Ⅰ. INTRODUCTION

Ensuring sustainability, accessibility, and quality of care remains a significant challenge for healthcare systems worldwide[1]. Rising healthcare costs, demographic shifts, and the growing prevalence of chronic diseases exacerbate these issues, and disparities in access and outcomes further complicate the effectiveness [2][3]. Overcoming these barriers requires coordinated action by policymakers, healthcare providers, and communities to promote equitable access and implement innovative solutions[4].

Russia faces similar pressures; however, these challenges must be understood in the context of its healthcare system. Historically, Russian healthcare has evolved from a centralized Semashko model, fully state-funded, hierarchical, and nationally uniform, to a mixed system following post-Soviet reforms[5]. Since 1993, the country has operated a compulsory health insurance (OMC) system funded by employer contributions, self-employed payments, and government transfers for unemployed and socially vulnerable groups [6][7][8]. Today, service provisions are shared between federal and regional public facilities, and a growing number of private providers are contracted within the OMC.

Government sources cover approximately 65% of total health expenditure, while out-of-pocket (OOP) payments account for approximately 35%, one of the highest shares among countries with universal coverage[8]. Although the OMC package guarantees a uniform benefit package nationwide, regional disparities in budgetary capacity result in uneven service availability, long waiting times, and gaps in drug provision[9]. Access remains formally universal, but is practically constrained by shortages the of medical personnel, limited funding for high-cost technologies, and a persistently high financial burden on households[9][10]. Understanding this structural context is essential for evaluating the feasibility and public acceptance of introducing the introduction of new co-payment mechanisms[11].

Addressing the growing funding gap, especially to meet national goals for reducing mortality and increasing life expectancy, would require an annual growth in health expenditure of 10–15% in real terms, which appears unrealistic under current fiscal conditions [9]. Patient co-payments can help address system inefficiencies for several reasons.

Many European countries use co-payments to reduce moral hazards and generate additional funds[16]. While the results vary, the European experience can guide Russia in designing fair and effective co-payment systems[14]. A relevant example is South Korea, where a mandatory co-payment system within its National Health Insurance (NHI) has, for decades, balanced universal coverage with individual cost-sharing. In Korea, patients pay a fixed percentage of service fees at the point of care, which helps prevent excessive demand, while maintaining affordable healthcare for most households[17]. The Korean model also uses exemptions and ceilings to protect vulnerable groups and limit catastrophic expenditure[18]. Such mechanisms ensure that cost-sharing does not become a barrier to necessary care, a lesson highly relevant for Russia, where introducing co-payments must be carefully designed to avoid worsening inequalities or discouraging timely treatment. Moreover, Korea’s experience shows that clear communication with the public and transparent regulations are essential for building trust and acceptance of cofinancing measures[19].

Building on these insights, this study uses the Theory of Planned Behavior (TPB )[20] to analyze how patients’ attitudes, perceived behavioral control, and subjective norms influence their intention to accept and use a co-payment system in Russian healthcare. The Theory is appropriate for this study because it has proven to be effective in explaining and predicting individual health-related decisions, including willingness to pay and the adoption of new payment schemes [21][22][23]. The TPB allows researchers to capture the role of attitudes, perceived social pressure, and perceived control in shaping behavioral intentions toward cost-sharing[20]. Using this framework helps identify the factors that most strongly influence patients’ acceptance of co-payments and can inform targeted policy measures to increase public support. By comparing these findings with lessons learned from South Korea’s long-standing experience with mandatory co-payments, this study seeks to identify practical insights into designing fair and sustainable cost-sharing models that balance financial responsibility with universal access. The results are expected to offer both academic and policy implications for adapting balanced cost-sharing mechanisms in Russia and provide valuable perspectives for Korean audiences interested in international health policy reforms.

Ⅱ. METHODS

1. Research Model

This study established the research model shown in <Figure 1> to analyze the factors influencing patients’ intention to use a co-payment system in Russian healthcare. Based on the TPB, the model examines how attitudes toward co-payments, perceived behavioral control, and subjective norms affect the intention to use co-payments.

Actual behavior—i.e., the use of services under a co-payment system—was not included, and this decision was theoretically and contextually justified for several reason:

To ensure robustness of the analysis, control variables that may influence this intention, such as age, gender, education, marital status, current health status, income, and OOP expenses, were included in the model.

The independent variables (attitude, subjective norms, and perceived behavioral control) and control variables (age, gender, education, marital status, income, health status, and OOP expenses) were defined following previous studies that successfully applied TPB to healthcare payment contexts[21][23].

2. Two-Stage Research Design

This study employed a two-stage research design consisting of:

This sequential structure aligns with recommendations for behavioral research using the TPB and reflects the methodological distinction between developing a measurement tool and applying it to the main survey.

3. Stage 1: Instrument Development

The questionnaire items were adapted from validated TPB-based studies related to healthcare payment behavior [21][23]. The items were translated into Russian and back-translated into English by independent bilingual experts to ensure semantic accuracy. A panel of experts reviewed all the items for cultural appropriateness and clarity. A small pilot test confirmed that the questionnaire wording was clear and understandable, requiring no major revisions.

4. Measurement Tool

The measurement tool used in this study was a structured questionnaire developed based on the TPB constructs of attitude, perceived behavioral control, subjective norms, and behavioral intention. All items were adapted from previous TPB-based studies and revised for the Russian context. The questionnaire was then translated into Russian and reviewed by bilingual experts. All items used on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree). Higher scores indicate more positive evaluations, stronger perceived control, stronger social influence, and higher intention to use co-payment. A complete list of the questionnaire items is provided in <Table 1>. Reliability: Cronbach’s alpha values indicated excellent internal consistency: Attitude (α =.981), Perceived Behavioral Control (α =.946), Subjective Norms (α =.934), and Intention (α =.959).

5. Stage 2: Data and Participants

Data were collected through an online survey conducted from March 3 to March 30, 2024. A total of 141 participants were recruited using the surveys.yandex.ru and anketolog.ru. The study population included adult Russian residents enrolled in the mandatory health insurance system. The questionnaire <Table 1> was translated into Russian to ensure complete comprehension.

6. Ethical Considerations

This study was approved by the Inje University IRB (IRB No. INJE 2023-11-037-001). Participants were informed of the study's purpose, and data confidentiality was strictly maintained.

7. Operational Definitions of Variables

1) Dependent Variable

The dependent variable in this study was the patients’ intention to use a co-payment system in Russian healthcare. This variable measures the degree to which participants are willing to accept and use co-payments (OOP costs) within the current social health insurance system.

2) Independent Variables

Subjective norms refer to the perceived social pressure or expectations of significant others regarding the use of a co-payment system. They were measured using the mean score of the three items on a 5-point Likert-type scale. Attitudes toward co-payments represent an individual’s overall positive or negative evaluation of the co-payment system, including beliefs and opinions about its introduction. Perceived behavioral control indicates an individual’s perception of how feasible or beneficial it is to use a co-payment system to achieve healthcare goals.

3) Control Variables

Control variables included key sociodemographic factors such as age, sexgender, education, marital status, self-rated health status, annual OOP health expenses, and monthly income. Income was self-reported and categorized into two groups: below or above 50,000 rubles per month, based on the average monthly income of medical personnel in Russia, as reported by the Federal State Statistics Service[10].

8. Analytical Methods

Data were analyzed with SPSS 27.0, using descriptive and frequency analyses of respondent characteristics. Factor analysis and Cronbach's alpha for scale validity and reliability. ANOVA or T-test was used to compare means by demographics, Pearson's correlation to assess variable relationships, and regression analysis to assess factors affecting co-payment intention. Multicollinearity was checked using Variance Inflation Factors (VIFs), which were below the recommended threshold of 10, confirming the absence of multicollinearity problems. Additionally, Pearson’s correlation coefficients were calculated and presented to demonstrate acceptable relationships among the variables.

Ⅲ. RESULTS

The study included respondents of various age groups, with the fewest in the 18–24 years age range (3.6%) and a relatively even distribution across other age groups<Table 2>. Most of the participants were female (82.3%) and married (63.1%).

Regarding perceived health status, 63.8% rated their health as "Fair," 31.9% held secondary vocational education, and 44.7% held a specialist degree in medicine. Higher educational levels were also reported (23.4%). The income distribution showed that 64.5% earned more than 50,000 rubles. Most respondents (85.8%) used OOP expenses for healthcare, with 31.9% spending 5,000–10,000 rubles, and 14.9% spending over 15,000 rubles.

The exploratory factor analysis confirmed the validity of the measurement tools within the Theory framework. KMO was .946, and Bartlett's test was significant at p <.001. All factor loadings were greater than 0.4.

Cronbach's Alpha values showed high reliability for all variables:

Therefore, the reliability of the Theory variables used in this study was deemed satisfactory. No items were found to compromise reliability, and the analysis proceeded without item removal<Table 3>.

The t-test and ANOVA results indicated that age and income had statistically significant influences on attitudes and perceived behavioral control, whereas no significant effects were observed for subjective norms or intentions. Although post hoc comparisons were conducted, they did not reveal any stable or statistically significant pairwise differences; therefore, detailed post hoc values are not reported in the table.

Pearson's correlation analysis showed significant positive correlations between intention and:

attitude (r =.875, p < .001)

perceived behavioral control (r =.884, p < .001)

subjective norms (r =.839, p < .001)

Multiple regression analysis (<Table 5>) confirmed that attitude (β =.406, p <.001) and perceived behavioral control (β =.509, p<.001) significantly predicted intention, while subjective norms were not significant (β =.072, p =.326). Control variables such as education (β = –.076, p<.05) and income (β =.092, p<.05) also showed significant effects on intention. Multicollinearity diagnostics indicated moderate but not problematic multicollinearity. The VIF values for the main predictors were as follows: perceived behavioral control (mean BC) = 3.952, attitude (mean Att) = 5.115, and subjective norms (mean SBN) = 5.297. All VIF values were below the commonly accepted threshold of 10, indicating the absence of severe multicollinearity.

Ⅳ. DISCUSSION

This study examined the factors influencing patients’ intention to use a co-payment system in Russian healthcare using the TPB. The results confirm that attitudes toward co-payment and perceived behavioral control significantly affect patients’ intentions to engage in a future co-payment system, whereas subjective norms do not. These findings align with, while also partially diverging from, previous research that applied the TPB to similar healthcare payment contexts.

First, the significant positive effect of attitude on intention(β =.406, p <.001) indicates that patients who perceive co-payments positively— such as viewing them as a mechanism for improving service quality or accessibility—are more willing to use services that require OOP payments in the future. This result is consistent with Jeetoo & Jaunky[21] study in Mauritius, which found that favorable attitudes strongly shaped the willingness to pay (WTP) for better public healthcare services. This result suggests that, despite differences in national health systems, individual cognitive evaluations of the benefits of OOP payments remain central determinants of behavioral intention.

Second, perceived behavioral control(PBC) emerged as the strongest predictor of intention in this study (β =.509, p <.001). This finding aligns with the TPB literature, indicating that when actual behavior cannot be observed yet, PBC reliably reflects individuals’ perceived ability to engage in behavior under current or anticipated conditions[22]. For example, Han & Han[23] demonstrated that PBC significantly predicted online medical purchasing intentions during the COVID-19 pandemic. Similar dynamics appear in the Russian context; the more feasible respondents perceive co-payments to be, the more likely they are to express their willingness to use them. Interestingly, subjective norms did not significantly predict intention(β =.072, p =.326). This result contrasts with some studies—such as Chua & Koh[24]—which found that subjective norms influence the WTP for telemedicine. In Russia, co-payments remain largely hypothetical and have not yet formed a stable, normative environment. Consequently, social expectations may not be salient enough to affect behavioral intentions.

Additional findings showed that education had a slight adverse effect on intention (β = –.076, p <.05). In contrast, higher income had a positive influence (β =.092, p <.05). These results indicate that individuals with stronger financial capacity express greater readiness to accept co-payments, whereas those with higher education may have stronger expectations of free healthcare entitlements. South Korea’s experience offers valuable insights to contextualize these results. Hahm et al.[25] found that attitude and PBC predict screening intentions across free, co-payment, and full-payment scenarios, whereas subjective norms gain significance only under higher financial burdens. This result aligns with the present finding that moderate co-payments are more strongly shaped by personal evaluations and perceived feasibility than by social influences.

Beyond behavioral insights, Korea’s policy experience demonstrates the practical mechanisms through which co-payment systems can balance sustainability and equity[26]. For example, Sohn & Jung [18] showed that differential co-payment rates in Korea helped redirect patient flow toward primary care, reducing unnecessary hospital use without harming access. At the same time, Lee & Cheong[19] emphasized that co-payments must be accompanied by strong financial protection measures, such as annual caps and targeted exemptions, to prevent inequalities— particularly given that OOP payments still account for approximately 30% of total health spending in Korea.

However, the direct applicability of the Korean model to Russia is limited owing to differences in system design. Korea operates a centralized single-payer system, whereas Russia’s federal–regional dual structure leads to heterogeneous funding capacities and administrative fragmentation. Therefore, Korea should not be viewed as a model for direct policy transfer but rather as a source of generalizable policy design principles— predictable cost-sharing, transparent rules, simplified payment processes, and targeted protection for vulnerable groups—that can be adapted within Russia’s institutional context.

Interpreting the Korean case through the lens of the TPB strengthens the theoretical coherence of the present study. Clear communication and standardized rules foster positive attitudes, whereas streamlined digital payment systems and administrative consistency enhance PBC. This result explains why the two TPB constructs emerged as key predictors in the Russian context.

Finally, Russia’s policy implications must reflect its institutional heterogeneity, levels of public trust, and regional disparities. By situating the findings within the Russian setting while selectively drawing from international experience, this study provides balanced, context-sensitive policy recommendations and strengthens the practical relevance of its conclusions.

Ⅴ. CONCLUSIONS

The results indicate that attitudes toward co-payment and PBC significantly influence patients’ intentions to participate in a future co-payment system, whereas subjective norms do not. Unlike most previous studies, which were primarily conducted in high-income or fully insurance-based systems, this study examines the intention to accept co-payments in the Russian context, where OOP payments are common but largely informal, and public perceptions of co-payments remain understudied. By applying the Theory, this study provides empirical evidence on the psychological determinants of acceptance, offering a more structured explanatory framework than earlier descriptive or economic analyses.

Drawing on this study’s findings and South Korea’s experience, policymakers in Russia should recognize that well-designed co-payment systems could contribute to financial sustainability and more efficient resource use. It is essential to foster positive attitudes by clearly explaining how co-payments can improve service quality. Measures to strengthen perceived feasibility, such as affordable rates, annual caps, and targeted exemptions for low-income and high-need patients, are critical for maintaining equity.

Overall, this study contributes to the literature by identifying the behavioral components that most strongly shape public willingness to accept co-payment in a health system transitioning toward mixed financing. These insights can help guide the design of co-payment policies in Russia that uphold universal access while minimizing financial hardships.