Improving the entrepreneurial ability of rural migrant workers returning home in China: Study based on 5,675 questionnaires

China has experienced large-scale rural-to-urban migration since the mid-1980s. Recently, due to COVID-19 impacts, economic slowdown, high urban living costs, and strong policy support for rural entrepreneurship, more migrant workers are returning home to start businesses. Despite rising participation, success rates remain low (3-year survival: 43.49%; 5-year survival: 22.71%). Prior literature indicates entrepreneurial ability is shaped by internal personal qualities (creativity, risk attitude, experience, networks, information skills) and external environments (culture, policies, market conditions). However, the relative effect sizes of these factors are rarely quantified. This study’s purpose is to incorporate both internal and external factors into a unified framework and determine which factors most affect entrepreneurial ability and to what extent, in order to guide effective interventions to improve success among returning migrant workers.

Scholars link entrepreneurship to socio-economic development and identify key abilities such as creativity, risk-taking, vision, opportunity recognition, and learning-based skills formation. Entrepreneurial education can cultivate such abilities. External contexts—regional, cultural, political, social, and economic environments—also shape outcomes. For Chinese returning migrant workers, low education, insufficient technical and management skills are often cited as causes of low success rates. Building on this literature, the paper conceptualizes entrepreneurial ability as jointly determined by individual internal factors (capital, education, skills and experience, interpersonal connections, management ability, hard-working spirit, access to information, communication skills) and external environmental factors (preferential policy, market risk, government support, external financing conditions, industrial restructuring, industry system, industrial technology content, market competition). This integrated framework guides the empirical analysis.

Design: Mixed-method evaluation using SWOT analysis combined with Analytic Hierarchy Process (AHP) to identify and prioritize factors influencing entrepreneurial ability. Data collection: From July 2018 to October 2022, surveys were conducted in four Chinese provinces with large migrant worker populations (Sichuan, Henan, Jiangxi, Anhui), covering 36 prefecture-level cities. A total of 6,720 questionnaires were distributed; 5,675 valid responses were collected (effective rate 84.45%): 3,405 successful entrepreneurs and 2,270 entrepreneurial losers. Sample characteristics included average age ≈43 years; average household labor force 2.5; average household size ≈5. Education levels: 57.62% junior high or below; 27.67% senior high; 14.71% college or above. Training: 74.78% educated in some form; 75.38% skilled. SWOT identification: Based on Table 2’s "significant impact" selections and respondents’ self-assessments (+ favorable; − unfavorable), internal strengths (S) and weaknesses (W) and external opportunities (O) and threats (T) were identified. Top three in each category formed the SWOT matrix: S1 skills/experience, S2 interpersonal connections, S3 hard-working spirit; W1 insufficient funds, W2 low cultural quality (literacy/education), W3 low management ability; O1 government incentives, O2 government support, O3 industrial restructuring; T1 market risk, T2 external financing difficulties, T3 low technology content. AHP procedure: A hierarchical model was constructed with the goal (entrepreneurship choice outcomes), criteria (SWOT groups with 12 factors), and alternatives (successful vs failed entrepreneurship). Pairwise comparison judgment matrices were built within each SWOT group (3×3 per group) to obtain local priorities; consistency was tested using CI and CR with threshold CR < 0.1. Group weights were also estimated and combined with local priorities to yield overall priorities (global weights) of all 12 factors. The characteristic value method was used for weight derivation. All matrices in Table 4 passed consistency (CR < 0.1).

- From survey perception (Table 2), high proportions rated many factors as significantly impactful (e.g., B1 preferential policy 94.13%, A1 capital 92.25%, A2 education 91.05%). - SWOT selection produced: Strengths (S1 skills/experience; S2 interpersonal connections; S3 hard-working spirit), Weaknesses (W1 insufficient funds; W2 low literacy; W3 low management ability), Opportunities (O1 government incentives; O2 government support; O3 industrial restructuring), Threats (T1 market risk; T2 external financing difficulties; T3 low technology content). - Within-group AHP rankings (Table 4): S1 > S2 > S3; W3 > W2 > W1; O3 > O1 > O2; T3 > T1 > T2. - Group weights (Table 5): Strengths 0.2868; Opportunities 0.2553; Threats 0.2455; Weaknesses 0.2376. - Overall global priorities (Table 5): O3 industrial restructuring (0.1762) > T3 low technology contents (0.1750) > S1 skill experience (0.1724) > W3 low management ability (0.1511) > S2 interpersonal connections (0.0887) > W2 low literacy (0.0714) > O1 government incentives (0.0573) > T1 market risks (0.0532) > T2 external financing difficulties (0.0369) > S3 hard-working spirit (0.0307) > W1 insufficient fund (0.0236) > O2 government support (0.0186). All CR values < 0.1, indicating acceptable consistency. - Interpretation: The most influential determinants of entrepreneurial ability success among returning migrant workers are external opportunity from industrial restructuring, low industry technology content (as a threat), internal skills/experience, and management ability (weakness).

The findings confirm entrepreneurial ability is shaped by both personal/internal and external/environmental factors. By integrating both into a single SWOT–AHP framework, the study quantifies relative importance, addressing the gap in prior research that considered these domains separately. The high priority of industrial restructuring (opportunity) underscores the role of macro-level structural changes in creating viable niches for rural entrepreneurship. The salience of low technology content (threat) reflects competitive disadvantages in low-tech segments and the need for upgrading. Skills/experience (strength) and management ability (a key weakness) highlight that human capital and managerial capacity critically determine outcomes. The study contributes methodologically by applying a combined SWOT–AHP approach that can be adapted for comparative assessment across countries and regions. The authors note preliminary comparative explorations (America, Britain, Japan, Korea) suggest stronger internal capabilities in some developed contexts and relatively favorable external environments in China and the U.S., indicating the framework’s utility for cross-regional benchmarking. Policy relevance is substantial: recommendations include leveraging supply-side agricultural restructuring, upgrading technological content and fostering characteristic industries, intensive skills and management training, improving access to finance (streamlining formal channels and regulating informal finance), and harnessing interpersonal networks to promote an “entrepreneurship drives employment” model, alongside strengthening rural infrastructure and social security to create enabling ecosystems.

This study constructs and applies an integrated SWOT–AHP framework to prioritize internal and external factors affecting the entrepreneurial ability of returning migrant workers in China, using evidence from 5,675 questionnaires across four provinces. Results show industrial restructuring opportunities, low technology content, skills/experience, and management ability are pivotal determinants of success, followed by interpersonal connections, literacy, government incentives, and market risks; external financing difficulties, hard-working spirit, insufficient funds, and government support are relatively less influential. The paper offers targeted policy recommendations and a transferable methodology and indicator system to guide future research and policy design aimed at enhancing entrepreneurial capabilities and success rates among returnees.

- The identification of influencing factors in SWOT and the pairwise comparisons in AHP involve subjective judgments, which may introduce bias despite consistency checks. - The survey is limited to four provinces and may not capture all regional heterogeneity in China. - Generalizability to other countries or contexts may require adapting the indicator system to specific political, economic, social, and cultural environments, as noted by the authors. - Self-reported perceptions (e.g., Table 2 self-assessment) may be affected by respondent bias.