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Abstract

Institutional environment is the most fundamental soil for the survival and development of industries, and the synergistic effect of institutional environment factors can help the development of rural industries. The article uses the marketization index related data of 31 provinces (autonomous regions, municipalities directly under the central government) in China in the year of 2019, and adopts the fuzzy set qualitative comparative analysis method (fsQCA) to explore the path of institutional environment driving rural industrial development from the perspective of configuration. The results of the study show that: (1) One single institutional environment factor is not a necessary condition for generating high efficiency of rural industries. (2) The development of rural industries is influenced by multiple institutional environment factors, which are “multiple and concurrent” to form diverse groupings, that is, to promote the development of rural industries in the way of “different paths to the same destination.” (3) There are two types of institutional environment groupings that can enhance the efficiency of rural industries: legal environment-driven type and product market-driven type. The significance of this study is that analyzes the impact of the interaction of various institutional environment factors on rural industrial development, enriches the research on rural industrial development, and reveals the complex causal mechanism of the institutional environment in promoting rural industrial development.

Keywords

institutional environment configuration effect rural industry fsQCA

Introduction

The prosperity of industries leads to the prosperity of rural areas. Industrial prosperity is the foundation and key to rural revitalization. Affected by multiple factors such as history, economy, and natural geography, the problem of unbalanced and insufficient development of rural industries in China is prominent. The rural industrial benefits in the eastern coastal areas represented by Shanghai, Jiangsu, Beijing, etc. are relatively high, while those in the central and western regions such as Guizhou, Gansu, and Shanxi are relatively low (F. Lu, Pang, & Deng, 2022; R. Y. Lu, Zhang, & Chen, 2022). This hinders the realization of the goal of comprehensive rural revitalization and common prosperity. The strategic plan for rural revitalization from the year of 2018 to 2022, proposes to promote the comprehensive revitalization of rural industries with the institutional system as the driving force. Institution is an endowment, which is the key to determine the survival and development of the organization (S. Xu & Li, 2019). As a key component of rural activities, the existence and development of rural industries cannot be guaranteed without the institutional system (Wang, 2016).

Institutions provide guidelines and beliefs for organizations (Du et al., 2020). It has a direct impact on the strategic choices and economic output of the organization. A good institutional environment provides a sound rule of law environment for industrial clustering and compensates for market failures (Song & Li, 2023). For rural industries, the impact of institutional environment can be both an incentive and a hindrance. On the one hand, institutions and policies inject resources and momentum into the development of rural industries and effectively expand the space for industrial development (Wu, 2021). On the other hand, the lack of policy support and implementation is also a major dilemma for the development of rural industries (Hu & Sun, 2021).

A lot of research has been done on the relationship between institutional environment and rural industrial development, and shown that the institutional environment is a key factor affecting the development of rural industries (Ministry of Agriculture Project Team in China, 2018). The relationship between formal institutions and rural industrial development has been analyzed in terms of the legal environment (Zheng & Deng, 2010), government-market relationship (Song & Liu, 2015), and the degree of product market development (Guo & Zhang, 2020).

However, most of these studies have discussed individual aspects and have not sufficiently analyzed the synergistic effects of various dimensions of the institutional environment, and have not paid enough attention to informal institutions. Organizations are clusters of interrelated structures and practices that cannot be viewed and understood in isolation (Fiss, 2007). The institutional environment is diverse and consists of multiple dimensions whose effects are not simply additive and whose convergence produces a qualitatively different set of outcomes than a single dimension (X. Li & Liu, 2015).

The impact of different institutional environment factors on the development of rural industries is correlated, and the factors are matched to each other through linkage to produce different groupings to influence industrial efficiency. So, what kind of institutional environment grouping can promote the development of rural industries? This becomes an important theoretical question.

Based on the above, this paper uses 31 provinces (municipalities directly under the Central Government and autonomous regions) in China as research samples, and uses the fuzzy set qualitative comparative analysis (fsQCA) method to investigate the influence of formal and informal institutions on rural industrial development from a histological perspective, to identify the institutional environment histories that enhance the effectiveness of rural industries and the interaction between various institutional environment factors, and to reveal the complex causal mechanism of institutional environment for rural industrial development. The main structure of the article is as follows: Firstly, review relevant literature on institutional environment, rural industrial development, and the relationship between institutional environment and rural industrial development, and propose research questions. Secondly, conduct research design, mainly introducing research methods, data sources, variable measurement and calibration, etc. The third is the data analysis results, including the sufficiency analysis of necessary condition analysis and condition configuration, and robustness testing. Fourthly, research conclusion and discussion. This study is of great practical significance for timely summarizing and sequentially guiding relevant initiatives in the current practice area and policy level to promote the development of rural industries and rural revitalization.

The innovation of this paper: Firstly, based on the fuzzy set qualitative comparative analysis method to explore the influence of formal and informal institutions on rural industrial development and enrich the related research on rural industrial development. Most of the existing literature studies the influence of institutional environment on rural industrial development from the dimension of formal institutions, and most of them explore the relationship between variables with traditional regression analysis methods, while fewer focus on informal institutions, and even fewer explore the coupling effect of formal and informal institutions, which cannot comprehensively understand the relationship between institutional environment and rural industrial development. This study introduces the fsQCA method and selects four representative antecedent variables, namely, legal environment, government market relationship, product market development degree, and regional culture, to explore the impact of the synergistic linkage between formal and informal institutions on rural industrial development and to realize the linkage matching between elements at different levels. The study concludes that formal institutions are more important in promoting rural industrial development, while informal institutions play a supporting role, among which the degree of product market development in formal institutions is the main influencing factor and the rest are secondary factors, and under certain conditions, the secondary factors can be substituted for each other.

The second is to explore four paths to enhance the effectiveness of rural industries, and reveals the complex causal mechanism of the institutional environment to promote the development of rural industries. Existing studies clearly point out that the institutional environment is a key factor influencing the development of rural industries, but they do not further explore how the institutional environment promotes the development of rural industries. The path of its influence on the development of rural industries still needs to be clarified. This study analyzes the grouping effects among variables and identifies the combined relationships among multiple institutional environment factors, and finds that there are four paths to enhance the effectiveness of rural industry, namely, legal environment-driven with the help of government market relations, product market-driven with the help of government market relations, product market-driven with the help of legal environment, and product market-driven with the help of regional culture.

Literature Review and Theoretical Analysis Framework

Research Related to Institutional Environment

The institutional environment is the core construct of institutional theory, which consists of a variety of complex and variable elements. The most representative academic research on institutional environment is by North and Scott, who emphasize that an institution is a code of behavior, a rule, a set of incentives and constraints that regulate human social relations, and is divided into formal and informal institutions (K. Yu, 2006). Among them, formal institutions refer to a series of conditions that stimulate or constrain the production and consumption behavior of market economic agents, usually with the government as the main body, including administrative, legal, market and other measures (Luo et al., 2021; R. Yu et al., 2022). Informal institutions refer to unwritten values, social beliefs, ethical norms and customs (Y. Yang et al., 2021). Scott further suggests that informal institutions consist of institutional norms, institutional cognition, and use institutional regulation to represent formal institutions (Scott, 1995). Among them, institutional regulation is the system of rules formed in the process of industrial development, including various laws and regulations, policies, etc (C. Chen et al., 2022). Institutional norms are reasonable means and methods to achieve industrial goals, which are formed based on social beliefs and customs (B. Fan & Sheng, 2022). The system cognition is a collection of common cognition, beliefs and values of the members of the society, emphasizing the common understanding and acceptance of the society at large (B. Fan & Sheng, 2022). He also mentions that institutional norms are closer to institutional cognition, and the two are often difficult to distinguish in empirical studies and overlap more with culture. Although subsequent studies despite the more detailed delineation of the institutional environment dimension, such as Bae and Salomon have subdivided formal institutions into political, legal, and economic dimensions (Bae & Salomon, 2010). It is not difficult to find that the discussion is based on formal and informal institutions. This paper draws on relevant studies to define the institutional environment as a series of basic laws and regulations, government-market relations, social rules and cultural practices that affect the development of rural industries, including formal and informal institutions (Zhao & Guo, 2014).

Research Related to Rural Industrial Development

Most of the existing studies on rural industrial development are discussed in the context of rural revitalization strategy, and the research topics focus on the influencing factors, development paths, and industrial benefit evaluation of rural industrial development.

The First is the study of factors influencing the development of rural industries. It can be roughly divided into four aspects: resource base, mass psychology, institutional environment, and market demand. It is found that human resources, financial capital, hardware and software infrastructure and other basic resources are not necessary to produce high efficiency of rural industry alone, but the differentiated combination of these basic resources drives the development of rural industry (Hao et al., 2022). Farmers’ willingness to participate in green production affects their green production behavior, which in turn affects the efficiency of agricultural industry (Yuan et al., 2022). Government policies and institutions are inseparable from rural industrial development. At different stages of industrial development, the government should implement appropriate action strategies, including the formulation of policies and institutions, according to the changes in the resource context (X. Li et al., 2023). Market demand leads the direction of rural industrial reform, and digital technology provides new dynamic energy for development (Xia et al., 2019).

The second is the study of rural industrial development paths. Based on industrial practice, scholars put forward the paths of industrial integration development, inclusive development, and revitalized resource development. For example, some scholars believe that the revitalization of rural industry should take the development of the main body, internal and external, online and offline integration (Wu et al., 2022); to attach great importance to the role of agricultural industry and promote the integration of agricultural and rural economy (Jiang, 2022). We should integrate with the digital economy, and through digital empowerment, help the supply-side structural reform of agriculture and rural areas and improve the efficiency of rural industries (Y. Chen, 2021). Some scholars also propose to continuously increase farmers’ rights and take an inclusive development path of industrial development (Qiu, 2022), or develop rural industries through the revitalization and utilization of various idle resources in rural areas, such as residential bases and houses (L. Yang & Wang, 2022).

The third is the study on the evaluation of the benefits of rural industrial development. Scholars mainly evaluate the level of rural industrial development from the perspective of economic and social benefits. The economic benefits include evaluation indexes such as agricultural production efficiency, village food price difference, village per capita gross product, village collective economic income, rural residents’ per capita disposable income, tourism output value as a proportion of regional gross product, and per capita consumption level of rural tourism (Tu & Zhang, 2018). Social benefits include food security, rural tourism infrastructure level, types of tourism services provided, agricultural production conditions, rural non-farm employment ratio, farmers’ benefit rate and other evaluation indicators (Huang et al., 2020; B. Yang et al., 2022).

Research Related to the Relationship Between Institutional Environment and Rural Industrial Development

Institutional environment is an important part of rural industrial environment and is closely related to the development of industry. A review of the literature reveals that relevant studies have been conducted from the formal and informal institutional dimensions to analyze the relationship between individual elements and the development of rural industries, and the main contents are sorted out as follows.

Formal System and Rural Industrial Development

The first is the relationship between the legal environment and rural industrial development. Law is the institutional premise of market economy (S. Yu, 2012). The legal environment has a significant impact on the output value of local industries (Zhao et al., 2007). It is an important factor in determining the efficiency of local industries. A sound rule of law system, strict law enforcement procedures, and strong law enforcement provide a fair and just development space for rural industries (C. Xu, 2020). It helps protect the interests of investors, operators and consumers in rural industries, maintain market order (Cao, 2020). It also helps to form a fair and open market environment with orderly competition, attract capital to enter, and improve the efficiency of the industry. For example, the better the level of rule of law, the more efficient the local tourism industry, the more conducive to tourism development (C. Xu, 2012). Conversely, an inadequate rule of law system, strict law enforcement procedures and weak law enforcement will easily lead to price fraud, substandard products and rent-seeking by local officials in the agricultural market (Ma, 2016). This will disrupt the order of the rural market, reduce the efficiency of the industry, and hinder the healthy development of rural industries.

The second is the relationship between the government-market relationship and rural industrial development. Handling the relationship between the government and the market is a fundamental requirement for promoting the development of rural industries. In the development of rural industry, there is a responsibility boundary between the market and the government (X. Li et al., 2023). Take the old revolutionary areas in China as an example, the rural industry development foundation is weak, the early stage needs to invest a lot of resources (Shao, 2016). If the government does not act, it is difficult to rely on the market to develop rural industry, and the government needs to play a leading role. However, the government’s excessive action will inhibit the vitality of the main body of rural industrial development, leading to a disconnect between the product and the market, hindering industrial development, and even the extreme phenomenon which is “once the cadres leave, the industry will be scattered” (Z. Li & Zheng, 2022). Therefore, we must optimize the allocation of resources and reasonably play the role of the government and the market. On the one hand, in the market effective areas, the government needs to simplify and decentralize, to give full play to the decisive role of the market in the allocation of resources in rural industries. The market is the most effective way to allocate resources, and the rapid development of the country’s economy since the reform and opening up is the best example. On the other hand, the government must take the initiative to play a role in the areas of market failure such as the failure to formulate and maintain market rules in rural industries, the failure of externalities, the failure of information asymmetry and the failure of rural public goods (P. Xu, 2014). The government should provide good institutional supply, market supervision and policy environment for the agricultural market. It has become a social consensus that the government needs to take the key responsibility in the development of “three rural areas” (J. Yu & Gao, 2009).

The third is the relationship between the degree of product market development and the development of rural industries. The degree of product market development mainly reflects the local protection in the product market of rural industries and the degree of product prices determined by the market (G. Fan & Wang, 2011). After the reform and opening up, China carried out the reform of the fiscal tax sharing system, which stimulated the incentive of local governments to protect their tax revenue and take a series of measures to protect the interests of local enterprises (Bai et al., 2004). Local protection, as a trade barrier, causes market segmentation and industrial de-specialization (C. Li et al., 2015). It affects the formation of regional specialization of industries (Bai et al., 2004). It hinders the development of rural product markets. Local protection and market segmentation are common among Chinese provinces, and the degree of local protection and market segmentation vary greatly among provinces (J. Zhang et al., 2011). Market segmentation and its degree of segmentation have a significant impact on the productivity of regional enterprises, and the productivity of enterprises in regions with a high degree of market segmentation is lower (J. Zhang et al., 2011). The degree of market segmentation is mainly reflected in the degree of local government protection of local markets, that is, the degree of product market development (R. Yang, 2016). A more mature agricultural market can provide accurate and timely price signals, reflect the supply and demand situation, and facilitate the efficient transmission of product demand information (Dai & Liu, 2013). A less developed agricultural market means that the product market is segmented, barriers to entry exist between regions, information transmission is hindered, and prices are determined by the market to a lesser extent, which is not conducive to industrial development (Guo & Zhang, 2020).

Informal System and Rural Industrial Development

There is relatively little academic attention on the relationship between informal institutions and rural industrial development. The research results of Bae and Salomon have been used to analyze the situation from the perspective of regional culture (Bae & Salomon, 2010).

The relationship between the regional culture and rural industrial development. Regional culture is the beliefs, moral norms and way of life accumulated by the people of a place during the long-term production, life, work and social history evolution (X. Li & Zou, 2021). Rural industries are dependent on the region where they are located and their development is greatly influenced by regional culture. On the one hand, rural industry is rooted in the region, and only with the region and regional culture as the carrier, rural industry can have vitality and sustainability (Zhu, 2018). The production and operation management of rural industries and the regional affiliation of products are deeply influenced by the regional culture (Lewis et al., 2002). For example, the relationship and ethics of rural society enable rural industries to overcome production management problems and promote the development of rural industries (Fu, 2018). On the other hand, regional culture has high permeability and cohesive power, which can empower the integrated development of rural industries through the integration and efficiency of industrial elements, the extension and reconstruction of industrial chains, and the gathering of industrial space (F. Chen, 2022). In addition, it can form an industrial system with distinctive regional characteristics. Take rural tourism as an example, local culture not only transforms the production and operation mode of local farmers, realizes the adjustment of the industrial structure of rural tourism and the sustainable income of farmers, but also establishes the industrial chain for the protection and inheritance of rural folk cultural heritage (Cai et al., 2018).

Above all, the existing studies has the following shortcomings: more attention is paid to formal institutions, less analysis is made on informal institutions, and even less research is conducted on the joint role of formal and informal institutions, especially the mechanism of the synergy of multiple institutional environment factors affecting the development of rural industries. The institutional environment has different dimensions, and each dimension is coupled with each other to form diverse groupings that affect the development of rural industries. China is in the midst of economic transformation and upgrading, and the institutional environment is more complex, and the differences between regional institutional environments are more significant. Institutional complexity theory believes that in a complex institutional context, it is necessary to systematically consider the common influence of all dimensions of the institutional environment with a group thinking, and analyze the relationship and group effectiveness of each dimension of the institutional environment from a holistic perspective (R. Yu et al., 2022). The relationship between Different elements of the two dimensions are coupled with each other to form different groupings, which affect the behavior of regional industrial subjects and the benefits of rural industrial development by shaping rules, determining industry standards and changing costs (Z. Zhang & Peng, 2009). Therefore, this paper follows the theorizing process of group state-defining scope, connecting and naming (Furnari et al., 2020), by combing relevant literature and combining the actual situation of rural industry development in each province, the legal environment, government-market relationship, product market development degree, and regional culture are taken as the antecedent conditions affecting rural industry development, and a theoretical analysis framework is established from the perspective of histories, as shown in Figure 1, in order to explore the institutional environment histories driving efficient and effective industry. It should be noted that although fsQCA can analyze the grouping effects of multiple antecedent conditions, in practice, it necessarily cannot cover all the antecedent conditions, and only the more important influencing factors can be selected.

Figure 1.

Analytical framework: the group effect of institutional environment on rural industrial efficiency.

Study Design

Method

This paper uses fuzzy set qualitative comparative analysis (fsQCA) to analyze how the institutional environment promotes the development of rural industries from a group perspective. This is mainly based on the following considerations: (1) rural industrial development is influenced by a combination of several factors, and individual factors have a limited impact on it, so a holistic analysis of these factors is needed, but the traditional regression method adopts an isolated analytical perspective on variables, which is mainly applicable to analyzing individual factors, while fsQCA focuses on multiple concurrent causal relationships across cases from a holistic perspective, which is more in line with the organizational phenomenon of interdependence and causal complexity of organizational phenomena. (2) The interaction effects of more than three factors are more complex and difficult to be explained clearly by traditional regression methods, but fsQCA, in contrast, can identify the combined relationships and dissimilarities among the factors. (3) fsQCA can better answer the question of asymmetry of causality, that is, the causes of high efficiency and low efficiency of rural industries are not the same. (4) fsQCA is good at comparative analysis of small and medium samples, which can not only identify the mechanism of action of conditional variables, but also guarantee the external generalizability of the results to a certain extent (Du &, Jia, 2017).

Data Sources

The research data in this paper come from two main sources: The first is the data on the antecedent variable institutional environment, which is obtained from the Chinese provincial marketization index database and the research results of Zhao et al. (2015). Second, the data on the outcome variable, rural industrial development benefits, are obtained from the China Rural Statistical Yearbook. In addition, this study also searched relevant reports, news reports, and other information through government official websites and Baidu to analyze the grouping results found by QCA. Considering that after 2020, the new crown epidemic affects regular production and life and seriously interferes with the development of the industry, all variables are measured using 2019 data.

Variable Measurement and Calibration

Resulting variables

In this paper, the effectiveness of rural industry is taken as the outcome variable and measured by the per capita disposable income of rural residents and the number of township cultural stations. The development of rural industries is to revitalize the countryside, narrow the gap between urban and rural areas and promote common prosperity, and its starting point and anchor point is to make farmers richer. In the process of promoting the development of rural industries, local households and farmers should be the main participants and development subjects, so that the benefits brought by industrial development can benefit more farmers, of which the per capita disposable income of rural households is an important evaluation criterion (Wang, 2022). Township cultural stations are an important part of grassroots governance, and an appropriate increase in the number of rural township cultural stations is conducive to the exchange of information on the “three rural areas” and the promotion of rural economic growth (Mao & Li, 2016). The number of cultural stations in rural towns and villages is an important component of grassroots governance. Considering the different measurement units of these two indicators, the data need to be pre-processed before data analysis, referring to the suggestion of L. Xu and Li (2020). The higher the mean value, the higher the efficiency of the rural industry in the region.

Antecedent Conditions

(1) Legal environment. This article uses the arithmetic mean of two sub-indices, namely the legal environment for maintaining the market and intellectual property protection, in the market-oriented index system established by G. Fan and Wang (2011) to measure the legal environment of a region. The larger the value, the more sounds the legal system and the rule of law in the region.

(2) Government-market relationship. The relationship index between government and market, established by G. Fan and Wang (2011) in the market-oriented index system of Chinese provinces, is used to measure the government market relationship. The larger the value, the greater the role of the market in resource allocation, the less government intervention, and the more reasonable the government size.

(3) The degree of product market development. The arithmetic mean of three sub-indices, namely the degree to which prices are determined by the market, fair market competition conditions, and the reduction of local protection in the commodity market, established by G. Fan and Wang (2011) in the market-oriented index system of Chinese provinces, is used to measure the degree of product market development. The larger the value, the more mature the product market development in that region.

(4) Regional culture. Referring to the approach of R. Yu et al. (2022), this article uses the arithmetic mean of nine dimensions, namely social oriented collectivism, uncertainty avoidance, performance oriented, interpersonal relationship oriented, small group collectivism, power gap, future oriented, bullying, and gender equality (The “Global Leadership and Organizational Behavior Effectiveness Study” (GLOBE) classifies culture into nine dimensions: socially oriented collectivism, uncertainty avoidance, performance orientation, interpersonal orientation, small group collectivism, power gap, future orientation, bullying, and gender equality), to measure regional culture. The value represents the level of regional culture and reflects the differences in regional culture among provinces.

Data Calibration

Data calibration is one of the key steps in fsQCA, which is the process of assigning set affiliation to a sample, with the aim of converting the measured variables into set concepts and establishing the relationship between the measured variable values and the fuzzy set affiliation through calibration points (F. Lu, Pang, & Deng, 2022; R. Y. Lu, Zhang, & Chen, 2022) . Due to the lack of theory and mature external standards for reference, this paper follows the mainstream QCA approach and refers to existing studies (W. Li et al., 2022). The sample quantile values are used to determine the location of calibration points, and the 90%, 50%, and 10% quantile on the descriptive statistics of the case samples are used as the fully affiliated, crossover, and fully unaffiliated points of all variables. The results of calibration and information on the condition variables are shown in Table 1.

Table 1.

Variables, Calibration Points, Descriptive Statistics.

			Calibration points			Descriptive analysis
Variables		Variable measurement	CompletelyAffiliation	CrossoverPoint	CompletelyUnaffiliated	Average value	Standarddeviation	Minimum value	Maximumvalue
Result Variables	Rural Industry Benefits	Mean value of per capita disposable income of rural residents in 2019 and the number of township cultural stations after dimensionless	0.044	0.031	0.019	0.032	0.011	0.015	0.075
Conditional Variables	Legal Environment	Average value of rule of law environment and intellectual property protection for maintaining the market by province in 2019	14.022	10.755	6.541	10.531	2.492	4.895	14.090
	Government Market Relations	2019 Index of Government-Market Relations by Province	7.734	6.370	4.304	6.108	1.673	0.200	8.890
	Degree of product market development	Average values of the degree of market determination of prices, reduction of local protection in commodity markets, and fair market competition conditions by province in 2019	7.926	4.847	0.063	4.311	2.762	1.237	8.877
	Regional Culture	Socially oriented collectivism, uncertainty avoidance, performance orientation, interpersonal orientation, small group collectivism, power gap, future orientation, bullying, gender equality meaning.	4.456	4.418	4.330	4.404	0.048	4.300	4.492

Data Analysis and Empirical Results

Analysis of Necessary Conditions

A necessary condition is a condition that must exist to cause a certain result to appear. Necessary condition analysis can explore the degree to which the result set constitutes a subset of the condition set, and its important evaluation indicator is consistency. It is generally believed that the condition variable with a consistency indicator value greater than 0.9 is a necessary condition (Schneider & Wagemann, 2012). Import the calibrated fuzzy values into the fsQCA software for necessity condition analysis, and the results are shown in Table 2. The consistency index values of all conditional variables are less than 0.9, so there is no single necessary condition that affects the high efficiency and non high efficiency of rural industries. This also indicates that the independent explanatory power of legal environment, government market relations, product market development level, and regional culture on the benefits of rural industries is weak. Therefore, it is necessary to conduct a configuration analysis of these antecedent conditions and identify multiple combinations of conditions that affect the development of rural industries.

Table 2.

Analysis of Necessary Conditions.

Conditional Variables	High industrial efficiency		Non-high industrial efficiency
Conditional Variables	Consistency	Coverage	Consistency	Coverage
Legal Environment	0.767	0.770	0.534	0.523
∼ Legal environment	0.525	0.535	0.765	0.762
Government Market Relations	0.775	0.754	0.521	0.495
∼Government Market Relationship	0.481	0.507	0.741	0.763
Degree of product market development	0.804	0.823	0.482	0.481
∼ Degree of product market development	0.493	0.494	0.822	0.804
Regional Culture	0.683	0.700	0.618	0.619
∼ Regional Culture	0.628	0.628	0.700	0.683

Analysis of the Adequacy of the Conditional Configuration

Conditional configuration adequacy analysis is the core step of QCA method, which refers to exploring the adequacy of configurations formed by different antecedent conditions on the results from the perspective of set theory (F. Lu, Pang, & Deng, 2022; R. Y. Lu, Zhang, & Chen, 2022). These different configurations represent different institutional environmental ecosystems that achieve the same result. This article uses QCA software to sequentially analyze the conditional configurations that generate high and non high benefits, and names the configurations discovered in the study based on the theoretical process of configuration analysis (Furnari et al., 2020).

Grouping of Conditions that Generate High Efficiency in Rural Industries

When using fsQCA3.0 software for conditional configuration adequacy analysis, it is necessary to set parameter values reasonably according to the actual situation, mainly including case frequency threshold, original consistency threshold, and PRI consistency threshold.

(1) Case frequency threshold. This parameter needs to be determined based on the sample size, and the frequency threshold for small and medium-sized samples is usually set to one, while the frequency threshold for large samples is greater than one. This article only contains 31 samples, which is relatively small in scale, so the case frequency threshold is set to one. It should be noted that due to the small sample size, there is a certain issue of limited diversity. However, this article takes 31 provinces (municipalities, autonomous regions) as the research object, and there are certain difficulties in expanding the sample size. Moreover, there is strong heterogeneity in the formal systems and regional cultures of each region, which can ensure the maximum heterogeneity within the overall case population and fully compare the sample cases (R. Yu et al., 2022).

(2) Primal consistency threshold. Scholars have chosen different original consistency thresholds in different contexts, such as 0.75 (Schneider & Wagemann, 2012), 0.8 (Du et al., 2020). In this study, the original consistency threshold was set to 0.8, as suggested by Du and Jia (2017).

(3) PRI consistency threshold. In theory, to avoid the existence of simultaneous subset relationships between a certain configuration and its negative results, the PRI consistency threshold should be as high as possible. However, there is currently no unified conclusion in the academic community on how high the PRI value should be to consider a true value table row as a sufficient configuration for the result. Du and Jia (2017) suggested that the PRI value should be set at least 0.75 (Du & Jia, 2017). Therefore, this study set the PRI consistency threshold to 0.75.

The results generated by conditional groupoid adequacy analysis include complex, intermediate, and parsimonious solutions (Complex solutions do not include logical residuals and exclude all counterfactual combinations; intermediate solutions incorporate logical residuals that are expected and meaningful based on theoretical knowledge and experience; and parsimonious solutions include all logical residuals but do not assess their plausibility. Logical residuals are truth table rows that logically exist but do not have or lack sufficient empirical cases, that is, relationships that logically exist but are “not evidenced” or “not sufficiently evidenced” in reality, and truth table rows that are generally below the frequency threshold are identified as logical residuals). In general, intermediate solutions are considered to be the preferred choice for reporting and interpretation in QCA studies (Schneider & Wagemann, 2013). By comparing each grouping of intermediate and simple solutions in a nested fashion, the core and edge conditions of each grouping can be identified: those that occur in both intermediate and simple solutions are core conditions, and those that occur only in intermediate solutions are edge conditions (Du et al., 2020). The results of the analysis in this paper are shown in Table 3.

Table 3.

Conditional Grouping for Achieving High and Non-high Efficiency in Rural Industries in fsQCA.

Note. indicates the presence of core conditions, indicates the absence of core conditions, indicates the presence of marginal conditions, indicates the absence of marginal conditions; spaces indicate that the presence of conditional variables is irrelevant to the results.

From Table 3, it can be seen that there are four groupings that produce high efficiency in rural industries, where grouping 2, grouping 3, and grouping 4 constitute second-order equivalent groupings, that is, the core conditions of the groupings are the same (Fiss, 2011). The overall consistency of the solutions with high efficiency is 0.848 and the overall coverage is 0.808, both of which are higher than the critical value, and the consistency of the four conditional group states is 0.854, 0.944, 0.928, and 0.958 in order, all of which are higher than the consistency criteria, indicating that the empirical results are valid. Based on the conditional grouping, this paper further identifies the differential adaptation relationships among legal environment, government market relationship, product market development degree, and regional culture in promoting rural industrial development.

(1) Legal environment driven type, represented by configuration 1 as a typical example. Condition configuration 1 indicates that when the legal environment is sound, reducing government intervention, optimizing resource allocation, and fully leveraging the role of the market in resource allocation can break through the limitations of regional culture on rural industrial development and achieve higher industrial benefits. The legal environment is the core condition, while government market relations are the peripheral condition. This path represents provinces such as Hebei, Beijing, Shanghai, Shandong, and Sichuan. Taking Hebei Province as an example, according to relevant statistical data, the province issued 29 legal documents in 2019 to promote the development of rural industries (The data were retrieved from the website of https://pkulaw.com/law?way=listCrumbs, under the heading of “local regulations”, with the title of “rural or rural areas, and industry,” the publication department of “Hebei Province,” and the publication year of In 2019, the final search result was 29 articles, of which 12 were local regulatory documents and 17 were local working documents), formulated 107 county-level characteristic industry revitalization plans, streamlined administrative approval business, and achieved “fingertip handling” of 1,484 service items (The data were retrieved from the report on the work of the Hebei government in 2020), reducing government intervention in the market, stimulating the vitality of market entities, and enhancing the role of the market in resource allocation. The introduction of a series of laws and regulations has created a favorable legal environment for industrial development, coupled with reduced government intervention and optimized resource allocation, resulting in higher efficiency of rural industries in Hebei Province. This also echoes the views of scholars (Cao, 2020; C. Xu, 2020), that a sound legal system, strict law enforcement procedures, and strong law enforcement efforts provide a fair, just, and upright development space for rural industries, which can promote the development of rural industries.

(2) Product market driven type, including three paths: Configuration 2, Configuration 3, and Configuration 4. Condition configuration 2 indicates that with the development level of the product market as the core condition and the legal environment as the marginal condition, it can also support the high efficiency of rural industries. Typical provinces along this path include Zhejiang, Jiangsu, Guangdong, Shanghai, Anhui, Fujian, Shandong, Hubei, etc. These regions ranked among the top in terms of legal environment and product market development in 2019. Taking Zhejiang Province as an example, its legal environment score is 14.090, ranking first in the country, its product market development score is 7.187, ranking fifth in the country, and its corresponding industrial efficiency score is 0.043, ranking fourth in the country. From a practical perspective, in 2019, the satisfaction score of the public in Zhejiang Province’s “Optimizing the Rule of Law Business Environment” was 92.4, with 89.6% of the general public giving positive evaluations (The data were retrieved from the website of http://www.zgcsjs.org.cn/News-news_list-id-22313.aspx); the online retail sales of agricultural products in the county reached 81.9 billion yuan, and the number of Taobao villages and towns has consistently ranked first in China for many years. Under the joint influence of a mature product market and legal environment, Zhejiang Province has achieved high industrial benefits (The data were retrieved from the website of https://www.chinanews.com.cn/sh/2020/11-20/9343549.shtml).

Condition configuration 3 indicates that when the product market develops and matures, reducing government intervention, optimizing resource allocation, and fully leveraging the role of the market in resource allocation can promote higher efficiency in rural industries. The development level of the product market plays a core role, while government market relations play a complementary role. This path represents provinces such as Sichuan, Shanghai, Liaoning, and Shandong. Taking Sichuan Province as an example, its product market development score is 5.043, ranking 16th in the country. This factor alone is not enough to generate high efficiency, but its government market relationship ranking is eighth in the country. Under the combined effect of the two, Sichuan Province’s industrial efficiency ranks first in the country. In 2019, Sichuan Province continued to deepen the “streamlining administration, delegating powers, and improving services” reform, reducing 51 administrative power items and minimizing government intervention in the market (The data were retrieved from the website of https://sichuan.scol.com.cn/scol_sc_m/202001/57455066.html). The role of the market in resource allocation was enhanced, and the online retail sales of agricultural products increased from 4.017 billion yuan in 2015 to 20.775 billion yuan, with an average annual growth rate of 50.8%, 13.8 percentage points higher than the national average (The data were retrieved from the website of https://m.gmw.cn/baijia/2020-12/23/1301967000.html). Despite the lack of regional cultural support, the rapid development of the product market has still increased industrial returns in rural areas by reducing government intervention and optimizing resource allocation.

Condition configuration 4 indicates that provinces with well-developed product markets, supplemented by regional culture, will be more conducive to generating high efficiency in rural industries. The level of product market development is the core condition, while regional culture is the peripheral condition. According to the QCA analysis results, this path represents provinces such as Jiangxi, Henan, and Hunan. Although the role of the market in resource allocation is relatively weak in these regions, their mature product markets and highland cultures result in high industrial efficiency. Taking Hunan Province as an example, in 2019, the e-commerce retail sales of agricultural products in the province reached 18.891 billion yuan, a year-on-year increase of 39.73%; Cultivate 11533 demonstration online stores, selling a total of 2.02 billion yuan of agricultural products from poverty-stricken areas (The data were retrieved from the website of http://www.hunan.gov.cn/hnszf/hnyw/20180408_sxhy/20/jddp/202001/t20200104_11126338.html). The product market is relatively mature, and a new model of cultural and tourism integration development has been created based on the unique Hunan culture. Supported by local characteristic culture, the configuration effect driven by product market as the dominant logic drives the development of rural industries in Hunan Province. These also echoes the views of scholars such as (Dai & Liu, 2013), that a well-developed agricultural product market can accurately and timely provide price signals, reflect supply and demand conditions, and facilitate the efficient transmission of product demand information.

Comparison Analysis of Different Conditions of High Efficiency and Effectiveness Grouping

One is that different conditions and configurations have different paths leading to the same goal, all of which can promote the development of rural industries. One is driven by the legal environment. The role of law is to reduce market transaction costs and improve industrial efficiency. In a complete legal environment, the efficiency of market resource allocation is relatively high, that is, market efficiency. However, there is spontaneity in the market, and market failure can lead to inefficient or even ineffective resource allocation. In this case, the government needs to intervene appropriately, that is, the government takes action. Therefore, the linkage and matching of legal environment and government market relationship is conducive to better promoting the construction of a unified national market and promoting the development of rural industries. The second is product market driven. The development of product markets has a significant driving effect on economic growth and provides broader space for the development of rural industries (Guo & Zhang, 2020). In addition to the appropriate government intervention mentioned above, which can compensate for market failures, a good legal environment and unique regional culture can also to some extent suppress the negative impact of the market. The legal environment can provide legal compliance for market transactions of various economic entities and formal institutional guarantees for industrial development. Regional culture provides potential and informal regulations and guidelines for the economic behavior of various entities, and is an important factor affecting the development of industrial clusters.

Secondly, compared with informal institutions, formal institutions are particularly influenced by the level of product market development as the core prerequisite for the development of rural industries. Comparing the four configurations with high efficiency, it was found that the degree of product market development only became irrelevant in configuration 1, and under this path, the legal environment replaced it as the core condition. Government market relations often serve as auxiliary preconditions, becoming irrelevant only in configuration 2 and replaced by regional culture in configuration 4. From this, it can be seen that compared with informal institutions, formal institutions are more important for improving the efficiency of rural industries, especially with the degree of product market development as the main influencing factor, and other conditions as secondary factors.

The possible reasons are that on the one hand, most rural areas have weak economic foundations, insufficient resources for industrial development, high investment risks, and weak willingness for capital to enter. They need to rely on formal institutional guidance and support to attract capital to develop industries.

On the other hand, the product market is the end of the production chain, and the products produced by enterprises ultimately need to flow into the market and reach transactions with consumers before they can be considered complete. Therefore, the maturity of the product market directly affects industrial efficiency and becomes a key factor affecting industrial development.

Thirdly, under the premise of the same core antecedent conditions, different edge antecedent conditions can to some extent replace each other. Comparing configurations 2, 3, and 4, it is found that the core antecedent conditions for these configurations are the level of product market development, while the marginal antecedent conditions are legal environment, government market relations, and regional culture, all of which can generate high industrial benefits. This indicates that configurations with the same core antecedent conditions can to some extent replace each other’s edge antecedent conditions. Rural industries and regions are inseparable, and they first need to integrate with regional culture in order to survive and develop better.

However, due to significant regional cultural differences, with the continuous integration of industries and regional cultures, problems such as non-standard and small-scale industrial development within the region have gradually become prominent, inhibiting the growth and development of industries. At this time, it is necessary to handle the relationship between the government and the market well and optimize the allocation of resources. Not only should the market play a role, but also the government needs to intervene appropriately and regulate industrial management.

However, with the continuous increase of government intervention, according to the law of diminishing marginal utility, its effect will show a downward trend at a certain point and eventually become negative, that is, excessive government intervention will also hinder the development of industries. At this time, it is necessary to constrain the legal environment and put power in the cage of the system.

The Grouping of Conditions that Generate Non-Efficient Benefits of Rural Industries

This group is formal system deficiency type. This paper identifies one grouping that generates non-efficient efficiency in rural industries, as shown in Table 3. Conditional configuration A indicates that non legal environments, non-governmental market relationships, and the degree of development of non-product markets will result in non-high efficiency in rural industries. As it is a non-aggregated component of formal institutions, the efficiency of rural industries is low when formal institutions are lacking. Therefore, it is named as formal institutional deficiency type. This reflects the irreplaceable role played by formal systems in the development of rural industries. In this configuration, non-governmental market relationships and the level of development of non-product markets are the core conditions, while the non-legal environment is the marginal condition. As the result shows a path, the consistency level of this configuration is the overall consistency level, with a score of 0.878, which is greater than the set critical value, indicating the validity of the empirical results. The original coverage, unique coverage, and overall coverage are all equivalent, with a score of 0.665. Typical provinces along the route include Xizang, Xinjiang, Inner Mongolia, Qinghai, Hainan, Ningxia, Shanxi, Jilin, Gansu, Guizhou, Heilongjiang and Guangxi. Most of these provinces are economically underdeveloped regions with unsound laws, immature product market development, and the role played by the market in resource allocation needs to be enhanced. Because of the weak institutional foundation and the need to invest a lot of resources in the early stage of industrial development, which may face the risk of not obtaining the expected output or even zero output, the willingness of capital to enter these regions is also relatively weak, which leads to low benefits of industrial development. Taking Ningxia as an example, according to relevant statistics, the province’s 2019 legal environment, government market relations, and product market development scores ranked low, and accordingly its rural industrial efficiency ranked at the bottom of the country with a score of 0.015.

Robustness Tests

Robustness testing is one of the more important aspects of QCA research analysis, and the theoretical community generally analyzes only the adequacy of the conditional grouping. On the one hand, scholars are more interested in the adequacy of the conditional grouping of the results, and on the other hand, the adequacy of the conditional grouping is more sensitive and subjective (Du & Jia, 2017). Commonly used robustness tests include adjusting parameter values (including calibration thresholds, original consistency thresholds, case frequency thresholds, PRI consistency thresholds) and adding antecedent conditions, etc., of which one method test can be chosen (F. Lu, Pang, & Deng, 2022; R. Y. Lu, Zhang, & Chen, 2022). In this paper, the original consistency threshold was adjusted from 0.8 to 0.85 in the conditional grouping adequacy analysis by using the method of increasing the original consistency threshold, and the results are shown in Table 4. The robustness test shows that the grouping results after adjusting the original consistency threshold are consistent with the previous analysis, indicating that the results have good robustness.

Table 4.

Grouping Results After Adjusting the Original Consistency Threshold.

Note. indicates core condition present, indicates core condition missing, indicates edge condition present, indicates edge condition missing; space indicates that the presence of the condition variable is irrelevant to the results; the original consistency threshold is adjusted to 0.85.

Conclusion and Discussion

Conclusion

This paper explores the driving paths and synergistic linkage effects of four antecedent variables, namely, legal environment, government market relations, product market development, and regional culture, on the development of rural industries, revealing the nature of the complex interactions among the conditions. The findings of the study are as follows:

(1) A single institutional environmental factor is not a necessary condition for generating high efficiency in rural industries. The legal environment, government market relations, level of product market development, and regional culture cannot be considered as necessary conditions for generating high efficiency in rural industries alone.

(2) The development of rural industries is influenced by multiple institutional environmental factors, with multiple concurrent factors forming a diversified configuration, that is, promoting the development of rural industries in a “different paths leading to the same destination” manner. From the former conditional configuration, it can be seen that compared with informal institutions, formal institutions are more important in improving the efficiency of rural industries, especially with the development level of product markets as the main influencing factor, while legal environment, government market relations, and regional culture are marginal conditions, and these marginal conditions can be replaced by each other. Under specific conditions, a sound legal environment coupled with optimized resource allocation and reduced government intervention can break through regional cultural limitations and generate high efficiency. At this point, the importance of product market development decreases.

(3) There is an asymmetric relationship between the driving mechanisms that generate high efficiency and non-high efficiency. There are four key paths for generating high efficiency in rural industries, which can be specifically divided into two types: legal environment driven and product market driven. There is one path to generate non high efficiency in rural industries, which is the formal institutional deficiency type. This configuration is based on the core conditions of non-governmental market relations and the degree of development of non-product markets, with non-legal environments as peripheral conditions.

Discussion

Based on the institutional grouping perspective, this paper analyzes the impact of synergistic linkages among the factors of the institutional environment on the development of rural industries and attempts to make the following innovations:

Firstly, based on the fuzzy set qualitative comparative analysis method to explore the influence of formal and informal institutions on rural industrial development and enrich the related research on rural industrial development. Most of the existing literature studies the influence of institutional environment on rural industrial development from the dimension of formal institutions, and most of them explore the relationship between variables with traditional regression analysis methods, while fewer focus on informal institutions, and even fewer explore the coupling effect of formal and informal institutions, which cannot comprehensively understand the relationship between institutional environment and rural industrial development. This study introduces the fsQCA method and selects four representative antecedent variables, namely, legal environment, government market relationship, product market development degree, and regional culture, to explore the impact of the synergistic linkage between formal and informal institutions on rural industrial development and to realize the linkage matching between elements at different levels. The study concludes that formal institutions are more important in promoting rural industrial development, while informal institutions play a supporting role, among which the degree of product market development in formal institutions is the main influencing factor and the rest are secondary factors, and under certain conditions, the secondary factors can be substituted for each other.

This paper can bring the following insights to rural industrial development practices:

Firstly, to improve the legal system and the legal environment. In the context of China’s complex institutional environment, improving the legal system and giving play to the decisive role of the market in resource allocation is one of the important paths for the development of rural industries, as shown in Group 1. Therefore, on the basis of sufficient research, the existing laws and regulations related to the development of rural industries should be timely repealed and revised and improved. We should increase the protection of intellectual property rights, make full use of digital technology to empower the construction of the legal system of rural industry and improve the efficiency of legal services.

Secondly, cultivate product markets and create a good market environment. The degree of product market development is the main factor influencing the development of rural industries, and it is used as a core antecedent condition in Group 2, Group 3 and Group 4, which means that we should vigorously cultivate product markets and create a good market environment. We should strengthen product brand cultivation, improve market influence and seize market share; we should reduce local protection in the commodity market, follow the law of value and form a fair market competition environment.

Thirdly, the relationship between government and market is handled well to optimize resource allocation. Both Configuration 1 and Configuration 3 show that the proper way of resource allocation can promote the development of rural industries. The boundary of government intervention in industrial development should be reasonably determined. At the stage of industry introduction and growth, the government should play a leading role, actively intervene and support industrial development; at the stage of industry maturity, it should play more of a guiding role and change from a manager to a service provider, so that the market can play a decisive role in resource allocation, and at the stage of transformation and upgrading, not only should the government play a leading role in the market, but also in the construction of new technology support and policy support, the government should also make efforts and play a leading role .

Fourthly, the regional culture is integrated to innovate the industrial development model. Group 4 shows that the regional culture of informal system as a marginal antecedent condition and the degree of auxiliary product market development promote the development of rural industry. It is necessary to find the integration point of rural industrial revitalization and rural cultural revitalization, integrate various resources, create a new development model of agricultural, cultural and tourism integration, vigorously implement the project of industrial integration to enrich the people, promote farmers’ income through tourism and leisure, tourism entrepreneurship, tourism services and other forms, and realize the efficient development of rural industry.

Shortcomings and Prospects

There are also some shortcomings in this paper: First, the conditional variables selected in this paper do not include all the institutional environmental factors that may affect the development of rural industries, and new research perspectives or more possible influencing factors can be adopted or considered in the future. Second, there is room for improvement in the measurement of variables and the source of data in this paper. For example, due to the limitation of data availability, the outcome variables in this paper are measured by the mean value of the per capita disposable income of rural residents and the number of township cultural stations in 2019, but the benefits of rural industrial development are not limited to these two aspects, and subsequently, when conditions allow, more precise measurement of each variable should be conducted, or research should be conducted based on the local and municipal levels to deepen the understanding of the relationship between institutional environmental factors and rural industrial The relationship between institutional environment factors and rural industrial development should be better understood. Thirdly, in the future, traditional data analysis methods should be combined to conduct heterogeneity analysis and impact mechanism analysis using panel data or county-level data.

Footnotes

ORCID iD

Xu Weiwei

Consent for Publication

All authors have agreed to publish.

Author Contributions

Xu Weiwei, initial draft and revision of the paper.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Jiangxi Province Science and Technology Plan Project (Provincial Vocational Early Career Youth Science and Technology Talent Training Special Project) “Empowering Jiangxi with Science and Technology to Build a New Business Model of Smart Elderly Care” (20252BEJ730335); Doctoral Research Initiation Fund of Jiangxi Science and Technology Normal University “Research on the Mechanism and Path of the Impact of Digital Technology on the High-Quality Development of Rural Industries” (2024BSQD77).

Declaration of Conflicting Interests

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

Data Availability Statement

Please refer to the data sources in the main text for details. If readers need it, they can contact the author.

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What Kind of Institutional Environment Can Promote The Development of Rural Industries in China?—Based on Fuzzy Set Qualitative Comparative Analysis

Abstract

Keywords

Introduction

Literature Review and Theoretical Analysis Framework

Research Related to Institutional Environment

Research Related to Rural Industrial Development

Research Related to the Relationship Between Institutional Environment and Rural Industrial Development

Formal System and Rural Industrial Development

Informal System and Rural Industrial Development

Study Design

Method

Data Sources

Variable Measurement and Calibration

Resulting variables

Antecedent Conditions

Data Calibration

Data Analysis and Empirical Results

Analysis of Necessary Conditions

Analysis of the Adequacy of the Conditional Configuration

Grouping of Conditions that Generate High Efficiency in Rural Industries

Comparison Analysis of Different Conditions of High Efficiency and Effectiveness Grouping

The Grouping of Conditions that Generate Non-Efficient Benefits of Rural Industries

Robustness Tests

Conclusion and Discussion

Conclusion

Discussion

Shortcomings and Prospects

Footnotes

ORCID iD

Consent for Publication

Author Contributions

Funding

Declaration of Conflicting Interests

Data Availability Statement

References