Abstract
Background
China has entered a stage of moderate aging, characterized by a “90-7-3” eldercare pattern: 90% of the older adults opt for home-based care, 7% utilize community-based care, and 3% reside in institutional care facilities. With the rapid development of the digital economy, innovative solutions such as smart eldercare devices and telemedicine have emerged, offering new possibilities to enhance the efficiency and quality of eldercare services. However, while benefiting from digital technologies, the older adults generally face challenges posed by the “digital divide,” making digital literacy a critical factor constraining the digital transformation of eldercare services.
Methods
This study utilizes data from the 2020 China Longitudinal Aging Social Survey (CLASS 2020) to examine the impact of digital literacy on older adults’ utilization of community-based home care services (CHCS). Factor analysis was employed to measure digital literacy levels, while probit regression and Heckman’s two-stage model were applied for empirical analysis.
Conclusion
Our empirical analysis yields three key findings: (1) A significant negative relationship is found between digital literacy and the utilization of CHCS. This indicates that, on average, higher digital literacy is associated with a lower propensity to use CHCS. (2) Dimension-specific analysis reveals divergent impacts: Digital application literacy was positively associated with service utilization. However, device operation literacy, information acquisition literacy, and digital social literacy all exhibited significant negative correlations with service use. (3) Mechanism analysis indicates that digital literacy reduces older adults’ reliance on CHCS through multiple pathways, including increased alternative consumption expenditures, strengthened social and family support, and improved self-efficacy.
Discussion
The findings suggest that improved digital literacy may reduce older adults’ utilization of CHCS, providing important implications for optimizing the elderly care system in the digital era. While promoting digital literacy among older populations, policymakers should establish integrated online-offline service delivery models to achieve precise matching between seniors’ needs and care provision.
Keywords: Health service, Digital literacy, Community-based home care services
Background
China has entered a critical phase of population aging, with profound implications for its social and economic development. According to the latest demographic statistics released by China’s National Bureau of Statistics (2024), the population aged 65 and above has reached 220 million, accounting for 15.6% of the nation’s total population [1]. This demographic shift indicates that China has officially entered the phase of a moderately aging society [2]. Addressing the multifaceted challenges posed by population aging has become a pressing societal and policy imperative. The Guidelines on Deepening the Reform and Development of Elderly Care Services issued by the Central Committee of the Communist Party of China and the State Council proposed to optimize the elderly care service delivery system characterized by “home-based care as the foundation, community care as the support, institutional care as the supplement, and integrated medical and elderly care services.” [3] In China, most older adults receive care at home or in communities, forming a distinctive “90-7-3” pattern where 90% of Seniors age in place at home, 7% utilize community-based care services, and 3% reside in professional care institutions [4].
At the same time, the rapid development of digital technology has catalyzed significant innovations in elderly care, such as intelligent care system, telemedicine solutions, etc. These technological developments not only facilitate the transformation of traditional care models but also enhance the capacity to meet the diverse needs of aging populations [5]. Nevertheless, older adults frequently encounter substantial barriers in accessing these digital solutions, a phenomenon commonly referred to as the “digital divide” [6]. This persistent gap underscores how digital literacy among elderly populations has emerged as a critical determinant in the successful digital transformation of care services. In response to these challenges, policymakers have implemented targeted interventions to reduce technological barriers for older adults. A notable example is the Implementation Plan for Addressing Older Adults’ Challenges in Adopting Smart Technologies, issued by the State Council General Office. This policy framework explicitly aims to: (1) eliminate technological access barriers, (2) accelerate the development of age-friendly smart communities, and (3) ensure equitable access to digital services for elderly populations [7].
A body of relevant studies has found that Internet use can improve the accessibility and convenience of health services for older adults [8]. However, there is a paucity of literature exploring the impact of digital literacy enhancement on the utilization of home and community-based care services (CHCS) for older adults. Based on data from the China Longitudinal Aging Social Survey 2020 (CLASS2020), this study empirically examines the impact of digital literacy on older adults’ utilization of CHCS and investigates the underlying mechanisms. It is found that as digital literacy increases, the utilization of CHCS by the older adults decreases significantly. Further analysis reveals that digital application literacy shows a significant positive correlation with community-based home care utilization, whereas device operation literacy, information acquisition literacy, and digital social literacy demonstrate negative correlations. The observed reduction in service dependency among digitally literate older adults operates through three pathways: (1) increased substitution with alternative consumption options, (2) enhanced social and familial support, and (3) improved self-efficacy.
The contributions of this study are twofold: First, different from conventional perspectives, we reveal the negative impact of digital literacy enhancement on older adults’ utilization of CHCS and elucidate its underlying mechanisms. Second, from a demand-supply matching standpoint, we provide concrete, actionable policy recommendations for optimizing CHCS.
Literature review
Definition of digital literacy and its impact among older adults
The concept of digital literacy traces back to Paul Gilster (1997), who originally defined it as “the ability to understand and use information presented in diverse digital formats,” with emphasis on technical competencies like information retrieval and evaluation [8]. As digital technologies evolved, this conceptualization expanded significantly. For example, Eshet-Alkalai (2004) subsequently proposed a five-dimensional framework encompassing visual, reproduction, branching, information, and socio-emotional literacies [9]. Recent scholarship has specifically adapted these concepts to older populations. Borka and Andrej (2020) conceptualized elderly digital literacy as multidimensional competence integrating technical, cognitive and emotional capacities to navigate digital environments [10]. Chinese scholars have further refined this construct. Hu et al. (2023) emphasized knowledge/skill and awareness dimensions, while Chen and Chen (2024) framed it as an integrated set of skills, competencies and attitudes for solving real-world problems through digital technology in aging contexts [11, 12].
Existing studies demonstrate that digital literacy positively correlates with older adults’ quality of life and health-promoting behaviors, contributing significantly to healthy aging [13]. First, digital literacy enables older adults to conveniently access health information, social welfare policies, and news updates, supporting better personal decision-making [14]. At the same time, social media and communication tools enhance older people’s contact with family and friends, increasing their social participation, which plays an important role in alleviating social isolation and preventing depressive moods [15, 16]. Moreover, digital literacy improves health management and independent living confidence. Older adults with e-health literacy can more effectively use online health resources, such as wearable devices for health monitoring, mobile apps for chronic disease tracking, and telemedicine platforms for professional consultations [17, 18].
Digital literacy and the utilization of CHCS
First, digital literacy improves older adults’ access to service information. Those with higher digital proficiency can more actively and efficiently search for and understand service details, eligibility criteria, and application procedures, effectively overcoming information barriers [19, 20]. According to Andersen’s healthcare utilization model, this improved information accessibility directly translates to higher service adoption rates. Second, digital literacy streamlines service access processes [21]. Many elderly care services, including meal delivery, home nursing, and daycare center activities, now offer online booking, payment, and feedback functions. Digital skills enable older adults to complete these service interactions independently and conveniently, reducing the time and communication costs associated with traditional methods, thereby improving service experience and usage frequency [22]. Furthermore, digital advancement has spawned new service models like online senior courses, remote emotional support, and virtual reality social activities. These innovative “smart elderly care” services both require and foster corresponding digital competencies among users, creating a mutually reinforcing cycle of supply and demand [23].
In summary, Existing studies have thoroughly examined both the concept of digital literacy among older adults and its positive welfare effects, with some scholars demonstrating its beneficial impact on the use of CHCS. However, current literature pays insufficient attention to digital literacy’s potential substitution effects and its inhibitory influence on care service utilization. Addressing this gap, our study specifically investigates the negative impact mechanisms of digital literacy on CHCS use, thereby complementing existing research by revealing these substitution effects.
Theoretical analysis
Andersen’s Healthcare Utilization Model suggests that individuals’ use of health services is influenced by predisposing, enabling, and need factors [21]. Within this framework, digital literacy serves as a key predisposing factor that may directly increase CHCS utilization by enhancing information access and service availability. The Health Belief Model further indicates that digital literacy can influence service use by modifying health perceptions [24]. For instance, older adults with higher digital literacy are more likely to recognize the convenience and benefits of CHCS (e.g., efficient chronic disease management), making them more proactive in using these services [25]. Together, these theories explain how digital literacy could potentially promote CHCS utilization.
However, digital literacy may also reduce older adults’ reliance on formal care services. According to social support theory, digital tools like WeChat and telemedicine can strengthen informal support from family and friends, creating substitution effects for CHCS [26]. Resource substitution theory further suggests that when individuals have more health management options, formal service utilization may decline [27]. This effect appears particularly strong in China, where cultural preferences for family support over public services [28]. Additionally, highly digitally literate older adults may reduce their dependence on CHCS due to increased self-efficacy in health management and greater confidence in independent living [29, 30].
In summary, digital literacy may have either positive or negative effects on CHCS utilization. This study specifically examines its negative effect and underlying mechanisms. We propose the following hypothesis:
Hypothesis 1: Increased digital literacy is associated with decreased utilization of CHCS.
Consumption of market-based services
digital literacy empowers older adults to proactively access functionally similar market-based services through internet platforms, which partially reduces their dependence on certain components of the traditional CHCS package. This substitution is manifested in concrete behaviors: using WeChat’s social features replaces the need for emotional support hotlines, using e-commerce platforms for shopping replaces community-organized shopping assistance, ordering meals via food delivery apps replaces community meal delivery or senior dining tables, and booking services via Online-to-Offline domestic platforms replaces in-home household chores provided by the community. Data indicates the proportion of the middle - aged and older population over 40 shopping online has increased yearly, even surpassing that of young people [31]. A study in Heilongjiang Province showed 63% of older people accessed health information via the Internet, while 22.5% searched for food, medicine, and disease - related knowledge [32]. As the digital literacy of the older adults improves, network services can, to some extent, meet the needs of the older adults’ lives, reducing their dependence on traditional CHCS. Based on these research findings, this paper proposes the following hypotheses:
Hypothesis 2: Higher digital literacy levels correlate with increased use of market-based services options and corresponding decreased utilization of traditional CHCS.
Social and family support
Older adults with higher digital literacy enjoy more convenient communication with family members. Through the Internet, children, friends, and relatives can remotely offer greater psychological comfort and financial support, such as showing concern via video calls and assisting with online tasks [33, 34]. The proper use of short-video apps strengthens the connection and interaction between middle - aged and elderly individuals and their families and friends [35]. Thus, enhancing digital literacy can fortify older people’s bonds with friends and family, boost social support networks, and make them more likely to seek help through family and social relationships instead of relying solely on CHCS. Based on this, the paper proposes the following hypotheses:
Hypothesis 3: As older adults become more digitally literate, social and family support increases, reducing the utilization of CHCS.
Self-efficacy and health effects
Self-efficacy refers to an individual’s subjective judgments and beliefs about his or her ability to successfully complete a task or reach a goal and emphasizes that individuals control their own actions through their beliefs about their abilities and self-regulation [36]. Studies have found that older adults who receive health literacy training via the Internet exhibit higher levels of skill and comfort in using digital tools. This increased proficiency boosts their confidence in operating technology independently [37, 38] Additionally, Internet use enables older adults to access health information and conduct online medical consultations, enhancing their autonomy and independence, which in turn increases their self - efficacy [30]. Research from China has also shown that enhancing the digital literacy of older adults significantly improves their quality of life and social adaptability [39]. Thus, increased self-efficacy enables older adults to be more willing to solve problems independently rather than relying on CHCS.
Several studies have shown that Internet use positively impacts older adults’ physical and mental health. By increasing social participation and interaction, Internet use improves older adults’ mental health and social adaptation levels, indirectly enhancing their self - rated health [40–42]. Du Peng et al. (2023) found that older adults using the Internet are psychologically healthier, with more frequent use, greater proficiency, and more features used correlating with better health [43]. Thus, digital literacy can reduce the utilization of CHCS by improving the physical and mental health of older adults. So, this paper proposes the following hypotheses:
Hypothesis 4: As older adults become more digitally literate, their health and self-efficacy increase, reducing the utilization of CHCS.
Methods
Data
This study utilizes data from the 2020 China Longitudinal Aging Social Survey (CLASS2020), a nationally representative longitudinal study tracking social and economic challenges among older adults. Conducted by the China Social Survey Network, the survey employs a multistage sampling approach: first selecting households via Secondary Sampling Unit mapping, then randomly choosing one resident aged 60 + per household. The 2020 wave includes 11,398 initial respondents.
To assess digital literacy, we first created an “internet use” variable based on survey responses to the question: “How often do you go online? 1. Daily; 2. Weekly; 3. Monthly; 4. A few times yearly; 5. Never.” Respondents answering “Never” (n = 3118, 27.36% of sample) were coded 0, while all others were coded 1. For internet-using respondents, we developed a composite digital literacy index encompassing five dimensions: device operation, information acquisition, social Literacy, application skills, and security awareness. Non-internet users were assigned a baseline value of 0 due to lack of observable digital behaviors.
Variables description
Explanatory variable
The dependent variable is community-based home care service utilization, measured using CLASS survey data. The survey categorizes services into nine types:1. Home visits;2. Elderly care hotline;3. Medical appointment accompaniment; 4. Daily shopping assistance;5. Legal aid;6. Housekeeping services;7. Meal delivery/elderly dining services;8. Day care centers;9. Psychological counseling. Respondents were coded as “1” if they used any of these services and “0” if they used none.
Core explanatory variable
The core explanatory variable is digital literacy, building upon existing multidimensional measurement frameworks developed for older adults. Prior research includes Huang et al.‘s (2021) [44] five-dimension media and information literacy assessment (awareness/knowledge, access/needs, evaluation/understanding, application/management, and ethics/security) and Wu et al.‘s (2023) three-dimension self-assessment scale (digital practice skills, learning awareness, and payment awareness) [45]. We use the following indicators, combined with the questions related to digital literacy in the CLASS2020 questionnaire, to measure the digital literacy of the older adults (Table 1). Using the factor analysis method, we subjected the digital literacy measurement data to Kaiser-Meyer-Olkin (KMO) and Bartlett’s test of sphericity. The results showed that the KMO was 0.710 and that the chi-square value of the Bartlett’s test of sphericity was 4669.21 (P < 0.001, degrees of freedom = 66). These results reject the original hypothesis that the variables are not related to each other, indicating that the data are suitable for factor analysis.
Table 1.
Digital literacy measurement indicators
| Dimensions | Questionnaire questions | Assignment | Factor loadings |
|---|---|---|---|
| Digital device operation | Don’t know how to use the Internet device | 0–1 | 0.774 |
| Don’t know how to use network software | 0–1 | 0.809 | |
| Information acquisition | Go online for news | 0–1 | 0.812 |
| Go online for information other than news | 0.761 | ||
| Go online for listening to music and playing games | 0–1 | 0.558 | |
| Digital Social | Go online for text chatting | 0–1 | 0.903 |
| Go online for voice and video chatting | 0–1 | 0.566 | |
| Digital application | Go online for shopping | 0–1 | 0.603 |
| Go online for transportation trips, manage health | 0–1 | 0.746 | |
| Go online for learning, training, investing | 0–1 | 0.782 | |
| Digital security | Do you think photos you see online can be edited or tampered with? | 1–3 | 0.819 |
| Do you think videos you see online can be edited or tampered with? | 1–3 | 0.785 |
Control variables
Referring to studies such as Hu et al. (2023) and Luo et al. (2023) [11, 46], this paper controls for variables related to personal characteristics of the older adults (age, gender, ethnicity, education level, marital status, and self-care ability), household characteristics (number of co-residing family members, number of housing units, and household income), and community characteristics (community location and type). Variable names and descriptions are presented in Table 2.
Table 2.
Variable name and variable description
| Type | Variable Name | Variable Description |
|---|---|---|
| Explanatory Variable | Utilization of CHCS | 1 = Yes;0 = No |
|
Personal Characteristic Variables |
Age | birth age |
| Gender | 1 = male;0 = female | |
| Education level | 1 = illiterate; 2 = private school; 3 = elementary school; 4 = middle school; 5 = high school; 6 = college; 7 = bachelor’s degree and above | |
| Ethnicity | 1 = han ethnic group;0 = others | |
| Marital status | 1 = married;0 = others | |
| Self-care ability | Whether need help in daily Life, 1= Yes;0 = No | |
|
Household Characteristic Variables |
Co-residing family members | Number of persons living together in the household on a regular basis |
| Number of housing units | Number of houses owned by the family | |
| Household income | Average monthly household income in the past 12 months | |
|
Community Characteristic Variables |
Community location | 1 = urban;0 = rural |
| Community type | 1 = old urban communities; 2 = unit communities; 3 = protected housing neighborhoods; 4 = commercial housing neighborhoods; 5 = transitional communities; 6 = rural |
Of the respondents, 16.87% Live in old urban communities, 4.59% in unit communities, 2.39% in protected housing neighborhoods, 20.61% in commercial housing neighborhoods, 11.65% in transition communities, and 43.89% in rural communities
Mechanism variables
Social support and family support
We used the frequency of contact with children and friends as proxies for family and social support, respectively. According to the questionnaire item, “How many friends do you see or contact at least once a month? Answer options: 0 = None; 1 = 1; 2 = 2; 3 = 3–4; 4 = 5–8; 5 = 9 and above.” According to the respondents’ answers, the higher the value, the stronger the social support is. According to the questionnaire question: “In the past 12 months, how often have you been in contact with this child (including various means of communication, such as telephone or WeChat)?1. Almost every day;2. At least once a week;3. At least once a month;4. A few times a year;5. Almost never;6. No need to be in contact.” The highest frequency of contact between the older adult and all children was generated by transforming the data into an ordered categorical variable with four levels:3 = Strong support: At least one child is contacted almost every day; 2. Moderate support = At least one child is contacted weekly, but not every day; 1 = Weak support: All children are contacted monthly or less; 0. No support/independence: All children do not need to be contacted.
Self-efficacy
Referring to Kong and Yan (2023) [47] and the questions in the CLASS2020 questionnaire based on the respondents’ evaluation of themselves: “do you feel that the following descriptions are consistent with your current reality? 1. I would be happy to take part in some work in the village/neighborhood committees if I have the opportunity; 2. I often want to do something for the society again; 3. I Like to study at present; 4. I feel that I am still a useful person to the society”, we assign values to the responses: not at all = 1, a little = 2, average = 3, more in line = 4, fully in line with the table = 5. Self-efficacy was calculated by summing up the scores of the respondents’ answers to the four questions, with higher the score indicating a higher sense of self-efficacy.
Self-related health
According to the questionnaire question: How do you feel about your current health? (1) very healthy (2) relatively healthy (3) average (4) relatively unhealthy (5) very unhealthy 9. unable to answer, a self-rated health variable was set. We reverse coded based on responses, with higher values indicating better self-rated health.
Mental health
The variable was set based on a set of questionnaire questions, which ask respondents to rate their mood in the last week. This questions include the following: 1.“Did you feel in good spirits?“;2.“Did you feel lonely?“;3.“Did you feel sad?“;4.“Did you feel your life was going well?“;5.“Did you have poor appetite?“;6.“Did you have trouble sleeping?“;7.“Did you feel useless?“;8.“Did you feel you had nothing to do?“;9.“Did you find life enjoyable (with many interesting things)?” The nine dimensions were assigned the following values: no = 1, sometimes = 2, often = 3. The responses for dimensions 1, 4, and 9 were reverse-adjusted, and the nine dimensions were summed to calculate mental health, with higher scores indicating lower levels of mental health.
Descriptive statistics
Table 3 shows descriptive statistics for some variables. Only 10.9% of the older adults have used CHCS, indicating a low utilization rate. The older adults’ digital Literacy has a mean of 1.055 and a maximum of 2.226, indicating a low overall digital literacy level among the older adults.
Table 3.
Descriptive statistics
| Variables | Samples | Mean | sd. | min | max |
|---|---|---|---|---|---|
| CHCS utilization | 11,396 | 0.109 | 0.311 | 0 | 1 |
| Digital literacy | 3,118 | 1.055 | 0.450 | −1.065 | 1.153 |
| Age | 11,398 | 71.588 | 6.603 | 60 | 98 |
| Gender | 11,398 | 0.504 | 0.500 | 0 | 1 |
| Education level | 11,398 | 2.981 | 1.345 | 1 | 7 |
| Ethnicity | 11,398 | 0.940 | 0.237 | 0 | 1 |
| Marital status | 11,398 | 0.754 | 0.431 | 0 | 1 |
| Self-care ability | 11,398 | 0.066 | 0.248 | 0 | 1 |
| Number of housing units | 11,396 | 1.063 | 0.393 | 0 | 5 |
| Co-residing members | 11,398 | 2.701 | 1.320 | 1 | 10 |
| Household income | 10,196 | 2606.314 | 3641.194 | 20 | 100,000 |
| Community location | 11,398 | 0.550 | 0.497 | 0 | 1 |
| Family support | 11,398 | 1.782 | 1.153 | 1 | 3 |
| Social support | 11,396 | 2.460 | 1.128 | 0 | 5 |
| Self-efficacy | 11,398 | 11.370 | 4.276 | 0 | 20 |
| Self-related health | 11,381 | 3.368 | 0.901 | 1 | 5 |
| Mental Health | 9,878 | 15.782 | 3.269 | 9 | 27 |
Empirical models
Probit model
This paper utilizes Probit modeling to estimate the effect of digital literacy on the utilization of CHCS for the older adults.
 |
1 |
In Eq. (1), 
is the binary variable indicating whether older adult i in county c utilizes community - based home care services. 
represents the level of digital literacy, and 
denotes the control variables. 
represents county fixed effects, capturing time-invariant characteristics at the county level. 
stand for error term, respectively. The parameter 
captures the total effect of digital literacy on CHCS utilization. A significantly positive 
suggests that digital literacy considerably promotes such utilization among the older adults.
Heckman two-stage model
Since digital literacy is only calculable for internet - using older adults, selecting such a sample for regression may cause sample selection bias. This paper employs a Heckman model to correct for this bias. The estimation of digital literacy’s effect unfolds in two steps: first, constructing a selection equation to identify factors influencing elderly internet use, estimating this probability via Probit model, and computing the inverse Mills ratio; then, in the second stage, adding the inverse Mills ratio as a control variable to Eq. (3) and estimating it using the Probit model.
Therefore, this paper sets up the two - stage Heckman estimation model as follows:
 |
2 |
 |
3 |
Where, Eq. (2) is Heckman’s first - stage choice model for estimating the probability of Internet use among the older adults. 
is a binary variable of whether they use the Internet, 1 = yes, 0 = no. 
is a series of control variables affecting the older adults’ Internet use, and 
is the number of landline telephones in the city in 1984, serving as an exclusionary variable.
Results
Baseline regression results
Table 4 presents the estimation results using the Probit model. Column (1) controls for older adults’ individual characteristics, including age, gender, ethnicity, education level, marital status, self-care ability, and regional effects. The results show a significantly negative coefficient for digital literacy. Column (2) additionally incorporates household characteristics—co-resident numbers, housing quantity, and family income—with the digital Literacy coefficient remaining statistically significant at the 1% level. In Column (3), community characteristics (location and type) are further controlled for, and the digital Literacy coefficient persists negatively at the 1% significance level. Column (4) introduces county fixed effects to account for time-invariant county-level factors, with the conclusion remaining robust. These findings demonstrate that, after controlling for individual, household, and community characteristics as well as regional effects, higher digital literacy levels are significantly associated with reduced utilization of CHCS among older adults.
Table 4.
Baseline regression results
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| Digital literacy | −0.014 | −0.203*** | −0.243*** | −0.307*** |
| (0.074) | (0.077) | (0.079) | (0.111) | |
| Individual characteristics | Yes | Yes | Yes | Yes |
| Family characteristics | No | Yes | Yes | Yes |
| Community characteristics | No | No | Yes | Yes |
| county-level fixed effects | No | No | No | YES |
| Pseudo R2 | 0.011 | 0.055 | 0.084 | 0.237 |
| Observations | 3,118 | 2,855 | 2,852 | 1,866 |
(1) Robust standard errors in parentheses; (2) ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. (3) Columns 1–4 report marginal effects
Heckman two-stage model and IV estimation
This study employs the Heckman two-stage model to address sample selection bias. In Table 5, Column (1) represents the selection equation examining factors influencing older adults’ internet usage. Following Huang et al.‘s (2019) approach, we selected the 1984 urban landline telephone number as the exclusion restriction variable in the selection equation - a factor demonstrating significant exogenous influence on internet adoption rates [48]. The selection equation results indicate the probability of internet use among the older adults. The significantly positive coefficient of the exclusion restriction variable confirms its validity, while the significantly negative inverse Mills ratio suggests that increased digital literacy may reduce CHCS utilization. Column (2) reports the marginal effects of outcome equation, showing that after controlling for selection bias, digital literacy maintains a statistically significant negative coefficient (β=−0.025, p < 0.01). These results robustly demonstrate that higher digital literacy levels correspond with significantly lower probabilities of utilizing CHCS.
Table 5.
Baseline regression results
| Explanatory variable | (1) | (2) | (3) | (4) |
|---|---|---|---|---|
| Heckman Model | IV Model | |||
| First stage | Second stage | First stage | Second stage | |
| Internet use | CHCS utilization | Digital literacy | CHCS utilization | |
| Urban landline telephone number |
0.081*** (0.020) |
0.208*** (0.050) |
||
| Digital literacy |
−0.025*** (0.008) |
−0.388* (0.202) |
||
| Lambda |
−0.139** (0.065) |
|||
| Individual characteristics | Yes | Yes | Yes | Yes |
| Family characteristics | Yes | Yes | Yes | Yes |
| Community characteristics | Yes | Yes | Yes | Yes |
| Observations | 9,276 | 2586 | 2819 | 2819 |
(1) standard errors are in parentheses; (2) ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. (3) Columns 2 and 4 report marginal effects. (4) In the instrumental variable estimation, we no longer control for county fixed effects because the instrumental variable is at the city level and controlling for county fixed effects at the same time would result in the validity of the instrumental variable being absorbed. Here we control for GDP, the city variable that may affect both Internet penetration and utilization of CHCS
Columns (3) and (4) in Table 5 present the IV estimation results. Column (3) demonstrates the relationship between the instrumental variable (city-level internet penetration rate) and digital literacy, showing a statistically significant positive correlation. Validity tests confirm the instrument’s strength: the Cragg-Donald Wald F statistic of 16.71 exceeds the critical value of 10, indicating no weak instrument problem, while the Hansen J statistic of 0 suggests no overidentification issues. Column (4) reports the second-stage IV estimates, revealing that digital literacy maintains a statistically significant positive coefficient.
The above results further indicate that the probability of utilizing CHCS decreases significantly as the level of digital literacy increases.
Other robustness tests
In the baseline regression, we use principal component analysis to calculate the digital literacy of the older adults. To further validate the robustness of the baseline regression, this paper changed the method of calculating the core explanatory variable digital literacy, recalculating the values of the five categories of digital literacy using the arithmetic averaging method. Column (1) of Table 6 reports the regression results after recalculating the core explanatory variables and shows that the coefficient of digital literacy is still significantly negative. We also replace the explanatory variables to test the robustness of the baseline regression. The continuous variable “type of CHCS utilization” is used to replace the explanatory variable “whether to utilize CHCS”. The regression results in column (2) of Table 6 show that digital Literacy significantly reduces the type of utilization of CHCS for the older adults. In the baseline regression, this paper chooses a sample of older adults over the age of 60, and column (3) of Table 6 readjusts the age range of the sample size to 65 years old and above, and the regression results are still robust. The methods through which older adults learn internet skills may influence both their digital literacy levels and CHCS utilization. We therefore incorporated controls for these learning pathways in our analysis. As shown in Column (4) of Table 6, the estimated results remain robust after accounting for this variable.
Table 6.
Other robustness tests (Heckman Model)
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| Digital literacy | −0.053*** | −0.086*** | −0.062*** | − 0.050*** |
| (0.014) | (0.311) | (0.017) | (0.014) | |
| Individual characteristics | Yes | Yes | Yes | Yes |
| Family characteristics | Yes | Yes | Yes | Yes |
| Community characteristics | Yes | Yes | Yes | Yes |
| Observations | 9,276 | 9,276 | 7,570 | 9,276 |
(1) Standard errors are in parentheses; (2) ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively
Dimension-specific analysis
Our analysis examines how five specific dimensions of digital literacy influence older adults’ use of CHCS. The results in Table 7 demonstrate varying effects across different digital competency areas. Digital application literacy showed a significant positive correlation (β = 0.017, p < 0.01), indicating that seniors proficient in using digital applications tend to utilize more care services. In contrast, we find statistically significant negative associations for device operation literacy, information acquisition literacy, and digital social literacy, suggesting that older adults with stronger skills in these areas may rely less on formal care services.
Table 7.
Dimension-specific analysis
| (1) | (2) | (3) | (4) | (5) | |
|---|---|---|---|---|---|
| Digital device operation | −0.034*** | ||||
| (0.006) | |||||
| Information acquisition | −0.013** | ||||
| (0.006) | |||||
| Digital Social | − 0.021*** | ||||
| (0.007) | |||||
| Digital application | 0.017*** | ||||
| (0.006) | |||||
| Digital security | 0.000 | ||||
| (0.006) | |||||
| Individual characteristics | Yes | Yes | Yes | Yes | Yes |
| Family characteristics | Yes | Yes | Yes | Yes | Yes |
| Community characteristics | Yes | Yes | Yes | Yes | Yes |
| Observations | 9,276 | 9,276 | 9,276 | 9,276 | 9,276 |
(1) Standard errors are in parentheses; (2) ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively
Mechanism testing
Digital literacy and the utilization of substitutable elderly care services
With the deep integration of the internet and digital technologies, traditionally provided elderly care services within the community that involve relatively low technical thresholds and minimal information dependence—such as daily grocery shopping, basic information inquiries, and routine communication—are increasingly being replaced by more efficient and convenient online alternatives. Against this backdrop, older adults with higher digital literacy are generally more capable of using smartphones and the internet to proactively seek out, evaluate, and adopt substitute services such as online medical consultations, fresh grocery e-commerce delivery, smart safety monitoring, and virtual interest-based communities. Due to the current lack of empirical data on the usage of these alternative services, this study indirectly examines the above proposition by analyzing the relationship between digital literacy and the uptake of traditionally more easily substitutable eldercare services. Our analysis using CLASS 2020 data examines this phenomenon across different service categories (Table 8, Columns 1–4). The results demonstrate statistically significant negative associations between digital literacy and the utilization of four key services: Elderly care hotlines (β=−0.023, p < 0.01), daily shopping (β=−0.007, p < 0.1), housekeeping services (β=−0.027, p < 0.01), and meal delivery (β=−0.025, p < 0.01). This pattern emerges because digitally proficient older adults increasingly turn to modern alternatives: replacing senior hotlines with telehealth platforms, obtaining groceries through e-commerce apps rather than shopping assistance, hiring help via on-demand domestic service applications instead of traditional housekeeping services, and ordering meals through food delivery platforms rather than community meal delivery.
Table 8.
Digital literacy and the utilization of substitutable elderly care services(Heckman)
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| Explanatory variables | Elderly care hotlines | Daily shopping assistance | Housekeeping services | Meal delivery services |
| Digital literacy | −0.023*** | −0.007* | −0.027*** | −0.025*** |
| (0.008) | (0.004) | (0.008) | (0.009) | |
| Individual Characteristics | Yes | Yes | Yes | Yes |
| Family Characteristics | Yes | Yes | Yes | Yes |
| Community Characteristics | Yes | Yes | Yes | Yes |
| Observations | 9,276 | 9,276 | 9,276 | 9,276 |
(1) Standard errors are in parentheses; (2) ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively
Social and family support
Using frequency of contact with children as a proxy for family support, Table 9 (Column 1) shows that digital literacy has a statistically significant positive coefficient (p < 0.1), indicating that Seniors with higher digital Literacy maintain more frequent contact with their children. Similarly, Column 2 demonstrates that digital literacy is positively associated with increased contact frequency with friends. These findings suggest that digital literacy enhances both family and social support among older adults, consistent with prior research [49, 50].
Table 9.
Digital literacy and social/family support, health
| Explanatory variables | (1) | (2) | (3) | (4) | (5) |
|---|---|---|---|---|---|
| Family support | Social support | Self-related health | Mental health | Self-efficacy | |
| Digital literacy | 0.090* | 0.106* | 0.292*** | −1.230 *** | 1.020*** |
| (0.048) | (0.055) | (0.038) | (0.158) | (0.169) | |
| Personal characteristics | YES | YES | YES | YES | YES |
| Family characteristics | YES | YES | YES | YES | YES |
| Community characteristics | YES | YES | YES | YES | YES |
| Observations | 9276 | 9276 | 9276 | 9276 | 9276 |
(1) Standard errors are in parentheses; (2) ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively; (3) Columns (1) and (2) incorporate controls for internet learning pathways while maintaining all original covariates
Self-efficacy and health
Digital literacy may influence the utilization of CHCS by improving self-efficacy and health of older adults. In Table 9, columns (3)-(5) test the above mechanisms. Column (3) reports the effect of digital literacy on older adults’ self-rated health. The results show that the coefficient on digital Literacy is significantly positive at the 1% statistical level. This indicates that digital literacy is significantly and positively associated with self-rated health of older adults. Column (4) reports the effect of digital Literacy on mental health of older adults and the results show that the coefficient of digital Literacy is significantly negative at 1% level. This indicates that the digital literacy is significantly negatively associated with the level of psychological depression among older adults. Column (5) reports the effect of digital Literacy on self-efficacy of older adults and the results show that the coefficient of digital Literacy is 1.020, which is significant at 1% statistical level test. It shows that digital literacy is significantly and positively related to self-efficacy of older adults.
The above empirical results suggest that as digital literacy increases, it may reduce the utilization of CHCS by enhancing the self-efficacy and health of older adults.
Discussion
This study reveals a negative association between digital literacy and older adults’ utilization of community-based home care services, challenging conventional research that predominantly reports positive effects [17]. However, the cross-sectional design imposes limitations on causal inference. First, the lack of temporal data prevents definitive exclusion of reverse causality—for instance, older adults using fewer formal services may be more motivated to improve digital skills. Second, despite employing Heckman corrections and instrumental variables, residual bias from unobserved confounders may persist. Additionally, the static data cannot capture dynamic interactions between digital literacy and service use or disentangle age effects from cohort effects. These constraints necessitate cautious interpretation of the observed correlations. Future research should leverage panel data or natural experiments to strengthen causal claims.
Our mechanism analysis reveals that older adults with higher digital literacy tend to acquire more information online and increasingly turn to market-based services. This shift likely reflects structural gaps in China’s current community-based elderly care system, including mismatches between service supply and demand, as well as insufficient integration of medical and care resources, which fail to meet seniors’ actual needs [51]. Specifically, our findings demonstrate significant negative correlations between digital literacy and the use of four services: elderly care hotlines, daily shopping assistance, housekeeping services, and meal delivery programs, suggesting these are the most substitutable services. However, due to data limitations, we could not directly measure older adults’ consumption of market-based services or assess the precise extent of digital literacy’s impact on such substitutions. Future research should incorporate social service usage data to establish a comprehensive, dynamic monitoring system for elderly service utilization patterns.
Conclusion
In the digital age, smart ageing at home is a basic trend. As a key factor, the digital literacy of the older adults greatly affects the utilization pattern of CHCS. Using data from the CLASS2020 questionnaire, this paper empirically explores the effects and mechanisms of digital literacy on the utilization of CHCS for the older adults. The results indicate that as digital literacy increases, the utilization of CHCS for the older adults significantly decreases. After a series of robustness tests, including the Heckman two-step approach to address endogeneity, changing the method of calculating digital literacy, replacing explanatory variables, and adjusting the sample range, the conclusions remain basically unchanged. The mechanism analysis reveals that as digital literacy improves, the older adults’ utilization of easily replaceable CHCS decreases. Digital literacy also reduces the older adults’ dependence on CHCS by improving their social support, family support, self-efficacy, and health level significantly.
Digital literacy significantly improves the self-efficacy and health of older adults while increasing their social adaptability. This provides a way of thinking about coping with aging for Chinese society, which has entered moderate aging. Community-based home care, as the mainstream model of aging in China, is gradually promoting the development of wisdom, and the application of digital technology in the lives of the older adults will become more popular. The level of digital literacy among the older adults will not only affect the mode of utilization of elderly services but will also affect the quality of life of the older adults. However, there is still a huge “digital divide” among the older adults in China, and the level of digital literacy is generally low. There is also a mismatch between the supply of CHCS and the actual needs of the older adults. Therefore, policymakers not only need to recognize the multidimensional nature of digital literacy and its multiple impacts on older adults, but also need to pay attention to the acceptance and trust of different digital literacy groups. In addition, policymakers also need to pay attention to the substitution effect of digital technology on traditional CHCS, balance the differentiated needs of low and high digital literacy, and safeguard the well-being of different groups of older adults in the digital age.
Acknowledgements
We would thank all the participants enrolled in the China Longitudinal Aging Social Survey for their contribution to science. We would also like to thank the CLASS research team for providing the dataset.
Authors’ contributions
XXQ designed the research, collected the data analyzed the data, ZMB contribute in interpreted the results and writing the manuscript. All authors reviewed the manuscript.
Funding
This work was supported by the National Social Science Foundation of China “Research on digital literacy and social integration of migrant workers under the background of digital economy” [21CRK011].
Data availability
Data used in this study are available in publicly accessible repositories as follows: The CLASS datasets are publicly available from the National Survey Research Center (NSRC) at Renmin University of China (http://class.ruc.edu.cn) and can be accessed after submitting a data use agreement to the CLASS team.
Declarations
Ethics approval and consent to participate
The data used in this study were obtained from the China Longitudinal Aging Social Survey (CLASS), a nationally representative longitudinal social survey project designed and implemented by the Institute of Gerontology at Renmin University of China. The CLASS study strictly complied with the Ethical Guidelines of the Academic Ethics Committee of Renmin University (Trial) and China’s Ethical Review Measures for Life Sciences and Medical Research Involving Humans. All participants provided written informed consent, with explicit authorization for using anonymized data in academic research.
Consent for publication
Not applicable. No experiments were conducted, nor were patients involved in this study.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.National Bureau of Statistics of China. Statistical communique of the People’s Republic of China on the 2024 national economic and social development. 2025. https://www.stats.gov.cn/sj/zxfb/202502/t20250228_1958817.html.
- 2.Lu JH. Promoting high-quality development of elderly care services in the new era by enhancing people’s well-being. Popul Dev. 2022;28(06):12–6. [Google Scholar]
- 3.Central Committee of the Communist Party of China and State Council. Guidelines on Deepening the Reform and Development of Elderly Care Services. Beijing: State Council. 2024. https://www.gov.cn/gongbao/2025/issue_11826/202501/content_7001310.html.
- 4.Bian SS, Qing G, Wang XX. Construction of senior service system in the context of a healthy China. Glob Health J. 2018;2(4):28–34. [Google Scholar]
- 5.Hu Z, Yin SW. Chinese path to elderly care in the age of digitization and intelligence: digital opportunities and algorithmic challenges. Popul Econ. 2024;45(06):69–81.
- 6.Du P, Han WT. Internet and life of older adults: challenges and opportunities. Popul Res. 2021;45(03):3–16. [Google Scholar]
- 7.State Council General Office. Implementation plan for effectively solving difficulties in the use of intelligent technology by the older adults. Guo Ban Fa 2020. No 45. 2020.
- 8.Gilster P. Digital literacy. New York: Wiley; 1997. [Google Scholar]
- 9.Eshet-Alkalai Y. Digital literacy: a conceptual framework for survival skills in the digital era. J Educ Multimedia Hypermedia. 2004;13(1):93–106. [Google Scholar]
- 10.Borka J, Andrej J. Overcoming the digital divide with a modern approach to learning digital skills for the elderly adults. Educ Inf Technol. 2020;25(1):259–79. [Google Scholar]
- 11.Hu YP, Wang QY, He JX. Digital Literacy in China’s Elderly Population: Influencing Factors and Improvement Paths. Library Tribune. 2024;44(09):144.
- 12.Chen MM, Chen Y. Digital literacy among Chinese residents: fundamental connotations, level assessment, and structural features. E-Government. 2024;21(09):91–101. [Google Scholar]
- 13.Oh SS, et al. Measurement of digital literacy among older adults: systematic review. J Med Internet Res. 2021;23(2):e26145. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Choi NG, Dinitto DM. The digital divide among low-income homebound older adults: internet use patterns, e-health literacy, and attitudes toward computer/internet use. J Med Internet Res. 2013;15(5):e93. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Cotten SR, Anderson WA, McCullough BM. Impact of internet use on loneliness and contact with others among older adults: cross-sectional analysis. J Med Internet Res. 2012;14(2):e59. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Heo J, Chun S, Lee S, Lee KH, Kim J. Internet use and well-being in older adults. Educ Gerontol. 2015;41(5):456–68. [DOI] [PubMed] [Google Scholar]
- 17.Heart T, Kalderon E. Older adults: are they ready to adopt health-related ICT? Int J Med Inform. 2013;82(11):e209–31. [DOI] [PubMed]
- 18.Sen K, Prybutok G, Prybutok V. The use of digital technology for social wellbeing reduces social isolation in older adults: A systematic review. SSM - Popul Health. 2021;17:101020. [DOI] [PMC free article] [PubMed]
- 19.Vicente MR, López AJ. A multidimensional analysis of the disability digital divide: some evidence for internet use. Inf Soc. 2010;26(1):48–64. [Google Scholar]
- 20.Zeng QY, He ZP, Zeng YC. The Cause of Paradoxical between Willingness and Behavior of Elderly People’s Demand for Home-based Care Services. Popul Econ. 2022;23(2):87–103.
- 21.Andersen RM. Revisiting the behavioral model and access to medical care: does it matter? J Health Soc Behav. 1995;36(1):1–10. [PubMed] [Google Scholar]
- 22.Hunsaker A, Hargittai E. A review of Internet use among older adults. New Media Soc. 2018;20(10):3937–54. [Google Scholar]
- 23.Zhang CG, Ning XS. The formation logic and practical path of smart elderly care in grassroots communities: a case exploration based on TOE framework. J Public Manage. 2025;22(03):89–101. [Google Scholar]
- 24.Rosenstock IM. Historical origins of the health belief model. Health Educ Monogr. 1974;2(4):328–35. [DOI] [PubMed] [Google Scholar]
- 25.Portz JD, Miller A, Foster B, et al. Persuasive features in health information technology interventions for older adults with chronic diseases: a systematic review. Health Technol. 2016;6(2):89–99. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Bonsang E. Does informal care from children to their elderly parents substitute for formal care in Europe?? J Health Econ. 2009;28(1):143–54. [DOI] [PubMed] [Google Scholar]
- 27.Thoits PA. Mechanisms linking social ties and support to physical and mental health. J Health Soc Behav. 2011;52(2):145–61. [DOI] [PubMed] [Google Scholar]
- 28.Liu Y-W. Complementary or alternative: social pension and family pension–based on the analysis of the differences between urban and rural areas. J Public Manage. 2016;13(04):77–88. [Google Scholar]
- 29.Li SJ, Cui GH, Yin YT, Xu HL. Associations between health literacy, digital skill, and ehealth literacy among older Chinese adults: a cross-sectional study. Digit Health. 2023;9:20552076231178431. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Slegers K, Boxtel MV, Jolles J. User preferences for web design compared: are age-specific interface guidelines necessary? Gerontechnology. 2005;4(3):153–65. [Google Scholar]
- 31.Shen C, Zhou L, Lin S. Older adults' online shopping behavior in China. In: International Conference on Cross-Cultural Design. Cham: Springer International Publishing; 2014. p. 482–8.
- 32.Sun X, Yan W, Zhou H. Internet use and need for digital health technology among the elderly: a cross-sectional survey in China. BMC Public Health. 2020;20(1):1386. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Peng XZ, et al. Does Internet Use Make Older People Happier? Empirical Research from CGSS. Nanjing J Social Sci. 2019;30(10):57–68. [Google Scholar]
- 34.Ren HJ, et al. Internet use, family intergenerational support, and older adults’ Well-being in the digital Economy——Empirical study based on CHARLS data. Psychol Explor. 2024;44(4):341–6. [Google Scholar]
- 35.Jin YA, Hu WB, Feng Y. Internet use and the life of older adults aged 50 and above in digital era: findings from a national survey. Popul Res. 2024;48(1):40–55. [Google Scholar]
- 36.Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191–215. [DOI] [PubMed] [Google Scholar]
- 37.Shapira N, Barak A, Gal I. Promoting older adults’ well-being through internet training and use. Aging Ment Health. 2007;11(5):477–84. [DOI] [PubMed] [Google Scholar]
- 38.Zolbin MG, Huvila I, Nikou S. Health literacy, health literacy interventions and decision-making: a systematic literature review. J Doc. 2022;78(7):405–28. [Google Scholar]
- 39.Du P, Luo YS. Can internet use promote the social adaptation level of older adults?? An analysis based on the perspective of different usages. Popul Res. 2023;47(6):3–20. [Google Scholar]
- 40.Fan CB, Wen Y. Research on the impact of internet use on the health of Middle-aged and elderly people. Northwest Popul J. 2023;44(3):80–92. [Google Scholar]
- 41.Zhao JG, Liu ZQ. The Impact of Internet Use on the Health of the Elderly. Chin J Popul Sci. 2020;34(5):14–26. [Google Scholar]
- 42.Zhou Y, Bai Y, Wang J. The impact of internet use on health among older adults in China: a nationally representative study. BMC Public Health. 2024;24(1):1065. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Du P, et al. The impact of internet use on mental health of the elderly: analysis on the moderating role of education. Northwest Popul J. 2023;44(2):1–13. [Google Scholar]
- 44.Huang DY, Qiu ZQ. Media and information literacy assessment framework for the elderly in digital environment. Libr Tribune. 2021;41(8):96–107. [Google Scholar]
- 45.Wu JX, et al. Development and application of Self-Rating digital literacy measurement for older adults. Mod Distance Educ Res. 2023;35(6):30–40. [Google Scholar]
- 46.Luo QQ, et al. Being digital senior citizens: the influence mechanism of digital literacy on the sense of digital fulfillment of the elderly. Library Tribune. 2023;43(5):130–9. [Google Scholar]
- 47.Kong ZY, Yan XM. Age identity and life satisfaction of the Elderly——Micro-Evidence and mechanism. Popul Dev. 2023;29(6):59–71. [Google Scholar]
- 48.Huang QH. Internet Development and Productivity Growth in Manufacturing Industry: Internal Mechanism and China Experiences. China Industrial Econ. 2019;37(8):5–23. [Google Scholar]
- 49.Jin YA, Zhao MH. Internet Use and the Elderly’s Active Aging in China——A Study Based on 2016 China Longitudinal Aging Social Survey. Popul J. 2019;41(06):44–55. [Google Scholar]
- 50.Li Z, Luo YN. A study of the relationship and mechanism between internet use and health of the migrant elderly. J Yunnan Minzu Univ (Philosophy Social Sci Edition). 2024;41(01):95–109. [Google Scholar]
- 51.Cui SY, et al. Optimizing Community Home-Based Elderly Care Services Through Supply-Demand Alignment: Evidence from 1,200 Senior Citizens in Shandong, China. Shandong Soc Sci. 2020;34(9):127–33.
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data used in this study are available in publicly accessible repositories as follows: The CLASS datasets are publicly available from the National Survey Research Center (NSRC) at Renmin University of China (http://class.ruc.edu.cn) and can be accessed after submitting a data use agreement to the CLASS team.