Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6, 2719-2730 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i6.8467 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 15 April 2025; Revised: 13 June 2025; Accepted: 17 June 2025; Published: 30 June 2025 * Correspondence: pattama@rmutt.ac.th Cultural foundations of welfare: The role of traditional values and community norms in shaping elderly well-being in China Ming Anqi1, Pattama Pasitpakakul2*, Sanitdech Jintana3 1,2,3Faculty of Liberal Arts, Rajamangala University of Technology Thanyaburi, Pathumthani, Thailand, anqi_m@mail.rmutt.ac.th (M.A.) pattama@rmutt.ac.th (P.P.) sanitdech_j@rmutt.ac.th (S.J.) Abstract: As China deals with a rapidly aging population, understanding the cultural factors that influence elderly welfare is becoming increasingly important. This study examines how Traditional Family Values (TFV) and Community Cultural Norms (CCN) affect Elderly Welfare Outcomes (EWO). It focuses on the roles of Policy Awareness (PA) and Policy Implementation (PI) in this process. The research was conducted in Zigong, a city with diverse demographics, using a stratified sampling method to represent different ethnic, socioeconomic, and urban-rural groups. Data were collected from 513 elderly individuals and analyzed using Structural Equation Modeling (SEM) through SmartPLS to test ten proposed relationships. The results indicate that TFV has a direct positive effect on EWO, but its influence through PA and PI is less significant. Conversely, CCN has a greater impact on welfare outcomes by enhancing policy awareness and implementation. These findings suggest that being informed about public policies strengthens the positive effects of cultural norms on elderly welfare. The study concludes that increasing policy literacy and adopting inclusive implementation strategies can help translate cultural alignment into tangible welfare improvements. This provides practical insights for policymakers aiming to develop aging policies that address cultural needs. Keywords: Community cultural norms, Elderly welfare, Policy awareness, Policy implementation, Traditional family values. 1. Introduction China is entering a phase of “super-aged” society status, with projections estimating that by 2030, nearly one in four citizens will be over the age of 60, equating to more than 400 million individuals [1]. This demographic transformation is largely the result of sustained increases in life expectancy—now exceeding 77 years—and persistently declining fertility rates, which have dropped to record lows of 1.09 in 2023 [2]. The "4-2-1 family structure," a direct outcome of the One Child Policy (1979–2015), places the responsibility of caring for two parents and four grandparents on a single child. This demographic shift has resulted in substantial caregiver burdens, emotional strain, and financial challenges for younger generations [3]. As a result, traditional family-based eldercare arrangements are eroding, particularly in urban centers, where younger adults migrate for work and leave aging parents in rural or suburban areas. This rapid shift is accompanied by rising rates of “empty-nest elderly” households, where older adults live alone or only with a spouse—often in poor health or with limited support [1]. The prevalence of social isolation among the elderly is 33%, with higher rates in those over 80, living alone, and lacking higher education. This highlights the importance of addressing social isolation as a public health concern [4]. The weakening of intergenerational co-residence is also accelerating the need for institutional eldercare solutions, which many older Chinese still view with cultural ambivalence or stigma due to Confucian ideals that associate institutionalization with filial failure [3, 5]. These dynamics have profound implications for social welfare systems, which were historically underdeveloped but are now expected to shoulder an increasing share of responsibility for eldercare. Although the Chinese government has launched several policy initiatives—such as the https://orcid.org/0000-0002-3657-3674 2720 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 2719-2730, 2025 DOI: 10.55214/25768484.v9i6.8467 © 2025 by the authors; licensee Learning Gate Healthy China 2030 agenda and the Filial Piety Law—to encourage active aging and formalize intergenerational support, implementation gaps and regional disparities remain [1, 3]. In this context, there is a pressing need to develop welfare policies that are not only scalable and efficient but also culturally congruent—recognizing the enduring influence of traditional values even amidst structural modernization. Amid China’s demographic transformation, traditional cultural values—particularly filial piety (xiào), a foundational element of Confucian morality—continue to play a central role in shaping societal expectations regarding eldercare [6, 7]. Filial piety encompasses a multidimensional duty for adult children to provide emotional, financial, and physical support to their aging parents, guided by a moral obligation that transcends legal mandates [3, 8, 9]. Historically, this cultural ethos fostered strong intergenerational cohesion and informal caregiving arrangements within extended family systems. However, the forces of urbanization, labor mobility, and socio-economic modernization have weakened the practical observance of these values. As families become more geographically dispersed and nuclear in structure, the capacity—and in some cases, the willingness—of adult children to fulfill filial responsibilities has diminished significantly [1, 10]. This erosion is increasingly evident in the rise of elder neglect and isolation, now widely reported in both public health data and media narratives. To address this normative decline, the Chinese government enacted the Filial Piety Law in 2013, legally mandating adult children to maintain frequent contact and provide support to parents over the age of 60. While symbolically significant, enforcement mechanisms are largely ineffective, as they depend on elderly parents initiating formal complaints—a process deterred by stigma and a deeply ingrained cultural aversion to “public shaming” of family issues [3, 11]. Moreover, public opinion surveys have shown a shift in perceived responsibility: fewer than 5% of Chinese respondents now believe that children should be the primary providers of eldercare, with the majority assigning this role to the government [1, 3]. Despite these shifts, cultural norms still exert a latent influence on eldercare policy and practice. Studies have demonstrated that perceptions of filial duty continue to shape attitudes toward institutional care, with many older adults associating nursing homes with abandonment and family failure [6, 12, 13]. This cultural stigma limits the scalability of formal care services and underscores the need for culturally embedded welfare systems that integrate traditional values into modern caregiving models. Rather than displacing filial piety, public policies must find ways to reinforce and adapt it—through intergenerational engagement programs, community-based services, and culturally sensitive messaging—to sustain eldercare in a rapidly changing society [14, 15]. This study is anchored in a multi-dimensional theoretical framework integrating five interrelated constructs: Traditional Family Values (TFV), Community Cultural Norms (CCN), Policy Awareness (PA), Policy Implementation (PI), and Elderly Welfare Outcomes (EWO). Traditional Family Values (TFV) are rooted in Confucian moral expectations, particularly filial piety, which dictates that adult children provide for aging parents out of reverence and familial duty. These values represent micro- level cultural capital that influences care behaviors and expectations within households [16, 17]. Community Cultural Norms (CCN) expand this lens to the meso-social level, reflecting shared local attitudes and behaviors that reinforce or challenge individual caregiving roles, especially in semi-urban or rural settings where collectivist traditions persist [18]. Policy Awareness (PA) reflects the extent to which older adults and their families are informed about existing welfare programs and rights. It is a crucial intermediary that can empower individuals to access state support, especially in rapidly changing policy environments [19]. Policy Implementation (PI), on the other hand, denotes the efficacy with which these programs are operationalized at the community level—factoring in bureaucratic capacity, service delivery, and local governance responsiveness [20, 21]. Finally, Elderly Welfare Outcomes (EWO) encompass multidimensional indicators of well-being, including physical health, social integration, financial security, and psychological well-being. These outcomes are not only shaped by institutional support but also by the socio-cultural matrix in which older adults reside. The integrative model underscores that cultural constructs like TFV and CCN may influence EWO both directly and indirectly through the mediating mechanisms of PA and PI. As suggested by prior research, such 2721 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 2719-2730, 2025 DOI: 10.55214/25768484.v9i6.8467 © 2025 by the authors; licensee Learning Gate models are essential for understanding the interaction between traditional norms and evolving welfare systems in China’s unique socio-political context [17, 19]. While previous studies have extensively explored demographic and socioeconomic determinants of elderly welfare in China, there remains a significant gap in understanding how cultural and policy constructs interact within a unified framework. Much of the current literature focuses on either traditional family dynamics [3, 22] or institutional welfare mechanisms [23] yet few have empirically examined their intersection—particularly the mediating role of policy awareness and implementation within culturally embedded contexts. Moreover, while cultural constructs like filial piety and community norms are acknowledged as important influences on elder care expectations, their direct and indirect effects on welfare outcomes have not been rigorously modeled using statistical approaches like Structural Equation Modeling (SEM). Recent work emphasizes the importance of capturing these dynamics, suggesting that traditional values and modern governance must be co-evaluated to understand evolving eldercare systems [24-26]. Yet empirical studies applying SEM to integrate Traditional Family Values (TFV), Community Cultural Norms (CCN), Policy Awareness (PA), and Policy Implementation (PI) in predicting Elderly Welfare Outcomes (EWO) remain rare. This study seeks to bridge that gap by employing SEM to test a multi-dimensional framework that includes both cultural and institutional factors. Specifically, we examine the direct and indirect pathways from TFV and CCN to EWO, mediated by PA and PI. This approach aligns with calls for more context-sensitive and culturally grounded policy research in rapidly aging societies [1]. It also addresses implementation gaps noted in previous policy evaluations, highlighting the importance of both awareness and action in translating policy into effective care [23, 26]. Through this investigation, we aim to (1) empirically validate the role of cultural values in shaping elderly welfare, (2) identify mediating policy mechanisms, and (3) offer insights for culturally responsive welfare policymaking in China and similar contexts. 2. Methods 2.1. Participants A total of 513 elderly individuals from Zigong participated in this study, exceeding the minimum target sample size of 400. Stratified sampling was employed to ensure broad representation across subgroups defined by ethnicity, socioeconomic status, and residence (urban vs. rural). The sample included 262 males (51.07%) and 251 females (48.93%). Participants ranged in age from 60 years and above, with the largest group aged 65–69 (37.23%). Most respondents were single (67.84%), had primary school education (49.12%), and reported monthly incomes between 2000–6000 RMB. A majority (59.06%) resided in rural areas. This sampling approach aimed to capture the demographic diversity of Zigong’s elderly population, enhancing the generalizability of the study’s findings. 2.2. Measures The study utilized a structured questionnaire comprising five core constructs: Traditional Family Values (TFV), Community Cultural Norms (CCN), Policy Awareness (PA), Policy Implementation (PI), and Elderly Welfare Outcomes (EWO), each measured using multiple items rated on a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). TFV (Cronbach's α = 0.887) was operationalized through three subdimensions—Filial Piety, Patrilineal Structures, and Gender Roles—capturing normative expectations around familial duty and hierarchy. CCN (α = 0.633) included Social Practices, Social Behavior, and Cooperation/Fairness/Trust, reflecting communal influences on behavior and cohesion. PA (α = 0.721) assessed individuals' awareness and understanding of elderly welfare policies, covering four subdomains: knowledge of policy existence, understanding of policy objectives, perception of policy impacts, and knowledge of implementation processes. PI (α = 0.772) measured the degree of policy compliance, adequacy of financial provisions, and stakeholder engagement in implementation. EWO (α = 0.708) encompassed five dimensions, including health outcomes, satisfaction, psychosocial support, caregiving experiences, and broader societal impacts. The Cronbach’s alpha values indicated 2722 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 2719-2730, 2025 DOI: 10.55214/25768484.v9i6.8467 © 2025 by the authors; licensee Learning Gate acceptable to high internal consistency for all constructs [27] supporting the reliability of the measurement scales employed in the study. 2.3. Data Collection Data collection for this study employed a mixed-method approach, combining face-to-face interviews and online surveys to accommodate the diverse accessibility needs and preferences of the elderly population in Zigong. Trained interviewers conducted in-person interviews, adhering to standardized protocols to ensure accuracy, consistency, and participant comfort. This method proved particularly effective for elderly individuals facing cognitive, physical, or literacy-related challenges, and helped foster trust and candid responses [28, 29]. Simultaneously, online surveys were distributed through local community organizations and widely-used social media platforms to engage participants more familiar with digital technology, particularly in urban settings. These digital instruments featured clear instructions and intuitive interfaces to minimize usability barriers and improve response rates, addressing concerns related to the digital divide among older adults [30]. By leveraging both traditional and digital tools, and disseminating through trusted community channels, the study ensured inclusivity and representativeness. This mixed-method strategy supported the collection of high- quality, comprehensive data and facilitated a richer understanding of the elderly’s perspectives on welfare policies [31]. 2.4. Data Analysis Data analysis for this study will involve both descriptive and inferential statistical techniques. Descriptive statistics will be employed to summarize the demographic characteristics of the sample, including age, gender, marital status, educational level, income, and residence. These will include measures of central tendency (mean, median, mode) and variability (standard deviation, range) for continuous variables, along with frequency distributions for categorical variables, providing a foundational understanding of the dataset and highlighting patterns or outliers [32]. To examine the hypothesized relationships among Traditional Family Values (TFV), Community Cultural Norms (CCN), Policy Awareness (PA), Policy Implementation (PI), and Elderly Welfare Outcomes (EWO), Structural Equation Modeling (SEM) will be conducted using SmartPLS software. SEM allows simultaneous assessment of multiple relationships between observed and latent variables, integrating factor analysis and multiple regression [33]. The analysis will proceed in two stages. First, the measurement model will be evaluated to ensure the reliability and validity of the constructs through Confirmatory Factor Analysis (CFA), assessing convergent validity via factor loadings (>0.70), Average Variance Extracted (AVE >0.50), and composite reliability, as well as discriminant validity by comparing AVE values with squared inter-construct correlations [27, 34, 35]. Second, the structural model will test hypothesized paths among variables, using path analysis and model fit indices such as the Standardized Root Mean Square Residual (SRMR <0.08), Normed Fit Index (NFI >0.90), and Comparative Fit Index (CFI >0.90) to evaluate model adequacy [36]. The significance and stability of path coefficients will be determined using a bootstrapping procedure with 5000 resamples [33], enabling robust conclusions about the relationships within the conceptual framework. 3. Results 3.1. Assessment of Measurement Model Before testing the structural model, the measurement model must be rigorously assessed to ensure the constructs are measured reliably and validly. In this study, all constructs were modeled reflectively and evaluated using SmartPLS 4. Following established PLS-SEM guidelines, internal consistency reliability was assessed using Cronbach’s alpha and composite reliability (CR), with acceptable thresholds of ≥ 0.70 for both metrics. Convergent validity was examined through Average Variance Extracted (AVE), where values ≥ 0.50 indicate that a construct explains more than half the variance of its indicators. To assess discriminant validity, both the Fornell-Larcker criterion and the Heterotrait- 2723 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 2719-2730, 2025 DOI: 10.55214/25768484.v9i6.8467 © 2025 by the authors; licensee Learning Gate Monotrait (HTMT) ratio were used. Fornell-Larcker requires that a construct’s AVE square root exceeds its correlations with other constructs, while HTMT values should be < 0.85 (or < 0.90 in more lenient contexts) to confirm discriminant validity [37, 38]. These criteria collectively ensure that each construct accurately reflects its theoretical definition and is distinct from other constructs in the model. Table 1. Measurement Model Assessments. Item Loadings VIF α rho_a rho_c AVE Community Cultural Norms (CCN) 0.772 0.784 0.776 0.538 CFT 0.650 1.381 SB 0.731 1.835 SP 0.811 1.786 Elderly Welfare Outcomes (EWO) 0.881 0.882 0.881 0.598 CAS 0.748 2.515 ECBI 0.774 1.925 HIO 0.817 2.657 PPSN 0.746 2.406 SAC 0.778 2.480 Policy Awareness (PA) 0.771 0.775 0.772 0.532 KPE 0.765 1.610 PPI 0.750 1.653 UPO 0.669 1.502 Policy Implementation (PI) 0.786 0.788 0.785 0.549 COM 0.686 1.759 FA 0.737 1.866 SE 0.796 1.475 Traditional Family Values (TFV) 0.906 0.909 0.907 0.764 GR 0.923 2.971 FP 0.867 2.986 PS 0.831 2.896 Table 1 presents the results of the measurement model assessment, confirming that all constructs demonstrate acceptable levels of reliability and validity. The indicator loadings for all items exceed the recommended threshold of 0.650, indicating strong individual item reliability [37]. Variance Inflation Factor (VIF) values for all indicators are below the conservative cutoff of 3.0, suggesting no problematic multicollinearity among indicators [38]. Internal consistency reliability is confirmed by Cronbach’s alpha (α), rho_A, and composite reliability (rho_c), with all values surpassing the minimum acceptable threshold of 0.70. Convergent validity is also supported, as the Average Variance Extracted (AVE) for each construct is above the 0.50 criterion, indicating that the constructs capture more than half of the variance of their respective indicators [37]. These results affirm the robustness of the measurement model and ensure the constructs are appropriately measured for further structural model analysis. 2724 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 2719-2730, 2025 DOI: 10.55214/25768484.v9i6.8467 © 2025 by the authors; licensee Learning Gate Table 2. Cross Loadings. CCN EWO PA PI TFV CFT 0.650 0.437 0.514 0.500 0.542 SB 0.731 0.490 0.579 0.526 0.513 SP 0.811 0.553 0.633 0.549 0.575 CAS 0.471 0.748 0.491 0.566 0.563 ECBI 0.502 0.774 0.524 0.572 0.590 HIO 0.585 0.817 0.541 0.585 0.630 PPSN 0.507 0.746 0.483 0.542 0.573 SAC 0.543 0.778 0.523 0.574 0.588 KPE 0.587 0.516 0.765 0.594 0.549 PPI 0.558 0.524 0.750 0.610 0.508 UPO 0.581 0.403 0.669 0.519 0.390 COM 0.500 0.526 0.519 0.686 0.384 FA 0.540 0.539 0.586 0.737 0.380 SE 0.550 0.568 0.646 0.796 0.422 GR 0.658 0.662 0.574 0.460 0.867 FP 0.665 0.681 0.643 0.503 0.923 PS 0.615 0.655 0.524 0.434 0.831 Table 2 displays the cross-loadings of each item on all latent constructs to assess discriminant validity. In PLS-SEM, an indicator is expected to load highest on its corresponding construct compared to other constructs, thereby confirming item specificity [37]. The results show that all indicators exhibit their highest loading on their intended constructs, supporting discriminant validity. For instance, the item SP (Social Practices) loads highest on Community Cultural Norms (0.811), while HIO (Health Impacts and Outcomes) loads most strongly on Elderly Welfare Outcomes (0.817), and FP (Filial Piety) demonstrates the highest loading on Traditional Family Values (0.923). These findings confirm that each item is conceptually and statistically aligned with its designated latent variable, ensuring that the constructs are empirically distinct from each other [38]. Table 3. Heterotrait-Monotrait ratio. CCN EWO PA PI TFV CCN EWO 0.677 PA 0.794 0.661 PI 0.720 0.734 0.786 TFV 0.746 0.762 0.662 0.532 Table 3 presents the Heterotrait-Monotrait (HTMT) ratios used to evaluate discriminant validity among the latent constructs. HTMT is considered a superior criterion for detecting lack of discriminant validity, especially in models with conceptually related constructs [39]. A commonly accepted threshold is 0.85, although a more lenient cut-off of 0.90 is occasionally used in exploratory research [37]. In this study, all HTMT values fall below 0.85, with the highest being 0.794 between Community Cultural Norms (CCN) and Policy Awareness (PA). This confirms that the constructs are empirically distinct and do not suffer from multicollinearity or conceptual overlap. The results strengthen the argument that each latent variable captures a unique dimension of the theoretical model, validating the appropriateness of the measurement structure for further structural model analysis. 2725 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 2719-2730, 2025 DOI: 10.55214/25768484.v9i6.8467 © 2025 by the authors; licensee Learning Gate Table 4. Fornell-Larcker criterion. CCN EWO PA PI TFV CCN 0.733 EWO 0.676 0.773 PA 0.787 0.663 0.729 PI 0.715 0.734 0.790 0.741 TFV 0.739 0.762 0.666 0.534 0.874 Table 4 presents the Fornell-Larcker criterion, a classic method for assessing discriminant validity in reflective measurement models. According to this criterion, the square root of each construct’s Average Variance Extracted (AVE)—shown on the diagonal—should be greater than its correlations with any other construct in the model [34]. In this study, the diagonal values (e.g., 0.773 for Elderly Welfare Outcomes, 0.729 for Policy Awareness, and 0.874 for Traditional Family Values) are all higher than the off-diagonal correlations in their respective rows and columns. This confirms that each construct shares more variance with its indicators than with other latent variables, thus satisfying the Fornell-Larcker criterion. When used alongside HTMT and cross-loading analyses, these results robustly affirm that all constructs in the model possess adequate discriminant validity [37]. 3.2. Assessment of Structural Model Following the validation of the measurement model, the next step involves assessing the structural model to evaluate the hypothesized relationships among latent constructs. Structural model assessment in Partial Least Squares Structural Equation Modeling (PLS-SEM) focuses on examining the model’s explanatory power and the significance of path coefficients. This includes evaluating the coefficient of determination (R²) for endogenous variables, path coefficients and their significance using bootstrapping procedures, and effect sizes (f²). In addition, potential issues of collinearity are assessed through Variance Inflation Factor (VIF) values to ensure that predictor constructs do not distort estimation results. These analyses provide insight into how well the theoretical model explains and predicts the target constructs, particularly how Traditional Family Values, Community Cultural Norms, Policy Awareness, and Policy Implementation influence Elderly Welfare Outcomes [37, 38]. Table 5. Model Fit. Saturate model Estimated model SRMR 0.035 0.039 d_ULS 0.188 0.228 d-G 0.170 0.175 Chi-square 348.569 358.569 NFI 0.914 0.912 Table 5 reports model fit indices comparing the saturated and estimated models, providing evidence of how well the proposed structural model represents the observed data. The Standardized Root Mean Square Residual (SRMR), a key absolute measure of fit in PLS-SEM, is well below the recommended threshold of 0.08 for both models, indicating an excellent overall fit [39]. The d_ULS (squared Euclidean distance) and d_G (geodesic distance) values—used to compare the empirical and model- implied correlation matrices—are both low, suggesting minimal discrepancy and reinforcing the model's adequacy [37]. The Chi-square values, though traditionally used in covariance-based SEM, are provided here for completeness but are not emphasized in PLS-SEM due to its non-parametric nature. The Normed Fit Index (NFI) values exceed 0.90, further supporting acceptable model fit. Collectively, these metrics demonstrate that the structural model fits the data well and is appropriate for hypothesis testing. 2726 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 2719-2730, 2025 DOI: 10.55214/25768484.v9i6.8467 © 2025 by the authors; licensee Learning Gate Figure 1. SEM model. Table 6. R-square. R-square R-square adjusted EWO 0.735 0.733 PA 0.635 0.634 PI 0.624 0.623 Table 6 presents the R-square (R²) and adjusted R-square values for the endogenous constructs— Elderly Welfare Outcomes (EWO), Policy Awareness (PA), and Policy Implementation (PI). The R² value indicates the proportion of variance in an endogenous variable that is explained by its predictors in the structural model. According to established benchmarks, values of 0.75, 0.50, and 0.25 can be considered substantial, moderate, and weak, respectively [37]. In this study, EWO exhibits a substantial R² of 0.735, suggesting that a significant portion of its variance is accounted for by Policy Implementation and Traditional Family Values. PA and PI also show moderately strong R² values (0.635 and 0.624, respectively), indicating that Traditional Family Values and Community Cultural Norms are meaningful predictors of policy-related constructs. The adjusted R² values are closely aligned with the R² values, demonstrating that the model remains robust even when accounting for model complexity and sample size. Table 7. f-square matrix. CCN EWO PA PI TFV CCN 0.014 0.527 EWO PA 1.656 PI 0.475 TFV 0.560 0.043 2727 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 2719-2730, 2025 DOI: 10.55214/25768484.v9i6.8467 © 2025 by the authors; licensee Learning Gate Table 7 provides the f-square (f²) values, which indicate the effect size of each exogenous construct on the endogenous variables in the model. In PLS-SEM, f² values help quantify the individual contribution of a predictor construct, with standard thresholds being 0.02 (small), 0.15 (medium), and 0.35 (large) [37]. The results show that Policy Awareness (PA) has a very large effect on Policy Implementation (f² = 1.656), highlighting its dominant role in shaping policy execution. Traditional Family Values (TFV) demonstrate a large effect on Elderly Welfare Outcomes (f² = 0.560) and a small effect on Policy Awareness (f² = 0.043). Community Cultural Norms (CCN) exhibit a large effect on PA (f² = 0.527) but only a negligible effect on EWO (f² = 0.014). These findings underscore the relative strengths of the predictors in the structural model and help identify the most influential pathways driving welfare outcomes. Table 8. Relationship between Variables. Relationship Original sample (O) Sample mean (M) Standard deviation (STDEV) t- statistics p-values CCN -> EWO -0.111 -0.117 0.146 0.756 0.450 CCN -> PA 0.651 0.662 0.137 4.751 0.000 PA -> PI 0.790 0.790 0.044 17.889 0.000 PI -> EWO 0.508 0.520 0.093 5.491 0.000 TFV -> EWO 0.573 0.569 0.114 5.011 0.000 TFV -> PA 0.185 0.174 0.154 1.200 0.230 CCN -> PA -> PI 0.514 0.525 0.119 4.327 0.000 TFV -> PA -> PI -> EWO 0.074 0.070 0.065 1.135 0.256 TFV -> PA -> PI 0.146 0.136 0.121 1.209 0.227 CCN -> PA -> PI -> EWO 0.261 0.275 0.088 2.982 0.003 PA -> PI -> EWO 0.401 0.412 0.080 5.037 0.000 Table 8 summarizes the results of the hypothesis testing via path coefficients and bootstrapping in SmartPLS, providing estimates for direct and indirect relationships between constructs. Among the direct effects, Traditional Family Values (TFV) significantly predict Elderly Welfare Outcomes (EWO), while Community Cultural Norms (CCN) do not exhibit a significant direct influence on EWO. Both Policy Awareness (PA) and Policy Implementation (PI) show strong and statistically significant relationships with their respective endogenous variables, highlighting the mediating power of policy mechanisms. Specifically, PA strongly influences PI, and PI in turn significantly predicts EWO, validating the policy chain’s operational significance. Regarding indirect effects, CCN significantly impacts PI via PA, and further contributes to EWO through the full mediation of PA and PI, indicating a robust policy-mediated pathway. Conversely, the indirect pathways from TFV to EWO and PI via PA are not statistically significant, suggesting that TFV’s influence on welfare outcomes is largely direct rather than policy-mediated. These findings reflect nuanced cultural-political interactions and confirm the necessity of separating normative values from procedural engagement in elderly welfare models. 4. Discussion This study provides novel empirical insights into the interplay between cultural norms and policy mechanisms in shaping elderly welfare outcomes (EWO) within the context of China’s rapidly aging population [1]. Consistent with existing literature, Traditional Family Values (TFV)—particularly those rooted in filial piety—demonstrated a significant direct effect on EWO, confirming the continued salience of Confucian moral expectations in guiding caregiving behaviors and perceptions of elder support [3, 6, 16]. In contrast, Community Cultural Norms (CCN) did not directly influence EWO, but instead exerted an indirect effect through the sequential mediation of Policy Awareness (PA) and Policy Implementation (PI), highlighting the importance of culturally embedded yet institutionally mediated pathways [19, 20]. These findings validate prior theoretical propositions that while normative beliefs 2728 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 2719-2730, 2025 DOI: 10.55214/25768484.v9i6.8467 © 2025 by the authors; licensee Learning Gate remain potent, their impact is increasingly channeled through formal policy systems as traditional family structures erode under the pressures of urbanization and demographic transformation [2, 3, 10]. Notably, TFV did not significantly affect EWO via PA or PI, suggesting that traditional values function more as direct motivators of care rather than as facilitators of state-mediated welfare access—a divergence that underscores the separation between normative ideals and procedural engagement in modern eldercare models [17]. From a policy perspective, the strong mediating role of PA and PI affirms the necessity of enhancing public awareness and ensuring local implementation efficacy to optimize welfare outcomes, particularly in regions with weak institutional capacity or low digital literacy among older adults [21, 23]. However, the study also highlights critical limitations. The relatively low internal consistency of the CCN construct (Cronbach’s α = 0.633) may limit the generalizability of findings regarding community-level cultural influences. Moreover, the cross- sectional design constrains causal inference, and the reliance on self-reported data may introduce social desirability bias, especially in domains tied to cultural identity and policy evaluation. Future research should incorporate longitudinal designs to trace evolving value-policy linkages over time and explore cross-regional comparisons to assess how local governance, digital access, and socio-economic diversity mediate the observed effects. There is also a need for more nuanced operationalization of community norms and policy engagement that captures both attitudinal and behavioral dimensions. Overall, this study underscores the importance of integrating traditional cultural values with state mechanisms in welfare policy design, advocating for culturally congruent approaches that reinforce—rather than replace—Confucian ideals through intergenerational programs, community-based services, and responsive governance frameworks [5, 14, 24]. 5. Conclusion This study sheds light on the complex interplay between cultural values and institutional mechanisms in shaping elderly welfare in China’s rapidly aging society. By integrating Traditional Family Values (TFV), Community Cultural Norms (CCN), Policy Awareness (PA), Policy Implementation (PI), and Elderly Welfare Outcomes (EWO) into a unified framework, the research underscores that while TFV directly enhance welfare outcomes, their indirect influence through policy channels remains limited. In contrast, CCN play a pivotal role in promoting welfare outcomes indirectly through robust mediation by PA and PI. These findings emphasize the importance of aligning culturally rooted norms with effective policy communication and implementation strategies to improve elderly well-being. The study’s insights support the development of culturally congruent policies that reinforce, rather than replace, traditional caregiving ideals. Nevertheless, limitations such as the use of cross- sectional data and potential measurement inconsistencies warrant cautious interpretation. Future research should pursue longitudinal and regional comparative studies to deepen understanding of evolving value-policy linkages and enhance the adaptability of eldercare systems across diverse sociocultural settings. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). https://creativecommons.org/licenses/by/4.0/ 2729 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 6: 2719-2730, 2025 DOI: 10.55214/25768484.v9i6.8467 © 2025 by the authors; licensee Learning Gate References [1] L. 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