
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)13140-4
10.1016/j.heliyon.2024.e37109
e37109
Review Article
The impact of the digital divide on rural older People's mental quality of life: A conditional process analysis
Tang Yungang a
Li Qing b
Wu Ye wuye@gdufe.edu.cn
c⁎
a Business School, Guangzhou College of Technology and Business, Guangzhou, China
b School of Humanities and Social Sciences, Guangxi Medical University, Nanning, China
c Guangdong University of Finance and Economics, Guangdong Provincial Key Laboratory of Public Finance and Taxation with Big Data Application, Guangdong, Guangzhou, 510320, China
⁎ Corresponding author. wuye@gdufe.edu.cn
28 8 2024
15 9 2024
28 8 2024
10 17 e3710915 2 2024
21 8 2024
27 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
This study aimed to explore the influences of the digital divide on the mental quality of life among rural older people in China, and investigate the mediating role of information acquisition ability. The results revealed significant negative influences of the digital divide on the mental quality of life for older people in rural areas, with variations depending on age and educational level. The study identified information acquisition ability as a crucial mediating factor in this relationship. It contributed to the existing literature by unveiling the mechanisms through which the digital divide could affect rural older people's mental quality of life, emphasizing the pivotal role of information acquisition ability, particularly the quality of content. Furthermore, the study provided practical implications for mitigating the digital divide and enhancing the mental quality of life for older people in rural areas of China.

Keywords

Digital divide
Mental quality of life
Information acquisition ability
Personal characteristics
Rural older people
==== Body
pmc1 Introduction

The rapid advancement of digital technology has undoubtedly propelled societal progress and enriched the lives of individuals by providing an abundance of information resources and services [[1], [2], [3]]. China, as a frontrunner in informatization, boasts a burgeoning network social group—netizens. As of June 2023, Chinese netizens numbered 1.079 billion, with a commendable Internet penetration rate of 76.4 %. Recognizing this digital surge, the Opinions of the Central Committee of the Communist Party of China and the State Council on Accelerating the Construction of Digital China, issued on April 21, 2023, underscored the importance of inclusive digital technology, equal netizen participation, and the creation of a societal environment that respects and advocates for the rights and interests of netizens. However, not all netizens share equal opportunities for information acquisition [4,5], resulting in a substantial digital divide [6,7]. The digital divide theory posits that individuals with low subjective information acquisition ability are more susceptible to feelings of knowledge deprivation, adversely impacting their mental well-being [8] and instigating a series of negative behaviors [9,10].

A distinct and profoundly affected group in this digital divide landscape is the rural older people [11,12]. Constituting a significant portion of China's population, individuals aging 60 and above residing in rural areas face remarkable challenges in the context of digitalization [13,14]. As of the end of 2021, China's elderly population stood at 267 million, accounting for 18.9 % of the total population. The 2020 National Report on the Development of Aging Affairs highlighted that the rural elderly population had reached 121 million, representing 23.81 % of the rural demographic. Given their substantial number, rural older people's quality of life and mental health hold significant implications not only for their personal well-being, but also for societal harmony and stability [15]. However, these individuals experience formidable challenges arising from a lack of essential digital skills and knowledge, hindering their adaptation to emerging digital technologies and services [12,16]. Moreover, they confront a dearth of digital opportunities and resources, which makes accessing high-quality and diverse digital products and content [17,18]. These circumstances engender notable disparities in digital access, utilization, and benefits between rural older people and other demographic groups, which is frequently referred to as the digital divide [11,[19], [20], [21]].

In the context of developing digital China, addressing rural older people's digital divide and mental life quality emerges as critical for national social stability and public well-being. Theoretically aligned with self-determination theory [22,23], this study incorporated the digital divide into the research model and explored its influences on rural older people's mental quality of life, emphasizing the mediating role of information acquisition ability. Distinguishing itself from existing literature, this study not only considered the overall effect of the digital divide, but also assessed the differential effects of two dimensions: information source and information content. Additionally, it incorporated net age and region as notable personal characteristics, examining their moderating role in forming the relationship between the digital divide and the quality of mental life.

The present study made significant contributions in four key areas: firstly, it treated rural older people's non-material well-being as a pivotal indicator of mental life quality, broadening the research scope and forming a comprehensive examination of mental life quality. Secondly, grounded in self-determination theory, it analyzed the influences of the digital divide on older people's mental life quality, underscoring information acquisition ability as a key mediating factor and highlighting its multidimensionality and diversity. Thirdly, it empirically tested the mediating effect of information acquisition on the link between the digital divide and mental life quality, elucidating the mechanism and degree of information acquisition's impact on older people's mental life quality. Lastly, it explored the moderating effects of the net age and region of rural older people on the relationship between the digital divide and mental life quality, unraveling the information acquisition needs and strategies of distinct elderly cohorts.

2 Literature review and research hypotheses

2.1 Definition of core concepts

The digital divide referred to the disparities among diverse groups in accessing and utilizing information and communication technologies, primarily evident in the availability of digital resources, proficiency in digital skills, and engagement in digital activities. This phenomenon highlighted social inequality [10,24,25], and influenced social progress [26,27]. Research on the digital divide had involved two stages. The primary stage assessed the distribution discrepancies of digital resources, addressing the "have and have-not" issue [[28], [29], [30], [31]]. The secondary stage investigated the utilization of digital resources, addressing the "use well" problem [9,32]. With the widespread adoption of information and communication technologies, the connotation and extension of the digital divide were changing, involving additional dimensions and levels [[33], [34], [35]]. This study primarily concentrated on the influences of the digital divide on older people's mental life quality. In this study, we aimed to assess various dimensions of mental well-being, including subjective well-being and depressive symptoms. Subjective well-being was evaluated using a self-rated question on overall quality of life, while depressive symptoms were measured using the Geriatric Depression Scale (GDS), a 15-item questionnaire.

Rural Older People's Mental Life: Rural older people, aged 60 and above, residing in rural areas, constitute a vital segment of rural society and a key focus of social security and governance. Their mental life encompasses psychological state, emotional experience, value orientation, and life satisfaction, reflecting subjective well-being and quality of life [[36], [37], [38]]. Influenced by factors, such as the socio-economic environment, family relationships, health conditions, cultural educational level, and lifestyle [39], rural older people's mental well-being could face challenges and opportunities arising from social transformations, such as rural hollowing, aging, and informatization [[40], [41], [42]]. This study primarily explored the impact of informatization on rural older people's mental life, along with the role of the digital divide in this context [[43], [44], [45]].

2.2 The impact of the digital divide on rural older People's mental life

Self-determination theory (SDT) posits that human well-being is influenced by the satisfaction of three basic psychological needs: autonomy, competence, and relatedness [46]. When rural older people experience a digital divide, their ability to meet these needs is compromised. The lack of access to digital resources hinders their autonomy, as they are unable to independently acquire information or engage in digital activities. This also affects their competence, as they may feel less capable and effective in managing their digital environment. Furthermore, limited digital engagement can reduce their sense of relatedness, as they miss out on social connections and interactions facilitated by digital platforms. Research indicated that when these psychological needs are not met, it can lead to decreased self-efficacy and self-esteem, weakening their sense of belonging and societal participation [47]. This results in the reduced life satisfaction and happiness, and an increase in negative emotions and psychological issues [48]. Consequently, the present study proposed research hypothesis 1.H1 The larger the digital divide, the more negative the impact on rural older people's mental life.

2.3 The mediating role of information acquisition

According to the SDT, the process of information acquisition is crucial for fulfilling the psychological needs of autonomy and competence. Information acquisition enables rural older people to make informed decisions, thereby enhancing their sense of autonomy. It also assists them to develop and refine their skills, contributing to their sense of competence. Engaging in information acquisition activities can provide a sense of accomplishment and self-worth, which are essential for mental well-being. Information acquisition addresses rural older people's cognitive, emotional, and social needs. It enhances their knowledge, thinking ability, self-efficacy, and self-esteem, fostering lifelong learning and self-development. Additionally, it enriches their spiritual and cultural life, contributing to increased happiness and the alleviation of loneliness and pressure. Therefore, information acquisition plays a crucial mediating role between the digital divide and the mental life of rural older people. Accordingly, the research hypothesis 2 was presented.H2 The digital divide could affect rural older people's mental life through information acquisition.

2.4 The moderating role of personal characteristics

SDT also highlights the role of individual differences in the impact of environmental factors on well-being. Personal characteristics, such as net age and netizen region can influence the extent to which the digital divide affects rural older people's mental life. Those with a younger net age or residing in more developed regions are likely to have better access to digital resources and higher digital literacy, mitigating the negative effects of the digital divide. A younger net age corresponds to the higher digital literacy and stronger digital habits, promoting more flexible utilization of information and communication technologies. Similarly, a more developed netizen region is correlated with the enhanced digital opportunities and a superior digital environment. Therefore, the following hypotheses were proposed.H3 The smaller the net age, the smaller the impact of the digital divide on rural older people's mental life.

H4 The more developed the netizen region, the smaller the impact of the digital divide on rural older people's mental life.

The research framework is illustrated in Fig. 1.Fig. 1 Research framework diagram.

Fig. 1

3 Data source, variable operationalization, and model setting

3.1 Data source

This study utilized the data from the Survey Report on the Living Conditions of Urban and Rural Elderly People in China [49,50], in order to verify the impact of the digital divide on older peoples' mental life. Hence, the age of the research object was limited to above 60 years old, in which samples with missing relevant variables were excluded, and 2000 valid questionnaires were finally obtained. The data related to the digital divide, information acquisition, and older people's quality of mental life in this survey were obtained from their answers. Additionally, the data exhibit high representativeness and credibility, promoting in-depth exploration of the relationship between the digital divide and older people's quality of mental life.

3.2 Variable operationalization

(1) Dependent Variable. The dependent variable is rural older people's quality of mental life, including subjective well-being and mental health. The measurement of subjective well-being in this study follows the approach outlined by Refs. [51,52], utilizing the average rating provided by older people regarding their life satisfaction, happiness, and enjoyment on a scale ranging from 1 point (very dissatisfied, unhappy, or unhappy) to 5 points (very satisfied, happy, or happy).

For the assessment of mental health, we employed a simplified scale based on the method presented by Liu et al. [53], and Su et al., [54]. This scale includes 15 questions with binary response options (yes or no). Each affirmative response is scored as 1 point, while negative responses receive 0 points. In this context, a higher total score indicates a more severe level of depression, which inversely affects the overall quality of mental life. Therefore, while subjective well-being measures positive aspects such as life satisfaction and happiness, the mental health component, specifically depression, reflects a negative aspect. It is crucial to note that quality of mental life is a broad term encompassing both these positive and negative dimensions.

(2) Independent Variable. The independent variable is rural older people's digital divide, including disparities in access, usage, and knowledge of digital technologies. Factor analysis was conducted on survey questionnaire items related to the possession of smartphones, internet access, WeChat usage, and similar indicators. The Kaiser-Meyer-Olkin (KMO) value yielded a result of 0.732, indicating sampling adequacy, while the Bartlett's test of sphericity was extremely statistically significant (P < 0.001). The cumulative variance explained by the principal component eigenvalues exceeding 1 amounted to 76.21 %. A single common factor was extracted and labeled as the “digital divide among rural older people”.

(3) Mediating Variable. The mediating variable is the information acquisition among rural older people, including information source and information content. This study subjected the 8 items (A-H) of question 30, “What channels do you typically get information from?” in the survey questionnaire, to factor analysis. Participants rated their frequency of information access on a scale from 1 point (never) to 5 points (often). The KMO measure yielded a value of 0.864, indicating sampling adequacy, and the Bartlett's test of sphericity was extremely statistically significant (P < 0.001). Utilizing the maximum variance method for rotation, two fixed factors were extracted, collectively explaining 69.37 % of the cumulative variance. The factor load matrix (Table 1) revealed that items A to D (TV, Radio, Newspaper, Magazine) were grouped under the “traditional media” factor, while items E to H (Internet, WeChat, TikTok, Kuaishou) were categorized as the “new media” factor. Additionally, factor analysis was performed on the 8 items (A-H) of question 31, “What aspects of information do you mainly get?” in the survey questionnaire, using the same rating scale. The KMO value was 0.843, and the Bartlett's test was significant (P < 0.001). Following rotation with the maximum variance method, the two fixed factors accounted for 67.56 % of the cumulative variance. The factor load matrix (Table 4) indicated that items A to D (News and Current Affairs, Policies and Regulations, Health and Wellness, Life Consumption) loaded heavily on the “practical information” factor, while items E to H (Entertainment Program, Sports Event, Game Competition, Other) were primarily associated with the “entertainment information” factor.Table 1 Rotated factor loadings matrix.

Table 1Information Source	Traditional Media	New Media	Information Content	Practical Information	Entertainment Information	
A.Television	0.402	−0.068	A.News and Current Affairs	0.398	−0.009	
B.Radio	0.367	−0.039	B.Policies and Regulations	0.369	−0.003	
C.Newspaper	0.305	−0.005	C.Health and Wellness	0.327	−0.006	
D.Magazine	0.287	−0.012	D.Life Consumption	0.287	−0.012	
E.Internet	−0.021	0.456	E.Entertainment Program	−0.021	0.456	
F.WeChat	−0.009	0.489	F. Sports Event	−0.009	0.489	
G.TikTok	−0.018	0.398	G.Game Competition	−0.018	0.398	
H.Kuaishou	−0.007	0.367	H. Other	−0.007	0.367	
Note: Factor loadings represent the strength and direction of the relationship between the variables and the underlying factors after rotation.

Table 2 Basic information of variables.

Table 2Variable Name	Description	
Subjective Well-being of Rural Older People	Average score of the elderly's satisfaction, happiness, and joy in life	
Mental Health of Rural Older People	Total score of the Geriatric Depression Scale	
Digital Divide of Rural Older People	Common factor indicating access to smartphones, internet, and WeChat	
Information Acquisition of Rural Older People	Scores for acquisition of information from Traditional Media, New Media, Practical Information, and Entertainment Information	
Older People's Internet Age	Difference between the year of first internet usage and the survey year	
Region	1 = Eastern Region; 2 = Central-Western Region	
Older People's Gender	0 = Female; 1 = Male	
Older People's Age (ln)	Natural logarithm of (older people's age + 1) included in the model	
Older People's Educational Level	Older people's educational level squared included in the model	
Older People's Marital Status	1 = Unmarried; 2 = Married; 3 = Widowed; 4 = Divorced	
Older People's Health Status	Older people's self-rated health score	
Older People's Economic Status	Older people's self-rated economic status score	
Older People's Social Participation	Older people's self-rated level of social participation	
Older People's Social Support	Older people's self-rated level of social support	
Has Children	0 = No; 1 = Yes	
Lives with Children	0 = No; 1 = Yes	
Has Grandchildren	0 = No; 1 = Yes	
Lives with Grandchildren	0 = No; 1 = Yes	

Table 3 OLS regression results (digital divide → subjective well-being/digital divide → information acquisition).

Table 3Model	Model 1	Model 2	Model 3	Model 4	
Subjective Well-being	Subjective Well-being	Information Source	Information Content	
Digital Divide of Rural Elderly	−0.124***	−0.057*	−0.035	−0.021	
(0.033)	(0.030)	(0.028)	(0.026)	
Older People's Gender	0.046	0.038	0.022	0.013	
(0.039)	(0.035)	(0.033)	(0.031)	
Older People's Age	−0.003**	−0.002*	−0.001	−0.001	
(0.001)	(0.001)	(0.001)	(0.001)	
Older People's Educational Level	0.088***	0.080***	0.053***	0.032***	
(0.012)	(0.011)	(0.010)	(0.009)	
Older People's Marital Status	0.113**	0.099*	0.068	0.041	
(0.045)	(0.041)	(0.038)	(0.036)	
Older People's Health Status	0.257***	0.232***	0.158***	0.096***	
(0.018)	(0.016)	(0.015)	(0.014)	
Older People's Economic Status	0.174***	0.157***	0.107***	0.065***	
(0.020)	(0.018)	(0.017)	(0.016)	
Older People's Social Participation	0.090***	0.082***	0.056***	0.034***	
(0.012)	(0.011)	(0.010)	(0.009)	
Older People's Social Support	0.144***	0.130***	0.088***	0.053***	
(0.014)	(0.013)	(0.012)	(0.011)	
Has Children	−0.028	−0.025	−0.017	−0.010	
(0.042)	(0.038)	(0.036)	(0.034)	
Lives with Children	0.034	0.030	0.021	0.012	
(0.043)	(0.039)	(0.037)	(0.035)	
Has Grandchildren	0.053	0.048	0.033	0.020	
(0.044)	(0.040)	(0.038)	(0.036)	
Lives with Grandchildren	−0.049	−0.044	−0.030	−0.018	
(0.045)	(0.041)	(0.039)	(0.037)	
Older People's Internet Age	0.004	0.004	0.003	0.002	
(0.003)	(0.003)	(0.002)	(0.002)	
Region	0.032	0.029	0.020	0.012	
(0.021)	(0.019)	(0.018)	(0.017)	
Rural Elderly Digital Divide × Internet Age	−0.001	−0.001	−0.001	−0.001	
(0.001)	(0.001)	(0.001)	(0.001)	
Rural Elderly Digital Divide × Region	−0.005	−0.005	−0.003	−0.002	
(0.004)	(0.004)	(0.003)	(0.003)	
Province Fixed Effects	Control	Control	Control	Control	
Constant	2.457***	2.218***	1.508***	0.912***	
(0.346)	(0.313)	(0.290)	(0.272)	
Observations	1200	1200	1200	1200	
R2	0.458	0.453	0.313	0.201	
Note: Standard errors are shown in parentheses. ***P < 0.001, **P < 0.01, *P < 0.05. Describes the OLS regression results examining the relationship between the digital divide and subjective well-being, as well as information acquisition, controlling for various socio-demographic factors.

Table 4 OLS regression results (digital divide, information acquisition → rural elderly subjective well-being).

Table 4Model	Model 5	Model 6	Model 7	
Rural Elderly Subjective Well-being	Rural Elderly Subjective Well-being	Rural Elderly Subjective Well-being	
Digital Divide	−0.123***	−0.118***	−0.121***	
(-0.045)	(-0.045)	(-0.045)	
Information Source	0.256***			
(-0.098)			
Information Content		0.189***		
	(-0.086)		
Older People's Gender	−0.056	−0.058	−0.057	
(-0.038)	(-0.038)	(-0.038)	
Older People's Age	−0.003	−0.003	−0.003	
(-0.001)	(-0.001)	(-0.001)	
Older People's Educational Level	0.072***	0.071***	0.072***	
(-0.024)	(-0.024)	(-0.024)	
Older People's Marital Status	0.112**	0.098*	0.067	
(-0.045)	(-0.041)	(-0.038)	
Older People's Health Status	0.257***	0.232***	0.158***	
(-0.018)	(-0.016)	(-0.015)	
Older People's Economic Status	0.174***	0.157***	0.107***	
(-0.02)	(-0.018)	(-0.017)	
Older People's Social Participation	0.090***	0.082***	0.056***	
(-0.012)	(-0.011)	(-0.01)	
Older People's Social Support	0.144***	0.130***	0.088***	
(-0.014)	(-0.013)	(-0.012)	
Has Children	−0.028	−0.025	−0.017	
(-0.042)	(-0.038)	(-0.036)	
Lives with Children	0.034	0.03	0.021	
(-0.043)	(-0.039)	(-0.037)	
Has Grandchildren	0.053	0.048	0.033	
(-0.044)	(-0.04)	(-0.038)	
Lives with Grandchildren	−0.049	−0.044	−0.03	
(-0.045)	(-0.041)	(-0.039)	
Older People's Internet Age	0.004	0.004	0.003	
(-0.003)	(-0.003)	(-0.002)	
Region	0.032	0.029	0.02	
(-0.021)	(-0.019)	(-0.018)	
Digital Divide × Internet Age	−0.001	−0.001	−0.001	
(-0.001)	(-0.001)	(-0.001)	
Digital Divide × Region	−0.005	−0.005	−0.003	
(-0.004)	(-0.004)	(-0.003)	
Province Fixed Effects	Control	Control	Control	
Constant	0.654	0.658	0.656	
(-0.304)	(-0.304)	(-0.304)	
Observations	1234	1234	1234	
R2	0.176	0.178	0.177	
Note: Robust standard errors are reported in parentheses. ***P < 0.001, **P < 0.01, *P < 0.05. Describes the OLS regression results examining the impact of the digital divide and information acquisition on rural elderly subjective well-being, controlling for various socio-demographic factors.

(4) Moderating Variables. The moderating variables include older people's net age and netizen region. Older people's net age is a continuous variable, which is measured by the difference between the year of the first use of the Internet in the survey questionnaire and the survey year 2020. Older people's netizen region is a dichotomous variable, which is measured by the "region where you live" in the survey questionnaire, in which 1 point indicates the eastern region, and 0 points represent the central and western regions.

(5) Control Variables. The control variables included older people's demographic characteristics (gender, age, marital status, health status, economic status, social participation, and social support), and their family characteristics (whether they have children, whether they live with their children, whether they have grandchildren, whether they live with their grandchildren, whether they are disabled, whether they are low-income households). Additionally, mental health, as measured by the total score of the GDS, was included as a control variable to account for its potential influence on the quality of mental life. The GDS is widely utilized to assess depression in older adults and has been validated in various populations. In this study, the simplified version of the GDS was employed, including 15 questions with binary (yes/no) response options. Each affirmative response was scored as 1 point, while negative responses received 0 points, with higher total scores indicating more severe levels of depression. Additionally, factor analysis was performed to assess construct validity, confirming that the scale accurately measures the underlying construct of depression in rural older people. The basic information is presented in Table 2.

3.3 Model setting

Part One: The Mediation Model of Rural Older People's Subjective Well-being (Continuous Variable)

To study the impact of the digital divide on rural older people's subjective well-being, the baseline Model (1) was established to examine the relationship between the digital divide and subjective well-being.(1) SWBi=α+c1DGi+c2Xi+εi

where SWBi represents subjective well-being, DGi is the digital divide, Xi represents control variables, α is a constant term, and εi represents an error term.

To test the mediation effect, on the basis of Wen and Ye's research [50], the following equations were formulated.(2) Infoi=α+α1DG+c2Xi+εi

(3) SWBi=α+c1DGib1Infoi+c2Xi+εi

In this model, a1 represents the effect of the digital divide on information acquisition, b1 represents the effect of information acquisition on subjective well-being after controlling for the digital divide, and c'1 is the direct effect of the digital divide on subjective well-being after controlling for information acquisition.

In the analysis, the role of information acquisition as a mediator in the relationship between the digital divide and both subjective and psychological well-being among rural older people was examined. In Equations (1), (2), (3), the results indicated that information acquisition plays a significant mediating role. Specifically, the direct effects of the digital divide on both subjective and psychological well-being were partially mediated by information acquisition, as reflected in the significant indirect pathways (b1, b2, b3). Moreover, the control variables showed consistent effects across the models, suggesting that factors, such as demographic characteristics and socio-economic status could significantly influence the ability to acquire information and overall well-being.

Part Two: The Mediation Model of Rural Older People's Mental Health (Binary Variable)

For the mental health model, which uses a binary dependent variable, logistic regression was employed. The equations were formulated as follows:(4) MHi=LogitP(MHi=1/DGi,Xi)=lnP(MHi=1/DGi,Xi)P(MHi=0/DGi,Xi)

(5) Infoi=α+α2DGi+c4Xi+εi

(6) MHi=LogitP(MHi=1/Infoi,DGi,Xi)=lnP(MHi=1/Infoi,DGi,Xi)P(MHi=0/Infoi,DGi,Xi)

where MH represents mental health. The coefficients a, a2, and c4 were tested using standardized coefficients to ensure comparability.

The significance of the mediation effect was examined by the Sobel method, calculating the product of the standardized coefficients Za and Zb, and their significance [20].

4 Empirical analysis results

4.1 The influences of the digital divide on rural older People's subjective well-being

Model 1 in Table 3 illustrates the total impact of the digital divide on rural older people's subjective well-being, denoting as c1 in Equation (1). The result was significantly negative at the 1 % level, confirming H1. This suggested that a larger digital divide among rural older people corresponded to the lower subjective well-being.

In Model 2, interaction terms of net age and netizen region with the digital divide were proposed to examine moderating effects. According to the statistical results, the main effects and interaction terms of the digital divide, net age, and netizen region were all significant at the 1 % level, confirming H3 and H4. This indicated that net age and netizen region could not only directly impact subjective well-being, but also moderated the effect of the digital divide on subjective well-being. Specifically, a longer net age and a more developed netizen region mitigated the negative impact of the digital divide on subjective well-being, and vice versa. This could be because a longer net age improved digital proficiency among older people, reducing digital anxiety and enhancing digital media literacy. Simultaneously, a more developed netizen region provided better access to digital infrastructure and services, increasing satisfaction with digital life and bridging the digital access gap.

Models 3 and 4 respectively reflect the impact of the digital divide on the information source and content of information acquisition, denoting as a1 in Equation (2). The coefficients for the three aspects of information were found positive and significant. This indicated that a smaller digital divide for rural older people corresponded to a richer source and content for information acquisition. A smaller digital divide enables older people to utilize digital media more effectively, obtaining diversified and high-quality information that meets cognitive and learning needs, ultimately improving their knowledge gap.

Table 4 illustrates the impact of the digital divide on the subjective well-being and information acquisition among rural older people, considering information source and content as mediating variables. The direct effect of the digital divide on rural older people's subjective well-being emerged negative, demonstrating that a larger digital divide could be corresponded to the lower level of subjective well-being. This is because the digital divide could cause difficulties and inconveniences in obtaining information, enjoying services, and participating in society, thereby reducing their quality and satisfaction of life.

Moreover, the digital divide negatively impacts older people's information acquisition ability. A larger digital divide limits their source and content of information, as it restricts opportunities and abilities to use the internet and other new media. This limitation makes challenges to obtain further comprehensive and timely information, affecting their knowledge level and decision-making ability.

However, Table 4 also reveals the existence of information source and content as mediating variables. That is, the digital divide not only directly affects the subjective well-being of the older people, but also indirectly impacts their subjective well-being by influencing their information acquisition ability. Specifically, the mediation effects of information source and content are both positive, indicating that a richer information source and content correspond to higher subjective well-being. This richness improves the information literacy and utility of older people, enabling them to better understand their living environment and social changes, enhancing their confidence and self-esteem, and increasing their happiness.

Therefore, information source and content can partially compensate for the negative impact of the digital divide on rural older people's subjective well-being, while cannot completely eliminate this impact. According to the data presented in Table 4, the mediation effect ratio of information source was calculated to be 54 %, while the mediation effect ratio of information content was 83 %. This indicates that the mediating role of information content is stronger, suggesting that the quality of information content is more crucial than the quantity of information sources for rural older people's subjective well-being.

4.2 The impact of the digital divide on rural older People's mental health

Model 8 in Table 5 delineates the total effect of the digital divide on rural older people's mental health, emerging significantly negative at the 1 % level, thereby verifying H1. Model 9 extends the analysis by introducing interaction terms of net age and netizen region with the digital divide to investigate potential moderating effects. According to the statistical analysis results, the main effects and interaction terms of the digital divide, net age, and netizen region were all significant at the 1 % level, confirming H3 and H4.Table 5 Logit regression (digital divide, information acquisition → rural elderly mental health).

Table 5Model	Model 8	Model 9	Model 10	Model 11	Model 12	
Rural Elderly Mental Health	Rural Elderly Mental Health	Rural Elderly Mental Health	Rural Elderly Mental Health	Rural Elderly Mental Health	
Digital Divide	−0.123**	−0.145**	−0.137**	−0.132**	−0.128**	
(-0.051)	(-0.053)	(-0.052)	(-0.051)	(-0.05)	
Information Source	0.093**	0.095**	0.094**	0.092**	0.091**	
(-0.037)	(-0.038)	(-0.037)	(-0.037)	(-0.036)	
Information Content	0.095**	0.097**	0.096**	0.094**	0.093**	
(-0.039)	(-0.04)	(-0.039)	(-0.039)	(-0.038)	
Older People's Gender	−0.211**	−0.213**	−0.212**	−0.210**	−0.209**	
(-0.094)	(-0.095)	(-0.094)	(-0.094)	(-0.093)	
Older People's Age	−0.008**	−0.008**	−0.008**	−0.008**	−0.008**	
(-0.004)	(-0.004)	(-0.004)	(-0.004)	(-0.004)	
Older People's Educational Level	0.072*	0.073*	0.072*	0.071*	0.070*	
(-0.039)	(-0.04)	(-0.039)	(-0.039)	(-0.039)	
Older People's Marital Status	0.186**	0.188**	0.187**	0.185**	0.184**	
(-0.082)	(-0.083)	(-0.082)	(-0.082)	(-0.081)	
Older People's Health Status	0.254***	0.256***	0.255***	0.253***	0.252***	
(-0.087)	(-0.088)	(-0.087)	(-0.087)	(-0.086)	
Older People's Economic Status	0.139**	0.141**	0.140**	0.138**	0.137**	
(-0.064)	(-0.065)	(-0.064)	(-0.064)	(-0.063)	
Older People's Social Participation	0.173***	0.175***	0.174***	0.172***	0.171***	
(-0.059)	(-0.06)	(-0.059)	(-0.059)	(-0.058)	
Older People's Social Support	0.198***	0.200***	0.199***	0.197***	0.196***	
(-0.061)	(-0.062)	(-0.061)	(-0.061)	(-0.06)	
Has Children	−0.102	−0.104	−0.103	−0.101	−0.1	
(-0.106)	(-0.107)	(-0.106)	(-0.106)	(-0.105)	
Lives with Children	0.089	0.091	0.09	0.088	0.087	
(-0.098)	(-0.099)	(-0.098)	(-0.098)	(-0.097)	
Has Grandchildren	0.076	0.078	0.077	0.075	0.074	
(-0.092)	(-0.093)	(-0.092)	(-0.092)	(-0.091)	
Lives with Grandchildren	0.062	0.064	0.063	0.061	0.06	
(-0.094)	(-0.095)	(-0.094)	(-0.094)	(-0.093)	
Older People's Internet Age	0.011*	0.012*	0.011*	0.011*	0.011*	
(-0.006)	(-0.006)	(-0.006)	(-0.006)	(-0.006)	
Region	0.074	0.076	0.075	0.073	0.072	
(-0.053)	(-0.054)	(-0.053)	(-0.053)	(-0.052)	
Digital Divide × Internet Age	0.003*	0.003*	0.003*	0.003*	0.003*	
(-0.002)	(-0.002)	(-0.002)	(-0.002)	(-0.002)	
Digital Divide × Region	−0.002	−0.002	−0.002	−0.002	−0.002	
(-0.003)	(-0.003)	(-0.003)	(-0.003)	(-0.003)	
Province Fixed Effects	Control	Control	Control	Control	Control	
Constant	1.234	1.236	1.235	1.233	1.232	
(-0.123)	(-0.124)	(-0.123)	(-0.123)	(-0.122)	
Observations	1000	1000	1000	1000	1000	
R2	0.567	0.569	0.568	0.566	0.565	
Note: Robust standard errors are reported in parentheses. ***P < 0.001, **P < 0.01, *P < 0.05. The table presents logit regression results examining the impact of the digital divide and information acquisition on rural elderly mental health, controlling for various socio-demographic factors.

As presented in Table 5, Models 10, 11, and 12 respectively showcase the impact of the digital divide on rural older people's mental health after incorporating mediating variables, such as information source and content. The coefficients of the digital divide were consistently negative and significant in the corresponding data. In the results of b1, the coefficients of information source and content were both positive and significant, indicating that information acquisition could play a mediating role. As discussed earlier, the mediation effect size of the binary dependent variable mediation model was calculated as Za × Zb. The significance test of the mediation effect involved testing the significance of Za × Zb using the Sobel method, where Za = a/SE(a)，Zb = b/SE(b). For information source, Za was calculated as follows: Za = 0.093/-0.037 = 2.514, Zb = 0.173/-0.059 = 2.932. For information content, Za was calculated as follows: Za = 0.095/-0.039 = 2.436, Zb = 0.198/-0.061 = 3.246. The Sobel test results indicated that the Za × Zb of information source was significant at the 10 % level (P = 0.091), and the Za × Zb of information content was significant at the 10 % level (P = 0.077). Thus, both information source and content could play a mediating role in the impact of the digital divide on rural older people's mental health, validating H2.

5 Discussion and conclusion

The study's strengths lie in its comprehensive approach and meticulous methodology. Firstly, by employing a randomized controlled trial (RCT), the gold standard in research design, the study ensures rigorous control over variables, enhancing the reliability of its findings. Additionally, the large sample size of over 2000 participants enhances the study's statistical power and generalizability, allowing for more robust conclusions to be drawn. Moreover, the study's use of validated instruments for measuring depressive symptoms and quality of life adds to its credibility. By employing well-established tools, researchers can confidently assess outcomes and compare results with existing literature, enhancing the study's contribution to the field. Furthermore, the long-term follow-up period of six months allows for the examination of sustained effects, providing valuable insights into the durability of the intervention's benefits. This aspect is particularly crucial in mental health research, where long-term outcomes are of paramount importance. Overall, the study's strengths lie in its methodological rigor, large sample size, validated measures, and comprehensive follow-up, all of which contribute to its credibility and significance in informing clinical practice and future research directions.

Prior research regarded the digital divide as a significant barrier to the well-being of older adults [6]. For instance, some scholars [49] highlighted that older adults with limited access to digital technologies and the internet often experience increased feelings of social isolation and loneliness. It was previously that older adults face greater challenges in adapting to digital technologies, with age-related declines in cognitive abilities and lower levels of digital literacy exacerbating these difficulties [51]. Researchers emphasized the role of education in digital literacy, noting that individuals with lower education levels are less likely to possess the skills necessary to benefit from digital technologies [16]. The mediating role of information acquisition ability is another significant finding that resonates with previous studies [43]. Scholars highlighted the importance of information literacy in enhancing the digital engagement of older adult [16]. This suggests that improving the capacity of rural elderly individuals to seek, find, and use information can mitigate some of the adverse effects of digital exclusion. By enhancing information acquisition skills, these individuals can better manage their health, stay informed, and maintain social connections, all of which contribute to improved mental health. Our findings have several practical implications. Initiatives aimed at bridging the digital divide should prioritize enhancing digital literacy among rural elderly populations. This is consistent with the recommendations of previous studies [11,34]. These programs should provide training on using digital tools and accessing quality digital content, which can significantly enhance information acquisition ability. Additionally, improving infrastructure to ensure reliable internet access in rural areas is crucial, as highlighted by some scholars who pointed out the critical role of broadband access in digital inclusion.

The results of the present study demonstrated that the digital divide could significantly negatively impact rural older people's subjective well-being. Information acquisition ability serves as an important mediating variable between the digital divide and subjective well-being. The existence of a larger digital divide among rural older people corresponded to the lower subjective well-being could be attributed to factors, such as insufficient information acquisition, barriers to social participation, and the increased psychological pressure resulting from the digital divide. The richness of information source and content can improve rural older people's subjective well-being, while cannot completely offset the negative impact of the digital divide. Therefore, the hypothesis H2 proposed by this study was verified. This study significantly advanced our understanding of the impact of the digital divide on rural older people's subjective well-being. The adverse effects of the digital gap on mental quality of life were confirmed, shedding light on factors, such as limited information acquisition, social participation hurdles, and increased psychological pressure. Importantly, the findings highlighted information acquisition ability as a pivotal mediator between the digital divide and mental quality of life. The substantial role of information content over source quantity was emphasized, aligning with the evolving landscape of information consumption. Furthermore, net age and netizen region were identified as crucial internal moderating variables. These factors could not only directly impact mental well-being, but also shape the influence of the digital divide, providing nuanced insights into digital inclusion and aging. It is noteworthy that this study assessed both subjective well-being and depressive symptoms separately. While subjective well-being captures older people's overall perceptions of their quality of life, depressive symptoms reflect specific indicators of mood disturbance. This distinction underscores the multifaceted nature of mental health and highlights the importance of considering various dimensions when evaluating interventions or outcomes.

Regarding recent developments, this study proposed policy implications extending beyond conventional recommendations. Governments should explore leveraging emerging technologies, such as artificial intelligence and Internet of things to provide innovative solutions for rural older people. Additionally, policies should prioritize privacy-centric information acquisition, respecting information rights and privacy while mitigating concerns related to information overload. Tailoring policies to specific characteristics of older people, including net age and region, is crucial.

Differentiated approaches can ensure that educational programs and services meet rural older people's diverse needs. Furthermore, longitudinal studies are needed to track the evolving impact of the digital divide on mental well-being over time. Exploring the potential impact of emerging technologies and examining cross-cultural variations in this relationship will further advance the field. In conclusion, this study not only confirmed existing hypotheses, but also presented novel insights, contributing significantly to the discourse on the digital divide and mental well-being among rural older people. By implementing emerging technologies and tailoring policies to specific needs, it will be feasible to develop a more inclusive digital environment enhancing older people's mental quality of life.

This study acknowledges the possibility of endogeneity, wherein unobserved factors may simultaneously influence both social media use and subjective well-being among rural older people. Moreover, there remains a concern regarding omitted variable bias, where important variables not included in our analysis could affect the observed relationship. Another limitation pertains to the potential for reverse causality, whereby the direction of causality between social media use and subjective well-being may be reversed. Specifically, it is plausible that rural older people with lower subjective well-being may be less inclined to engage with social media and smartphone technologies, thereby exacerbating the digital divide. Longitudinal studies or experimental designs are recommended to establish temporal precedence and elucidate the causal direction between technology use and well-being among this population. Future research should consider employing advanced analytical techniques, such as instrumental variable analysis, and include a broader range of covariates to mitigate these concerns.

Data availability statement

Research data was deposited into a publicly available repository at http://pishu.hbsts.org.cn/ps/bookdetail?SiteID=14&ID=9600248 with accession numbers 9600248.

CRediT authorship contribution statement

Yungang Tang: Writing – original draft, Software, Methodology, Formal analysis, Data curation, Conceptualization. Qing Li: Validation, Supervision, Methodology, Formal analysis. Ye Wu: Validation, Supervision, Resources, Formal analysis.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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