Entrepreneurship and Intervention Strategies of Land-Lost Farmers in Urbanization Process of Zhejiang Province

Abstract

Urbanization aims to promote citizenization, in which people’s social and spiritual needs must be satisfied. However, vulnerable land-lost farmers face many difficulties during the urbanization process. This is partly caused by the GDP-oriented policies that specifically target land-lost farmers. The government must change its governance from GDP (land) oriented to people oriented to improve its management performance. Central government is encouraging urbanization. However, only a few studies have investigated the entrepreneurial activities of land-lost farmers at the micro-level in China, which engenders ineffective policies for promoting such activities during urbanization. Based on grounded theory, this study develops a consciousness-situation-behavior model to explain the entrepreneurship mechanism of land-lost farmers. The authors discovered that entrepreneurial awareness motivates entrepreneurial behavior through the perceptions of desirability and feasibility. Land acquisition plays a mediating role in the relationship between entrepreneurial consciousness and behavior. For various situation factors, land location and entrepreneurship policy significantly mediate the relationship between entrepreneurial consciousness and behavior, whereas the settlement of land-lost farmers produces an insignificant effect on such relationship. A higher compensation can also lower the possibility of entrepreneurship. Based on these findings, practical policies are proposed.

Keywords

governmental performance management land-lost farmers’ entrepreneurship urbanization China

Introduction

China’s GDP per capita increased from US$4,682 in 2010 to US$5,000 in 2011, but its urbanization rate over these years has only increased from 49.5% in 2010 to 50% in 2011, which was significantly lower than that of the United States (Chen & Huang, 2012). By the end of 2014, according to Zhejiang Statistics Yearbook, the urbanization rate in Zhejiang province reached 64%, whereas that in the Hangzhou and Ningbo regions amounted to 75.1% and 70%, respectively (Statistics Bureau of Zhejiang Province, 2015). Hangzhou and Ningbo have reached the mature stage of urbanization (Northam, 1975). Most local governments have included the rate of urbanization in the governmental performance management system. The new office for examination and assessment was established in Hangzhou in 2006 to accelerate the urbanization process and comprehensive evaluations. As a capital city of Zhejiang, Hangzhou is the first to change its focus from scale expansion to quality improvement and from land urbanization to civilization. The Hangzhou government has also increased its efforts to satisfy the social and spiritual demands of its people and to promote the sustainable development of its towns.

Based on the urbanization level and economic growth rate of China, the number of land-lost farmers in the country will reach approximately 100 million in 10 years. Land-lost farmers face various risks and losses in terms of economy, society, culture, capital, opportunities, and rights (Bao and Peng, 2016). Therefore, the government must promote the development of these peasants instead of guaranteeing their subsistence (Zheng & Sun, 2006). Providing opportunities for land-lost farmers and maintaining their rights to development can effectively solve the problems of this disadvantaged population. Previous studies reveal that self-employment people are highly satisfied with their lives and demonstrate the highest citizenization. Therefore, the government must increase non-agricultural employment channels of land-lost farmers and create favorable conditions for these peasants to start their own businesses. Solving the problems of land-lost farmers, providing new knowledge and skills, offering new social roles, establishing new social relations, and integrating peasants into the urban social life are all directly related to the urbanization level and management performance of the Chinese government. To improve its management performance, the government should change its governance from GDP (land) oriented to people oriented. The government should also implement feasible policies for enhancing entrepreneurship awareness of land-lost farmers.

Previous studies have primarily focused on the necessity of peasants’ entrepreneurship and have proposed some countermeasures to promote entrepreneurial peasants. However, their findings provide insufficient theoretical basis for the implementation of supporting policies from the government. Therefore, land-lost farmers are not provided with an effective guidance. Thus, investigating the entrepreneurial awareness of land-lost farmers (including the directions of the path, effect, and regulating mechanism), as well as analyzing their intrinsic mechanism, is crucial.

Literature Review

Protection Settlement for Land-Lost Farmers and Policy Practice

More than 5,000 land-lost farmers were recorded in 2008 in the whole country (Bao, 2012). Land-lost farmers are different from “migrant workers” and “traditional peasants,” under the dual urban–rural division policy ( Zhang, 2011; Lai, Peng, Li, & Lin, 2014). Land expropriation has resulted in land-lost farmers, whose priority is to secure their lands and lives. These peasants face several difficulties or become “marginal people” when they attempt to adapt to the urban life.Their transition from peasants to urban citizens is characterized by the seizing of lands in a non-market manner and the settlement of land-lost farmers in a market fashion. Given the development of a labor market system, land-lost farmers cannot easily participate in the market competition for employment, thereby increasing their vulnerability. From the early 1980s to the 1990s, China’s policy for land-lost farmers principally focused on the protection of this disadvantaged population, which can be found in the following measures.

Production resettlement

The relocation of people depends on the existing building area and the relevant compensation provisions, such as the exchange of property rights from old to new residential buildings. Production resettlement can effectively promote the rural economical and intensive utilization of land as well as reduce the relocation costs. However, some deficiencies are also observed, requiring huge workload and additional housing. These deficiencies engender poor quality of housing, weak residential property management, and insufficiency of funds.

Welfare resettlement

Welfare resettlement refers to the provision of work and permanent residence for land-lost farmers. Demolition parties provide capable land-lost farmers with job. Welfare resettlement is relatively difficult due to the limitations of land-lost farmers in terms of their skills and age. The provision of permanent residence can allow land-lost farmers to be identified as urban residents and enjoy relevant benefits. Urban identity enables land-lost farmers to enjoy the benefits of urban hukou. With the deepening of reforms and opening up, the current policy aims to solve the problems of agriculture, rural areas, and peasants (also San Nong Problems; Shen, Lu, Peng, & Jiang, 2011). The difference between rural and urban residents is narrowed gradually.

Currency settlement

Monetary settlement is one of the most important methods. The dismantler offers compensation for demolition according to the value of demolished houses. Land-lost farmers must solve the resettlement housing problem by themselves. The currency approach can improve the efficiency of settlement velocity and demolition as well as relieve resettlement work stress. However, the demolition cost will increase, and the capital pressure will be intensified in the short term.

Life guarantee

Life guarantee refers to safeguarding the basic life of land-lost farmers through endowment and basic life insurance. The required funds are obtained from the government, and the benefits are to be shared among collective economic organizations and individuals. Endowment insurance and medical insurance can improve the medical and old-age payments for land-lost farmers, expands insurance coverage to the greatest extent but also heavily burdens the local government.

Land usufruct returning

Land usufruct returning refers to the return of requisitioned land (usually by 10%), to landless villages and collective economic organizations. This land is subsequently used to ensure a stable economic income. “Land usufruct returning” is kind of new experiences but is only applicable in developed areas with abundant land resources.

Developmental Settlement for Land-Lost Farmers and Policy Trend

From the urbanization perspective, the performance management of the Chinese government can be divided into two phases: GDP-oriented governmental performance management (urbanization) and people-oriented governmental performance management (citizenization). In the 21st century, China’s policy has gradually shifted its focus on supporting land-lost farmers. The resettlement of land-lost farmers is a sustainable development process that consists of four core elements: economic compensation support, housing, employment, and social security. Building a long-term protection mechanism can help achieve the sustainable development of the land-lost farmers’ livelihood (Shen, Peng, Zhang, & Wu, 2012).

The current development goal of the Chinese government is to promote human-centered urbanization and to make the agricultural population live together with urban residents. As experimental unit, Zhejiang province has issued a series of relevant policies to support the entrepreneurship of land-lost farmers. They provide land-lost farmers with enterprise grants, rewards, small loans, and discount interest, and low workplace rent; training for self-employment, which is performed step by step by government agencies; and humanistic care and security of interests.

Scholars have realized that compensation and protection of rights cannot withstand the damages from the problems that land-lost farmers are facing. To fundamentally solve this problem, development of land-lost farmers must be prioritized instead of guaranteeing their subsistence (Zheng & Sun, 2006). Some scholars have conducted entrepreneurship countermeasure research to relieve the employment pressure of land-lost farmers. The entrepreneurship of these peasants requires guidance from the government, but the current entrepreneurial policy for land-lost farmers remains deficient. This policy can be improved using appropriate policy making, implementation, and supervision mechanisms Han, 2009. An entrepreneurship policy guiding system and entrepreneurial platform must also be established to improve the employment of these peasants. Moreover, the policy guidance system must be established based on entrepreneurial ideas, entrepreneurship training, entrepreneurship financing, policy support, and social security. Entrepreneurship training services can promote human capital and the development of land-lost farmers (Bao, 2012). A policy support system for land-lost farmers can be established through a self-employment process (entrepreneurial opportunity identification, social relation network, development of entrepreneurial opportunities, and entrepreneurial results) to promote self-employment. The Chinese government has conducted employment training sessions in a few areas, which brings about certain positive effects. Yet, given their low position in the society, land-lost farmers tend to be ignored. The current entrepreneurial training programs for peasants and the follow-up services of government departments and social organizations cannot meet the business needs of these people.

Land-Lost Farmers’ Entrepreneurial Behavior Model

Model

The research period begins from June 2014 to January 2015. We build the model through in-depth interviews, and then verify this model through a questionnaire survey. In-depth interviews were conducted with 23 land-lost farmers who were randomly selected from 100 entrepreneurial land-lost farmers. The respondents comprise 11 males and 12 females. Each interview lasts for less than an hour. We explore how these respondents understand the concept of entrepreneurship, and then investigate their entrepreneurial behavior. We obtained research notes, audio, video, and other data from the interviews. Through open coding, axial coding, and selective coding, we build a conceptual model to understand the relationship among the entrepreneurial intention, situation, and behavior of land-lost farmers, which can be found in Figure 1.

Figure 1.

The conceptual model for the relationship of land-lost farmers.

According to the conceptual model, the entrepreneurial consciousness is mainly composed of land-lost farmers’ achievement motivation, innovation orientation, awareness of social capital, and market opportunity, which can be divided into perceptions of desirability and feasibility (Shapero & Sokol, 1982; Krueger, 1993; Krueger, Reilly, & Carsrud, 2000). According to Shapero, entrepreneurial behavior is triggered by certain external change or events. The responses of people to emergencies depend on their existing “alternative solution” cognitive. Perceptions of desirability and feasibility are two basic cognitive means of entrepreneurship. Perception of desirability refers to the prospect of entrepreneurial activity as well as reflects whether entrepreneurial activity is in line with the will of the peasants, which is how much value a business can make. Land-lost farmers have a higher tendency to start a business if the value that such business can satisfy is also high. Perceptions of desirability also include the achievement motivation and innovation orientation of peasants, which can explain their potential to become entrepreneurs. Perception of feasibility, which includes the awareness of social capital and market opportunity, refers to the belief of land-lost farmers in their ability to start a business. In other words, this perception reflects the feasibility of entrepreneurship perceived by peasants. This perception is interpreted in this article as the land-lost farmers’ perception of their knowledge, skills, and experience. A stronger feasibility of perception will increase the tendency for these peasants to start a business. A field investigation has determined that peasants with stronger perceptions of desirability and feasibility hold more positive attitudes toward entrepreneurship. They are more sensitive to market information and are excellent in mining connections and resources.

Based on this model, we propose the following hypotheses:

Hypothesis 1 (H1): Entrepreneurial awareness of land-lost farmers has a significant influence on their entrepreneurial behavior.

Hypothesis 2 (H2): The achievement motivation of land-lost farmers has a significant influence on their entrepreneurial behavior.

Hypothesis 3 (H3): The innovation orientation of land-lost farmers has a significant influence on their entrepreneurial behavior.

Hypothesis 4 (H4): The perceived social capital of land-lost farmers has a significant influence on their entrepreneurial behavior.

Hypothesis 5 (H5): The perceived market opportunities for peasants have a significant influence on their entrepreneurial behavior.

Hypothesis 6 (H6): Land expropriation location can significantly affect the relationship between entrepreneurial intention and entrepreneurial behavior.

Hypothesis 7 (H7): Resettlement mode can significantly affect the relationship between entrepreneurial intention and entrepreneurial behavior.

Hypothesis 8 (H8): Entrepreneurship policy can significantly affect the relationship between entrepreneurial intention and entrepreneurial behavior.

Hypothesis 9 (H9): The amount of compensation can significantly affect the relationship between entrepreneurial intention and entrepreneurial behavior.

Samples and Data Sources

The data were primarily gathered from a field investigation of land-lost farmers in a few areas of Hangzhou and Ningbo in Zhejiang province. Hangzhou is a provincial capital city, whereas Ningbo is a city that is specifically designed in the state plan. Given that these two areas are leading the urbanization process in China, the land-lost farmers in these areas are facing severe problems. To ensure the reliability and validity of the questionnaire, the items are derived from the previous literature. We also adjust the questionnaire based on the characteristics of the targeted land-lost farmers. All of the items are measured on a 5-point Likert-type scale, ranging from 1 (strongly disagree) to 5 (strongly agree). Prior to the formal investigation, we requested 20 land-lost farmers from Jiubao to answer the questionnaires, and then modified the items according to their feedback. We also distributed 100 questionnaires to peasants in Tonglu, of which 88 valid questionnaires are returned. Through reliability validity analysis, we exclude achievement motivation and innovation orientation as two measurement variables. The remaining variables have high reliability and validity. We then begin the formal investigation.

The questionnaire is divided into two parts. The first part pertains to the demographic characteristics of the respondents, whereas the second part includes nine potential variables and 23 measuring items. A total of 350 questionnaires were collected, of which 43 invalid questionnaires were excluded. Therefore, 307 valid questionnaires were used in this study. A total of 250 questionnaires (218 valid) were collected from Hangzhou, whereas 100 questionnaires (89 valid) were collected from Ningbo. Table 1 shows the statistical characteristics of the sample.

Table 1.

Statistical Characteristics of the Sample, N = 307.

Variable	n	%
Gender
M	151	49.2
F	156	50.8
Age
<30	57	18.6
31-40	107	34.9
41-50	101	32.9
51-60	42	13.7
Education
Elementary school	31	10.1
Junior high school	109	35.5
Senior high school	119	38.8
College	48	15.6

Reliability and Validity Analysis

We used SPSS software to analyze the reliability of our data. According to De Vaus (2002), Cronbach’s alpha values of .70 to .80 indicate a fair reliability, whereas Cronbach’s alpha values of .80 to .90 suggest an excellent reliability. Shen, Lu, Peng, & Jiang (2011) argued that .70 was a low yet acceptable scale boundary value. Table 2 shows the reliability of the test results. The comprehensive reliability of each potential variable is above .70, which validates the authenticity and reliability of our data.

Table 2.

Reliability Test, N = 307.

Factor	Number of items	Cronbach’s alpha values
Achievement motivation (AM)	3	.888
Innovation orientation (IO)	3	.905
Market opportunity (MO)	3	.934
Social capital (SC)	3	.858
Entrepreneurial behavior (EB)	3	.864
Land location (LL)	2	.901
Setting mode (SM)	2	.926
Business policy (BP)	2	.931
Amount of compensation (AC)	2	.866

Principal components analysis is applied to the exploratory factor analysis. Kaiser (1974) considered a Kaiser–Meyer–Olkin (KMO) index of above 0.80 as excellent. The KMO value of our sample data is 0.873, which indicates the suitability of our data for a principal components analysis. Table 3 shows the results of the principal components analysis. Nine eigenvalues are above 0.60, which explain 86.39% of the variance. The factor structure is clear, the load factor on the value is greater than 0.60, and the cross measure factor is below 0.50, which indicate that the scale has a favorable convergent validity and discriminant validity.

Table 3.

Factor Matrix After Maximum Variance Rotating, N = 307.

Items	Factor
Items	1	2	3	4	5	6	7	8	9
EB1	0.809	0.193	0.163	0.174	0.107	0.087	0.077	0.100	0.183
EB3	0.778	0.197	0.111	0.182	0.093	0.047	−0.025	0.120	0.204
EB2	0.745	0.143	0.202	0.237	0.268	0.009	0.061	0.034	0.231
AM2	0.166	0.811	0.194	0.291	0.115	0.040	−0.002	0.033	0.063
AM1	0.133	0.780	0.124	0.253	0.267	0.071	0.037	0.019	0.146
AM3	0.294	0.765	0.197	0.273	0.217	0.089	0.050	0.002	0.091
SC1	0.175	0.116	0.878	0.146	0.162	0.072	0.002	0.066	0.081
SC3	0.043	0.144	0.738	0.259	0.285	0.048	−0.004	0.039	0.168
SC2	0.316	0.290	0.729	0.007	0.307	0.089	0.073	−0.045	0.067
IO3	0.172	0.282	0.181	0.784	0.328	0.075	−0.010	−0.003	0.128
IO1	0.297	0.316	0.164	0.769	0.206	0.061	−0.045	0.042	0.140
IO2	0.226	0.341	0.140	0.742	0.186	0.044	0.044	0.095	0.050
MO3	0.107	0.255	0.322	0.277	0.798	0.066	0.016	0.085	0.154
MO2	0.198	0.283	0.356	0.251	0.744	0.100	0.040	0.110	0.086
MO1	0.282	0.196	0.266	0.275	0.732	0.081	0.069	0.039	0.197
SM1	0.039	0.047	0.097	0.040	0.077	0.938	0.090	0.140	0.089
SM2	0.075	0.089	0.050	0.073	0.060	0.924	0.053	0.177	0.129
BP2	−0.036	−0.030	−0.023	−0.012	−0.022	−0.059	0.963	−0.051	0.003
BP1	−0.034	−0.021	−0.014	0.014	−0.041	−0.072	0.959	−0.052	−0.005
LL2	0.071	0.043	0.007	0.047	0.034	0.135	0.095	0.925	0.140
LL1	0.118	−0.004	0.054	0.035	0.088	0.176	0.017	0.921	0.091
AC1	−0.323	−0.172	−0.185	−0.122	−0.148	−0.107	0.032	−0.184	−0.796
AC2	−0.333	−0.099	−0.116	−0.135	−0.184	−0.202	−0.030	−0.150	−0.795

Note. EB = entrepreneurial behavior; AM = achievement motivation; SC = social capital; IO = innovation orientation; MO = market opportunity; SM = setting mode; BP = business policy; LL = land location; AC = amount of compensation.

Correlation Analysis

To avoid multiple collinearity problems during the regression analysis, we used SPSS to calculate the Pearson correlation coefficient between the variables. The results are shown in Table 4.

Table 4.

The Correlation Coefficient Matrix, N = 307.

qs	AM	IO	MO	SC	AC	SM	LL	BP	EB
AM	1
IO	.709**	1
MO	.623**	.673**	1
SC	.523**	.502**	.692**	1
AC	−.418**	−.446**	−.508**	−.430**	1
SM	.079	.115*	.171**	.050	.391**	1
LL	.119*	.164**	.213**	.130*	.363**	.484**	1
BP	−.078	−.033	−.102	−.072	.046	−.089	−.126*	1
EB	.541**	.582**	.550**	.498**	−.643**	.125*	.247**	−.1	1

Note. AM = achievement motivation; IO = innovation orientation; MO = market opportunity; SC = social capital; AC = amount of compensation; SM = setting mode; LL = land location; BP = business policy; EB = entrepreneurial behavior. ** indicates the significance level of 0.05, * indicates the siginificance level of 0.1.

Achievement motivation, innovation orientation, social capital, and market opportunities have a low correlation (ranging between .3 and .8), which indicates the absence of collinearity problems. The dependent variable, namely, entrepreneurial behavior, and the regulating variables (i.e., land location, resettlement way, and land compensation) show a significant correlation, which indicates the fitness of the data for the regression analysis.

Regression Analysis

First, we analyzed how the factors of entrepreneurial intention affect entrepreneurial behavior. The multiple regression model was established as follows:

EB = α + α_{1} AM + α_{2} IO + α_{3} SC + α_{4} MO + ε,

where α is a constant term, α_i is the regression coefficient, and ε is the error term. The results of this model are shown in Tables 5 to 7. The multiple correlation coefficient (R) of the four variables and entrepreneurial behavior is .645, whereas the determination coefficient (R²) of the four variables is .416. Therefore, these variables can explain 76.2% of entrepreneurship. The F test value is 53.890, which exceeds the significance level of .01. Therefore, at least one of the selected variables has reached the significance level. The Durbin–Watson value is close to 2, which indicates the absence of related problems.

Table 5.

Multivariate Model Summary, N = 307.

R	R ²	Adjusted R²	SE of estimate	Change of the statistics					Durbin–Watson
R	R ²	Adjusted R²	SE of estimate	ΔR²	ΔF	df1	df2	ΔSignificance F	Durbin–Watson
.645	.416	.409	0.76217	.416	53.890	4	302	.000	2.015

Table 6.

Multiple Regression ANOVAs, N = 307.

	Sum of squares	df	M ²	F	Significance
Regression	125.219	4	31.305	53.890	.000
Residual	175.433	302	0.581
Total	300.652	306

Table 7.

Multivariate Regression Coefficients, N = 307.

	Unstandardized coefficients		Standardized coefficients	t	Significance
	B	SE	Trial version	t	Significance
(Constant)	0.936	0.185		5.047	.000
AM	0.163	0.067	0.160	2.438	.015
IO	0.273	0.065	0.290	4.229	.000
MO	0.123	0.068	0.131	1.815	.070
SC	0.185	0.064	0.178	2.888	.004

Note. AM = achievement motivation; IO = innovation orientation; MO = market opportunity; SC = social capital.

The results of the regression coefficient significant t test indicate that innovation orientation and social capital have reached the .01 significance level, achievement motivation has reached the .05 significance level, and market opportunities have not reached the significance level, which are consistent with the interview results. These findings suggest that the entrepreneurial opportunities for land-lost farmers are relatively few, and that most of the land-lost farmers have weak ability in discovering market opportunities. The entrepreneurial opportunities for land-lost farmers are created by “drive” rather than by “discovery.”

Second, we tested the influence of entrepreneurial awareness on entrepreneurial behavior, using the following linear regression model:

EB = α + α_{1} EI + ε .

EI denotes entrepreneurial awareness, and EB indicates entrepreneurial behavior. Tables 8 and 9 show the results of the regression model. The correlation coefficient (R) is .642, and the determination coefficient (R²) is .413, which indicates that entrepreneurial awareness can explain 76.1% of entrepreneurship. The F test value reaches the .01 significance level.

Table 8.

Linear Regression Model Summary, N = 307.

R	R ²	Ra²	SE of estimate	Change of the statistics					Durbin–Watson
R	R ²	Ra²	SE of estimate	ΔR²	ΔF	df1	df2	ΔSignificance F	Durbin–Watson
.642	.413	.411	0.76086	.413	214.342	1	305	.000	2.008

Table 9.

Linear Regression ANOVA, N = 307.

	Sum of squares	df	M ²	F	Significance
Regression	124.085	1	124.085	214.342	.000
Residual	175.567	305	0.579
Total	300.652	306

The significant t test results indicate that the coefficient of entrepreneurial awareness is 0.743, which is significant at the .01 level, as shown in Table 10. We obtain the following regression equation:

EB = 0.970 + 0.743 EI .

Table 10.

Linear Regression Coefficient, N = 307.

	Unstandardized coefficients		Standardized coefficients	t	Significance
	B	SE	Trial version	t	Significance
(Constant)	0.970	0.178		5.454	.000
EI	0.743	0.051	0.642	14.640	.000

Note. EI = entrepreneurial awareness.

Variable Adjustment Analysis

Given that the explanatory and regulating variables are continuous variables, we performed a centralized process. The regulating effect of the situational variables model is shown as follows:

EB = β + β_{1} X + β_{j} Y_{j} + β_{1 j} X_{1} Y_{j} + ε,

where X (entrepreneurial awareness) is the explanatory variable; Y_i is the regulating variable, Y₁, Y₂, Y₃, and Y₄ correspond to LL, SM, AC, and BP, respectively, and X_iY_j is the interaction item that indicates how Y_j can regulate the relationship between X and EB. Y_j can adjust the relationship between X and EB when the interaction coefficient reaches a significance level. Hierarchical regression is applied to test the regulation effect. Model 1 shows the effect of entrepreneurial awareness on entrepreneurship. Given that regression analysis was not performed for the situational variables, all of the regulating variables in Model 1 are the same. Model 2 includes the Sland acquisition situation variables in the regression model and analyzes the main effect of these variables regardless of their interactive items. Model 3 includes the land acquisition situation variables and interactive items in the regression model to analyze the regulating effect of specific situational variables. The analysis results are shown in Table 11.

Table 11.

Inspection Results of Land Situation Adjusting Variable, N = 307.

	Land location			Setting mode		Business policy		Compensation
	Mode 1	Mode 2	Mode 3	Mode 2	Mode 3	Mode 2	Mode 3	Mode 2	Mode 3
β	3.494	3.493	3.513	3.494	3.496	3.493	3.501	3.492	3.510
X	0.743**	0.714**	0.698**	0.730**	0.711**	0.739**	0.714**	0.484**	0.471**
Y_i		0.143**	0.127**	0.056	0.047	−0.049	−0.052	−0.522*	−0.515**
X × Y_i			−0.134**		0.043		0.120*		0.051
R	.642	.655	.666	.644	.645	.644	.651	.734	.735
R ²	.431	.430	.444	.415	.416	.415	.423	.539	.540
Adjusted R²	.411	.426	.439	.411	.410	.411	.418	.536	.536
ΔR²	.413	.017	.014	.002	.001	.002	.008	.126	.002
F	214.342	114.485	80.666	107.641	71.831	107.748	74.127	177.533	118.713
ΔF	214.342	9.003	7.861	0.964	0.539	1.090	4.443	83.056	1.054
Significant (ΔF)	.000**	.003*	.005**	.327	.463	.297	.036*	.000*	.305
Significant	.000**	.000**	.000**	.000**	.000**	.000**	.000**	.000**	.000**

Note: ** indicates the significance level of 0.05, * indicates the siginificance level of 0.1.

In terms of land location, Model 2 shows that land location has a significant and positive main effect, which indicates that land-lost farmers tend to start their own businesses when they are located closer to the city outskirts. Land-lost farmers in villages or urban–rural fringes have a better life quality. Influenced by the external environment of land requisition, those land-lost farmers with excellent location can effectively receive information, market, and policy as well as access more resources. Therefore, land-lost farmers in villages or urban–rural fringes have a stronger entrepreneurial awareness. Model 3 shows that land location has a reverse adjustment effect on the relationship between entrepreneurial awareness and behavior. In other words, a higher urbanization level will decrease the tendency for land-lost farmers to start their own businesses. Our findings indicate that land-lost farmers are given more job opportunities and higher wages in edge areas. However, land-lost farmers have low risk-taking ability and prefer to live a more secure lifestyle.

In terms of setting mode, Model 2 shows that setting mode has no significant main effect, which indicates that this mode will not affect the entrepreneurial behavior of land-lost farmers. Model 3 shows that setting mode has no adjusting effect on the relationship between entrepreneurial awareness and behavior. In other words, setting mode has no role in the entrepreneurial behavior of land-lost farmers. China currently has a complex setting mode. Given our limited time and survey scope, data may not be considerably representative, which may influence the empirical analysis results.

In terms of business policy, Model 2 shows that business policy has no significant main effect. A business policy is not effectively implemented in some areas, which may lead to peasants having an insufficient understanding of such policy. Land-lost farmers in certain areas demonstrate a bolshie attitude. Moreover, the entrepreneurship policy in some areas is trivial, which indicates that land-lost farmers cannot start their own business. Model 3 shows that business policy has a positive adjusting effect on the relationship between entrepreneurial awareness and behavior. In other words, with more favorable entrepreneurial policy incentives, land-lost farmers with a stronger entrepreneurial awareness are more likely to demonstrate an entrepreneurial behavior. Relevant business policies can promote entrepreneurship.

In terms of compensation amount, Model 2 shows that the amount of compensation has a negative significant main effect, which indicates that land expropriation compensation has a certain inhibiting role. Many land-lost farmers in economically developed areas became rich overnight because of their high compensation. Consequently, they lost their willingness to fight as well as resort to gambling and drug abuse. Therefore, these peasants can induce serious social problems. In this manner, a high compensation does not promote entrepreneurship. From Model 3, amount of compensation has no adjusting effect on the relationship between entrepreneurial awareness and behavior. In general, land-lost farmers with a strong entrepreneurial awareness will not give up on their low-salary jobs.

Confirmatory Test of the Model

This part tests the adaptation degree of the model. The results are shown in Table 12. The absolute adaptation statistic x²/df is less than 5, which means that the model and the sample data have an acceptable fit. The root mean square error of approximation (RMSEA) is 0.093, which is less than 0.1. The goodness-of-fit index (GFI) is greater than 0.80, which indicates that theoretical construction copy matrix can favorably explain the observation matrix sample data. The value-added adaptation degree statistic normative fit index (NFI), incremental fit index (IFI), Tucker–Lewis index (TLI), and comparative fit index (CFI) are all above 0.9. Therefore, the overall adaptation degree has an acceptable theoretical model.

Table 12.

Overall Adaptation Model Analysis, N = 307.

Statistic test	Standards	Results
x²/df	<5.0	3.621
RMSEA	<0.1	0.093
GFI	>0.8	0.890
NFI	>0.90	0.927
IFI	>0.90	0.946
CFI	>0.90	0.946
TLI	>0.90	0.929

Note. RMSEA = root mean square error approximation; GFI = goodness-of-fit index; NFI = normative fit index; IFI = incremental fit index; CFI = comparative fit index; TFI = Tucker–Lewis index.

Path analysis was performed via structural equation model (SEM) analysis. The estimated parameters reach the .05 significance level when the critical ratio value exceeds 1.96. The absolute value of C. R. is greater than 2.58, which indicates that the estimated parameters reach .01 significance level. The standardized and unstandardized path road maps are shown in Figures 2 and 3, whereas the hypothesis testing results are shown in Table 13. The inspection results are consistent with the SPSS results. Using the maximum-likelihood method, AMOS concludes that market opportunities have no significant effect on entrepreneurial awareness, whereas the effects of achievement motivation, innovation orientation, and social capital are all significant.

Figure 2.

Unstandardized path road map.

Figure 3.

Standardized path road map.

Table 13.

Hypothesis Testing Results, N = 307.

			Unstandardized estimate	Standardized estimate	SE	CR	p
Entrepreneurial consciousness	←	Achievement motivation	0.196	0.204	0.087	2.241	.025
Entrepreneurial consciousness	←	Innovation orientation	0.263	0.329	0.073	3.602	***
Entrepreneurial consciousness	←	Market opportunity	−0.002	−0.003	0.072	−0.033	.974
Entrepreneurial consciousness	←	Social capital	0.302	0.293	0.091	3.331	***

Note. CR = critical ratio. *** indicates the significance level of 0.01.

Data Analysis

SPSS is applied for the regression analysis and for adjusting the variables whereas the AMOS software is used for the confirmatory and path analyses. The findings verify the theoretical model, and the research hypotheses are summarized in Table 14.

Table 14.

Hypothesis Test Results Summary.

Hypotheses	Relational assumption	Test results	Conclusion
H1	Entrepreneurial consciousness → entrepreneurial behavior	Accept	Positive
H2	Achievement motivation → entrepreneurial behavior	Accept	Positive
H3	Innovation orientation → entrepreneurial behavior	Accept	Positive
H4	Social capital → entrepreneurial behavior	Accept	Positive
H5	Market opportunity → entrepreneurial behavior	Reject	No significant influence
Adjustment of land expropriation situational variables
H6	Land locationConsciousness → behavior	Accept	Positive main effect; negative interaction term
H7	Setting modeConsciousness → behavior	Reject	No significant main effectsNo significant interaction term
H8	Amount of compensationConsciousness → behavior	Accept	No significant main effects; positive interaction term
H9	Business policyConsciousness → behavior	Reject	Positive main effect; no significant interaction term

Conclusion

Based on the above-mentioned investigations, five important perspectives can be summarized as follows:

Most of the proposed hypotheses are accepted. The possible reasons behind these findings are summarized in the subsequent chapter.

The entrepreneurial awareness of land-lost farmers could be a decisive factor of their entrepreneurial behavior. The entrepreneurial awareness of land-lost farmers significantly affects their entrepreneurial behavior. Empirical studies reveal that awareness is the actual behavior of “forecast” variables. Shapero and Sokol (1982) proposed that inertia would dominate people’s behavior until interrupted by other things or “replacement.” When replacement occurs, those potential entrepreneurial individuals will make their decision according to their perceptions of desirability and feasibility. The “requisition of land” is equivalent to “replacement” in such a way that the lifestyle of land-lost farmers will change after their lands are acquired. Potential entrepreneurial individuals are faced with various life choices. These people will make their next move and change their perceptions toward the direction of entrepreneurship when they think that entrepreneurial behavior is more desirable and feasible. Otherwise, they will never start a business. In conclusion, the entrepreneurial awareness of land-lost farmers directly determines their entrepreneurship.

Entrepreneurial awareness of land-lost farmers is affected by achievement motivation, innovation orientation, and social capital. Entrepreneurial awareness includes achievement motivation, innovation orientation, market opportunities, and social capital. The main effect of achievement motivation on entrepreneurial awareness is 0.163, which indicates that a stronger achievement motivation induces a stronger entrepreneurial awareness. The main effect of innovation orientation on entrepreneurial awareness is 0.273, which indicates that a stronger innovation orientation engenders a stronger entrepreneurial awareness. Entrepreneurship itself contains a certain degree of innovation consciousness. The main effect of market opportunities on entrepreneurial awareness does not reach the significance level because most land-lost farmers are aware of their limited market opportunities. These entrepreneurial opportunities are created, not by “discovery” but by “drive.” Moreover, peasants have limited entrepreneurial opportunities. The main effect of social capital on entrepreneurial intention is 0.185, which indicates that a higher social capital leads to a stronger entrepreneurial awareness for land-lost farmers.

Land location and entrepreneurship policy can adjust the relationship between entrepreneurial awareness and behavior. Land location can be divided into urban villages, urban–rural fringes, and remote rural areas according to their distance from urban areas. Land location will enhance the entrepreneurial feasibility judgment of land-lost farmers. The closer these peasants are located to the city outskirts, the more they tend to start their own business. Land-lost farmers in villages or urban–rural fringes have a better life quality. Influenced by the external environment of land requisition, those land-lost farmers in excellent locations demonstrate superior performance in receiving information, market, and policy as well as access more entrepreneurial resources. Therefore, land-lost farmers in villages or urban–rural fringes have a stronger entrepreneurial awareness. Land location has a reverse adjusting effect on the relationship between entrepreneurial awareness and behavior. In other words, a higher urbanization level will decrease the tendency for land-lost farmers to start their businesses. These peasants have more job opportunities and higher compensation in edge areas. However, these peasants tend to opt for a more secure lifestyle because of their weak risk-taking ability. Business policy has a positive adjusting effect on the relationship between entrepreneurial awareness and behavior. In other words, under favorable entrepreneurial policy incentives, land-lost farmers with stronger entrepreneurial awareness are more likely to demonstrate an entrepreneurial behavior. The implementation of relevant business policies can promote entrepreneurship among peasants.

The amount of compensation may inhibit the entrepreneurial behavior of land-lost farmers but cannot adjust relationship between the entrepreneurial awareness and behavior. The amount of compensation will affect the entrepreneurial funds and loans of individuals. However, the current compensation standard differs across areas. A certain amount of compensation can be used to expand the business activities of land-lost farmers. However, the entrepreneurial awareness of those peasants with high land expropriation compensation is weakened along with a higher amount of compensation, thereby inducing a “gather-gambling” phenomenon. In Figure 4, two potential land-lost farmers are represented as A and B. B has a higher compensation than A. However, implementing the same amount of compensation fees will produce a greater regulating effect on A than on B. This indicates that the entrepreneurial awareness of those peasants under certain business conditions is strengthened by providing them with a higher compensation. The behavior consistency of these peasants will also increase the tendency for them to enhance their entrepreneurial behavior. However, increasing the compensation for those peasants with fair compensation will not produce considerable influence. Therefore, the key problem is how to appropriately use compensation funds.

Resettlement mode will not affect the entrepreneurial awareness of land-lost farmers and will not adjust the relationship between entrepreneurial awareness and behavior. The resettlement mode is presently not unified. Peasants in certain areas adopt the land usufruct returning mode, and their houses can be used for rental, business, and production purposes to promote entrepreneurship. However, land-lost farmers in most areas adopt monetary resettlement and have to look for jobs. Most of these peasants become migrant workers. Given the diversity of resettlement mode and the difference in the economic development of various regions, resettlement mode cannot adjust the relationship between entreprenerial awareness and behavior.

Figure 4.

Regulating effect of the amount of compensation.

Limitations and Future Research

Given the limitations in personal ability and time, several shortcomings must be addressed. First, we adopted grounded theory to construct our theoretical model, which may have some defects and deficiencies. Second, the data are primarily gathered through a field investigation of land-lost farmers, who are confined to a few areas of Hangzhou and Ningbo in Zhejiang province. Given that these two areas are leading the urbanization process in China, the peasants in these areas are facing severe problems. Although these areas are suitable for our research, they cannot entirely represent the Zhejiang province. Finally, a large sample size cannot be adopted in this study for several inevitable reasons. In the future, we will enlarge the sample size to obtain more objective results. We will also apply regional comparative analysis on the basis of the theoretical model.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors are grateful for the financial support from the Key Project of Higher Education of Zhejiang Province (Project#2013GH007).

Author Biographies

Haijun Bao is a professor at School of Public Administration, Zhejiang University of Finance & Economics, Hangzhou, China. His research interest is in the area of land expropriation.

Yingying Cen is a postgraduate student at School of Public Administration, Zhejiang University of Finance & Economics, Hangzhou, China. Her research interest is in the area of entrepreneurial management.

Yi Peng is an associate professor at School of Public Administration, Zhejiang University of Finance & Economics, Hangzhou, China. His research interest is in the area of sustainable urbanization.

Dinghuan Yuan is a PhD candidate at the Department of Public Policy, City University of Hong Kong, Hong Kong. Her research interest is in the area of entrepreneurial management.

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