Keyword

1 Introduction

China has clearly stated in multiple policy documents that it is necessary to “promote green and low-carbon building materials and green construction methods, accelerate the industrialization of new buildings”, and vigorously promote industrialized construction methods such as prefabricated buildings [1]. The development of industrialization cannot be separated from the construction migrant workers on the production line. The Ministry of Housing and Urban Rural Development has issued the Guiding Opinions on Accelerating the Cultivation of a New Era Construction Industry Worker Team, proposing that construction industry workers are an important component of China's industrial workers and the foundation for the development of the construction industry. Promoting the gradual transformation of traditional construction workers from migrant workers to industrial workers is not only necessary to adapt to the transformation of the construction industry, but also provides stronger talent support for the sustainable development of the construction industry [2]. Therefore, the research on the transformation of migrant workers in the construction industry has practical significance and research value.

2 Analysis of Transition Factors for Migrant Workers

2.1 Identification of Influencing Factors

Literature Research Identifying Factors.

For the influencing factors of the transition from migrant workers to industrial workers in the construction industry, scholars have already made a summary, but there are some differences in research perspectives. Starting from 2001, we searched and selected the core journal literature that had been cited more than 5 times, and summarized that the identity contradiction problem of migrant workers who are also workers and farmers is the key (Table 1). The transition of migrant workers to industrial workers is essentially a transformation of their professional and social identities [3]. Focusing on this theoretical foundation, from the perspective of identity transformation, we further selected representative literature in the past ten years, and summarized six types of key factors after sorting: human capital factors, institutional factors, industry factors, enterprise factors, macro-occupational factors and policy factors.

Research Interview Identification Factors.

The development of prefabricated buildings in China is in Its early stages, and there is relatively little research on industrial workers [4]. Therefore, team members conducted in-depth visits to relevant management personnel engaged in construction industrialization and industrial workers in representative construction companies, and comprehensively screened the influencing factors from the perspectives of enterprises, workers, and labor subcontracting. Based on the interview results, the identified influencing factors in the literature were ultimately reduced to 30.

Table 1. Literature Identification Impact Factor
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Process of Extracting Data from Questionnaires.

Based on the 30 factors identified above, rate the importance of each factor. Based on statistical theory, the designed questionnaire will be in the form of a likert rating scale, set at 1–5 levels (1 = very unimportant, 2 = unimportant, 3 = average, 4 = important, and 5 = very important). During the research process, a total of 900 questionnaires were distributed, and 721 valid questionnaires were initially collected. The respondents in the valid questionnaire not only include migrant workers from traditional construction sites, but also 450 frontline workers engaged in PC component production and prefabricated construction installation workers, 15 project managers in the construction industry, and 51 frontline labor management personnel.

2.2 Selection of Key Factors

Selection Method.

Due to the complex and dispersed factors that affect the transformation of migrant workers, it is difficult to extract key factors using traditional regression analysis methods. Therefore, this article uses factor analysis to identify the main influencing factors of the transformation of construction migrant workers. Through SPSS 26.0, a model analysis was conducted on 721 valid questionnaires collected from the survey. The specific analysis elements include reliability testing, applicability testing, factor analysis process, key factor extraction, and importance ranking of key factors.

Reliability Test.

Reliability test is an important method to determine whether the data is reliable or not, using this test on the data of 721 valid questionnaires, the value of Clonbach's coefficient (assessment tool) was obtained to be 0.825, which is greater than the basic requirement of 0.7, and meets the basic requirements of the reliability test, which indicates that the data is reliable, and can be analyzed by factor analysis.

Suitability Test.

KMO and Bartlett's test of sphericity were used to determine the suitability of the research data for factor analysis. According to the commonly used KMO metrics given by Kaiser: KMO > 0.9 means very suitable; KMO > 0.8 means suitable; KMO > 0.7 means average; KMO > 0.6 means not very suitable; KMO < 0.5 means very unsuitable. As shown in Table 2, KMO takes the value of 0.825, which is greater than 0.8, indicating that it is suitable for factor analysis, and the Bartlett's test of sphericity and the statistical values meet the requirement of significance level.

Table 2. KMO and Bartlett sphericity test tables
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Key Factor Selection.

When extracting key factors, Kunhui Ye [10] proposed that the factor load of key indicators should be greater than 0.5, and the cumulative explanatory variance of key factors should also be higher than 60%. Finally, 16 key factors that meet the above conditions were selected, with a cumulative explanatory variance of 63.794% (>60%) (Table 3). Finally, based on the specific content and internal relationships of factors, they are classified into six key factors, namely human capital factor (F1), institutional factor (F2), industry factor (F3), enterprise factor (F4), macro occupational factor (F5), and policy factor (F6).

Table 3. Extraction of key factors
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Importance of Key Factors.

The importance of the above 16 key indicators was calculated and ranked by applying the importance index calculation method. Importance index P = ∑(kX)*100/5, where: k is the respondents’ rating of an indicator, and the rating weights are from 1 to 5 respectively; X = n/(N-n), N is the number of questionnaires making the same rating of a factor; and N is the total number of questionnaires recovered. At the same time, the arithmetic mean of the coefficients of the indicators within the six key factors was calculated as a way to represent the importance of each key factor, and the final ranking of the importance index was F1 (79.1), F2 (66.85), F5 (59.5), F4 (56.1), F3 (47.95), and F6 (41.6).

3 Empirical Research on the Factors Influencing the Willingness of Migrant Workers to Transition

3.1 Sample Characteristics and Content Analysis

In order to obtain first-hand information and ensure the accuracy of the results, a total of 23 construction sites in Chongqing were visited, 900 construction migrant workers were selected for questionnaire surveys, 623 questionnaires were recovered, and the final number of valid questionnaires was 418 after eliminating invalid questionnaires with low reliability and incomplete data, and the 418 questionnaires were analyzed for measurement. The research object of this paper is mainly the traditional construction site migrant workers, and the survey contains six aspects: age, education level, wage income, wage payment, labor contract signing and willingness to transition [5].

Age.

In the 418 sample data involved in this research, the age ranges of 36–54 years old and 55 years old and above accounted for the largest proportion of the number of people, accounting for 61%, close to 2/3 of the total number of people, indicating that the sample of the number of middle-aged and elderly people is larger, and the aging is relatively more serious. Compared with the middle-aged and old-aged groups, the youth group, that is, those aged 25 and below, accounted for a lower proportion, accounting for only 5.5%.

Educational Level.

From the survey results of the education level of construction migrant workers, the middle school gradient accounts for the largest proportion, accounting for 52.9%, more than half. in addition the number of people at the level of secondary school, high school as well as technical school is also higher, accounting for 33%, as high as one-third. Elementary school and below accounted for 7.7%, so the overall literacy level of migrant workers in the construction industry remains low.

Wage Income.

Through the research, it is found that there is a large gap in the demand for construction workers in Chongqing, and the wages of migrant workers in the construction industry depend firstly on the type of work, with the wages of general handymen ranging from 4,000 to 8,000 yuan, and the wages of reinforcing steel workers, masonry workers and other difficult coefficients and specialties being higher than 8,000 yuan. Secondly, their wages are mainly based on piecework, and the difference in wages for the same type of work mainly lies in the number of pieces completed, with more work getting more pay [6].

Wage Payment.

The results of the survey show that because of the national macro-control policies in place, and because of the institutional initiatives for the protection of the rights and interests of migrant workers in recent years, there is basically no wage arrears for construction migrant workers, but there are still 23.7% of the migrant workers who said that they could not get their wages on time [7].

Signing of Labor Contracts.

Among the surveyed migrant workers in the construction industry, only 40% of the number of people have signed a contract, less than half. it can be clearly seen that the situation of signing labor contracts by migrant workers in the construction industry is not optimistic.

Willingness to Transform.

In order to facilitate quantitative analysis and accurately investigate the degree of willingness of migrant workers, this study set up the questionnaire with five levels, and the results were: very willing (28.2%), willing (21.8%), quite willing (26.1%), not too willing (15.3%), and unwilling (8.6%).

Some of the researched construction migrant workers have the willingness to transition, but in their choice, they are still constrained by factors such as age, education, and wage payment, so it is necessary to analyze the specific significance impact situation of each factor [8].

3.2 Significance Empirical Analysis

Selection and Definition of Explanatory Variables.

Based on the results of the factor analysis in Sect. 2, among the main influencing factors (i.e., explanatory variables) extracted for the transformation of traditional construction migrant workers, the influencing factors that ranked in the top five in terms of importance indexes (importance indexes greater than 70) and were easy to analyze quantitatively were selected, which were, in the order of ranking in terms of their importance indexes, age (88.3), wage payment situation (86.1), wage income (80.9), labor contract signing (79.1) and workers’ education level (76.3).

Research Assumptions

Assumption 1: Age has a negative effect on the willingness of construction migrant workers to transform, the older the willingness to transform the less strong; Assumption 2:The degree of education has a positive effect on the willingness of construction migrant workers to transition, the higher the degree of education, the stronger the willingness to transition; Assumption 3: Wage income has a negative effect on the willingness to transition of construction migrant workers, the higher the income the less willing to transition; Assumption 4: Wage payment has a negative effect on the willingness to transition of migrant workers in the construction industry, and the willingness to transition is not high if wages can be paid on time; Assumption 5: The contract signing situation has a negative effect on the willingness to transition of construction migrant workers, and the willingness to transition is stronger if the labor contract is not properly signed.

Analysis of Multiple Linear Regression Models.

The collected valid data were measured by model analysis, and the multivariate linear regression model was established and econometrically analyzed by SPSS26.0 to analyze the significance of the impact of each factor [9] (Table 4).

Descriptive statistics of model variables:

Table 4. Means and standard deviations of the variables
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Summary of the model: The adjusted R-squared represents the fit of the model, i.e. how well the linear equation responds to the real data. As shown in Table 5, the adjusted R-squared of the determinable coefficient of the model of this study is 0.639, which indicates that this regression equation is able to explain 63.9% of the real data, suggesting that the model has a good explanatory power. Meanwhile, the DW value in the regression model of this paper is 1.992, which is very close to 2, indicating that the residual terms are independent of each other and are not interfered with, which indicates that the data have a good explanatory ability and the regression analysis results are more accurate.

Table 5. Model summary
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Analysis of variance table: As shown in Table 6, the corresponding F-value in the regression variance is 46.746, which corresponds to a significance of 0.000b, which is less than 0.05, indicating that the regression equation is valid.

Table 6. Analysis of variance
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Regression coefficient table:

Table 7. Regression coefficient
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Multicollinearity test：Multicollinearity generally refers to the strength of linear correlation between explanatory variables. In this paper, the variance inflation factor (VIF) is mainly selected as a way to determine whether there is multicollinearity between variables. The specific determination method to judge the strength of multicollinearity from the perspective of the size of the VIF value is as follows: the larger the VIF is, especially when the VIF ≥ 10, it indicates that the multicollinearity between the explanatory variables is more serious; the closer the VIF is to 1, it indicates that the multicollinearity between the explanatory variables is weaker. From Table 7, the tolerance range of the five independent variables is 0.619 ~ 0.932, which is close to 1, and VIF < 10, indicating that the multicollinearity among variables is relatively weak.

Significance analysis: As can be seen from Table 7, at the 10% significance level, the five indicators of age, wage, wage payment, workers’ education, and contract signing passed the significance test, and the significance level is high, indicating that the results of factor analysis are more accurate.

Analysis of Results

Age: As shown in Table 7, the regression coefficient value (Beta value in Table 7) of age on the degree of transition intention is −0.543, with a significance level of P = 0.000 < 0.05, meeting the requirements for significance level. It indicates that age affects the willingness to transition of migrant workers in the construction industry to a greater extent under the same conditions. The regression coefficient is negative and the absolute value is large, indicating that there is a significant negative correlation between age and willingness to transition.

Wages: The value of regression coefficient of wage on the degree of willingness to transition is −0.167 with significance level P = 0.003 < 0.05, which satisfies the requirement of significance level. It indicates that under the same conditions, the degree of influence of wages on the willingness to transition of migrant workers in the construction industry is greater. The negative regression coefficient indicates that wages are negatively correlated with the willingness to transform.

Wage payment situation: The value of regression coefficient of wage payment situation on the degree of willingness to transform is −0.249, and the significance level P = 0.000 < 0.05, which satisfies the requirement of significance level. It indicates that under the same conditions, the wage payment situation has a greater degree of influence on the willingness of transformation of migrant workers in the construction industry. The negative value of the regression coefficient indicates that the willingness to transform is lower for the migrant workers who can get their wages on time.

Educational level of workers: The value of regression coefficient of age on the degree of willingness to transition is 0.178, and the significance level P = 0.006 < 0.05, which satisfies the requirement of significance level. It indicates that under the same conditions, the degree of education of workers has a greater degree of influence on the willingness of transformation of migrant workers in the construction industry. The regression coefficient is positive, that is, the higher the education level of the construction migrant workers’ willingness to transition is higher, and there is a significant positive correlation between the education level and the willingness to transition, which supports the original hypothesis.

Contract signing situation: The value of regression coefficient of contract signing situation on the degree of willingness to transform is −0.201, with significance level P = 0.000 < 0.05, which satisfies the requirement of significance level. It indicates that under the same conditions, the contract signing situation has a greater degree of influence on the willingness of transformation of migrant workers in the construction industry. The negative value of the regression coefficient indicates that the transformation willingness of construction industry migrant workers who do not sign labor contracts is stronger.

4 Conclusions and Strategies

Further analysis through empirical evidence found that the younger the age, the higher the education level and the lower the wage of construction migrant workers, the greater the willingness to transition; And the older, the less educated, and the higher the wages of the construction industry migrant workers, the smaller the willingness to transition; at the same time, the wage payment situation and contract signing situation have a negative impact on the willingness to transition of the construction migrant workers, i.e., the willingness to transition is stronger for those migrant workers whose wages can not be paid on time and at the same time do not have a labor contract signed, and the results of the analysis provide ideas for the path of the transition. In view of the difficulties in transition, this paper provides the following theories and suggestions for the transition of future migrant workers in the construction industry:

Firstly, establish policy pathways to attract young migrant workers to transform. Increase the implementation of policy publicity, popularize and update the knowledge of industry development for migrant workers, and promote the active transformation of young individuals. At the same time to promote the realization of industrial workers own process, improve the construction industry migrant workers vocational training system, the establishment of intelligent construction industrial workers training base, increase the attractiveness of the industry to form a benign transformation closed loop.

Secondly, lowering the cost of transition and increasing the transition of migrant workers. Establish a system of subsidies and salary guarantees for high-paying skilled trades to allay their transition concerns and address the skills gap for industrial workers. And set up an incentive mechanism to boost the transformation of lower-wage construction migrant workers.

Thirdly, improving the remuneration system to reduce the loss of migrant workers. By improving the pay and benefits system and implementing a system of payment of wages by banks, the long-standing problem of wage arrears will be eliminated, and their livelihood needs will be met while providing them with appropriate social security, thus reducing the outflow of rural migrant workers and increasing the base for transition [10].

Fourthly, standardize the entry process and increase the contract signing rate for migrant workers. Specialized departments have been set up to regulate and institutionalize the management of construction industry workers, and simple labour contracts have been introduced to increase the supervision of the labour market in the construction industry and to ensure that the basic interests of migrant workers are protected.

Fifthly, a long-term training system should be established. Vocational education and continuing education for migrant workers in the construction industry should be implemented to help migrant workers in the construction industry to become quality construction industrial workers in the new era and to efficiently complete the transition from migrant construction workers to industrial workers.