The nexus between talent rooting environment and economic growth: Empirical evidence from the related questionnaires analysis

Mingyang Liu a, b, c, Yang Li b, *, Tiande Li b, Junwei Zheng d, Shaowei Chen e

a Post-doctoral, Guangdong Polytechnic of Science and Technology, Zhuhai 519090, Guangdong, China. Email: myliu86@yeah.net b School of Ecomomics, Sichuan University (SESCU), Chengdu 610065, China. *Corresponding author. Email: yangli_scu@yeah.net; Email: tdli@scu.edu.cnc Economy & Management, Sichuan University of Arts and Science, Dazhou 635000, Sichuan, China. d Ph.D. student, Department of Economics, School of Economics, Xiamen University, Xiamen 361005, China. Email: zhengjunweizjw@163.come lecturer, Xi'an University of Finance and Economics, 710100, Xi'an, China. Email: chenswufe@163.com

Abstract

The primary objective of this study was to estimate a model for analysing talent rooting environment and regional economic growth. In order to examine the effect of talent environment and other factors on economic growth, this paper surveyed the related literatures and conducted questionnaires. The factors affecting the development of regional economy have been assessed by a conceptual path model of technology acceptance model (TAM) and organizational context variables. Collected data were analyzed by SPSS using regression analysis. The final model was tested by structural equation modeling (SEM) and represented by IBM SPSS Amos 22. The relationship between economic growth and its predictors can be described by the structural equation: YEG=0.201XEI+0.149XCAPP+0.217XPG+0.148XTE + 0.480XNR+1.042. Motivated by the studies that have been used for estimating the error correction model, which suggested that the economic growth driven by talent accumulated. Simultaneously, there was selection bias towards talent rooting environment. Compared with the talent introduction, it was more profound to pay more attention to the future development of talent. The model demonstrated that increased education investment will unambiguously conditionally promote the attraction to talented person. Empirical results also illustrated that policy guidance had positive and significant effect on talent introduction and talent development. Moreover, the genuine growth effect of talent for economic is homogeneous across studies, but varies according to several factors.

Key Words: talent rooting environment; SEM; economic growth; TAM; talent development

1. Introduction

Economists generally agreed that higher education and talents are the driving force of regional economic development. Undoubtedly, aggregation of talent is an important way to enhance the overall strength of the region and the country. Abilities, skills and talents of people living in a society affect the overall economic development of the society—among psychologists this idea has become known through the work of Lynn and Vanhanen (2006). Similar ideas are popular outside of academic circles. Politicians in several countries have been concerned about the talent pool of their nation. In the United Kingdom, a review of skills was ordered by the government with the aim of finding ways to ensure continued prosperity and productivity in Britain; the resulting report proposed that UK should commit to becoming a world leader in skills (Leitch, 2006). In order to properly evaluate allocation of talent as a possible causal determinant of economic growth, the regression analysis should control for alternative possible determinants of growth. Previous research (Hanushek and Woessmann, 2008) has found many economic and social variables to have an impact on growth. This variable was known to have a strong positive effect on economic growth.

Higher education is the main way of human capital investment, which plays an important role in social and economic development. The relevance research contains different aspects of the education. While there are studies that point to correlations between higher education and innovation (e.g., Pillay, 2011), there are

counter arguments that point to limited interactions between the key variables of higher education expansion, growth, productivity and technological change (e.g., Ca, 2006). Once we acknowledge that there is more to higher education’s developmental impact than economic development alone (e.g., Walker, 2015; Glenda et al., 2015), we will restrict our argument to the economic sphere. China undertook a rapid, largescale expansion of higher education beginning at the turn of the 21st century. In light of this increase in the number of college educated workers, fundamental economic and sociological theories provide several reasons to expect that the economic returns to higher education in China have undergone substantial change. There has been are cent wave of empirical studies focusing on the regional economies returns to higher education in China (Gustafsson and Li, 2000; Chen and Ju, 2003; Chen and Hamori, 2009; Wu, 2010; Li et al., 2012; Wang, 2012). Scholars consider heterogeneous returns to education through variation in returns across a third variable’s distribution. For instance, Heckman et al. (2004) develop a marginal treatment effects method to investigate varying returns to higher education across the spectrum of the odds of attending college. Sociologist Xie et al. (2012) use propensity score matching methodology to examine the same type of heterogeneity. Another common third variable approach involves modeling variation in educational returns across the income distribution. Quantile regression models are widely used in this vein. Quantile regression studies examine the independent variable’s effects separately across quantiles of the dependent variable. Wang (2013) uses a related methodological approach to examine educational attainment’s effect on the earnings distribution in urban China from 1995 to 2002, adjusting for end ogeneity bias with an instrumental variable.

Furthermore, prior work indicates that cultural and economic context influence the availability and deployment of entrepreneurial talent (Zhang et al., 2010). Innovation studies presume that talent, either generated individually or through teams, originates creative activities leading to innovation (Holbrook et al., 2010). Studies (e.g., Langford et al., 2003; et al., 2005; Phillips et al., 2008; Holbrook et al., 2010) based on semi-structured interviews with managers of innovative firms found that firms prefer to locate near thick labor markets. Thus, assessing factors that attract and retain talent is central to examining a regional innovation system. Various hypotheses examine a region’s quantitative characteristics to attract and retain creative individuals, ranging from a central role of a rich and diverse culture (e.g., Florida, 2004; Wolfe and Gertler, 2003) to simple market factors (e.g., Shearmur, 2007).

Owing to the unpredictable and often catastrophic market, economic development needs the help from government policy guidance. The policy guidance is based on external governance of the economy and correctly handing the relationship between the market and government. Government policy provides institutional arrangements, operational incentives, technological innovation. Ideally, talent environment should have a series of inquisition to study the economic development. However, these investigations (e.g., Vanhanen, 2006; Phillips et al., 2008; Chen and Hamori, 2009; Zhang et al., 2010; Acikgoz and Mert, 2014) have tended to frequently address the so-called life-style, the cultural and the HR management environment etc., rather than on talent rooting environment (i.e. educational investment, communication and promotion platform, policy guidance, talent environment). While respecting the analytical strengths of each of the approaches, which need a better approach to examine these human factors. On the basis of above studies, talent accumulated environment in green development of regional economy focus chiefly on surveying the relationship among educational investment, communication and promotion platform, policy guidance, talent environment, talent introduction, talent development, talent rooting environment and economic growth. Its purpose is to clarify the relationship between the factors that affect the talent accumulated environment. After all, good conditions are the basis of attracting, retaining and developing talents.

This study is different from three categories of empirical approaches: cross-section, panel data, and time-series ones. We created an integrative framework model for understanding and advancing further research in regional talent management, which highlights several selected variables in regional economic development,

and several drivers of those variables. This study is organized as follows. Section 2 formulates hypotheses of the study. Methods are described in Section 3. Section 4 presents the results of data analyses. Discussion is given in Section 5. Finally, conclusions and suggestions are arranged in Section 6.

2. Hypotheses of the study

TRE is defined as a region environment to attract talent and make them root. Regions attract talent mainly through two interrelated mechanisms (Weng and McElroy, 2010). Conventional wisdom holds that places attract talents by matching them to jobs and economic opportunity. Another Perspective indicates that places attract talents by providing a series of comfort talent environments (Gottlieb, 1995). Such environments are seen as the pivotal to modern cities (Lloyd and Clark, 2001) and as necessary ingredients in attracting highly educated, talented individuals who possess scarce resources and are economically mobile.

Fig. 1. Hypotheses and the proposed conceptual path model of organizational context elements.Notes: EI-educational investment, CAPP-communication and promotion platform, PG-policy guidance, TE-talent environment, TI-talent introduction, TD-talent development, TRE-talent rooting environment, NR-natural resources, EG-economic growth.

Undoubtedly, the economic downturn will cause local government to reevaluate. Research suggests, however, (Rowland, 2011) that regional government that implement or maintain their talent-management commitments are likely to reap positive returns. Studies (Weng and McElroy, 2010; Ahmad Khan et.al., 2013; Feldstein, 2017) show there are a series of factors that contribute to the economic growth (EG). It needs to consider not only natural resources factors associated with regions, but also consider talent rooting environment so as to understand the role of talent in regional economic growth.

H1. Talent rooting environment is positively associated with EG.H2. Natural resources is positively associated with EG.Start with the end in mind talent introduction strategy must be tightly aligned with talent development

strategy. Any kind of practice and activity in talent rooting environment, is supposed to be aligned with its vision and long-term objectives. Every strategy requires appropriate talent to serve. Investing in talent-development systems and implementing strategies is a crucial activity, an activity that the most stable and innovative organizations rarely sacrifice (Tarique and Schuler, 2010; Rowland, 2011). Advanced talent introduction is the important project during the process of regional economic development. In this study, we draw upon the talent introduction and talent development to capture these multiple dimensions of TRE. TI

and TD, the two basic factors in talent rooting environment model, have been found to control talents’ intention to stay specific region as presented in H3 and H4:

H3. Talent introduction is positively associated with TRE.H4. Talent development is positively associated with TRE.Talent environment impacts the cultivation and development of talents directly, as well as attraction

power of the area. It is the most important productive factor for the sustainable economic development of a region, namely, talented personnel will bring about a competitive advantage to their owners. The key to attracting talented personnel is create a good environment for them (Liu and Xu, 2012). The formation of evaluation index system for talent environment will provide a scientific guidance for the optimized configuration of the talent environment. Thus, hypothesis H5 and H6 have been followed by this study:

H5. Talent environment is positively associated with TI.H6. Talent environment is positively associated with TD.Government policy has always had a significant influence on economic growth and new talent

introduction. During the past four decades, policy uncertainty has grown in China. There was less attention to the debilitating impact of poorly fashioned policies, and policy uncertainty, on talent introduction and the impact on the economy. Those issues were most significant for regional development and innovation. Preliminary questionnaires found that contemporaneous policy guidance may be having a greater impact upon talent introduction, talent development and economics activity than previously identified and is an area in need of further study. Hence, H7 and H8 have been put forth in this study:

H7. Policy Guidance is positively associated with TI.H8. Policy Guidance is positively associated with TD.Talent environment, communication and promotion platform, policy guidance and educational

investment; were the organizational contextual factors that have been analyzed in this study. These factors are commonly identified on the basis of literature review and prior empirical studies (Zhang, 2010; Weng, 2010; Pillay, 2011; Oketch, 2014; Walker,2015;). Innovative ideation is a core factor to evaluate the innovation of regional talents. Communication and promotion platform provides chance for regional talents to study and communication. Furthermore, the platform is also a necessary, key process as well as an effective approach to improve innovation ability. Therefore, the following hypotheses, H9 and H10, have been put forth:

H9. Communication and promotion platform is positively associated with TI.H10. Communication and promotion platform is positively associated with TD.Management should consider that an effective way to attract and retain talent is to create an environment

where talented people can develop. In today’s knowledge economy, talent is not only critical, it’s also scarce. Management should consider that an effective way to attract and retain talent is to create an environment where talented people can develop. The way is educational investment that comprises mixed pool of talent, some are explicitly defined for special tasks and some of them have overlapping success profile. Educational investment is not only beneficial for talent attraction but it also plays a key role for talent development. Consequently, hypotheses, H11 and H12, have been presented as follows:

H11. Educational investment is positively associated with TI.H12. Educational investment is positively associated with TD.Talented people seek out opportunities to grow, and they will flock to organizations that provide ample

opportunities to do so. Retention also becomes a non-issue; if people are developing more rapidly than they could anywhere else, why would they leave? If organizations are truly serious about attracting, retaining, and developing high-quality talent, they need to view themselves as growth platforms for talent where people can develop themselves faster than they could elsewhere. So, government or manager should provide high-level

policy guidance for talents in order to provide more in-depth guidance on certain of the department’s priorities and to encourage increased coordination in advancing communication and promotion platform.

Educational investment is a good way to help people develop a specific skill or knowledge set. As the pace of change increases, it can become increasingly difficult to predict what skills people will need, and the half-life of trainings may get shorter and shorter. The need for self-improvement is deeply satisfying to people of many skill levels and positions, and becoming a platform for talent can help you attract and retain highly skilled people. Leaders who understand the importance of creating communication and promotion platform where talents at many levels have opportunities for growth may find that they have tapped into the fundamental human motivation for progress, and they may see significant progress in innovation, productivity, and efficiency. Therefore, the following hypotheses, H13, H14 and H15, have been put forth:

H13. Policy Guidance is positively associated with TE.H14. Communication and promotion platform is positively associated with PG.H15. Educational investment is positively associated with CAPP.In order to discuss the impact of these factors on talent cultivation and regional economic growth, this

study incorporated contextual factors into the technology acceptance model (TAM), which is based on the assumption that talents behavioral intention predicts their willingness to root and cognition about the development of regional economy. The purpose of this study was to determine the organizational contextual factors that may affect talent rooting environment and EG. Fig. 1 summarizes hypotheses and presents the proposed a conceptual path model of AMOS and organizational contextual factors.

3. Methods

This study was conducted from July 2015 to April 2017 in school of economics, Sichuan University (SCU). For measuring the elements of the proposed model, a questionnaire was designed and undergone several drafts before it was finally approved by experts (including academics and practitioners) with significant experiences. The questionnaire was presented to talents whose age from 22 to 45. Participants who had bachelor's degree, master's or doctoral degree background were contacted and requested to fill up the questionnaire. Both male and female participants from 16 main cities in China were included in the sample of the study. However, due to inaccessibility to talents data base, it was not possible for the researchers to contact each and every participant. Therefore, convenience sampling ways was taken to collecting the data by field survey questionnaire, email, QQ and WeChat. For statistical analysis of questionnaires, we have used of the SPSS AMOS 22 software package.

3.1. data collection and Sample

Data were collected from diverse industries in sixteen large and medium-sized cities: Beijing, Tianjin, Shanghai, Chongqing, Shenzhen, Chengdu, Xi'an, Guangzhou, Hangzhou, Nanjing, Xiamen, Changsha, Wuhan, Zhuhai, Shenyang and Urumchi. Fig. 2 shows the questionnaires percentage of sample in the sixteen large and medium-sized cities. A one-way analysis of variance was used to test for differences in EG among sixteen large and medium-sized cities. The main hypotheses of the study were tested using hierarchical regression analysis.

Fig. 2. Questionnaires percentage of sample citiesParticipants were included in the study only if they were willing to respond to researchers’ request. All

participants were ensured that their provided information will only be used for academic purposes and will be kept secret. In total, more than 7153 questionnaires were distributed among participants of the study, however only 6326 were resumed for final analysis. This provides 88.4% response rate for the study which is quite adequate to conduct the further analysis. Among these respondents, 3214 were female and 3112 were male participants. Finally, sample error was calculated and it was ±6.26% with 95% confidence level (p ≤ 0.05). Moreover, in 7153 questionnaires, 6995 people believe that talent and natural resources contribution to economic development accounted for 50%, only a small number of people think production tools or other factors to promote economic growth.

3.2. Measures

The core of the questionnaire was a set of items relating to issues that have been proposed in the literature to influence talents satisfaction. The respondents indicated their agreement or disagreement with the above items using a seven-point Liker scale with 1 representing strongly disagree and 7 representing strongly agree. Nine variables were measured: five primary latent variable variables (TE, PG, CAPP, EI and NR), two intermediate latent variables (TI and TD), and the final latent variable EG.

3.3. Reliability analysis

The structural equation model (SEM) analysis procedure is applied in two stages based on related noticeable literatures. The first stage involves performing the reliability analysis and confirmatory factor analysis specific to dimensions and items. Although, previous studies (Mohsen and Reg, 2006; Auewarakul et al., 2015) mentioned quite reasonable Alpha reliability for the measurements that are used in this study, however, validation was fairly essential before conducting the tests for hypotheses of this study. Consequently, Cronbach’s Alpha reliability analysis were conducted in IBM SPSS. The results of reliability analysis are provided in Table 1.

Table 1 Items used to measure the various constructs in the model.

ConstructItemnumber

ItemsCronbach’s

Alpha reliabilityEducational investment

1 Good hardware environment is conducive to the development of myself and my kids.

0.91

(EI)2

Software environment affects the future development of myself and my kids.

3 Education investment is critical for attracting talents and innovation.

Communication and promotion platform(CAPP)

4 Communication and promotion platform is helpful in my job.

0.755

In the long run, good communication and promotion platform will help me to develop in future.

6Communication and promotion platform help me to promote work efficiency.

Policy Guidance (PG)

7 Forward-looking for policy guidance is critical for regional development.

0.868 The continuity of policy guidance is vital to regional development.

9The stability of policy guidance is very important for regional development.

Talent environment(TE)

10 I think that humanistic environment is very important.

0.7911 I believe good medical environment attracts me.12 The better the working environment is, the more attractive it is..13 It is very important for me to have a good living environment.14 Crowded traffic environment will affect my mood.

Talent development(TD)

15 I will pay attention to my future development prospects.0.8716 How much holiday and freedom should be of concern.

17 I will consider income and development opportunities.

Talent introduction(TI)

18 Wages and salaries are very important for me.0.8519 I think talents have the right to enjoy tax incentives.

20 I will care whether it is convenient for children to go to school.

Talent rooting environment (TRE)

21 Regional commitment is important to me.

0.9022It is very important for me to have good ecological environment and service platform.

23 Keeping promises is crucial to decide whether I will stay or leave.

Natural resources(NR)

24 Structure of natural resources is important to economic growth.

0.8325

Quantity of natural resources will affect the introduction of enterprises, especially, high-tech enterprises.

26Quality of natural resources will affect the development of regional industry.

Economic growth(EG)

27 An efficient economic growth is necessary for regional development.0.8828 Green growth is essential for economic.

29 Sustainable growth is very important for regional development.

Table1 validates the claim of previous researches (Weng and McElroy, 2010) about the reliability of talent introduction and talent rooting environment. All the measures used in present study postulated higher Cronbach’s Alpha reliability values i.e. all these values are above 0.60, which is suggested threshold for the Cronbach’s alpha reliability and acceptability (Pallant, 2013). Hence, all the variables were highly reliable for present study and for the assessment of the hypotheses regarding talent rooting environment, CFA and SEM analysis in next sections.

4. Results

4.1. Correlation and validation

Demographic information of the study participants (in Table 2) shows the mean and standard deviation for demographics variable. Table 2 shows the majority of the participations (44.9%) were female. The mean age of the respondents was 31.6, and 27.4% of the participants were in the age range of 28–33. About 41% of

the participants have the university degrees. The data also showed the mean year of 5.63 for the participants’ work experience while less than half of them (39.1%) had 1–5 years of work experience.

Table 2Demographic information of the sample.

Demographics Category Freq. Per. Mean Standard deviation

GenderMale 3112 43.5

Female 3214 44.9Total 6326 88.4

Age

20–24 798 11.1

31.6 2.56

25-28 1577 22.028-33 1964 27.433-36 1305 18.236≤ 709 9.9

Total 6353 88.6

Degree

Bchelor 2936 41.0Master 1701 23.7Ph.D. 905 12.6Total 5542 77.3

Work experience

1–5 2803 39.1

5.63 2.915–10 1681 23.510≤ 1306 18.2

Total 5790 80.8

According to the findings, shown in Table 3, there was a positive and significant correlation between TAM variables i.e. EG, NR and TRE. This table also shows a positive and significant correlation between TRE and TI, TRE and TD, TI and TE, TI and PG, TD and PG, TD and EI, EI and CAPP, PG and CAPP, PG and TE. It was also found that there is no significant correlation between TE and TD, CAPP and TI, EI and TI.

Table 3Correlation between latent variables of proposed conceptual path model

Constructs EG NR TRE TI TD TE PG CAPP EIEG 1NR 0.536*** 1TRE 0.589*** 0.218* 1TI 0.236** 0.347* 0.547*** 1TD 0.282** 0.139* 0.601*** 0.315*** 1TE 0.361* 0.046* 0.461*** 0.626*** 0.341 1PG 0.284*** 0.135** 0.504*** 0.481*** 0.437*** 0.196*** 1

CAPP 0.179* 0.132* 0.364*** 0.197 0.561*** 0.391*** 0.378*** 1EI 0.373** 0.257* 0.410*** 0.316 0.704*** 0.325*** 0.436*** 0.402*** 1

Notes: (*) P-value is significant at 0.05 levels, (* *) P-value is significant at 0.01 levels, (* * *) P-value is significant at 0.001 levels.

Validation of the model has been conducted by various fitness measures. Standard values of fit indices stated by Byrne (2010) and values obtained from the test have been listed in Table 4 and Table 5 respectively. Structural equation modeling (SEM) is advocated because it expands the explanatory ability and statistical efficiency for model testing within a single comprehensive method (Hair et al., 1998). Because chi-squared statistics are sensitive to sample size, the root-mean-square error of approximation (RMSEA), the normed fit index (NFI), and the comparative fit index (CFI) were used to assess model fit (Bentler and Bonette, 1980). Both the NFI and CFI with values in the upper 0.80s indicate an acceptable fit, while those

over 0.90 indicate a good fit (Bryne, 2000). The RMSEA with values under 0.1 represent mediocre fit, values between 0.08 and 0.05 indicate responsible fit, and those under 0.05 show a close fit to the approximation of population (Bryne, 2000).

Table 4Recommended goodness-of-fit measure.

Fit Indices Desired Range

χ2/degrees of freedom<2 excellent fit≤ 3 okay fit>5 poor fit

RMSEA (Root Mean Square Error of Approximation)

Values less than 0.05 show good fitValues as high as 0.08 represent reasonable fitValues from 0.08 to 0.10 show mediocre fitValues > 1.0 show poor fit

Goodness-of-fit index (GFI) ≥ .90 Closer to 1 the better the fitAverage Goodness-of-fit index (AGFI) ≥ .90 Closer to 1 the better the fitComparative Fit Index (CFI) ≥ .90 Closer to 1 the better the fit

The goodness-of-fit (GFI) statistics are shown in Table 5. Both the fit indices GFI and AGFI are within the desired range i.e. 0.913 and 0.982 respectively. As RMSEA values of about 0.05 or less would indicate a close fit of the model, and values of 0.08 or less would indicate a reasonable error of approximation. The Chi-square value is also satisfactory and the value of χ2/df is also considerable and RMSEA value is quite small as it should be. CFI and NFI were considered and appeared to be favorable. As the values of all fitness parameter indices are well within permissible range it can be stated that policy guidance plays a vital role in the process of talent introduction and talent development.

Table 5Model Fitting Parameters.

Chi-Square(χ2) df χ2/df GFI RMSEA AGFI NFI CFI728.91 365 1.997 0.913 0.0371 0.982 0.930 0.968

4.2. Hypothesis testing result

Fig. 3 presents the path diagram developed by IBM SPSS Amos 22 software which demonstrates the hypothesized relationships among latent constructs. The values over the arrows indicate the associated standardized regression weights obtained after execution of SEM analysis. The inferences drawn here are on the basis of the path estimate values.

Fig. 3. Standardized estimates result of the structural equation model.Notes: OV-observation variable, e-error, Z-disturbance or residuals.

Regarding the standard coefficient of TRE and EG (β = 0.528, p = 0.001), indicating the association between TRE and EG, H1 was supported. Furthermore, it was found that NR positively affected EG (β = 0.480 with p = 0.001), which supports H2. Concerning the association between TI and TRE, the results showed that TI had positive and significant effect on TRE (β = 0.489, p = 0.001); hence, H3 was supported. Also, the findings (β = 0.548, p = 0.001) revealed positive and significant correlation between TD and TRE, supporting H4 and showing that TD is positively associated with TRE. Concerning the association between PG and TI, the results showed that PG had positive and significant effect on TI (β = 0.411, p = 0.001); hence, H7 was supported. Also, the findings (β = 0.384, p = 0.001) revealed positive and significant correlation between PG and TD, supporting H8 and showing that PG is positively associated with TD. As for the relationship between TE and TI, the findings were favorable (β = 0.572, p = 0.001) and supported H5. Similarly, the findings (β = 0.512, p = 0.001), (β = 0.694, p = 0.001), (β = 0.181, p = 0.001), (β = 0.309, p = 0.001) and (β = 0.353, p = 0.001), show H10, H12, H13, H14 and H15 were supported, respectively.

As can be seen from Table 3 and Fig. 3, concerning the relationship between TE and TD, CAPP and TI, EI and TI, unexpectedly, the findings showed there were no significant relationship between TE and TD, CAPP and TI, EI and TI, unexpectedly. Thus H6, H9 and H11 were not supported in this study.

4.3. Structural equation model

The measurement model as a part of SEM is recognized as confirmatory analysis model, which examines relationship between the latent and observed variables. Universally, it consists of two equations, which distinguishes the relationship between the exogenous latent variables and exogenous observed variables, endogenous latent variables and endogenous observed variables. They are expressed by equation like this:

（1） （2）

Where is vector of exogenous observed variables, is vector of endogenous observed

variables, is vector of exogenous latent variables, is vector of endogenous latent

variables, is matrix, called factor loading matrix on exogenous latent variables, is

matrix, called factor loading matrix on endogenous latent variables, is vector of measurement

error, is vector of measurement error, and represent the part that can not be explained by latent variables. The following measurement model equations can be obtained from Fig. 3.

（3） （4） （5）

（6） （7） （8）

（9） （10） （11）

The structure model is known as cause and effect model. It mainly explains the relationship between thelatent variables; it is expressed by equation like this:

or （12）Where is the vector of endogenous latent variables, is the vector of exogenous latent variables, is the relationship between endogenous latent variables, is the value of exogenous latent variables on endogenous latent variables, is the measurement error in structure equation model, representing the part that the model can not be explained. The following structure model equations can be obtained from Fig. 3.

（13） （14） （15）

（16）

Considering Eq. (13), (14), (15) and Eq. (16), the structural equation for EG was proposed as follows:

（17） Using the model to predict EG described in the above equation, it is clear that TRE will be more effective

if they were targeted at fostering CAPP, TE, especially EI and PG.

5. Discussion

Confirmatory factor analysis (CFA) is a multivariate statistical procedure that is used to test how well the measured variables represent the number of constructs. CFA is a special case of the structural equation model (SEM), also known as the covariance structure. SEM consists of two components: a measurement model linking a set of observed variables to a usually smaller set of latent variables and a structural model linking the latent variables through a series of recursive and non-recursive relationships. By analysis of moment structures, the equations of measured model were obtained. As shown in the fig.3 and formulas from 3 to 11, the normalized estimated loadings were between 0.6 and 0.95 to support convergent validity. Consequently, the design of the sample is reasonable and feasible. This means that if we attract more talents to stay, EI should focus on hardware and software environment; CAPP should be got more attention; PG should keep forward-looking, continuity and stability; Manager that is challenged by TE should spend more time on creating favourable humanistic, medical, work, living and traffic environment. Meanwhile, it is essential to give people enough salary, time and space.

Using structure model to predict intention described in the equation (13), it was clear that the growth of regional economy depended mainly on talent rooting environment (TRE) and natural resources (NR), and TRE has played a major role. TRE consisted of talent introduction (TI) and talent development (TD). From the regression coefficient of Eq. (14) can be seen that talented people paid more attention to their future development while considering the attraction of the environment, namely, TD was more important than TI. According to Eq. (15), policy guidance (PG) and talent environment (TE) were very critical two factors during the process of talent introduction, PG having a positive effect on TE. Similarly, TD was made up of three parts: EI, PG and communication and promotion platform (CAPP). Eq. (16) showed that EI had a positive effect on CAPP and CAPP had a positive effect on PG. As can be seen from Eq. (17), obviously, economic growth (EG) relied strongly on EI, CAPP, PG, TE and NR.

Motivated by the studies that have been used for estimating the error correction model, which suggested that the economic growth driven by talent accumulated. Simultaneously, there was selection bias towards talent rooting environment. Compared with the talent introduction, it was more profound to pay more attention to the future development of talent. The model demonstrated that increased education investment will unambiguously conditionally promote the attraction to talented person. Empirical results also illustrated that policy guidance had positive and significant effect on talent introduction and talent development. Furthermore, the genuine growth effect of talent for economic was homogeneous across studies, but varied according to several factors. In a word, sustainable economic growth can be achieved only when the relative conditions were good enough to attract and stay more talent.

It should be noted that this study concentrates on only talent rooting environment. However, we have to point out that we rarely consider the impact of natural resources on regional economic growth. Although our sample of talents is large, we only investigated the TRE in some cities in China. This article suggests that those who see the TRE solely as a function of economics have a limited view. These results suggest that economics is indeed important, but they are not the only factors. Hopefully this integrative framework may guide further academic research on region talent management and might also inform the work of regional talent introduction.

6. Conclusions and suggestions

In this study, we had investigated the impact of talent rooting environment on regional economic growth. Data was used to analyse some of the important variables affecting regional economic development. These are tested empirically regarding the influence of the local talent attraction and the relationship between them examined. Empirical results suggest that the economic growth driven by talent accumulated and also illustrate that policy guidance had positive effect on talent introduction and development. The resultant model appears robust and can be used to draw some important policy lessons for economic policy in China and other countries.

Consistent with Weng and McElroy (2010) finding EI and TI the two neutral variables leads to a more favorable parameter estimates of the TRE variables. Therefore, basing our interpretation and discussion on results of model, which suggest talent is a key factor for economic growth, but not the only factor. The TRE that includes educational investment, communication and promotion platform, policy guidance, and talent environment is the key to increasing region attraction and developing talent.

TRE for most organizations today is complex, dynamic and highly competitive, and extremely volatile, which is likely to remain for years to come. In addition to these external conditions, regional development is also facing several challenges. If we want our talents to stay, we must make the following changes. Based on the global vision, firstly, a system of talent system should be established in line with international standards. Starting from regional situation, secondly, management and human resources department should further optimize management structure, build livable environment and strengthen the soft power of the region so as to attract and keep the best use professional people. Most important of all, they should provide a good environment for talent innovation and keep policy guidance consistent, timely and forward-looking.

Acknowledgements

This research was supported by the Key Program of National Social Science Foundation of China, Grant No. 11AZD101; the Major Projects Science Foundation of Sichuan Provincial Education Department of China, Grant No. 17SA0147.

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