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Article

Unraveling the Dynamics of Digital Inclusion: Exploring the Third-Level Digital Divide Among Older Adults in China

by
Qian Cheng
and
Maowei Chen
*
Department of Global Convergence, Kangwon National University, Chuncheon 24341, Republic of Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(21), 11647; https://doi.org/10.3390/app152111647 (registering DOI)
Submission received: 7 September 2025 / Revised: 24 October 2025 / Accepted: 28 October 2025 / Published: 31 October 2025
(This article belongs to the Special Issue Digital Health, Mobile Technologies and Future of Human Healthcare)
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Abstract

The COVID-19 pandemic catalyzed engagement in photographic activities among older adults, yet research remains limited on how these activities promote digital inclusion within the context of the third-level digital divide. Thus, in this study, we conducted a digital-inclusion-focused survey to assess how generalized photography influences older adults’ subjective perceptions of digital inclusion in this context. We developed a theoretical framework that integrates elements of photography and narrative characteristics to examine the roles of social identity and perceived digital inclusion in fostering digital inclusion. Using a sample of 1627 older adults in China, we tested the model with structural equation modeling (SEM). The findings reveal that participation in photographic activities and photographic visual narratives indirectly increases older adults’ subjective perceptions of digital inclusion through fully mediating and chain-mediating effects by enhancing perceived digital inclusion and social identity. Additionally, the moderating effects of age and regional differences were examined. Therefore, photography within the environmental divide can effectively promote digital inclusion among older adults of different ages and from different regions, demonstrating cross-group consistency. This study leverages widespread experiences regarding photographic activities to fill a research gap in the third-level digital divide, offering insights into promoting deeper digital inclusion of older adults in our digital society.

1. Introduction

Under the guidance of global policies promoting active aging and the “digital inclusion” initiative, photography, as the most basic and accessible form of digital media practice, is gradually revealing its unique potential in promoting social integration among older adults [1]. As a tool that enables the creation and dissemination of visual content without requiring one to surmount high technical barriers [2], photography not only provides older adults with convenient pathways for self-expression, participation in public discourse, and maintenance of social connections [3] but also features strong cross-cultural adaptability and psychological resonance [4,5]. The United Nations Educational, Scientific and Cultural Organization (UNESCO) has emphasized in its policies on “active aging” that efforts should be made to promote the application of open, low-threshold digital skills to help older adults overcome educational and technological barriers, thereby facilitating lifelong learning and inclusive social participation [6]. Although countries are generally advancing digital infrastructure development and striving to bridge the digital divide between older adults and the digital age [7], older adults still face the challenge of the “the third-level digital divide” in terms of their use of digital media [8,9,10], meaning that access to technology does not necessarily translate into effective social integration capabilities, thereby further limiting their social participation and identity formation [11].
The COVID-19 pandemic accelerated the migration of social life to online environments [12]. According to the 47th statistical report released by the China Internet Network Information Center (CNNIC) in February 2021, the number of internet users in China reached 989 million in the year in question, an increase of 85.4 million compared to March 2020. Additionally, the number of online video, live-streaming, and video-conferencing-app users grew to 927 million, 617 million, and 346 million, respectively [13]. During the pandemic, the average weekly time spent on social media in China increased from 17.2 h to 21.4 h [14], reflecting users’ greater reliance on digital media during this period. This social transformation has brought new opportunities for social integration but may also exacerbate existing inequalities and social isolation [15], particularly for older adults with relatively weaker digital skills [16], making the exploration of media tools that can effectively promote older adults’ social integration and digital inclusion increasingly urgent.
Research has treated photography as an everyday visual medium, focusing on its potential to foster digital inclusion and social participation among older adults [17,18]. However, much of the literature still positions photography primarily as a technical means of data collection and expression or as an auxiliary component within training processes, with emphasis placed on image outputs and technological applications [19,20]. This orientation partly overlooks how photography itself shapes mechanisms such as social identity, affective feedback, and improvement in digital literacy. To address these gaps, we adopt an operational definition of “generalized photographic activity”. Any everyday activity aimed at generating, processing, and disseminating static digital images that does not require professional equipment or specialized training or entail commercial intent qualifies.
Accordingly, this study investigates two questions in depth: (1) Do photographic activities affect digital inclusion? (2) Through what specific pathways and mechanisms do they shape older adults’ digital inclusion? To address these questions, at the theoretical level, we shift “Photography” from a traditional tool/outcome perspective to an “everyday media activity” perspective within the context of the third-level digital divide, proposing dual and sequential mediation mechanisms—“Participation in photographic activities/Photographic visual narratives → Perceived digital inclusion → Social identity → Digital inclusion”—thereby supplementing access- and skill-oriented accounts with an affect-identity pathway. At the conceptual and measurement levels, we offer an operational definition of generalized photographic activity and develop/validate multidimensional scales for “Participation in photographic activities” and “Photographic visual narratives,” which, together with perceived digital inclusion and social identity, form a testable structural model. Empirically, drawing on structural equation modeling of a large sample of older adults in China (n = 1627) collected in 2024–2025, we confirm the indirect and sequential effects of photographic activities on digital inclusion and find that age and region have non-significant effects on the downstream paths, revealing a cross-group media intervention pathway that enriches debates on economic determinism and regional disparities. At the policy and practice levels, we further propose low-barrier, affectively friendly intervention, using image-based practices as the entry point and prioritizing perceived digital inclusion to catalyze social identity formation.
The remainder of this paper is organized as follows: in Section 2, we provide a review of the literature and present the hypotheses we developed; Section 3 details the model, scales, and sample; Section 4 reports on reliability/validity, the structural model, and mediation/moderation tests; Section 5 discusses the theoretical–practical implications and limitations; and Section 6 concludes this study with contributions, limitations, and future directions.

2. Literature Review and Hypothesis Development

2.1. The Objectives of the Third-Level Digital Divide: The Relationship Between Older Adults and Digital Inclusion

The term “digital divide” refers to the differences in access to and use of electronic information and technology driven by various social identities [21]. This divide exacerbates existing inequalities rooted in factors such as age, gender, race, and socioeconomic status [22,23]. Photography, as a digital media practice, is often regarded by scholars as one of the effective methods for fostering social equity and personal expression in the digital age [24], thereby sparking significant academic interest in the relationship between photography and the digital divide.
The digital divide is commonly categorized into three levels: the physical divide, the skills divide, and the environmental divide [25]. The physical divide refers to the gap between those with and without access to computers and the Internet [26]. The skills divide pertains to the digital competencies needed to use information and communication technologies (ICTs) effectively [27]. Lastly, the environmental digital divide, also referred to as the outcome digital divide, encompasses variations in internet access and benefits among users across different regions, economic classes, and levels of social development.
Digital inclusion, a positive concept corresponding to the digital divide, refers to the extent to which all groups can integrate into a digital society through digital technology [28]. In other words, digital inclusion is aimed at bridging the digital divide and enabling disadvantaged groups to equally share in the benefits of the development of the information society [29]. Through qualitative interviews with older adult internet users in China, one study [30] found that insufficient digital skills, declining physical ability, and lack of assistance from others interact to amplify the fear and anxiety older adults feel when learning to use new devices. Additionally, older adults are more accustomed to traditional face-to-face communication, and this generational cultural difference reduces their willingness to use social media [31], often making older adults one of the groups most affected by the third-level digital divide [32], which involves a complex set of challenges for this group in terms of resource access, psychological acceptance, and cultural integration.
However, in the literature, researchers often view older adults as passive recipients of technology, focusing on their disadvantages in terms of hardware and skills while neglecting the potential for digital social integration that their agency can create [33]. This “one-dimensional” understanding makes it difficult to see the possible paths through which older adults can integrate into the digital society and gain a sense of identity through self-practice. This perspective provides a theoretical entry point for this study and reveals a research gap, and it is also one of the cognitive biases that we aim to address: In the context of the third-level digital divide, we need to move beyond a “one-size-fits-all” approach and adopt a technological mindset, conduct in-depth research on older adults’ motivations and practical experience in regard to participating in the digital realm, and understand how they seek social identity and digital inclusion through specific media practices (such as photography) in an environment of inequality.

2.2. Photography and Visual Narrative: From Youth Exuberance to Older Adults’ Empowerment

In the era of digital media, young people take photographs with their smartphones and share them on social platforms, producing a “visualized–publicized” mode of everyday expression [34,35]. By contrast, older adults have long remained at the margins of this visual narrative space [36,37,38]. As noted in the Introduction, we adopt an operational definition of generalized photographic activity (incorporating everyday acts of smartphone shooting, light editing, saving, sharing, and interaction into “photographic activity”). Within this framework, we divide the independent variable into two dimensions: (i) participation in photographic activities (emphasizing behavioral engagement and self-efficacy) and (ii) photographic visual narratives (emphasizing meaning-making and self-presentation through photographs).
Regarding perceived digital inclusion (a sense of familiarity and ease with respect to the digital environment): participatory photography and photovoice studies indicate that the shoot–narrate–share cycle is low-barrier and easy to learn, effectively enhancing older adults’ usage confidence, platform familiarity, and technological self-efficacy, thereby lowering the psychological and skill thresholds for entering the digital ecology [39,40]. Such sustained participation in photographic activities causes individuals to interact with devices and platforms more frequently and proactively, thereby strengthening their sense of affinity with—and control over—the digital environment [39,41]. Meanwhile, participation in photographic activities, via organizing personal memories and present experiences, helps older adults embed their life contexts into the digital ecology, cultivating a virtuous cycle of being able, willing, and confident to express things, thereby consolidating their fitting into and comfort within digital spaces [42].
Regarding social identity (self-positioning and belonging vis-à-vis groups and social roles), image-based visual narratives enable older adults to be “seen and heard,” present positive self-images in public and semi-public settings, and challenge and recalibrate stereotypes about “older people” [43,44,45]. When individuals share experiences, achievements, and mutual aid through photographs (e.g., showcasing volunteer work), they are more likely to receive positive feedback from family, peers, and communities, reinforcing emotional bonds with social networks and confirming social roles [44,45]. At the same time, sustained participation in photographic activities not only creates opportunities for expression but also, through peer interaction, commenting, and recirculation, deepens group embeddedness and identity [45].
H1a. 
Participation in photographic activities significantly impacts perceived digital inclusion.
H1b. 
Participation in photographic activities significantly impacts social identity.
H2a. 
Photographic visual narratives significantly impact perceived digital inclusion.
H2b. 
Photographic visual narratives significantly impact social identity.

2.3. Social Identity and Digital Inclusion: A Dual Bridge Between Emotions and Behavior

Older adults’ digital inclusion depends not only on skill acquisition but also on socio-psychological mechanisms [46]. We define perceived digital inclusion as a sense of familiarity, trust, and ease with respect to the digital environment and social identity as a sense of belonging and role confirmation within the digital society [47,48]. Prior research has shown that these two psychological resources are closely associated with older adults’ willingness to use platforms, sustained participation, and subjective evaluations of “digital inclusion” [46,47,48]; they also co-occur with active aging and increased social participation [49], with convergent evidence in regard to Chinese samples [32].
Within the aforementioned framework of “generalized photographic activity” (encompassing everyday shooting–light editing–saving–sharing–interaction), photography combines low operational barriers with strong affective activation. On the one hand, sustained participation in photographic activities leads to high-frequency, low-pressure, salutary interactions between older adults and devices/platforms, boosting usage confidence and environmental familiarity, thereby enhancing perceived digital inclusion [50]. On the other hand, photographic visual narratives organized around lived experience increase individuals’ visibility among family and communities and elicit positive feedback, thereby strengthening social identity and alleviating marginalization anxiety [51,52].
Furthermore, a sequential chain from perceived digital inclusion to social identity is theoretically grounded: trust and ease in the digital environment can reduce identity threats and usage anxiety and improve interaction quality and collective efficacy, thereby reinforcing emotional belonging with respect to digital communities [46,47,48,49]. Building on the foregoing literature and theorization, we specify two mediating paths—social identity and perceived digital inclusion—linking photographic activities (including the variables “Participation in photographic activities” and “Photographic Visual Narratives”) to digital inclusion. Specifically, we not only hypothesize that the two mediators exert positive indirect effects on digital inclusion but also infer a sequential chain from “Perceived Digital Inclusion to Social identity”. Once older adults adapt to and trust the digital environment and subsequently attain greater psychological group identification, they tend to be more motivated and willing, in turn fostering a heightened perception of the digital divide narrowing. In sum, we propose the following research hypotheses:
H3a. 
Perceived digital inclusion significantly impacts digital inclusion.
H3b. 
Social identity significantly impacts digital inclusion.
H4. 
Perceived digital inclusion significantly impacts social identity.
H5a. 
Perceived digital inclusion plays a significant mediating role between “Participation in photographic activities and digital inclusion”.
H5b. 
Social identity plays a significant mediating role between “Participation in photographic activities and digital inclusion”.
H5c. 
Perceived digital inclusion plays a significant mediating role between “Photographic visual narratives and digital inclusion”.
H5d. 
Social identity plays a significant mediating role between “Photographic visual narratives and digital inclusion”.
H6a. 
Perceived digital inclusion and social identity play chain-mediating roles between “Participation in photographic activities and digital inclusion”.
H6b. 
Perceived digital inclusion and social identity play chain-mediating roles between “Photographic visual narratives and digital inclusion”.

2.4. Research Gaps and Theoretical Positioning: The Equalizing Role of the Photographic Medium

Photography simultaneously has features pertaining to technological use and emotional expression, meeting the dual needs of “skill enhancement–psychological adaptation” in regard to older adults’ digital inclusion; its capacity to “help people to see” [17] provides low-barrier entry points for low-skill users and, through narrative resonance, fosters subjective inclusion, thereby narrowing the intergenerational digital divide from both objective and subjective sides [53]. Accordingly, we position photography as a potential equalizing medium and, on this basis, focus on three core gaps, as follows:
Taken together, despite a growing body of research, salient theoretical and practical gaps remain. First, quantitative evidence and operationalization remain insufficient. The existing research concentrates on skill training and age-friendly infrastructures [54], with inadequate attention to intrinsic motivation and lived experience [20]. Although there is an abundance of visual narrative research pertaining to health communication and intergenerational interaction [55], quantitative evidence that operationalizes photographic attributes and practices into structured scales within digital divide models is lacking. Macro-level explanations centered on the economy/education dominate [30], while micro-level evidence grounded in everyday media practice remains scattered [56]. Small-sample studies on topics such as photovoice point the way, yet the overall empirical base remains thin [39]. Aisi-Heikkinen et al. [57] likewise note the scarcity of empirical and theoretical resources on later-life digital inclusion, underscoring the need for new practical pathways and theoretical concepts [58,59,60].
Second, the age-based digital divide requires reconceptualization. The paradigm equating “advanced age” with “technological resistance” is being challenged [61,62]. Neves advocates for reconceptualizing the age-based digital divide by shifting toward mechanisms and pathways that enable older adults to actively embrace new media [31]. Within our framework, perceived digital inclusion and social identity may be pivotal to the “psychological–behavioral translation,” yet whether their downstream effects on digital inclusion vary by age stage remains insufficiently tested.
Third, there is a mechanistic debate over regional differences. One view emphasizes economic determinism, positing that regional economic and technological conditions drive the third-level digital divide [63]. Another highlights cultural capital and social support networks, arguing that under comparable economic conditions, differences in cultural orientations also yield divergent levels of digital participation [44]. Accordingly, we treat “region” as a contextual test variable to examine the external validity and boundary conditions of the downstream paths “Perceived digital inclusion → Digital inclusion” and “Social identity → Digital inclusion” across areas. This design is intended to assess the cross-regional robustness of photography as an “equalizer” while providing a baseline for incorporating finer-grained contextual factors and temporal dynamics (e.g., family support structures and neighborhood digital infrastructures) in subsequent research. In sum, we advance the following research hypotheses.
H7a. 
Age plays a significant role in moderating the “Perceived digital inclusion → Digital inclusion” relationship.
H7b. 
Age plays a significant role in moderating the “Social identity → Digital inclusion” relationship.
H8a. 
Region plays a significant role in moderating the “Perceived Digital inclusion → Digital inclusion” relationship.
H8b. 
Region plays a significant role in moderating the “Social identity → Digital inclusion” relationship.

3. Research Methods

3.1. Models and Scales

Building on the established hypotheses, we now provide the specifics of the conceptual model, as depicted in Figure 1. The model comprises five core constructs: digital inclusion, perceived digital inclusion, social identity, participation in photographic activities, and photographic visual narratives.
Each construct was operationalized with 24 measurement items in total, as detailed in Table 1. The measurement items in Table 1 were adapted from prior studies and refined through context-specific wording adjustments and pilot testing. These items span from dimensions of digital self-efficacy to social connectedness, learning motivation, and practice preferences, ensuring content validity of the constructs.
Based on the established research hypotheses, this study further delineates the required conceptual model, as illustrated in Figure 1.

3.2. Acquisition and Processing of the Sample Data

We employed structural equation modeling (SEM) to analyze the research questions, primarily using questionnaire surveys to collect sample data for empirical analysis. The authors developed the “Survey Questionnaire on the Impact of Photography on Bridging the Digital Divide Among Older Adults” based on established scales and conducted a survey using the “Wenjuanxing” survey platform tool.
As we aimed to explore the overall impact of the photographic medium on Chinese older adults’ digital participation, the survey was not limited to specific cities or regions. Instead, a nationwide sample collection method was used to increase the diversity of the sample and the universality of the research conclusions. To ensure that the data collection process met quality control standards, the researchers clearly stated the purpose of this study, ensured anonymity and data confidentiality, and provided instructions for completing the questionnaire at the beginning of the questionnaire to increase the respondents’ enthusiasm and the authenticity of their answers.

3.3. Research Objects

To ensure close alignment between the research questions and the local context, we designated older adults in China as the sole empirical focus. China combines a large older population base with very high mobile-internet penetration; as of July 2025, 99.4% of internet users now access the web via mobile phones, and video-based applications such as short videos are widely pervasive [69], providing ample heterogeneity and statistical power with which to test the mechanism chain of “Photography → Perceived digital inclusion → Social identity → Digital inclusion”. On this basis, we treat the Chinese sample as a contextualized site for mechanism testing and report the theoretical inferences and statistical results accordingly.
The older-adult respondents selected for this study were aged 60 and above, a range widely accepted worldwide as the age threshold for studies on aging issues. At the same time, we further accounted for the impact of cognitive ability on the validity of questionnaire responses and therefore did not include older adults aged 80 and above with significant cognitive impairment or significantly declining health, thereby ensuring the operability of the sample and the validity of the survey.
In terms of geographical coverage, we targeted older adults nationwide, avoiding the issue of insufficient sample representativeness caused by geographical homogeneity. The survey sample covers the eastern (Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Shandong, Fujian, Guangdong, and Hainan), central (Shanxi, Henan, Hubei, Hunan, Anhui, and Jiangxi), western (Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, and Xinjiang), northern (Heilongjiang, Jilin, Liaoning, and Inner Mongolia), and southern (Guangdong, Guangxi, Fujian, and Hainan) regions, with varying levels of economic development, ensuring that the research conclusions better reflect the overall situation regarding older adults in China.
In addition, instead of focusing on specific types of samples (such as specialized older-adult care institutions or communities), we focused on ordinary older adults with diverse characteristics, thereby avoiding sample bias caused by a single social background. This research design effectively controlled the influence of potential confounding variables such as management systems, community environments, and social support resources to a certain extent, improving the internal validity of the research results and the robustness of the conclusions.

3.4. Data Cleaning and Sample Description

Although online surveys can effectively cover an entire country, they also pose many challenges, such as the lack of on-site supervision during the completion process, insufficient attention from respondents, or misunderstandings [70]. To ensure the data in this study were of high quality and reliable, the researchers conducted a rigorous data-cleaning process on the raw data collected.
We collected a total of 3593 online survey questionnaires. First, to ensure that the data met the age requirements pertaining to the research subjects, the researchers excluded questionnaires in which “59 years old and below” was selected for the age option, as this age range did not meet the criteria for older adults defined in this study. Therefore, these questionnaires were not included in the subsequent analysis. A total of 475 such incomplete responses were deleted, leaving 3118 datasets. Second, to exclude data quality issues precipitated by low-quality responses, random responses, or duplicate responses, the researchers established strict screening criteria. This task included deleting duplicate questionnaires obtained from individuals with the same IP address, questionnaires with identical answers for all options, and questionnaires with obvious alternating patterns in answers (e.g., ABAB). A total of 167 such questionnaires were deleted, leaving 2951 datasets. Additionally, we considered the reasonable amount of time required to complete the questionnaire and excluded those with a total completion time of less than 5 min to ensure the respondents had sufficient time to think about and respond to the questions, thereby ensuring data validity. This step resulted in the exclusion of an additional 1324 questionnaires, leaving 1627 valid samples.
Although this rigorous screening process resulted in a low questionnaire response rate, the researchers believe that it ensured the data used for this analysis are of high quality, thereby improving the accuracy and reliability of the research results. In addition, the sufficient amount of raw data allowed us to maintain a substantial sample size even after the screening process, ensuring the reliability of the subsequent statistical analyses and the robustness of the research conclusions.

4. Empirical Analysis

4.1. Descriptive Statistics of the Sample

The study sample was well structured, with diverse and balanced distributions across age bands and regions, supporting the representativeness and external validity of the analyses. Descriptive statistics were performed on 1627 valid cases; the results are summarized in Table 2. By age, the largest shares were 60–65 (39.46%) and 66–70 (29.44%). Respondents aged 71–75 (20.96%) and 76–80 (10.14%) also accounted for substantial proportions. Geographically, the sample covered five Chinese regions: eastern (17.33%), central (19.36%), western (26.24%), northern (14.20%), and southern (22.86%). This breadth and diversity provide a solid basis for examining how photography, as a medium, shapes the digital divide among older adults.

4.2. Reliability and Validity Analysis

4.2.1. Reliability Analysis

In this study, to determine the reliability of the measurement items, we conducted a reliability analysis using Cronbach’s α coefficient to measure internal consistency. When the α coefficient reaches 0.7–0.8, a measurement tool can be considered to have sufficient reliability; when the α coefficient is greater than 0.8, the measurement tool has very good reliability.
As reported in the reliability analysis (Table 3), Cronbach’s α coefficients for the scales of digital inclusion, perceived digital inclusion, participation in photographic activities, and photographic visual narratives all exceeded 0.80. Moreover, deleting any single item did not increase the overall Cronbach’s α of the scale. These results indicate that the measurement instrument has good reliability.

4.2.2. Validity Analysis

We conducted exploratory and confirmatory factor analyses with SPSS 26.0 and AMOS 23.0 to evaluate scale validity (Table 4). Constituent factors were extracted using principal component analysis with varimax rotation. The number of factors was determined by the criteria eigenvalues ≥ 1.0 and factor loadings ≥ 0.50. The Kaiser–Meyer–Olkin (KMO) measure was 0.935, and Bartlett’s test of sphericity was significant: χ2(276) = 17,221.256, with p < 0.001. Given the KMO > 0.80 and p < 0.05, the data were suitable for factor analysis. Within the theoretical framework, the five factors—digital inclusion, perceived digital inclusion, social identity, participation in photographic activities, and photographic visual narratives—explained 12.500%, 11.191%, 8.461%, 16.316%, and 15.579% of the variance, respectively; the total variance explained was 64.047%. These results indicate that the measurement scales exhibit good construct validity.
We constructed a structural equation model using AMOS 23.0 and tested the model’s structural validity, and discriminant validity. As shown in Table 5, the measurement model demonstrated excellent structural validity, with the following fit indices: χ2 = 253.291, df = 244, χ2/df = 1.038, SRMR = 0.031, GFI = 0.987, AGFI = 0.984, PGFI = 0.803, NFI = 0.985, RFI = 0.983, IFI = 0.999, TLI = 0.999, CFI = 0.999, RMSEA = 0.005, and PNFI = 0.871.
Convergent validity was examined using composite reliability (CR) and average variance extracted (AVE). As shown in Table 5, the CR values for all the measurement dimensions were greater than the standard value of 0.7. Except for “digital inclusion”, whose AVE value was slightly below 0.5 (AVE = 0.486), the AVE values for all the other measurement dimensions exceeded the standard threshold. Since the composite reliability (CR = 0.825) of this measurement dimension significantly exceeds 0.7, it can still be considered to have convergent validity according to the recommendation of Fornell and Larcker (1981) [71], thereby validating that the measurement model possesses convergent validity [72]. As shown in Table 6, the square root of the AVE for each latent variable was greater than the correlation coefficient between that variable and other latent variables, indicating that the measurement items across different dimensions satisfied discriminant validity [71].

4.3. Structural Model and Hypothesis Testing (H1–H8)

4.3.1. Common Method Variance

When measurement results are influenced by data collection tools or the survey environment rather than the actual construct being measured, common method bias (CMB) may exist. Given that this study collected data through an online questionnaire, it may be affected by CMB, as pointed out by Batista-Foguet et al. [62]. If not controlled, SEM may misinterpret CMB as a true causal relationship between variables, thereby affecting the explanatory power of the model and the credibility of the research conclusions [63].
In SEM analysis, researchers typically first establish a measurement model to verify the relationships between observed variables and latent variables and then construct a structural model to explore the path relationships between latent variables. Since CMB involves controlling for bias between observed variables, this issue is typically addressed after establishing a measurement model.
We adopted Harman’s single-factor test method to assess the existence of common method bias [63]. Exploratory factor analysis was conducted on all measurement indicators related to the variables in the model, using principal component analysis without rotation extraction. The results showed that the first factor explained 32.373% of the variance, which was lower than the critical value of 40%, indicating that there was no significant common method bias in this study.
To further explore common method bias, we employed confirmatory factor analysis. A structural equation model with a confirmatory single-factor structure was constructed using AMOS 24.0, with all measurement indicators loaded onto a single common factor. The model fit indices did not meet the required standards (χ2/df = 29.064, RMSEA = 0.131, CFI = 0.585, TLI = 0.545, NFI = 0.577, and SRMR = 0.152), indicating that the single-factor model cannot explain the overall data structure and the degree of common method bias is not severe [64].

4.3.2. Structural Equation Model Analysis

This study draws on the theoretical framework of social identity and perceived digital inclusion to examine the impact of these factors on the digital divide. A structural equation model was constructed to address the following question: “Does photography influence digital inclusion?” In this model, photography participation and photographic visual narratives are designated as independent variables; social identity and digital inclusion are mediating variables; digital inclusion is the dependent variable; and age and region are moderating variables.
The structural equation model (SEM) assessing digital integration underwent rigorous goodness-of-fit tests. Table 5 presents the test results: χ2 = 253.291, df = 244, χ2/df = 1.038, SRMR = 0.031, GFI = 0.987, AGFI = 0.984, PGFI = 0.803, NFI = 0.985, RFI = 0.983, IFI = 0.999, TLI = 0.999, CFI = 0.999, RMSEA = 0.005, and PNFI = 0.871. According to the SEM fit criteria described by Wu [65], the assessment indicates that the hypothesized path relationships exhibit good consistency with respect to the observed data. Therefore, the constructed model and its related hypotheses are robust and reflect empirical reality.
Path analysis was performed on SEM, and the results are shown in Table 7. According to the joint significance test, perceived digital inclusion significantly and positively predicted digital inclusion (β = 0.225, p < 0.001). Social identity significantly and positively predicted digital inclusion (β = 0.284, p < 0.001). Participation in photography significantly and positively predicted perceived digital inclusion (β = 0.446, p < 0.001). and social identity (β = 0.313, p < 0.001), and photographic visual narratives significantly and positively predicted digital inclusion (β = 0.294, p < 0.001), while photographic visual narratives also significantly and positively predicted social identity (β = 0.283, p < 0.001). Additionally, perceived digital inclusion significantly and positively predicted social identity (β = 0.162, p < 0.001).
Therefore, it can be inferred that perceived digital inclusion plays a fully mediating role in the relationship between participation in photographic activities and photographic visual narratives and digital inclusion and that social identity plays a fully mediating role in the relationship between participation in photographic activities and photographic visual narratives and digital inclusion. Additionally, perceived digital inclusion and social identity act as chain mediators between participation in photographic activities and digital inclusion and between photographic visual narratives and digital inclusion. Furthermore, as shown in Figure 2, the R2 values for perceived digital inclusion, social identity, and digital inclusion are 0.397, 0.382, and 0.192, respectively.
As shown in Figure 2, the exogenous constructs—participation in photographic activities and photographic visual narratives—jointly explain R2 = 0.397 of perceived digital inclusion and R2 = 0.382 of social identity. Together with the mediators, the model explains R2 = 0.192 of digital inclusion. Although the explanatory power for digital inclusion variance is relatively low, this result is common in related research fields, as the digital divide itself deeply reflects the widespread resource and opportunity inequalities in society [66,67]. All the hypothesized structural paths are positive and significant: from perceived digital inclusion (H1a β = 0.446*, H2a β = 0.294*) to social identity (H1b β = 0.313*, H2b β = 0.283*), from perceived digital inclusion to social identity (H4 β = 0.162*), and from both mediators to digital inclusion (H3a β = 0.225*; H3b β = 0.284*). Bias-corrected bootstrapping (5000 resamples) indicated that the indirect effects via perceived digital inclusion and social identity, as well as the sequential indirect effect (perceived digital inclusion → social identity), are all significant, with 95% CIs not crossing zero (Table 8). Overall, the model exhibits acceptable global fit (Table 7), and the pattern of R2 values indicates moderate explained variance for the mediators and modest explained variance for the outcome, consistent with a mechanism in which photography-related practices influence older adults’ digital inclusion primarily through perceived digital inclusion and social identity.
In summary, the conceptual model constructed in this study successfully passed path testing and model fitness testing based on the theoretical framework. This result indicates that the theoretical framework established in this study applies to the third-level digital divide, where photography among older adults promotes digital inclusion. Furthermore, it also proves that participation in photographic activities and photographic visual narrative characteristics promote social identity and digital inclusion among older adults, in turn further influencing digital inclusion.

4.3.3. Mediating Effect Analysis

We employed the bootstrap method, with 5000 repeated samples, to test the parallel and chain mediation mechanisms between “Perceived digital inclusion” and “Social identity” in the relationship between “Participation in photographic activities/Photographic visual narratives and Digital inclusion” under a 95% confidence interval, aiming to clarify Question 2: “What are the specific mechanisms influencing the digital inclusion of older adults?” As shown in Table 8, participation in photographic activities and photographic visual narratives exerts the greatest mediating effect through digital inclusion, followed by social identity. The chain-like pathway formed by the two, although with smaller coefficients, remains significant, indicating that enhancing inclusion first and then strengthening identity can further promote the narrowing of the digital communication gap among older adults. By combining the effect ratios, it is evident that digital inclusion is the key lever, while the chain mechanism reveals a two-stage progressive psychological process, providing a prioritized pathway for future interventions.
Further analysis indicates that “Perceived digital inclusion” and “Social identity” occupy central positions in all the intermediary pathways, collectively accounting for over 80% of the total effect, suggesting that cognitive and emotional mechanisms play a crucial role in older adults’ digital inclusion. Perceived digital inclusion enhances older adults’ technical confidence and social participation skills relating to photography, while social identity reinforces their subjective sense of being acknowledged and accepted during digital interactions. The synergistic effects of these two factors collectively shape the gradual psychological process through which older adults actively integrate into the digital society.
It is worth noting that although the chain-mediated effect of “Participation in photographic activities/Photographic visual narratives → Perceived digital inclusion → Social identity → Digital inclusion” is relatively weaker than the other pathways, it still reached statistical significance (p < 0.01), indicating that this path has a substantial transmission effect between variables. This chain structure reveals the progressive psychological mechanisms through which older adults progress from technical exposure to emotional resonance to social identity recognition in the process of photography, highlighting the core role of perceived digital inclusion in facilitating the construction of social identity. This mechanism emphasizes older adults’ emotional journey toward being “seen” and “accepted” in the digital realm, particularly in a media environment where visual expression and self-presentation are increasingly important. The chained mediating pathway helps us gain a deeper understanding of this group’s digital inclusion process. Therefore, even though the effect size is relatively small, this pathway should be retained to ensure the model is theoretically complete and has good explanatory power.

4.3.4. Modulation Effect Analysis

Using the PROCESS Model 14 with 5000 bootstrap resamples, we tested whether age (60–65/66–70/71–75/76–80) and region (Eastern/Central/Western/Northern/Southern) moderate the downstream paths from perceived digital inclusion and social identity to digital inclusion. As shown in Table 9, all the interaction terms were deemed non-significant (absolute coefficients ≤ 0.078; t = −0.98 to 1.61; p = 0.108–0.946), and the bias-corrected 95% CIs for each interaction straddled zero. Accordingly, H7a–H8b were not supported. The conditional effects estimated at each age band and region were comparable in magnitude, indicating that—within this already-accessed sample—the two downstream links are statistically stable across age groups and regions.
From a theoretical standpoint, the third-level digital divide emphasizes post-access meaning-making and identity affirmation. Within the low-threshold context of “generalized photographic activity” (everyday shooting–light editing–saving–sharing–interaction), the affect–identity translation achieved via perceived digital inclusion and social identity among older adults may depend less on macro-demographic differences (e.g., age brackets and the five macro-regions) and more on practice intensity, interaction quality, and supportive contexts. Accordingly, we interpret the non-significant age and region interactions as preliminary evidence that—depending on prior access—the two psychological pathways activated by photography exhibit cross-group robustness.
At the same time, we cautiously delineate the study’s boundaries: age and region were grouped into five-year bands and five macro-regions, respectively, and we did not incorporate finer-grained contextual indicators (e.g., family support structures, neighborhood digital infrastructures, prior technological exposure, and objective digital literacy tests). These more proximate contextual and capability differences may, in future work, reveal that micro-level heterogeneity regarding “for-whom” and “in-which-settings” queries in interventions has a greater effect. Accordingly, we treat the age/region moderation tests as baseline evidence that provides a reference point for incorporating finer-grained contextual variables and temporal dynamics (e.g., repeated measures or experience sampling) in subsequent research, rather than as an assertion that contextual differences do not exist.

5. Discussion

Starting from the notion of “generalized photographic activity,” this study distinguishes between “Participation in photographic activities” (behavioral investment in shooting–light editing–saving–sharing–interaction) and “Photographic visual narratives” (meaning-making and self-presentation through photographs) and tests a dual and sequential mediation mechanism whereby these two dimensions affect “Digital inclusion” via “Perceived digital inclusion” and “Social identity.”
The results show that both mediation paths are significant, and the sequential link “Perceived digital inclusion → Social identity” also holds, suggesting that—given prior access—older adults first cultivate familiarity and ease with the digital environment through frequent, low-pressure image-capturing activities [73], then consolidate group belonging and role confirmation through visibility and feedback [74], and ultimately elevate their subjective evaluation of digital inclusion.
Building on existing “Digital inclusion/Social identity” models, our theoretical advancement consists of foregrounding media specificity: photography compresses “production–light editing–publishing–interactive feedback” into the same device and temporal window, yielding a low-friction “image–recognition” closed loop. This loop supplies an operational micro-mechanism for the sequential process “affective accessibility/controllability (Perceived digital inclusion) → relational recognition (Social identity) → subjective inclusion,” thereby not merely placing the two constructs side by side but rather explaining their directionally coupled relation, to show how micro-elements such as practice intensity, visibility, and feedback quality serve as proximate drivers of narrowing the third-level digital divide [74]. Consequently, when image-based activities reduce usage threat and increase the likelihood of being seen/accepted, the “inclusion → identity → integration” pathway is more readily activated.
Notably, according to our analyses (see Table 8 and Figure 2), age and region do not exhibit significant moderation effects on the downstream paths (perceived digital inclusion/social identity → digital inclusion). Within the “already-accessed” sample, the two photography-driven psychological pathways display relatively stable cross-group effects, whose magnitudes do not materially vary by age band or macro-regional division. This does not imply that differences are absent; rather, it suggests that once image-based activities substantially lower the threshold for entering the digital environment, felt inclusion is shaped mainly by the intensity and quality of activity and by the affective and identity-related returns of “being seen—being responded to” [75]. Substantively, this stability is meaningful because it identifies a post-access decoupling from demographic partitions: after a sufficiency of access is met, the drivers of integration shift from age/region to practice-centered micro-processes (visibility, feedback, and recognitional gains), thereby specifying where “the third-level digital divide” mechanism actually operates. Conceptually, this result localizes demographic influence to the access/skills phase while assigning the post-access phase to recognitional dynamics activated efficiently by photography, clarifying the division of labor across stages. Theoretically, this refines the third-level digital divide as a two-stage process consisting of (1) the structural preconditions of access and basic skills (where demographic and regional inequalities matter most) and (2) an affect–identity translation zone in which recognitional dynamics—activated efficiently by photography—become the proximal levers of inclusion and remain relatively invariant across ages/regions. In policy terms, such invariance supports scalable and equitable interventions: Low-barrier “shoot–tell–share” groups can be replicated across regions and age cohorts without redesigning the core mechanism, concentrating resources on improving practice quality (goal clarity, feedback density, and peer support) rather than tailoring by demographics [76,77,78,79].
At the same time, we exercise methodological and contextual restraint in interpreting “non-significance”. First, age and region are operationalized in five-year bands and five macro-regions, respectively, yielding a relatively coarse measurement resolution. Second, the sample distributions by region, urban–rural status, and occupational structure have not been calibrated to the population (e.g., via stratified quotas or weighting); consequently, sample composition may influence downstream effect estimates and reduce our ability to detect finer-grained heterogeneity. Third, the model does not explicitly incorporate key contextual and capacity indicators, such as family support structures, neighborhood digital infrastructure, prior occupational technology exposure, objective digital-literacy assessments, device and network conditions, and platform accessibility/usability. In the future, within a higher-resolution measurement framework, age and region may manifest—via more specific “context variables” (e.g., intergenerational resource allocation within families, community public spaces and digital facilities, and local cultural-capital density)—as conditional effects or as components of sequential mediation [74,80,81]. Put differently, our results offer baseline evidence of cross-group robustness but do not preclude observing structured differences—for which effects are stronger—in finer-grained contexts [80,81].
With respect to explanatory power and theoretical extrapolation, this study also offers several reflections. First, its explanatory power for digital inclusion is limited, indicating the presence of omitted variables and measurement insufficiencies: education and household income; caregiving burden and social-support density; occupational technology exposure and objective digital literacy; device and network conditions; and the accessibility and usability of platform interfaces may all jointly operate with the “inclusion–identity–integration” chain [74,80,81]. Second, the data derive primarily from a single-wave self-report survey; although standard tests for method bias were conducted, common-source bias and social desirability effects could not be fully ruled out. Third, we treated “photographic activity” as an integrative construct; at the scale level, we did not differentiate instrumental/affective/public subdimensions, nor did we distinguish observable behavioral differences such as editing intensity, sharing frequency, audience strata, and privacy strategies. Taken together, these limitations delineate the boundary conditions of our conclusions.
Regarding cross-cultural applicability, we employed older adults in China as a contextualized sample, and the conclusions are initially bounded by China’s cultural context and platform ecology. At the mechanistic level, photography’s low-barrier and visibility-feedback features have potentially transferable applicability across contexts; however, differences in cultures/platforms (e.g., acquaintance-based vs. open social networking), family structures, and community-governance regimes may alter practice visibility, feedback density, and affective returns, thereby shaping the strength of the “inclusion → identity → integration” pathway and the design of interventions. Accordingly, other cultural settings require measurement equivalence and strategy adaptation: on the one hand, we should retain the common core of “low-threshold image practice → visibility → feedback”; on the other hand, we should calibrate to local platform and community-culture preferences regarding “sharing scope, audience strata, and privacy strategies” [76,77,78,79]. Looking ahead, cross-context replications (e.g., in South Korea), following measurement-invariance checks, will be pursued to assess generalizability and guide context-specific adaptation.
In sum, conceiving of photography as an everyday media practice that ordinary older adults can continually engage in helps shift the core problem of the third-level digital divide from “whether access is sufficient” to “how post-access is effectively translated into affect and identity.” This practice-centered perspective indicates that genuinely advancing digital inclusion hinges on building low-threshold, replicable, feedback-rich everyday micro-interventions around narrative experiences of being seen and accepted [75,82,83]. Further unpacking contextual and temporal dynamics is a necessary step to convert “robust average effects” into “precise, context-grounded action” [80,81].

6. Conclusions

6.1. Research Contributions

6.1.1. Theoretical Advances in Media Specificity: From Generic “IT Use” to an “Image-Recognition” Micro-Loop

In contrast to models that treat “Digital inclusion” as generic technology use, we situate photography within a “generalized everyday media practice” perspective and propose, and validate, a media-specific micro-mechanism: a low-friction closed loop consisting of “production–light editing–publishing–feedback,” completed on the same device and within the same sitting, in which practical experience—via visibility and responses—is translated into affective accessibility and relational recognition, thereby enhancing digital inclusion. This “image-recognition” micro-loop furnishes a psychological–interactional micro-foundation for the third-level digital divide [84,85], moving beyond instrumental views of photography as mere data collection or a training aid and offering an operational unit for explaining who achieves inclusion through being seen and accepted, how it is achieved, and on what grounds it is achieved.

6.1.2. Directional Restatement of Process Mechanisms: Defining “Perceived Digital Inclusion” as the Affective Substrate of Social Identity

Within an integrated “Perceived digital inclusion–Social identity” framework, we reconceptualize “inclusion” not as the endpoint of ability/access but as an affective and control state (familiarity, controllability, and low threat) and show via sequential mediation that it directionally precedes social identity (“Perceived digital inclusion → Social identity”), ultimately propelling subjective digital inclusion. This recasts the previously parallel constructs of “Perceived digital inclusion” and “Social identity” as a testable process theory: photographic activity first reduces environmental unfamiliarity and usage threat to form a stable affective substrate and then—through visibility and positive feedback—crystallizes into group belonging and role confirmation. Accordingly, we shift the key to the third-level digital divide from “whether skills are present” to a “sequential translation from affect to identity.”

6.1.3. The “Equalizer” Proposition and Cross-Group Robustness: From Demographic Differences to a Practice-Centered Universal Pathway

Observing non-significant age/region moderation on downstream paths, we advance a testable proposition that, in access-established contexts, photography functions as an equalizing medium that yields a cross-group transferable psychological pathway: what most determines felt inclusion is likely practice intensity, interaction quality, and the recognition loop rather than age or macro-regional partitions per se. This finding provides baseline evidence for a practice-centered account of the third-level digital divide and points to a future research blueprint that incorporates temporal dynamics and contextual granularity (e.g., family support and neighborhood digital infrastructure) to delineate “for whom” and “where” the effects are stronger. Put differently, we move beyond debates about “demographic partitions” toward a theoretical basis for constructing low-threshold, replicable, and feedback-rich pathways grounded in narrative experiences of being seen and accepted.

6.2. Future Development Directions

6.2.1. Enhancing Sensitivity to Time and Context

Taking our finding of relative stability in downstream paths across age and region as a baseline, subsequent work can situate the “photography → perceived inclusion → Social identity → inclusion” chain within more every day, dynamic contexts—such as family support, neighborhood digital infrastructures, community norms, and peer climates—attending to short-term fluctuations and enduring effects across different life rhythms and stages. The goal is not to draw directional conclusions immediately but to more clearly delineate the boundary conditions under which effects are stronger or weaker, thereby addressing reviewers’ calls for greater temporal and contextual sensitivity.

6.2.2. Refining the “Spectrum of Photographic Activity” and Testing Shallow-to-Deep Transfer

This study approaches photography as a generalized everyday practice. Future work can decompose it into identifiable sub-dimensions (e.g., instrumentality, affectivity, and publicness) and key stages (shooting, light editing, sharing/narrating, and interactive feedback) to avoid treating photography as a single, undifferentiated act. In parallel, we recommend extending outcomes beyond “subjective inclusion” to institutional and high-barrier scenarios (e-government services, telemedicine, mobile finance, etc.) to assess whether image-based practices rooted in leisure and socializing can transform into deeper digital competencies and confidence. Moreover, the risks of “functional monofocus/shallow gratification” and the pathways out of them should be continuously tracked (e.g., deepening practice via more goal-directed tasks and feedback mechanisms).

6.2.3. Strengthening External Generalizability and Robustness of Evidence

To address concerns about “generalizability and explanatory power,” researchers can conduct replication–comparisons across cultures and platform ecologies and improve measurement to reduce bias, e.g., by balancing questionnaire wording and triangulating self-reports with multiple sources such as objective behaviors and third-party observations. Key variables should also be added—education and income, prior occupational tech exposure, caregiving burden, objective digital literacy, device and network conditions, and social-support density—to better account for sources of variation in the “inclusion → identity → integration” pathway.

Author Contributions

Conceptualization, Q.C. and M.C.; methodology, Q.C. and M.C.; software, Q.C. and M.C.; validation, Q.C. and M.C.; formal analysis, Q.C.; investigation, Q.C.; resources, Q.C. and M.C.; data curation, Q.C. and M.C.; writing—original draft preparation, Q.C.; writing—review and editing, M.C.; visualization, Q.C. and M.C.; supervision, M.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki. The Department of Global Convergence, Kangwon National University, waived the need for ethics approval (15 April 2025). The department determined that this project is scientifically sound, ethical, and poses no risk to participants, given that it does not involve invasive procedures.

Informed Consent Statement

Informed consent was obtained from all subjects involved in this study.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Applsci 15 11647 g001
Figure 1. Conceptual model.
Figure 1. Conceptual model.
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Figure 2. SEM model diagram.
Figure 2. SEM model diagram.
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Table 1. Main variables and references used in this study.
Table 1. Main variables and references used in this study.
VariableMeasurement ItemsReference(s)
Digital inclusionDI1After taking part in photography activities, are you more willing to use digital devices for other daily activities?Lishinski et al. (2021) [64]
Andrade et al. (2009) [65]
DI2Do you feel that participating in photography has noticeably increased your involvement in digital society?
DI3Do photography activities make you feel that the gap between your digital skills and those of younger people has decreased?
DI4Has photography helped increase your confidence in using digital technology in daily life?
DI5Is your main reason for participating in photography activities to improve your own digital skills?
Perceived digital
inclusion		PDI1Do you feel that photography makes it possible for you to participate equally in the digital society?Johansson et al. (2021) [66]
PDI2Has participating in photography increased your sense of connection with digital technology?
PDI3Do you think photography activities have helped eliminate your sense of distance from digital technology?
PDI4Has participating in photography made you more willing to try out new digital technologies?
Social identitySI1Has photography made it easier for you to keep in touch with family and friends?Seifert et al. (2020) [67]
SI2Do you feel that photography has helped you gain more recognition from others?
SI3Has participating in photography enhanced your sense of Social identity?
Participation in photographic activityPPP1Do you think participating in photography has made your social life richer?Neves et al. (2018) [31]
PPP2Do you think digital photography sparks your interest in learning?
PPP3Do you see participating in photography as a way to improve your quality of life?
PPP4Do you feel that photography is an easy way to record and understand your life?
PPP5Do you think photography is an enjoyable way to express your feelings and stories?
PPP6Are you more willing to record and share important moments with others through photography?
Photographic visual narrativesPVN1Do visual stories make you more interested in learning digital technology?Canorro et al. (2024) [68]
PVN2Do you find it easy and simple to operate photographic equipment?
PVN3Do digital photography apps give you clear instructions and guidance on how to use them?
PVN4Do you think digital photography tools are designed to be very user-friendly?
PVN5Does using photographic equipment reduce your anxiety about learning new technology?
PVN6Do you feel more connected to society through photography activities?
Table 2. Descriptive analysis of the data samples.
Table 2. Descriptive analysis of the data samples.
Age RangeNPercentage (%)RegionNPercentage (%)
60–6564239.46Eastern28217.33
66–7047929.44Central31519.36
71–7534120.96Western42726.24
76–8016510.14Northern23114.2
Southern37222.86
Age rangeEastern (%)Central (%)Western (%)Northern (%)Southern (%)
60–65116 (18.07%)118 (18.38%)163 (25.39%)92 (14.33%)153 (23.83%)
66–7087 (18.16%)83 (17.33%)132 (27.56%)68 (14.20%)109 (22.76%)
71–7550 (14.66%)74 (21.70%)97 (28.45%)49 (14.37%)71 (20.82%)
76–8029 (17.58%)40 (24.24%)35 (21.21%)22 (13.33%)39 (23.64%)
Table 3. Results of the reliability analysis.
Table 3. Results of the reliability analysis.
VariableNumber of ItemsCronbach’s α
Digital inclusion50.825
Perceived digital inclusion40.847
Social identity30.779
Participation in photographic activities60.891
Photographic visual narratives60.874
Table 4. Results of validity and reliability analysis.
Table 4. Results of validity and reliability analysis.
CategoryFactor LoadingsCommon Factor
Variance
(Extracted)		EigenvalueVariance
Explained (%)		CRAVE
12345
Digital inclusion10.766 0.5913.00012.5000.8250.486
20.748 0.600
30.747 0.587
40.746 0.557
50.727 0.617
Perceived digital inclusion1 0.727 0.6882.68611.1910.8470.581
2 0.770 0.694
3 0.768 0.685
4 0.761 0.680
Social identity1 0.780 0.6782.0318.4610.7790.541
2 0.780 0.704
3 0.756 0.704
Participation in photographic activities1 0.777 0.6503.91616.3160.8910.577
2 0.768 0.656
3 0.765 0.642
4 0.758 0.666
5 0.756 0.647
6 0.740 0.632
Photographic visual narratives1 0.7600.6183.73915.5790.8740.536
2 0.7560.606
3 0.7560.634
4 0.7500.625
5 0.7460.609
6 0.7350.602
Total variance explained = 64.047%; KMO = 0.935; Bartlett’s test of sphericity, χ2 = 17,221.256, df = 276.
Table 5. Results of the model-fitting analysis.
Table 5. Results of the model-fitting analysis.
Statistical TestingAbsolute Fitness IndicesValue-Added Adaptation IndicesParsimonious Fitness Indices
χ2/dfRMSEAGFINFIIFICFIPNFIPCFIPGFI
Adaptation standard
Parameter		<3≤0.05>0.90>0.90>0.90>0.90>0.50>0.50>0.50
1.0380.0050.9870.9850.9990.9990.8710.8840.803
Other parameters: n = 1627; χ2 = 253.291; df = 244; p = 0.328.
Table 6. Results of discriminant validity analysis.
Table 6. Results of discriminant validity analysis.
CRAVEMSVMaxR(H)AVE > MSVDigital InclusionPerceived Digital InclusionSocial IdentityParticipation in
Photographic Activities		Photographic Visual
Narratives
Digital inclusion0.8250.4860.3590.825TRUE0.697
Perceived digital inclusion0.8470.5810.3590.847TRUE0.599 ***0.762
Social identity0.7790.5410.3230.779TRUE0.525 ***0.568 ***0.736
Participation in photographic activities0.8910.5770.3580.891TRUE0.539 ***0.598 ***0.518 ***0.760
Photographic visual narratives0.8740.5360.3170.874TRUE0.488 ***0.563 ***0.39 ***0.427 ***0.732
Other parameters: *** p < 0.001.
Table 7. Model path verification.
Table 7. Model path verification.
Hypothesis PathStd
Estimate		S.E.C.R.pResults
H1a. Participation in photographic activities → Perceived digital inclusion0.4460.0314.853***Supported
H1b. Participation in photographic activities → Social identity0.3130.038.972***Supported
H2a. Photographic visual narratives → Perceived digital inclusion0.2940.03210.251***Supported
H2b. Photographic visual narratives → Social identity0.2830.0318.725***Supported
H3a. Perceived digital inclusion → Digital inclusion0.2250.0276.604***Supported
H3b. Social identity → Digital inclusion0.2840.0347.916***Supported
H4. Perceived digital inclusion → Social identity0.1620.0314.473***Supported
Other parameters: *** p < 0.001.
Table 8. Results of the mediation effect test.
Table 8. Results of the mediation effect test.
Hypothesis PathStd
Estimate		Std.
Error		Bias-Corrected Confidence Interval (95%)p-ValueEffect Ratio
LowerUpper
H5a. Participation in photographic activities → Perceived digital inclusion → Digital inclusion0.2100.0210.1690.252***38.36%
H5b. Participation in photographic activities → Social identity → Digital inclusion0.0460.0140.0200.072**8.41%
H5c. Photographic visual narratives → Perceived digital inclusion → Digital inclusion0.1600.0190.1220.196***29.23%
H5d. Photographic visual narratives → Social identity → Digital inclusion0.0480.0170.0180.078**8.77%
H6a. Participation in photographic activities → Perceived digital inclusion → Social identity → Digital inclusion0.0370.0100.0190.055**6.76%
H6b. Photographic visual narratives → Perceived digital inclusion → Social identity → Digital inclusion0.0380.0090.0200.055**6.94%
Total effect0.5480.0480.4660.627***100%
Other parameters: ** p < 0.01, *** p < 0.001.
Table 9. Results of mediation analysis.
Table 9. Results of mediation analysis.
Hypothesis PathPath
Coefficient		Std. Errort-Valuep-ValueTesting Results
Age (60–65) × Social identity → Digital inclusion0.0060.0410.1540.878Failed
Age (66–70) × Social identity → Digital inclusion−0.0430.044−0.9780.328Failed
Age (71–75) × Social identity → Digital inclusion0.0420.0510.8210.412Failed
Age (76–80) × Social identity → Digital inclusion0.0040.0620.0680.946Failed
Region (Eastern) × Social identity → Digital inclusion−0.0780.052−1.5010.134Failed
Region (Central) × Social identity → Digital inclusion0.0210.0530.4050.686Failed
Region (Western) × Social identity → Digital inclusion0.0740.0461.6070.108Failed
Region (Northern) × Social identity → Digital inclusion0.0480.0560.8500.396Failed
Region (Southern) × Social identity → Digital inclusion−0.0620.047−1.3310.183Failed
Age (60–65) × Social identity → Digital inclusion0.0130.0390.3330.739Failed
Age (66–70) × Social identity → Digital inclusion0.0190.0410.4480.654Failed
Age (71–75) × Social identity → Digital inclusion−0.0390.045−0.8530.394Failed
Age (76–80) × Social identity → Digital inclusion−0.0100.063−0.1530.879Failed
Region (Eastern) × Social identity → Digital inclusion0.0130.0500.2640.792Failed
Region (Central) × Social identity → Digital inclusion−0.0050.047−0.1120.911Failed
Region (Western) × Social identity → Digital inclusion−0.0210.043−0.4740.635Failed
Region (Northern) × Social identity → Digital inclusion0.0330.0530.6170.537Failed
Region (Southern) × Social identity → Digital inclusion−0.0130.045−0.2810.779Failed
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Cheng, Q.; Chen, M. Unraveling the Dynamics of Digital Inclusion: Exploring the Third-Level Digital Divide Among Older Adults in China. Appl. Sci. 2025, 15, 11647. https://doi.org/10.3390/app152111647

AMA Style

Cheng Q, Chen M. Unraveling the Dynamics of Digital Inclusion: Exploring the Third-Level Digital Divide Among Older Adults in China. Applied Sciences. 2025; 15(21):11647. https://doi.org/10.3390/app152111647

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Cheng, Qian, and Maowei Chen. 2025. "Unraveling the Dynamics of Digital Inclusion: Exploring the Third-Level Digital Divide Among Older Adults in China" Applied Sciences 15, no. 21: 11647. https://doi.org/10.3390/app152111647

APA Style

Cheng, Q., & Chen, M. (2025). Unraveling the Dynamics of Digital Inclusion: Exploring the Third-Level Digital Divide Among Older Adults in China. Applied Sciences, 15(21), 11647. https://doi.org/10.3390/app152111647

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