Bridging the Digital Gradient: How Digital Literacy and Information Perception Shape Innovation and Entrepreneurship Across Urban, County and Township Students

Abstract

Background: In China, a paradox has emerged: while the digital access gap narrows, a pronounced digital gradient—a sequential decline in outcomes from urban to county to township students—persists in innovation and entrepreneurship. This study investigates the hidden, cognitive mechanisms behind this enduring gradient inequality. Methods: Analyzing a national survey of 31,779 students, we employed statistical models designed to trace sequential pathways and account for institutional influences. Results: We found a clear urban > county > township gradient in students’ digital literacy, information perception, and innovation capabilities. The disparity is primarily driven by a cognitive mediation chain: rural students’ lower digital literacy inhibits their ability to perceive and evaluate information effectively, which in turn suppresses their innovation and entrepreneurial potential. This “digital literacy → information perception” pathway explains over 80% of the gap in entrepreneurial intention and one-third of the gap in innovation capacity. Crucially, elite “Double First-Class” universities mitigate this gradient; their robust offline support systems compensate for deficits in students’ digital literacy, reducing its necessity for entrepreneurial success. Conclusions: The contemporary digital divide is fundamentally a cognitive gradient. Moving forward, policy must look beyond infrastructure to foster a cognitive capacity to transform digital access into innovation capability, rather than merely expanding digital access. Our findings affirm that universities can act as powerful institutional compensators. A dual strategy that combines cognitive empowerment with targeted institutional support is essential to bridge the digital gradient and close the innovation gap across urban, county, and township student populations.

1. Introduction

A clear paradox has emerged in China’s digital transformation: although access to digital infrastructure and devices has become increasingly equalized across regions, substantial gaps in university students’ innovation capacity and entrepreneurial intention persist along the urban–county–township spectrum [1,2], where “county” denotes county-level administrative divisions (xian ji xingzheng qu), which serve as crucial socio-economic spaces bridging largely rural townships and core urban centers. This divergence between access convergence and capability divergence challenges the assumption that expanding digital connectivity will naturally lead to more equitable developmental outcomes [3]. The key question, therefore, is not simply who has access to digital tools, but who can effectively convert that access into innovative action.

Existing research offers important but incomplete explanations. Structural perspectives emphasize unequal distributions of socioeconomic and educational resources associated with the household registration (hukou) system—an institution that controls population mobility and determines access to local public services based on registration location [4]. Yet such frameworks struggle to explain why significant performance gaps persist even among students within the same universities and similar curricular environments. Conversely, perspectives treating digital technology as a neutral tool [5] overlook the cognitive processes through which individuals interpret information, recognize opportunities, and translate digital input into innovation outcomes [6,7]. In practice, students from different positions along the urban–rural gradient are socialized into distinct digital cognitive environments. Those from urban cores are more likely to have been exposed to structured digital and inquiry-based learning, enabling more efficient information processing. Students from county and township backgrounds, by contrast, may possess access to similar technologies yet lack the habitual cognitive strategies required to apply them—forming what van Dijk (2020) describes as the shift from material to cognitive exclusion [2].

This observation points to two key gaps in current research. First, while the “second- and third-level digital divide” literature [8] highlights disparities in digital skills and usage outcomes, the mechanisms linking access, cognition, and capability remain underdeveloped. In particular, it is unclear whether the urban–rural gradient operates through a sequential cognitive pathway, whereby digital literacy shapes individuals’ information perception, which subsequently influences innovation capacity and entrepreneurial intention. Second, although universities are recognized as key institutional actors in shaping students’ developmental trajectories, public policy in China has largely focused on infrastructure expansion [9]. Whether university environments—especially those of elite “Double First-Class” universities (a national strategic initiative in China that designates top-tier universities and disciplines for concentrated funding, with the dual goals of developing world-class universities and world-class disciplines, and which employs a competitive, dynamic selection mechanism)—can compensate for initial digital disadvantages originating across the urban–county–township gradient remains insufficiently examined.

To address these gaps, this study proposes and tests a technology-to-cognition pathway in the context of higher education. We argue that digital literacy does not directly lead to innovative outcomes; instead, its effectiveness depends on students’ capacity to interpret, evaluate, and apply information within institutional and market environments. Furthermore, we examine whether university-level resources shape this transformation process, with a focus on the compensatory role of high-quality educational environments.

This study contributes to the literature in two ways. First, it provides micro-level evidence that contemporary digital inequality is fundamentally cognitive, mediated through a sequential pathway linking digital literacy to information perception. Second, it identifies the institutional conditions under which this pathway can be strengthened or weakened, demonstrating how universities may function as compensatory environments capable of reducing inequalities rooted in early digital socialization across the urban–rural gradient.

The remainder of this paper is structured as follows. Section 2 develops the theoretical framework and presents three hypotheses concerning the urban–county–township gradient in digital competence, the sequential cognitive pathway linking digital literacy to information perception, and the shaping role of university institutional resources. Section 3 describes the data, variable construction, and analytical strategy. Section 4 reports the empirical results, including main effects, mediation analysis, and moderation tests. Section 5 discusses the theoretical implications and policy relevance of the findings, followed by research limitations and directions for future work.

2. Literature Review and Research Hypotheses

2.1. The Urban-County-Township Digital Gradient and Its Innovative Consequences

Recent research on the digital divide has shifted focus from disparities in access to deeper differences in how individuals leverage digital resources [6]. However, most studies continue to rely on a binary urban–rural framework, obscuring the potential for a continuous competence gradient across diverse settlement types. This perspective is particularly limiting in China, where county-level units constitute a distinct socio-spatial unit situated between major cities and rural townships [10].

Traditional explanations for spatial disparities emphasize structural mechanisms, such as the hukou system’s role in allocating educational resources [11] and the constraints of rural social networks [12]. Yet, as digital access has improved nationally, inequality has increasingly manifested as a divide in skills and outcomes [13]. The critical question is no longer who has access, but who can use it effectively.

This shift has led to the recognition of digital competence as a form of capital embedded in social and educational environments [14]. Students’ positions along the urban–rural gradient shape their acquisition of this capital. Urban students often benefit from early exposure to structured digital learning and cultures that reward innovation [15]. In contrast, the digital engagement of students from township backgrounds can be more fragmented and utilitarian, oriented toward consumption or exam preparation [13,16].

Crucially, counties represent a pivotal, yet under-examined, intermediate layer in this gradient. They act as a transitional zone, receiving spillovers from urban innovation ecosystems while retaining local social constraints [10]. Consequently, county students often exhibit levels of digital exposure and self-efficacy that are intermediate between their urban and township peers. This establishes a systematic urban > county > township gradient in digital competence, which we posit is a fundamental driver of inequality.

In innovation and entrepreneurship, digital competence is a key resource for opportunity recognition and resource mobilization [5]. Limited digital skills constrain information filtering and opportunity identification, while weak awareness of institutional environments undermines entrepreneurial intention and innovation [8,17]. Thus, the hypothesized gradient in digital competence is likely to produce a corresponding gradient in innovation capacity and entrepreneurial intention.

Although digital inequality is well-established, the prevailing binary framework overlooks this continuous spatial reality and the unique transitional role of counties. This gap limits our understanding of how stratification operates across urbanization levels. Accordingly, we propose:

H1: 

A significant gradient exists in university students’ innovation capacity and entrepreneurial intention, with outcomes decreasing sequentially from urban to county to township backgrounds.

2.2. The Sequential Mediation of Digital Literacy and Information Perception

Digital literacy involves not only operational skills but also the cognitive capacity to apply technology for problem-solving and value creation [7]. In rural China, students often develop technical proficiency but show limited ability to leverage technology for innovative purposes [18]. This gap underscores that a critical cognitive transformation is required to convert digital access into practical efficacy.

Family and school environments are pivotal in cultivating this capacity. Family social capital facilitates the transmission of implicit institutional knowledge, enhancing children’s ability to interpret and utilize information [19]. Schools, meanwhile, provide structured training that helps students develop efficient information-processing frameworks [20]. These differential socialization experiences lead to divergent outcomes: urban students typically become more adept at identifying key information, whereas rural students are more vulnerable to information overload.

Evidence from cognitive neuroscience lends further support to these disparities. Socioeconomic status has been linked to variation in the development of brain regions supporting higher-order cognitive functions, such as the prefrontal cortex [21]. Such differences may shape how individuals evaluate and respond to opportunities. For instance, eye-tracking studies suggest that systematic training improves the ability to focus on relevant information, while a lack of training is associated with greater distractibility [22]. These findings at the neural level reinforce the profound impact that early and structured environmental exposure has on the development of cognitive habitus.

Within innovation and entrepreneurship, digital competence acts as a key form of capital for opportunity identification and resource integration [5]. However, its effectiveness hinges on a crucial cognitive intermediary: information perception. We conceptualize information perception as the capacity to evaluate opportunities, parse environmental cues, and comprehend institutional support. It functions as the cognitive hub through which technological skills are translated into actionable outcomes, forming a chain that progresses from assessing motivation to integrating resources and finally to judging feasibility.

Despite recognizing the importance of both digital literacy and cognitive factors, existing research has largely overlooked their function as a sequential mediating chain. The specific pathway through which digital literacy enhances information perception to ultimately drive innovation and entrepreneurship remains empirically underdeveloped. Therefore, building on the premise that technology requires cognitive transformation to yield practical outcomes, we propose the following hypothesis:

H2: 

Digital literacy and information perception act as a sequential mediating chain linking urban–county–township disparities with innovation capacity and entrepreneurial intention.

2.3. The Compensating Role of University Institutions

Universities are critical institutional actors capable of shaping the impact of pre-existing urban–rural disparities. While digital technologies are often seen as inherently inclusive, their equalizing potential depends heavily on the institutional context [23,24]. Without robust support, students from under-resourced backgrounds may fall into a “technology conversion trap,” where access to technology fails to translate into meaningful outcomes. Universities can help bridge this gap through two primary channels.

First, they can provide resource substitution. Through offline mentoring networks, shared laboratories, and incubator programs, universities can offer tangible resources that partially compensate for deficits in students’ personal digital literacy [25]. Second, they engage in cognitive reshaping. By offering structured training—such as workshops on algorithmic bias or policy simulations—universities can directly strengthen students’ critical thinking and environmental awareness, thereby influencing the very process of how digital literacy is transformed into information perception.

The effectiveness of this institutional compensation is likely heterogeneous, varying between entrepreneurial intention and innovation capacity. Entrepreneurial intention, as a short-term motivational state, is more readily shaped by accessible resources and mentoring, which can quickly boost confidence and perceived feasibility [26]. In contrast, innovation capacity requires long-term knowledge accumulation and systematic research training, where the benefits of university resources, such as faculty mentorship and research projects, manifest over a longer timeframe [27,28]. This temporal distinction is crucial in the urban–rural context. Urban students often enter university with a foundation of cultural capital that supports innovation, whereas rural students may rely more heavily on the university itself to activate and cultivate this capacity [29]. Elite “Double First-Class” universities, with their superior infrastructure and systematic training, are thus uniquely positioned to narrow these initial gaps.

Although the importance of universities is acknowledged, few studies explicitly test how institutional resources shape the specific cognitive pathway from digital literacy to innovation and entrepreneurship. A key unresolved question is whether elite universities exert a compensatory effect that is uniform across outcomes or one that differs for short-term entrepreneurial intention versus long-term innovation capacity. Therefore, we hypothesize:

H3: 

University type moderates the pathway of “digital literacy → information perception → innovation capacity/entrepreneurial intention,” and this moderating effect is heterogeneous across the two outcomes.

3. Research Methodology

3.1. Data Source

This study utilizes primary data from the “University Innovation and Entrepreneurship Education Quality Survey” conducted by a research team at Xi’an Jiaotong University, China. The survey was administered online between September 2024 and January 2025 via the ‘Wenjuanxing’ platform (www.wjx.cn). The data collection procedure adhered to ethical standards for academic research, ensuring anonymity, confidentiality, and voluntary participation. A multistage stratified cluster sampling strategy was designed to capture institutional and regional diversity. Universities were first stratified by type (‘Double First-Class’, regular undergraduate, and vocational colleges) across six provinces (Shaanxi, Hubei, Zhejiang, Jiangsu, Hebei, Heilongjiang). Within each stratum, universities were purposively selected in collaboration with local partners to ensure institutional variation. Within participating universities, the survey was disseminated through official channels to student groups across multiple majors and cohorts. While participation was voluntary, this approach enhanced sample heterogeneity. Accordingly, the sample provides broad diversity across institutional contexts rather than strict population representativeness.

Regarding the survey distribution, the online platform distributed the survey link to the selected student groups. The database recorded a total of 36,011 completed submissions. The data cleaning process followed three steps to ensure quality and robustness. (1) Removal of cases with completion times shorter than 200 s (n = 2864 excluded). (2) Exclusion of cases with missing values on key demographic or socioeconomic variables (e.g., parental education) (n = 146 excluded). A sensitivity analysis using multiple imputation confirmed that this exclusion did not introduce substantial estimation bias, as the results were consistent with the main analysis presented herein. (3) Removal of respondents from universities with fewer than 100 valid responses to ensure stable university-level estimates (n = 1222 excluded). After cleaning, the final analytic sample comprised 31,779 students. The authors were granted full access to the anonymized dataset for the purposes of this analysis.

3.2. Variable Specification

1

Dependent Variables

Innovation capacity was measured using a 16-item scale sourced from the “University Innovation and Entrepreneurship Education Quality Survey” project. This scale, which was developed for use in this national survey, comprises two dimensions: creative thinking (7 items, e.g., “able to analyze problems from multiple perspectives”) and creative personality (9 items, e.g., “curious about complex matters”). Responses were collected on a 4-point Likert scale (1 = not consistent to 4 = consistent) to reduce central tendency bias. The scale demonstrated excellent reliability in the present sample (Cronbach’s α = 0.973). Standardized factor scores were used in subsequent analyses.

Entrepreneurial intention was assessed using a four-item short form adapted from the Entrepreneurial Intention Scale (EISU) by Gollwitzer and Brandstätter (1997) [30]. We selected these four items (e.g., “My career goal is to become an entrepreneur”; “I am interested in starting a company”) as they capture the core construct of goal-oriented intention, which is theorized to be more stable and less context-dependent than execution-oriented intention. This adaptation was also empirically driven, as the short form demonstrated superior model fit and parsimony in our pilot analysis compared to the full scale. Responses were rated on a 7-point Likert scale (1 = strongly disagree to 7 = strongly agree). The adapted scale showed high composite reliability in our study (CR = 0.936). Standardized factor scores were used in the analysis.

2

Core Independent Variable

Urban–County–Township Gradient was operationalized based on students’ family permanent residence, creating three categories: township (rural/township areas, reference group), county (county-level units), and urban (prefecture-level cities and above). This classification captures a sequential gradient of lived experience and digital socialization beyond the traditional hukou system, reflecting the theorized urban > county > township continuum in students’ pre-university environments.

3

Mediating Variables

Digital literacy was assessed using a 9-item scale specifically developed for the present research project to capture students’ self-perceived competence in using digital and intelligent tools for learning (e.g., “I can use digital tools to solve learning problems”). Despite being project-specific, the scale exhibited strong psychometric properties in our sample. Responses were recorded on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). We conducted a confirmatory factor analysis (CFA), which confirmed the unidimensionality of the scale, with all factor loadings exceeding 0.70. The scale also showed strong composite reliability (CR = 0.936) and convergent validity (AVE = 0.831). Standardized factor scores were used in the analysis.

Information perception was assessed with a four-item scale capturing three dimensions—value evaluation efficacy, environmental parsing ability, and institutional cognition validity (see

Table 1. Measurement of the Three-Dimensional Construct.

Dimension	Measurement Items
Value evaluation efficacy	The current forms and methods of innovation and entrepreneurship education can stimulate our enthusiasm to participate.
Environmental parsing ability	National entrepreneurship policies provide tangible support for students to engage in entrepreneurial activities. Our university attaches great importance to entrepreneurship education and actively encourages students to participate.
Institutional cognition validity	Our university actively implements entrepreneurship support policies introduced by governments at various levels.

Note: Although abbreviated, the scale is designed to capture the essential theoretical dimensions of information perception while reducing respondent burden in a large-scale survey.

). This construct, following the operationalization proposed by Liñán and Fayolle (2015) [31], reflects internal information processing and cognitive capacity through recognition of and trust in a supportive environment. The scale demonstrated strong psychometric properties (CR = 0.908, AVE = 0.671). Standardized factor scores were used in the analysis.

4

Moderating and Control Variables

University type was coded as a binary variable (1 = “Double First-Class”, 0 = others) to assess whether concentrated institutional resources alter the relationship between digital literacy and student outcomes. In the context of interaction terms, the sign of the coefficient is substantively meaningful: β < 0 indicates a compensatory (substitution) effect, where institutional resources reduce the extent to which individual digital literacy predicts the outcome; β > 0 indicates an accumulative (reinforcement) effect, where institutional resources amplify the role of individual digital literacy. This interpretation distinguishes the statistical meaning of the interaction from the broader conceptual role of universities in capability formation.

Control variables included gender, political affiliation (membership in the Chinese Communist Party, CCP, which may confer political capital and access to institutional resources), leadership experience, grade level, academic performance, family socioeconomic status, parental education (cultural capital), and family entrepreneurial atmosphere.

All continuous variables were standardized prior to modeling. Summaries are provided in

Table 2. Variable Definitions and Descriptive Statistics (N = 31,779).

Variable	Operational Definition	Statistics
Dependent Variables
Innovation capacity (Innov. Cap.)	Continuous variable; creative thinking (7 items) + creative personality (9 items), 4-point scale	Standardized factor scores, range [−3.48, 1.58]
Entrepreneurial intention (Entr. Intent.)	Continuous variable; 4 items from the EISU, 7-point scale	Standardized factor scores, range [−2.03, 1.57]
Core Independent Variable
Urban–County–Township Gradient	Township: family permanent residence = rural/township (reference group)	40.6% (n = 12,907)
	County: family permanent residence = county-level administrative unit	24.1% (n = 7676)
	Urban: family permanent residence = prefecture-level or above	35.2% (n = 11,196)
Mediating Variables
Digital literacy (Dig. Lit.)	Continuous variable; 9 items from CSLAiI scale, 5-point scale	Standardized factor scores, range [−3.23, 1.47]
Information perception (Info. Percep.)	Continuous variable; 4 items across three dimensions, 5-point scale	Standardized factor scores, range [−3.20, 1.38]
Moderating Variable
University type	Double First-Class = 1; non-Double First-Class = 0	22.7% (n = 7202)
Control Variables
Gender	Male = 1, Female = 0 (reference)	47.0% (n = 14,929)
Political affiliation	Communist Party member = 1, otherwise = 0 (reference)	5.4% (n = 1728)
Leadership experience	Yes = 1, No = 0 (reference)	54.4% (n = 18,041)
Grade level	Ordered variable, 1 (freshman)—6 (PhD)	M = 2.181, SD = 1.150
Academic performance	Ordered variable, 1 (top 10%)—5 (bottom 25%)	M = 2.436, SD = 1.128
Family socioeconomic status	Continuous variable; self-reported 10-point scale	M = 4.127, SD = 1.650
Family cultural capital	Continuous variable; parental years of education	Standardized factor scores, range [−3.23, 2.62]
Family entrepreneurial atmosphere	Relatives in entrepreneurship = 1; otherwise = 0 (reference)	48.3% (n = 15,346)

Note: All continuous variables were standardized prior to modeling. Dummy variables were coded with the first category as the reference group. Abbreviations: Innov. Cap. = Innovation capacity; Entr. Intent. = Entrepreneurial intention; Dig. Lit. = Digital literacy; Info. Percep. = Information perception.

, with full details below.

Measurement Model Assessment: Confirmatory factor analysis (CFA) supported the construct validity of all multi-item scales (see

Table 3. Reliability and Validity Tests (N = 31,779).

Variable	CFA Indicators				Model Fit Indices
	Std	SMC	CR	AVE	Cronbach’s α	CFI	TLI	RMSEA	SRMR
Innov. Cap.	0.764–0.872	0.584–0.760	0.973	0.697	0.973	0.930	0.919	0.106	0.031
Entr. Intent.	0.807–0.957	0.651–0.916	0.936	0.785	0.934	0.998	0.994	0.063	0.005
Dig. Lit.	0.888–0.936	0.789–0.876	0.978	0.831	0.978	0.943	0.925	0.165	0.025
Info. Percep.	0.600–0.955	0.359–0.912	0.908	0.671	0.931	0.998	0.994	0.062	0.006

). All factor loadings were significant and substantial (>0.60), with composite reliability (CR) values exceeding 0.90 and average variance extracted (AVE) values above 0.67. Although the RMSEA values (0.106–0.165) were slightly elevated—a known phenomenon in very large samples [32]—the other fit indices (CFI > 0.93, TLI > 0.91, SRMR < 0.08) collectively indicated acceptable model fit [33].

3.3. Analytical Strategy

We sought to model an individual-level cognitive mediation pathway while simultaneously accounting for students’ nesting within universities and examining how institutional environments alter the strength of these relationships. To avoid conceptual ambiguity, we frame this institutional role in terms of testing interaction effects rather than a “moderating role.” No single statistical model could simultaneously and efficiently address all these aspects. Therefore, we adopted an integrated, staged approach that leverages the distinct strengths of multilevel modeling (MLM) for handling clustering and cross-level interactions and structural equation modeling (SEM) for testing complex mediation pathways.

Stage 1: Establishing Main Effects and Quantifying Institutional Clustering. We first estimated multilevel linear models (MLM) with students nested within universities. This step was crucial not only to obtain unbiased standard errors for the urban–rural gradient but also to empirically quantify the intra-class correlation (ICC), verifying the extent of university-level variance in our outcomes. Establishing significant institutional clustering at this stage provided the foundational justification for introducing university-level moderators in Stage 3.

Stage 2: Modeling the Sequential Cognitive Pathway. To formally test our core hypothesis H2—the sequential mediation via “digital literacy → information perception”—we employed structural equation modeling (SEM). SEM is the most appropriate framework for this task, as it allows for the simultaneous estimation of the entire pathway, provides model fit indices to assess its plausibility, and offers robust bootstrap methods for testing the significance of the indirect effects. This approach provides a holistic test of the cognitive mechanism that MLM alone cannot capture with the same elegance and precision.

Stage 3: Examining How Institutional Environments Shape These Relationships. To test H3 regarding institutional compensation, we investigated moderation through two complementary lenses, each suited to a specific type of question. Resource Substitution: We extended the Stage 1 MLM by adding a cross-level interaction term (university type × digital literacy). MLM is the gold standard for testing such interactions, as it correctly partitions the variance and allows us to cleanly estimate how a university-level resource alters an individual-level relationship. Cognitive Reshaping: Within the more flexible SEM framework, we tested whether university context alters the strength of the association in the “digital literacy → information perception” path. This allowed us to examine whether the university environment changes the efficiency of the core cognitive transformation process itself.

4. Empirical Results

4.1. Main Effects of the Urban–County–Township Gradient

Analysis of standardized data from 31,779 observations (Figure 1) indicates significant gradient differences across the urban–county–township groups in digital literacy, information perception, innovation capacity, and entrepreneurial intention. Urban students reported higher mean scores than township students in digital literacy (M = 0.220), information perception (M = 0.129), innovation capacity (M = 0.177), and entrepreneurial intention (M = 0.080), with county students occupying an intermediate position.

This pattern is broadly consistent with van Dijk’s (2020) argument that the absence of effective cognitive transformation constitutes a new form of digital exclusion [2]. Effect size analysis using Cohen’s d (

Table 4. Cohen’s d Effect Sizes Across the Urban–County–Township Gradient.

Variable	Urban vs. Township	County vs. Township	Urban vs. County
Dig. Lit.	0.428	0.211	0.219
Info. Percep.	0.262	0.139	0.125
Innov. Cap.	0.350	0.188	0.165
Entr. Intent.	0.157	0.091	0.067

Note: Effect size thresholds follow Cohen (1988) [34]: |d| < 0.20 = small effect; 0.20 ≤ |d| < 0.50 = medium effect; |d| ≥ 0.50 = large effect.

) further reveals medium-level differences between urban and township groups in digital literacy (d = 0.428), information perception (d = 0.262), and innovation capacity (d = 0.350). The county–township contrast also displayed a small but non-trivial difference in digital literacy (d = 0.211), suggesting that counties—owing to their transitional geographic and institutional role—may function as hubs in mitigating the digital divide.

By contrast, disparities in entrepreneurial intention were relatively modest (urban vs. township: d = 0.157). This aligns with George’s (2021) observation that entrepreneurial motivation tends to rely more on regional economic ecosystems than on structural resources, implying that the determinants of entrepreneurial intention differ from those of innovation capacity [24].

After controlling for individual characteristics such as gender and family capital, the associations across the urban–county–township gradient with both innovation capacity and entrepreneurial intention remained statistically significant (

Table 5. Results of Mixed-Effects Models.

	Innovation Capacity	Entrepreneurial Intention
Urban–County–Township Gradient
County vs. Township	0.071 *** (0.014)	0.018 (0.015)
Urban vs. Township	0.149 *** (0.014)	0.044 ** (0.014)
Individual characteristics
Gender	0.073 *** (0.011)	0.166 *** (0.011)
Political affiliation	0.098 *** (0.026)	0.022 (0.026)
Leadership experience	0.100 *** (0.011)	0.090 *** (0.011)
Grade level	−0.039 *** (0.005)	−0.070 *** (0.005)
Academic performance	−0.112 *** (0.005)	−0.075 *** (0.005)
Family capital
Family socioeconomic status	0.062 *** (0.003)	0.044 *** (0.004)
Family cultural capital	0.086 *** (0.006)	0.024 *** (0.006)
Family entrepreneurial atmosphere	0.087 *** (0.011)	0.182 *** (0.011)
Intercept	−0.095 ** (0.032)	−0.113 (0.066)
Wald χ2	2592.60 ***	1659.15 ***

Note: Coefficients are standardized estimates; standard errors in parentheses. *** p < 0.001, ** p < 0.01.

). For innovation capacity, urban students scored higher than township students (β = 0.149 ***), with county students occupying an intermediate position (β = 0.071 ***). For entrepreneurial intention, urban students reported higher scores than township students (β = 0.044 **), whereas the difference between county and township students was not statistically significant. This gradient differentiation is broadly consistent with Logan and Molotch’s theory of the “spatial monopoly of opportunity,” suggesting that the agglomeration advantages of cities in technological infrastructure and knowledge networks may contribute to the institutional environment for innovation and entrepreneurship [35]. These findings lend support to H1, indicating that the urban–county–township gradient is associated with significant stratified differences in students’ innovation capacity and entrepreneurial intention (urban > county > township).

Among the control variables, gender was positively associated with both innovation capacity (β = 0.073 ***) and entrepreneurial intention (β = 0.166 ***), reflecting a male advantage. This aligns with Ridgeway’s “gender frame” theory, which posits that implicit gender norms influence behavioral choices through role expectations [36]. Furthermore, political affiliation (CCP membership) showed a significant positive effect on innovation capacity (β = 0.098 ***), which can be interpreted as the influence of political capital and privileged access to institutional resources that may enhance innovative activities. Family entrepreneurial atmosphere exerted the strongest effect on entrepreneurial intention (β = 0.182 ***), surpassing that of family socioeconomic status (β = 0.044 ***). This finding is consistent with Aldrich and Cliff’s “family embeddedness” theory [37], highlighting the importance of non-economic capital in entrepreneurial decision-making. Academic performance was negatively related to both innovation capacity (β = −0.112 ***) and entrepreneurial intention (β = −0.075 ***), resonating with Robinson’s critique that “standardized education may stifle creativity” [3]. Grade level also showed a negative association, suggesting that senior students may experience reduced innovation motivation due to structural factors such as employment pressures. The overall model fit was significant (Wald χ² = 2592.60 ***), demonstrating reasonable explanatory adequacy.

4.2. Mediation Analysis: The Sequential Role of Digital Literacy and Information Perception

The structural equation model (SEM, see

Table 6. Mediation Analysis Results (Standardized Estimates).

Path and Effect	Standardized Coefficient (SE)	95% CI
Main pathways
County vs. Township → Dig. Lit.	0.036 *** (0.006)	[0.024, 0.048]
Urban vs. Township → Dig. Lit.	0.099 *** (0.007)	[0.085, 0.113]
Dig. Lit. → Info. Percep.	0.656 *** (0.005)	[0.646, 0.666]
Info. Percep. → Innov. Cap.	0.377 *** (0.008)	[0.362, 0.392]
Info. Percep. → Entr. Intent.	0.216 *** (0.008)	[0.200, 0.232]
Mediating pathways
County → Dig. Lit. → Info. Percep. → Innov. Cap.	0.009 *** (0.002)	[0.012, 0.059]
Urban → Dig. Lit. → Info. Percep. → Innov. Cap.	0.024 *** (0.002)	[0.021, 0.028]
County → Dig. Lit. → Info. Percep. → Entr. Intent.	0.005 *** (0.001)	[0.003, 0.007]
Urban → Dig. Lit. → Info. Percep. → Entr. Intent.	0.014 *** (0.001)	[0.012, 0.016]
Total effects
County → Innov. Cap.	0.030 *** (0.004)	[0.022, 0.038]
Urban → Innov. Cap.	0.072 *** (0.004)	[0.064, 0.080]
County → Entr. Intent.	0.006 *** (0.002)	[0.003, 0.009]
Urban → Entr. Intent.	0.017 *** (0.002)	[0.013, 0.021]

Note: Standard errors in parentheses. *** p < 0.001; Bootstrap = 5000; Log-likelihood = −455,121.13. Residual variances for the urban–rural pathways were 0.549 for information perception and 0.573 for innovation capacity.

) indicates that the urban–county–township gradient is associated with differentiation in capabilities through the sequential pathway of “digital literacy → information perception.” Significant gradient associations were observed with digital literacy (urban vs. township: β = 0.099 ***; county vs. township: β = 0.036 ***), illustrating the paradox of “access convergence–capability divergence.” The strong link between digital literacy and information perception (β = 0.656 ***) resonates with Castells’ (1996) [38] notion of “cognitive decoding,” whereby technological resources require cognitive transformation to become practically effective.

The contribution of the mediating pathway displayed notable domain heterogeneity. In the domain of innovation capacity, the sequential mediation explained about 33.3% of the total association for urban students (0.024/0.072) and 30.0% for county students (0.009/0.030). By contrast, for entrepreneurial intention, the mediation contribution rose to 82.4% (0.014/0.017) for urban students and 83.3% (0.005/0.006) for county students, suggesting that cognitive transformation of technology may play a particularly salient role in shaping opportunity-oriented behaviors. The direct association of information perception with innovation capacity (β = 0.377 ***) was stronger than with entrepreneurial intention (β = 0.216 ***), consistent with van Dijk’s (2020) argument that the ability to decode information is a central mechanism in the transformation of social power [2].

Figure 2 visually illustrates how the urban–rural digital divide relates to innovation capacity and entrepreneurial intention through the sequential pathway of “digital literacy → information perception,” based on standardized path coefficients. These results lend support to H2, suggesting that digital literacy and information perception jointly function as sequential mediators linking the urban–county–township gradient with both innovation capacity and entrepreneurial intention.

4.3. Institutional Boundaries of University Type and Multilevel Empowerment Mechanisms

Before interpreting the coefficients, it is important to clarify direction: a negative interaction coefficient indicates that institutional resources substitute for individual digital literacy (compensatory effect), while a positive coefficient indicates reinforcement. The multilevel mixed-effects model suggests that the type of “Double First-Class” university is associated with moderation of the relationship between digital literacy and innovation/entrepreneurship outcomes (

Table 7. Moderating Effects of University Type on the Relationship Between Digital Literacy and Innovation/Entrepreneurship Outcomes.

Variables and Effects	Innovation Capacity	Entrepreneurial Intention
Fixed effects
Dig. Lit.	0.534 *** (0.009)	0.304 *** (0.012)
Double First-Class university (DFC)	0.026 (0.038)	−0.030 (0.029)
DFC × Dig. Lit.	0.010 (0.012)	−0.039 ** (0.016)
Test of moderation	χ2 = 0.67 (p = 0.414)	χ2 = 5.65 * (p = 0.017)
Marginal effects
Non-DFC	0.534 *** (0.009)	0.304 *** (0.012)
DFC	0.544 *** (0.009)	0.265 *** (0.012)
Random effects
Variance across universities	0.005 *** (0.002)	0.019 *** (0.005)
Residual variance	0.650 *** (0.012)	0.856 *** (0.038)
Model fit	Log-likelihood = −38,261.38	Log-likelihood = −42,650.18
	Wald χ2 = 188,078.02 ***	Wald χ2 = 27,653.18 ***

Note: *** p < 0.001, ** p < 0.01, * p < 0.05; standard errors in parentheses; control variables included.

). For entrepreneurial intention, Double First-Class universities showed a significant negative moderating effect (β = −0.039 **), whereby offline resources (e.g., mentoring networks, incubators) appeared to reduce the marginal contribution of digital literacy by 12.8% (from 0.304 to 0.265). This pattern is consistent with the proposition that institutional compensation may partially substitute for individual technological capital [39]. However, no significant moderating effect was observed for innovation capacity (β = 0.010, p = 0.414), underscoring the behavioral distinction that entrepreneurial intention is often shaped by short-term opportunity recognition, whereas innovation capacity requires sustained knowledge accumulation. Variance decomposition across universities indicated that institutional resource differences explained entrepreneurial intention (σ² = 0.019 ***) more than innovation capacity (σ² = 0.005 ***), suggesting that university-level support systems play a relatively stronger role in shaping opportunity-oriented behaviors.

To further examine how university type shapes the cognitive transformation pathways, we tested its moderating effect on the sequential mediating chain (“digital literacy → information perception → innovation capacity/entrepreneurial intention”) (

Table 8. Moderating Effects of University Type on Mediating Pathways.

Path and Moderating Effect	Standardized Coefficient (SE)	Moderating Effect Δβ (SE)
Main pathway
Dig. Lit. → Info. Percep.	0.620 *** (0.009)	-
Moderating pathway
DFC moderation (Dig. Lit. → Info. Percep.)	−0.021 * (0.009)	-
Suppression of mediating pathways
Dig. Lit. → Info. Percep. → Innov. Cap.	-	−0.025 ** (0.009)
Dig. Lit. → Info. Percep. → Entr. Intent.	-	0.002 (0.011)

Note: *** p < 0.001, ** p < 0.01, * p < 0.05; standard errors in parentheses; control variables included.

). The results reveal a dual-pathway moderation mechanism (Figure 3).

First, Double First-Class universities significantly weaken the direct link between digital literacy and entrepreneurial intention (β = −0.039 **, Table 7). This represents a resource substitution effect: robust institutional support systems (e.g., incubators, mentorship) provide an alternative pathway to entrepreneurial engagement, thereby reducing the marginal necessity of individual digital capital for forming entrepreneurial intention. Second, a cognitive reshaping effect is observed at the mediation stage. The pathway from digital literacy to information perception is also attenuated in these universities (β = −0.021 *), which consequently suppresses its indirect effect on innovation capacity (Δβ = −0.025 **). This indicates that the institutional environment, by offering pre-structured informational frameworks and expert guidance, partially externalizes the cognitive labor of information decoding that students would otherwise need to perform independently.

Collectively, these findings support H3 by demonstrating that elite universities reconfigure the innovation-entrepreneurship mechanism. The negative coefficients do not signify suppression of student potential but rather a fundamental shift in the agency of entrepreneurial development—from a model reliant on individual digital prowess to one facilitated by institutional resource orchestration.

5. Discussion and Conclusions

5.1. The Cognitive Pathway: Reproducing Inequality Through Digital Habitus

Our core finding—the potent mediating chain of “digital literacy → information perception”—specifies a key cognitive mechanism that bridges individual skills and collective outcomes, thereby extending the digital divide literature. It empirically validates Castells’ (1996) [38] classic proposition that technology must be cognitively decoded to be effective by identifying information perception as a concrete form of this “cognitive decoding” in the innovation domain.

More importantly, it moves beyond the “third-level digital divide” [8], which identifies outcome gaps, by unpacking the specific cognitive process that leads to them. The stark gradient differences in digital literacy (d = 0.428) and information perception (d = 0.262) suggest that what we term “digital habitus”—the ingrained disposition to convert technology into opportunity—acts as a new form of cultural capital [29]. This conceptualization not only clarifies the cognitive process leading to outcome gaps but also highlights its nature as a new form of cultural capital that is unevenly distributed along the urban-rural spectrum. It integrates prior work by aligning with Hargittai (2010) on early socialization [40], yet offers a more precise mechanism focusing on internalized cognitive schemas, and it resonates with van Dijk’s (2020) “cognitive exclusion” by specifying how it operates through a failure to transform skills into effective environmental perception [2].

5.2. The Urban–Rural Gradient: Beyond a Simple Binary

The clear urban > county > township gradient across all variables underscores the inadequacy of a binary urban–rural framework, a limitation persistently noted in development and geography studies [10]. Our study demonstrates that the county is a pivotal, transitional category in China’s stratification system. This finding challenges the theoretical sufficiency of dualistic models and urges a move toward a continuous or gradient-based model to more accurately reflect the socio-spatial reality in many developing contexts. Therefore, policymakers should view counties not as peripheral areas but as strategic leverage points where interventions could have the highest marginal returns in narrowing the broader regional inequality.

5.3. The Compensating University: Substitution and Reshaping

The institutional shaping role of university type reveals a complex institutional mechanism that challenges purely individualistic models of digital entrepreneurship [5]. Our results show that Double First-Class universities weaken the direct link between digital literacy and entrepreneurial intention (β = −0.039 **), indicating a resource substitution effect. This finding provides robust empirical support for Marginson’s (2016) proposition of higher education as a compensatory institution [39], demonstrating that offline support systems can partially substitute for pre-existing deficits in individual digital capital.

This institutional influence extends to the cognitive core of our model. Elite universities also attenuate the “digital literacy → information perception” pathway (β = −0.021 *), demonstrating a cognitive reshaping effect. This result offers a novel explanation for mixed findings in prior research on university impact: the institution does not merely add resources but can reconfigure foundational cognitive pathways. Crucially, this suppression of the cognitive pathway significantly reduces the indirect effect of digital literacy on innovation capacity (Δβ = −0.025 **). This divergence is theoretically meaningful. It suggests that while entrepreneurial intention is readily shaped by short-term resource substitution [24], innovation capacity—as a deeper cognitive capability—is influenced through the suppression of its foundational cognitive mediators. Together, these two institutional mechanisms demonstrate that elite universities do not merely provide resources but can actively reconfigure the very pathways through which pre-existing inequalities translate into innovative outcomes.

6. Conclusions and Implications

6.1. Theoretical and Policy Implications

In conclusion, this study provides robust evidence that contemporary inequality across the urban–county–township spectrum is reproduced through a cognitive chain and can be partially mitigated through institutional compensation. We contribute to theory by (1) conceptualizing “digital habitus” as a key mechanism of stratification; (2) delineating the sequential “technology–cognition” pathway that underlies disparity in outcomes; and (3) revealing the dual (substitution and reshaping) role that elite institutions can play in moderating these pathways. Most importantly, we theorize and empirically validate the urban–county–township gradient as a necessary refinement to the binary models that have long dominated the field.

These theoretical insights carry direct and actionable implications for policy and practice. First, policy must move beyond one-size-fits-all approaches to implement gradient-tailored strategies that reshape the cognitive chain. For township students, a “Digital Habitus Foundation” strategy is essential to build foundational competence and self-efficacy. For county students, a “Cognitive Bridge” strategy should strengthen cognitive transformation capacity via structured mentorship. For urban students, a “Deep Innovation” strategy is needed to promote advanced, interdisciplinary challenges that foster sustained knowledge integration.

Second, the documented resource substitution effect (β = −0.039 *) provides a solid evidence base for formalizing and targeting institutional support. “Double First-Class” universities should be encouraged and funded to implement mechanisms—such as dedicated mentorship and incubator access—explicitly designed to compensate for pre-university disparities.

Finally, given the stronger effect of information perception on innovation capacity (β = 0.377) than on entrepreneurial intention (β = 0.216), policymakers should prioritize long-term investments in fostering innovative thinking over short-term entrepreneurial metrics. This requires sustained knowledge accumulation and research exposure, which are critical for building a robust national innovation system.

6.2. Limitations and Future Research

This study has limitations. Its cross-sectional design precludes definitive causal inferences. Future longitudinal research is needed to trace the formation of the “digital habitus” over time. Secondly, the binary measure of university type, while providing a clear initial test, masks internal heterogeneity. Future work should disaggregate institutional effects by discipline and specific policy configurations. Finally, comparative studies in other national contexts with similar spatial hierarchies (e.g., Vietnam, India) are essential to test the generalizability of the “gradient–cognition–institution” framework.