Abstract
Objective
Community senior canteens have become an important component of community-based meal services and healthy aging strategies in China. However, insufficient alignment between service provision and the diverse needs of older adults may constrain service utilization and long-term sustainability. This study aimed to examine the associations among factors related to older adults’ use of community senior canteens and to identify patterns of behavioral association that may inform the optimization of community meal services.
Methods
A community-based cross-sectional survey was conducted among adults aged 60 years and older across the 11 administrative districts of Nanjing, China. Using a combination of stratified sampling, quota sampling, and snowball sampling, 1,568 complete cases were included in the final SEM analysis. An adapted SICAS framework was used to construct a structural equation model (SEM) to examine the sequential relationships among awareness, interaction, purchase, and sharing. Reliability, validity, and model fit were evaluated before structural path analysis.
Results
The measurement and structural models demonstrated satisfactory fit. All three hypothesized associations were statistically significant (p < 0.001). Awareness was positively associated with Interaction, Interaction was positively associated with Purchase, and Purchase was positively associated with Sharing.
Conclusion
Older adults’ use of community senior canteens was characterized by a pattern of positive associations among awareness, interaction, purchase, and sharing. Given the cross-sectional design, these findings should not be interpreted as evidence of temporal or causal relationships. The observed associations may nevertheless provide useful evidence for improving service communication, user engagement, and service quality in community meal services.
Keywords: chain service utilization, community senior canteens, healthy aging, silver economy, structural equation modeling
1. Introduction
Population aging has become one of the most profound demographic transformations of the twenty-first century, fundamentally reshaping public health systems, social security, and long-term care worldwide (1). According to the United Nations World Population Prospects 2024 medium-variant projection, the global population aged 65 years and older is projected to exceed 1.6 billion by 2050, accounting for approximately 16% of the world’s total population (1). This unprecedented demographic transition presents substantial challenges to sustainable economic and social development. In response, the World Health Organization (WHO) introduced the concept of Healthy Aging, emphasizing that healthy aging extends beyond disease prevention and medical care to encompass the maintenance of functional ability, independence, and social participation throughout later life (2, 3).
In recent years, community meal services have become an integral component of community long-term care systems in many countries, including the United States, Japan, the United Kingdom, and several Nordic countries (4). Growing evidence suggests that these services not only improve nutritional status and food security among older adults but also reduce social isolation, strengthen social connectedness, and facilitate aging in place, thereby contributing to healthy aging. As population aging continues to accelerate, an increasing number of countries have recognized the silver economy as an important strategy for addressing aging-related challenges while promoting sustainable economic development. Accordingly, community meal services have gradually evolved from traditional welfare programs into integrated service models that simultaneously generate social value and economic sustainability, providing new opportunities for building resilient community-based care systems for older adults. Against this global backdrop, China is experiencing one of the largest and most rapid demographic transitions in the world. The continuous growth of the older population has substantially increased the demand for care, healthcare, and long-term care services for older adults, while simultaneously positioning the silver economy as an emerging driver of economic growth (5–8).
Although community meal services have become an important topic in aging research worldwide, considerable differences exist in their development across countries. In Europe and North America, research has primarily focused on the public health benefits of community meal services, including improvements in nutritional health, social participation, and reductions in healthcare utilization (9, 10). In contrast, Japan has placed greater emphasis on integrating community meal services into local care networks for older adults and community governance systems (11). These studies provide valuable evidence for improving community-based care for older adults and demonstrate that community meal services have become an effective public health intervention for promoting healthy aging (12).
Compared with many developed countries, China has continuously strengthened its response to population aging by promoting the integration of community-based and home-based care services for older adults, with community meal assistance becoming an increasingly important component of the care system for older adults (13). Meanwhile, declining household size, increasing numbers of empty-nest households, and weakening family caregiving capacity have intensified the dietary care needs of older adults, particularly those who are living alone, living with disabilities, or of advanced age (14, 15). Under these circumstances, community senior canteens have evolved into an important platform linking families, communities, and care services for older adults. Beyond providing nutritional support and food safety, they also help alleviate family caregiving burdens, encourage social participation, and support active and healthy aging.
As one of China’s economically developed metropolitan cities and one of the earliest cities to enter a deeply aging society, Nanjing represents a typical setting for investigating community meal service. In recent years, the city has continuously expanded its community meal service network by establishing a diversified service delivery system involving government agencies, market actors, and social organizations. Innovative practices, including central kitchens combined with community meal delivery and smart meal assistance, have been widely implemented, substantially improving service accessibility, operational efficiency, and resource integration. These developments provide a valuable practical context for examining the operational mechanisms of community senior canteens (16–18).
Despite the continuous expansion of service provision, community senior canteens still face several challenges, including insufficient identification of heterogeneous needs among older adults, mismatches between service provision and actual demand, limited operational sustainability, and inadequate market vitality. Therefore, understanding the determinants of older adults’ willingness to use community senior canteens and optimizing service provision under the framework of the silver economy have become critical issues for promoting the high-quality development of community-based care services for older adults (19–23).
Nanjing was selected as the study setting because of its representativeness in terms of population aging, the development of community-based care services for older adults, and the advancement of the silver economy. Therefore, Nanjing provides an appropriate setting for the present study (24–26). This study focuses on community senior canteens in Nanjing and employs a structural equation model (SEM) based on questionnaire data collected from older adults to systematically examine the key determinants of older adults’ willingness to use community senior canteens and to test the relationships among these determinants. The findings are expected to contribute to the literature on older adults’ use of community meal services in the context of the silver economy by providing empirical evidence for optimizing service provision, improving resource allocation and operational efficiency, and informing policies aimed at promoting the high-quality development of community-based care services for older adults.
2. Participants and methods
2.1. Participants
This study employed a community-based cross-sectional survey conducted in Nanjing, Jiangsu Province, China, between October and November 2025. The study population comprised adults aged 60 years and older residing in the 11 administrative districts of Nanjing. The survey assessed older adults’ awareness of community meal services, utilization patterns, dining behaviors, dining scenarios, and open-ended feedback. As the direct users of community meal services, older adults are best positioned to provide firsthand information regarding key aspects of service quality, including food taste, nutritional and health value, the convenience of dine-in and meal delivery services, and age-friendly environmental design. Participants were eligible for inclusion if they met the following criteria: (1) were aged ≥60 years; (2) were able to understand the questionnaire and voluntarily agreed to participate in the study; and (3) were able to complete the questionnaire independently or, if necessary, with assistance from a caregiver according to their own responses and intentions.
2.2. Methods
2.2.1. Survey instrument
A structured questionnaire was used for data collection. The questionnaire was developed based on the findings of semi-structured interviews conducted with 21 older adults, 20 adult children or primary caregivers, and 9 community senior canteen operators or community workers from the target population. In addition, focus group discussions were conducted to identify the core needs of participants across different age groups, family structures, and service-use scenarios. The interview and focus group findings adequately reflected the diverse perspectives of service users, family decision-makers, and service providers regarding community senior canteens in Nanjing. Based on these findings, together with relevant policy documents, community meal service policies, and the SICAS theoretical framework, the questionnaire was developed (27).
The questionnaire consisted of five sections: (1) sociodemographic characteristics, including gender, age, educational level, pension income, living arrangement, and mobility status; (2) awareness and utilization of community senior canteens, including awareness channels, previous use of community senior canteens, and overall dining experiences, to assess participants’ awareness of and attitudes toward community senior canteens; (3) utilization characteristics of community senior canteens, including dining mode, dining time, frequency of use, meal price, payment method, and the main reasons for choosing community senior canteens; (4) the SICAS scale, comprising four dimensions: Awareness, Interaction, Purchase, and Share; and (5) open-ended questions, which collected participants’ suggestions and comments regarding community senior canteens.
To ensure the appropriateness of the questionnaire design, a pilot survey was conducted prior to the formal investigation. A total of 78 pilot questionnaires were randomly distributed online. Based on the issues identified during the pilot survey, the questionnaire was revised item by item, and the validity of the returned questionnaires was subsequently evaluated.
2.2.2. Data collection
A multi-stage sampling strategy was adopted to improve geographic coverage while maintaining the feasibility of field-based data collection. The 11 administrative districts of Nanjing were first treated as geographic strata, and quotas were allocated according to the population size of each district. Within the district-level quotas, participants were recruited through snowball sampling from eligible older adults.
A pilot survey was first conducted online, and the questionnaire was revised according to problems identified during the pretest, including ambiguous wording and inappropriate response options. The formal survey was subsequently conducted using paper questionnaires. The formal survey was conducted using paper-based questionnaires through face-to-face recruitment in community senior canteens. For older adults who experienced difficulties completing the questionnaire because of age-related visual or operational limitations, adult children or caregivers could provide assistance with the participant’s informed consent. Importantly, assistants were instructed to record the participant’s own responses rather than provide their own evaluations or preferences.
A total of 1,698 questionnaires were distributed, of which 1,678 were returned, yielding a response rate of 98.82%. After systematic quality screening based on predefined data-cleaning criteria, 28 invalid questionnaires were excluded, leaving 1,650 questionnaires for further analysis. Among these, 1,568 complete cases were included in the final SEM analysis after excluding cases with missing values on the model variables. Thus, the final analytical sample comprised 1,568 older adults.
2.2.3. Reliability and validity
The results of the formal survey demonstrated satisfactory internal consistency. Cronbach’s α coefficients for the four SICAS dimensions (Awareness, Interaction, Purchase and Sharing) were 0.872, 0.808, 0.873, and 0.721, respectively. All Cronbach’s α coefficients exceeded the recommended threshold of 0.70, indicating good internal consistency of the measurement scale.
The Kaiser–Meyer–Olkin (KMO) test was performed to evaluate the suitability of the data for factor analysis. The KMO value was 0.942, indicating good structural validity and excellent sampling adequacy for factor analysis. Subsequently, confirmatory factor analysis (CFA) was conducted to further assess the construct validity of the measurement model.
2.2.4. Statistical analysis
Structural equation modeling (SEM) was selected as the primary analytical approach because the proposed framework involves multiple theoretically related latent constructs—Awareness, Interaction, Purchase, and Sharing—and examines their interrelationships simultaneously. Unlike traditional multiple linear regression, which generally treats predictors as directly observed variables and typically evaluates one dependent relationship at a time, SEM allows latent constructs to be represented by multiple observed indicators while explicitly accounting for measurement error (27). SEM also enables the assessment of the measurement model through confirmatory factor analysis (CFA) and the structural model within a unified analytical framework. This approach was therefore considered appropriate for simultaneously evaluating the reliability and validity of the adapted SICAS constructs and testing the hypothesized associations among them. The methodological advantages of SEM over conventional regression analysis have also been discussed in previous comparative research.
Data were analyzed using SPSS and AMOS 28.0. Structural equation modeling (SEM) was employed to examine the associations among the four latent constructs and to evaluate the hypothesized structural model. Model fit was assessed using the chi-square to degrees of freedom ratio (χ2/df), the Comparative Fit Index (CFI), the Incremental Fit Index (IFI), the Root Mean Square Error of Approximation (RMSEA), the Relative Fit Index (RFI), and the Tucker–Lewis Index (TLI). The significance of path coefficients was assessed using p values, with p < 0.05 considered statistically significant. Standardized coefficients were reported to facilitate interpretation of the relative strength of associations (28, 29).
2.3. Ethics
This study employed an anonymous questionnaire survey and did not collect any personally identifiable information. Before completing the questionnaire, all participants were informed of the purpose of the study, participated voluntarily, and were free to withdraw at any time. This study was a non-interventional survey and did not involve any procedures that posed potential risks or harm to the participants. According to the regulations of the authors’ affiliated institution, formal ethical approval was not required for this type of anonymous, non-interventional research.
3. Model construction
3.1. Theoretical model
The SICAS framework was selected as the theoretical basis because its central proposition is particularly relevant to service-utilization contexts in which users move from initial perception and information processing to interaction, behavioral action, and post-use sharing. Originally developed to characterize consumer behavior in an increasingly interactive and connected environment, the SICAS framework comprises five stages: Sense, Interest and Interactive, Connect and Communicate, Action, and Share. Unlike traditional linear consumer behavior models, SICAS emphasizes the role of user engagement, bidirectional communication, and post-use sharing in behavioral conversion.
To investigate the behavioral pathways underlying older adults’ use of community senior canteens, this study developed a structural equation model based on an adapted SICAS framework. Community senior canteen utilization is a service decision that involves not only an individual’s recognition of service availability but also the acquisition and evaluation of information, communication with service providers or family members, actual service use, and subsequent evaluation or recommendation. These characteristics correspond conceptually to the major behavioral stages represented by SICAS.
Based on this contextual adaptation, the original five-stage framework was operationalized into four latent constructs. First, “Sense” was conceptualized as Awareness, referring to older adults’ recognition and understanding of community senior canteens and their perceived value. Second, “Interest and Interactive” and “Connect and Communicate” were combined into Interaction, because both stages represent users’ active engagement with service-related information, communication, and participation, and the distinction between them was not sufficiently meaningful in the context of community senior canteens. Third, “Action” was operationalized as Purchase, representing actual or intended utilization-related behavior, such as dining at the canteen, convenient payment, and continued use. Finally, “Share” was retained as Sharing, reflecting users’ willingness to evaluate, recommend, or communicate their service experiences to others.
Accordingly, a four-stage behavioral association model (Awareness–Interaction–Purchase–Sharing) was established (Figure 1), and the 13 items of the Q23 scale were classified into four latent constructs to examine whether community senior canteen users’ behavioral transitions exhibit significant sequential transmission effects.
Figure 1.
Four-stage chain model of awareness, interaction, purchase, and sharing.
3.2. Model estimation
3.2.1. Measurement model assessment
The measurement model demonstrated a satisfactory overall fit to the data, with all fit indices meeting the recommended criteria. As shown in Table 1, with all fit indices meeting the recommended criteria. Specifically, the χ2/df (CMIN/DF) was 1.404, the root mean square error of approximation (RMSEA) was 0.029, the relative fit index (RFI) was 0.944, the incremental fit index (IFI) was 0.988, and the Tucker–Lewis index (TLI) was 0.983, indicating that the proposed measurement model was appropriate for subsequent analyses.
Table 1.
Results of confirmatory factor analysis.
| Indicator | Reference criteria | Observed results |
|---|---|---|
| CMIN/DF | 1–3 Excellent, 3–5 Good | 1.404 |
| RMSEA | < 0.05 Excellent, < 0.10 Good | 0.029 |
| RFI | > 0.90 Excellent, > 0.80 Good | 0.944 |
| IFI | > 0.90 Excellent, > 0.80 Good | 0.988 |
| TLI | > 0.90 Excellent, > 0.80 Good | 0.983 |
After establishing an adequate measurement model fit, the convergent validity and composite reliability of the latent constructs were further evaluated. The CFA model of the behavioral intention scale is presented in Figure 2. As shown in Table 2, all constructs exceeded the recommended thresholds for average variance extracted (AVE) and composite reliability (CR), demonstrating satisfactory convergent validity and internal consistency. As shown in Table 3, all item-to-construct paths were statistically significant (p < 0.001), with standardized loadings exceeding the 0.5 threshold. This confirms that the items are valid measures for the four dimensions: Awareness, Interaction, Purchase, and Sharing.
Figure 2.
Confirmatory factor analysis (CFA) model diagram.
Table 2.
Measurement model: convergent validity and composite reliability.
| Item | Path relationship | Construct | Estimate | AVE | CR |
|---|---|---|---|---|---|
| Q23_s1 | ← | Awareness | 0.799 | 0.661 | 0.854 |
| Q23_s2 | ← | Awareness | 0.794 | ||
| Q23_s3 | ← | Awareness | 0.845 | ||
| Q23_s4 | ← | Interaction | 0.751 | 0.531 | 0.819 |
| Q23_s5 | ← | Interaction | 0.734 | ||
| Q23_s6 | ← | Interaction | 0.718 | ||
| Q23_s7 | ← | Interaction | 0.710 | ||
| Q23_s8 | ← | Purchase | 0.871 | 0.727 | 0.914 |
| Q23_s9 | ← | Purchase | 0.788 | ||
| Q23_s10 | ← | Purchase | 0.883 | ||
| Q23_s11 | ← | Purchase | 0.866 | ||
| Q23_s12 | ← | Sharing | 0.804 | 0.668 | 0.801 |
| Q23_s13 | ← | Sharing | 0.830 |
Table 3.
Measurement model: standardized factor loadings.
| Item | Path relationship | Construct | Estimate |
|---|---|---|---|
| Q23_s1 | ← | Awareness | 0547 |
| Q23_s2 | ← | Awareness | 0.554 |
| Q23_s3 | ← | Awareness | 0.583 |
| Q23_s4 | ← | Interaction | 0.539 |
| Q23_s5 | ← | Interaction | 0.568 |
| Q23_s6 | ← | Interaction | 0.584 |
| Q23_s7 | ← | Interaction | 0.546 |
| Q23_s8 | ← | Purchase | 0.632 |
| Q23_s9 | ← | Purchase | 0.588 |
| Q23_s10 | ← | Purchase | 0.584 |
| Q23_s11 | ← | Purchase | 0.587 |
| Q23_s12 | ← | Sharing | 0.634 |
| Q23_s13 | ← | Sharing | 0.613 |
3.2.2. Structural model evaluation
Following validation of the measurement model, the structural model was further evaluated. As shown in Figure 3 and Table 4, the structural model demonstrated a satisfactory overall fit. Specifically, the χ2/df was 1.551, RMSEA was 0.034, RFI was 0.938, IFI was 0.982, and TLI was 0.977. All model fit indices satisfied the recommended criteria, indicating that the proposed four-stage sequential structural model showed a satisfactory fit to the observed data.
Figure 3.
Four-stage behavioral association model.
Table 4.
Goodness-of-fit indices for the four-stage chain structural model.
| Indicator | Reference criteria | Observed results |
|---|---|---|
| CMIN/DF | 1–3 Excellent, 3–5 Good | 1.551 |
| RMSEA | < 0.05 Excellent, < 0.10 Good | 0.034 |
| RFI | > 0.90 Excellent, > 0.80 Good | 0.938 |
| IFI | > 0.90 Excellent, > 0.80 Good | 0.982 |
| TLI | > 0.90 Excellent, > 0.80 Good | 0.977 |
| CFI | ≧ 0.95 Excellent,≧ 0.90 Good | 0.996 |
| GFI | ≧0.905Excellent,≧ 0.90 Good | 0.989 |
| AGFI | ≧ 0.90 Excellent,≧ 0.80 Good | 0.982 |
4. Results
4.1. Demographic characteristics of the study participants
As shown in Table 5, a total of 1,568 older adults participated in the survey. Of these, 51.3% were male and 48.7% were female. Participants aged 65–74 years accounted for more than half of the sample (53.1%). Most had a junior or senior high school education (58.5%) and a monthly income of RMB 3,001–8,000 (56.1%). Nearly half lived with their spouse (45.3%), 50.8% could travel independently, and 51.0% were proficient in smartphone use. Additionally, 38.5% reported being eligible for or receiving meal subsidies.
Table 5.
Demographic characteristics of participants (N = 1,568).
| Characteristic | Number of participants | Percentage (%) |
|---|---|---|
| Gender | ||
| Male | 805 | 51.3 |
| Female | 763 | 48.7 |
| Age | ||
| 60–64 years | 389 | 24.8 |
| 65–69 years | 418 | 26.7 |
| 70–74 years | 414 | 26.4 |
| ≥75 years | 347 | 22.1 |
| Educational level | ||
| Primary school or below | 308 | 19.6 |
| Junior high schoo | 463 | 29.5 |
| Senior high school/technical secondary school | 454 | 29.0 |
| Junior college | 276 | 17.6 |
| Bachelor’s degree or above | 67 | 4.3 |
| Monthly income | ||
| ≤ RMB 3,000 | 337 | 21.5 |
| RMB 3,001–5,000 | 469 | 29.9 |
| RMB 5,001–8,000 | 411 | 26.2 |
| RMB 8,001–10,000 | 264 | 16.8 |
| ≥ RMB 10,001 | 87 | 5.5 |
| Living situation | ||
| Living alone | 417 | 26.6 |
| living with spouse | 710 | 45.3 |
| Living with children | 441 | 28.1 |
| Ease of movement | ||
| Convenient (can travel independently) | 796 | 50.8 |
| Moderate (some difficulty traveling) | 462 | 29.5 |
| Inconvenient (mostly requires assistance from others) | 310 | 19.8 |
| Whether eligible for or receiving meal subsidies or related benefits | ||
| Yes | 603 | 38.5 |
| No | 721 | 46.0 |
| not sure | 244 | 15.6 |
| Level of proficiency in using smartphones | ||
| Proficient (can use QR code payments, view WeChat/short video content) | 800 | 51.2 |
| Average (can use QR code payments or basically view information) | 503 | 32.1 |
| Not very skilled (needs assistance from others) | 159 | 10.1 |
| Cannot do it | 106 | 6.8 |
4.2. Descriptive statistics and distributional characteristics
As shown in Table 6, the mean scores of the 13 measurement items ranged from 3.07 to 3.23, slightly above the midpoint of the 5-point Likert scale (3.00). Awareness and purchase had relatively higher scores, while interaction had the lowest. Skewness ranged from −0.28 to −0.09 and kurtosis from 1.77 to 1.91, with all values within the recommended thresholds for approximate normality (absolute skewness < 3; absolute kurtosis < 8).
Table 6.
Descriptive statistics and distributional characteristics of the measurement items.
| Construct | Measurement item | Item description | Mean | SD | Skewness | Kurtosis |
|---|---|---|---|---|---|---|
| Awareness | Q23_s1 | Consumption trust | 3.16 | 1.33 | −0.15 | 1.77 |
| Q23_s2 | Price transparency | 3.12 | 1.29 | −0.19 | 1.90 | |
| Q23_s3 | Nutritional matching | 3.20 | 1.33 | −0.22 | 1.81 | |
| Interaction | Q23_s4 | Information awareness | 3.07 | 1.34 | −0.11 | 1.80 |
| Q23_s5 | Activity participation | 3.10 | 1.30 | −0.15 | 1.83 | |
| Q23_s6 | Contact channels | 3.11 | 1.33 | −0.14 | 1.81 | |
| Q23_s7 | Communication accessibility | 3.14 | 1.30 | −0.14 | 1.85 | |
| Purchase | Q23_s8 | Consumption convenience | 3.23 | 1.30 | −0.28 | 1.91 |
| Q23_s9 | Payment convenience | 3.16 | 1.37 | −0.24 | 1.78 | |
| Q23_s10 | Continued use | 3.16 | 1.32 | −0.20 | 1.84 | |
| Q23_s11 | Future frequency of use | 3.10 | 1.33 | −0.09 | 1.81 | |
| Sharing | Q23_s12 | Satisfaction sharing | 3.15 | 1.30 | −0.20 | 1.87 |
| Q23_s13 | Recommendation to relatives/friends | 3.12 | 1.33 | −0.23 | 1.86 |
4.3. Multicollinearity assessment
As shown in Table 7, the tolerance values ranged from 0.422 to 1.000, while the VIF values ranged from 1.000 to 2.372. All values were within the commonly accepted thresholds, indicating that no substantial multicollinearity was present among the predictor variables.
Table 7.
Multicollinearity diagnostics of the predictor variables.
| Dependent variable | Predictor | Tolerance | VIF |
|---|---|---|---|
| Interaction | Awareness | 1.000 | 1.000 |
| Purchase | Awareness | 0.534 | 1.874 |
| Interaction | 0.534 | 1.874 | |
| Sharing | Awareness | 0.439 | 2.276 |
| Interaction | 0.437 | 2.288 | |
| Purchase | 0.422 | 2.372 |
4.4. Structural path analysis
Structural path analysis was conducted to examine the hypothesized associations among the four latent constructs. The structural model showed satisfactory overall fit, with χ2/df = 1.551, RMSEA = 0.034, RFI = 0.938, IFI = 0.982, and TLI = 0.977.
All three hypothesized adjacent associations were statistically significant. Awareness was positively associated with Interaction, Interaction was positively associated with Purchase, and Purchase was positively associated with Sharing. The measurement-model results, including factor loadings and their significance tests, are presented in Table 8, whereas the structural path results are presented in Table 9. Overall, the results were consistent with the hypothesized associations among Awareness, Interaction, Purchase, and Sharing.
Table 8.
Measurement model: Item loadings and significance.
| Item | Path relationship | Construct | Estimate | S. E. | CR | p |
|---|---|---|---|---|---|---|
| Q23_s1 | ← | Awareness | 0.600 | 0.201 | 10.989 | *** |
| Q23_s2 | ← | Awareness | 0.594 | 0.177 | 10.981 | *** |
| Q23_s3 | ← | Awareness | 0.644 | 0.092 | 11.689 | *** |
| Q23_s4 | ← | Interaction | 0.575 | 0.145 | 11.882 | *** |
| Q23_s5 | ← | Interaction | 0.596 | 0.122 | 10.880 | *** |
| Q23_s6 | ← | Interaction | 0.594 | 0.135 | 11.686 | *** |
| Q23_s7 | ← | Interaction | 0.578 | 0.122 | 10.372 | *** |
| Q23_s8 | ← | Purchase | 0.641 | 0.142 | 11.540 | *** |
| Q23_s9 | ← | Purchase | 0.622 | 0.185 | 12.085 | *** |
| Q23_s10 | ← | Purchase | 0.609 | 0.212 | 11.754 | *** |
| Q23_s11 | ← | Purchase | 0.600 | 0.082 | 11.687 | *** |
| Q23_s12 | ← | Sharing | 0.661 | 0.164 | 10.298 | *** |
| Q23_s13 | ← | Sharing | 0.624 | 0.218 | 11.106 | *** |
“***” indicates p < 0.001.
Table 9.
Results of structural path analysis for the four-stage chain structural equation model.
| Path relationship | Unstandardized estimate | S. E. | CR | p |
|---|---|---|---|---|
| Awareness → Interaction | 1.024 | 0.068 | 15.149 | *** |
| Interaction → Purchase | 0.970 | 0.062 | 15.754 | *** |
| Purchase → Sharing | 1.031 | 0.063 | 16.320 | *** |
“***” indicates p < 0.001.
5. Discussion
5.1. Key findings
This study identified a consistent pattern of positive associations among Awareness, Interaction, Purchase, and Sharing among older adults using community senior canteens. Awareness was positively associated with Interaction, Interaction with Purchase, and Purchase with Sharing. These findings were consistent with the hypothesized ordering of the four constructs, although the cross-sectional design does not permit conclusions regarding temporal or causal relationships.
One possible explanation is that greater awareness may be associated with lower perceived uncertainty, while greater interaction may be associated with higher confidence and perceived accessibility. Similarly, favorable utilization-related experiences may be associated with greater satisfaction and willingness to share information.
These findings may have several practical implications for community senior canteen management. Service strategies could be organized around the four behavioral domains identified in the present study. At the awareness stage, providers could communicate core service attributes such as food safety, price transparency, and nutritional suitability through accessible channels (30). At the interaction stage, accessible menu information, nutritional guidance, community activities, and convenient communication channels may strengthen engagement with potential users (31, 32). At the utilization stage, attention to meal quality, service attitude, payment convenience, and accessibility may improve the user experience. Finally, mechanisms that facilitate feedback and voluntary recommendation may help strengthen the sharing dimension. These recommendations should be regarded as practice-oriented implications derived from the observed associations rather than as experimentally demonstrated causal interventions (7, 33–35).
5.2. Comparison with international research
In recent years, the focus of international research on meal assistance services for older adults has shifted from basic support to technology-enabled solutions and sustainable development; this trend offers a meaningful contrast to the practical explorations of community senior canteens in China.
At the level of technology application, French research—based on the AGGIR framework for assessing older adults’ dependency—systematically examined the acceptance of smart meal assistance systems. Its core findings indicate that whether technology can effectively enhance service efficiency depends heavily on the accurate identification of older adults’ functional decline and cognitive characteristics, rather than the simple transplantation of technological solutions (36). Research from Spain and other countries further points out that while Internet of Things (IoT) devices can replace some manual care functions in areas such as route planning and nutritional balancing, risks such as privacy infringement, data security, and the digital divide are becoming structural barriers to the implementation of these technologies. This warning reveals the inherent tension between the logic of efficiency and the vulnerability of the older population (37).
In terms of service organization models, Japan’s meal delivery services have evolved through four stages—emergence, inception, adjustment, and optimization. Their core experience lies in matching diverse dining scenarios—such as home meal delivery, centralized community meal service, and family-style dining—to the varying levels of mobility among older adults, thereby achieving a refined alignment between service provision and individual functional status. In contrast, while China’s community senior canteens have achieved rapid expansion of their facility networks under policy impetus, current research is largely limited to policy narratives and descriptive accounts of operational challenges. There remain significant shortcomings in the precision of identifying heterogeneous user needs, the mechanistic analysis of behavioral decision-making pathways, and the quantitative empirical testing of segmentation strategies (38–41).
Against this background, the present study complements existing research by focusing on the behavioral associations underlying community senior canteen utilization, rather than primarily examining service delivery models or technological implementation. This perspective may help explain how older adults move from awareness and interaction to utilization-related behavior and sharing.
5.3. Impact on health policy and practice
Meal assistance services for older adults should be integrated into broader strategies for the integration of medical care, care for older adults, and nutrition, as well as community health promotion. Given the importance of perceived service attributes, including food safety, price transparency, and nutritional suitability, regulatory authorities should strengthen the existing institutional compliance framework by adopting a risk-based inspection approach (42–45). More targeted and rigorous supervision should be implemented for meal service providers serving vulnerable populations, such as the oldest-old and older adults with disabilities, to improve food safety protection (46, 47).
Beyond ensuring food safety, meal assistance services should also strengthen their health promotion function. As the observed associations span awareness, interaction, utilization-related behavior, and sharing, health promotion strategies should be embedded throughout the service process (32). This includes improving the transparency of nutritional information, integrating nutritional screening and health assessments into meal service settings, and promoting the community-wide diffusion of healthy eating behaviors through continuous improvements in service experience (48–50).
In addition, a collaborative governance mechanism involving governments, communities, healthcare institutions, and social organizations should be established to support coordinated provision of both basic and personalized services. Such efforts would facilitate the transformation of meal assistance services from a model focused primarily on food safety and nutrition toward a comprehensive health promotion approach characterized by precise service matching and person-centered care (51, 52).
Taken together, these findings suggest that community meal services should evolve from a food assistance model toward an integrated community health service that combines nutritional support, health promotion, and social participation (53–55).
5.4. Limitations
This study has several limitations. Although stratified and quota sampling ensured coverage across Nanjing’s 11 municipal districts, the use of snowball sampling may have introduced selection bias and limited generalizability. The cross-sectional design and self-reported data limit causal inference and may be subject to self-report and common method bias. Caregiver-assisted questionnaire completion may have also introduced response bias, although caregivers were instructed to report participants’ own responses. In addition, discriminant validity was not independently assessed using established criteria such as the Fornell–Larcker criterion or HTMT, and potentially relevant factors, including socioeconomic status and previous service experience, were not included in the model. Future studies should adopt longitudinal and multi-site designs with probability-based sampling to further validate the proposed framework.
6. Conclusion
Based on 1,568 valid questionnaires from older adults across 11 districts in Nanjing, this study developed and empirically examined a four-stage behavioral association model—Awareness → Interaction → Purchase → Sharing—using structural equation modeling. The results showed significant positive associations between each pair of adjacent constructs. The findings were consistent with the hypothesized association pattern, although causal and temporal relationships cannot be established because of the cross-sectional design.
Funding Statement
The author(s) declared that financial support was not received for this work and/or its publication.
Footnotes
Edited by: Flávia Dos Santos Barbosa Brito, Rio de Janeiro State University, Brazil
Reviewed by: Krittaphas Kangwanrattanakul, Burapha University, Thailand
Made Indra Wijaya, University of Warmadewa, Indonesia
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and the institutional requirements.
Author contributions
SW: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. YY: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft. SD: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft. HL: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft. HZ: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing. XW: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft. JS: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing. JL: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – review & editing.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2026.1934658/full#supplementary-material
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Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.