As a core component of social policy, urban and rural basic public services refer to government-led provision of basic services directly related to the right to survival and development of urban and rural residents, covering such areas as education, health care, social security, infrastructure, public health, etc. (4, 21). Its essence is to guarantee the basic rights of citizens through its organizational conditions; however, in developing countries affected by the dual economic structure and institutional barriers, the non-equalization of basic public services in urban and rural areas has become a prominent problem (1).

In China, the distribution of basic public services between urban and rural areas is constrained by institutional factors. It links public identity to access to public services, forming a “rural–urban” dichotomy (22). This manifests in rural residents being excluded from urban public service systems even after prolonged urban employment. Rural migrant workers can only enroll in the New Rural Cooperative Medical Scheme (NRCCM) at their place of residence, where hospitalization reimbursement rates are significantly lower than those under the basic medical insurance for urban employees (23).

Under the TOE framework perspective, the essence of urban–rural basic public services is a comprehensive reflection of organizational, environmental, and technological conditions. Government governance needs to integrate various tools such as finance, land, and industry, and the non-equalization of urban and rural public services in China exposes the dual deficiencies of governance capacity and financial capacity (12). Existing research also reveals that there are significant differences in the “soft” and “hard” structures of public services. “Soft” public services (education, healthcare, social security) have a much higher moderating effect on income disparity than “hard” public services (infrastructure) (24). Differential investment in early education has the highest contribution to income disparity in China, and subsidizing early education is seen as the most effective policy (8), while insufficient investment in education in rural areas directly constrains the accumulation of human capital and creates a cycle of intergenerational poverty (5, 6).

In summary, the disparity in basic public services between urban and rural areas stems from the combined effects of government governance, economic development, and technological innovation. Addressing this challenge requires focusing on organizational conditions within the TOE framework, such as optimizing fiscal transfer mechanisms and enhancing local governments’ collaborative governance capabilities (20, 25). These theoretical studies provide a research framework for analyzing urban–rural income disparities and lay the theoretical foundation for this study’s exploration of pathways for public service grouping.

The urban–rural income gap is a concentrated manifestation of structural contradictions in developing countries. In China, this gap shows typical stage characteristics, and although it has declined after policy interventions such as rural revitalization, the absolute gap still persists along with the non-equalization of public services (26). The perpetuation of the gap not only inhibits the expansion of the consumer market but also creates a “poverty cycle” through the intergenerational distribution of resources such as education and healthcare, and the lack of investment in the human capital of rural families has led to barriers to the career advancement of their children, exacerbating the solidification of the social stratification (5, 6).

Institutional constraints form the core explanatory factor for the urban–rural income gap. By restricting access to public services, they result in inadequate coverage of pension insurance for migrant workers, forcing them to enroll in the New Rural Cooperative Medical Scheme only in their registered hometowns (9). The double-edged sword effect of economic structural transformation is also pronounced: industrial upgrading in eastern regions widens the gap initially through an “N-curve” before narrowing it, while western regions experience an expanding gap due to labor skill mismatches (27). Urbanization, while widening disparities in the short term through factor concentration, converges gaps in the long run via the spillover effects of public services, thereby constraining human capital development (28).

Social capital differences amplify income differentiation through the human capital pathway. Unequal distribution of educational resources contributes 35% to the income gap, with insufficient investment in early education directly constraining rural labor mobility to higher-skilled jobs (7, 8), and urban–rural disparities in healthcare resources reduce labor productivity through health human capital depletion (29, 30), as evidenced by the fact that urban households are generally better equipped with soap and water hand-washing facilities than rural households (31). Under the perspective of the TOE framework, the misconfiguration of technological, organizational, and environmental conditions exacerbates the structural contradictions, and most of the existing research focuses on the linear effects of single factors and seldom studies the group relationship among variables of each dimension. Based on the TOE framework, this study introduces fuzzy set qualitative comparative analysis (fsQCA), aiming to reveal the multiple concurrent causal relationships of the condition variables, break through the explanatory limitations of the traditional linear model, and provide a new theoretical perspective for the systematic optimization of urban–rural integration policies.

The impact of urban–rural basic public services on the urban–rural income gap is a core topic of development economics and institutional research, and its mechanism runs through the multiple dimensions of resource allocation, human capital accumulation, and government governance. As a core public service area, the distribution of educational resources is an important factor affecting the urban–rural income gap. Becker & Chiswick (32) were the first to reveal the decisive role of education differences in income differentiation. Yang & Qiu (8) further found that differences in China’s investment in early childhood education contributed 35% to the income gap and proposed that the subsidization of rural preschool education is the most effective policy tool. The rural–urban split in social security, on the other hand, exacerbates imbalances through labor market discrimination, with insufficient levels of social security in rural areas directly widening the income gap (33), while research in India suggests that the lack of formal insurance leads to labor mismatches and the formation of a spatial wage divide (34).

The impact of “hard” public services such as infrastructure varies regionally. For instance, China’s high-speed rail construction has shown a weak convergence effect on urban–rural income disparities (35). However, in underdeveloped regions, low internet penetration rates (IPR) may create a “digital poverty trap” hindering rural residents’ access to emerging employment opportunities like e-commerce (36). A study employing triadic graph analysis found that disparities in basic healthcare service levels between urban and rural areas are highly correlated with residential space. Across 73 countries, basic healthcare coverage in urban areas significantly exceeds that in rural areas, suggesting that unequal distribution of public service resources may exacerbate urban–rural income gaps through human capital pathways (31). However, institutional constraints weaken the poverty-reducing effects of the former (24). Disparities in educational investment reinforce income stratification through the intergenerational transmission of human capital (6). Inadequate coordination between policy design and governance capacity exacerbates structural contradictions; achieving equitable public services requires integrating multidimensional policy instruments across finance, land, and industry (20). Regions with significant income disparities often lag in basic public services and social governance, leading to economic imbalances (25).

Established studies on the impact of urban–rural basic public services on the urban–rural income gap mostly focus on the linear impact of a single factor and seldom explore the group effects among the dimensions of public services from the perspective of the TOE framework. Based on the TOE framework, this study introduces fuzzy set qualitative comparative analysis (fsQCA), aiming to reveal how conditional variables affect the urban–rural income gap through synergistic or substitution effects and to provide a nonlinear explanatory framework and policy recommendations for solving the urban–rural basic public service allocation challenges.

The TOE framework was proposed by Tornatzky & Fleischer (13), which centers on summarizing the factors affecting organizational behavior and technology adoption into three types of systemic conditions, namely, technology, organization, and environment, and emphasizes the interaction mechanism among the three. The framework integrates the diffusion of innovation theory and technology acceptance model (14) and has been widely used in management research fields such as technology adoption and digital transformation because it can analyze complex organizational phenomena from multilevel dimensions (15, 16). In industries such as healthcare and e-commerce, scholars have used the framework to identify the differential impacts of technological capabilities, organizational resources, and external market environment on technology adoption (37, 38). Its theoretical strength lies in capturing the synergistic logic of technology-organization-environment through a systemic perspective (39), providing a structured analytical framework for analyzing multifactor interactions.

In the technology dimension, the TOE framework focuses on the technological status of organizations, covering descriptive indicators such as technological capabilities, government pressure, and resources (40). For example, in the context of Industry 4.0, digital technologies such as artificial intelligence and big data governance have become key enablers of entrepreneurial activities by reshaping the global value chain (41, 42), suggesting that technological sophistication drives organizational effectiveness (43). Organizational dimensions focus on internal attributes, including resource availability, organizational rules, degree of technological need, and strategic orientation (44, 45). Organizational absorptive capacity for technology, top management support, and human resource reserves directly affect the depth and breadth of technology adoption (46, 47). The environmental dimension involves the organization’s external ecosystem, which contains market competition, policies and regulations, and industry characteristics (48). For example, a favorable business environment (e.g., low entry costs, land availability) significantly promotes SME growth (49), whereas government pressure and industry competition patterns constitute external constraints on technology adoption (40).

The TOE framework provides researchers with a strong theoretical foundation and explains the influence of many factor groups on the technological activities of firms (48), and thus the integration of the TOE framework with fuzzy-set qualitative comparative analysis (fsQCA) provides a unique advantage in the analysis of groupings of complex social phenomena. fsQCA’s nonlinear causal logic is highly compatible with the TOE framework’s The concept of interaction of technological, organizational, and environmental conditions is highly compatible (18, 19) and is able to identify synergistic paths of multiple conditions through multi-case comparisons. In the research scenario of urban–rural basic public services and income disparity, this combination can break through the limitations of traditional single-factor analysis and systematically analyze the configuration effect in the TOE framework. Through the configuration analysis of fsQCA, this study aims to reveal the key combinations of conditions affecting the urban–rural income gap and to provide an empirically based analytical paradigm and theoretical basis for the expansion of the application of the TOE framework in urban–rural basic public services.

This study builds a theoretical model based on the TOE framework and constructs conditional variables of urban–rural basic public services based on technological, organizational, and environmental conditions: technological conditions cover human capital and informationization level, organizational conditions focus on government governance and resource allocation, and environmental conditions focus on the level of economic development. Through the interaction effect among the three types of conditions, the degree of equalization of urban–rural basic public services is presented so as to analyze the complex causal mechanism of the urban–rural income gap under the multi-conditional grouping. Therefore, this study proposes the following theoretical model (Figure 1) for subsequent analysis using the fsQCA 4.1 software (Figure 1).

The data for this study are derived from the 2023 China Statistical Yearbook, provincial and municipal statistical yearbooks or bulletins, the GuotaiAn Regional Economic Database, the China Urban Statistical Yearbook, and the Evaluation Report on Political-Business Relationships in Chinese Cities.

Technological, organizational, and environmental conditions are taken as the measurement dimensions of the condition variables, and the subdivided variables under each dimension are intergovernmental relations, government-market relations, government-society relations, economic development, human capital and informatization level, as shown in Table 1.

In this study, the observed variables are calibrated using the direct method, which involves changing them into fuzzy-sets. Based on the treatment in the published literature (55), “three anchor points” for the four condition variables and one outcome variable (Thiel index) are selected: “full membership,” “crossover point,” and “full non-membership.” Specifically, the anchor points for the sample data are the upper quartile (75%), median, and lower quartile (25%). The calibration of the non-high urban–rural income gap is achieved by excluding the high urban–rural income difference. Table 2 shows the specific calibration findings for each study variable.

This study empirically examines the possible adequacy or necessity of the relationship between conditional variables and outcome variables in terms of the dimensions of consistency and coverage. Using the fsQCA 4.1 analysis tool, reference was made to established research (56), and in order to ensure the reliability of the study’s conclusions, a systematic analysis was also carried out to analyze the situations where the condition variable had a missing status (marked with a “~“). The empirical results show (Table 3 for details) that the consistency of individual conditional necessities is below the threshold of 0.9 for determining the necessity condition, a finding that suggests that, in the case of urban–rural basic public services, none of its dimensional indicators meets the criteria of the necessary preconditions that drive the urban–rural income gap.

In this study, the consistency threshold is set at 0.8, and the case cutoff value is set at 2. Based on the uncertainty of the direction of the influence of environmental conditions, the symmetry assumption is used to conduct a counterfactual analysis by comparing the intermediate solution with the parsimonious solution to identify the core conditions and the edge conditions (55). The histogram analysis paths are shown in Table 4, with several driving paths (H1-H6) for high urban–rural income gap and several inhibiting paths (L1-L7) for non-high urban–rural income gap. The results of the group analysis are shown in Table 5, with total coverage of 0.685 and 0.736, which can explain 68.5 and 73.6% of the sample cases, respectively, and the overall consistency is higher than 0.9, which verifies the robustness of the analysis results. Each grouping that affects the urban–rural income gap is analyzed in detail below.

This study conducted robustness tests on the pre-configurations of high urban–rural income disparities and non-high urban–rural income disparities (57). In the fsQCA4.1 software, the consistency threshold for configurations generating high and non-high urban–rural income disparities was raised from 0.8 to 0.85, yielding configurations largely consistent with existing results.

Robustness tests revealed no substantial deviation in configuration paths, maintaining a clear subset relationship with the original solution, indicating stable findings.

Notably, the stricter parameter threshold essentially eliminated peripheral solutions while preserving core configurations that satisfy enhanced logical rigor (18). This strategic refinement confirms the persistence of core causal configurations under stricter analysis. Based on these tests, the study’s findings demonstrate robustness.

Based on the TOE framework, this study uses fuzzy set qualitative comparative analysis (fsQCA) to explore the group relationship between urban–rural basic public service allocation and urban–rural income disparity in the Yangtze River Delta (YRD) urban agglomeration and finds that both the high urban–rural income disparity and the non-high urban–rural income disparity are caused by multifactorial interactions, and there is a differentiated driving path.

The high urban–rural income gap is driven by three types of groupings: first, the path of insufficient public services and digital infrastructure, characterized by a core lack of public service expenditures and Internet penetration, and the superimposition of marginal conditions such as poor government-business relations and weak human capital; second, the path of scarcity of total factor resources, which presents a lack of total factors such as the fiscal self-sufficiency rate, per capita GDP, and public service expenditures; and, third, the path of fiscal and economic decline accompanied by a lack of human capacity in the system. The third is the path of fiscal and economic recession accompanied by institutional manpower shortage, characterized by the core lack of fiscal self-sufficiency rate and GDP per capita, accompanied by the irregularity of government-business relations and the loss of human capital.

Three types of paths exist for non-high urban–rural income gaps: digital technology synergy of financial capacity, synergistic promotion of financial digital services, and public service-led human capital support. The study shows that single conditions are not necessary for the urban–rural income gap and that the grouping effects of technological, organizational, and environmental conditions are key, providing a non-linear explanatory framework for urban–rural integration policies.

This study combines the TOE framework with the fsQCA method, breaks through the theoretical limitations of traditional linear causal logic, and provides a new theoretical perspective of “group dependence” for the study of the urban–rural income gap. Existing literature mostly focuses on the influence of single factors such as education investment, city size, and social security level on the urban–rural income gap but seldom explores the interaction mechanism of technology, organization, and environmental conditions. This study confirms that the formation of the urban–rural income gap is not the result of a single condition but the product of the synergy or substitution of multiple groupings of technological, organizational, and environmental conditions, which expands the boundary of the application of the TOE framework in the field of public services and provides a theoretical paradigm for the understanding of non-linear causality of complex social phenomena.

At the methodological level, the study identifies the differentiated driving paths of the high urban–rural income gap and the non-high urban–rural income gap through the fsQCA method, revealing the characteristic of “multiple concurrent causation,” and fills the gap in the traditional measurement method in terms of capturing the conditional combination effect of urban–rural basic public services. This research paradigm, which combines the TOE framework with group analysis, not only enriches the multidimensional analytical framework of urban–rural disparity research but also provides methodological innovations for the study of complex issues in the field of public services.

The study provides systematic governance ideas for cracking the urban–rural dichotomy: in the technological dimension, accelerating the penetration of digital technology and talent cultivation in rural areas, and breaking through geographic boundaries through digital services such as educational resource sharing platforms and telemedicine; in the organizational dimension, focusing on innovations in governmental governance and resource allocation, for example, linking public service expenditures to the degree of standardization of the relationship between the government and the business sector; in the environmental dimension, building a collaborative governance network of public services across administrative regions based on the strategy of regional economic integration. In the environmental dimension, relying on the regional economic integration strategy, a collaborative governance network of public services across administrative regions is constructed. This kind of systematic governance idea can fundamentally break down the systemic barriers of non-equalization of urban and rural public services, provide practical inspiration for the integrated development of urban and rural areas in the new era, and provide practical paths for realizing the goal of common prosperity.