Influencing Factors of Sustainable Rural Entrepreneurship: A Four-Dimensional Evaluation System Encompassing Entrepreneurs, Economy, Society, and Environment

Abstract

The implementation of rural entrepreneurship emerges as a pivotal pathway for fostering rural economic growth. However, unsustainable entrepreneurial endeavors have posed notable ecological threats and environmental degradation. Drawing upon the triple bottom line framework, this research devised a comprehensive evaluation system for sustainable rural entrepreneurship, spanning four dimensions: entrepreneurs, economic, social, and environmental aspects. Employing the fuzzy Decision-Making Trial and Evaluation Laboratory (DANP) approach, we delineated the intricate causal relationships among influencing factors and identified key determinants along with their respective weights. Our findings underscore the prominence of economic dimensions in fostering sustainable rural entrepreneurship. Specifically, entrepreneurial motivation, type of entrepreneurship, financial backing, economic value, favorable policy frameworks, and a conducive business environment emerged as pivotal indicators. Notably, the type of entrepreneurship, financial support, economic value, and favorable policies emerged as propelling factors driving sustainable rural entrepreneurial progress. Conversely, entrepreneurial motivation and the business environment manifested as dependent factors within this causal nexus. This study offers valuable managerial implications for entrepreneurial teams and pertinent government agencies, enabling decision-makers to formulate evidence-based strategies aimed at realizing sustainable rural entrepreneurship.

1. Introduction

Rural entrepreneurship has always been regarded as an important channel for rural economic development. Successful rural entrepreneurship can drive local economic growth and is significant for the comprehensive implementation of the rural revitalization strategy [1]. Rural entrepreneurship can rationally allocate rural resources, change the traditional agricultural structure, drive the employment of rural labor and increase farmers’ income, and contribute to the formation of new industries, and new business modes, which provides a new path for the construction of new rural areas and the integrated development of urban and rural areas [2,3]. According to the Implementation Progress of the Chinese Government’s Rural Revitalization Strategic Plan (2018–2022), 2210 rural entrepreneurship and innovation park bases have been promoted by supporting entrepreneurship to drive employment. In total, 11.2 million people have been attracted to return to rural areas for entrepreneurship, with an average of 6–7 stable employment positions created by each entity. New business models, such as live streaming and crowdfunding, driven by modern information technologies such as big data and the Internet of Things, have been emerging endlessly [4]. However, rural entrepreneurship still suffers from the short life cycle of startups, mountain loss, environmental pollution, and other issues [5]. These problems are detrimental to the economic development of rural areas and widen the wealth gap between rural and urban areas. To a large extent, this is because entrepreneurship does not prioritize sustainability [6,7]. Therefore, it is urgent to achieve high-quality development of rural industries and transform the agricultural economic system through economically viable, low-carbon, and circular sustainable rural entrepreneurship [8]. Based on the analysis presented above, this article contends that identifying the pivotal factors influencing the sustainable development of rural entrepreneurship can pave the way for the formulation of effective support policies, optimization of resource allocation, and an elevation in entrepreneurial success rates, alongside the promotion of innovation. This, in turn, contributes to mitigating poverty and inequality, safeguarding the environment, fostering community participation, and preserving cultural heritage, all while bolstering the economic resilience of rural areas and facilitating balanced regional development. Clarifying these factors enables rural entrepreneurship to assume a more prominent role in advancing the comprehensive development of the socio-economy [9,10].

The advantage of sustainable rural entrepreneurship lies in its ability to balance economic and ecological benefits. For example, the contributions made by farmers to rural areas and agriculture are not merely instrumental in bolstering regional economic outcomes; rather, they possess the capacity to evolve into pivotal guardians of local environmental sustainability [11]. Achieving sustainable rural entrepreneurship is influenced by multiple factors. Barth and Zalkat (2021) found that entrepreneurial success is motivated by personal competence, previous entrepreneurial experience, and family and government support. It is challenged by management and technical problems and insufficient capital [12]. Soleymani et al. (2021) noted that indicators such as facility utilization, cost management of products and services, transparency of financial operations, and social altruism can affect the sustainability of rural entrepreneurship [1]. Tur-Porcar et al. (2018) argued that entrepreneurial motivation, self-efficacy, economic returns, and business management are the keys to promoting sustainable entrepreneurship [13]. Burchi et al. (2021) found that financial literacy and other competencies of entrepreneurs positively impact sustainable entrepreneurship [14]. Zahrani (2022) stated that sustainable entrepreneurial culture, education, and training all contribute to the success of sustainable entrepreneurship [15]. Existing studies have examined the factors influencing sustainable entrepreneurial success at different economic, social, environmental, and cultural levels. Still, there is a lack of literature on rural entrepreneurship, and few studies have provided insight into the correlations and dynamics among the factors influencing sustainable rural entrepreneurship. Therefore, the purpose of our research is to try to fill this research gap and identify the factors that influence sustainable rural entrepreneurship. Specifically, this study attempts to provide insights into the following issues: (i) What are the essential factors that drive talents to implement sustainable entrepreneurship in rural areas? (ii) In the process of promoting sustainable entrepreneurship in rural areas, how do these factors interact and influence each other? (iii) What factors should be considered when promoting sustainable entrepreneurship strategies in rural areas?

The study of the influencing factors of sustainable rural entrepreneurship involves multiple dimensions, and analyzing the relationship and importance of the factors is a multi-criteria decision-making problem. The Decision-Making Trial and Evaluation Laboratory (DEMATEL)-based Analytical Network Process (DANP) method, as one of the multi-criteria decision-making methods, combines the advantages of both DEMATEL and ANP to obtain the optimal solution to the complex relationship of multiple factors perfectly [16,17]. By integrating network analysis and multi-criteria decision analysis, the DANP model can more effectively quantify the key factors affecting rural entrepreneurship. It constructs a network of interrelationships among factors to identify core drivers. Compared to statistical regression and qualitative research, this method is particularly suitable for tackling complex decision-making problems that involve multiple criteria and factors. It can analyze the interdependencies between decision factors, integrate qualitative and quantitative data, and enhance decision quality through expert opinions. Additionally, the visualization tools of DANP facilitate intuitive understanding of the relationships between decision factors, making it a powerful tool in decision support systems. However, given that the traditional multi-attribute group decision-making method (multi-criteria decision-making method, MCDM) has empirical and domain limitations, the inherent decision-making framework tends to restrict decision-makers from expressing their opinions, making the decision results lack comprehensiveness [18]. This paper combines fuzzy set theory with the DANP method to determine the factors affecting sustainable rural entrepreneurship, and the data are fuzzified by the triangular fuzzy number method to overcome the subjectivity of indicator evaluation. Therefore, this paper employs the fuzzy DANP method to quantify the key factors in rural entrepreneurship, effectively filling the research gap and providing data support for policy formulation and resource allocation.

This study emphasizes that under the concept of sustainable development, rural entrepreneurial operations require fundamental operational changes. These changes affect value creation and cost-effectiveness models, leading to new market models. The innovations of this study are summarized as follows: First, this study proposes a complete sustainability assessment framework, including thirteen indicators, divided into four perspectives: entrepreneurs, economic level, social level, and environmental level. Second, this study combines triangular fuzzy theory with the DANP model to improve the ability to evaluate uncertain environments comprehensively. Finally, the research analysis results reveal key impact indicators and their priorities to decision-makers, providing suggestions for rural entrepreneurs to root in rural areas for sustainable development.

This study is structured as follows: Section 2 provides a literature review of research on sustainable rural entrepreneurship; Section 3 describes the methodology; Section 4 presents the research findings; Section 5 discusses the findings; and Section 6 summarizes the conclusions and future research directions.

2. Literature Review

2.1. Rural Entrepreneurship

Rural entrepreneurship refers to the behavior of entrepreneurs who utilize rural resources, establish enterprises to carry out commercial activities, and obtain economic returns [19]. Attracting entrepreneurs to start businesses in rural areas can enhance rural industries and inject new vitality into local economic development [20]. Entrepreneurs mainly combine local resources to start businesses. Rural entrepreneurship includes agricultural entrepreneurship, rural e-commerce, rural tourism, and other types of businesses. As entrepreneurs’ capabilities and literacy advance alongside rapid scientific and technological progress, modern, large-scale, and standardized agricultural production has thrived, significantly enhancing both the efficiency and quality of farming practices [12]. With the popularity and promotion of Internet technology, some rural areas have made full use of local resources to develop e-commerce and sell local agricultural products and cultural and creative products to consumers through live e-commerce [21]. In contemporary times, as living standards rise, there is a burgeoning desire for a high-quality life and personal fulfillment. This trend has led many urban dwellers to seek the tranquility and natural charm of rural landscapes. Entrepreneurs can bolster local economic development by leveraging the rural sector’s natural ecology, historical and cultural heritage, and local customs to enhance the service industry and foster the growth of rural tourism [22]. However, there are still some problems with rural entrepreneurship in China, such as the misuse of chemicals such as pesticides and fertilizers. The government could improve rural infrastructure, issue relevant laws and policies to guide rural entrepreneurs to care for the environment, reduce the ecological damage caused by entrepreneurial activities, and achieve sustainable development of the rural economy.

2.2. The Driving Force of Sustainable Rural Entrepreneurship

Sustainable rural entrepreneurship, grounded in the principles of sustainable development, is adapted to local conditions and aims to develop the rural economy and enhance the living standards of residents while ensuring the preservation of ecological balance [23]. Sustainable rural entrepreneurship seeks a dynamic balance between economic, environmental, and social interests. Economically, entrepreneurial teams should focus on achieving long-term, sustainable profit margins. Socially, they must consider the effects of their business operations on local communities and their inhabitants. Environmentally, it is crucial to prioritize resource renewability, ecological preservation, and restoration, as well as the long-term implications for future generations.

Implementing sustainable rural entrepreneurship is a complex project influenced by the interaction of economic, social, and environmental factors. Most entrepreneurial activities begin with the identification of market opportunities. Meanwhile, startup costs, profit returns, and other economic factors also influence the success of sustainable rural entrepreneurship [12,13]. In the social sphere, the prevailing views on entrepreneurship and altruism, along with cultural education and training in entrepreneurial skills and the prevailing social norms, all significantly influence the decision to embark on sustainable rural entrepreneurship [1,15,24]. Environmental factors such as guidance and support from relevant government departments, understanding and assistance from families, and a good legal and business environment positively impact the realization of sustainable rural entrepreneurship [12,25]. Digitalization brings new opportunities for rural entrepreneurship and promotes the development of new industries and business models. However, there are constraints in the technological infrastructure of rural areas. The main issues encompass outdated network facilities, a lack of digital skills, and insufficient infrastructure construction, including poor roads, inadequate power grids, unreliable water supply, and low internet penetration rates [26,27]. Factors such as the ability and accomplishment of entrepreneurs and teams, experience, self-efficacy, and entrepreneurial motivation are also crucial for the success of sustainable rural entrepreneurship [12,13]. Existing studies have analyzed the factors influencing the success of sustainable rural entrepreneurship at different levels. However, there is still a lack of research that deeply analyzes the interactions and mutual influences among the factors that influence sustainable rural entrepreneurship.

2.3. New Framework for Sustainable Rural Entrepreneurship

Under the promotion of the rural revitalization strategy, rural reform has been continuously deepened, and the concept of green development is widely known. Rural entrepreneurship has become important for promoting supply-side reform in China’s rural areas [8].

The entrepreneur’s personal characteristics play a pivotal role in the decision-making processes of sustainable rural entrepreneurship [14]. Specifically, it is further subdivided into three indicators: entrepreneurial motivation, entrepreneurial ability, and type of entrepreneurship [13,14,26]. In addition, to comply with the requirements of the era of sustainable and high-quality development, sustainable rural entrepreneurship is inseparable from the impact of the “Triple Bottom Line” value orientation, which includes economic, social, and environmental considerations [1]. At the economic level, the successful implementation of rural entrepreneurship, as an important channel for improving the rural economy, needs to consider the influence of market factors such as consumer demand and market opportunities, as well as factors such as profit returns [12,13]. The rapid development of information technology and the digital economy has brought challenges to traditional business models, and there is an urgent need for sustainable business models to drive the value creation of enterprises [28,29]. As for startups, financial support is an important influencing factor for the sustainability of rural entrepreneurship [14]. Therefore, this study divides the economic dimension into four indicators: market factors, business model, financial support, and economic value. At the social level, the concepts of rural revitalization, such as industrial integration, green concepts, and resource integration, bring new opportunities for rural entrepreneurship development and contribute to rural enterprises’ sustainable development. From a corporate perspective, those enterprises that fully emphasize social responsibility in their operations and have more social capital are also more likely to achieve sustainable rural entrepreneurship [30,31]. This study divides the social dimension into three indicators: favorable policy, social responsibility, and social capital. In the environmental dimension, a favorable business environment, good infrastructure for entrepreneurship, such as transportation, logistics, and Internet, and abundant natural resources, such as land, water, and tourism resources, will help enterprises achieve successful and sustainable rural entrepreneurship [32,33,34]. This study divides the environmental dimension into three indicators: business environment, infrastructure, and resource conditions.

In a comprehensive analysis of the above, this study constructs the specific dimensions, indicator descriptions, and references of sustainable rural entrepreneurship in China, as shown in

Table 1. Sustainable rural entrepreneurship impact factor indicator framework.

Dimension	Criteria	Explanation	Reference
Entrepreneurs (C1)	Entrepreneurial motivation (C11)	Entrepreneurs are driven to undertake a range of sustainable rural entrepreneurship activities, such as “seizing opportunities” or “doing what they want to do”.	[13,19,21,35]
	Entrepreneurial ability (C12)	The personal ability of entrepreneurs, such as the ability to find rural value, to find entrepreneurial opportunities, and to cultivate, generate, and derivative new enterprises	[14,36,37]
	Type of Entrepreneurship (C13)	Types that are driven by factors such as market opportunities or updates to products, processes, and organizational forms	[38,39]
Economic-level (C2)	Market factors (C21)	Market opportunities or niches exist, and consumers seek green and sustainable products.	[40,41]
	Business model (C22)	Considering a wide range of stakeholders, including the environment and society, and attaching importance to the development of products or industries in the direction of energy saving, emission reduction, and low carbon emissions	[42,43,44]
	Financial support (C23)	Access to financing and access to capital, loans (especially risky capital) easily	[37,39,40,44]
	Economic value (C24)	Generated profit returns, helped farmers increase their income	[45,46]
Social-level (C3)	Favorable policy (C31)	Relevant departments release policies and guidance favorable to entrepreneurship, such as rural revitalization strategy, tax incentives, and reductions	[47,48]
	Social responsibility (C32)	Valuing social trust, altruism, and empathy, and taking responsibility among stakeholders such as employees and consumers	[47,49]
	Social capital (C33)	Social relations of the entrepreneurs or entrepreneurship team, geo-relative networks such as relatives of the employees of the enterprise, or institutional-type relationship networks	[50,51]
Environmental-level (C4)	Business environment (C41)	Reducing and removing various restrictions by the law, improving the quality of rural government services and sound factor markets	[52,53]
	Infrastructure (C42)	Communication, municipal, and transportation infrastructure for entrepreneurship	[54,55,56]
	Resource conditions (C43)	Natural and socio-economic resources such as land, forestry, and water resources	[57]

.

3. Methodology

The fuzzy DANP method efficiently combines fuzzy set theory, DEMATEL, and ANP methods. It can not only analyze the influence relationships among indicators, calculate the weights of indicators, and obtain optimal solutions for complex multi-factor relationships, but also effectively reduce the subjective fuzziness caused by experts’ evaluations [15]. First, collect the original data through a questionnaire survey and convert the data into the corresponding clear numbers. Second, the degree of influence, degree of being influenced, degree of centrality, and degree of causality among the influencing factors are calculated using the DEMATEL method, and the causal relationship between the influencing factors is established. Finally, the ANP method is employed to calculate the weights of each influencing factor. The specific steps are as follows [17,58,59]:

Step 1: Transformation of questionnaire data.

Based on the constructed indicator model, a 5-level survey questionnaire is designed, with levels ranging from no impact (0) to high impact (4). Invite the k experts to fill in the questionnaire and convert expert scores into corresponding triangular fuzzy numbers according to the fuzzy semantic conversion table in

Table 2. Fuzzy semantic conversion table.

Influence Level	Influence Value	Triangular Fuzzy Number	Clear Number
Very high impact	4	(0.7, 0.9, 1.0)	0.8667
High impact	3	(0.5, 0.7, 0.9)	0.7000
Small impact	2	(0.3, 0.5, 0.7)	0.5000
Little effect	1	(0.1, 0.3, 0.5)	0.3000
No effect	0	(0, 0.1, 0.3)	0.1333

, and then the triangular fuzzy number is transformed into the corresponding clear number according to the defuzzification formula of the center of gravity method in Equation (1).

$$f={\displaystyle \frac{(c-a)+(b-a)}{3}}+a$$

(1)

where f represents the clear number value and a, b, c are the upper limit, most probable value, and lower limit of the triangular fuzzy number A, respectively.

Step 2: Calculate the initial average matrix E.

Calculate the average matrix E of n × n according to Equation (2). $e_{ij}^m$ represents the score of the m-th expert on the degree of direct influence of the i-th factor on the j-th factor.

$$E={\left[e_{ij}\right]}_{n\times n},e_{ij}={\displaystyle \frac{1}{k}}{\displaystyle \sum _{m=1}^k{e}_{ij}^m}$$

(2)

where i, j = 0, 1, 2, …, n.

Step 3: Verify the consistency of the original data.

According to Equation (3), calculate the consistency of expert opinions. If the ratio is less than 0.05, the confidence level is above 95%. There is a 95% probability that the expert opinions are the same.

$$ratio={\displaystyle \frac{1}{n(n-1)}}{\displaystyle {\sum }_{i=1}^n{\displaystyle {\sum }_{j=1}^n\left(\left|e_{ij}^k-e_{ij}^{k-1}\right|/e_{ij}^k\right)}}$$

(3)

Step 4: Normalize the initial average matrix E to get the direct influence matrix Y.

Sum the data for each row and column, select the maximum value, and divide each element in the initial average matrix E by the maximum value to obtain the direct influence matrix Y. The Equation is shown in (4). The elements in the matrix Y indicate the strength of the relationship between the corresponding influencing factors.

$$\mathit{Y}=v\times \mathit{E},v=\min \left[1/{\max }_i{\displaystyle \sum _{j=1}^n{e}_{ij}},1/{\max }_j{\displaystyle \sum _{i=1}^n{e}_{ij}}\right]$$

(4)

Step 5: Calculate the comprehensive influence matrix T.

Multiplying the direct impact matrix Y can represent the indirect relationship of each influencing factor, and adding up all the indirect influences gives a comprehensive impact matrix T. The specific Equation (5) is as follows:

$$\mathit{T}=\mathit{Y}+{\mathit{Y}}^2+\cdots +{\mathit{Y}}^K=\mathit{Y}{(\mathit{E}-\mathit{Y})}^{-1}={\left[t_{ij}\right]}_{n\times n}$$

(5)

Step 6: Drawing the Influence Relationship Map (INRM).

Calculate the degree of impact, the degree of being impacted, centrality, and cause degree of each factor in the comprehensive impact matrix T. The sum of each row (${\displaystyle \sum _{j=1}^n{t}_{ij}}$) indicates the corresponding factor’s impact level (ri). The sum of each column (${\displaystyle \sum _{i=1}^n{t}_{ij}}$) indicates the corresponding factor’s being impacted level (cj). When i = j, ri + cj represents centrality, and ri − cj represents cause degree. The influence relationship map is drawn with centrality as abscissa and cause degree as ordinate.

Step 7: Standardize the comprehensive influence matrix T.

The integrated impact matrix T can be divided into a dimension-based matrix T_D and different sub-matrices T_C based on indicators. Divide each element in T_D by the sum of all the elements in its corresponding row to get ${\mathit{T}}_D^a$, as shown in Equation (6). The standardization processing of the T_C matrix is similar, but considering the different weights of indicators in each dimension, the comprehensive influence matrix of indicators is first divided into sub-matrices of various sizes, and then calculated with sub-matrices as the unit to obtain, as shown in Equation (7). Where, ${\mathit{T}}_C^{a12}$ is a submatrix of the matrix ${\mathit{T}}_C^a$, and the calculation method is shown in Equation (8).

$${\mathit{T}}_D^a={\left[t_D^{aij}\right]}_{n\times n}=\left[\begin{array}{cccc}{t}_D^{11}/d_1& \cdots & t_D^{1j}/d_1& \begin{array}{cc}\cdots & t_D^{1n}/d_1\end{array}\\ ⋮& & ⋮& \begin{array}{cc}& ⋮\end{array}\\ t_D^{i1}/d_i& \cdots & t_D^{ij}/d_i& \begin{array}{cc}\cdots & t_D^{in}/d_i\end{array}\\ \begin{array}{c}⋮\\ t_D^{n1}/d_n\end{array}& \begin{array}{c}\\ \cdots \end{array}& \begin{array}{c}⋮\\ t_D^{nj}/d_n\end{array}& \begin{array}{cc}\begin{array}{c}\\ \cdots \end{array}& \begin{array}{c}⋮\\ t_D^{nn}/d_n\end{array}\end{array}\end{array}\right],d_i={\displaystyle {\sum }_{j=1}^n}{t}_D^{ij},i=1,2,3,\dots ,n$$

(6)

$${\mathit{T}}_C^a=\left[\begin{array}{cccc}{T}_C^{a11}& \cdots & T_C^{a1j}& \begin{array}{cc}\cdots & T_C^{a1n}\end{array}\\ ⋮& & ⋮& \begin{array}{cc}& ⋮\end{array}\\ T_C^{ai1}& \cdots & T_C^{aij}& \begin{array}{cc}\cdots & T_C^{ain}\end{array}\\ \begin{array}{c}⋮\\ T_C^{an1}\end{array}& \begin{array}{c}\\ \cdots \end{array}& \begin{array}{c}⋮\\ T_C^{anj}\end{array}& \begin{array}{cc}\begin{array}{c}\\ \cdots \end{array}& \begin{array}{c}⋮\\ T_C^{ann}\end{array}\end{array}\end{array}\right]$$

(7)

$${\mathit{T}}_C^{a12}=\left[\begin{array}{cccc}{t}_{11}^{12}/t_1^{12}& \cdots & t_{1j}^{12}/t_1^{12}& \begin{array}{cc}\cdots & t_{1n_2}^{12}/t_1^{12}\end{array}\\ ⋮& & ⋮& \begin{array}{cc}& ⋮\end{array}\\ t_{i1}^{12}/t_i^{12}& \cdots & t_{ij}^{12}/t_i^{12}& \begin{array}{cc}\cdots & t_{i{n}_2}^{12}/t_i^{12}\end{array}\\ \begin{array}{c}⋮\\ t_{n_{1}1}^{12}/t_{n_1}^{12}\end{array}& \begin{array}{c}\\ \cdots \end{array}& \begin{array}{c}⋮\\ t_{n_{1}j}^{12}/t_{n_1}^{12}\end{array}& \begin{array}{cc}\begin{array}{c}\\ \cdots \end{array}& \begin{array}{c}⋮\\ t_{n_1{n}_2}^{12}/t_{n_1}^{12}\end{array}\end{array}\end{array}\right],t_i^{12}={\displaystyle {\sum }_{j=1}^{n_2}}{t}_{ij}^{12}$$

(8)

Step 8: Calculate the unweighted super matrix W.

Transpose the indicators-based standardized comprehensive influence matrix ${\mathit{T}}_C^a$ to obtain the unweighted supermatrix W. The specific calculation of Equation (9) is as follows:

$$\mathit{W}={\left(T_C^a\right)}^T$$

(9)

Step 9: Calculate the weighted super matrix Wa.

Multiply the dimension-based standardized comprehensive influence matrix and the unweighted supermatrix W to get the weighted supermatrix Wa. The specific calculation of Equation (10) is as follows:

$${\mathit{W}}_a={\mathit{T}}_D^a\mathit{W}=\left[\begin{array}{cccc}{t}_D^{a11}\times W_{11}& \cdots & t_D^{a1j}\times W_{1j}& \begin{array}{cc}\cdots & t_D^{a1n}\times W_{1n}\end{array}\\ ⋮& & ⋮& \begin{array}{cc}& ⋮\end{array}\\ t_D^{ai1}\times W_{i1}& \cdots & t_D^{aij}\times W_{ij}& \begin{array}{cc}\cdots & t_D^{ain}\times W_{in}\end{array}\\ \begin{array}{c}⋮\\ t_D^{an1}\times W_{n1}\end{array}& \begin{array}{c}\\ \cdots \end{array}& \begin{array}{c}⋮\\ t_D^{anj}\times W_{nj}\end{array}& \begin{array}{cc}\begin{array}{c}\\ \cdots \end{array}& \begin{array}{c}⋮\\ t_D^{ann}\times W_{nn}\end{array}\end{array}\end{array}\right]$$

(10)

Step 10: Calculate the limit weighted supermatrix L.

Multiply the weighted supermatrix Wa, and then obtain the stable limit supermatrix L by taking the limit and determining the weight of each indicator, as shown in Equation (11).

$$\mathit{L}=\underset{\lambda \to \infty }{\lim }{({\mathit{W}}_a)}^{\lambda }$$

(11)

4. Results Analysis

To effectively analyze the degree of interaction and correlation among various factors affecting sustainable rural entrepreneurship, this study sent two rounds of questionnaires via email to 20 scientific research workers in the fields of entrepreneurship and agricultural and rural development at well-known universities. The first round of the questionnaire collected experts’ opinions on the questionnaire to modify and improve the indicator system of factors affecting sustainable rural entrepreneurship. In the second round of the questionnaire, experts were invited to score the interaction influence degree of each factor among the indicators. The questionnaire needs to be designed based on the DANP methodology. For example, if indicator A has a very strong impact on indicator B, then the impact value of A on indicator B is 4. Conversely, if indicator B has no impact on indicator A, then its value is 0. A total of 10 valid questionnaires were received, including responses from 7 professors with 20 years of experience in the fields of innovation and entrepreneurship and business management and 3 managers in agricultural business management and entrepreneurial management. The detailed backgrounds of these experts are cataloged in

Table 3. The detailed backgrounds 10 experts.

No.	Work Department	Job Title	Educational Qualifications	Working Experience (Years)
1	Entrepreneurial management	Professor	Master’s	21
2	Entrepreneurial Characteristics	Professor	Ph.D.	20
3	Rural entrepreneurship	Professor	Ph.D.	24
4	Innovation and entrepreneurship	Professor	Ph.D.	20
5	Rural entrepreneurship	Professor	Ph.D.	21
6	Entrepreneurial management	Professor	Ph.D.	20
7	Entrepreneurial management	Professor	Master’s	22
8	Agricultural science	manager	Master’s	15
9	Agricultural technology	manager	Ph.D.	10
10	Rural e-commerce	manager	Master’s	12

.

According to Equation (3), the consistency level of the 10 questionnaires is 0.04, and the confidence level is above 95%, indicating that the expert opinions show good consistency. The average evaluation matrix can be seen in

Table 4. The average evaluation matrix.

	C11	C12	C13	C21	C22	C23	C24	C31	C32	C33	C41	C42	C43
C11	0.00	0.44	0.56	0.45	0.62	0.60	0.65	0.48	0.61	0.52	0.54	0.41	0.47
C12	0.64	0.00	0.62	0.54	0.61	0.68	0.72	0.50	0.69	0.71	0.54	0.41	0.50
C13	0.48	0.52	0.00	0.58	0.72	0.72	0.72	0.57	0.58	0.70	0.65	0.52	0.58
C21	0.73	0.51	0.70	0.00	0.74	0.71	0.69	0.59	0.52	0.54	0.69	0.50	0.50
C22	0.65	0.58	0.67	0.52	0.00	0.73	0.76	0.54	0.63	0.70	0.68	0.56	0.61
C23	0.73	0.65	0.70	0.66	0.69	0.00	0.73	0.67	0.56	0.65	0.66	0.60	0.54
C24	0.71	0.56	0.67	0.63	0.63	0.71	0.00	0.67	0.75	0.67	0.71	0.52	0.64
C31	0.78	0.63	0.76	0.69	0.75	0.71	0.74	0.00	0.56	0.58	0.80	0.64	0.62
C32	0.67	0.50	0.56	0.56	0.64	0.59	0.67	0.61	0.00	0.69	0.58	0.41	0.45
C33	0.76	0.66	0.71	0.48	0.65	0.71	0.56	0.52	0.70	0.00	0.62	0.43	0.50
C41	0.78	0.63	0.77	0.69	0.73	0.71	0.69	0.67	0.60	0.59	0.00	0.65	0.46
C42	0.67	0.63	0.69	0.60	0.65	0.67	0.57	0.54	0.56	0.58	0.75	0.00	0.56
C43	0.71	0.62	0.78	0.65	0.71	0.56	0.58	0.58	0.52	0.53	0.64	0.60	0.00

.

After the collected questionnaire data were transformed and processed according to the DEMATEL method, the comprehensive impact matrix T was obtained, as seen in

Table 5. The total evaluation matrix.

	C11	C12	C13	C21	C22	C23	C24	C31	C32	C33	C41	C42	C43
C11	0.58	0.54	0.64	0.55	0.64	0.64	0.65	0.55	0.59	0.59	0.61	0.49	0.51
C12	0.72	0.55	0.71	0.62	0.71	0.71	0.72	0.61	0.66	0.67	0.68	0.55	0.57
C13	0.73	0.62	0.66	0.64	0.74	0.74	0.74	0.63	0.66	0.69	0.71	0.57	0.59
C21	0.76	0.63	0.74	0.58	0.75	0.74	0.74	0.64	0.66	0.67	0.72	0.57	0.59
C22	0.76	0.65	0.76	0.65	0.68	0.76	0.76	0.65	0.69	0.71	0.73	0.59	0.62
C23	0.79	0.67	0.78	0.68	0.77	0.70	0.78	0.68	0.69	0.72	0.75	0.61	0.62
C24	0.79	0.66	0.78	0.68	0.77	0.78	0.70	0.68	0.71	0.72	0.76	0.60	0.63
C31	0.83	0.70	0.82	0.72	0.81	0.81	0.81	0.63	0.72	0.74	0.80	0.64	0.66
C32	0.71	0.59	0.69	0.61	0.69	0.69	0.70	0.61	0.56	0.65	0.67	0.53	0.55
C33	0.74	0.63	0.73	0.62	0.72	0.73	0.71	0.62	0.66	0.60	0.70	0.56	0.58
C41	0.81	0.68	0.80	0.70	0.79	0.79	0.79	0.69	0.71	0.72	0.69	0.63	0.62
C42	0.75	0.64	0.75	0.65	0.74	0.74	0.73	0.64	0.67	0.68	0.73	0.52	0.60
C43	0.76	0.64	0.75	0.65	0.75	0.73	0.73	0.64	0.66	0.68	0.72	0.59	0.54

.

The impact, being impacted, centrality, and cause degree of factors at all levels were calculated, as shown in

Table 6. Factors influencing sustainable rural entrepreneurship at all levels.

Dimensions	ri	cj	ri + cj	ri − cj	Criteria	ri	cj	ri + cj	ri − cj
C1	2.53	2.82	5.34	−0.29	C11	7.58	9.73	17.31	−2.15
					C12	8.47	8.21	16.69	0.26
					C13	8.72	9.60	18.32	−0.87
C2	2.79	2.84	5.63	−0.06	C21	8.79	8.35	17.14	0.44
					C22	9.02	9.56	18.57	−0.54
					C23	9.23	9.54	18.77	−0.32
					C24	9.27	9.56	18.83	−0.29
C3	2.71	2.63	5.34	0.08	C31	9.70	8.26	17.96	1.44
					C32	8.23	8.64	16.87	−0.41
					C33	8.61	8.84	17.45	−0.23
C4	2.76	2.49	5.26	0.27	C41	9.39	9.25	18.63	0.14
					C42	8.84	7.46	16.29	1.38
					C43	8.83	7.69	16.52	1.14

.

To more intuitively show the key factors in the sustainable development of rural entrepreneurship and the relationship between the influencing factors at all levels, and with centrality as the horizontal coordinate and cause degree as the vertical coordinate, a causal diagram of influencing factors of sustainable rural entrepreneurship is drawn, as shown in Figure 1.

As can be seen from the figure above, favorable policies (C₃₁) and business environment (C₄₁) are located in the first quadrant, with high centrality and cause degree. They are the driving factors for sustainable rural entrepreneurship and play a key role in realizing sustainable rural entrepreneurship. Therefore, they have the greatest impact on the sustainable development of rural entrepreneurship. In less developed regions, rural entrepreneurs, in particular, require preferential policies and a conducive business environment. The Chinese government has instituted a range of measures, encompassing tax exemptions and reductions, start-up subsidies, guaranteed loan facilities, and the establishment of entrepreneurship parks and incubation centers, in addition to training programs and skill upgrading initiatives, aimed at diminishing the hurdles to entrepreneurship and attracting both talent and capital. For returning entrepreneurs specifically, the government offers one-time subsidies, social insurance subsidies, and tax incentives as strategies to bolster the rural economy and facilitate rural revitalization. These policies are devised to ignite entrepreneurial zeal and to augment employment opportunities and economic development within rural communities.

In the second quadrant, entrepreneurial ability (C₁₂), market factors (C₂₁), infrastructure (C₄₂), and resource conditions (C₄₃) are the supporting factors of sustainable rural entrepreneurship; they play an auxiliary role in realizing sustainable rural entrepreneurship. Entrepreneurial motivation (C₁₁), social responsibility (C₃₂), and social capital (C₃₃) are located in the third quadrant, and the cause degree is less than zero, so they are vulnerable to other factors in the model, which are called independent factors of sustainable rural entrepreneurship. Entrepreneurship type (C₁₃), business model (C₂₂), financial support (C₂₃), and economic value (C₂₄) are located in the fourth quadrant. They are the core problem factors of sustainable rural entrepreneurship and are most susceptible to other factors in the model. In economically prosperous rural regions, market dynamics, infrastructural development, and resource availability constitute pivotal factors in the success of rural entrepreneurship. For instance, Zhejiang Province in China has facilitated the transformation of farmers into “peasant entrepreneurs” and has established a scalable sales paradigm for agricultural commodities through the organization of “theory + practice”-oriented village broadcasting training programs tailored to farmer-students. Furthermore, cultivation modalities, including those driven by talent, park clustering, leading enterprises, characteristic industries, and industry integration with an emphasis on innovation, have catalyzed the advancement and nurturing of rural innovative entrepreneurship talents.

Based on whether the cause degree is greater than zero, the influencing factors can be divided into causal factors and outcome factors. The cause degree of causal factors is greater than zero, indicating that they can affect other factors. On the contrary, the cause degree less than 0 is the outcome factor. As can be seen from Table 6, in the cause degree statistics of the first-level dimension, the causal factors are environmental factors (C₄) and social factors (C₃), and the outcome factors are economic factors (C₂) and entrepreneurs (C₁). Sustainable rural entrepreneurship is mainly affected by the above factors. Centrality is the sum of the degree of influence and the degree of being influenced. Centrality shows the importance of factors to sustainable rural entrepreneurship. The ranking of the centrality of the first-level dimensions is in the order of economic factors (C₂), entrepreneurs (C₁), social factors (C₃), and environmental factors (C₄), indicating that economic factors are the most critical dimensions in the index system of sustainable rural entrepreneurship. This is consistent with the research of Del et al. [37]. They believe bank financing is crucial for rural entrepreneurship and that raising startup capital is the first problem entrepreneurs must solve in their ventures’ initial stage. At the same time, most rural entrepreneurship is based on market opportunities. If the market positioning of entrepreneurial projects is inaccurate or lacks market competitiveness, it will lead to slow sales of products, the investment funds not being recovered, and even entrepreneurial failure [36]. Therefore, in realizing sustainable development of rural entrepreneurship, it is necessary to comprehensively consider the influence of economic factors such as market factors, financial support, business models, and profit returns.

As can be seen from

Table 7. The weights of dimensions and criteria.

Dimensions	Local Weight	Ranking	Criteria	Local Weight	Ranking	Global Weight	Ranking
C1	0.261	3	C11	0.353	1	0.092	1
			C12	0.299	3	0.078	7
			C13	0.348	2	0.091	2
C2	0.264	2	C21	0.226	4	0.059	13
			C22	0.258	2	0.068	11
			C23	0.258	3	0.068	12
			C24	0.258	1	0.068	10
C3	0.244	1	C31	0.321	3	0.078	6
			C32	0.336	2	0.082	5
			C33	0.343	1	0.084	4
C4	0.231	4	C41	0.379	1	0.088	3
			C42	0.306	3	0.071	9
			C43	0.315	2	0.073	8

, there are six causal factors influencing sustainable rural entrepreneurship, among which favorable policies (C₃₁), infrastructure (C₄₂), and resource conditions (C₄₃) are the most critical. It shows that the favorable policies issued by relevant government departments, the construction of rural entrepreneurial infrastructure, and local resource conditions can easily affect other factors. In addition, in the causal diagram of influencing factors at all levels, favorable policies belong to the driving factor, indicating that releasing relevant policies supporting rural entrepreneurship is important to achieving sustainable development of rural entrepreneurship. There were seven outcome factors, among which the cause degree of entrepreneurial motivation (C₁₁) was the smallest, indicating that the entrepreneurial motivation of entrepreneurs was most susceptible to other factors. Consistent with the research of Meshram et al., entrepreneurial motivation is an important prerequisite for enterprise growth, and many factors such as entrepreneurs’ perception of entrepreneurial opportunities, self-fulfilling career aspirations, business experience, finance availability, personal and family security, and work independence factors can stimulate individual entrepreneurial motivation [35]. Therefore, creating a good entrepreneurial atmosphere and business environment through policies such as “mass innovation and mass entrepreneurship” and the awareness and guidance of sustainable rural entrepreneurship can be clarified to strengthen the entrepreneurial motivation of rural entrepreneurs to achieve a circular economy and sustainable development, as well as promoting the sustainable development of rural entrepreneurship projects effectively.

The degree of centrality reflects the importance of various factors influencing the realization of sustainable rural entrepreneurship. As can be seen from Table 7, economic value (C₂₄), financial support (C₂₃), business environment (C₄₁), business model (C₂₂), and entrepreneurial type (C₁₃) are the top five factors in the ranking of centrality, and there is little difference in centrality among these five factors. It shows that they have an important impact on the sustainable development of rural entrepreneurship. In sustainable rural entrepreneurship, it is necessary to attach great importance to the types of entrepreneurial projects, business models, financial support, business environment, and whether they can bring sustainable economic value. Three of the top five factors in the index centrality of second-level indicators belong to the economic dimension, reflecting that the economy is the key first-level dimension in the index system of sustainable rural entrepreneurship. In addition, combined with Figure 1, it can be found that the business environment (C₄₁) is a driving factor for sustainable rural entrepreneurship, indicating that it significantly impacts sustainable rural entrepreneurship and plays a key role in the sustainable development of rural entrepreneurship. Peng et al. (2022) found that the legal environment and market environment significantly positively impact the competitiveness of startups [25]. Therefore, relevant government departments creating a good entrepreneurial environment can help sustainable agricultural entrepreneurship.

According to the weight results and ranking of indicators, it can be found that entrepreneurial motivation (C₁₁), entrepreneurial type (C₁₃), business environment (C₄₁), social capital (C₃₃), and social responsibility (C₃₂) play an important role in the process of realizing sustainable rural entrepreneurship. From the perspective of cause degree, the cause degree of entrepreneurial motivation (C₁₁), entrepreneurial type (C₁₃), social capital (C₃₃), and social responsibility (C₃₂) is less than zero, so they are easily affected by other factors. From the perspective of centrality, there is a difference between the centrality ranking and the weight ranking of the indicators. The three factors with the top centrality ranking are all economic factors, but their weight ranking is lower, which may be because they are outcome factors and can be easily affected by other factors. Based on the centrality and weight ranking of influencing factors, it can be seen that economic level and entrepreneurs are the most important dimensions in the sustainable rural entrepreneurship indicator system, indicating that the influencing factors of economic factors and entrepreneurs greatly impact sustainable rural entrepreneurship. The study of Tur-Porcar et al. (2018) also points out that entrepreneurial motivation, self-efficacy, and other behavioral factors, as well as business factors such as economic benefits and enterprise management, play a key role in realizing sustainable rural entrepreneurship [13]. To realize the sustainable development of rural entrepreneurship, it is necessary to identify the existing market opportunities, obtain the corresponding financial support, and pay attention to improving the ability and literacy of entrepreneurs.

5. Discussion

5.1. Theoretical Implications

First, this article believes that policies provide key support and incentives, an important reason entrepreneurs return to rural areas for entrepreneurship. The government’s supportive policies, such as tax incentives, fiscal subsidies, and convenient loans, have reduced the cost of entrepreneurship. Industrial guidance and infrastructure construction provide entrepreneurs with clear development directions and a favorable business environment. Talent cultivation and market access policies have attracted talent and simplified the entrepreneurial process. In addition, risk management measures and innovation incentives help entrepreneurs diversify risks and stimulate creativity. These policies collectively create an environment conducive to rural entrepreneurship and encourage the return of entrepreneurs. This research finding aligns with existing perspectives on rural return migration entrepreneurship (e.g., Wang et al. [41]; Chen and Barcus [60]).

Second, we find that entrepreneurial motivation is vital for rural entrepreneurs as it propels them to pursue personal goals, meet economic needs, create social impact, preserve culture, assume environmental responsibility, respond to policy incentives, cater to market demands, apply new technologies, leverage educational backgrounds, and offer community services. Another research paper discusses the motivations of farmers for entrepreneurship and their contributions to sustainable rural development, including contributions to vulnerable groups in rural areas [19]. This provides us with a further insight that attracting young people to start businesses in less developed rural areas is key to stimulating rural development. These motivations help them overcome challenges, achieve business success, and foster economic growth and social advancement in rural areas.

Third, this article has significant advantages in using the DANP method to study key factors of rural entrepreneurship, including comprehensiveness, hierarchy, fuzziness handling, priority ranking, multi-criteria decision-making, consensus formation, strategy formulation, and visualization. These advantages help decision-makers systematically identify and evaluate influencing factors, form effective strategies, and promote the sustainable development of rural entrepreneurship. This systematic approach not only aids decision-makers in meticulously identifying and evaluating influencing factors but also enables the formulation of targeted strategies that resonate with the complex needs of rural entrepreneurship [1,19].

Lastly, this study extends the triple bottom line theory by adding the personal factors of entrepreneurs based on environmental, social, and economic factors. This expansion, as proposed in the four-dimensional evaluation system, represents a groundbreaking contribution to the theoretical landscape of sustainable rural entrepreneurship. By acknowledging the pivotal role of entrepreneurs’ personal motivations and ability [14,19,21], it deepens our understanding of the intricate interplay between individual agency and the broader sustainability objectives. Consequently, this study not only enriches the theoretical foundation but also serves as a valuable theoretical reference point for future research endeavors, inspiring scholars to explore the nuanced relationships between personal factors and the triple bottom line in the context of rural entrepreneurship.

5.2. Managerial Implications

The key to sustainable entrepreneurship in rural areas lies in stimulating and cultivating the potential of entrepreneurs. It is necessary to enhance the entrepreneurial awareness of farmers and returnees through education and training so that they realize that entrepreneurship is not only an opportunity for personal development but also an important way to promote rural development [61]. Local government departments should provide necessary entrepreneurial skills training to help them master key skills such as market analysis, financial planning, and risk management. In addition, providing financial, technological, and market information resources to entrepreneurs can lower the threshold for entrepreneurship and increase the success rate. For instance, training programs that enhance digital literacy can help rural entrepreneurs leverage e-commerce and other digital platforms to expand their markets [26]. Finally, it is necessary to educate entrepreneurs on how to conduct risk assessment and management, help them cope with the uncertainty of the market and natural conditions, and ensure that entrepreneurial activities can continue and operate stably. There is little literature on the issue of risk control in rural entrepreneurship, which is also a direction for future research exploration.

From an economic perspective, sustainable entrepreneurship in rural areas requires achieving industrial diversification and market linkage. By developing various industries such as agriculture, handicrafts, and rural tourism, farmers can increase their income and enhance the economic vitality of rural areas. At the same time, an effective market linkage mechanism to help agricultural products enter broader markets and improve their market competitiveness should be established. Additionally, through processing and brand building, the value chain of agricultural products can be extended, increasing the products’ added value and boosting farmers’ incomes [60]. Finally, financial products and services suitable for rural entrepreneurship, such as microcredit and agricultural insurance, should be provided to support entrepreneurs financially.

The impact of sustainable entrepreneurship in rural areas on society is multifaceted. Entrepreneurship activities can promote community development and improve residents’ quality of life and social cohesion. They can also protect and utilize local cultural heritage, inherit and promote local culture through cultural entrepreneurship activities, and enhance the community’s cultural identity [62], as well as improving local education and sanitation conditions through entrepreneurial activities, enhancing residents’ health and education levels, and promoting social equity. It is noteworthy that rural entrepreneurship necessitates a strong emphasis on adaptability, innovation, community participation, and risk management, all of which serve as essential guarantees for its sustained development [63,64]. Finally, all sectors of society should be encouraged to participate in rural entrepreneurship, including non-governmental organizations, enterprises, and social capital, to form a diversified social support network.

6. Conclusions

Through a literature review, this study establishes a model framework based on the theoretical basis of the triple bottom line. It creates an index system of influencing factors of sustainable rural entrepreneurship from four dimensions: entrepreneurs, economic-level, social-level, and environmental-level. The fuzzy DANP method is used to identify the key factors and the associated relationship among them. The results show that economic levels and entrepreneurs significantly affect the realization of sustainable rural entrepreneurship. Among the secondary indicators, entrepreneurial motivation, entrepreneurial type, financial support, economic value, favorable policies, and business environment are the key factors affecting the success of sustainable rural entrepreneurship. All factors other than entrepreneurial motivation and business environment are causal factors that cover different dimensions of sustainable rural entrepreneurship. It shows that sustainable rural development cannot be achieved without various subjects and efforts in rural entrepreneurship. Sustainable rural entrepreneurship can be realized only when all stakeholders, such as entrepreneurial teams, investors, government, and residents, play their roles and give full effort to their roles.

For entrepreneurs seeking to promote the sustainable development of rural entrepreneurship and the realization of a circular economy, the following methods can be adopted to help startups carry out sustainable rural entrepreneurship: First, entrepreneurs need to improve their competence and literacy, cultivate correct and sustainable entrepreneurial ideas, enhance entrepreneurial risk awareness, be familiar with the types of sustainable rural entrepreneurial development, take the initiative to assume social responsibilities, consider the well-being of local farmers, and achieve sustainable income increase. Second, relevant government departments should strengthen policy guidance and support to encourage all kinds of groups with entrepreneurial intentions to make use of rural resources for innovation and entrepreneurship. At the same time, government departments can increase pollution charges through environmental regulations and other means to promote green agriculture, leisure agriculture, rural tourism, and other types of rural entrepreneurship. Third, optimize the entrepreneurial environment for rural entrepreneurs, reduce or even cancel unnecessary restrictions, reduce taxes and fees for initially established small, medium, and micro enterprises, and vigorously develop inclusive financial services to ensure financial support for entrepreneurial projects.

The principal contributions of this study are threefold. First, it constructs an evaluation framework for the influencing factors of sustainable rural entrepreneurship, elucidating the intricate interplay between these factors. This endeavor enriches the research landscape in the field of sustainable rural entrepreneurship and facilitates the promotion of both rural entrepreneurship’s sustainable development and the realization of circular economy principles. Second, by providing a theoretical foundation, it aids governmental policymaking, guiding rural entrepreneurial teams towards sustainable rural development initiatives. Last, the utilization of the fuzzy DANP methodology endeavors to minimize the potential biases inherent in expert subjective judgments.

There are still some limitations in this study. On the one hand, the expert samples selected are all from university scientific research institutions, and there is a lack of entrepreneurs who have taken root in rural entrepreneurship’s sustainable development and achieved certain results. Furthermore, the fact that all research samples are from China poses a challenge to the generalizability of our findings. In the future, comparative studies across different countries would be valuable and necessary. On the other hand, this study builds an index system of sustainable rural entrepreneurship based on a literature review and triple bottom line theory, within which it is difficult to cover all factors related to the sustainable development of rural entrepreneurship. For instance, the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) can enhance operational efficiency and sustainability [65]. In the future, the impact of AI and IoT applications on entrepreneurship can be considered. Meanwhile, machine learning and other methods can be adopted to explore the influencing factors of sustainable rural entrepreneurship and further improve the index evaluation system of sustainable rural entrepreneurship. Future research could focus on studying the risk management of rural entrepreneurship projects and the early warning signs of talent loss in rural entrepreneurship.