Managing digital bridges: How online social capital translates digital literacy into higher education access

Abstract

Background

In the modern computer-driven economy, digital technologies have reshaped the distribution of opportunities and tasks in higher education.

Objective

Whether digital literacy and online social capital, as products of the digital age, serve as “equalizers” to promote equity by enabling individuals to improve their socioeconomic conditions/status, or function as “new barriers” that reinforce disparities due to the effects of established social stratification, is a core issue that needs to be explored.

Methods

Using a quantitative research design with a sample of 384 high school graduates recruited from a province in western China, this study explores the mechanisms by which digital literacy and online social capital affect access to higher education through multivariate logistic regression and group regression comparative analysis.

Results

The results reveal that digital literacy had a significant positive effect on access to higher education, and consequently this effect differed significantly by institution type and socioeconomic background. The study confirms that network social capital plays a mediating role between digital literacy and access to higher education.

Conclusions

These findings provide: first, empirical evidence for understanding inequality in education in the digital age and second, important insights for the development of digital policies to promote educational equity.

Keywords

digital literacy higher education access online social capital SES

Introduction

In an era when AI is hugely reshaping higher education in terms of how students study and subjects are taught, AI-powered language tools are reconstructing the way knowledge is acquired, and algorithm-driven personalized learning recommendation systems are continuously being updated and refined.^1,2 It appears that the digital transformation of higher education has eliminated traditional geographical and informational barriers, increasing the inclusivity of higher education.³ However, despite these technological advances significantly enhancing the availability of resources, students from low-income families, rural areas, and first-generation college students still lack access to elite institutions when compared to more advantaged groups.^4–6 This phenomenon reveals a key issue and that is the widespread adoption of digital technology does not automatically translate into equitable distribution of learning opportunities. In the situation where everyone has access to the same technological tools, educational inequality persists.

This is explained by the fact that technological tools by themselves are insufficient to address systemic inequality. The effectiveness of digital tools depends on the dynamic interaction of two key elements: technology access and digital literacy, as well as the social support network that translates these technological capabilities into tangible resources.⁷ However, in current research, the role of the gap in social resources formed by digital connections is severely underestimated. Similarly, the challenge for educational equity in the digital age is not the breadth of access to technology, but the establishment of effective digital societies, that is, online support networks and virtual community resources that translate technological competencies into tangible educational opportunities. This online social capital built through the internet serves as an important bridge between technological tools and such opportunities.⁷ The educational equity promised by the digital revolution can only be truly realized when technological access, digital literacy and online social support systems interact in productive and capacity-building ways.

In other words, when disadvantaged students lack the online social capital to transform their digital skills into resources and opportunities, the digitalization of education may instead exacerbate existing opportunity disparity.⁹ This tech-society interaction dilemma poses a challenge to the equity of higher education in the digital age. The question needs to be asked: are we creating a double divide, not only due to differences in digital skills but also because of the unequal distribution of online social capital, permanently excluding disadvantaged students from good quality higher education? This is the question that has motivated the present study.

Research scope and gap

The digital divide, a phenomenon that refers to differences among various socioeconomic status (SES) groups in terms of access to information technology, usage capabilities, and application benefits, has been validated by existing research.¹⁰ A number of factors contribute to the emergence of the digital divide, such as inequalities in infrastructure related to geographic location (rural vs urban) and variations in resource access regardless of SES.¹⁰ It is worth noting that the digital divide is present not only in the fundamental dimension of technology access but also in the differentiation of digital literacy. This capability gap determines how effectively different groups can translate digital technology into tangible opportunities.¹¹ Similarly, the differences in levels of digital literacy affect individuals’ opportunities in accessing education.¹²

However, existing research on educational equity in the digital age has limitations. While focusing on the physical aspects of technology access, it has relatively neglected the critical role of individual digital literacy and online social capital, which together determine whether technological tools can be effectively translated into real educational opportunities. This cognitive gap leads to two unanswered queries. The first question is, is there a double divide among students from different social backgrounds in digital environments, including differences in technology access, digital literacy, and online social capital? Second, assuming that this double divide does exist, by what mechanisms does it affect the distribution of higher education opportunities? Responses to these queries will: first, provide new theoretical perspectives for understanding educational inequality in the digital age and second, reveal new mechanisms for the distribution of educational opportunities, which is the scope for this study.

Research aim and questions

This study builds on the theoretical framework of the digital divide and seeks to broaden the theoretical explanation of the distribution of higher education access in the digital age. It does so by investigating the interaction effects of technological tools and online social capital, thereby promoting educational equity for students from various SES backgrounds. To achieve this goal, the study will concentrate on addressing the following research questions.

(1) What are the differences in digital literacy, online social capital and access to different types of higher education amongst students from diverse SES backgrounds?

(2) How does digital literacy influence access to different types of higher education among diverse SES background students?

(3) Does online social capital play a mediator role in the relationship between digital literacy and access to different types of higher education amongst diverse SES background students?

(4) What are the intervention measures that could promote equitable access to higher education in the digital era?

The introduction is followed by a literature review, which in turn is followed by the research approach, research results, and discussion. This paper concludes with a summation of the study’s key points.

Literature review

Following a critical analysis of the influences of SES on the differences that exist in digital literacy, this section investigates how these variations affect education. It then highlights the role of digital literacy as a key transformation tool in the process of building online social capital. Lastly, it assesses the influence of online social capital on education.

SES-driven digital literacy and its role in access to education

The concept of digital literacy has been continuously expanding as technology has also developed and become more sophisticated. Gilster¹³ first defined it in his pioneering research as “the core ability to understand and use information technology,” laying the foundational framework for this concept. With the evolution of the digital economy, academic understanding of digital literacy has gradually diversified. UNESCO¹¹ has expanded its meaning from technical skills to incorporate broader dimensions such as information evaluation, digital security, ethical awareness, and critical thinking. This development trend is further reflected in the “Digital Competence Framework,” which clearly defines digital literacy as “the ability to confidently, critically, and responsibly use information technology in a digital environment.” Furthermore, it systematically divides it into five core dimensions: information and data literacy, communication and collaboration, digital content creation, safety, and problem-solving.¹⁴

The OECD¹² report shows that the value of digital literacy in educational development is being widely recognized globally. Wang et al.¹⁵ point out that the widespread adoption of digital literacy is profoundly reshaping the education ecosystem. By promoting the construction of digital infrastructure and the sharing of online educational resources, it has significantly enhanced equity and quality.^3,9 Specifically, the development of digital literacy not only enhances students’ ability to access and process information and expands digital learning methods but also cultivates critical thinking and innovative practice skills.¹⁶ This effectively narrows the education gap, allowing more students, especially those in remote areas, to access good quality resources and obtain learning opportunities.³

However, this optimistic scenario is encountering challenges from empirical research. Darvin¹⁷ revealed that the distribution of digital literacy is significantly uneven in various SES groups. Students from privileged family backgrounds are exposed to digital devices and receive guidance earlier, while students from disadvantaged groups lack basic digital operation skills.¹⁸ This initial disparity accumulates as students’ progress through different learning stages, ultimately resulting in a significant digital literacy gap or discrepancy.¹⁹ This gap led to a dual differentiation: disparity in learning outcomes and inequality in accessing education-related information.²⁰ Notably, technologies like algorithmic recommendations may further reinforce the echo chamber effect, making students with insufficient levels of digital literacy susceptible to misinformation.²¹ This digital divide will entrench existing inequalities, ultimately marginalizing students from disadvantaged groups in the competition for learning opportunities in the digital age.²²

Digital literacy as a gateway to online social capital

Social capital theory, first proposed by Bourdieu,²³ emphasizes resource exchange and reciprocal behaviors in social networks. With the development of digitalization, the concept of Online Social Capital has attracted the attention of researchers, which refers to the support, information, and opportunities that an individual receives through virtual social networks, for instance, social media and online learning communities.⁸

Digital literacy, which serves as an individual’s ability to access, evaluate, and utilize information in digital environments, determines a person’s ability to access resources in online social interactions.^24–26 Referring to the essential competence dimension, technological skills, that is, skills in using digital devices and applications, constitute the fundamental threshold for building online social capital, and individuals who are skilled in the use of social media are more likely to build extensive online social networks.²⁷ However, the technological divide created by differences in socioeconomic status puts low-income groups at an immediate disadvantage.¹⁸ Second, information literacy determines the quality of an individual’s ability to identify and utilize online social network resources. Individuals with high levels of information literacy are able to filter invalid social information and build effective and accurate social networks.²⁷

On the dimension of expansion competence, social collaboration competence affects the effectiveness of maintaining network social relations, and well-organized social collaboration competence contributes to form a wider reciprocal network, thus acquiring heterogeneous online social capital.²⁸ In addition, innovative production capacity is the key to add value to social capital. High-quality content output can significantly increase an individual’s online social network.²⁹ Last but not least, the ability to institute safety and security features ensures the stability of online social capital; individuals with an organized awareness of online safety can effectively avoid risks. However, overprotective behaviors hinder the expansion of online social capital.³⁰ It is important to note that high SES groups gain an advantage in all dimensions of digital literacy through intergenerational inheritance.³¹ This disparity in the growth of digital literacy in turn influences the ultimate effect of online social capital development.¹⁷

Online social capital and its impact on access to education

Studies revealed that online social capital has a significant impact on education outcomes. Online bonding social capital provides emotional support through strong relational networks such as online study groups and communities, effectively reduces academic anxiety, enhances learning resilience, and promotes knowledge construction based on trust.⁸ Online bridging social capital relies on weak relational connections such as cross-college students and experts and breaks down information barriers by integrating heterogeneous resources.⁸ This process not only facilitates cross-group knowledge flow but also provides students with college differences, professional outlook, and other non-redundant information, expanding access to information about educational opportunities.

Despite the fact that online social capital has a positive role to play in education, its negative effects should not be ignored. As a virtual form of capital, online social capital has an amplified effect over time, and its distribution is affected by SES, revealing an imbalance.⁸ This imbalance is first reflected in the intergenerational inheritance of families. Children from advantaged families have access to better digital devices and technological guidance, and more importantly, inherit a range of tacit knowledge, including skills to use specialized learning platforms and strategies to build high-quality academic networks.¹⁸ Second, differences in digital technology usage habits further exacerbate the dynamics of inequality. Students who employ digital technologies for learning and academic interactions accumulate high-quality social capital through ongoing interactions, including access to academic resources and building networks of expert contacts. Meanwhile those who remain stuck in entertainment-related socialization are trapped in a low-value social cycle dominated by entertainment content.¹⁹ The difference in usage preferences leads to the build-up of high-quality social capital by the former, while the latter fall into a vicious cycle of low-value socialization.

With a focus on examining the distinct performance of various SES groups in this process, this study employs a comparative research methodology, grounded in the theoretical characteristics mentioned above. Here the aim is to examine the mediating mechanism of online social capital in the relationship between digital literacy and access to higher education. In light of this the following hypotheses are posited.

H1: Significant differences in the digital literacy, online social capital and access to higher education are evident among diverse SES groups.

H2: Access to higher education is significantly positively impacted by their digital literacy.

H3: Access to higher education and digital literacy are mediated by online social capital.

Methodology

Research design and participants

The purpose of this study is to examine the issue of educational inequality in the digital age, specifically how digital literacy and online social capital interact to influence educational pathways. To examine the differentiated performance of this mechanism across SES, the study employs the quantitative method utilizing a cross-sectional design. By comparing the higher education access obtained by candidates from diverse SES backgrounds, revealed here are new mechanisms of learning attainment in the digital age. This quantitative research³² makes it possible to examine the statistical correlation between variables, ensuring the objectivity of the research results, and meets the research needs for detailed comparisons of group differences.

Upper secondary school graduates who were able to secure higher education access in 2024 constitute the participants in this study. As a typical digital native generation, this cohort is representative in terms of online social capital and digital literacy, reflecting the digital characteristics of contemporary youth. The survey was conducted from November 15, 2024, to January 10, 2025.

Population and sampling procedures

The research population consists of high school graduates in China who are eligible for higher education enrollment in 2024, totaling approximately 10 million people and distributed across 15,800 regular high schools nationwide. These schools can be categorized into provincial model high schools (PMHS), urban general high schools (URHS) and rural general high schools (RGHS) which also reflect the location of the school. However, since this study aims to compare between diverse SES background students, 2 regions were selected, specifically the eastern and western regions. The eastern region in China is highly economically developed with better amenities characterized by technological advancement while the western region is considered the most backward region in China; consequently, the regions represent significant SES variations. One province in eastern and one in western China were selected as the provincial sampling units.

A multi-level sampling approach which involves identifying different steps, and then selecting target individuals within each step, and then sampling the participants was used to achieve a representative sample. Therefore, the total population of interest include 9681 students from 6 high schools in Jiangsu and Sichuan Province, in eastern and western region of China, respectively. Since the schools are categorized based on location, one school from each category of high school, that is, PMHS, URHS, and RGHS was purposively selected from the two provinces (3 from the eastern and 3 from the western region in China). This was done using the simple random sampling technique.

Hence, in total 6 high schools were sampled. Subsequently, the minimum sample size was determined using the finite population sample size calculation formula proposed by Yamane,³³ where n represents the required sample size, N represents the target population size (i.e., the total number of graduates), and e stands for the allowable sampling error (set to 5% or 0.05 in this study). This error range is a standard setting in the field of education research, which can ensure statistical validity while taking into account the feasibility of actual research.³⁴

n = \frac{N}{1 + N {(e)}^{2}}

An effective sample size of 384 was determined and it was stratified among the six schools selected (in the sampling process) based on the total population of students in each school. Furthermore, simple random sampling was conducted at each sampled school to sample the specific participant using an interval of 3 based on the schools’ student list. The schools’ academic staff assisted in the sampling work. This sampling strategy ensured the representativeness of school types through stratified sampling and utilizing random sampling methods to control selection bias. The demographic distribution of the sampling population is summarized in Table 1.

Table 1.

Descriptive statistics of controlled variables.

Control variables	Number	Percentage %
Gender
Male	185	48.2
Female	199	51.8
Ethnicity
Han	379	98.7
Minority	5	1.3
Household registration
Urban	208	54.2
Rural	176	45.8
School category
PMHS	168	43.7
UGHS	146	38.1
RGHS	70	18.2

Note: n = 384. PMHS refers to provincial model schools, UGHS refers to urban general senior high schools, and RGHS refers to rural general senior high schools. Given the limited number of minority-ethnicity graduates in the sample, this variable was excluded from the analysis.

Measures

This study collected primary data (online social capital) through questionnaires and obtained secondary data (digital literacy, higher education admission results, and family background information) from the school’s academic affairs system to ensure data source triangulation and mitigate biases related to self-reported data.

Online social capital was measured using a 5-point Likert scale designed by Paige et al., and it incorporates two dimensions: online bridging social capital (7 items), adapted from the scale by Ellison et al. and online bonding social capital (5 items), adapted from the questionnaire by Williams.^35–37 The pilot study indicated that the scale has good reliability (Cronbach’s α = 0.85). Establishing a three-part scale means it can be divided into three groups: high level, medium level, and low level.

Access to higher education was divided into three categories based on the type of institution: research universities (representing elite higher education institutions), applied universities, and vocational colleges. These corresponded to levels 3, 2, and 1 classification standards, respectively. Meanwhile digital literacy was measured using information technology course standardized exam results taken during students’ school years. SES is measured using a composite indicator method proposed by Green³⁸ that includes three key indicators: parents’ education/qualification level, occupational status, and family income were converted into standardized scores for calculation, with the formula widely used in the Chinese context (SES = (0.82*parental education level +0.81* parental occupation +0.76 * income)/0.63).³⁹ The Components and Classification Criteria of SES are summarized in Table 2. Both of these two variables were classified into three groups—high, medium, and low—using the tertile method to reflect the distribution features of students from various SES backgrounds.

Table 2.

Components and classification criteria of SES.

Dimension	Level	Classification description	Weight
Parental education	1	Primary school or below	0.82
	2	Junior high school
	3	Senior high school/vocational high school
	4	Associate degree/bachelor’s degree
	5	Master’s degree or above
Parental occupation	1	Unemployed/not in labor force	0.81
	2	Manual laborers (e.g., farmers and construction workers)
	3	Clerical workers/small business owners
	4	Skilled technicians/middle-level managers
	5	Professionals/senior executives
Household monthly income (CNY)	1	Less than 2,000 yuan	0.76
	2	2,000–3,000 yuan
	3	3,001–4,000 yuan
	4	4,001–5,000 yuan
	5	5,001–6,000 yuan
	6	6,001–7,000 yuan
	7	7,001–8,000 yuan
	8	8,001–9,000 yuan
	9	9,001–10,000 yuan
	10	10,001–11,000 yuan
	11	11,001–12,000 yuan
	12	12,000 yuan and above

Data analysis

To address research question 1, the ratio model served to examine the group differences in digital literacy, online social capital, and access to higher education among students of diverse SES groups (RQ1). Subsequently, multiple regression and subgroup regression analyses were conducted to explore the role of digital literacy in access to higher education and its heterogeneous effects across different SES groups (RQ2). Then, stepwise regression analysis was employed to examine the mediating effect of online social capital between digital literacy and higher education access (RQ3). Finally, the results of the three analyses were integrated to comprehensively address the core issue of equitable access to educational opportunities in the digital age (RQ4).

Results and discussion

Uneven distribution of digital literacy, online social capital, and access to higher education among graduates across diverse SES backgrounds

To address RQ1, which investigates the differences in digital literacy, online social capital and access to different types of higher education amongst students from diverse SES backgrounds, descriptive statistics were computed. The results in Figure 1 highlight the differences in digital literacy levels among graduates based on their SES background. The superiority of digital literacy in the high SES group is notable: 42.97% of high SES graduates are highly digitally literate, which is significantly higher than the medium SES group (35.94%) and the low SES group (21.09%). In terms of medium digital literacy levels, although the differences between the three groups were relatively small (35.94% for the high SES group, 33.59% for the medium SES group, and 30.47% for the low SES group), a gradient distribution from high to low was observed.

Figure 1.

Comparison of digital literacy among SES groups (in %).

However, the pattern of distribution of low digital literacy levels reveals an opposite pattern of ratios to that of high digital literacy levels. Specifically, the low SES group has a high proportion of 48.44% at this level, which is much higher than that of the medium SES group (30.47%) and the high SES group (21.09%). Together, these results suggest that the higher the SES, the greater the likelihood that graduates will be highly digitally literate, while the low SES group faces challenges in building digital literacy.

The results in Figure 2 show that graduates’ level of online social capital is also correlated with their SES background. The high SES group wields significant dominance in terms of high online social capital: 46.88% of high SES graduates have high levels of online social capital, much higher than the middle SES group (34.38%) and the low SES group (18.75%). In terms of the distribution of medium-level online social capital, the difference between the three groups is small. However, it reveals a noticeable pattern, that is, the medium SES group (34.38%) is slightly higher than the high SES group (33.59%) and the low SES group (32.03%).

Figure 2.

Comparison of online social capital among SES groups (in %).

On the other hand, at the low online social capital level, the data shows a diametrically opposite distributional trend: the low SES group has the highest percentage at this level (49.22%), which is significantly higher than that of the medium SES group (31.25%) and the high SES group (19.53%). This contrast further highlights the difference of SES on online social capital - the higher the SES, the more likely graduates are to accumulate richer online social capital, while groups with low SES are more likely to experience a lack of online social capital.

The data in Figure 3 illustrates the distribution of higher education access for diverse SES groups. In terms of access to research universities, there is a disparity among the SES groups: more than half (53.49%) of high SES graduates enroll in research universities, which is almost twice as many as the middle SES group (29.07%) and more than three times as many as the low SES group (17.44%). The distribution of applied universities is similarly hierarchical, with the high SES group (36.16%), the medium SES group (35.03%), and the low SES group (28.81%) in descending order, but the gap is small.

Figure 3.

Comparison of access to HE among different SES groups (in %).

Access to vocational college, however, demonstrates a contrary distribution pattern: more than half of the low SES group (51.24%), which is not only much higher than the middle SES group (33.88%) but also 3.4 times higher than the high SES group (14.88%). Indicated here is a SES gradient in access to higher education - the advantaged groups are more likely to have access to academically oriented higher education, while the disadvantaged groups are more concentrated on career paths.

Influence of digital literacy on access to higher education

To answer RQ2, a multivariate logistic regression and group regression were conducted to examine the relationship between digital literacy and access to different types of higher education among students from diverse SES backgrounds. As shown in Table 3, the results reveal that digital literacy has a significant positive association with access to different types of higher education, using vocational colleges as the reference category. Controlling for other variables, for every 1 unit increase in digital literacy, the odds ratio of graduates’ admission to research universities significantly increased to 9.616 times to that of the reference group of vocational colleges (OR = 9. 616, 95% CI [4.211, 21.949], p < 0.001). Additionally, the odds ratio of admission to applied universities would increase to 5.032 times (OR = 5.032, 95% CI [2.595, 9.757], p < 0.001), an outcome that demonstrates the different patterns of effect of digital literacy on access to higher education.

Table 3.

Influence of digital literacy on access to different types of HE.

Variables	Or (95% CI)
Variables	Access to RU	Access to AU
Gender_male	2.072* (1.130, 3.799)	1.335 (0.814, 2.190)
HR_rural	0.273*** (0.136, 0.548)	0.533* (0.315, 0.902)
School category: RGHS as reference
PMHS	1.409 (0.410, 4.840)	3.282* (1.150, 9.365)
UGHS	1.468 (0.424, 5.088)	2.244 (0.766, 6.578)
Digital literacy	9.616*** (4.211, 21.949)	5.032*** (2.595, 9.757)

Note: *** p<.001, **p < 0 .01, *p<.05. Vocational colleges as reference. HR refers to household registration. PMHS refers to provincial model schools, UGHS refers to urban general upper secondary schools, and RGHS refers to rural general upper secondary schools.

As shown in Table 4, the multivariate logistic regression analyses of subgroups based on SES (with vocational college as the reference category) indicated that the role of digital literacy differed significantly across groups. The results of the analysis for research university access showed that digital literacy had a significant positive effect on all SES groups. Here the strongest effect occurred in the high SES group (OR = 10.516, 95% CI [1.941, 56.967], p < 0.05), followed by the low SES group (OR = 8.992, 95% CI [1.666, 48.536], p < 0.05) and the medium SES group (OR = 6.800, 95% CI [1.765, 26.198], p < 0.01), with the high SES group having a 56% higher odds ratios than the medium SES group. The analysis of applied university access highlighted a different trend than research universities: the effect of the low SES group (OR = 4.488, 95% CI [1.582, 12.730], p < 0.01) was close to that in the medium SES group (OR = 4.401, 95% CI [1.312, 14.763], p < 0.05), whereas the influence of the high SES group (OR = 8.877, 95% CI [1.937, 40.671], p < 0.01) was twice as high as theirs.

Table 4.

Influence of digital literacy on access to different types of HE among diverse SES groups.

Variables	Access to RU			Access to AU
Variables	Low SES	Medium SES	High SES	Low SES	Medium SES	High SES
Gender_male	2.965 (0.714, 12.304)	1.910 (0.704, 5.183)	3.583* (1.176, 10.914)	0.806 (0.359, 1.811)	1.846 (0.754, 4.523)	2.141 (0.790, 5.804)
HR_rural	0.422 (0.091, 1.967)	0.306* (0.105, 0.891)	0.197* (0.051, 0.754)	0.444 (0.186, 1.058)	0.438 (0.172, 1.117)	0.778 (0.267, 2.264)
School category: RGHS as reference
PMHS	0.358 (0.032, 3.935)	3.159 (0.260, 38.364)	1.826 (0.245, 13.606)	3.942 (0.578, 26.900)	3.841 (0.544, 27.117)	3.015 (0.510, 17.829)
UGHS	0.105 (0.008, 1.346)	2.814 (0.267, 29.608)	0.975 (0.139, 6.860)	2.717 (0.434, 17.025)	2.453 (0.395, 15.224)	4.150 (0.796, 21.637)
Digital literacy	8.992* (1.666, 48.536)	6.800** (1.765, 26.198)	10.516* (1.941, 56.967)	4.488** (1.582, 12.730)	4.401* (1.312, 14.763)	8.877** (1.937, 40.671)

Note: *** p<.001, **p < 0 .01, *p<.05. Vocational colleges as reference. HR refers to household registration. PMHS refers to provincial model schools, UGHS refers to urban general upper secondary schools, and RGHS refers to rural general upper secondary schools.

The finding provides a direct response to RQ2, revealing the dual role of digital literacy in access to higher education. Consistent with the findings reported by Ahel and Lingenau,⁹ digital literacy is positively associated with the acquisition of higher education access, but this study further found significant SES differences in its outcome. The results demonstrate that the association between digital literacy and access to elite higher education (such as research universities) is stronger among high SES students, which confirms the manifestation of the Matthew effect in the field of education. Here the advantaged class can better use digital resources to consolidate their upper hand in education.⁴⁰

It is relevant to note that digital literacy may serve as an important resource for empowerment and inclusion for low SES groups, which provides new evidence for compensatory education approaches in the digital age. Although low SES students are at a disadvantage in obtaining digital resources, once they develop considerable digital capabilities, they may experience greater opportunities to overcome learning obstacles. This suggests that digital literacy could be an effective means of improving learning for disadvantaged groups. It is consistent with the broader discussions argued by Selwyn⁴¹ on the potential of technological conditioning, which can serve as a means to free oneself from socioeconomic challenges.

Influence of digital literacy on online social capital

To address RQ3, the initial step was to examine the effect of the independent variable (digital literacy) on the proposed mediator (online social capital). The full-sample regression results that are reported in Table 5 show that digital literacy has a significant positive effect on online social capital (β = 0.397, p<0.001). Each standard deviation increase in digital literacy is associated with an increase in online social capital of nearly 0.4 standard deviations.

Table 5.

Influence of digital literacy on online social capital.

Variables	All sample	Low SES	Medium SES	High SES
Constant	2.301***(0.278)	2.207***(0.459)	2.333***(0.491)	2.594***(0.540)
Gender_male	0.011 (0.071)	0.006 (0.129)	0.011 (0.118)	0.038 (0.121)
HR_rural	−0.273***(0.078)	−0.294**(0.145)	−0.241 (0.126)	−0.292**(0.135)
School category: RGHS as reference
PMHS	0.114 (0.156)	0.174 (0.283)	0.358 (0.265)	0.105 (0.265)
UGHS	0.085 (0.143)	0.189 (0.265)	0.242 (0.247)	0.143 (0.231)
Digital literacy	0.397***(0.091)	0.358*(0.158)	0.338*(0.155)	0.388*(0.182)
Observations	384	128	128	128
R-squared	0.085	0.088	0.108	0.065

Subgroup regression further showed that there were SES differences in this effect: the effect was stronger in the high SES group (β = 0.388, p<0.001) than in the low SES group (β = 0.358, p<0.001) and in the middle SES group (β = 0.338, p<0.001). Suggested here is that students of high SES are able to more efficiently translate digital literacy strengths into online social capital collection and expansion. Meanwhile the medium SES group had the relatively least efficient conversion.

These results corresponding to RQ3 reveal the significant role of digital literacy is linked to online social capital, and the finding provides a new theoretical perspective for understanding the mechanism of social capital formation in the digital age. It is worth noting that the conversion efficiency appears to be greater in the high SES group than in the low SES group. This gap may be due to: advantaged families develop digital social skills earlier; their offline social networks are naturally extended in digital space; and they have a better grasp of elite-oriented online social norms. This difference confirms Bourdieu’s²³ view on the unequal reproduction mechanism of cultural capital, indicating that the process of digital literacy transforming social capital is also constrained by the existing social structure.

Access to higher education: Mediating the impact of online social capital

Building on the previous step, the direct relationship between online social capital and access to higher education was then examined to address RQ3. The results of the multivariate logistic regression analysis indicated that online social capital wielded a significant effect on access to different types of higher education. Using vocational institutions as the reference group, for each unit increase in online social capital, the odds of a student being admitted to a research university significantly increased to 3.114 times when compared to the control group (OR = 3.114, 95% CI [2.006, 4.835], p<0.001). In the meantime, the odds of being admitted to an applied university increased to 2.408 times in comparison to the control group (OR = 2.418, 95% CI [1.689, 3.461], p<0.001) (Table 6).

Table 6.

Influence of online social capital on access to different types of HE.

Variables	Or (95% CI)
Variables	Access to RU	Access to AU
Gender_male	1.843* (1.013,3.356)	1.240 (0.758, 2.028)
HR_rural	0.393** (0.197, 0.785)	0.713 (0.421, 1.208)
School category: RGHS as reference
PMHS	1.442 (0.413, 5.037)	3.219* (1.116, 9.279)
UGHS	1.376 (0.392, 4.833)	2.031 (0.687, 6.000)
Online social capital	3.114*** (2.006, 4.835)	2.418*** (1.689, 3.461)

Note: *** p<.001, **p < 0 .01, *p<.05. HR refers to household registration. PMHS refers to provincial model schools, UGHS refers to urban general upper secondary schools, and RGHS refers to rural general upper secondary schools.

The subgroup regression analysis further reveals the heterogeneity of the relationship of online social capital and access to higher education. As shown in Table 7, using vocational colleges as a reference group, the influence of online social capital on access rate to research universities appears to vary across different SES groups. The effect sizes for the low SES group (OR = 3.617, 95% CI [1.428, 9.158], p<0.01) and the high SES group (OR = 3.510, 95% CI [1.540, 8.000], p<0.01) are similar and significantly higher than that of the middle SES group (OR = 2.651, 95% CI [1.251, 5.617], p<0.05).

Table 7.

Influence of online social capital on access to different types of HE among SES groups.

Variables	Access to RU			Access to AU
Variables	Low SES	Medium SES	High SES	Low SES	Medium SES	High SES
Gender_ male	2.422 (0.618, 9.497)	1.637 (0.616, 4.353)	3.180* (1.039, 9.730)	0.819 (0.368, 1.825)	1.653 (0.684, 3.993)	1.833 (0.680, 4.942)
HR_rural	0.653 (0.140, 3.052)	0.398 (0.138, 1.149)	0.364 (0.093, 1.421)	0.554 (0.233, 1.314)	0.540 (0.212, 1.375)	1.440 (0.483, 4.294)
School category: RGHS as reference
PMHS	0.318 (0.029, 3.467)	3.362 (0.267, 42.286)	2.507 (0.294, 21.359)	3.410 (0.514, 22.615)	3.925 (0.538, 28.624)	3.698 (0.566, 24.169)
UGHS	0.102 (0.008, 1.280)	2.271 (0.209, 24.653)	1.354 (0.171, 10.753)	2.388 (0.392, 14.554)	1.993 (0.313, 12.689)	5.415 (0.950, 30.871)
Online social capital	3.617** (1.428, 9.158)	2.651* (1.251, 5.617)	3.510** (1.540, 8.000)	2.007* (1.149, 3.506)	2.214* (1.129, 4.342)	3.305** (1.598, 6.834)

In terms of access to applied universities, the influence of online social capital shows a clear SES gradient. The high SES group (OR = 3.305, 95% CI [1.598, 6.834], p<0.01) is higher than the middle SES group (OR = 2.214, 95% CI [1.129, 4.342], p<0.05), which is higher than the low SES group (OR = 2.007, 95% CI [1.149, 3.506], p<0.05). Among them, the advantage effect of the high SES group is 1.64 times larger than that of the low SES group. This outcome confirms two things: first, online social capital may contribute to obtaining higher education access and second, it reveals the key role of SES in the transformation of the utility of online social capital.

The stepwise regression method was used to test the mediating effect of online social capital (taking vocational colleges as the reference group). As shown in Table 8, in the baseline model (Model 1), digital literacy highlighted a significant positive impact on the odds ratio of entering a research university (OR = 9.616, 95% CI [4.211, 21.949], p<0.001). When the online social capital variable was introduced (Model 3), the effect of digital literacy remained statistically significant, but the magnitude dropped to OR = 7.122 (95% CI [3.061, 16.567], p<0.001), and online social capital itself also confirmed a significant effect (OR = 3.114, 95% CI [2.006, 4.835], p<0.001). Indicated here is that online social capital plays a partial mediating role in the relationship between digital literacy and enrolling in a research university. Based on the calculation of effect size changes, the mediating effect of online social capital accounts for 25.9%.

Table 8.

Mediating the role of online social capital.

Variables	Access to RU			Access to AU
Variables	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Gender_ male	2.072* (1.130, 3.799)	1.843* (1.013,3.356)	2.166* (1.161, 4.042)	2.072* (1.130, 3.799)	1.240 (0.758, 2.028)	1.391 (0.836, 2.315)
HR_rural	0.273*** (0.136, 0.548)	0.393** (0.197, 0.785)	0.337** (0.165, 0.688)	0.273*** (0.136, 0.548)	0.713 (0.421, 1.208)	0.631 (0.366, 1.089)
School category: RGHS as reference
PMHS	1.409 (0.410, 4.840)	1.442 (0.413, 5.037)	1.579 (0.436, 5.721)	3.282* (1.150, 9.365)	3.219* (1.116, 9.279)	3.438* (1.161, 10.179)
UGHS	1.468 (0.424, 5.088)	1.376 (0.392, 4.833)	1.445 (0.395, 5.286)	2.244 (0.766, 6.578)	2.031 (0.687, 6.000)	2.132 (0.700, 6.492)
Digital literacy	9.616*** (4.211, 21.949)		7.122*** (3.061, 16.567)	5.032*** (2.595, 9.757)		3.941*** (2.017, 7.700)
Online social capital		3.114*** (2.006, 4.835)	2.635*** (1.673, 4.152)		2.418*** (1.689, 3.461)	2.157*** (1.489, 3.124)

Similarly, in the analysis of applied universities, Model 4 shows that the initial effect of digital literacy is OR = 5.032 (95% CI [2.595, 9.757], p < 0.001); after the inclusion of online social capital (Model 6), the effect of digital literacy dropped to OR = 3.941 (95% CI [2.017, 7.700], p < 0.001), and the mediating effect accounted for 21.6%. It is worth noting that the intensity of the mediating effect of the research university path (25.9%) is higher than that of the applied university (21.6%). The results support the hypothesis that online social capital is a key mediating mechanism for the transformation of digital literacy into access to higher education.

The group mediation effect analysis reveals the heterogeneous characteristics of digital literacy in affecting the acquisition of higher education opportunities through online social capital (taking vocational colleges as the reference group). As shown in Table 5, in terms of research university, digital literacy has a significant initial effect on low SES (OR = 8.992), middle SES (OR = 6.800), and high SES (OR = 10.516) groups. After the introduction of online social capital, as shown in Table 9, the direct effects of each SES group dropped to OR = 5.606 (mediation effect accounted for 37.6%), OR = 5.525 (23.1%), and OR = 7.250 (31.1%), respectively, confirming group differences. This group mediation effect shows that low SES students can more effectively transform digital literacy into online social capital advantages. As well, their conversion efficiency is 1.21 and 1.62 times greater than that of the high SES group and middle SES group, respectively.

Table 9.

Group mediation effect of online social capital.

Variables	Access to RU			Access to AU
Variables	Low SES	Medium SES	High SES	Low SES	Medium SES	High SES
Gender_ male	3.015 (0.701, 12.958)	1.998 (0.718, 5.562)	3.648* (1.150, 11.568)	0.826 (0.362, 1.886)	1.952 (0.778, 4.897)	2.145 (0.764, 6.021)
HR_rural	0.596 (0.121, 2.933)	0.363 (0.121, 1.086)	0.277 (0.068, 1.128)	0.524 (0.215, 1.278)	0.502 (0.192, 1.311)	1.092 (0.347, 3.431)
School category: RGHS as reference
PMHS	0.301 (0.026, 3.503)	3.734 (0.283, 49.281)	2.673 (0.313, 22.814)	3.393 (0.478, 24.071)	4.323 (0.568, 32.902)	4.029 (0.615, 26.416)
UGHS	0.093 (0.007, 1.245)	2.527 (0.229, 28.879)	1.279 (0.161, 10.133)	2.401 (0.368, 15.659)	2.216 (0.338, 14.518)	5.189 (0.913, 29.600)
Digital literacy	5.606* (1.028, 30.569)	5.525* (1.404, 21.736)	7.250* (1.269, 41.411)	3.925* (1.363, 11.306)	3.651* (1.080, 12.342)	5.793* (1.268, 26.467)
Online social capital	2.895* (1.128, 7.434)	2.343* (1.084, 5.061)	3.100** (1.325, 7.254)	1.840* (1.036, 3.266)	2.018* (1.012, 4.024)	2.951** (1.391,6.262)

In terms of applied university access, as shown in Table 5, the initial effects of digital literacy were high SES group (OR = 8.877), middle SES group (OR = 4.401) and low SES group (OR = 4.488). After adding online social capital, as shown in Table 9, the effect values dropped to OR = 5.793 (mediation effect accounted for 34.7%), OR = 3.651 (17.0%) and OR = 3.925 (12.5%), respectively. The mediating effect of the high SES group was higher than that of the middle SES group by 17.7 percentage points and that of the low SES group by 22.2 percentage points. These findings confirm that SES shapes the indirect impact path of digital literacy through online social capital, especially in access to quality higher education, showing an advantage growth effect.

With reference to RQ3, these findings reveal the role of online social capital in the process of converting digital literacy into higher education access. Online social capital plays a significant if partial role in the relationship between digital literacy and higher education access. This finding is consistent with Spottswood and Wohn,⁸ indicating that online social networks have become a channel for delivering resources in the digital age. The intrinsic role of online social capital presents a complex social class picture. The high SES group showed the strongest intrinsic effect in the research university path, confirming Bourdieu’s²³ classic discussion on capital transformation, the dominant mainstream is better at converting mass capital into educational opportunities.

Educational equity in the digital era: A systems perspective on managing access gaps in higher education

The rapid development of digital technology is reshaping the distribution mechanism of higher education access, but this process will not naturally lead to a more equitable education landscape.¹⁵ On the contrary, it may exacerbate existing social inequality. This study found that the interaction between digital literacy and online social capital has formed a new education stratification mechanism: high SES groups can more efficiently transform digital capabilities into high-quality learning opportunities by virtue of their resource advantages, while disadvantaged groups face the digital conversion barrier. This phenomenon reflects the increased advantage-based process, whereby initial resource differences in the digital learning ecosystem are amplified over time through interaction.⁴² However, although digital technology cannot in itself be a booster of educational equity, it may become a new tool for class solidification.²² Therefore, comprehensively and systematically reconstructing the equity governance system of higher education in the digital age has become a core issue that needs to be urgently addressed in current education policies, as outlined in RQ 4.

From the perspective of system management, the achievement of equity in higher education in the digital era requires the construction of a dynamic and balanced governance system, which regards digital technology, education system and social structure as interacting subsystems and continuously optimizes system functions through continuous feedback regulation. Thus, dynamic governance should recognize the distinctions among the students and provide a balanced system that promotes the participation of all types of students. Policymakers should therefore promote inclusive policies that mainstream equity and rural inclusion.

Similarly, a digital monitoring system for educational equity should be established to track the differences and changes in key indicators such as the development of digital literacy and online social capital, and access to educational opportunities among different social groups in real time, while at the same time providing data support for changes in policy. By establishing a collaborative governance mechanism involving multiple parties, we can build a sustainable development ecosystem that adapts to technological changes and ensures educational equity.

This can be done, for example, by setting up a platform-based digital literacy tutoring program that provides free digital courses for students from the low SES. Likewise, access to free artificial intelligence recommendations can provide targeted and personalized learning paths to enhance the digital competence of students from low SES. A digital mentor volunteer program could also be set up where skilled university students are encouraged to provide tutoring and college application counseling/guidance to low SES secondary school students in order to increase their digital competency and awareness.

Limitations and future studies

Although the empirical data of this study can reflect the digital literacy and access to higher education of high school graduates in China in 2024, there are still certain limitations. First, as a cross-sectional research design, although it can reveal the correlation between variables, it is limited by the time of data collection. Due to digital literacy development in China having only a recent history, it is impossible to track the long-term process of students’ digital literacy and online social capital from basic education to higher education. Second, the research sample is concentrated in the group of academic high school graduates, and the coverage of vocational high school students is insufficient, which may limit the generalizability of the research conclusions in the field of vocational education.

Future research suggests expanding the sample coverage, including comparative samples from the vocational high school students in order to test the education type applicability of the research findings. Second, a longitudinal tracking method is used to examine the evolution of students’ digital capabilities from basic education to higher education through panel data, with a particular focus on changes in the online social capital transformation mechanism at the key education transition stage (admission stage). In addition, qualitative analysis or mixed methods research such as conducting in-depth interviews should be conducted so that the differences in the acquisition and transformation of digital resources among students from various regions and family backgrounds can be explored in more detail.

Conclusions and implications

This study reveals the differential impact mechanism of digital literacy and online social capital on the acquisition of higher education opportunities through empirical analysis. The study found that digital literacy has a positive effect on all types of higher education opportunities, but the intensity of its effect varies according to institution type and social class. In the application of research universities, the high SES group showed the strongest advantage effect. This differentiation phenomenon confirms the extension of Bourdieu’s capital conversion theory in the digital field, indicating that the advantaged class can more effectively transform digital capabilities into educational opportunities.

The study also reveals the dual effects of digital technology in education. It not only provides a new upward channel for disadvantaged groups but also may exacerbate educational inequality due to differences in social capital conversion capabilities. These findings convincingly show that the issue of equity in the digital age has shifted from traditional inequality in resource access to more complex inequality in digital capability conversion, which poses new challenges to the formulation of educational equity policies.

Footnotes

Acknowledgments

The authors sincerely thank all the sampled senior secondary schools that voluntarily provided secondary data.

ORCID iDs

Shiwei Wang

Gazi Mahabubul Alam

Karima Bashir

Miao Lei

Ethical approval

The studies involving humans were approved by the Ethics Committee of University Putra Malaysia UPM (JKEUPM) (REFERENCE NO: JKEUPM-2024-439). The studies were conducted in accordance with the local legislation and institutional requirements.

Author contributions

Conception: GMA, WSW, and VS; methodology: GMA, WSW, and ML; data collection: WSW; interpretation or analysis of data: GMA, WSW, KB, and ML; preparation of the manuscript: GMA, WSW, and ML; revision for content: VS, KB, and ML; and supervision: GMA.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

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

Third-party material

No third-party materials are used in this paper.

Use of artificial intelligence (AI) tools

AI tools are not used to generate the contents. However, the accuracy of language in some instances was checked using software.

Data Availability Statement

A copy of the regression analysis is included in the supplementary files. Further access to the data is available upon reasonable request from the first author (WSW). Due to the size of our data set, full disclosure is thought to risk the identity of the senior secondary schools, risking their privacy and compromising the conditions under which they agreed to participate.*

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