Discrete Dynamics in Nature and Society
Volume 2015 (2015), Article ID 219865, 10 pages
http://dx.doi.org/10.1155/2015/219865

Research Article

Factors Influencing Mechanism of Construction Development Transformation in China Based on SEM

Yiyue Wang,¹ Zhongfu Li,² and Feng Shi³

¹School of Management, Harbin Institute of Technology, Harbin 150001, China
²Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China
³Department of Engineering Management, Dalian Nationalities University, Dalian 116650, China

Received 4 May 2015; Revised 7 September 2015; Accepted 8 September 2015

Academic Editor: Juan R. Torregrosa

Copyright © 2015 Yiyue Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Construction industry development transformation is one of the most important issues in China. Along with the improving construction industry development transformation, problems have increased and need to be solved. For effectively improving the construction industry development transformation, this paper studied the factors influencing mechanism of construction industry development transformation. Construction industry development transformation is influenced by many factors. Firstly, 10 significant influential factors were extracted and 25 observable variables were used. Secondly, Structural Equation Modeling (SEM) was used to analyze the relationship between construction industry development transformation and its influential factors. Then, SEM hypothesis based on the research on the factors influencing construction development transformation was constructed. Through empirical results and its analysis, the role of the basic production factors for construction cannot be ignored. The internal industry directly influenced the transformation in the construction. The external industry indirectly influenced construction transformation. And industrial demands stimulated the basic influencing factors.

1. Introduction

Construction industry is one of the most important industries in China. From 1978 to 2014 construction industry in China has achieved remarkable results; proportion of the construction in the national economy has improved from 3.8% to 7.8%. The construction plays an irreplaceable role in supporting the national economy. However, the extensive mode of development for long-term presence in the development of the construction has been restricting the development of the construction. With the rapid development of the construction, the current development is unsustainable. With the strategic decision of economic development transformation in China being made, changing the development mode of the construction is also imperative.

The transformation of development pattern was proposed for the Chinese original economic system, government functions, and technology development, and its aim was not only to achieve economic growth but also to pay more attention to improving the quality of economic, structural optimization, and social benefits. The construction is very important to economic growth in China [1–3], but it was found that the output was mainly from capital invested with diminishing returns, and the growth of Total Factor Productivity and its contribution to economic growth was negative through the analysis of the total factor in the construction of China from 1998 to 2014. Only in the period of 2000–2005, the scale of the industry was economic. As the economic develop pattern of construction industry is single, the inputs and using of factors are low efficient. So, the development mode of construction industry cannot adapt to the changes in the market and bring diseconomies of scale. Some scholars proposed that it is helpful for the transformation of development pattern in the construction of China that the construction service was expanded by flexible structure of factor inputs. Because it would improve the construction products added value [4, 5]. In contrast with countries and regions where the industry went well, it is found that the profits, the growth of contracts, and operating performance were the main aspects of the competition among the construction enterprises. With the rapid development of urbanization, the investment of urban infrastructure, real estate construction, and other aspects is huge, which are stimulating the development of related industries [6–8]. The operation innovative of the construction factors was the direct manifestation of the added value in the industry, and its mode, business philosophy, and production philosophy must be closely integrated with the factors of industrialization and information technology, so that the innovation was to solve the problem and contradictions posed by the traditional and modern technology and management. Therefore, the economics of construction was promoted by human capital, technology improvement, and technological innovation capability enhancement [9, 10]. The research about the terms of physical capital, human capital, technology, industry structure, management, industrial demand, policy institution, urbanization, housing markets, and so forth based on this would reflect the development status of the construction and the development direction of construction in China.

2. The Model of Factors Influencing Mechanism

2.1. Method Chosen

Structural Equation Modeling (SEM) is valid to research the factors, which is a theory-driven statistical method put together with factors analysis, canonical correlation analysis, and multiple regression analysis. SEM could deal with multiple dependent variables, whose variables allow containing deviation and being posed by multiple observable variables if it is latent. It is a measuring mode more flexible than the traditional. The researchers could assume the relationship of the latent variables and then verify the regression level of the data in the model [11–13].

2.2. SEM Hypothesis

Based on the research on the factors influencing of the development transformation in the construction around the world [14, 15], the model hypothesis are as below combined with observable variables of the factors in this paper [16].

(1 ) The Influence of the Basic Production Factors to the Transformation in the Construction. According to the economic growth theory, the economy could be stimulated by the basic production factors, which are the endogenous factors of the economic development in the construction [17]:

(H1a)direct positive influence of physical capital to the transformation in the construction;(H1b)direct positive influence of human capital to the transformation in the construction;(H1c)direct positive influence of technical level to the transformation in the construction.

(2 ) The Influence of Industrial Internal Environment Factors to the Transformation in the Construction. The industrial structure and management level directly positively influencing are assumed [18]:

(H2a)direct positive influence of industrial structure to the transformation in the construction;(H2b)direct positive influence of management level to the transformation in the construction.

(3 ) The Influence of Industrial External Environment Factors to the Transformation in the Construction. The influence that industrial demands impact on the basic production factors could affect the transformation in the construction [19]. The influence that policy system impact on industrial internal environment could affect the transformation in the construction. At the same time, the tendency of the policies would change the industrial demands:

(H3a)direct positive influence of the industrial demands to physical capital;(H3b)direct positive influence of the industrial demands to human capital;(H3c)direct positive influence of the industrial demands to technical level;(H4a)direct positive influence of the policy system to industrial structure;(H4b)direct positive influence of the policy system to management level;(H4c)direct positive influence of the policy system to industrial demands;(H5a)direct positive influence of urbanization to industrial demands;(H5b)direct positive influence of urbanization to housing market;(H6a)direct positive influence of housing markets to industrial demands.

2.3. The Selection of the Index

Based on the analysis above, the latent variables are collected as physical capital, human capital, technical level, industrial structure, management level, industrial demands, urbanization, housing markets, and the transformation in the construction. The latent variables, observable variables, and their meaning and interpretation are shown as Table 1.

Table 1: The factors influencing indicators analysis of economic development transformation.

Latent variables
Observable variables
Variables symbol

The transformation in the construction
Gross output value of construction
JF1

Value added of construction
JF2

Value added rate of construction
JF3

The rate of gross output value of construction to GDP
JF4

Overall labor productivity of construction
JF5

TFP of construction
JF6

Physical capital
Net value of machinery and equipment owned
WZ1

Assets of construction enterprises
WZ2

Human capital
Number of persons employed
RZ1

Years of education persons employed
RZ2

Technical level
Power of machines per laborer
JS1

Value of machines per laborer
JS2

Industrial structure
Total output value rate of construction enterprises of special and first grade general contractors
SC1

Total profits rate of construction enterprises of special and first grade general contractors
SC2

Management level
The yield of unit construction
GS1

The yield of floor space completed of buildings constructed by construction enterprises
GS2

Industrial demands
Gross scale of construction
CX1

the length of road
CX2

Bridges in cities
CX3

Policy system
Gross output value rate of nonstate-owned
ZZ1

Fixed investments rate
ZZ2

Urbanization
The proportion of urban population
UR1

The total number of foreign employees
UR2

Housing market
Housing industry output value
HM1

Housing industry investment
HM2

Note: the influence of taxes to the transformation in the construction in the factors of management level is not significant, so that the taxes variable will not be considered.

2.4. Data Collection and Processing

(1 ) Data Collection. SEM has high demand of sample size, at least 200. In this paper, the statistical data was collected from 30 provinces from 1998 to 2013. The data as JF1, JF2, CX2, CX3, and ZZ2 were from “CHINA STATISTICAL YEARBOOK”; the data as JF5, WZ1, WZ2, JS1, JS2, SC1, SC2, GS1, GS2, CX1, and ZZ1 were from “CHINA STATISTICAL YEARBOOK ON CONSTRUCTION”; the data as RZ1 and RZ2 were from “CHINA LABOUR STATISTICAL YEARBOOK.” JF3, JF4, and JF6 were calculated by the data above. JF6 was calculated by Malmquist based on DEA; JF4 was the ratio of JF1 and GDP; JF3 was the ratio of JF2 and JF1. All the data could be used by Table 1.

(2 ) Data Processing and Testing. The data should be firstly standardized for being valid because of the large scale and amount of statistical calibers. In this paper, the data were standardized by logarithm function transformation, and the function is as follows:

in which is the standardizing data.

It is necessary to test the reliability of the data which describe the level of consistency and stability. The consistency mainly reflects the relationship among internal subjects to make sure whether each subject measures the same content or quality. The stability is the reliability coefficient among the repeated measurements for the same testers at different timing using one kind of measure method.

The data in this paper are official, so that the consistency could reflect the reliability. Split-half reliability is a method where items number is classified by odd and even or directly cut into two segments from the middle, using the function of Spearman-Brown to estimate the corresponding coefficient. In 1951, a new method was developed by Cronbach, which is Cronbach’s Alpha coefficient. Its principle is any item can be compared with another in the measurement tool. More importantly, Cronbach’s Alpha coefficient has more strict and careful requirements for the consistency estimation. So, it could eliminate the shortcomings of split-half reliability. The result of data consistency was in Table 2 using SPSS18.0.

Table 2: Results reliability analysis.

Cronbach's alpha
of items

.817
25

From the result shown, the reliability reached 0.896, much over 0.7, which showed that these data have enough reliability to do the research of SEM. The reliability of seven latent variables was shown as Table 3. From Table 3, the reliability of each latent variable is over 0.7; therefore, the data in this paper are of well reliability.

Table 3: Latent variables reliability analysis.

Latent variables
The transformation in the construction industry
Physical capital
Human capital
Technical level
Industrial structure
Management level
Industrial demands
Policy system
Urbanization
Housing market

of items
6
2
2
2
2
3
2
2
2
2

Cronbach's alpha
0.841
0.818
0.829
0.774
0.728
0.752
0.763
0.716
0.759
0.716

Validity reflects the degree of characteristics that could be measured by tools correctly, which contains Content Validity, Criterion Validity, and Construct Validity. Content Validity and Criterion Validity are difficult to achieve in practice. Because they demand the experts to do qualitative research or the measurements should be taken in an accepted standard environment.

3. The Empirical Results and Analysis

3.1. The Original SEM Model and Its Estimation

(1 ) The Drawing of the Original SEM. The original SEM drawn and then built was shown as Figure 1 using AMOS18.0.

Figure 1: The initial set of structural equation model.

(2 ) The Estimation of the SEM. The standardized results of the parameter estimation operated using the maximum likelihood estimation run by Amos18.0 were shown in Figure 2, in which process the data were fitted with the theoretical model.

Figure 2: Structural equation model estimation results.

3.2. The Evaluation of SEM

(1 ) Significance Evaluation of Path Coefficient. Significance evaluation of path coefficient shows whether the parameter estimates are statistically significant in the model. There is a test of Critical Ratio (C.R.) in the Amos18.0, and the results were in Tables 4 and 5. The C.R. and of the standard deviation estimate were shown as Table 5, which showed the results were significant.

Table 4: Standardized regression weights.

The path of SEM
Estimate
S.E.
C.R.

The transformation in the construction ← physical capital
.543
.147
2.843

The transformation in the construction ← human capital
.271
.754
7.312

The transformation in the construction ← technical level
.450
.884
72.863

The transformation in the construction ← industrial structure
.574
.416
−15.251

The transformation in the construction ← management level
.064
.905
5.253

Physical capital ← industrial demands
.685
.001
16.933

Human capital ← industrial demands
.375
.029
24.479

Technical level ← industrial demands
.342
.162
10.753

Industrial structure ← policy system
.127
.970
1.422
.075

Management level ← policy system
.253
.049
20.735

Industrial demands ← policy system
.248
.008
71.146

Industrial demands ← urbanization
.413
.023
6.419

Housing market ← urbanization
.295
.062
23.483

The transformation in the construction ← housing market
.103
.952
2.842

JF1 ← the transformation in the construction
.764
.000
−5.943

JF2 ← the transformation in the construction
.636
.000
4.307

JF3 ← the transformation in the construction
.916
.063
26.846

JF4 ← the transformation in the construction
.982
1.030
25.431

JF5 ← the transformation in the construction
.562
.000
3.014

JF6 ← the transformation in the construction
.948
.000
16.138

WZ1 ← physical capital
.959
.215
8.846

WZ2 ← physical capital
.767
.437
9.835

RZ1 ← human capital
.954
.163
5.175

RZ2 ← human capital
.274
.257
35.436

JS1 ← technical level
.905
.000
13.063

JS2 ← technical level
1.052
.464
2.742

SC1 ← industrial structure
.983
1.873
14.962

SC2 ← industrial structure
.836
.034
8.772

GS1 ← management level
.334
1.743
8.346

GS2 ← management level
.291
.235
43.374

CX1 ← industrial demands
1.257
.007
.374

CX2 ← industrial demands
.516
.036
4.236

CX3 ← industrial demands
.451
.035
31.336

ZZ1 ← policy system
.052
.126
.141

ZZ2 ← policy system
.447
.914
2.746

UR1 ← urbanization
.735
.052
.244

UR2 ← urbanization
.301
.062
5.714

HM1 ← housing market
.529
.951
13.195

HM2 ← housing market
.436
.052
24.053

Note: reflects , significant at 1%.

Table 5: Variances estimation results.

Estimate
S.E.
C.R.

e2
.002
.252
3.723

e7
.258
.336
.486

e3
.253
.123
2.735

e4
.774
.255
2.386

e5
.736
.695
3.365

e6
.174
1.852
2.235

e1
.985
.906
-.017

e10
.194
.046
3.058

e11
.127
.0984
3.984

e12
.903
.036
3.853

e13
.084
.085
3.735

e8
.732
.263
2.763

e9
.843
.053
4.257

e14
.743
.074
1.743
.083

e15
.003
.524
3.732

e16
.898
.263
1.753

e17
.173
1.163
3.732

e18
.123
.061
2.962

e19
.087
.723
3.028

e20
1.623
.762
1.623

e25
.489
.484
−.917

e26
.729
.909
3.129

e28
1.298
.362
−1.372
.228

e21
.326
.591
2.437

e22
.286
.749
−.324

e23
.137
1.126
2.828

e24
.082
.027
.977

e31
.316
.763
−.624

e33
1.833
.046
.926

e34
.733
1.105
.537

e32
.457
.942
1.025

e35
.036
.627
−4.015

e36
.258
.920
2.722

Note: reflects , significant at 1%.

(2 ) Degree Evaluation of the Model Fit. Model fit index is used for inspecting the matching degree between the data and SEM. Amos18.0 provides many model fit index. From Table 6, the fit of data and SEM is not perfect, we need to revise it. But in the SEM, model fit index just reflects the degree of fit, but not the judgment of whether the establishment of SEM is correct or not. More importantly, the rationality should be demonstrated according to the research background and theoretical basis. It is significant that the SEM could be testified by practical experience and economic theory in this research, although there is no perfect fit.

Table 6: Model fit summary.

Model fit index
Estimate
Evaluation criteria

8.216
Bigger, Better

RMR
0.064
<0.08

GFI
0.717
>0.9

RMSEA
0.569
<0.08

NFI
0.923
>0.9

CFI
0.86
>0.9

3.3. Hypothesis Test of SEM

From Table 4, the standardizing path coefficients among the latent variables were significant at the level of 1%, except path of the industrial structure and policy system. Path coefficient is 0.083 ( value) and less than 0.1, which could be accepted at the significant level of 10%. The standardizing path regression coefficient between each latent variable and its observing variables were significant at the level of 1%. Therefore, hypothesis test results of SEM were shown in the Table 7.

Table 7: Hypothesis testing results.

The content of hypothesis
Estimate
Conclusion

H1a direct positive influence of physical capital to the transformation in the construction
0.543
Positive

H1b direct positive influence of human capital to the transformation in the construction
0.271
Positive

H1c direct positive influence of technical level to the transformation in the construction
0.450
Positive

H2a direct positive influence of industrial structure to the transformation in the construction
0.574
Positive

H2b direct positive influence of management level to the transformation in the construction
0.064
Positive

H3a direct positive influence of the industrial demands to physical capital
0.685
Positive

H3b direct positive influence of the industrial demands to human capital
0.375
Positive

H3c direct positive influence of the industrial demands to technical level
0.342
Positive

H4a direct positive influence of the policy system to industrial structure
0.127
Basic positive

H4b direct positive influence of the policy system to management level
0.253
Positive

H4c direct positive influence of the policy system to industrial demands
0.248
Positive

H5a direct positive influence of urbanization to industrial demands
0.413
Positive

H5b direct positive influence of urbanization to housing market
0.295
Positive

H6b direct positive influence of housing markets to construction development transformation
0.103
Positive

3.4. Result Analysis

(1) There is direct and indirect influence in the factors of the transformation in the construction. The results were in Table 8.

Table 8: The influence of the change of the pattern elements of construction development.

Influence factors
Relationship of influence
Influence coefficient

Physical capital
Direct
0.543

Human capital
Direct
0.271

Technical level
Direct
0.450

Industrial structure
Direct
0.574

Management level
Direct
0.064

Housing market
Direct
0.103

Policy system
Indirect
0.089

Industrial demands
Indirect
0.512

Urbanization
Indirect
0.242

The path coefficient from reason variables to outcome variables is used to measure the direct effect of the two variables. Physical capital, human capital, technology level, industry structure, management level, and housing market were the direct effect of variables. It indicates that the influence of these five factors was direct to the transformation in the construction, and the standardizing influence coefficients were 0.543, 0.271, 0.450, 0.574, 0.064, and 0.103.

Indirect effect is the influence that reason variables affect one or more intermediate variables, and then the outcome variables are affected by the intermediate variables. If there is only one intermediate variable, indirect effect is measured by the product of two path coefficients; if there are multiple intermediate variables, indirect effect is measured by the sum of indirect effect of each intermediate variable. Industry demands, policy system, and urbanization were variables of indirect effects, and its influence to the transformation in the construction was indirect.

There were three path of indirect influence to the transformation in the construction from industrial demands. They were as follows: industrial demands → physical capital → the transformation in the construction, industrial demands → human capital → the transformation in the construction, industrial demands → technical level → the transformation in the construction. And influence coefficient was 0.512. As the same, policy system affect the construction transformation through industrial structure, management level and industrial demands which is an indirect effect variable. Therefore, the path was policy system → industrial structure → the transformation in the construction, policy system → management level → the transformation in the construction, policy system → industrial demands → physical capital → the transformation in the construction, policy system → industrial demands → human capital → the transformation in the construction, policy system → industrial demands → technical level → the transformation in the construction. And influence coefficient was 0.089. Urbanization affects the transformation in the construction through four paths. They were as follows: urbanization → industrial demands → physical capital → the transformation in the construction; urbanization → industrial demands → human capital → the transformation in the construction; urbanization → industrial demands → technical level → the transformation in the construction; urbanization → housing market → the transformation in the construction. And influence coefficient was 0.242.

(2) Suppose (H1a), (H1b), and (H1c) were correct, which is the direct positive influence of physical capital, human capital, and technical level to the transformation in the construction, and their effects were technical level > physical capital > human capital, which is consistent with the theoretical analysis. It reflected that technology level in construction is the most critical factor for the transformation; but the influence of human capital is less than others. From the observing variables, RZ1 is the most reflection in the human capital, and the value of RZ2 is 0.26, which could not fully reflect the factor. It showed that the level of human capital in construction is low in China, and it is an entrance to promote human capital for the transformation in the construction. The coefficient of the observing variables of physical capital and technical level identically reflect the status of the factor.

(3) Suppose (H2a) and (H2b) were correct, which is the direct positive influence of industrial structure and management level to the transformation in the construction. The coefficients of observing factors GS1 and GS2 were 0.33 and 0.26, which showed that the management level is low. It showed the reality in construction at some level, although the two observing variables could not fully reflect the status.

(4) Suppose (H3a), (H3b), (H3c), (H4a), (H4b), and (H4c) were correct, which is the indirect positive influence of industrial demands and policy system to the transformation in the construction. The influence coefficient of industrial demands through physical capital is the biggest for the transformation. It showed that the construction increases the input of capital and equipments to pull the industrial demands, which is harmful for the transformation and the development of the construction. Because of the specific fact in China, the Industrial demands would increase when the investment of government increases. The influence for the transformation is industrial demands through physical capital in which the coefficient of ZZ1 was relatively low, but the coefficient of the test is significant. It reflected the factor of property rights to some extent, which needs further research on its accurate observing variable.

(5) Suppose (H6a) was correct, which is direct positive influence of housing market to the transformation in the construction. The results showed that the effect of housing market on the construction transformation was obvious. The development of housing market drives investment of the construction, and further the construction development transformation will be promoted. Suppose (H5a) and (H5b) were correct, which is the indirect positive influence of urbanization to the transformation in the construction. In the process of urbanization in China, with a large number of rural populations who were into the city, the demands of the urban infrastructure and other production and living facilities extremely expanded. To meet these demands, the construction products were developed in large scale to promote the development of the construction, and market competition should be standardized further to form a competitive mechanism which determines the direction of development in construction followed by the law of supply and demand. This is also the only way which must be passed. The construction should take the initiative to adapt to the new requirements in the process of urbanization to adapt to the development of new urbanization by the development transformation actually.

4. Conclusions

It is a complex process that the influence of factors affects the transformation in the construction, which involves the factors of physical capital, human capital, technology level, industrial structure, management level, industrial demand, policy system, urbanization, and housing market. The construction transformation includes two aspects which are development effect and development efficiency. In this paper, the factors’ relationship and the influence path were thoroughly studied in SEM for the transformation in the construction. The conclusions and enlightenment were as follows.

Firstly, the role of the basic production factors for the transformation played in the construction could not be ignored. Comparing the influence coefficient of seven factors, physical capital and human capital were still important, which noted that the construction was still labor and capital intensive, so that human capital, mechanical equipment, and so forth still played an important role in construction currently. The influence coefficient of technical level and technological innovation were relatively large, which shows that the improvement of technical level was important for the transformation in the development of the construction.

Secondly, the internal industry directly influenced the transformation in the construction. The influence of industrial structure was the highest, whereas the management level is the lowest. The reason could be due to that the construction contained entire industry and social dimension, but management level influences the transformation on the construction enterprises in a microcosmic way. In other words, the transformation in the construction industry not only needs to improve the management level, but also needs all the hard work of the construction industry. The influence of industrial structure is high on the industry, as its influence coefficient was second. Therefore, adjusting and optimizing industrial structure is the most important key for the transformation.

Last but not least, the external industry influenced the transformation in the construction directly and indirectly. The development of the Housing market directly affects the construction development transformation. Industrial demands which stimulated the basic factors of production have the highest influence. Influence coefficient of urbanization to the construction development transformation is in the middle of all of the factors. Through affecting the housing market and industrial demands, urbanization puts an indirect influence on the construction development transformation in China. The reason would be that industrial demands were the main driving force of the industry development which increases every year with the rapid economic development and the rapid progress of urbanization in China. The more the industrial demands increases, the more the material capital, human resources and technology will put in the construction industry. The influence of policy system was relatively comprehensive, and the coefficient was not high, but its influence path was the most, which indicate that policy system affected a wide range of other factors which could affect the transformation in the construction.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

We are particularly grateful to the academic editor and reviewers for their thoughtful, valuable comments and suggestions.

References

Y. G. Dai and C. Chen, “Total factor productivity for Chinese construction industry and its convergence trend,” Science-Technology and Management, vol. 1, pp. 81–84, 2010.

X. Wang, Y. Chen, B. Liu, Y. Shen, and H. Sun, “A total factor productivity measure for the construction industry and analysis of its spatial difference: a case study in China,” Construction Management & Economics, vol. 31, no. 10, pp. 1059–1071, 2013.

M. I. Kamenetskii, “Construction sector as a factor of prospective development of the national economy,” Studies on Russian Economic Development, vol. 24, no. 3, pp. 249–258, 2013.

J. Weixing, “New economic growth points and growth forces of the construction in China,” Construction Economy, vol. 9, pp. 16–20, 2003.

B. Liu, X. Chen, X. Wang, and Y. Chen, “Development potential of Chinese construction industry in the new century based on regional difference and spatial convergence analysis,” KSCE Journal of Civil Engineering, vol. 18, no. 1, pp. 11–18, 2014.

R. Zhou, S. O. Marxism, H. University et al., “Research on the development dimension and realization path of China's new urbanization,” Construction Economy, vol. 36, no. 5, pp. 100–102, 2015.

Y. Liang and C. Z. Cai, “Exploration of priority strategies for harmonious development between urbanization and the construction of new countryside in China based on the theory of unbalanced growth,” Asian Agricultural Research, vol. 3, no. 8, pp. 69–73, 2011.

Y. J. Feng and C. Zhen-Huan, “Research on dynamic relativity of population urbanization and construction industry development in China—an empirical test based on VAR model,” Resource Development & Market, vol. 9, no. 11, pp. 1154–1159, 2013.

P. Garcia-Castrillo and M. Sanso, “Human capital and optimal policy in a Lucas-type model,” Review of Economic Dynamics, vol. 3, no. 4, pp. 757–770, 2000.

F. H. Struliz, “Inequality and growth: the dual roal human capital in development,” Journal of Development Economies, vol. 66, pp. 173–197, 2000.

H. Qin, H. Guan, and G. Zhang, “Analysis of the travel intent for park and ride based on perception,” Discrete Dynamics in Nature and Society, vol. 2012, Article ID 516197, 14 pages, 2012.

K. Fukao, T. Inui, K. Ito, Y. G. Kim, and T. Yuan, “An international comparison of the TFP levels and the productivity convergence of Japanese, Korean, Taiwanese, and Chinese listed firms,” Global COE Hi-Stat Discussion Paper Series 168, 2008.

B. W. Y. So, “Reassessment of the state role in the development of high-tech industry: a case study of Taiwan's Hsinchu Science Park,” East Asia, vol. 23, no. 2, pp. 61–86, 2006.

K. A. Bollen and J. S. Long, Eds., Testing Structural Equation Models, Sage, Nesbury Park, Calif, USA, 1993.

L. Ding, W. F. Velicera, and L. L. Harlow, “Effects of estimation methods, number of indicators per factor, and improper solutions on structural equation modeling fit indices,” Structural Equation Modeling, vol. 7, no. 2, pp. 119–143, 1995.

R. Inklaar and M. P. Timmer, “International comparisons of industry output, inputs and productivity levels: methodology and new results,” Economic Systems Research, vol. 19, no. 3, pp. 343–363, 2007.

R. Y. Sunindijo, B. H. W. Hadikusumo, and T. Phangchunun, “Modelling service quality in the construction industry,” International Journal of Business Performance Management, vol. 15, no. 3, pp. 262–276, 2014.

H. R. Thomas and I. Yiakoumis, “Factor model of construction productivity,” Journal of Construction Engineering & Management, vol. 113, no. 4, pp. 632–639, 1987.

N. T. Aleksandr and K. V. Ye, “Outsourcing as a factor in the effectiveness of the construction industry,” Problems of Economy, vol. 4, pp. 129–135, 2012.