural interpretations to link physical space with social culture. By doing so, we can identify the dynamics of scenes that drive the aggregation of innovative talents, providing them with a favorable local quality that fosters innovation, rationality, and transgression. This approach meets the needs of talents in the context of “post-industrial society” and “knowledge society” while also being attentive to and mitigating potential urban inequalities and other complex issues.

Lastly, we propose three directions for future research. First, there is a need for more nuanced qualitative analysis. Our empirical analysis is based on the aggregation of innovative talent groups or, more directly, specific industry groups. As such, it does not account for individual attributes such as the age and income of these innovative talents, nor does it consider whether these attributes affect their aggregation. Further research should conduct quantitative and/or qualitative analyses at the micro level. Second, research confined to the urban spatial level is not sufficiently in-depth. Refining the unit of analysis to the community or neighborhood level can better highlight the impact of different scenes on the aggregation of innovative talents. Third, the scene perspective can be expanded to the study of typical cases. This paper posits that the stages of urban development and local cultural differences in China are vast. Therefore, the specific situation of innovative talent aggregation in each city requires a case-by-case approach, even a community-by-community discussion, thus still necessitating support from typical case studies.