From Theory to Debt Decisions: Evidence on Financial Literacy Among University Students

Abstract

Financial literacy represents a fundamental competence in contemporary knowledge-based economies, particularly in the context of increasingly complex corporate financing instruments. Insufficient financial literacy may lead to suboptimal debt decisions, inefficient capital structures, and heightened financial vulnerability of firms. The aim of this paper is to assess the level of financial literacy of university students in the field of corporate debt financing and to identify key determinants influencing the correctness of their responses. The empirical analysis is based on a quantitative questionnaire survey conducted among university students in the Slovak Republic (n = 403) using a convenience sampling approach. The questionnaire included 16 knowledge-based items focused on debt financing instruments, interest mechanisms, leasing, bonds, and alternative sources of financing. Data were analysed using descriptive statistics and inferential methods, primarily Pearson’s χ² test of independence and Cramer’s V. The results reveal considerable variability in students’ performance across thematic areas. Higher success rates were observed for basic concepts of debt financing and traditional bank products, while lower performance was recorded for analytically demanding tasks, particularly those related to interest rate comparisons, capital market instruments, and alternative financing forms. Field of study emerged as the most significant determinant of financial literacy, followed by the level of study, whereas gender and region showed only marginal effects. The findings highlight the need to strengthen application-oriented financial education in higher education, with a stronger focus on practical aspects of corporate debt financing.

1. Introduction

Financial literacy has become established in the 21st century as a key competence influencing the quality of economic decision-making by both individuals and business entities. With the growing complexity of financial markets, products, and regulatory environments, increasing demands are placed on the ability to understand financial instruments, assess risks, and make informed decisions. Despite the growing importance of financial literacy, several studies suggest that young adults frequently lack the basic financial knowledge necessary for effective financial decision-making, including understanding borrowing conditions, budgeting, and long-term financial planning (Adesina et al., 2025). This trend is particularly relevant for young individuals in the phase of formal education, who will soon participate in both individual and corporate financial decision-making processes.

However, numerous empirical studies point to persistent deficiencies in the level of financial literacy. Low financial literacy is associated with negative consequences for financial behaviour, stability, and performance of economic agents. Research further suggests that financial literacy is not determined solely by education, but is also influenced by socioeconomic, cultural, and demographic factors. In this context, particular attention should be paid to corporate debt financing, where the level of financial knowledge affects strategic decision-making, capital structure, and the long-term sustainability of firms in the market.

The concept of financial literacy began to take shape systematically in the United States at the turn of the 1980s and 1990s, and after 2000, it experienced a significant increase in attention because of the implementation of extensive national and supranational educational initiatives (Gedvilaitė et al., 2022). Today, financial literacy is perceived as a standardized concept, with the OECD defining it as a process of increasing understanding of financial products, strengthening the ability to identify risks and opportunities, and promoting informed financial decision-making (OECD, 2020). From a theoretical perspective, financial literacy can be interpreted as a form of human capital that enables individuals to process economic information and make informed decisions regarding saving, investing, and borrowing (Lusardi & Mitchell, 2014). Individuals with higher levels of financial literacy are therefore better equipped to evaluate financial products, assess financial risks, and choose appropriate financing options (Remund, 2010).

In the academic literature, financial literacy is considered a fundamental prerequisite for rational financial behaviour and informed choice (Kaiser et al., 2022). Financial decision-making is commonly viewed as a rational process in which individuals identify their financial needs, search for relevant information, and evaluate available alternatives before selecting the most appropriate financial product. In this context, access to reliable financial information and the ability to interpret it correctly play a crucial role in enabling individuals to make informed financial decisions (Sharma et al., 2025). Contemporary approaches further emphasize that financial literacy extends beyond purely numerical and technical knowledge and encompasses attitudes, behaviour, and subjective perceptions of financial well-being, while also highlighting its broader environmental and social implications (Gedvilaitė et al., 2022; B. Li & Zhang, 2025).

Recent research has also emphasized the importance of distinguishing between general financial literacy and debt literacy. While financial literacy refers to the broader understanding of financial concepts such as inflation, diversification, and investment, debt literacy focuses specifically on the ability to evaluate borrowing conditions, interest costs, and repayment obligations. These two forms of literacy influence different financial decisions and should therefore be analyzed separately when studying borrowing behavior (Khan & Rabbani, 2025).

Empirical findings, however, are not unequivocal. Kawamura et al. (2021) note that a higher level of financial literacy may also be associated with increased risk-taking and speculative behaviour. In contrast, Gedvilaitė et al. (2022) emphasize a positive relationship between financial literacy and stable investment behaviour. Van Rooij et al. (2011) distinguish between basic and advanced financial literacy, with the advanced level being linked to specific professions, age groups, and situations in which individuals actively manage or provide financial resources.

Cultural and national specificities play a significant role in shaping financial literacy. Beckker et al. (2020) point out that although most research focuses on socioeconomic determinants, cultural factors have long been underestimated. Cupak et al. (2018) and Beckker et al. (2020) demonstrate that cultural dimensions, such as uncertainty avoidance and individualism, influence individuals’ attitudes toward risk, financial education, and the search for financial information.

International comparisons also reveal differences in preferences for financial products. Zureck and Svoboda (2015) identified that German students prefer traditional financial products, whereas students in the Czech Republic show greater interest in capital markets and real estate investments. An important factor influencing financial behaviour is also self-confidence in one’s financial knowledge. Yeh and Ling (2022) show that a mismatch between actual financial literacy and subjective self-confidence can lead to suboptimal decisions. Similarly, Chen and Volpe (1998) highlight the relationship between financial self-confidence and the quality of retirement planning.

Numerous studies confirm the positive impact of financial literacy on the quality of financial decision-making (Bannier & Schwarz, 2018; Darriet et al., 2020; Fanta & Mutsonziwa, 2021). Kadoya and Khan (2018) identified its positive effect on psychological well-being in old age, while Watanapongvanich et al. (2021) pointed to a relationship between financial literacy and a lower tendency toward risky behaviour. Previous research suggests that young adults frequently lack the financial knowledge required to make informed financial decisions in areas such as borrowing, budgeting, and investment planning. Insufficient financial literacy may lead to suboptimal financial behavior and difficulties in managing debt obligations. At the same time, many studies measuring financial literacy focus primarily on knowledge tests and lack an explicit theoretical framework explaining how financial knowledge translates into actual financial decision-making. These gaps highlight the importance of examining not only the level of financial literacy among university students but also its implications for specific financial decisions, including corporate debt financing (Adesina et al., 2025; Norvilitis & Mendes-Da-Silva, 2014).

From the perspective of rational decision-making theory, individuals typically go through several stages before making a financial decision, including problem identification, information search, and evaluation of available alternatives. The effectiveness of this process largely depends on the individual’s financial knowledge and ability to interpret financial information correctly (Sharma et al., 2025).

The development of financial literacy is therefore an integral part of public policies in many countries, including the Slovak Republic. Research repeatedly emphasizes the importance of financial education as a tool for minimizing the negative consequences of incorrect financial decisions (Toth et al., 2015). Polednakova (2019) also notes that a low level of financial literacy persists even at universities, and that differences between economic and non-economic fields of study may not always be pronounced.

Financial literacy is also closely related to the understanding of external sources of financing. Rijssegem et al. (2023) demonstrate that deeper knowledge of debt financing on the part of founders increases the likelihood of obtaining external capital, particularly in firms involved in international trade Ni and Gao (2025) highlights the importance of debt financing for start-up enterprises, which often lack sufficient retained earnings.

Gajdosikova et al. (2023) identify the growing popularity of debt financing through banks, bonds, and financial institutions, while emphasizing the need for debt management and monitoring financial performance. Manyanga et al. (2023) confirm the positive impact of debt financing on firm performance in market economies, whereas Githaigo and Kabiru (2015) and Valaskova et al. (2021) warn against the risks of excessive indebtedness.

Research by Abd Rahman et al. (2019) and W. Li et al. (2022) points to a preference for short-term debt on the part of both firms and banks, while long-term debt, including bonds, plays a significant role in the stable financing of investments (Gajdosikova et al., 2023; Valaskova et al., 2021).

Traditional debt financing is primarily based on bank loans, which remain the dominant source of corporate financing (Xie et al., 2024; Hu & Liu, 2025). Bubeliny et al. (2021) define a bank loan as the temporary provision of financial resources with the obligation to repay them under agreed conditions, while Valaskova et al.(2021) emphasizes its interest-bearing nature.

Debt instruments of the capital market also include bonds, the use of which is conditional upon the issuer’s creditworthiness and regulatory requirements (Fernandes et al., 2014; Hayashi & Routh, 2025). Finke et al. (2017) identify credit risk as a key limiting factor of this form of financing.

Alongside traditional forms, alternative sources of financing are increasingly gaining importance. (Hu & Liu, 2025) identifies their dynamic development particularly in Asia, while Yasar (2021) and Rohatgi et al. (2023) emphasize the role of technological innovation and digital platforms. Leasing represents a significant alternative to conventional forms of financing (Yan, 2006), with its development and definitions analysed by Mazure (2009). According to Mollick (2014) and Miglo and Miglo (2019), crowdfunding is a financing tool particularly suitable for new and innovative enterprises, while Fisher et al. (2017) also highlight its social dimension.

Despite the growing body of research on financial literacy, relatively limited attention has been devoted to distinguishing between general financial knowledge and borrowing-specific competencies. Recent studies indicate that debt literacy represents a distinct dimension of financial capability that specifically affects borrowing behavior and debt management decisions (Khan & Rabbani, 2025). While a substantial portion of the literature focuses on saving behavior and investment decisions, considerably less attention has been paid to the role of financial literacy in borrowing decisions and debt financing. This gap is particularly evident in studies examining university students, who represent an important group entering the financial decision-making environment. Therefore, examining financial literacy in the context of corporate debt financing among university students represents an important extension of the existing literature.

Therefore, the aim of this study is to assess the level of financial literacy of university students, specifically in the field of corporate debt financing, and to identify the key determinants influencing their knowledge in this domain.

2. Methodology

Building on the findings of previous empirical studies that point to an insufficient level of financial literacy (FL) among young adults, a quantitative study was conducted to assess the level of financial literacy of university students in corporate debt financing. The aim of the research was to quantify respondents’ knowledge levels and to identify relationships between the correctness of responses and selected sociodemographic and educational characteristics. This study employs a quantitative research design based on a structured questionnaire survey. The quantitative approach was selected because it allows systematic measurement of students’ financial knowledge and enables statistical comparison between groups of respondents. The questionnaire method is widely used in financial literacy research as it allows the collection of standardized data from a relatively large sample of respondents.

Primary data were collected using a questionnaire survey (see Appendix A). After the completion of data collection, the dataset was subjected to descriptive and inferential statistical analyses. For each questionnaire item, research hypotheses were formulated and subsequently tested for statistical significance.

2.1. Sample Selection

The study employed a non-probability purposive sampling strategy combined with convenience-based recruitment. The target group consisted of university students enrolled in economics and business-related programs in higher education institutions in the Slovak Republic. Respondents were recruited based on their accessibility and willingness to participate in the online questionnaire. Participation in the survey was voluntary. Although this sampling approach does not ensure full representativeness of the entire student population, it is commonly used in exploratory studies focusing on financial literacy within specific target groups.

Prior to the main data collection, a pilot study was conducted to verify the clarity, unambiguity, and methodological adequacy of the questionnaire. The pilot testing was carried out on a homogeneous sample of six respondents, which is consistent with recommendations in the methodological literature suggesting that a pilot sample should include at least 4 to 12 participants. Based on the feedback obtained, minor deficiencies in the wording of several items were identified and subsequently corrected.

The minimum required sample size was determined using a formula for calculating sample size for a finite population. The target population consisted of first- and second-cycle university students in the Slovak Republic, with a total population size of 94,358 individuals, according to data from the Ministry of Education as of 31 October 2023 (including both full-time and part-time students).

The minimum number of respondents was calculated assuming a 95% confidence level and a 5% margin of error, as follows:

$$n={\displaystyle \frac{\frac{z_{\alpha }^2\cdot p\cdot (1-p)}{e^2}}{1+\frac{z_{\alpha }^2\cdot p\cdot (1-p)}{e^2\cdot N}}}$$

(1)

where

• $\alpha$—level of significance;
• $z_{\alpha }$—critical value of the standard normal distribution;
• $p$—assumed proportion of the occurrence of the observed characteristic (0.5);
• $e$—acceptable margin of error;
• $N$—size of the target population.

Based on this calculation, the minimum required sample size was determined to be 383 respondents. A total of 423 respondents participated in the survey, of whom 403 completed the questionnaire in full and were therefore included in the subsequent analysis.

The target group consisted of university students enrolled in higher education institutions in the Slovak Republic, studying in either full-time or part-time programmes, aged 18 to 25 years.

2.2. Data Collection and Questionnaire Design

Data were collected in the Slovak Republic using an online questionnaire distributed via the internet to university students enrolled at Slovak higher education institutions. The data collection period extended from 31 January 2024 to 13 February 2024. Completion of the questionnaire was anonymous and not time-limited.

The questionnaire consisted of two sections. The first section (see Appendix B) included four identification questions focusing on basic respondent characteristics (gender, region of origin, level of study, and field of study). The second section comprised sixteen knowledge-based questions addressing issues related to corporate debt financing.

The questions were designed to assess not only theoretical knowledge but also the ability to apply financial concepts in practical situations. Each question offered four response options, with only one correct answer. Responses to all questions were mandatory. The use of a calculator was permitted; however, given the nature of the questions, it was not essential.

The research complied with standard ethical principles for social science research. Participation in the survey was voluntary and anonymous, and respondents were informed about the purpose of the research before completing the questionnaire.

The questionnaire was developed based on existing financial literacy studies and relevant literature. To ensure content validity, the questionnaire items were reviewed by experts in finance and economics education. A pilot test was conducted with a small group of students prior to the main data collection to verify the clarity and comprehensibility of the questions.

2.3. Applied Analytical Methods

To evaluate the relationships between respondents’ levels of financial literacy and selected sociodemographic and educational characteristics, standard statistical methods for the analysis of categorical data were applied. The analytical procedures allow the identification of statistically significant relationships and the assessment of their strength.

Prior to statistical analysis, the collected data was cleaned and prepared for analysis. Incomplete questionnaires and responses with missing key variables were excluded from the dataset. The remaining responses were coded and organized into a structured database. Descriptive statistics were calculated to verify the consistency and distribution of the variables.

2.3.1. Analysis of (In)dependence of Categorical Variables

To examine the relationships between response correctness and respondent characteristics, an analysis of (in)dependence between categorical variables was conducted using Pearson’s chi-square test of independence. This method enables a comparison between observed and expected frequencies in contingency tables.

The following hypotheses were formulated:

H_A: 

Field of study has an effect on the selection of the correct answer.

H_B: 

Level of study has an effect on the selection of the correct answer.

H_C: 

Respondent region of origin has an effect on the selection of the correct answer.

H_D: 

Respondent gender has no effect on the selection of the correct answer.

The null hypothesis assumed no existence of a statistically significant association between the examined variables. Hypothesis testing was conducted at a significance level of α = 0.05.

For analytical purposes, respondents’ answers were dichotomised into correct and incorrect categories. Contingency tables containing observed and expected frequencies were constructed. Prior to the analysis, the assumptions of the chi-square test were examined, including minimum expected cell frequencies and adequate sample size.

2.3.2. Strength of Association—Cramér’s V

In cases where a statistically significant dependence was identified, its intensity was quantified using Cramér’s V coefficient, calculated as follows:

$$V=\sqrt{{\displaystyle \frac{{\chi }^2}{n\cdot \mathrm{m}\mathrm{i}\mathrm{n}(r-1,c-1)}}}$$

(2)

where n denotes the total number of observations, r the number of rows, and c the number of columns in the contingency table.

The values of the coefficient were interpreted according to the following thresholds:

• 0.00–0.30: weak association;
• 0.30–0.80: moderate association;
• 0.80–1.00: strong association.

Given the number of item-level hypothesis tests performed, the results should be interpreted with caution due to the increased risk of Type I error.

2.3.3. Sign Scheme Analysis

To facilitate a more detailed interpretation of statistically significant relationships identified by Pearson’s chi-square test of independence, a sign scheme analysis based on adjusted residuals was applied. This post hoc method was used only for selected questionnaire items in which a statistically significant association had been confirmed.

The analysis was performed using the SPSS 27 statistical software, with respondents’ answers retained in their original categorical form rather than aggregated into binary categories. Instead of reporting numerical residual values, the sign scheme replaces them with symbolic indicators that facilitate intuitive interpretation of both the direction and magnitude of deviations. A positive sign indicates that the observed frequency exceeds the expected value, whereas a negative sign denotes a lower observed frequency compared to the theoretical expectation; cells without meaningful deviations are marked accordingly. The number of symbols reflects the associated error risk (5%, 1%, and 0.1%), thereby highlighting statistically significant over- or under-representation of specific category combinations (Kicova et al., 2025; Vrtana & Duricova, 2026).

The method was applied to identify which respondent groups (e.g., field of study, level of study, or region of origin) contributed most strongly to the statistically significant relationships detected by the chi-square test. The sign scheme analysis thus serves as an auxiliary interpretative tool that complements the chi-square test and Cramér’s V without replacing the primary inferential results.

3. Results

This chapter presents the main empirical findings derived from the questionnaire survey conducted to assess the level of financial literacy of university students in corporate debt financing. The results are organized into several thematic sections that successively outline the characteristics of the research sample, respondents’ performance across individual knowledge domains, and the identified relationships between response correctness and selected sociodemographic and educational characteristics. The final part of the chapter focuses on the analysis of the aggregate financial literacy score, which enables a comprehensive comparison of knowledge levels across different respondent groups.

3.1. Characteristics of the Research Sample

A total of 403 respondents who completed the questionnaire in its entirety (16 knowledge-based items) were included in the analysis. With respect to educational and demographic characteristics, the sample was relatively balanced:

• Level of study: first cycle 52.11% (n = 210), second cycle 47.89% (n = 193).
• Gender: female 52.36% (n = 211), male 47.64% (n = 192).
• Field of study: non-economic programmes constituted the dominant group (52.11%, n = 210), while economic programmes accounted for the remainder of the sample.
• Region of origin: Western Slovakia 30.27% (n = 122), Central Slovakia 36.48% (n = 147), Eastern Slovakia 33.25% (n = 134).

This sample structure enabled the testing of relationships between response correctness and the variables defined in the methodological section, namely gender, region of origin, level of study, and field of study.

3.2. Respondents’ Performance in the Area of Corporate Debt Financing

The knowledge-based section of the questionnaire consisted of 16 questions covering key concepts related to corporate debt financing, including credit products, interest rate mechanisms, leasing, capital market instruments, and alternative forms of financing. Respondents’ performance varied across different knowledge domains.

Table 1. Success rate based on knowledge domain.

Knowledge Domain	Questionnaire Items	Success Rate (%)
Fundamental concepts of corporate debt financing	Q5–Q7	46–62
Interest rate mechanisms and loan evaluation	Q8–Q10	43–62
Bank credit instruments	Q11	67
Leasing financing instruments	Q12	49
Payment and contractual conditions	Q13	64
Capital market debt instruments	Q14–Q15	56–60
Receivables financing instruments	Q16	47
Alternative financing instruments	Q17–Q20	55–61

Source: Authors’ own processing based on the results of the questionnaire survey.

provides a synthetic overview of success rates across the main areas of corporate debt financing literacy assessed in the questionnaire.

3.3. Hypothesis Testing and Determinants of Response Correctness

The hypothesis testing results reveal several systematic patterns in the determinants of respondents’ performance. Relationships between response correctness and selected respondent characteristics were examined using Pearson’s χ² test of independence (α = 0.05), with the strength of associations assessed using Cramér’s V.

As shown in

Table 2. Statistical analysis of field of study.

Field of Study	$\mathit{p}$-Value	Conclusion	Strength (Value)	Strength (Description)
Q5	<0.0001	Dependent	0.362	Moderate
Q6	<0.0001	Dependent	0.303	Moderate
Q7	<0.0001	Dependent	0.265	Weak
Q8	<0.0001	Dependent	0.382	Moderate
Q9	<0.0001	Dependent	0.316	Moderate
Q10	<0.0001	Dependent	0.316	Moderate
Q11	<0.0001	Dependent	0.424	Moderate
Q12	<0.0001	Dependent	0.260	Weak
Q13	<0.0001	Dependent	0.374	Moderate
Q14	<0.0001	Dependent	0.424	Moderate
Q15	<0.0001	Dependent	0.367	Moderate
Q16	<0.0001	Dependent	0.383	Moderate
Q17	<0.0001	Dependent	0.397	Moderate
Q18	<0.0001	Dependent	0.370	Moderate
Q19	<0.0001	Dependent	0.367	Moderate
Q20	<0.0001	Dependent	0.410	Moderate

Source: Authors’ own processing based on the results of the questionnaire survey.

, a statistically significant association between field of study and response accuracy was confirmed for all analysed questionnaire items (Q5–Q20), with $p$-values below 0.0001 in every case. Therefore, the hypothesis H_A was accepted.

The strength of the identified relationships, measured using Cramér’s V, ranged from 0.260 to 0.424, indicating weak to moderate dependence according to the applied interpretation thresholds. Most of the observed associations reached a moderate level of dependence, while only a small number of items showed weak associations.

The strongest relationships were observed for Question 11 and Question 14 (Cramér’s V = 0.424), followed by Question 20 (0.410) and Question 17 (0.397), suggesting that respondents’ academic orientation played a particularly important role in questions related to bank credit instruments and alternative financing concepts. The weakest associations were observed for Question 7 (0.265) and Question 12 (0.260), although these relationships remained statistically significant.

Overall, the results indicate that students enrolled in economically oriented study programmes selected correct answers more frequently, whereas respondents from non-economic fields showed a higher tendency to choose incorrect alternatives or the option “I do not know.” This pattern was also supported by sign scheme analyses (Appendix C) conducted for selected questionnaire items, which confirmed a consistent direction of differences between respondent groups.

As shown in

Table 3. Statistical analysis of level of study.

Level of Study	$\mathit{p}$-Value	Conclusion	Strength (Value)	Strength (Description)
Q5	<0.0001	Dependent	0.161	Weak
Q6	0.074	Independent	-	-
Q7	0.017	Dependent	0.119	Weak
Q8	0.002	Dependent	0.157	Weak
Q9	0.021	Dependent	0.115	Weak
Q10	<0.0001	Dependent	0.115	Weak
Q11	<0.0001	Dependent	0.207	Weak
Q12	<0.0001	Dependent	0.226	Weak
Q13	0.030	Dependent	0.108	Weak
Q14	0.049	Dependent	0.098	Weak
Q15	0.018	Dependent	0.117	Weak
Q16	0.028	Dependent	0.110	Weak
Q17	0.001	Dependent	0.160	Weak
Q18	0.001	Dependent	0.160	Weak
Q19	0.001	Dependent	0.162	Weak
Q20	0.001	Dependent	0.244	Weak

Source: Authors’ own processing based on the results of the questionnaire survey.

, statistically significant associations between level of study and response accuracy were identified for most of the analysed questionnaire items (15 out of 16 questions). The only exception was Question 6, where no statistically significant relationship was observed ($p$ = 0.074). Therefore, the alternative hypothesis H_B was accepted.

The strength of the identified relationships, measured using Cramér’s V, ranged from 0.098 to 0.244, indicating weak dependence according to the applied interpretation thresholds. This suggests that although level of study influenced the correctness of responses in several items, the magnitude of this effect remained relatively limited.

The strongest association was observed for Question 20 (Cramér’s V = 0.244), followed by Question 12 (0.226) and Question 11 (0.207). In contrast, the weakest relationships were recorded for Question 14 (0.098) and Question 13 (0.108). Despite their statistical significance, these values indicate only a modest influence of level of study on respondents’ financial knowledge.

Overall, the results suggest that second-cycle students demonstrated a slightly higher probability of selecting correct answers compared to first-cycle students. This tendency was particularly noticeable in questions requiring analytical reasoning or the comparison of financial alternatives, such as loan evaluation or the interpretation of financial instruments. However, compared to the effect of field of study, the influence of level of study remained substantially weaker.

As presented in

Table 4. Statistical analysis of region.

Region	$\mathit{p}$-Value	Conclusion	Strength (Value)	Strength (Description)
Q5	0.277	Independent	-	-
Q6	0.171	Independent	-	-
Q7	0.554	Independent	-	-
Q8	0.578	Independent	-	-
Q9	0.812	Independent	-	-
Q10	0.289	Independent	-	-
Q11	0.025	Dependent	0.622	Moderate
Q12	0.659	Independent	-	-
Q13	0.825	Independent	-	-
Q14	0.907	Independent	-	-
Q15	0.264	Independent	-	-
Q16	0.280	Independent	-	-
Q17	0.171	Independent	-	-
Q18	0.925	Independent	-	-
Q19	0.658	Independent	-	-
Q20	0.315	Independent	-	-

Source: Authors’ own processing based on the results of the questionnaire survey.

, statistically significant associations between region and response accuracy were identified only for one questionnaire item (Question 11), while all other analysed items showed no statistically significant dependence for hypothesis H_c.

Specifically, Question 11, which focused on the identification of overdraft credit, yielded a statistically significant result ($p$ = 0.025), with a Cramér’s V value of 0.622, indicating a moderate association according to the applied interpretation thresholds. However, for the remaining 15 questionnaire items (Q5–Q10 and Q12–Q20), the null hypothesis of independence could not be rejected, as all $p$-values exceeded the significance level of α = 0.05.

Overall, these findings suggest that region of origin did not represent a systematic determinant of respondents’ performance in the analysed questionnaire. The isolated statistically significant result observed for Question 11 should therefore be interpreted with caution, as it may reflect specific sample characteristics or local contextual factors rather than a consistent regional effect.

As presented in

Table 5. Statistical analysis of gender.

Gender	$\mathit{p}$-Value	Conclusion	Strength (Value)	Strength (Description)
Q5	0.897	Independent	-	-
Q6	0.060	Independent	-	-
Q7	0.603	Independent	-	-
Q8	0.067	Independent	-	-
Q9	0.424	Independent	-	-
Q10	0.007	Dependent	0.134	Weak
Q11	0.322	Independent	-	-
Q12	0.067	Independent	-	-
Q13	0.416	Independent	-	-
Q14	0.254	Independent	-	-
Q15	0.711	Independent	-	-
Q16	0.761	Independent	-	-
Q17	0.757	Independent	-	-
Q18	0.947	Independent	-	-
Q19	0.724	Independent	-	-
Q20	0.153	Independent	-	-

Source: Authors’ own processing based on the results of the questionnaire survey.

, statistically significant associations between gender and response accuracy were identified only for one questionnaire item (Question 10), while all other analysed items showed no statistically significant dependence for hypothesis H_D.

Specifically, Question 10, which required respondents to compare loan alternatives based on the annual percentage rate of charge (APR), yielded a statistically significant result (p = 0.007). The strength of this relationship, measured using Cramér’s V, reached 0.134, indicating a weak association according to the applied interpretation thresholds.

For the remaining 15 questionnaire items (Q5–Q9 and Q11–Q20), the null hypothesis of independence could not be rejected, as all p-values exceeded the significance level of α = 0.05. These results suggest that gender did not represent a systematic determinant of respondents’ performance in the analysed questionnaire.

Although male respondents achieved slightly higher success rates in the item related to loan evaluation, the overall influence of gender on financial knowledge remained limited.

3.4. Aggregate Financial Literacy Score

The aggregate financial literacy score was calculated as the proportion of correct answers across all 16 knowledge-based questions (maximum score = 16 points). This indicator enabled a comparative assessment of financial knowledge across respondent groups.

The results confirmed the patterns observed in the item-level analysis. Field of study emerged as the most important determinant of performance, with students enrolled in economically oriented programmes achieving systematically higher aggregate scores than students from non-economic fields.

A weaker but still noticeable effect was observed for level of study, as second-cycle students achieved higher average scores than first-cycle students, indicating a cumulative effect of education and experience.

Gender differences were relatively small, with male respondents achieving approximately 57% of the maximum score, and statistical significance was confirmed in only one questionnaire item.

The maximum possible score (16 points) was achieved by 8.18% of respondents (n = 33). All these respondents were enrolled in economically oriented programmes, and most were second-cycle students, highlighting the role of formal economic education in achieving higher levels of financial literacy in corporate debt financing.

4. Discussion

The findings of this study are consistent with a substantial body of international empirical literature, which repeatedly documents that the level of financial literacy among young adults and university students is, on average, only moderate to low, even in environments with relatively well-developed financial markets (He et al., 2025). Review studies indicate that weaknesses are systematically concentrated in areas such as interest compounding, the time value of money, risk assessment, and the comparison of financial products, which have measurable consequences for the quality of financial decision-making (Lusardi & Mitchell, 2014).

Our findings can also be interpreted through the perspective of rational decision-making theory, which suggests that individuals evaluate financial alternatives through a structured process involving information search and comparison of available options before making financial decisions (Sharma et al., 2025).

About performance determinants, our results confirm the robust conclusion of Van Rooij et al. (2011) that financial knowledge is strongly differentiated according to individuals’ exposure to financial content, whether through field of study or specific educational experiences (Showkat et al., 2025). These findings are also consistent with recent research distinguishing between financial literacy and debt literacy. Khan and Rabbani (2025) demonstrate that while financial literacy is more closely related to investment behavior, debt literacy plays a crucial role in shaping borrowing decisions and debt management outcomes among university students. Field of study also emerged in our questionnaire survey as the most consistent determinant of response correctness, which aligns with evidence that individuals with higher levels of financial knowledge tend to make more sophisticated choices across various segments of financial markets (Ni & Gao, 2025).

At the same time, the results indicate that the mere presence of formal economic education does not automatically imply an adequate level of understanding of specific financial instruments, particularly when questions require application and analytical interpretation (e.g., comparison of loan alternatives, APR, interest rate periodicity) (Fernandes et al., 2014). Meta-analytical research also suggests that although financial education often has a statistically significant impact on behavior, its substantive effect tends to be modest and depends on the quality and timing of the intervention as well as its connection to real decision-making situations. (Fernandes et al., 2014; Ni & Gao, 2025).

Particularly relevant is the distinction between objective (tested) and subjective (perceived) financial literacy. Several studies demonstrate (Allgood & Walstad, 2016; Rodríguez-Correa et al., 2025; Croitoru et al., 2025) that subjective self-confidence in financial matters may be at least as important as actual knowledge, while overconfidence can lead to riskier or more costly decisions, especially in the context of borrowing and repayment. This observation is consistent with our findings of a higher frequency of “I do not know” responses among non-economically oriented respondents, as well as weaker performance in application-based questions, where decision-making under uncertainty often shifts toward heuristic reasoning.

Our findings may also be interpreted through the life-cycle perspective of financial sophistication. Previous research shows that financial decision-making and the ability to assess costs and risks evolve with age and experience, with young adults being more prone to errors in credit products and fee structures (Agarwal et al., 2009; Nogueira et al., 2025).

Our findings regarding weaker orientation in capital market instruments and alternative forms of financing are also consistent with Van Rooij et al. (2011), who argue that knowledge of market-based instruments and risk diversification is particularly deficient among young adults (Pratiwi & Fytaloka, 2025; Hossain et al., 2025). As the importance of combining bank and non-bank financing sources grows in corporate practice, limited understanding of bonds, factoring, or alternative capital forms may increase the likelihood of suboptimal capital structure choices, mispricing of capital costs, and underestimation of risks (Molina-García et al., 2025). Empirical evidence further shows that financial literacy is associated with participation in capital markets and the use of more sophisticated financial products, which reinforces the need for targeted education in this domain (Hong Shan et al., 2023; Lusardi & Mitchell, 2023).

From the perspective of the higher education environment, previous studies indicate that students’ financial knowledge and behaviour are influenced not only by formal education but also by financial socialization, family background, and work experience (Shim et al., 2010; Rodríguez-Correa et al., 2025). This is also relevant for interpreting differences between study groups: part of the observed variation may reflect selection effects (students with a stronger interest in finance choosing economic programmes), part curricular effects, and part informal or extracurricular learning (Lam & Mueggenburg, 2025).

From an applied perspective, these findings support the need to strengthen application-oriented financial education that connects theoretical concepts with decision-making tasks typical of real practice, such as APR interpretation, repayment schedule simulations, comparison of financing alternatives, valuation of debt instruments, and work with cash flow and financing costs (Hastings et al., 2013; Lanciano et al., 2025). Such educational approaches are frequently recommended in the literature as more effective than isolated “declarative” learning of concepts, particularly when embedded in realistic scenarios and combined with immediate feedback.

Overall, the findings suggest that although university students demonstrate partial knowledge of corporate debt financing instruments, important gaps remain in application-oriented financial decision-making, particularly among students outside economically oriented fields of study.

Limitations and Future Research

Despite the valuable insights provided by this study, several limitations should be acknowledged. First, the research sample consisted exclusively of university students, which limits the generalizability of the findings to other population groups. Students represent a specific segment of the population with relatively similar educational backgrounds and financial experience, which may influence their responses and decision-making behaviour. Previous research indicates that financial literacy levels and financial behaviour may differ substantially across demographic groups such as age, income, and education level, which should be considered when interpreting results based on student samples (Cordero & Pedraja-Chaparro, 2022).

Second, the study was conducted within a single national context. Financial literacy and financial decision-making may be influenced by institutional, cultural, and economic factors that differ across countries. Cross-country studies demonstrate that national environments, financial systems, and educational frameworks significantly shape financial knowledge and financial behaviour, which may limit the transferability of findings obtained in a single country (Grohmann et al., 2018; Nicolini et al., 2013). Therefore, the results should be interpreted with caution when applied to other geographical contexts.

Third, the research relied on self-reported questionnaire data. Although this approach is commonly used in studies examining financial literacy, respondents’ answers may be influenced by subjective perceptions, misunderstandings of financial concepts, or social desirability bias. Previous research highlights that survey-based measurements of financial literacy may be affected by response biases and measurement errors, which may influence the reliability of the obtained results) (Robb & Sharpe, 2009).

Future research could expand the scope of the analysis by including respondents from different countries and educational backgrounds to enable international comparisons. In addition, further studies could examine the relationship between financial literacy and financial decision-making using experimental or longitudinal research designs, which may provide deeper insights into how financial knowledge influences real financial behaviour over time. Moreover, future research could incorporate behavioural and psychological factors, such as cognitive biases or self-control, which have been identified as important determinants of financial behaviour alongside financial literacy (Klapper et al., 2015).

5. Conclusions

This paper presented an empirical assessment of the level of financial literacy among university students in corporate debt financing, with a particular focus on identifying determinants of response correctness and differences across respondent groups. The results indicate that the level of financial literacy in this domain is heterogeneous and often limited, especially in questions requiring the application of financial concepts rather than simple recognition of definitions.

The analysis of individual questionnaire items revealed that respondents achieved relatively better results in questions related to basic concepts of debt financing and traditional banking products. In contrast, lower success rates were observed in application-oriented questions requiring analytical comparison of loan alternatives, interpretation of the annual percentage rate of charge (APR), differentiation between leasing types, or understanding of capital market instruments. These findings suggest that declarative knowledge is not always accompanied by the ability to apply financial concepts in practical decision-making contexts.

With respect to determinants of financial literacy, field of study emerged as the strongest and most consistent factor influencing the correctness of responses. Students enrolled in economically oriented study programs achieved higher success rates both in individual questionnaire items and in the overall financial literacy score. The level of study showed a weaker but occasionally statistically significant effect, particularly in analytically demanding questions, suggesting a cumulative effect of education and experience. In contrast, gender and region of origin did not appear to be significant determinants in most cases.

The results have several practical implications. In the context of higher education, they point to the need for a systematic strengthening of application-oriented financial education that links theoretical knowledge with decision-making situations typical of entrepreneurial and managerial practice. Attention should be devoted to debt financing, capital markets, and alternative sources of financing, which play an increasingly important role in modern economies yet represent areas of relatively weak financial knowledge among students.

From a broader economic perspective, a low level of financial literacy in debt financing may lead in the future to suboptimal corporate capital structure decisions, mispricing of risk, and increased financial vulnerability of business entities. Enhancing the financial literacy of young people and future managers, therefore, represents not only an educational challenge but also an important economic and policy-related issue.

The limitations of this study lie primarily in its geographical focus on the Slovak Republic and in the use of a questionnaire-based instrument that predominantly captures the objective component of financial literacy. In addition, the use of a non-probability student sample limits the generalizability of the findings to the broader population. The results should therefore be interpreted primarily within the context of university students and their educational environment. Future research could expand the scope of the analysis by including respondents from different countries and educational systems to enable international comparisons. Further studies could also examine the relationship between financial literacy and financial decision-making using experimental or longitudinal research designs, which would allow a deeper understanding of how financial knowledge translates into actual financial behavior. In addition, incorporating behavioral and psychological factors such as cognitive biases, financial self-confidence, or self-control could provide a more comprehensive explanation of financial decision-making.

Overall, the findings highlight the importance of strengthening application-oriented financial education focused on corporate debt financing and capital market instruments, particularly among students outside economically oriented fields of study.