The Moderating Role of Financial Literacy and Awareness on the Acceptance Towards Attitude on Cryptocurrency Adoption Among Faculty and Students of Eastern Visayas State University

The Moderating Role of Financial Literacy and Awareness on the Acceptance Towards Attitude on Cryptocurrency Adoption Among Faculty and Students of Eastern Visayas State University | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Moderating Role of Financial Literacy and Awareness on the Acceptance Towards Attitude on Cryptocurrency Adoption Among Faculty and Students of Eastern Visayas State University Jeremiah Edison Nicart, Miguel Abas, Jr This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7160135/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study aimed to analyze the role of financial literacy and awareness on cryptocurrency acceptance and attitude among students and faculty members of Eastern Visayas State University. The findings show that the faculty members and students have moderate cryptocurrency awareness, relatively high financial literacy, neutral cryptocurrency acceptance, and a negative attitude. The relationships among these constructs were analyzed using SEM in AMOS. The results show that financial literacy negatively and significantly influences cryptocurrency attitude. Additionally, financial literacy does not necessarily moderate the relationship between acceptance of and attitude towards cryptocurrency. The findings further showed that awareness positively and significantly influences attitude, and that it does not also moderate the relationship between acceptance and attitude towards cryptocurrency. Finally, it was found in this study that acceptance of cryptocurrency positively and significantly influences the attitude towards it. cryptocurrency awareness acceptance attitude financial literacy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction In January 2009, the oldest surviving cryptocurrency known as Bitcoin was introduced, offering an avenue of decentralized notion in the finance and accounting sectors. Decentralization is achieved when financial transactions are initiated and completed without the intervention of a centralized authority. Since then, it has instigated arguments as to its regulations, digital security, and legitimacy. Despite these, several countries have legalized cryptocurrency and its transactions. El Salvador has even made Bitcoin as a legal tender, in accordance with their new Digital Securities Law (Al Jazeera, 2023 ). On the other hand, there are still other countries that issued a ban on Bitcoin and other cryptocurrencies (Bajpai, 2019 ). Most cryptocurrencies derive their value based on their blockchain technology and liquidity provided in the crypto market, indicating that most individuals and institutions engage in crypto transactions by trading. However, with the increasing popularity of Bitcoin and other cryptocurrencies, online investment scams have also emerged. The Department of Justice in the United States charged Bitconnect’s founder for creating a multinational Ponzi scheme, obtaining around $ 2.4 billion. Bitconnect is an organization that misled the public by providing high yields through their lending platform, trading bot, and volatility software, when in fact the returns of the early investors were given from the investments of the new entrants (U.S. Department of Justice, 2022 ). Cryptocurrency is commonly used as a currency, a speculation instrument, and as an investment tool, which evidences that it can provide financial awareness and freedom. Several news globally and locally has erupted saying that certain individuals were able to build houses, buy cars, and become rich by engaging in cryptocurrency activities. It was reported in CNBC that a 25-year-old became a millionaire after investing early in Ethereum and Bitcoin (Locke, 2021 ) and in another report by Umoh ( 2018 ) mentioned that a 19-year-old Bitcoin millionaire has been offering crucial investment advice. Studies on cryptocurrencies reveal that most people are aware of cryptocurrencies and their basic premise (Anil Kumar & Swathy, 2019 ). As also supported by Shukla ( 2019 ), people’s awareness in general is visible, and they would like it to be integrated into their individual investment portfolios due to its huge potential returns. While there are several studies that support the proposal to incorporate cryptocurrency in a formal academic setting, as in the case of Phillips ( 2021 ), Naveen Kumar & Sanskriti ( 2022 ), Doblas ( 2019 ), among others, these studies were conducted on students and working professionals who are not on the academic point of view. Most of the mentioned study did not use structural equation modelling (SEM) in analyzing the relationships of the variables, except for Magbitang et al. ( 2023 ), to which this research’s instrument was adopted from, however, no discussion in model fit determination was disclosed. This study was built from these gaps by conducting it to an academic perspective and an extended acceptance framework by adding financial literacy, cryptocurrency awareness, perceived benefits and risks that may influence the acceptance and attitude towards cryptocurrency, and by adapting technology acceptance model, diffusion of innovation theory, and theory of reasoned action. Furthermore, a comprehensive procedure in arriving at a good model fit for structurual equation modelling (SEM) is discussed critically in this study. Literature Review Financial Literacy Financial literacy is one’s capability to manage financial information and make educated judgements according to Lusardi and Mitchell (2014) as cited by Gui et al. ( 2021 ). Since cryptocurrency may be used as a currency and a financial tool, its adoption may or may not be impacted by an individual’s financial literacy.Panos et al. ( 2020 ) concluded that financially literate people have a lower tendency of owning cryptocurrency despite their awareness. This is aligned with the findings of Santoso & Modjo ( 2022 ). On the other hand,Jariyapan et al. ( 2022 ) found that financial literacy affects positively the adoption of cryptocurrency. The latter’s findings are concreted byPrasetyo & Kurniasari ( 2023 ) who found that financial literacy has a significant effect on the intention to engage into cryptocurrency transactions. Magbitang et al. ( 2023 ), found that financial literacy positively and significantly affects acceptance but negative on attidude. For this study, the relationship of financial literacy towards acceptance and attitude on cryptocurrency were examined. Accordingly, the following hypotheses were formulated: H1. Financial Literacy has a significant influence on attitude towards cryptocurrency. H2. Financial Literacy moderates the relationship of acceptance and attitude towards cryptocurrency. Awareness Awareness is a situation when an individual is informed of a novelty and frames an overall insight as cited by Prakosa & Sumantika ( 2023 ). Citing further from Alaeddin & Altounjy ( 2018 ), awareness influences comportments. Despite the awareness on the existence of cryptocurrency, realizations on how cryptocurrency functions are still at minimal (Arutgeevitha et al., 2022 ; Jadhav et al., 2023 ; Ku-Mahamud et al., 2019 ; Mathur, 2022 ; Scott et al., 2022 ). Doblas ( 2019 )) discovered that level of awareness substantially affects adoption of cryptocurrency. Magbitang et al. ( 2023 ) found that awareness has signifivant and positive Accordingly, hypotheses are enumerated below: H3. Awareness has a significant influence on attitude towards cryptocurrency. H4. Awareness moderates the relationship of acceptance and attitude towards cryptocurrency. Perceived Benefits and Risks As cited by Sciarelli et al. ( 2022 ), perceived benefit is the view of the favorable outcomes because of certain behavior. The study ofAlqaryouti et al. ( 2020 ) found that there is no substantial association between perceived benefit and behavioral use of cryptocurrency. This finding is the same with Tyagi et al. ( 2023 ), where they claimed that there is low considerable association between perceived benefit and the intent to use cryptocurrency. On the other hand,Hasan et al., ( 2022 ) discovered that perceived benefit improves perceived value and motive to utilize cryptocurrency, which is concreted byDewi et al. ( 2023 ) through their findings that perceived benefit materially induce investment in cryptocurrency.Magbitang et al. ( 2023 ) found that a significant relationship between perceived benefits towards the acceptance of cryptocurrency exists. For this study, perceived benefits construct was considered a direct factor of acceptance. Perceived risk is a user’s insight of the ambiguity and potential unfavorable outcomes from acquiring goods or services, as in the case of a behavioral research perspective (Faqih, 2016 ).Anser et al. ( 2020 ) claimed that when people recognize greater risks associated with Bitcoin, there may be no adoption even though a motivation to use it is relatively robust. Interestingly,Arias-Oliva et al. ( 2019 ) found that perceived risk has no effect on cryptocurrency adoption. Voskobojnikov et al. ( 2021 ), andJariyapan et al. ( 2022 ) found no negative influence of perceived risk towards cryptocurrency adoption.Magbitang et al. ( 2023 ) found that higher perceived risk tends to have higher acceptance. Same with perceived benefits, for this study, perceived risks construct was also treated as a direct factor of acceptance. Acceptance As defined by (Cambridge Dictionary, n.d.) in marketing, acceptance is the motivation of individuals to adopt new goods or services or to agree to novel theories. In this study, acceptance is the people’s intention to use cryptocurrency. Arutgeevitha et al., 2022 ), majority of their respondents showed high level of willingness to integrate cryptocurrency in their individual investment portfolio as influenced mainly by lucrative returns. Magbitang et al. ( 2023 ) claimed that there is a high level of acceptance among the respondents and that acceptance significantly influences attitude. Because of this, a hypothesis was formed, as shown below: H5. Acceptance significantly influences attitude towards cryptocurrency. Attitude Attitude can be defined as how an individual perceives and values a thing or a person, a propensity to react positively or negatively concerning a specific theory, thing, individual or condition (Vargas-Sánchez et al., 2016 ). Doblas ( 2019 ) found that there is a negative attitude towards cryptocurrency adoption owing to price instability despite having a positive impression of being used as a means of exchange. This is supported by Parilla & Abadilla ( 2022 ), where they also found that there is a negative attitude towards cryptocurrency, which is influenced by lack of regulation. They further claimed that there is no substantial association between attitude and acceptance of cryptocurrency as a medium of exchange. Magbitang et al. ( 2023 ) also supports the negative attitude towards cryptocurrency but not the impact of awareness towards attitude. This study examined the attitude towards cryptocurrency among the respondents to confirm or negate these findings. Technology Acceptance Model (TAM) Technology Acceptance Model (TAM) by Davis (1989) was formulated to define how end users accept and adopt a technology (Bharadwaj & Deka, 2021 ). According to TAM, behavioral intention is influenced by perceived usefulness and perceived ease of use. In the literature review by Bhuvana & Aithal ( 2022 ), the factors influencing the behavioral intention towards cryptocurrency use include, ease of use, social impact, convenience, trust, volatility, individual beliefs, risks, and decision-making. As in the case of Gil-Cordero et al. ( 2020 ), they suggested that TAM be extended to include perceived risk. Furthermore, Arias-Oliva et al. ( 2019 ), Alomari & Abdullah ( 2023 ), andMagbitang et al. ( 2023 ) included awareness and financial literacy as added variables in their respective studies. Since TAM has been tested to work as an adoption model, this study referred to it by merging perceived ease and use and perceived usefulness into Perceived Benefits. This is backed byAbramova & Böhme ( 2016 ) where they found that perceived ease of use has a significant positive effect on perceived benefits andAl-Fraihat et al. ( 2020 ) where they found that amongst strong supporters of benefits, is the perceived usefulness. Perceived risks and financial literacy as suggested by the above were also included among the constructs. Diffusion of Innovation Theory (DIT) Diffusion of Innovation Theory is a structure that aims to explain a procedure through advancement of technology employed by individuals. As illustrated by Rogers, innovation is a theory, procedure, or thing seen as new by a person or other element of acceptance. Furthermore, diffusion is the methodology by which innovation is disseminated to the people through various media for a particular period. Consequently, DIT highlights the explanation of how novel ideas obtain massive application (Alotaibi & Wald, 2013 ). This study referred to this theory as it supports perceived benefits (relative advantage) as one of the main motivators of adoption, with awareness being the initial stage of innovation adoption, as mentioned by Lamorte ( 2022 ). This theory therefore also supports the above recommendations to include perceived benefits and awareness constructs in TAM extension. Theory of Reasoned Action (TRA) This theory by Ajzen and Fishbein in 1967 was developed to create a model that would identify, support, and motivate human behavior. This model is moderated by two constructs: attitude and subjective norms, with attitude having a direct or indirect influence on human behavior. This model however is not for specific behavior or technology as it was meant to explain any human behavior (Momani et al., 2017 ). For the context of this study, only the attitude construct was considered as it did not examine the actual behavior of the respondents, as is also the case for Doblas ( 2019 ). The framework of this study was anchored on TAM, DOI and TRA. Figure 1 represents the schematic diagram of the theoretical framework based on the literature reviewed and theories adapted. In Fig. 1 , the constructs are color coded based on the theories they were referred to. Perceived Benefits, Perceived Risks and Financial Literacy were adapted from the TAM coded by the green color, Acceptance and Awareness were adapted from DIT represented by the orange color, and Attitude was adapted from TRA, with the gray color. The diagram is designed to explain how individuals accept cryptocurrency based solely on the adapted influencing factors, excluding the other constructs from these theories. Accordingly, the conceptual framework is disclosed in Fig. 2 , which is designed to explain directional relationships based on the formulated hypotheses. Figure 2 shows the directional relationships between the variables. That is, Perceived Benefits and Perceived Risks directly influence Acceptance. Attitude is influenced by Financial Literacy as represented by the first hypothesis, H1. The relationship between Acceptance and Attitude is hypothesized to be moderated by Financial Literacy as noted by the broken arrow in H2. Awareness is theorized to influence Attitude as shown in H3, and that it also serves as a moderator of the relationship between Acceptance and Attitude, as shown in H4. Attitude is postulated to be influenced by Acceptance. Method Research Design This quantitative study used descriptive-correlation design. A quantitative type of research expresses gathered data in mathematical format and charts to verify hypotheses and expectations (Thattamparambil, 2020 ). Facts about the topic were collected from surveys, as the primary data source, and from published peer-reviewed articles, journals, reports, books, and other reliable online sources, as secondary data sources. The data were processed to determine the research topic’s status to build a framework useful to identify relationships among the variables and in the formulation and confirmation of hypotheses. Therefore, a descriptive-correlation design was appropriate as it gave a picture of the current awareness, financial literacy, perceived benefits and risks, acceptance, and attitude towards cryptocurrency among the respondents, and determined the effect of the direct and indirect relationships between the dependent and independent variables. Research Environment This study was conducted in Eastern Visayas State University (EVSU) in Tacloban City, including its external campuses in Ormoc, Tanauan, Carigara, and Burauen, all in Leyte Province as exhibited in Fig. 3 . Respondents The students and regular faculty members of EVSU were the respondents of this study. In determining the sample size, a reference to Krejcie & Morgan (1970) sample size table was made. The student population of 22,372 would need about 377 sample size, the regular faculty member’s population of 493 needs 217 sample size. To ensure that each population subgroup (colleges and campuses) was represented, a stratified random sampling was employed. This method gives a true representation of the sample for each population subgroup (Bhardwaj, 2019 ). Accordingly, the 377 and 217 samples for students and regular faculty, respectively, were distributed based on the college/campus population’s proportion to the total population. Research Instrument The instrument was adopted from Magbitang et al. ( 2023 ). The questionnaire was divided into six (6) parts. The first part aimed to obtain the profile of the respondents. The second part were multiple choice questions that measured the respondent’s financial literacy. The third to fifth part contained a seven 4-Point Likert Scale that measured the respondent’s awareness on cryptocurrency, perceived risks and benefits, and acceptance of cryptocurrency. And the sixth part included five 4-Point Likert Scale items that measured the attitude of the respondents. Data Gathering Data was gathered in a hybrid manner, meaning, the survey questionnaires were distributed using Google Forms, for both onsite and online respondents. In cases where the onsite respondent did not have connectivity to access Google Forms, a print was provided. Data Analysis An inferential analysis, using structural equation modelling (SEM), was conducted to verify the hypotheses. The analysis included both confirmatory factor analysis (CFA) and path analysis, which are both models in SEM, which examined and explained the direct and indirect impact of the independent towards the dependent variable. In the conduct of CFA, no exploratory factor analysis (EFA) was conducted as the instrument is fully adopted. As mentioned by Hair et al. ( 2010 ), the factor structure is already proven when a researcher uses an existing instrument, and that the emphasis is on the confirmation that the instrument measures the intended constructs.Brown ( 2015 ) asserted that in CFA, the researcher may proceed directly when an existing measure is used. This is supported byByrne ( 2016 ) where it was mentioned that the EFA can be bypassed and proceed directly to CFA to verify the structure of the factors. Furthermore,Kline ( 2016 ) mentioned that the factor is already known when using an established measure, and that the aim is to validate these constructs. Before arriving at a model fit, each of the latent variables was individually tested using factor loadings and modification indices. This was done to identify which constructs may theoretically be correlated or may reasonably be removed from the model. This method has been done and suggested by various literature such as Xiong et al. ( 2025 ), where they concluded that the deletion of indicators is the best way to modify the model and that establishing correlation can be theoretically justified, and Brown ( 2015 ), where the importance of model fit was highlighted and that removing constructs can improve it. Hence, to arrive at a good model fit, the CFA was tested five times; first without modification; second using deletion as a modification based on the factor loading coefficients; third using deletion as a modification based on modification indices, fourth by establishing correlations, and by combining deletion and establishing correlation. Results This study analyzed the role of financial literacy, perceived benefits and risks, awareness of the respondent’s acceptance and attitude towards cryptocurrencies. The total sample collected was 377 students and 217 for faculty members, respectively. Not included in those samples were the 59 who opted not to participate in this study. Respondent’s Profile The respondents’ age consisted of 213 below 20 years old, 245 were within the 21–30 age range, 76 for 31–40 age range, 34 for 41–50 age range and 26 for above 50 years old. There were 394 females and 200 males. Among them were 490 single, 95 married, 3 separated, and 6 widowed/widower. For educational background (faculty members), 39 were college graduates, 55 graduate level, 58 graduate degree, 16 post-graduate level and 49 post graduate degree. For students, 2 were in grades 7, 8 and 10, respectively, and 3 were in grade 9. There were 93 first years, 164 second years, 60 third years and 51 fourth years. And for income range, 362 had below 20,000, 72 had income between 21,000 and 30,000, 98 had 31,000 to 40,000, 21 for 41,000–50,0000 and 41 earning above 50,000, respectively. Descriptive Analysis Table 1 summarizes the results for the descriptive analysis for the level of awareness, financial literacy, perceived benefits and perceived risks, acceptance and attitude towards cryptocurrencies among the respondents. Table 1 Descriptive Summary Faculty Members Descriptive Statistics n = 217 Mean Mode Deviation Std. Error of Mean Kurtosis Awareness 2.53 3.00 0.69 0.047 -0.249 Financial Literacy 3.17 3.43 0.44 0.030 1.118 Perceived Benefits 2.56 3.00 0.67 0.046 -0.112 Perceived Risks 2.88 3.00 0.74 0.050 0.151 Acceptance 2.49 3.00 0.63 0.043 -0.018 Attitude 2.47 3.00 0.63 0.043 0.172 Students Descriptive Statistics n = 377 Mean Mode Deviation Std. Error of Mean Kurtosis Awareness 2.50 3.00 0.64 0.033 0.001 Financial Literacy 3.10 3.14 0.46 0.023 0.241 Perceived Benefits 2.58 3.00 0.62 0.032 0.159 Perceived Risks 2.75 3.00 0.70 0.036 -0.150 Acceptance 2.54 3.00 0.57 0.029 0.314 Attitude 2.51 3.00 0.58 0.030 0.579 Total Sample Descriptive Statistics n = 594 Mean Mode Deviation Std. Error of Mean Kurtosis Awareness 2.51 3.00 0.66 0.027 -0.095 Financial Literacy 3.12 3.43 0.45 0.020 0.491 Perceived Benefits 2.57 3.00 0.64 0.026 0.048 Perceived Risks 2.80 3.00 0.71 0.029 -0.073 Acceptance 2.52 3.00 0.59 0.024 0.181 Attitude 2.49 3.00 0.60 0.025 0.399 Cryptocurrency Awareness Level The level of cryptocurrency awareness among the respondents is moderate, with the faculty members having a mean of 2.53, students 2.50, and the total sample 2.51. The mode of 3.00 implies that majority of the respondents have a moderate level of cryptocurrencies awareness. Faculty members had a slightly higher mean which may suggest that they are more exposed to financial technology trends especially those handling financial education courses. Student’s score is lower indicating minimal exposure to financial and digital assets. The standard deviations (0.64–0.69) imply average divergence. Faculty members portrayed greater variability as may be caused by diverse academic disciplines being taught (e.g., finance and non-finance subjects). The standard error (0.027–0.047) ascertains a precise estimate while the negative kurtosis (-0.10 to 0.00) indicates a normal distribution. Financial Literacy Level There is a relatively high financial literacy level among the respondents. Faculty members acquired the highest mean score at 3.17, and the students at 3.10, the total at 3.12. The mode of 3.43 for the total sample shows a bias toward high literacy, mirroring comprehension of basic financial concepts. Faculty member’s higher score with a positive kurtosis of 1.12 indicates a strong consensus. The low standard deviation (0.44–0.46) and standard error of mean (0.020–0.030), verify reliability. Perceived Benefits and Risks Level Perceived benefits level is moderate. Faculty members’ mean score at 2.56 was slightly lower than the students’ at 2.58 while overall is at 2.57. The uniform mode of 3.0 for the faculty, students and the overall, implies recognition of the potential and existing advantages of cryptocurrencies, such as improved business and remittance transactions, profitable investments, etc. The students may be seen as keener towards digital currencies while the faculty members’ lower score may imply doubt on cryptocurrency’s stability. The standard deviations (0.62–0.67) reveal average diversity, with the faculty members being more variable which may possibly be attributed to varied tech familiarity. The standard error (0.026–0.046) confirms precision and the kurtosis (-0.11 to 0.16) suggests normality. As to the perceived risks, the level is relatively high. Faculty members’ mean score is 2.88, students at 2.75 and for the total sample at 2.8. This coincides with the perceived benefits mean scores. Meaning, since the faculty members had lower perceived benefits level, they have higher perceived risks level. The mode of 3.0 shows a general skepticism on cryptocurrencies, possibly due to its volatility and other factors. The high standard deviation (0.70–0.74), suggests divided perceptions. The standard error (0.029–0.050) verifies reliability and the kurtosis (-0.15 to 0.15) implies a normal distribution. Cryptocurrency Acceptance Cryptocurrency acceptance is neutral with the students mean score at 2.54, higher than the faculty members at 2.49, while the total sample at 2.52. This level coincides with the perceived benefits and risks among students and the faculty members. That is, since students perceived higher benefits and lower risks, they are more likely to accept cryptocurrency than the faculty members. The 3.0 mode implies a moderate intention to accept cryptocurrency, but the low mean scores may indicate barriers. Furthermore, faculty members’ resistance coincides with their financial literacy and perceived risks and benefits scores. The standard deviations (0.57–0.63) demonstrate average diversity, with students less contrasted as evidenced by their respective kurtosis. Cryptocurrency Attitude With financial literacy being the highest construct, attitude towards cryptocurrency is the lowest, with the students’ mean score of 2.51 slightly outperforming the faculty members at 2.47 and the total sample’s 2.49. The results on attitude correspond with the perceived benefits and risks, and acceptance constructs, suggesting that students have a slightly positive attitude towards cryptocurrency. The mode of 3.00 shows average positivity, however, low mean scores suggest doubt. The student’s keenness towards cryptocurrency may be attributed to their higher risk tolerance. The standard deviations (0.58–0.63) and kurtosis (0.17–0.58) indicate average concurrence, specifically among students. Confirmatory Factor Analysis In arriving at a model fit, five CFA tests were conducted to ensure that it can be further used in the SEM. The summary of the values, the indicators deleted, and the theoretical justifications for establishing correlations among indicators are discussed in the foregoing subsections. First CFA – No Modification Figure 4 illustrates the preliminary CFA test, processed to evaluate the measurement model of the latent variables without any deletions or establishing correlations among the indicators. As observed in Fig. 4, the CFA was examined without deleting or establishing correlations among the indicators. The latent variables, Financial Literacy (FinLit), Perceived Benefits (PerBen), Perceived Risks (PerRis), Awareness (Aware), Acceptance (Accept) and Attitude (Attit) were covariated as part of the CFA procedure without deleting and/or correlating their individual indicators. The indices are disclosed in Table 2. In Table 2, the CMIN/DF value of 4.136 falls close to the upper limit of 5.0 and is above the ideal threshold of < 3.0, suggesting a moderate fit. The RMSEA of 0.073 meets the threshold of 0.9, suggesting that the model does not meet the criteria for a good fit. The PNFI of 0.734 and PCFI 0.768 exceed their > 0.05 threshold, indicating parsimony. Hoelter’s critical 0.05 of 160 and 0.01 of 167 respectively, both fall below the threshold of > 200, suggesting low sample stability. Overall, in some respects, the model is marginally acceptable as in the case of the RMSEA and the Parsimony-Adjusted indices, however, it does not meet all criteria for a good model fit as in the instance for CFI, TLI and Hoelter’s Critical Values . Second CFA – Deletion Based on Factor Loading Coefficients Prior to the deletion of indicators, the factor loadings from the first test were referred to, to identify which indicators have poor factor loadings (those standardized coefficients below 0.045), and therefore, may directly be deleted (Xiong et al., 2025). The factor loading coefficients are reported in Table 3. Table 3 Standardized Factor Loadings Financial Literacy Awareness Perceived Benefits Perceived Risks Acceptance Attitude FL1 0.332 FL2 0.418 FL3 0.238 FL4 -0.046 FL5 0.277 FL6 0.542 FL7 0.496 AW1 0.274 AW2 0.788 AW3 0.842 AW4 0.825 AW5 0.756 AW6 0.764 PB1 0.697 PB2 0.784 PB3 0.845 PB4 0.706 PR1 0.864 PR2 0.825 PR3 0.652 ACC1 0.758 ACC2 0.767 ACC3 0.784 ACC4 0.81 ACC5 0.212 ACC6 0.789 ACC7 0.834 ATT1 0.697 ATT2 0.741 ATT3 0.748 ATT4 0.707 ATT5 0.538 As documented in Table 3, financial literacy has only two indicators above 0.45, FL6 with a factor loading coefficient of 0.542 and FL7 with 0.496 respectively. The rest, FL1 to FL5, are removed directly. At least 3 indicators are required for a single-factor model, while for models with two or more factors, at least 2 indicators per factor are required (Xiong et al., 2025). The two remaining measurement indicators for financial literacy (FL6 and FL7), are sufficient, since the model for this study has more than two variables. Only AW1 has a factor loading coefficient below 0.45 for awareness, therefore this was deleted while the remaining are retained. For perceived benefits, perceived risks and attitude, no deletion is needed as the factor loading coefficients were all above 0.45. For acceptance, only AC5 is removed as this is the only indicator with factor loading below the threshold, while the rest are retained. Accordingly, the modified model is unveiled in Fig. 5, representing a CFA model with deleted indicators that have poor factor loading coefficients. Referring to Fig. 5, the model was tested by deleting the indicators based on the poorness of their factor loading coefficients. Financial literacy (FinLit) has only two remaining indicators, FL6 and FL7, while perceived benefits (PerBen), perceived risks (PerRis) and attitude (Attit), retained all their indicators. For awareness (Aware), AW1 was removed and for acceptance (Accept), AC5 was also deleted. Consequently, the values are posted in Table 4. Table 4 Model Fit Indices for Deletion Based on Poor Factor Loadings. Indices Value Threshold Interpretation Chi-Square/Degrees of Freedom (CMIN/DF) 4.754 ≤ 3.0 to 5.0 Reasonable Comparative Fit Index (CFI) 0.888 > 0.9 Suboptimal Root Mean Square Error of Approximation (RMSEA) 0.08 ≤ 0.08 Acceptable Tucker-Lewis Index (TLI) 0.87 > .9 Suboptimal Parsimony-Adjusted Normed Fit Index) (PNFI) 0.747 > 0.50 Parsimonious Parsimony-Adjusted Comparative Fit Index (PCFI) 0.769 >0.50 Parsimonious Hoelter's Critical (HOELTER) 0.05 144 > 200 Suboptimal Hoelter's Critical (HOELTER) 0.01 152 > 200 Suboptimal Reflected in Table 4, the CMIN/DF of 4.754 shows a moderate fit being above 3.0 and just below the 5.0 upper limit. Both the CFI at 0.888 and TLI at 0.87 showed values below 0.9, indicating suboptimal fit. Both the PNFI at .747 and PCFI at 0.769 indicate parsimony as their values are above 0.50. For Hoelter’s critical values at 144 for 0.05 and 152 for 0.01, respectively, both are below the > 200 threshold, indicating low sample stability. Third CFA – Deletion based on Modification Indices In this test, a reference to the modification indices (MI) from the first test is made to identify which paths have high modification indices. Accordingly, deleted indicators with their total modification indices (MI) are condensed in Table 5. Displayed in Table 5, AW1, AW3, AW4, PB1, PR1, AC4, and AC5 are the indicators, represented by the error terms, e8, e10, e11, e14, e18, e24, and e25 respectively, that are needed to be established with correlation among other error terms and variables. These indicators also had high modification indices. The total effect is the sum of the modification indices of the paths corresponding to one error term needed to be established correlation with one more error terms and variables, (e.g., e8 to Aware, e8 to PerRis, and so on). Thus, their deletion is empirically justified. Therefore, the model with the deleted indicators is tested accordingly as manifested in Fig. 6. Figure 6 demonstrates the model that has deleted indicators based on high modification indices. FinLit and Attit retained all their indicators as the error terms corresponding to the indicators showed MI. For Aware, the indicators that were retained are AW2, AW5 and AW6 as these were the ones that had low MI. PerBen and PerRis both had one indicator that had high MI and therefore deleted, PB1 and PR1 respectively. Accept had two indicators, AC4 and AC5, that showed high MI and therefore were also deleted. The model fit results for this test are summarized in Table 6. The model fit values have improved significantly as can be noted in Table 6. CMIN/DF have fallen below the ideal threshold of 3.0, suggesting a good model fit. Both CFI at 0.921 and TLI at 0.909 are above the threshold of 0.90, indicating an acceptable and good fit. The parsimony values, PNFI at 0.765 and PCFI at 0.798, are similar as the previous tests, above 0.5 and are confined between 0.7 to 0.8, indicating a reasonable parsimony. Both Hoelter’s critical values, 0.05 at 242, and 0.01 at 256, have exceeded the minimum threshold of 200, reflecting adequacy of sample size to ensure stability and reliability. Fourth CFA – Correlation of Error Terms For this test, correlations of some error terms were made. Theoretical justifications include shared method variance (correlated error terms are both 4-point Likert Scale), item content overlap (a particular indicator was a duplicate of the other indicator in the same variable), and some questions have similar structures and wordings. In the case of Xiong et al. (2025), correlation of error terms was conducted in reference to the variables’ modification indices. In the context of this study, only those indicators that were similarly structured, shared method variance, and content overlap were correlated. The correlated CFA model is demonstrated in Fig. 7. Specified in Fig. 7, error terms representing AW2, AW3, and AW4 were correlated as these are similarly structured indicators. AW2 is structured as “I understand basic knowledge of…,” AW3 is structured as, “I understand how cryptocurrency…,” and AW4 is structured as “I understand the technology…” The correlation of error terms representing AW5 and AW6, PB2 and PB3, PR1 and PR2, AC1 and AC2, AC3 and AC4, AC5 and AC6 were also built on the same justification. PB1 and PR2 are also based on similar structure, only that PB1 is structured positive, and PR2 negative. The correlation between error terms of AC1 and AC7 is based on content overlap. That is, AC7 is a duplicate of AC1. The values reflected are the standardized estimates and the model fit values are outlined in Table 7. Per results in Table 7, the CMIN/DF has improved at 3.356 compared to the first test at 4.136 and second test at 4.754. While this may indicate a moderate fit, this is still above the ideal level of 0.9 threshold. PNFI at 0.749 and PCFI at 0.783 are also confined between 0.7 and 0.8, indicating reasonable parsimony. For the Hoelter’s critical values, 0.01 has improved at 206 which is above > 200 threshold, indicating adequacy of sample to ensure reliability and stability. However, 0.05 fell shortly at 197, suggesting otherwise. Fifth CFA – Deletion of Indicators and Correlation of Error Terms Since the third test provided a good overall but not quite excellent fit, it was used as the basis for the fifth test, with the fourth test being the reference for the correlation. Thus, the model was tested and visualized in Fig. 8. To highlight Fig. 8, the indicators retained are based on the third test, while the correlation between the error terms are based on the fourth test. It is to be noted that since there were deleted indicators in the third test, the correlation of error terms representing those were also removed. Accordingly, only correlations between error terms of AW5 and AW6, PB2 and PB3, AC1 and AC2, AC1 and AC7 and AT4 and AT5 were established. The values represent their standardized estimates with the summary of model fit criteria posed in Table 8. Based on Table 8, all the indices have significantly improved compared to the prior tests. CMIN/DF of 2.445 indicates an acceptable fit. The CFI at 0.938 and the RMSEA of 0.049 suggest good model fit. The TLI of 0.927 is also above the acceptable threshold. PNFI and PCFI at 0.765 and 0.797 respectively, are above the minimum parsimony threshold of 0.5, And finally for the critical values, both are above the acceptable limit, indicating adequacy of the sample to ensure reliability and stability. Overall, the results from the fifth test provide a good model fit. Accordingly, the structural equation model (SEM) was based on CFA from this test. The Structural Equation Model After ensuring that the CFA from the fifth test is a good fit for potential modelling, the structural equation model (SEM) was established based on the conceptual framework. It is to be noted that presenting moderation in AMOS needs adding a new variable which is the interaction between the independent and the moderating variables. Thus, in this study, the interactions between financial literacy and acceptance as well as awareness and acceptance were established. The interaction is the product of the centered means of the independent and moderating variables. Although moderation can be made in a separate model, this study used a unified model to fully align with the conceptual structure. The structural equation model based on the framework is unveiled in Fig. 9. The structural equation model portrayed in Fig. 9 explains the directional relationships of the variables aligned with the conceptual framework. Among the highlights in the model is the existence of two new variables, Inter_FLxAC and Inter_AWxAC, which are the interactions between the independent (Accept) and the moderating variables (FinLit and Aware). Furthermore, the independent and moderating variables together with the interaction variables being exogenous, were correlated as this is necessary to run SEM in AMOS. It can also be observed that PerBen, PerRis and Attit have been assigned with error terms (e26, e27 and e28 respectively), indicating their role as endogenous variables. The values reflected are the standardized estimates and the model fit values are summarized in Table 9. Table 9 Model Fit Indices of the Structural Equation Model Indices Value Threshold Interpretation Chi-Square/Degrees of Freedom (CMIN/DF) 2.669 ≤ 3.0 to 5.0 Acceptable Comparative Fit Index (CFI) 0.915 > 0.9 Good Fit Root Mean Square Error of Approximation (RMSEA) 0.053 ≤ 0.08 Acceptable Tucker-Lewis Index (TLI) 0.903 > .9 Acceptable Parsimony-Adjusted Normed Fit Index) (PNFI) 0.762 > 0.50 Parsimonious Parsimony-Adjusted Comparative Fit Index (PCFI) 0.8 >0.50 Parsimonious Hoelter's Critical (HOELTER) 0.05 253 > 200 Acceptable Hoelter's Critical (HOELTER) 0.01 266 > 200 Acceptable Looking at the values of the indices in Table 9, it can be noted that overall, the SEM has a good and acceptable fit. CMIN/DF of 2.669, which is below 3.0, indicates an acceptable fit. The CFI and TLI, being above 0.90, suggest an acceptable fit. PNFI and PCFI are above 0.50, reflecting parsimony. And finally, the critical values, being above 200, proves adequacy of the sample to ensure reliability and stability. Test of Hypotheses In testing the hypothesis, a path analysis, based on the structural equation model, was performed. Each of the hypothesized paths were examined by analyzing their effect size, and p-values. A summary is provided in Table 10. Discussion Financial Literacy’s Influence Towards Attitude and Its Moderating Role Financial literacy has a small, negative and significant effect on attitude (β = -0.167, SE = 0.081, p = 0.039), supporting H1 but on the negative side. This implies that high financial literacy leads to adverse attitude towards cryptocurrencies. The same is found in the study of Magbitang et al. ( 2023 ). As to moderation, financial literacy does not necessarily moderate the relationship between acceptance and attitude (β = -0.12, SE = 0.081, p = 0.051), not supporting H2. Although the relationship is not significant, the small and negative effect size supports the negative influence of financial literacy on attitude towards cryptocurrencies. Awareness’s Influence Towards Attitude and Its Moderating Role Although small in effect size, awareness has a highly significant positive influence on attitude toward cryptocurrency (β = 0.158, SE = 0.057, p = 0.006), indicating that higher awareness is associated with a more favorable attitude. This supports H3. This corroborates the findings of Magbitang et al. ( 2023 ), where they also found that awareness positively and significantly influences attitude. In terms of moderation, akin to financial literacy, awareness does not necessarily moderate the relationship between acceptance and attitude (β = -0.019, SE = 0.03, p = 0.531), not supporting H4. Acceptance’s Influence Towards Attitude Acceptance’s effect size on Attitude is large. Their relationship is positive and highly significant (β = 0.843, SE = 0.078, p < 0.001) denoting that the higher the acceptance level, the more favorable attitude towards cryptocurrency is. This supports H5. This finding is the same as the finding of Magbitang et al. ( 2023 ). These direct effects of perceived benefits and perceived risks on cryptocurrency acceptance are both positive and highly significant (β = 0.97, SE = 0.064, p < 0.001 and β = 0.515, SE = 0.068, p < 0.001, respectively), proving their empirical inclusion in the model. Contribution to Literature This is the first study on cryptocurrency acceptance conducted within a state university in Eastern Visayas, encompassing both urban (Tacloban and Ormoc) and rural (nearby towns such as Tanauan, Carigara and Burauen) areas. This also adds to the limited literature of both students and educators being respondents of the studies involving cryptocurrencies. Series of comprehensive test for modification of the confirmatory factor analysis (CFA) in achieving an acceptable and good model fit based on empirical and theoretical justifications, including the deletion of indicators of a construct, and correlation of error terms representing the variables that have been employed in this study may be used as a reference for future studies that may utilize similar methods and/or variables. Finally, this study can be used for future exploratory studies to support the findings herein. Conclusion This study examined the impact of financial literacy and cryptocurrency awareness on the acceptance towards attitude on cryptocurrencies among faculty and students at Eastern Visayas State University. Results show that the respondents despite having above-average financial literacy, generally exhibited low to moderate awareness, negative perceptions, low acceptance, and unfavorable attitudes toward cryptocurrencies. Financial literacy was found to have significant and negative effect on attitude. Both awareness and acceptance positively and significantly affect attitude towards cryptocurrency. As to moderation, both financial literacy and awareness showed a negative and not significant effect on the relationship between acceptance and attitude. These results, evidenced by an acceptable model fit for SEM, highlighted the roles of financial literacy, awareness, and acceptance in influencing the attitudes toward cryptocurrency. Finally, the combination of deletion of indicators based on high modification indices and correlation of error terms, proved to be the best method of modifying a confirmatory factor analysis (CFA) in achieving an acceptable and good model fit for potential structural equation model (SEM). Educational initiatives at Eastern Visayas State University should make a focus on increasing awareness and addressing perceived risks to improve acceptance and attitudes, while considering that financially literate individuals may require tailored strategies to align their acceptance with more favorable attitudes. For business-related programs, cryptocurrency awareness and utilization can be incorporated in financial markets and related courses. While the technology behind cryptocurrencies, blockchain, can be introduced to programs related to information technology and auditing. Future studies could examine these relationships in diverse populations to improve the general applicability of the findings. Declarations Ethical Approval and Consent to Participate This is study is reviewed and approved by the Research Ethics Committe (REC) of the University of San Carlos. Approved permission to gather data is obtained from the Eastern Visayas State University President’s Office, with communications to conduct the study sent and received by the Commission on Higher Education (CHED) and Department of Education (DepEd). Consent for Publication Not Applicable Funding Not Applicable Data Availability The authors do not analyse or generate any datasets, because this work proceeds within a theoretical and mathematical approach. References Abramova, S., & Böhme, R. (2016). Perceived Benefit and Risk as Multidimensional Determinants of Bitcoin Use: A Quantitative Exploratory Study . Al Jazeera. (2023, March). El Salvador passes law on cryptocurrency transfers | Crypto News . https://www.aljazeera.com/news/2023/1/12/el-salvador-passes-law-on-cryptocurrency-transfers Alaeddin, O., & Altounjy, R. (2018). Trust, Technology Awareness and Satisfaction Effect into the Intention to Use Cryptocurrency among Generation Z in Malaysia. 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NON-ADOPTION OF CRYPTO-ASSETS: EXPLORING THE ROLE OF TRUST, SELF-EFFICACY, AND RISK . Xiong, Z., Xia, H., Ni, J., & Hu, H. (2025). Basic assumptions, core connotations, and path methods of model modification—using confirmatory factor analysis as an example. Frontiers in Education , 10 . https://doi.org/10.3389/feduc.2025.1506415 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7160135","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489247196,"identity":"aba68b68-6423-481d-a5ba-4df46c615761","order_by":0,"name":"Jeremiah Edison Nicart","email":"data:image/png;base64,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","orcid":"","institution":"Eastern Visayas State University","correspondingAuthor":true,"prefix":"","firstName":"Jeremiah","middleName":"Edison","lastName":"Nicart","suffix":""},{"id":489247197,"identity":"3c4b95b9-8d80-476e-a6d5-e772c3811569","order_by":1,"name":"Miguel Abas, Jr","email":"","orcid":"","institution":"University of San Carlos","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"","lastName":"Abas","suffix":"Jr"}],"badges":[],"createdAt":"2025-07-18 18:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7160135/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7160135/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87404079,"identity":"cad04270-bbae-4409-8825-53609863327a","added_by":"auto","created_at":"2025-07-23 12:33:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":144965,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTheoretical Framework\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7160135/v1/f69cf2687d7a787d2674fa32.png"},{"id":87403876,"identity":"775c2136-9646-4a9f-897c-dc585e5a810e","added_by":"auto","created_at":"2025-07-23 12:25:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":129475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConceptual Framework\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7160135/v1/38fd4caca16c26f877af4d70.png"},{"id":87404076,"identity":"e28767ad-d8a4-4e5b-b294-f4976aa37e7b","added_by":"auto","created_at":"2025-07-23 12:33:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":242041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eResearch Environment\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7160135/v1/3bb911d1e105c55c5839ce5e.png"},{"id":87404939,"identity":"6902af03-88f7-4767-942f-12b91ef799c1","added_by":"auto","created_at":"2025-07-23 12:41:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":187052,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eInitial CFA Without Modifications\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7160135/v1/fbb1452845ed197af8d6e88c.png"},{"id":87404077,"identity":"e267492e-80c1-4fb5-b5ae-e41918997979","added_by":"auto","created_at":"2025-07-23 12:33:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":161519,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eModified CFA - Deleted Indicators with Poor Factor Loadings\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7160135/v1/1f593f5bd9d6c145cbaceec1.png"},{"id":87403885,"identity":"0245d82e-843d-4e5d-98bc-fd5e3a756182","added_by":"auto","created_at":"2025-07-23 12:25:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":317485,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eModified CFA - Deletion Based on High MI\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7160135/v1/ef5c1550328e7618716a896c.png"},{"id":87403887,"identity":"3d4e5596-6dea-4d3e-a188-879a344dc3c0","added_by":"auto","created_at":"2025-07-23 12:25:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":199497,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eModified CFA - Correlation of Error Terms\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7160135/v1/c8aefc92aedb1762819d3ebb.png"},{"id":87404085,"identity":"6ba1931b-fcb2-4337-a9f4-32105fcbaa6a","added_by":"auto","created_at":"2025-07-23 12:33:54","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":133006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eModified CFA – Deletion Based on MI and Correlations\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7160135/v1/12c5295e3806ee0a0260b16c.png"},{"id":87404081,"identity":"03683605-b1f9-4574-8d92-e0ddc2a6de23","added_by":"auto","created_at":"2025-07-23 12:33:54","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":129405,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe Structural Equation Model Based on the Conceptual Framework\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7160135/v1/ce0f2ecc6ec9623afef9118c.png"},{"id":87911337,"identity":"ca701cd8-6803-4c79-a1f7-c8fa3db72dff","added_by":"auto","created_at":"2025-07-30 09:54:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2738382,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7160135/v1/da74e359-6ea7-48c9-95a4-b9cc7295a0c8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Moderating Role of Financial Literacy and Awareness on the Acceptance Towards Attitude on Cryptocurrency Adoption Among Faculty and Students of Eastern Visayas State University","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn January 2009, the oldest surviving cryptocurrency known as Bitcoin was introduced, offering an avenue of decentralized notion in the finance and accounting sectors. Decentralization is achieved when financial transactions are initiated and completed without the intervention of a centralized authority. Since then, it has instigated arguments as to its regulations, digital security, and legitimacy. Despite these, several countries have legalized cryptocurrency and its transactions. El Salvador has even made Bitcoin as a legal tender, in accordance with their new Digital Securities Law (Al Jazeera, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). On the other hand, there are still other countries that issued a ban on Bitcoin and other cryptocurrencies (Bajpai, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMost cryptocurrencies derive their value based on their blockchain technology and liquidity provided in the crypto market, indicating that most individuals and institutions engage in crypto transactions by trading. However, with the increasing popularity of Bitcoin and other cryptocurrencies, online investment scams have also emerged. The Department of Justice in the United States charged Bitconnect’s founder for creating a multinational Ponzi scheme, obtaining around \u003cspan\u003e$\u003c/span\u003e2.4\u0026nbsp;billion. Bitconnect is an organization that misled the public by providing high yields through their lending platform, trading bot, and volatility software, when in fact the returns of the early investors were given from the investments of the new entrants (U.S. Department of Justice, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCryptocurrency is commonly used as a currency, a speculation instrument, and as an investment tool, which evidences that it can provide financial awareness and freedom. Several news globally and locally has erupted saying that certain individuals were able to build houses, buy cars, and become rich by engaging in cryptocurrency activities. It was reported in CNBC that a 25-year-old became a millionaire after investing early in Ethereum and Bitcoin (Locke, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and in another report by Umoh (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) mentioned that a 19-year-old Bitcoin millionaire has been offering crucial investment advice.\u003c/p\u003e\u003cp\u003eStudies on cryptocurrencies reveal that most people are aware of cryptocurrencies and their basic premise (Anil Kumar \u0026amp; Swathy, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As also supported by Shukla (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), people’s awareness in general is visible, and they would like it to be integrated into their individual investment portfolios due to its huge potential returns. While there are several studies that support the proposal to incorporate cryptocurrency in a formal academic setting, as in the case of Phillips (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Naveen Kumar \u0026amp; Sanskriti (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Doblas (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), among others, these studies were conducted on students and working professionals who are not on the academic point of view. Most of the mentioned study did not use structural equation modelling (SEM) in analyzing the relationships of the variables, except for Magbitang et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), to which this research’s instrument was adopted from, however, no discussion in model fit determination was disclosed.\u003c/p\u003e\u003cp\u003eThis study was built from these gaps by conducting it to an academic perspective and an extended acceptance framework by adding financial literacy, cryptocurrency awareness, perceived benefits and risks that may influence the acceptance and attitude towards cryptocurrency, and by adapting technology acceptance model, diffusion of innovation theory, and theory of reasoned action. Furthermore, a comprehensive procedure in arriving at a good model fit for structurual equation modelling (SEM) is discussed critically in this study.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cp\u003e\u003cem\u003eFinancial Literacy\u003c/em\u003e\u003c/p\u003e\u003cp\u003eFinancial literacy is one’s capability to manage financial information and make educated judgements according to Lusardi and Mitchell (2014) as cited by Gui et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Since cryptocurrency may be used as a currency and a financial tool, its adoption may or may not be impacted by an individual’s financial literacy.Panos et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) concluded that financially literate people have a lower tendency of owning cryptocurrency despite their awareness. This is aligned with the findings of Santoso \u0026amp; Modjo (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). On the other hand,Jariyapan et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that financial literacy affects positively the adoption of cryptocurrency. The latter’s findings are concreted byPrasetyo \u0026amp; Kurniasari (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) who found that financial literacy has a significant effect on the intention to engage into cryptocurrency transactions. Magbitang et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), found that financial literacy positively and significantly affects acceptance but negative on attidude. For this study, the relationship of financial literacy towards acceptance and attitude on cryptocurrency were examined. Accordingly, the following hypotheses were formulated:\u003c/p\u003e\u003cp\u003e\u003cem\u003eH1. Financial Literacy has a significant influence on attitude towards cryptocurrency.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eH2. Financial Literacy moderates the relationship of acceptance and attitude towards cryptocurrency.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eAwareness\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAwareness is a situation when an individual is informed of a novelty and frames an overall insight as cited by Prakosa \u0026amp; Sumantika (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Citing further from Alaeddin \u0026amp; Altounjy (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), awareness influences comportments. Despite the awareness on the existence of cryptocurrency, realizations on how cryptocurrency functions are still at minimal (Arutgeevitha et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jadhav et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ku-Mahamud et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mathur, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Scott et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Doblas (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)) discovered that level of awareness substantially affects adoption of cryptocurrency. Magbitang et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that awareness has signifivant and positive Accordingly, hypotheses are enumerated below:\u003c/p\u003e\u003cp\u003e\u003cem\u003eH3. Awareness has a significant influence on attitude towards cryptocurrency.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eH4. Awareness moderates the relationship of acceptance and attitude towards cryptocurrency.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePerceived Benefits and Risks\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAs cited by Sciarelli et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), perceived benefit is the view of the favorable outcomes because of certain behavior. The study ofAlqaryouti et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that there is no substantial association between perceived benefit and behavioral use of cryptocurrency. This finding is the same with Tyagi et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), where they claimed that there is low considerable association between perceived benefit and the intent to use cryptocurrency. On the other hand,Hasan et al., (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) discovered that perceived benefit improves perceived value and motive to utilize cryptocurrency, which is concreted byDewi et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) through their findings that perceived benefit materially induce investment in cryptocurrency.Magbitang et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that a significant relationship between perceived benefits towards the acceptance of cryptocurrency exists. For this study, perceived benefits construct was considered a direct factor of acceptance.\u003c/p\u003e\u003cp\u003ePerceived risk is a user’s insight of the ambiguity and potential unfavorable outcomes from acquiring goods or services, as in the case of a behavioral research perspective (Faqih, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).Anser et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) claimed that when people recognize greater risks associated with Bitcoin, there may be no adoption even though a motivation to use it is relatively robust. Interestingly,Arias-Oliva et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that perceived risk has no effect on cryptocurrency adoption. Voskobojnikov et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), andJariyapan et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found no negative influence of perceived risk towards cryptocurrency adoption.Magbitang et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that higher perceived risk tends to have higher acceptance. Same with perceived benefits, for this study, perceived risks construct was also treated as a direct factor of acceptance.\u003c/p\u003e\u003cp\u003e\u003cem\u003eAcceptance\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAs defined by (Cambridge Dictionary, n.d.) in marketing, acceptance is the motivation of individuals to adopt new goods or services or to agree to novel theories. In this study, acceptance is the people’s intention to use cryptocurrency. Arutgeevitha et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), majority of their respondents showed high level of willingness to integrate cryptocurrency in their individual investment portfolio as influenced mainly by lucrative returns. Magbitang et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) claimed that there is a high level of acceptance among the respondents and that acceptance significantly influences attitude. Because of this, a hypothesis was formed, as shown below:\u003c/p\u003e\u003cp\u003e\u003cem\u003eH5. Acceptance significantly influences attitude towards cryptocurrency.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eAttitude\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAttitude can be defined as how an individual perceives and values a thing or a person, a propensity to react positively or negatively concerning a specific theory, thing, individual or condition (Vargas-Sánchez et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Doblas (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found that there is a negative attitude towards cryptocurrency adoption owing to price instability despite having a positive impression of being used as a means of exchange. This is supported by Parilla \u0026amp; Abadilla (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), where they also found that there is a negative attitude towards cryptocurrency, which is influenced by lack of regulation. They further claimed that there is no substantial association between attitude and acceptance of cryptocurrency as a medium of exchange. Magbitang et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) also supports the negative attitude towards cryptocurrency but not the impact of awareness towards attitude. This study examined the attitude towards cryptocurrency among the respondents to confirm or negate these findings.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTechnology Acceptance Model (TAM)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTechnology Acceptance Model (TAM) by Davis (1989) was formulated to define how end users accept and adopt a technology (Bharadwaj \u0026amp; Deka, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to TAM, behavioral intention is influenced by perceived usefulness and perceived ease of use. In the literature review by Bhuvana \u0026amp; Aithal (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the factors influencing the behavioral intention towards cryptocurrency use include, ease of use, social impact, convenience, trust, volatility, individual beliefs, risks, and decision-making. As in the case of Gil-Cordero et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), they suggested that TAM be extended to include perceived risk. Furthermore, Arias-Oliva et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Alomari \u0026amp; Abdullah (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), andMagbitang et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) included awareness and financial literacy as added variables in their respective studies. Since TAM has been tested to work as an adoption model, this study referred to it by merging perceived ease and use and perceived usefulness into Perceived Benefits. This is backed byAbramova \u0026amp; Böhme (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) where they found that perceived ease of use has a significant positive effect on perceived benefits andAl-Fraihat et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) where they found that amongst strong supporters of benefits, is the perceived usefulness. Perceived risks and financial literacy as suggested by the above were also included among the constructs.\u003c/p\u003e\u003cp\u003e\u003cem\u003eDiffusion of Innovation Theory (DIT)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eDiffusion of Innovation Theory is a structure that aims to explain a procedure through advancement of technology employed by individuals. As illustrated by Rogers, innovation is a theory, procedure, or thing seen as new by a person or other element of acceptance. Furthermore, diffusion is the methodology by which innovation is disseminated to the people through various media for a particular period. Consequently, DIT highlights the explanation of how novel ideas obtain massive application (Alotaibi \u0026amp; Wald, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This study referred to this theory as it supports perceived benefits (relative advantage) as one of the main motivators of adoption, with awareness being the initial stage of innovation adoption, as mentioned by Lamorte (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This theory therefore also supports the above recommendations to include perceived benefits and awareness constructs in TAM extension.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTheory of Reasoned Action (TRA)\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThis theory by Ajzen and Fishbein in 1967 was developed to create a model that would identify, support, and motivate human behavior. This model is moderated by two constructs: attitude and subjective norms, with attitude having a direct or indirect influence on human behavior. This model however is not for specific behavior or technology as it was meant to explain any human behavior (Momani et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For the context of this study, only the attitude construct was considered as it did not examine the actual behavior of the respondents, as is also the case for Doblas (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe framework of this study was anchored on TAM, DOI and TRA. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e represents the schematic diagram of the theoretical framework based on the literature reviewed and theories adapted.\u003c/p\u003e\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the constructs are color coded based on the theories they were referred to. Perceived Benefits, Perceived Risks and Financial Literacy were adapted from the TAM coded by the green color, Acceptance and Awareness were adapted from DIT represented by the orange color, and Attitude was adapted from TRA, with the gray color. The diagram is designed to explain how individuals accept cryptocurrency based solely on the adapted influencing factors, excluding the other constructs from these theories.\u003c/p\u003e\u003cp\u003eAccordingly, the conceptual framework is disclosed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which is designed to explain directional relationships based on the formulated hypotheses.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the directional relationships between the variables. That is, Perceived Benefits and Perceived Risks directly influence Acceptance. Attitude is influenced by Financial Literacy as represented by the first hypothesis, H1. The relationship between Acceptance and Attitude is hypothesized to be moderated by Financial Literacy as noted by the broken arrow in H2. Awareness is theorized to influence Attitude as shown in H3, and that it also serves as a moderator of the relationship between Acceptance and Attitude, as shown in H4. Attitude is postulated to be influenced by Acceptance.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cem\u003eResearch Design\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThis quantitative study used descriptive-correlation design. A quantitative type of research expresses gathered data in mathematical format and charts to verify hypotheses and expectations (Thattamparambil, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Facts about the topic were collected from surveys, as the primary data source, and from published peer-reviewed articles, journals, reports, books, and other reliable online sources, as secondary data sources. The data were processed to determine the research topic’s status to build a framework useful to identify relationships among the variables and in the formulation and confirmation of hypotheses. Therefore, a descriptive-correlation design was appropriate as it gave a picture of the current awareness, financial literacy, perceived benefits and risks, acceptance, and attitude towards cryptocurrency among the respondents, and determined the effect of the direct and indirect relationships between the dependent and independent variables.\u003c/p\u003e\u003cp\u003e\u003cem\u003eResearch Environment\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThis study was conducted in Eastern Visayas State University (EVSU) in Tacloban City, including its external campuses in Ormoc, Tanauan, Carigara, and Burauen, all in Leyte Province as exhibited in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cem\u003eRespondents\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe students and regular faculty members of EVSU were the respondents of this study. In determining the sample size, a reference to Krejcie \u0026amp; Morgan (1970) sample size table was made. The student population of 22,372 would need about 377 sample size, the regular faculty member’s population of 493 needs 217 sample size. To ensure that each population subgroup (colleges and campuses) was represented, a stratified random sampling was employed. This method gives a true representation of the sample for each population subgroup (Bhardwaj, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Accordingly, the 377 and 217 samples for students and regular faculty, respectively, were distributed based on the college/campus population’s proportion to the total population.\u003c/p\u003e\u003cp\u003e\u003cem\u003eResearch Instrument\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe instrument was adopted from Magbitang et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The questionnaire was divided into six (6) parts. The first part aimed to obtain the profile of the respondents. The second part were multiple choice questions that measured the respondent’s financial literacy. The third to fifth part contained a seven 4-Point Likert Scale that measured the respondent’s awareness on cryptocurrency, perceived risks and benefits, and acceptance of cryptocurrency. And the sixth part included five 4-Point Likert Scale items that measured the attitude of the respondents.\u003c/p\u003e\u003cp\u003e\u003cem\u003eData Gathering\u003c/em\u003e\u003c/p\u003e\u003cp\u003eData was gathered in a hybrid manner, meaning, the survey questionnaires were distributed using Google Forms, for both onsite and online respondents. In cases where the onsite respondent did not have connectivity to access Google Forms, a print was provided.\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eAn inferential analysis, using structural equation modelling (SEM), was conducted to verify the hypotheses. The analysis included both confirmatory factor analysis (CFA) and path analysis, which are both models in SEM, which examined and explained the direct and indirect impact of the independent towards the dependent variable.\u003c/p\u003e\u003cp\u003eIn the conduct of CFA, no exploratory factor analysis (EFA) was conducted as the instrument is fully adopted. As mentioned by Hair et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), the factor structure is already proven when a researcher uses an existing instrument, and that the emphasis is on the confirmation that the instrument measures the intended constructs.Brown (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) asserted that in CFA, the researcher may proceed directly when an existing measure is used. This is supported byByrne (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) where it was mentioned that the EFA can be bypassed and proceed directly to CFA to verify the structure of the factors. Furthermore,Kline (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) mentioned that the factor is already known when using an established measure, and that the aim is to validate these constructs.\u003c/p\u003e\u003cp\u003eBefore arriving at a model fit, each of the latent variables was individually tested using factor loadings and modification indices. This was done to identify which constructs may theoretically be correlated or may reasonably be removed from the model. This method has been done and suggested by various literature such as Xiong et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), where they concluded that the deletion of indicators is the best way to modify the model and that establishing correlation can be theoretically justified, and Brown (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), where the importance of model fit was highlighted and that removing constructs can improve it. Hence, to arrive at a good model fit, the CFA was tested five times; first without modification; second using deletion as a modification based on the factor loading coefficients; third using deletion as a modification based on modification indices, fourth by establishing correlations, and by combining deletion and establishing correlation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis study analyzed the role of financial literacy, perceived benefits and risks, awareness of the respondent’s acceptance and attitude towards cryptocurrencies. The total sample collected was 377 students and 217 for faculty members, respectively. Not included in those samples were the 59 who opted not to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRespondent’s Profile\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe respondents’ age consisted of 213 below 20 years old, 245 were within the 21–30 age range, 76 for 31–40 age range, 34 for 41–50 age range and 26 for above 50 years old. There were 394 females and 200 males. Among them were 490 single, 95 married, 3 separated, and 6 widowed/widower. For educational background (faculty members), 39 were college graduates, 55 graduate level, 58 graduate degree, 16 post-graduate level and 49 post graduate degree. For students, 2 were in grades 7, 8 and 10, respectively, and 3 were in grade 9. There were 93 first years, 164 second years, 60 third years and 51 fourth years. And for income range, 362 had below 20,000, 72 had income between 21,000 and 30,000, 98 had 31,000 to 40,000, 21 for 41,000–50,0000 and 41 earning above 50,000, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDescriptive Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 1 summarizes the results for the descriptive analysis for the level of awareness, financial literacy, perceived benefits and perceived risks, acceptance and attitude towards cryptocurrencies among the respondents.\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cem\u003eDescriptive Summary\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFaculty Members\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eDescriptive Statistics\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en = 217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStd. Error of Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAwareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial Literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived Benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived Risks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcceptance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudents\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescriptive Statistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en = 377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStd. Error of Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAwareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial Literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived Benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived Risks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcceptance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescriptive Statistics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en = 594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStd. Error of Mean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAwareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial Literacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived Benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived Risks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcceptance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cem\u003eCryptocurrency Awareness Level\u003c/em\u003e\n\u003cp\u003eThe level of cryptocurrency awareness among the respondents is moderate, with the faculty members having a mean of 2.53, students 2.50, and the total sample 2.51. The mode of 3.00 implies that majority of the respondents have a moderate level of cryptocurrencies awareness. Faculty members had a slightly higher mean which may suggest that they are more exposed to financial technology trends especially those handling financial education courses. Student’s score is lower indicating minimal exposure to financial and digital assets. The standard deviations (0.64–0.69) imply average divergence. Faculty members portrayed greater variability as may be caused by diverse academic disciplines being taught (e.g., finance and non-finance subjects). The standard error (0.027–0.047) ascertains a precise estimate while the negative kurtosis (-0.10 to 0.00) indicates a normal distribution.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFinancial Literacy Level\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThere is a relatively high financial literacy level among the respondents. Faculty members acquired the highest mean score at 3.17, and the students at 3.10, the total at 3.12. The mode of 3.43 for the total sample shows a bias toward high literacy, mirroring comprehension of basic financial concepts. Faculty member’s higher score with a positive kurtosis of 1.12 indicates a strong consensus. The low standard deviation (0.44–0.46) and standard error of mean (0.020–0.030), verify reliability.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePerceived Benefits and Risks Level\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePerceived benefits level is moderate. Faculty members’ mean score at 2.56 was slightly lower than the students’ at 2.58 while overall is at 2.57. The uniform mode of 3.0 for the faculty, students and the overall, implies recognition of the potential and existing advantages of cryptocurrencies, such as improved business and remittance transactions, profitable investments, etc. The students may be seen as keener towards digital currencies while the faculty members’ lower score may imply doubt on cryptocurrency’s stability. The standard deviations (0.62–0.67) reveal average diversity, with the faculty members being more variable which may possibly be attributed to varied tech familiarity. The standard error (0.026–0.046) confirms precision and the kurtosis (-0.11 to 0.16) suggests normality.\u003c/p\u003e\n\u003cp\u003eAs to the perceived risks, the level is relatively high. Faculty members’ mean score is 2.88, students at 2.75 and for the total sample at 2.8. This coincides with the perceived benefits mean scores. Meaning, since the faculty members had lower perceived benefits level, they have higher perceived risks level. The mode of 3.0 shows a general skepticism on cryptocurrencies, possibly due to its volatility and other factors. The high standard deviation (0.70–0.74), suggests divided perceptions. The standard error (0.029–0.050) verifies reliability and the kurtosis (-0.15 to 0.15) implies a normal distribution.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCryptocurrency Acceptance\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCryptocurrency acceptance is neutral with the students mean score at 2.54, higher than the faculty members at 2.49, while the total sample at 2.52. This level coincides with the perceived benefits and risks among students and the faculty members. That is, since students perceived higher benefits and lower risks, they are more likely to accept cryptocurrency than the faculty members. The 3.0 mode implies a moderate intention to accept cryptocurrency, but the low mean scores may indicate barriers. Furthermore, faculty members’ resistance coincides with their financial literacy and perceived risks and benefits scores. The standard deviations (0.57–0.63) demonstrate average diversity, with students less contrasted as evidenced by their respective kurtosis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCryptocurrency Attitude\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWith financial literacy being the highest construct, attitude towards cryptocurrency is the lowest, with the students’ mean score of 2.51 slightly outperforming the faculty members at 2.47 and the total sample’s 2.49. The results on attitude correspond with the perceived benefits and risks, and acceptance constructs, suggesting that students have a slightly positive attitude towards cryptocurrency. The mode of 3.00 shows average positivity, however, low mean scores suggest doubt. The student’s keenness towards cryptocurrency may be attributed to their higher risk tolerance. The standard deviations (0.58–0.63) and kurtosis (0.17–0.58) indicate average concurrence, specifically among students.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConfirmatory Factor Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn arriving at a model fit, five CFA tests were conducted to ensure that it can be further used in the SEM. The summary of the values, the indicators deleted, and the theoretical justifications for establishing correlations among indicators are discussed in the foregoing subsections.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFirst CFA – No Modification\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFigure 4 illustrates the preliminary CFA test, processed to evaluate the measurement model of the latent variables without any deletions or establishing correlations among the indicators.\u003c/p\u003e\n\u003cp\u003eAs observed in Fig. 4, the CFA was examined without deleting or establishing correlations among the indicators. The latent variables, Financial Literacy (FinLit), Perceived Benefits (PerBen), Perceived Risks (PerRis), Awareness (Aware), Acceptance (Accept) and Attitude (Attit) were covariated as part of the CFA procedure without deleting and/or correlating their individual indicators. The indices are disclosed in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eIn Table 2, the CMIN/DF value of 4.136 falls close to the upper limit of 5.0 and is above the ideal threshold of \u0026lt; 3.0, suggesting a moderate fit. The RMSEA of 0.073 meets the threshold of \u0026lt; 0.08, supporting an acceptable fit. However, the CFI of 0.849 and TLI of 0.833 both fall below the acceptable threshold of \u0026gt; 0.9, suggesting that the model does not meet the criteria for a good fit. The PNFI of 0.734 and PCFI 0.768 exceed their \u0026gt; 0.05 threshold, indicating parsimony. Hoelter’s critical 0.05 of 160 and 0.01 of 167 respectively, both fall below the threshold of \u0026gt; 200, suggesting low sample stability. Overall, in some respects, the model is marginally acceptable as in the case of the RMSEA and the Parsimony-Adjusted indices, however, it does not meet all criteria for a good model fit as in the instance for CFI, TLI and Hoelter’s Critical Values .\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSecond CFA – Deletion Based on Factor Loading Coefficients\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePrior to the deletion of indicators, the factor loadings from the first test were referred to, to identify which indicators have poor factor loadings (those standardized coefficients below 0.045), and therefore, may directly be deleted (Xiong et al., 2025). The factor loading coefficients are reported in Table 3.\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cem\u003eStandardized Factor Loadings\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFinancial Literacy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAwareness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePerceived Benefits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePerceived Risks\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAcceptance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAttitude\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFL5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFL6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFL7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAW1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAW2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAW3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAW4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAW5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAW6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePB3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePB4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePR3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACC6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eACC7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.748\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eATT5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAs documented in Table 3, financial literacy has only two indicators above 0.45, FL6 with a factor loading coefficient of 0.542 and FL7 with 0.496 respectively. The rest, FL1 to FL5, are removed directly. At least 3 indicators are required for a single-factor model, while for models with two or more factors, at least 2 indicators per factor are required (Xiong et al., 2025). The two remaining measurement indicators for financial literacy (FL6 and FL7), are sufficient, since the model for this study has more than two variables. Only AW1 has a factor loading coefficient below 0.45 for awareness, therefore this was deleted while the remaining are retained. For perceived benefits, perceived risks and attitude, no deletion is needed as the factor loading coefficients were all above 0.45. For acceptance, only AC5 is removed as this is the only indicator with factor loading below the threshold, while the rest are retained. Accordingly, the modified model is unveiled in Fig. 5, representing a CFA model with deleted indicators that have poor factor loading coefficients.\u003c/p\u003e\n\u003cp\u003eReferring to Fig. 5, the model was tested by deleting the indicators based on the poorness of their factor loading coefficients. Financial literacy (FinLit) has only two remaining indicators, FL6 and FL7, while perceived benefits (PerBen), perceived risks (PerRis) and attitude (Attit), retained all their indicators. For awareness (Aware), AW1 was removed and for acceptance (Accept), AC5 was also deleted. Consequently, the values are posted in Table 4.\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cem\u003eModel Fit Indices for Deletion Based on Poor Factor Loadings.\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndices\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eThreshold\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInterpretation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChi-Square/Degrees of Freedom (CMIN/DF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e≤ 3.0 to 5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReasonable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComparative Fit Index (CFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuboptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoot Mean Square Error of Approximation (RMSEA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e≤ 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcceptable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTucker-Lewis Index (TLI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt; .9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuboptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParsimony-Adjusted Normed Fit Index) (PNFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt; 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParsimonious\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParsimony-Adjusted Comparative Fit Index (PCFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParsimonious\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHoelter's Critical (HOELTER) 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt; 200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuboptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHoelter's Critical (HOELTER) 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt; 200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuboptimal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eReflected in Table 4, the CMIN/DF of 4.754 shows a moderate fit being above 3.0 and just below the 5.0 upper limit. Both the CFI at 0.888 and TLI at 0.87 showed values below 0.9, indicating suboptimal fit. Both the PNFI at .747 and PCFI at 0.769 indicate parsimony as their values are above 0.50. For Hoelter’s critical values at 144 for 0.05 and 152 for 0.01, respectively, both are below the \u0026gt; 200 threshold, indicating low sample stability.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThird CFA – Deletion based on Modification Indices\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn this test, a reference to the modification indices (MI) from the first test is made to identify which paths have high modification indices. Accordingly, deleted indicators with their total modification indices (MI) are condensed in Table\u0026nbsp;5.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cdiv\u003eDisplayed in Table 5, AW1, AW3, AW4, PB1, PR1, AC4, and AC5 are the indicators, represented by the error terms, e8, e10, e11, e14, e18, e24, and e25 respectively, that are needed to be established with correlation among other error terms and variables. These indicators also had high modification indices. The total effect is the sum of the modification indices of the paths corresponding to one error term needed to be established correlation with one more error terms and variables, (e.g., e8 to Aware, e8 to PerRis, and so on). Thus, their deletion is empirically justified. Therefore, the model with the deleted indicators is tested accordingly as manifested in Fig. 6.\u003c/div\u003e\n\u003cp\u003eFigure 6 demonstrates the model that has deleted indicators based on high modification indices. FinLit and Attit retained all their indicators as the error terms corresponding to the indicators showed MI. For Aware, the indicators that were retained are AW2, AW5 and AW6 as these were the ones that had low MI. PerBen and PerRis both had one indicator that had high MI and therefore deleted, PB1 and PR1 respectively. Accept had two indicators, AC4 and AC5, that showed high MI and therefore were also deleted. The model fit results for this test are summarized in Table 6.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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N2OOgOVMlvn5drkx0wr/RTlU3maWplmWZ6ka3P0ZY0y/WLE0tbDnkz1xTXvgjJlktN807ncU7AXnwcn12xhj+qP1yztmaaGvkPAyBy8aeNA1007pBTW9rOT6bYwx/WLF0hzBKdUbvqVZHmOmCeowy+DUaX2+EQP5m+HGGGNmj5fCjTHGGGNML3jG0hhjjDHG9IIVS2OMMcYY0wtWLI0xxhhjTC8U91hqc7sxxhhjjDFd8cs7xhhjjDGmF7wUbowxxhhjesGKpTHGGGOM6QUrlsYYY4wxphesWJqJhO84xy+lyOhTfOPAd6NjWG+//XblcvC76Njln/4zZimStxWZ2D7yNtoF2q/8c/8kQr8Q89VkyE/ME2a+oUxi/M8991zlMjpdyofwY3yz6ZObyOUqM6n1ZlqQXGdTT9qwYmkmEj63t2/fvupqMFi7du2A98xuueWWymZ0+Lzf9u3bq6tD0DHv3Lkzne/YsSMdjVnK0Fbuu+++6upQ+1u+fHllc7CN0p74XGbXd0Bpv4Q1DZAnTOwz+Dyo7PXpW/KE/UJBmbz11lvV1ezoUj5XXnnlYX3zXEFakHNMD/GO+ylWHpamFZTBcSc98nw//vjj6bht27Z0nAusWJolDx2zOq/LL788HY1Z6kQlgwevOMMvGJwW4zfXSw+gOa+99lp1ZiYd6u6WLVuqq+kChfLWW2+trkajpJBu2LAhHdevX5+Oc4EVS2OGvPrqq+npOM7IGLPUiYPPs88+W50dhAHrwgsvrK4WD+edd16alWuDvgLl20w2KJXnn39+dTVd0MY0Mz4qKJUlhVQzwV3q+LhYsTRTR9z/xX6buBentNwhN8zHH39c2R4iusd9JzTq6JaHne/Fik+GMU0Y7wsy00gcfPIZH7aNlGb487rftgQZ2zN7nfN2l+8F035oTFvYC0HsF0iryPcmAv0C5zGPssMgS5HLBVOaRYYYRuyXIE9HVxlGuS/0lqG87y3JFrCPSiVusS+OdTXat5VVXkY6x0R553U1plvk5aoy5RiVSs7jfU3tjHMplaw2yL2p7cQ8YWI+oj3xRr/FOjjUXI2ZSPbt28fGrWTWrl1b2R6Ea7ndd99932zfvn3m+q233qp8DR/LMrth4zzCjqPsCCfa4R8UH3GB4tu4cWO6ljso3fLLtcIxZtqgHlOfMWofoLofwQ5/1HnQfWoLoLYS24Tapdp5qU2C/BG+/MSw54LYt5TyDMo3hjzE9HMuoj/yItkqXLmB4lX+CTfKTPFAjA+jPofzmGaFKTnn18A5dnlc2EnWMR9zLX/FjVG9glguSquulWfkEtMakb3ky3mUQ7wvLyvSITcMxPREZEcYhK/8kDaVm+JVnKrfilN2IvoD+VFZcK/KP+YJ5JcwhPxyX+k6zy/Xygd+czxjaaaaYaVOU/snnXRSZXOI+LTP8hbcdddd6diG/Gk/Sr7kt3nz5nRcvXp1OsqdOD/77LN0zhMjT30smS3GfWhmaRBnJbXhn1mbq6++Op2XePDBB6uz/iDO4YCW2jxtSm163P1ncwXbak488cTqajD49NNPq7PDYY+mltLpR9RfDQfsdFy5cmU6qq/RC43yRzySQWSo4CT5aAYuzjwpLPVXiqNuDy1gr5cbV61alY6bNm1Kx0lB9ZK6ESnJB5CJZuAlA+ReJ4e8rCJDZS4d4+w+dTWH8qDMtL2EtGmcUXkobNpPXdojde2sy71C7Qp035o1a9KxNF6SX+oXpg4rlmbRoY58//796Zh3Nl1QRyq0L4UjnZIa4lVXXZWWAzS4ESeNc/g0mK6JOy6HGTNtMIBI2aFdUP/ffPPN4uDF2+S0E5RO2oVQW5wNxAm0PS3Diag8TRql7TeggRl5oZTs2rUrXSNj8qZ+S32NlCf6Gty75lkPtbHfWrZsWTpGdu/eXZ0dTlSMo8I8ybTJZs+ePdXZoSVm9fmlB4G8rNpQXY0oDO4nHFCcKlPGE2hLf5/tTGktjZP5OFhCdSpixdKYBtoaKrMDNHAZGjxwfKv6CxANFMZMK/ElHvZmnXzyydXVkTAzw742DZ59wwAY2xymafZk2kCJz/MHKPKcSwHgWJoZM6OBYhRl3UVx7BtmAWMamNlsY67b2WywYmkWLRr8Sk9UbajzLv2lSBzESk+mQgOBZns8CJhpJQ62PCjV/S0Xs/O0N83Yd0XLtk3E9jzJM5Tjon6lbZaIGUgeaGGU/yJsU761zJ0Ttxlpm880UXoIinmKs5d9kS+X16FxRrPVOaWZZejSzrq0KWh6SNTYNSpWLM2iJQ5+UupiR9z0FxTaW0Lj1Z4mBjMta6tBs09He3I4MpuDv9ioNdtT2gdqzLSgOs+xTkmJSlHdnr0mtGyb/7URxPYc32ilTS4GRTPuWY35U19CPiVTKfqjztTmCqmUKhSI0tYGwF4KkMolvhXOMu44Zb1QINuYJy0/A339qBMA+sPxeF/XWU/t4afd6P44zuSoXozazvBTly+2dyELxjqFpQkV7ckdmW+MmUCGFT29cZYb3nobDmyH2Q07xcOuMcNGksLhrbZor3t5g09Ed4zehMNPtCdNkVI6gLhztxifMdMI9Zq6rPZRItZ72kNsm7RdjK7lBxS2TN5+1J5zf5im9MyWPC6ZGGcpT/Eag/+8P8EoXwJ/dX6QSQy7TnZyi9fcK/J0RLe68oFor/KJ9/ZNnhYZ+uGSnPI8c7+I40kkH2d0T1tZRZlLFiV/eZqiPEWez3yciWHEuiA73KMf5SGmUfFGf9Ev5LKI+Yj2mDwchS+O4mfoYIwxxhhjWmBWcaiIpfOhcjbzxrg5iJfCjTHGGGNML1ixNMYYY4wxveClcGOMMcaYjvBC1b59h/5tZO3atZ3+ImipYMXSGGOMMcb0gpfCjTHGGGNML1ixNMYYY4wxvVBcCvfn54wxxhhjzKh4j6UxxhhjjOkFL4UbY4wxxphesGJpjDHGGGN6wYqlMcYYY4zphXlRLN9+++3BlVdeOfjBD35Q2ZT51a9+NTj77LOrq6XD//zP/wyee+656sq0cf/99w+OP/749JIZ9er//b//V7lMHnxTlnpNehca6thvf/vb6mr6oeznkkmvW8aYpQ19OnoVY2Id6F8//elPW/WvPpm1YsnASaI10POP9GSSDhl7uPTSSwfPP/98Ou8L4qXjJ07iZvCOcU4TL7300uD9999PeSkZZEperXweVCr//Oc/Dz7//PPB9u3bB6+99trgnXfeqVwP/qNBk5nPxgW333774J577hl88cUXlc3CQLt48cUXB5dffnllc5Au7TfKr02Z477onzDiNYYy0Dnx/vWvf63uPgSdYbwHEyHdp5xyymFh5YaHVOInrEjJbzSqI5dddtngtNNOK6ZvqUHfQ91APqojXdBDFfdRHjxE52CnOtgG6SAcwuuahrmG+hFl07W+kH7yjFmIvORth7JqQn2C8lkqy0mG/M71w+h8IoVx586dlU0Z9K8tW7ZUV/MEb4WPy3Bg543yb9atW/fNX/7yl2R34MCBZH/cccclN8H52rVrq6vZs2LFim82btyY4gPF2Wcc8wX5APIyHCxT3pQvji+//HKykwzlthShjN96663qqszWrVuTrHJ/XC9E/Rgql4e1hfkGedBGc0Zpv5IpZt++fZXtkdx3333JD/U41lPqeH6vwiSuujrNffjLwU5p5kg4akdAPDEflEFEcbfVEcKI7XEpQv9DuSIDjMqYYxPIPLZX1bdYByRv7DF1EC/lQL0inKY6ONcQN3mnvnFOPkkf5+SlqT4L7ic/3KO8tckTf331X8QrmcuoPdVB3OSZdOA3lu2kE/PbVjYLyahljPzJU1vdwU9fdacLY492ylBpwAJ19qLPjCnuvFJzPZ/C6wM6bTpKQfrr8qDBOR8klwp15Z7T5K+tAc4FxEl6FgI6KgYAOtaIZNS1/eKfeoldVOBy9GCU1+E6GSjMOuWN+0rlmKebMEplS5iEjTttTYxSR8hvKeylQkmxp9yoV03gnvdVdQqU6kEJlSFlXqoj8wV1hbpAHScPtKnYd4PqVZOSJiWnVB/zdhohrrxdjQv5yNPeBH5JX5Q/MkAW0wD5VR0r1edJYdQyVr1p65/w01fd6cLYS+F33XVXOrLMV+K73/3uYFiY1dXc8Le//a06O8h55503WL58eXU1HbAMftFFF1VXzdx5552DYWedZM5SoOkOS2gsHdxyyy2VzdLgySefHKxZs+aIdjFO+73wwgsHw84pLauU6h8yJq5vf/vblU03hh394E9/+tPgmmuuqWya0TJ4F0jLb37zm3T+s5/9LB3rqKsjV1999eDee+9NS4FLkeuuu646OwR1oW17B0u8+RaooeKU6tAo3HDDDSmuRx99dOS6NVsoc20DeOSRRwaXXHLJYO/evamO0KbypdVPP/10MFSCU/up4/XXX0/Hiy++OB2BsYu+XW45LK+z7NkHtB/a8EMPPTSz7aUNttFQblH+XFOek75VhPyxZerVV19NMn7ggQcql8mizzJeaMZSLKmYrOvTgJoUuccee6w6O0TcjxIbJWF2fcnh9NNPTxXk+uuvT2HEQS6Pk/jwQ3yEzb4QvSAU95gIXWufFRBG9Mu54tQ+B+zogIhDe1Vi3Bj8lRpx184Sf5JZ7ICUJ+Io7dfRXh7cuT+moSn9+MNN9+KHPImm/EWZEW6X/Tsqf+7J92MR1vnnn5/OOeJnlH1JdIwR0kSYhEG8hKf42sqtTd7cL3eVVw4v0cR9WTGMWCaEJX+SIfdKTm0yePzxxwdnnnlmdXWQ2bTfzZs3pyMKaw5umzZtqq66g+KCEkuayHcb1P1///d/r67aYdBGEW0bBPM6IrgfxaZu0F+KfPnll4N169ZVV2UefvjhJHP1OZQtdapJ6cqhvFBOeWDhwUPtJfYNcwFthPSec845af87igl9RlQGI+SPtrht27akwDRBnmh7OStXrhy88cYb1dUhSMsFF1yQ6iBtJLb7tn6zxI4dO9KRh7lbb7210z5i/J511lnV1UFUjn/84x/TcVIhvxs2bEjn9DNN/UDs26lnlHmkzZ1yUX8d62k+zsoPdpRhUxlzVPlyLL2ASXvUOFYak0rEvHS9pzPVzOVIaPp1+MRS2bSDf6bNNQ3NMgB2mo5nmYPrrkliqYHwdA9T3cMKU7keRPtAFCfuukewxBKvtewS88Y9WnrTEiHxAeFzjR/csMcvcekIyAy/ug/yZXAg3ia5MuVNfJr6Jgzt8QHyirvCxR/xkjb8ED/uCqMu/cCRdAP5QC7403VT/nCT3FVfmiDP3EsaYzq7Ll9G5C83uo+j8k28yhvpbctXm7zzOsf9eR1D7tgpDoWhZUOlDT/Kv/zEMsE/dgonB3vc5V9wjX1TPcvhHtU50kUaJQMgnaQNSnWYe4kzJ/rjHD+SHXBfnn7FE+E+pa+EwlZYkkFu8rgEeVYdMAf7xDpZRVRvqS95XxdR+eSo3nAv9Y06rfZEW5srCD+vw03gHxPbfh2EWwq7zl6U0sR1W79ZAr/44X78U56xPefgp9S+6uwnCfKmPpI6Q5pLbRl5UH6q1/Qz+FU9a3OnP1Ydj2WhfhhDWlQ2HLGLaeE6ljHhYac4FKfyo35M4wnxalyI7S0Pl7ibxrHZ0jza16DM5JW8iZJ/7GKlVCXvCkLhfgqb+/LOi/DyCqSOSpTixC6mtRRudOd+Cjaiwi0ZQdryxpyHnaP0S27qZHOjMPLwiA/3OChwnadflb5kuLctf8QZy7bUkIXiUmMRNEKMUL2LaS9R8keDjNfyQz4ibfnqIu88r1zjByT/vAHLj2QQwxTYRZmW8hmpc5d9Hn4T3KO4SXueFsJSPJznYeOXe3KiP2Qj+Uo+3BfTj3zyMoM8PTnEgx+FJRnEsPM6EinlaanCIFQqgxK0beo2fSimThlU+eSU5E4dwG9TnzJbiIPw6X/Ia943laDuqP6S7zrq6lKdvSDc6N6132xDfV7eJ0VwL7WvOvtJgTzl9YQyoi7mYy8yizKgrsY62+Sufr1kJB/O8/JVvy9yP7S1WJZ5v6XrvAzII0bk4aqe5qap/o3CWEvh3/rWt6qzuUVLqTJcR1gaZq/LRx99NBgKNk0jX3XVVTPL1Ewpn3zyyel8NvDXNuyDZAqbqWPCzcmXs997772UpqGMjzCRrsvgYv/+/em4bNmydGSJYli5joijbjlG8e3evTsdRZ6ODz74IO2hycPFsDTYlj+WTVhmYcqfKXb2h9ZBXJAvyw4H+rRs0Qc//vGPq7PDOeaYY6qzg7Tlq03epToXrz/88MN0POmkk9JRsHcLPvvss3ScS2bbflnaHHZ2M/sOtaxEvZgN1EGWPYeddVoiKi1XjboMLtgXByeeeGI6lqirI+YQlAnm7rvvrmzqob9kOZzlb9oNdYblPvXP40I/MRwYZx1OE8RBuvkrs3/5l39JS/HU+6ZlZuq/9vE99dRTle2RHHvssdXZkTS55fTVb1KWlM3HH39c2RwJeaoj70MnCbYmsJ806hHUxdLWFmR29NFHV1cHl/oZ+7Xk3+Sufj0fFzBN+/rV77NUXoJtQvRdtDn6RP46qAtXXHFFymcdo+oNozKWYokgqYgMogws8w2FEPc2MCBReEPtPl33vReKuLTX5oknnui8+XzXrl3V2ZHQQa1evbq66gayVr7ZjyNyJTHCCwuUkyqu9lGsWrUqHZvYs2dPY/k25Y/Oavh0lzqkrvt48rj67LDYG9VV8WnKFzTJuytfffVVdXaQ2GHNNX20X17+oXNm/xJ7ftavX1+5zA4GSe0zQwnhP0sjuKmj7wp1n0GBPOeDcGSUOrIUoa7QlpseEiMMhLzkA8idQev4448fPPjgg8muC9ynh4IIff4oSti4EA8KJWm48cYb08uWelguKbb4j31zCep1aXICO9xGpY9+E8VZkxUlcM/7RY0p5557bjpOGhpvcuXpwIEDaVzi5aUcyreJNvc6BbEO9ft1D7yULRNqtDv0haeffrpyaYY9l/R3TfQxjtUx9lvhPInCHXfckY4l2Ew6m6dKOqJYIaI2zZNIjv78OTYszfDVkW9IzqFyMgtKflGWug5qdIh0FHSuUQZ6mWOUt8EFsmYwZ0ZNAySVh0rH4K4OhiPxAoPlxo0bkyLA0xovc7z88sutAyidDHHpBRJBh8p1W/6w5/zdd99NT0ZQ6kxBHZo2lQvKjpmJ+aQtX23ypsNqUkzVgeQzGrxNCryY1heKK1diYbbtF3kgC96w5MUGyacPaGPMFlH/4lvFpKWts8yhbH7+85+n81KfMQpqc0sR5EhdeeaZZ5LyBPQDeqGsBOUX4T5m05jl6QozOjwU5AM2ih5/YD+f0GdSL6nvUKcgI6umtKnfj3nS+ShjQp/9Jm2rKW7yk080oJjQHkd90Jsv6h549bDArF3s3+i7md2ML8fgrjre5K6+lhnFOKtNuUb/Oa+88kqSYV3fwj8iIHN0H9LcdQJCL7zV0TaOzZqhwjY22h/A3qS4d4Zz3LTPRPsP4pq/7OJeHa3753sfcrSvIMY77HxSnISh+7V3RJupsc/3NAyVtHStfRLa04Ad+w0Ul8IgT7jjxrnyke9NID3DipjcoiF+0DFCWIQd88CReCSb/D7c8jgw2gtCupv2Q9WlHxRnNKQP2vJHeJwTvvIlGZYgLsJD3sAxXgN5Io64z6UE8bT5k9wo/0hbvtrkrfrEkXxTryRHyS6vl8SJH6VFZYKdwA92sSyVFo51EGeeR0GeuL+t/QJplQyE8prLmXQTL/kQiot8CNzxG+OOqBxVB7gu+cUOfzF9xEO6VJZ5GrvUkcgofhcbyFd1ODeSifrJWGfoA9ratFD4sc4IwsGdMsWdcuZ6EiA/tB/Va9pnnraSbGg7+CM/3Mt5Uz8NkgP3qE1z3VXGgnvVjogbeeZ1W3FF1KeD4mnqexYSte9SfQLSjTt5VNnpnmjoxyTLNnf167lR+JyXyipvM7GMuVZfSjiKg3u5j3IkDPwpHrUP5Z0j98TyVP5z01cfNyvFEsggDUsJI5NkLA4AMeEIgHtyO0y0a0JC5RjvI968IqkgSBcVQ4OhwL/C4Ei6OcY8yF2ViDAJj0LAXuEjhwj3U5hyV/pIe16ASlfJEC/3EncJwsKP/KrTgSifaLBXhcOU0o97LFvOY7nW5Q9IA/lUuto6Te7jfsLBP+HG/JbyUZJH7geTw31yy+UFTfmCJnkD18oH+eYa2cX0YhfDoG4K7GTId0yv7Lg/2uVpENjjvw7CjmWs/MZyjvFglA9kQtpjmed+CSu3K6W/Lo3cr/jy+gncF8OJhjJE/upwRclvE8gCP7EOLBWQnepyyUgmqqO0eYFb3qajO+R1G5PXZYUjd84npSw0vmDIX6kdlmQDGkcwpftyNN5Q55X/kozVXuqIbSbv0wV+CCtCXOoraPd5fiaFvG/J5ZG7Rz/0w7FfzvPY5q4yxT2v77KT/Lk/1wPyMuZ+hUfYlBXXsQ5gp3LBLW8f2Mtwn2gbx2bDUfwMA14ysJTLFPBSyTZ7MlnKLzFsJMU/PzaLB5Y32N/Kss+0LuWSBy2/LgQsDw077E4vrBhjTAm2og0Vu95ekJlkxt5jaaYD3vQbPr0kRTqa4RPaRL/NZ/oBhYx9YcMn2pm9NNPGQiqV7CNkT91NN91U2ZilQHyLuM7k/1JijDnIklMs9TLPtA6yo8DGXF7W4S8zYn4ZLNl43ecLF2ZyoZz5mywGwqVQ7/uCTfj/9m//Nvj973+/oMqtmX/yB/GSWQozT6Yf1O8ulf53SS2FM7DGN5OZtVvMfy9CJf6v//qv9IbYvup/zVasWJH+46rpv7XM4gRF6ZNPPvH2h47QXzDbu5TfBjfGzB5muMVSWA5fcnssjTHGGGPM3OA9lsYYY4wxphesWBpjjDHGmF6wYmmMMcYYY3rBiqUxxhhjjOkFK5bGGGOMMaYXrFgaY4wxxphesGJpjDHGGGN6wYplz/An1Mcff/zgH//4R2WzsPzqV79KX11ZKPiTdr4AdOqppw7efvvtynZu4ctCfN95vkHWxG2MMcZ0gXGR8aqPT4Sid6B/oIcsJLNWLMkIQiEz/Ls8CsT999+fFIqFGNyXAvF7tSUz2wpaCnNcnnzyycFtt9028+Wfuea5554bXHvttUd8XYb6SL2kfpIf6iv1U/UXkFue79xAnf2///u/Dy644IKUBmMWG7Qf2g2G8y7wsKWxgU+L5g/ctBW1SY0dJRgoNc4sJci35EP+u3wSMMqUSYX5eqCfVpBX7PuRGXbIrWs9nw2XXnrpYMuWLdXVkZAGpa3OTFwZ8+Wdcdm+fTtf7flm3bp13/zlL39JdgcOHEj2xx13XHJbzJDXtWvXVlfzy9atW5N833rrrcrmIFy3penll1/+5r777quuyqhsOc4W4iultW+og6ecckoqlwj21McVK1aktAjOsYtpwy/XuXyGinEKW9TJR2lQezBmMUB7oK3QDmhfnLf1Ibfffnvyh3/Mxo0bD2uf6ofkznmp7alPww0zzSCPrn0q+abfUt+E/Lr07dyj/kfjhPujMuguyIsyUb2kjiPrUl2cK6gXTWVL2mg7uR/syUPb2Er488nYrZSMIHgyVUID9GKGytjW0OcKyb9UodoaA42mzU9T+KPSZ1hNUBZ5p03Do+MoKZygBhvTRlpL8ol2TXlayHphTN8w0FLX40OZ6j9uJXRPdFdbVDtC6cmh3eCnBG6EOc0gN/pf8ojiXSc/oF/Cj5D8mhRT7sndkZv7oyNBttSnOqW7yzjZF13KqM4Pdao0Dgm1xflk7KXwu+66Kx3vueeedMz57ne/OxgWTHW1+FiofXxNaPr+lltuqWyOBD9N0+7TCvnes2dPWm6LsBT/xRdfpPr67W9/u7I9BHaqy3WwfMdyRJNcI6Rh586dXoIyi4LXX389HS+++OJ0hPPOO28wVHJm3HJkv3z58nQE2tqaNWsGL7zwQrrOt6vAhRdemNrrYgW5PfbYY4OPPvpocMwxxyR50F/kfQXjy1AhGPzwhz+sbA7J78UXX6xsDkf3nHTSSZXNQf71X/819UddltGXCsgK3QUdBV2lxJ133lmdTS7oINQpTAnKfN26ddXV/DGWYslAS0VdsWLFYR1HDg1IkMG434Z9DHGDqfY5MIBzjj8M57qX+7DTfdjrxRDsaKD40bXAHwWg/Qi4qyGTF6WLyoabXnbhOu694Bz/GPbS0QEiB9xId0wP4ed7I7gG3GSndBCX0o/pup8mknc4MW+ATK666qp0fuuttx4WfxdiGSmfhKF8CdIdy+Krr76qXA5BWpAzfkifwuBahrhyu7r0Pvvss4OVK1dWV4d4/PHH0/Giiy5KxxKkta5hQt3g2cTwyTKlyZhph76Jvj6H9vbGG29UV4fz5ZdfpmPeh5155pmDP/3pT9XVkXDfQgyE8w1KIg+qe/fuHfz4xz9OD7f0lfSryOzDDz9M/k4//fR0FMjvtddeq64O5+uvv07HvL8944wz0lFhmsHgd7/7XTpecskl6VhCZRRhTI3jNOe0jwhjVBzbSmM5Y6nGT8p8HIiX9ETiGA2cq70Rl8bUOaeauRwJLYO0Td1G8MvUMtP5GM4Jg+UVwmOKn2vsNa3LMjt2WjLgPsJhuh+YpsZd9yls/GCnZQbctBSK4VxhKA4MaWEZQUsx+NFSv5b2CUtwHWVAepQP5YE0YBeXNIBlIE3B44d4lF7JI8aVozLIjeKFmDeh+9qm+OVP4XFU3tivIXstJyjtyBd3lYfyFsNCztyHO2gfELLHjnu51lIZfpGz4ihBWZXyRDiYUdA90eRh5/LJIc2qY8ZMM7S92M+JOnugLdM+8mVZ2rb61xK0mbo2RVyEuVhRn0Jfgynltc4e6B9xy8cNlYX3WR5CdalU1yTjaPDH2ETd1biEvBnrsNPYhD+uFa6u8SekY8gP4VHv69qSUJqjifcoLuzjeLUQ7Was2MhAnqkmUCTwnysGCFODr8KMApGdCgDyhqXr6EcNTGGh2MTGlochwUvRERRS7BjxF/PMPbkMSumRYhRR5QQpZyVTR0k25DNeg/ImdJ9kU0cp/NK9uT/ccoVKHZv80Mi4zk2UkZRLjnlHWQK/pTwp7FHIw6Ks8rDzfOfgf9R4jZlEaJd5/wV19qBBGCOFhnGAvoF+qgTtjL6wDuJajG0KuZA3ZIMMkF1d/9HWr0hGGreQPXaUgzmE5FTXf1MGjFMY6QWlyYJcmcd/Pl5p/OOodhH1CiBc0tQE7tGPyjaicSmOV8rrfDLWUvi3vvWt6qwbH3zwQTrmy+bDDmZO/oaGeIYFPNi1a1e6ZnqYZXktl7MMXIKp78jnn3+ellC5n6ltlr3H4Sc/+Um6V1PmTF//r//1v9I5vPfee4NhRaDkjzCjwJLKQoPMmeKP5Ht+mJofNoAj8vrqq69WPg7ub6EM2Q9a2ovVlWEjrs7G5/LLL6/OjFl6HHvssdXZkdS50ZeyTM5y+fe+972ZLTH095dddlnl6xD0jZi77767slncsDSq7URPPfXUYPPmzWlZnL4O2bH/so6mPu2ZZ55J+wbZ8sQyrPZWMu6ZQ6je/u1vf0vHHMogGmCczsc26RpakmZsO/nkk9O50Dasjz/+OG1HYAtdPibm4XaBvaFnnXVWdTVZjKVYkqGhhj1TabuS+21qPLOFyiBFlg6LgnvppZeS8oUS1wUUynPOOWfw/vvvD5544om0b24cVPm0l4IXSuJGeJASPBsIs2mv4CSxe/fu6qweyhC5sZ91lHoWUYca99yOCuno+uKOMYsN2l/poRo73OpgnOBhkYdGlCYGVsaNXMmhbfOwPw0vS8wWxiL23J122mlJHuyXZJzJ++1zzz03HfM95YwTvMBTB30VkyjInImRVatWJWVn06ZNlQ8Derh54IEH0rErpXFIiqfQ/mKRu/fJpD6Ijf1W+MMPP5yOd9xxRzqW4MURNPlly5al6x07dqSj2L9/f1Ic+obCp9NbvXp1uqbzY3aUBpcrdHXQAfDURz4pvLo3x7py8803p9k3ws1B8SS9dDhxM+40PmXyJMjb2U2KIIMLA4k2qgNH8q/za665Jj0AoNADm47bwswbNNCh8oT/61//uvZ+OvbZKJ51kCZjph3NuEQlR+dNL8VFaF/33nvv4Pe//31lcxDaJGMIM20agOkjGTsWI3y8gXEJpY9xRZMfOZq8iQ/gyIq+tTTjWwI5rl+/frB9+/baeJYqjK1MFDGD3vXlGWSIkp6P4Tw0aeaQMnv++ecPG2s0pn/nO9+ZWe3tMrEy1QyfbMaGvQQEwZ6ZuDGYc9zYOyKGCmTaW6A9DRzjtfZhxj022psQw9F+RO170J4T4sMOwznxCaURtC8BO/ZHcI1friPaq6C9kNofxL1KD+fcS5za06D0xTSD9lYQRpQVkA7cuC8a8lEH6cJPvlcjR3mTvIBr0kk66u6X7KN7qYxkp/xKbsibfBEv59iRd9x1T26IC/+kOcpPYSLvmI8IssK9hPKSh0v6KDeVHSAT/DbJHkryiZCWtjCMmRZoI+rraDecx35AbTS2L8CedkD/lvd7se/NTaldyW9dH7DYQHaSG3lGjsggojEogn/Ki3vr+idzaB8ldSrXYajHGu+Fxmnu4RwYh7FTndTYRllhp3KL4XAey4Yjcem+EoSDH+JWXCUUf2yb1AW1m2g/l8xKsQQqvxQHDAJDOHknIgHjjj8ExL2gTklGg31uR4FEO+6Tvxg26YnClx/cqQhR+DFMCi4iNylE+M8rBO74I748zcqfII15JyBiJysZ1lWgGIdMiVxegrC5rqtk8R6ZrmUEapTYkQauyVvs5GJj4qh7Y5qFrnP7iNJXBx1BrCMY6glpE7m8MHkZQu4Hk0M8MWxjph31fxi1V6H2pzqva7XtvC/TIB3bUDTRf973YPL4Fyu50pHLkT4rjin4Q674lfJjmkHGed+PTEv1lnE66juc53LWeIc7ZUG7ieFwrjEYd+InnLoyIx2KT6ZU//N2Ij+kmXioR7leNlccxc8wEVML/9fEsupQqFOzv9DMDSyXs/Sz0FsIWFp/6KGHBu+++25lY4wxxiwNxt5jacykMXxCS29XlvaxzhfsrSEN2htqjDHGLCWmXrHUCxulr7uYpQUb3vmCBS9rLYRySZzMmpKG2b7sZYwxxkwjU70UrmVw4eVwAyh4v/vd7+b9rxh4q/3qq692HTTGGLNkmfo9lsYYY4wxZjLwHktjjDHGGNMLViyNMcYYY0wvWLE0xhhjjDG9YMXSGGOMMcb0ghVLY4wxxhjTC1YsjRkB/uLq+OOPHxx11FHpCz/8IfpS4H/+539Svv/xj39UNvMHcRI3aTDGGDPZjKVY8ifQDKy5ofNnsF3IL5/E9LR92g+lIPpHaVgoGDxJL+lAjr/61a9S+vhvxGmkro5Es5DyHgfS++c//3nw+eefD7Zv3z547bXXBu+8807lWkaKqDHGGLMk4H8sx4GPmXO7PnTOh9W3bt2a7Pjg+Vx9AJ8PvJc+wB5ROjBN6SAc/PBx9vxj8/MNH63nI/RKBx+mR458HH9ayetIBLu2cpw0KA8+9D8K1C1kQL013bj99ttHlrMxxpjJYOyl8PyTdd/+9rcH11133WA4KAy++OKLwY4dOyqXfnnppZeqs3rOOOOMwVAhS+cPPvhgOpZ4/PHHB0OFbnDqqaem9C8Ub7/99uBPf/pT+mqL0sHs5R/+8Id0Pq00fdbwlltuqc6mA8qIej0Kzz33XKpb8NRTT6Wjaef555+vzowxxkwbve+xPOaYY6qz/mGg3rJlS3XVzIUXXpiUS/yX9oUR1po1axZUocz529/+Vp0dhE8DLl++vLpaPKCkYaZNuRyVbdu2De67777BunXrkrK0EPsTpw22fuzbt6+6MsYYM230rlju378/HZctW5aOgv2C7Btkvxn7684+++wjNuPHfYaYuF8Tv1dddVU65/vguKOcNLF58+Z0fPLJJ9MxgtumTZuqq8MhzpgOBju9pKF9j3JjRkrpwO23v/1tsiO9yi+m6cWD008/fXDccccNrr/++hRvVEAee+yx6uwgMW0KF1lC3NcodI2bIIzol3PFSV7IH3Yo38RBPkB5VxniZ5z9tI888kh1dpC6ODnHjn2KXBOn5NhUV/JyIAzCrFPsmsIC7j///PPTOUf8tO0P1f3M2v74xz9O56+//no6RvI8kk6Mws/zonTquuQnz6/cdJ/CRu7Ks/KU20EMX3U9pltlhuEc/yov7JROwK2u/XCPHhwlZ4hxReryBbFOcU4bwR/Xxhhj5pBqSXwsuF375NhPxx5B7DjmsFdQewgx8qu9Z9ixh439kcDeSPYdxv2a7LuKcdaBP/lRGHEPJXGuW7cunZOuuI+RuHCLcXK/8sRRezIxnGOAOPFLGhUGfghffupAftqPJxkqDQI/uYx0jyC/8Zr4sYt55B7lX/sglT+lHz8qU/nlqL1vxE24bfkirJKJe+hKcWIne9Ku+Mg7ecrloHLmPJYD+/UkN445bWEJ1b2Y7ibIA/tkBeHlslKYyqPSR5qx437yIj+ESXpVp7Bryy9u5Ed5IZ/yB8ovdoqfa9yJB2L4pBmja9IkmVA/FDbhKp0x3/jnWvngPLorvwozxoWbaMuX7iFslS3tHrtYLsYYY/pl1oplNHT0pZcU1KFrEBBxUGFAiAMM4J/7GIyAQYbrOMCUwJ/8MIjk9zDYaeDiHCNIB/5LBhg8lR4gXLmBrhU+5H7qYKDFrwZFjnEQJJ0xbsjDxk8eV57HUrjRnfspy4jKsGRiXnNwJ42RqKAK/OVx4gd7KQuiS12RXEr1MdIlLFBamvIquD8PU/Uqv1/h5jLifpWJ8hLvVRp1Xym/1CfsckWKfGFPGIA/ZE+9oGxKileehlK6ZRfTqfvEOO0nj6trvjiP9RpiOMYYY/pn1kvhw06aUWEw7PzTCyglPvjgg3TM9wsOB5mZ/VTvvfdeWs6K4H844NUuYXaBpcPhID2499570zKclijZv1iCdChPuQGW5Vie1rIky/J9wX5P9h1+9NFHKQ28LMLyv/K/c+fOwcknn5zOZwN/l3PRRRelvLBESLg5+d5TynA4SBflUifLOm688cbq7HDq9rvm+3ZHqStHH310dVZmLuodL65Rr7Xci7nnnnuS27PPPpuObdA2SuUilMZdu3ZVNgeJ+f3www/T8aSTTkpHcckll6TjZ599lo7I/YknnkjntGHqxlzRR/vpmi9jjDHzT297LFEuUIb+4z/+o3bfHYpdJFcYcnfo4+Wau+66a+ZNdfZlrV+/vnIpkw/WEfKGIsLb6eydI8+zhT1gDLhCCubWrVvTdWlv3mwgrnPOOWfw/vvvJ4UChbELe/bsKZbRqFBXRlVGc/qsK33XO/5t4MCBA0co4MiZPYRdZEjb4IGoCdKYP6yV+Oqrr6qzg5SU7a+//jq9zMZDxzXXXFPZ9k+f7adLvowxxswvvb68gzK0cuXKwbXXXnvY4KkXefK/IOJFH2ZdgAGS2ZJcKd27d+/grLPOqq7GQ7OWDzzwQPpTa67rIB3MFLHxP85Y6Z4LLrggzSYx63LxxRcnuz7gDeKcyy+/PB2jAq6Xo+pokxXyZRb04YcfHtx9992NfwkUoQxRznn5IZYRL0zkZdaVXKHuSp91pe96R37q/m1ADzRdHhReeOGFFE4dtC/q6erVqyubIznxxBPTMf+ro08//TQdeWkMyDsPX48++miq16r/c0Ef7adrvowxxsw/YyuWGoi//PLLdBTPPPNMWgZkRkxvg6KUoUDedtttM2+ASqn4zW9+k655Q/u4445LSqkUOmYXmUG56aab0rVmuYiT+OuUEv62J1fANmzYkNLFABphgEaJkCKsdDCzhDKqpUzebgWUKy3fkwbNbpJmriWPOJsiu6aZKgZz5CS5Et4dd9yR5Kalydtvvz2lC7kA4eX5lBJKOLjjl/wRPkohM1PwySefpCNlhLvO69KoMkQJ+973vjcjF2bn6pRT5SWvI0DZXXrppSlvdXHmM1KiS10pxVmiS1ggpUXHEtRpFDItyeaoHPGjdiBQJGP81LH8XwtYRkdWGNUNPfCU8ovSTJ3hr45UZ4jjoYceSjOFKL+UEcoe7ZZrwsONesZb2iKv1zrGeCWbprrf1n5WrVqVrgmDNGOfx9UlX4ov1i2dl2RljDGmJ74Zg7VrD74gEk1ps70MsOGezfW8IIDdcFA87B7grVQ29+s+zoeDUOV6EG3Q52WIErpXRnEQPy9EcIThAHSEX+yAdJA+7Egvceb3Yc/bpnqphfTkYRJ3Lqs8z4Ad4eT+Y7yCeHBT/IpT4F9hcCQvHAlLb/3KHXkQJ2ESHi9DYK/wkX+EsPPyUZg5MR91hjSBrmOcpEv2pFNlI5rqSiwH5bGJprCglJc8zJheTJ7e3F1+ZE+cpJVz6l6Uq/KDvJCR/Jfqcim/uCtsjnpLGnQfeYQ8ndjH8DGPPvroYde45364zuVG2PKn+hvbD5An8q/6mKeH+wXnbfnCkI5SvowxZpqhj1QfXQd9H2PHfPZ5YymWZnLQQG2mEyk8TR2Dyhi/xiwU1EMUfkxTfY3w4CDlv/SgDLnSj8n9dfEz7ZAfZETekFn8h4cm2sqFh9S6iZKlBIoVD+SLBdoE5Um5NrVH+ZlPxbL3P0g3xhizuGAvNds12ArDv1Zwnv9hfQ7bPXiZk/3jw7Em2ZVeDMv/KWE4+B+xR7mLn2mDrR9si9F2DslmqPQNfv/73yfZ5VtmctrKhS0ibHXh3z8IFz/vvvtu2kqzlEAObAdj+0zcHjNpkLauH3Fga2CXzz6zrWveOahfmmlFT7hL8Ql0MZAvBZfQ9oeuMxjG9AnbQvL6pxnEfKtShFm3WK/po5g9if8/yv34a6KLn2lCy5fMInKOXDjm/TizUE357lIubA/Jw1Wfs5TQUjD5jltmJg3Vja6ovNtWEPDjGUvTCZ5s9Am8E044ofXp1kwWlNePfvSjdM7/XJZmgLDTf2Di12Vs5hv9i0F8i5/ZkqGSWPsPB8zGDZWbwQ9/+MPK5uDfY/FPBy+++GJlMxg8+OCDaUaFl8bq6nYXP5MOM2a0Zf5q680330wvmTFzSJ6QCzIZDvyHzcJyjQyRZYku5aKXOWM58eImM75LBWYB+TeYV199NcmGf4eZRDSDvSioFExjjDHmCJjtYXYthxmQuj1rpRk4YGaFWUvQDCb+ZIgr0sXPJMOMEjJi5pGZslweIp/dFeS3boata7noZThmKtlviXtdOhYjyE8zelr9iS9HRpCR9qNSJtTjSJu7Zpnlrtlk6oFmTblHfrCjLJhhzuu50qw2gx3HWB80Y0m+lDf86F6BPXFHYl5K98wGK5bGGGNqYUDKByWoswcGKQasnJI9g3wcPEuKVBc/k0bXZUqo89d0f9dyQWmRAoH9UlIqASVOWwOoR8ih9HCCokX9otwA5Ry/UkLb3FHspGgiY+LAnbg5YqKyyRG7mBauY9npAU1xKE7lR3WM8sWOeKVgRqU3D5e48ae6QHvK75kNViyNMcbUkisqos4eUIYYqHLq7IGBkYGbwbeOLn4mCQb+LjOWyGSuFEspOVJKSkrVYgVFKc8vShh1KC8LyigqVihz+JNS1+ROWMi2ZFR+nOflJeVT5H6oM7GuS5GUcqvrvI6QR4zIw9WDRm7y9I2L91gaY4yp5dhjj63OjqTOLf9cb2Q4GFdnh8Mf3/NFpqHyWNkcSRc/kwR7HvkYBHv8+GN+PhzCPrp832SdTKBOll3KRW8ZX3311SkdQ0XliI8fLGb4RwLyqw96YHiDng815PuDqVPxs7B8+IO9vfoASJP7hx9+mOyGOtURhi8S1qGPadTtHb7uuuvSB0y0/5KPinThiiuuSPmsA7ehUnpEWtmH2gdWLI0xxtTC39XwVy052OFW4txzz03HfMDkS0tNnyrlk8CntHwjv4ufSQOFGAUDJYHPsN56663pL4BQ9lD+kIm+QiUkO8kyp0u58BlllAh9tQ5FBeWSFwJ5oWgxI+U9V54OHDiQFHm+1JXz0ksvVWdl2txHfblMiqo+U5ujBwPqC/Xm6aefrlya4SGmrY3s3r27OusfK5bGGGNq0adI46Cpc7nlMIvDwBYHLwbJPXv2DC677LLK5kj+/ve/p8/vNtHFzyTDm+DMDD3xxBPpDXGUP2SCbJCRQHbIUDNmOV3KpfT50ssvvzwd9XnfxQr/D7p+/frq6hC8eU8ZoHBH5RplM34yGXDX7G6TuxRDZhT1KWugPKL/nFdeeSWVMQ8eJW644YZUJ6gvpDnOmDbB/3U2PcARJ8oqaVOd49jbW+lDDd4YY4yphT1c7MtiLxn7HDln87/QXi+9mADYDQfjmT1o7CeL+75wZw+Z4F72eMW9b138LBbIl/YDSnZRnpQBdshfdC0XwsWPyqGvvXSTCnWGfNfVE+SKO/KSPHVPNEMFLMmwi7temsmNwuec8pP/UhlTLipPypZr4lD5Kg7u5T7t88Sf4lE7U945cg92QvnPzUS9vENiYuNfSlDQ2txrjDGLFfo6BjFM/rIAAx0DUxwkgbGBgRE3KTcCv4SFG37yMKGLn8UCstELNuQ1lyV5RxZSIERTuQDhoFQQLn7yclhsIAPyKiNFTuTu0Q96jOprqQza3FUWuCPz6C47lEDdnytyXOOGH8qI+xWedA2u5Q7Yqd6Uyhd7Ge4TsW1y7LNtzVqxJBNkioxIYLkhs/jJFbDoB/cmCD/6lxBoZFGoCB+/pAtKlSg3Udg56jDrlEcVdF5BjDHGGGOgTddYTMxqjyXr87wVxQZk9i2wD2CoaCW3oUI3s1H2P//zP9OaPxuK454G3IdPAOkc96bNxE8++WQ6DjXrFKbetBoqlYPjjz8+2ZGWf/mXfxmcdtppM2HhDzfuGxZqijOaoeKY/NWhb9T+7ne/S8cc9r+88cYbg82bN9d+IcEYY8zSgv118W3kOlP64pYx08zYiiUbPW+77baZz82JfKMxCidvot1+++3pFX82KkfOOOOMpPABn+6q4/HHHx+sWLEifRJLn72i4bIBl79SkB0bXPMPs+PGfSV4W+7CCy+srg4H5ZS/iUAp1acTS5Dnu+66K22GNcYYYxhb8omMkmn6OxqzOIgvyCwFxlYsmUHkraO6t5lymv7XDMUO5RLlrTRryYwocUl5zPnb3/5WnR2EBt01XTwt1jVslOBf/OIXyaAUk446UGj5m4f4hp4xxhhjljYnnHBCOjIRxt8HLXbGViyZQTzzzDOrq3b279+fjsuWLUvHHJaSQUveEdw2bdpUXR3i9NNPT38BcP311yfFLiql/IluG01/AwDkkb9m0N8zvPjii+lYB8qxls6NMcYYY+IMdV9/Qj7JjKVYosDt27dvsGrVqsqmHv1jPLORGzduTApgCWYZWerGX5wu5j+hsC/NQDKDyf5GlqrZo8mRuEqznsCMYtzbgkJaR5wlxbCXs20f6FlnnZWWzo0xxhhjliJjKZafffZZdVYP+w1R3r73ve8N3n333cHLL7/cOot48803pyXnOGv58MMPD2688cbq6kjY3/jOO++kl4WYvUQxRREtLVvnL+/oxaES+Szpj3/843TM94hGWO5H4TbGGGOMWYqMvRTeht4K561r9hV0gdlMZh3vvffeNGupt6z1Oao6mFFkn+RHH32U4kU5veqqqxpnF4GXikqwTxIFkbRodvNHP/pRcmN53BhjjDHGHMmcKZYCpRBl7z/+4z86/R0Pb1fr7XH2QJY+ySRQAOPMpBRMzUTmH5kvUXpx55FHHil+oJ18oHDGTzYZY4wxxpiDjKVY6ruYX331VTq2gfK2cuXKwbXXXtv6ur1mLR944IG0X7FuT6bYtm1bdXYIvWzT9CZ6hDTpe6DMcsaP9kcUbtOH6Em7McYYY8xSZCzFkhdpUKA++OCDyuYQmpXMP37/zDPPpNm+c84557AZP/4qSG+Miw0bNiS/zF5GUAD37t17mHLKCzkon4oXxfCOO+5I+yz1If7SfYJZT17///73v5/u5SUdTAnyrReMSm+U79q1q/HD78YYY4wxi5pvxuS++w5+ID3CNUFGo29wgj6PWGfkl08yDhXXme9dElfuFzv88y1NjjHu+K3M0r25IS6IdnneILqX/PBpx/zbocYYY4wxS4Wj+BkqSSPD7B+fTmTZuOufkS9m2Ov50EMPpTfgjTHGGGOWImO/vMOLMvx9EMvGpSXmpQT55++JnnjiicrGGGOMMWbpMau3wtnbePbZZ6c9iktVuWRvJ/lnP2j+nXRjjDHGmKXE2EvhEV7G+eSTT2r/F3Ixw5d+rr766tb/2jTGGGOMWez0olgaY4wxxhgz53+QbowxxhhjlgZWLI0xxhhjTC9YsTTGGGOMMb1gxdIYY4wxxvSCFUtjjDHGGNMLU6FY8lUb/i/z/vvvr2wWP+T3V7/6VXW1cPD9dNJx/PHHVzZzD/8Nyt84zTf8L+tS/7N/Y4wx0wG6Ef+j3aQbvf3222k8xd98MZZiSUKPOuqoVoO/2SKh8OnI+YICyPMyLUptqWxmU6Fuv/32wT333DP44osvKpu5hYZy7bXXpv9EHaWe1dnnNMnnsssuS58pRbE1xhwOfSAPmJiu/aEeSmlnPLjxoJrTFi4Pe9xLGPghTD8AHqKLjM3BcR35LBYYy9CNdu7cWdmUufTSSwdbtmypruYJ/sdyVLZv3/7Nxo0bvzlw4EC6fuutt/gvzHQE7NetW5f89YHCv++++yqbuecvf/lLipN8TiOkm/Tv27evshmfoXKZwpprkPkpp5wyU69GrWdbt25N7l3qXZ18uHfFihUzcRpjvkl9L+2C9kLb4LytP6bfUFvC0OZi+4Yu4WKnfhh/XNPuFwP0WeRt3H66i4zNwXpDf4+ZZNmQtrVr11ZX7XTVjfAzSrizZawZy48//niwadOm9L3wEtjfdtttyd+0os8znnzyyek4bSjdy5cvT8fZcMwxx1Rnc8utt96aPo2pejVqPTvjjDPS8aSTTkrHJurkwxMt2xCefPLJysaYpQ0zYLTN//N//k9qL7S73/zmN8mubnYMe1Y6nn/++eQfc+eddw4+//zzmbbVJVxWMFgteeyxx9I1/vBDuKUViWnj8ssvT2PNmjVr0owa+e1KFxmbgzz44IODoWKVznfs2JGOk8jrr79enU03YymWt9xyS6vCQmPBnzFdYJDYs2fPYUsVC1XP+ETnvffe6+U2Y4ZosLv44ovTEfiE7XHHHVc7EMo+tl8UHxSoF154IV13CffFF18cnHrqqelcyM8rr7xS2UwvyIRtP3v37h1s3rx5Jr9sCWjrf7rI2BzcSvHaa68NXn311VRvHnjggcplsliodwvmgjl/eSffxxb3uMX9NAg130dTR75PTk+ufLOc2SaFofCpWL/97W9Tg8UPacG97ml7VAifCqF9LoRPfog/phNKdsA9XONOfvLNtoQnPzzVNsmnjrjRV/JQnBHikhzr9qRwj+6XXIFrGaU/2qmscp599tnBypUrq6uFhYGLWZK6QdOYpQT9wYoVK6qrQ9Be33jjjerqcL788st0zJWjM888c2a/fJdw//nPfxYVLPy899571dXigH6HPholCNjvzThQ12d2kbE5OEO5YcOGdL5x48bBvn37Ut0rEXUIxrV8BrnNvW5cjGM698gPdpQfusgFF1yQxh32TOKmcZmjdAuOjN051AV0AvnJx/QSMS9d7+lMtSQ+K/K9bxH2DOAW1/e130H7ArgePkmk/SagvXK6zvcR4J89Ni+//HK6Bs7Zb6L9E3G/HfcRPtf4YS8f1xybiHE2QVqUd9LGnhf2uYDSHvf9Kf+5neJSWqPMiCOXRxOEFf1wj8IlfUqv9k8SP0g2MS78x7C4R2lH3uzrURjxWmHgn7wojhLIq03WyrfSntPmHsnlk0OeyYcxSx3abuyLRJ090D/QvmIfB7Qp+hfoEm7sWyL0F4tln2UTyKGun+oiY3OwrsTxDZmV+nZ0COSm8YP6hV/pCW3uTeMiRwxpkd7CEbuYFq5jm1AZKw7FqfxozGO8wo54NabHepGH26Qv9UGzdtKRtgE9zxRgJ0UCwUbhklkElStS+EfAhCWBCCk/uVG8UiRUqF3Af5uyo8pRMpIHaYv5J+24RzsKOXaeuTvnMS2lhhFRfiNRjiIvO+LJw+ZaYSntJZOnDzuObWmF/P4SeVpz2twjJflEkEOUvzFLlbq20NRG6CcYhDFxYI4KYZdw1abpQwkTo8FW48Nig3GA/gnZ0XfW9WddZLzUoa7k4w91CZkhvwhyi4oVMo2ybXInLOpkyWhc4zyv7xonRe6HOk68Ih/jdJ2PneQRI/JwccMuN3n6xmUi/seSaeBhAVVXB/eJvPvuu2nvSeTPf/5zmi5mIzd+Ikz9D4VMCR1m2FcROfroo6uzI5fUMXXLDnV88MEHaVNwHi+GpQ24+eab0/S2lt6Zmh9WhMPshp1J437Cs846K21qZwqdKWs2ac8FpCl/YSlef/jhh+lYym/c60j6hpU3/c1BXo7GmOnh2GOPrc6OpM6N/pnlbJasv/e976V+66uvvkr9HH/rBV3CpQ8dKktpifCEE04YXHPNNTMv61100UXpuFjQcil7JOGjjz5KY53GkZwuMl7qbNu2LY1BcYxHVyhtdUJuUT9g/z4vQulF3ib3ruNiziWXXJKOdXqH9t+ydE/d4K+DunDFFVc0bofoqi+Ny0QolsCbbV0ZPo1VZ4eze/fu6mzuQbnT3hZeOintAxLqAFWRqSQ/+clPkjLN23tcozA3cffddw+GT0vpHhTMhf6/xS4KOB0fyiV5a5LPfDHOvlRjljq039J/5WHX1G8x4DJQMWAxOKIQnnLKKTP7truGy8s93K+Bb9euXWmvXNuLfdMA/aL23PGPGKtXr055RRnJJ09KtMl4KaPxUUqTzIEDB9I4+tBDDyX3yEsvvVSdlWlzH3ViSorqiSeemI451A/2ZTLmUzeefvrpyqUZ9lxSD5qYS31pXhRLlIsmeDrlaUAbWUXeONiUzBOa/EYQIsJnY6vC4Jj7i/AkmFe6uqfDCOHSudHwly1blp5+KPyo6KF46hp/KMNPPPFE2jDLW8fYkT8U6j/+8Y+tT9/kA//M5PKkAaVOebbQ4MhbHWoAPDlpYzLQoLSpGPkws8CsLHkG5BPLNofy02b0uYCyoJxGYTEMXMbMFvVNcdDUeddZQ/oK/mnh97//fWUzXrg8HNKPzNWKzXzD6tWbb74589bybBTCkoyXMoxH69evr64OobGXWbv4Ai9jH7Ob8eUY3DUh0eTeZVwswT8bMPbVjTU33HBDqu+qG3HGtAn0Cs18lxhHXxqJoTI1a7Txs27/Iuv2w4ykfQhDpXBmf9uwoNLeQvYocB0N/rVvQPsY8Qvsc+DeuHdCfnKjPRHa0Kq9CW0oTYpTkH72r8S4S/sVSH+EdGAf975ofwT3R7RfI9ojQ+LEDUP4TXuMtHeD9IpcjiA7lZ3KhiPxIAflT3mSLHNDXNyDf4UHyid5wL0E6W3b39FWz+rciZ80xf0xJflEcIv+jVnK0B/Qhmi/tBnOYz+iNl5qe7Q1+mv6kpy2cAE3wqV/kN+lyLgyXopoLKirK8gQd+qTxgDdEw1jHvLt4t40LgLnlJH8c+Q6lmes47QNrolD7UNxcC/3Ud6EgT/FQ12I7YQj92AnlP/c9DXmzVqxLCUuh8wjHNzINBlFGAhOwkBQZBw/cpN9DBv7kh0gFMXDUfYc5TdWhDooJPmvM7EAyA8Ko9w4zxu4CjcvONJDhY0oHAxpAfJAZVD+8s5X5LJRGCWZRbnIDjhSBtgRD9fkKcoNe/mh3NQ4ouyErnP7iNJXRx5G7rfknhvqWp18IpQd9mqYxphDbT72z0LtSv2ArtUPN7WlpnC5Jhz6mKX+oDcbGS8lVGdkkFMkd49+GIs1xnKUrEWbe924CLLTGMn9eZ3mGjf8UJ7cr/AIOyqSKm/spH/gJh1LYC8TxzriinnJ295sOIqfYcDGLDgsl7N0MZvloD5gOWDYQNO+VmOMMWa28OLQULFLy9qLnYl5eceY4RNT+vpE3Ks63xA3+51uuummysYYY4wxXbFiaSYG3nDkzUjeCF0I5ZJN1//2b/+WNr93eSPTGGOMaSO+ILMUsGJpJgqWwXnz/3e/+11lM388/PDDabYSBdcYY4zpA/6DFXgTnS1fix3vsTTGGGOMMb3gGUtjjDHGGNMLViyNMcYYY0wvWLE0xhhjjDG9YMXSGGOMMcb0ghVLY4wxxhjTC1OpWPLR97PPPru6WtroI/jHH398ZXPwT775l//4MfyFhv/v4oP3p556avow/1LiueeeS38xcf/991c240OZUtaU+0LRZ36A+jBOWNSpSf/rDtrmQv7hvzGmXyZN/2AsYEyYpPF+LMWSzhzFJTcIu6/BZqmCbEf5pOHtt98+uOeeewZffPFFZTOZPPnkk4PbbrttsG/fvsrmcKg3qkd8UrFEXu+moa6hNJGfnTt3VjZHUteeopkNpfDHVe675GdUzjvvvMH+/ftT/9H1D4RR1s4555zBz372s5SmPH8YOlvakhS7aF9S9krhdC2bOvt///d/T3/4jzI+7dDekB2mS9tjAC7JBVN6MEJG1AHc8/BLZbNY/myafNCmyBMP3l0VhDaZjBvupBPHimjII/ldLPViquF/LMeBD59zuz5cPlQY0sfPseNoRgcZIj9M/Ih8G3ycfhZF2QnSEz9gPw58UJ906oP/OeSfj/Tjp+6D+Pjhg/mjyGehIa1NeYK8PQnlt4kuZaO61Ufb7JKfcSBtXdJH/MgEmUXWrVuX0iWob8cdd1wy8rt169bkB7u6OkQa8Ce6ls327duTP44R7i+ld5og77RN8ozcOG8rf/KMPHLDvRGFh39kRxw5lEkMg7KeFEh7XuajQNslf8iBOkLdrOsjI20yGTfcaYA8UV/U73Gttk15cG0WjllpIxRi3rmoM5nmTnShoBOgoSC/OLC1QRlwz1xCx9mmvLRBp0Y6mzo3+cHUddazTcdCQH7aBuI6P233dS2bLmnoSp9hReg/2gY/4qWt5GBPuiLIBrs46KqN1Q1AhJOnoS6/0a6pfvfRfhYKPZSgqAvltaQEAv2/lJoIdrFvw51yoHzqlIFcgZ80kAX5QnHjIb9OJiVUP2PeqVNt+W2TybjhThO0p7xNUQ7ke5Tx0/RP73ssmY6Gr7/+Oh1NN5i+53OCr7766mDYQQ0eeOCBymXhYdmQJYb5guX9YQc4uOqqq9Jyz1KF5UKWfW655ZbK5kjmu2zmmg0bNqTvxddBO7n33nsH1113XWXTzEknnZSO//znP9NRDAee9Hm1a665prIZjS5lE2FJnu0D01ifX3/99XS8+OKL0xHYvkA/JbcSjz322BHf3Ge5+/LLL6+uBoMbbrghbeN59NFHa7/P/+CDDw4+//zzJMNJlB+yIK8fffTR4JhjjhmsWbOmc1pffPHFwVA5OizvXA8Vx+J2DdEmk3HDnXZOPvnkdPzyyy/T0SwMvSuW2t/wrW99Kx3Z14GyqX0QcRCkQXDNPiY6HPbusC8n7g3hSBhqPHTo8WUV/MaXQrSvh2saEP61Twq7fG+P7pV73NsT4+KcRhzDIT6uZeK9siPuLuzYsSMNqjB86mrsALDXXiTSlJO/XBH3iYlS+vBPXgmb/HHNkT1idP4MjNyjcEmHZIKhrFT+wHmU2VdffVW5NEPn/Pvf/z4NXJdeemlrRxjLBsO57on1g3pEfsnjn//851nVG2SAvdzz/UuqNxjV3VHJB+28rjeVTRe4v6l+R9ryU1cXSI/sMNwb7WL9g1WrVqW85PELZEJ+u37PXXXu2GOPTUeBYko7Iy7SOipNClUdDOzPPvtsdTU9ULYrVqyorg6xcuXK9F3/EqXyoY2gdEnZIdznn38+2aHgUx/ytkQdoj+jzPF7/vnnj1Ve8wH54kFj7969gx//+MfpAYn80GbJRwkebs4666zq6iCS3R//+Md0zOkik3HCXQxIoWQMAepYHP84V99CX4TcsIv6B9BHcR3HQuBcfaGI463CwXBOWeGfuLHLxwnSoLEcd/WbEOPivK5/jvELxcu93EMcMW7sMNwHpEN2CocwSA92HLm/1PcXqWYux4LbmfoHptw1DT3sQJMd0/Vca1qaI9dM0wNLB1wzPa+lE5ZE8KelK035a3kJe+7BANP7Codz/JMW7sFgxzX2+CMOgRvLMLiB0qc8xbjqwiFdXCu9gjzmdk2QVqUDWRBnTKvQXhnJlPjJA/51HeUhoh8gL9iprAiXNGCPIe3xfu6VXyCt+FGaFa/SrPC5ltwkT5VlCdwUL+f4V7pETAf2UR7EQ7zYcU5YkgflKvn97//9v2fs8YNfwupSbwhHdRg/uBEOfoFr0qAwCUvxNIGf3MR7OFeaowy5jjKpIw9P5SH7uvy25Qf7prqga5Uj/jhXmUVwJ2zJN4cw6/KqdBEGKF7sorzi/ZzjHtNCONE/4Cc3yr/gHuzzewX1hnxPG8ioJPM6+zqoI7FcVV7YqV5Qz7CjnUa4xr/Ks1R3JhHVibyuiDq3pntEk0xmE+60QN2jvgjyT/6Qh/oA2hv1DpAX7uqXJDf84IY9fjlXX4XBTjLjnHswQPkqHO5X25c/2rz6TdIb27/u1T26Vp5iXMSvNoKfvG+VH0Fc+FEeOMePtrNgx3Vsv4Qdw0GepAHUZyutbcxasSQyDOfKMIkGFSSJEjHhwHWsHIA7GVI4dDwxQwiD+wT+uY5+ZBfhPglSgs0HMBWA0pzHBTEcUFwxn8gkXjdBGlRRBDJBnpKBIN7cr9IskAPXUc5t+eCemGaO8X7ujXmmwWBXMsC9hBchn7g3VU7cYry6B3lIFnk68nhIO/dITiqfuEcMZD9OvSkZ7pXsY9krPTFfJXI/NOz8HqUvppnrKJM6SmngPuwjMb9d8tNWF4C+QJ0mYXNdB/fl6RQxbTmSDe6Kn/jyco/3U574wa/6AsKJ8oU8TaWykazye4XSN23UybzOvgRypvwjpfvztpuDO+HkbX7SoM6RN9JJXcn7cZHXK1FnX6Ikkz7CnXSQr/oU8oVBb4h9C+5xjM/rHPdwf4T2iyzV53GMMuN+7hNq99FPqS/APd5HvHk913inNOdxQZ6HPH7qHtdKvyBPeR2J4UAMh2OTHtbErJfCWb5l6n8YVtrzEffVMPWOPTC9yhRuibgPBL7zne+kKf7TTjstTcGy5MI+lj758MMP01F7sMQll1ySjp999lk6duEnP/lJWrZl3wswXUxely9fnq6ZbtY0M0bTz2Lbtm2DLVu2HOaHpQyWOvIlN5butI9E5NfjcOKJJ6byG1a8NOVO/pv2j7333nuDYcVL5Zsb2LVr1xHlncu6C6RlqLgkedxxxx2V7SFIRx4Pch822sOWC+Doo4+uzsZH9aaUb+S1e/fu5K6yh3g+CnEv2kLRJT9tdQHoC+gbKEeWpUtLpX3BPmXF/+677x62NzCHvoe+hvZLX8PSWRcmoWzmi3wbQaTJLUI/Rltuo67tCtypR8NBs7KZHFg61FaVp556arB58+Y0NrLtIh/jBPWuDi3ntlGSSR/hTgPoBrRxtXeWhWPfwph20UUXJXuWgxk/c/KyGXUsHBf6wnzsJq3w8ccfp+M4fPDBB+mY99NDJXGkdjMbPWzO/yCddX720FCZeTmlCwwEQ8043YfCRQFTMeaCfN/fOMoHFfOXv/xlSisdIvuo1q9fX7k2o4FMDUNm+JSQOoeHHnoouc81VEIq+vAJKpVTl71MKI/zwd13353ShXxLadKelEhdR94XnfeazALyMBcd2lzQpS7QWVKOdFZxP9BCQ93XXkH2rLL/to1pKpvZgkxKAzJ2uHWBh2c9tAvkjuKVg2ybFFYGOMaESYE+nH6JAZg6Tv/JeNVlEGaMy9uO+pZzzz03HbuQy6SvcKcdyoH/u33//fcHTzzxRNrn3MY4Y+G45C8Z9Tlu5ePiqA8Us9HD5lSx5OmNP+9+55130lNbrkHXwaDDUwOZQMPmCZaOqU8IH3iyjHz66afpePrpp6djVzRryewa6Y5P53EGBcO1QEYlJZQKRhhU8Pj0ThzjKHT5Ru4cOp09e/bMvN0oRa4OylIvPsT0Kd8MDIRXUvrG4c4770z1IE+TOoF8pokBqy3P46B6w0tFcTM08qMs1XjnQ/GcD7rkp60uAG2CP0GnflG3br311sZOatmyZdXZ4RBXX3UqohlVvRAxV0ySQtQVzaLEOqBzuTVBnaA95jPHKJr073ndwu9ll11WXR3J3//+95kXHSeBa6+9drB69eo0y8VDcNdxDshn3k+ySkA9GWVWP5dJX+FOM4wJ/LPIww8/nMqla76pj6OMheNCWdDXxDJS/8ls4bio7+SF4Aj9L2OoiOclZqOHja1YaiBveq1fbihrCC8OJAzKTQME07YImUZKAcQnWN2no+KJs48IEWIcnOuacFECKVgUAiA+ZghZ1tOTQx4XxHAE/pm1JDyOXSBeKmxd56wnfMkCCJtBnEInDZTDCy+8kNzqthqAFAT8cx9x04ETlpbmUQxoVOSFRhiXU3jSU76Je9OmTcldTzJawuctNLjxxhvTIM3fiZB27nvkkUeSG4p0PpgI6kppxog0oZATV0TpoHOXjMgbnfxNN92Uruvq6GzqDXn70Y9+NJNvnmopR5ZISc/Pf/7zmTxqdo6/yVFdy1F7Uvx1lNKcl00JySYPX/nSERQWdMlPW12g3dOu6KiBI2mm0y+9JQnMwJRgAOdBooTqTcxLjvImeUdQhLdu3VpdHaJr2eihVMccHgh5+p82qPP0idQBZEdd4px2ICWKcqPM8/IElsHpw3JQNKkHhKU+gj6IuqSHEsKNbYbwURR4kJ8UWIqND1GjwH3UdW3zIb+0K/IoaG96Mxi6yKRLuNMMdYXxi2Md+svDTz75JB2RE/fovOneprFQ9+movjiOM+oDYj8td91HWaCwUUbYYdhOR5vQQ1geF8ivyOOn7FEC+dIdeQCO9MO/+c1v0jWQN8mQuhX7dVaaoUkPa+SbMRhmPG3yjKa0qXMotLRZFHfu4XqY4WSnF3sww0KbefsI2DSKu+LhqA2kedz4jdekI/eDXbzGCO5XGjmy0Vrk4UC8lp0gjdiRzzZK6Y7k7tEPbsgMu2Hnnq6Rn9yVX+wFaYvylHyHT2PpnHvY9Ms1flRGQpuKuUdlgTvliT3p4V65AeHF8uca/3EzdQR/0eQyAeKMdQVkp/s4VxlEOZKWKEPZY7CXfKJdvMYI5K4yIE/kTZAehSU5ciTOKB+Rx4sp5b2UZiiVTaQu/Nwe4rXsuuQHu1JdiDLEP+T5kD3Q/ginDsIk/CifGIcM6c3J4y35AdKu8Otkl5P7weSQ7lhPpg3VeUwsM1AZlPJHeVI/SlCe6nMwpT6E+HBTnVtskF/1X+QxlyF5Rgbq07rKpC3caSVvx5g6GcR+izqqOqw+E8N1HFPwh6xUL7lX9TfvD1TvZUhHnj6uS/cB8cR+k/Sp/uf3QLzGlOIHtSvVE+JQnEL9OO5qd/gnDOoaR/woHRyVtjaO4md4k+kBnoKeeuqpxiW++YCnE2bQhhWp95eeJgGesHjaMosPZt15ks+XTSM8WTN7qBnQuaDvOqZZW2a3jDFmMTPnL+8sFRiIfv3rX6c/xp0U9Cf1iw0rlYsTlveGT9aNSiWw5IeCNpcPcH3WMfoG3hDm5QFjjFnsWLGcJexnYG/RCSeckAajtkFxrmC2lH04DLbsuWCAXiqbtM30wywkihef9muDdoZf9kEt9OpAG+zPZA8zX2FxezTGLAWsWM6SU6qXSdatWzd45pln0vlCoL9J4oUIluPjm+fGTDJs3WDjOXW260whShpvbPJN5EmGWVg2zI/7cocxxkwb3mNpjDHGGGN6wTOWxhhjjDGmF6xYGmOMMcaYXrBiaYwxxhhjesGKpTHGGGOM6QUrlsYYY4wxphemUrHkO5Znn312dbW04TueyEPfZQb+O6/uu70LBX8SzV+v8GUVfb/UdIf6ru+3TgILnR7qEP9jqe/cG2OMmQzGUizpzFFccsNgow+Zm/FAtqP8593tt98+uOeeewZffPFFZTOZPPnkk+mj+Pv27atsmkFx0J/Pq27xZ9jxY/mmP5BpbMslM5sHAu7Nw5uNUnjppZcOtmzZUl0ZY4yZGPgfy3Hg4+Tcro+e89FyfbSdoxkdZIj8MF0/9g58uH4WRdkJ0sNH6GcDH9wnnfnH8HNUj/QxfNBH8bGfbTomHeSkdjWfUMannHLKEfLFft26da3ltmLFiuqsHpWtynU2EN9irwvGGDNtjL0Unn+ebPny5YPHHnssfYmGmQSWY81oPPjgg4PhQJnOd+zYkY5dOOaYY6qzueP111+vzsZHXwdqgpkz6s/WrVsHt9xyS6pXwJFr7Bc7L730UnU2v/DVG7Yq5GB/4403VldlmEn+05/+VF3Vc/LJJ6ejynU2dP1KjzHGmPmj9z2WGpi+/vrrdDTdYA/ia6+9lj5rd9xxxw0eeOCBymXh4SGB/WxzDTK4995708PJddddV9keDvZ9KCWTCsv9k7bES9mfd955yZSg3PikqTHGGNO7YskgA9/61rfSkRdIUDa1ryoqKHEDPgMqL6DwQgBhYC//hKH9XfnLKviNL4Xgxn1coxDhX3tCseM6onvlHvfvxbg4154/hZPvG4v3yq7rPjJmKDds2JDON27cmPYi1s36Ys+eQ8Iv7cdElsSr9Cj/GFFKH/7JK2GTP645XnDBBWkP586dO9M9Cpd0xH2QlJXKHziPMvvqq68qlzLMihJPm5LCzLiI5YLhXHIbt25wVLlHWeNXYcM4dRskZ+7hSBqB8PjWO9x6663JnXBiWKA0y+AOhCs7gZ3SyHHUF7okp0gpPZqtJB7ZdyGWEWmT3DF5WpGj8iKZ5ZTyS3q5lsFPbmeMMaYnqiXxseB29vcB+7C0f0r7nrRncOvWremaI9fbt29P18cdd1y6Zl8Xeza5n71c+OMIhIG79ndhzz0YYC+awtGePO0Vw2DHNfb4Iw6BG/u0cAOlT3mKcdWFQ7q4VnoFecztmiCtSof2r8a0CtyITzIlfvKAf11HeYjoB8hL3KNGuKQBewxpj/dzb9zPRlrxozQrXqVZ4XMtuUmeKssc4svT3QThRlkQB3FixznhRFlgxz1tdYN8cg9GYUs+GOCe6K6601a3ccceO5BMCA+QDddRBgoryl/7VTlGqLvkSedKD3akIcZVIuZdJsYLpfTovjbIV/QXy0j1ibQSnmQN5AN/qjvIG/eYhqb8YtQGJHuuo7yMMcbMnvaRoAE6aQ22nNPxS5EAOnB17ILrOGhyTQcfwZ1BRuEwWERlJB/ENFhFP/kABtyngYiwcddAJPLBN48LYjiguGI+kUm8boI0EG9EClI+6BFv7ldpFiXlpC0f3BPTzDHez70xzwzI2JUMcC/hRcgn7rGcIpJjjLcJ0pDHQboJQzJSmKPUDeA89xPTP27dlkIkVFZKn65zGWAX0weEHf1RV1SfVb9LJg87ksuBfObxAuFEe86xa6Mke9nVlRF5oS3kbRU5Kg1d8osftSsp+cYYY/pl1kvhLN/u3buXEWDw+eefp2VKbarnBR/sQUtUJfJN+N/5zncGzz///OC0005LS24rV66s3d81Lh9++GE6nnTSSekoLrnkknT87LPP0rELP/nJT9K+SF6+AZbZyKv2AuZLl/lS4bZt29K+uuiHpUWWhfOXZliO1gsQIr8ehxNPPDGV33CwTsvJ5J+XZep47733BsMBO5VvbmDXrl1HlHcu65xRX0IiDXkcyHyoPByxfNsHF110UTru3r177LrNHlHaC0vM1G3+Nmdcbr755rQnVVBXlEbV77xsME3lmkM+zzrrrOpqYSAvtIW8/kSZd8kvZfHEE0+kc9qXZGWMMaY/et9jmcOeqTVr1iSlgZdTunDxxRcP3nrrrXQfChfKDvur5oJ831+XN5dzGLB++ctfprSi0Dz77LOD9evXV67NaM9ePhgeOHAgKasPPfRQcp9rUMgYbDdu3JjK6fzzz0+KTxMoj32igf6FF15Ixy7EPZ0iV+b6QuEuW7YsHcep26SXBwv2UK5evXrw9NNPVy6jg7xQuNQ23n///SPyrv2Xs+Huu++uziaftvzyUiFlxkPUNddcU9kaY4zpizlVLNlgz593v/POOyO9zcsMEDNoDJj79u1LM1DM6vUJ4cNTTz2VjuLTTz9Nx9NPPz0du6JZy9tvvz2lm1k/wZveUWnkWiCjkhKKgkAYKHtx9o04xlHo2madGJD37NmTZpw/+uijpGA2vZ1MWTJ7ivIZ06d8H3vssSm8kuJXB2EiP/Jc9yBBeFJ4pQzHF2qAGcG5mGXTyyTMoI9bt2+44YaUB+oAshrnQUZQRyinzZs3p7R9//vfr1wO1W9mRONLMJRz3Ysvk4peBGSmuI4u+aWe3HXXXYNHH3001XPVX2OMMf0xtmKpwfzLL79MxxJyQ1ljMI3KAp1/k9Kxbt26pLAwYDNjiaIidJ+OiifOPu7fvz8dYxyc61pKDEvuGniIjxlClng185PHBTEcgX9mLQmPYxeIF+WtbklOy/KSBRA2AyLKN2mgHDTDV7ccC1pmxj/3ETcKGGFpaZ5BloGYvLAEihIr1q5dO5Nv4t60aVNy14yylvB5mxf430Nm01CkSDv3PfLII8kNRbpuZummm25KDxK8HU08UWmlzhAecYPScO211874I1/MRhEOjFM3IrFu/PrXv051hrozbt3+5z//meSCO2G+8sorlcvB+7Tlg/ApK8JVWKUwr7766vTwRdqY6Req38T1ox/9aKZ8mImuq2+ET53gWIpLlNJz4YUXpiN2evu9hGQfy7VURrIjPOoi9Y9lf8mZo+ov9bYtv8iSfzd45plnZh7aaOfU36b0GmOMGZFvxkAb9aOJG+8FLzawwR537uGazfPY6eUHzFA5SJvpBZvt9dKA7mXjPeRx4zdek47cj16IiEZwv9LIUW/5Qh4OxGvZCb1AEF/oqKOU7kjuHv3ghsywGw6m6Rr5yV35xV6QtihPyZeXXDjnHt4w5ho/KiOhF1e4R2WBO+WJPenhXrkB4cXy5xr/XV6awI/CVnrIawwfSAN5lz/OJf+SjCWDaBevMSB/xMmR/HEuxq3byCCWHX65jnJVGSg+hYXBX45kUwJ7xUf6iL9Eqb7F+hOJfpQe5UP5zynJmXu7lhGykVyIh/qBXLGL7a0uvwpL6c3TU5KrMcbk0MfQt0wK9H/0eXV9+0IwlmJpylCwUYlYKDRocjTjIeXGGHMQHgIYwDB1Dx0RBuCovEejhwEeGPRgSLjckz88Rj8YzksPL4sRxhQ9wOYP7k3kD2fINYL85IfjNMmz9BCMQU6jyGhcrFi245GzJ6jMTTNC84kUy6XS+c4FViyNOQSDOf0bg5j6ujblUgpRbuKgzDnKABA21/nDOXYYKaP4J+xJh36YvIz7gM99KAy6n3zTL7WBnHKZx7GA8iNcrc4x+8/1XCtkfUJaqQOSB9f6L2HqyjTlZTHikXOWxCfpLo1+rkChpXOgk+CcxmXGB/lRpu6gzFJHikp8aNbDq5S9HBSZ0uwRdlGhyRVEhStlinDyuAkz+plkyCPjAvkk36P0J9wTZxq5V318E8i4yQ/u+fhAGrGfJkhzPuaSB+pG3NJm5p85/7uhxc6w8afjUMFMLwYsFHq7mJdeeNM9vnluRoOXmXjbHE444YR0NGapov/SjS+H8ZLZUMk54n92I/E/jQUvXV1++eXp/MUXXzzihUOFq5fa9I8AL730UjoCL8zhp+//Np4LeEmMvpi/I+MFsnPOOeeIf9Iogd+h0j744Q9/WNkcfEGUv8pCbnUQLi+k8RKqXvDMoQyuuOKK6uog//qv/zrzYtw0o/901st/ZmGwYjlL+I+/oYKeGmXeic4ndLK8DT0JaZl28r+HMmYpg5KzYsWK6uoQ/O3WG2+8UV0dDm/y5/CvByhG6pv4h4SS4kO4fPwA9LY/ypIUJf4Dti7eSYV8oGjz92T8fy1y4AG2TpnTH/7nf3t35plnNv5n7o4dO9KRB2PkxEdGKD+B4sk/J/ARksgZZ5yR7KPfaUQKZfwXFOSsf4ngXEo9/0yCkq9y4B9N9A8R1DWuzz777OSfa+AcP/r3E+BewsCPwsFwrn/JIG7saAMR0kAccic9ahMxLs55SMEfD2PKA8T4heLlXu4hjhg3dhjuA9IhO4VDGKQHO47cX/dvLkcwHDiNMcaYIqUlR6izr4NtQ3GJVsuW+XI6S8D5PkttOcJtsewd10sopSV9ueXU2UdYMmfrAGUjmWkJPt9qIOrsJxnyF5f0tceS7QLKb6xL2lahJX/8ST7auoFfziUzDHbaT6x6iAHkpXC4X/KTP7YyUL8Jh/QSrtC9ukfXylOMi/gJg7DwozzE+JVGIC78KA+c40dbSrDjOrZfwo7hIE/SALiRdqW1Dc9YGmOMmVOY/WCmTR9QAP6DFYaDV3LHMPsyHMTSf45GmKkbDo7Jjf+txe80ojwy68SHLoaKdu9L+swIs22BlRdme5FZ05aFaYa8acbv+uuvT3WJ2WzNirOKd9lll6Vz/R+uZvtwg6FiltyYUaZs+DoXMPtJOHwIQ+BOGIKy+8Mf/pDOWYZXWfI/zsBWBmarCYf/+iW94uc//3lqD7qHI2nQB0JiXHyWljAIixl95SHGL5hZ5P99+Z9n7sEQ7lAxHPzsZz9LfrDLIexI/C9h3Pi4RFesWBpjjKklfpwip8ktgmITlUpgUHz55ZfTEix7mfnE5scff5zc4p/4swwHDI5vvfVWGpy1hDctaFmTPZZvvvlmUrL19a0SWsotgSLUFbZqoVBIrtqzWkeb+6SBkvXuu++mLUsYlLG4DQPlkbqEPQooCldOrmTxFS/uQ26Uz2effZYUu75BgdSeUKF6r/Iahw8++CAdc0URpTsqtm2wXYIPvrCdgjaIrKUEt2HF0hhjTC3MHpYGZOzymcU6+CSvviQWYWaNLyihFKBoMYvHzKQGRfbIsb9y1apV6ZqBjRkpzepMOsweoQSzp3LZsmVpjyUKcj7o55x77rnpmO9pQz6ENQqKG1C6UEzzz6NyrZm7xQR1BGX+/fffHzzxxBOHzTbWQdlQv6iHPADw9S493PRN/pJRaSZxXPJZ/aaHlRK0TR7kqD/6wl7XNmfF0hhjTC2aRYlKjs7jzGIdzNahPMa3ykswo8dgeOedd1Y2g5llyQjKDy8TTfqbv8iIf+jgW/7k/7rrruusOJBHBvKoACKbPXv2zCztdgX5x3JiFg4FNcJ13ezptMJDCf+S8vDDD6eZ265KM+WGnHkA+Oijj5KCiWLVN5QvM4JRAdQSd/5y1SjoIUIvcgk+pxtfwiu9kBfhJR5mb1EmmenEPw+IXbBiaYwxphZmcO677760J4xBkMGPc/bvaeaNwZh9bvlbr8AyOMtwJQhPs3paHo7KF29FM5NGfBp0+X4/A53+tmhSYXaVQbnr8mEOgzjfx0dBQk533HHHzPKsQG4s8QqUAb3ZjbyYaVu/fv1hMkVxR3HS7BPy5Jo9edMC8kBZj0pZjh5KPvnkk3SknnGPzpvuRW7UaeSmWV6h+3T86quv0jE+6PCXWCA3iHsWAYWXeky5Yod58MEH06yqHsLyuEB+RR4/9QMl8Lbbbpt5AORIef/mN79J10DeJEPqit4Gp87xkAe0W9xo59S9rltfml8v6whv+k3qH5LyFlN8W8oYY8zo8IbrcIBNJu9T6WcZTuIfmYvhIFd8k5swuAf3pj/05t7hAJf8YjgvhbcYQS7DAT3lmzd7eZs3MlRCDnszmusucsKe+/DHPdMkT9WbaOrGeMkDGVJHVYeRq+7lGlkJ/FGPkbfulXyifDGq9zKkI08f16X7gHhUDqSD9KmM83sgXmNK8YPeBCdM7IlDcQrylNctte2hwpuO+FE6OOb1r46j+BneNDZo9mx0ffTRR9Pm69JenGGm0h+yljbAokk/8sgjaUoY8HvzzTenjaJM5bK3hj0OTQwFkI78b1cEe+IkjWzwzZ+GjTHGGGNMf8xqKZwpdJRKplhR2FDchhpuckOpQ2dlqpepepS+fAMs1yiN/JUE/vCPgskbUUy7su+DZQTsmZIFzqORUokCOdSmk2KK4VyKLHslSANTzsYYY4wxZm4YW7FkXZ41/PgfT5BvkGVtXv+hxAZY7f9gPZ/rrVu3JgVQe3U4co19BOWzhJRHQLmNJkIa2MOjPQfGGGOMMaZfxlYsn3zyyfQauhTCNvRNWDbUopSyQRRlkzflSmDfFrZmQKNy2cSGDRtG+pNPY4wxxhjTnbEVy8cff7x2FrGE3mLiD1h5S5A/xdXydh3MMtZBeMxAjgL7NdkDqrcLjTHGGGNMf4ylWKKYsSdSf1pbIr5az8wifzi6du3atFSuf5Uf9Q87QR9K50sNpGEU9EF//lrBGGOMMcb0y1iKJZ84aoOXcFj+RgHk5R7+ZPSZZ56pXMdHL+3wcg5L6aOgfZez+VySMcYYY4wpM/ZSeBvsZ+TPN1ECeXOcZW0pduPMVOYQVttSujHGGGOMmT/mTLFsQp+XeuGFF9JxXPhMkzHGGGOMmQzGUiz5fiTEzxWNAm978zkw9l3qs1I52ps5F+hbmsYYY4wxpj/GUixRDNnf+MEHH1Q2h9D/VMbvZpa46aab0h+Z85F4/tMyvqnNdzxvuOGGw75d+uc//zkdm97oRhmNJkf/YclXfYwxxhhjTL+MvRTOHkq+jBPhg/jf+9730jl/nM7b23V/SM4eST6zuH379rQkjqKKf174+b//9/+mT0SiwHI/9vrkI/6IJwfllBeFmAXFcK6Pqou//e1vSZnt+t+bxhhjjDGmO2N/K5wZwdNOOy0pcdOiqKG0Pvzww4OLL764sjHGGGOMMX0x9owlM4686c2b2aVl50mD75ozW2ml0hhjjDFmbhh7xlLwgg1L2q+++urM3wlNGiyJs9w+yWk0xhhjjJl2Zq1YAi/bfPLJJ7Xf/V5I2KP5yiuvpJeFrFQaY4wxxswdvSiWxhhjjDHGLMgfpBtjjDHGmMWHFUtjjDHGGNMLViyNMcYYY0wvWLE0xhhjjDG9YMXSGGOMMcb0wlQplnwn/Fe/+tXg+OOPr2wWF+SPvPH3TU109deVceSK/7rPdZruIMP806PGGGNMG88991z6xHXTGMIYw/+Nlz6FPVeMpViSQL7fHY2UDI65W18D5+23356+Qf7FF19UNv3x17/+NaV1tsoaBX322WfP5P3KK69MMsFeMpo0RpErX1kif8ccc8zgvPPOm8lnNIJyz91yGeTy4jyvL6VwcqNGU6qbdfWvS7iYUp0uGWjyW8obMty/f39y6/oFK8LgIQBTl7cc6jWfNCUddDKluEppn4avapmFg/pBH6f6wjl9aRM8lOaDHPeoH6Be19XRpQB9otoqx6b+q6kfQH7IUeH0NRExrVDnqJ+LBfprynfnzp2VTZlLL710sGXLlupqnuB/LMfhL3/5C/9/+c1QKalsDmfFihXJ/cCBA5VNP6xduzaFGyENb731VnU1HtxPuNu3b69sRoN8kufjjjsuhaF8I6d169alsO+7775kNyqEO9eU5FqCvGzdurW6OgjX3Pvyyy9XNodADrjleZC8TjnllMPu4xw73GLd4Rx70plD2UV71c2NGzdWNmVwj3mhfKIM9u3bl+LkCCrHHPmL5H5JE3nCrtRmSEtbeoE0Eg5xSoZt9Qr5UC/VRoinJEfsSZ8MeTCmCeqM6hVH6iN1LbbdCH6oW7H+UZcJR+2M/pMwSnV0GqBPifkZBfo/2jPyw6hPytt4l34A+ZEO3Ol/Yh+w1EBO6tfq6uYkQNpGqfdqT3nZ5+Rtbq5p1yQaaMoQmcC9b0rhMqjPtsGo4o0bjtJFAy7BIN1W+CWUrrmmS3khm1yBAlXuOtnhlldqrunoSp0vdrjRWUa4Jw9H5LIlzjZ50+lG8J/LgEFC+Sq5izyukl86Dezqwmirx6oLURGX7EtyFIQblVnSgXzjQxT3l8rWmDqoe3l/p/qYP3wC9Y42TT2L7Tg+iAu1n7r+dJIhL+Rf+RxlsqIkN/WVoks/QJxcR7ki06XaxunrkWNd3ZwUKLe6Ma6Eyj0ff3LwM0q4s2XqX95hKnjYmKqr8Vm+fHk6sjQ5KkxJMx09rLyD7373u5Xt4bDUPCosZQwV0upq4bnrrrsGv/jFL6qr8WGpR/KS3CPY4fanP/0p+W2DZaBbbrmluurOY489Vp3Vw2dK2+pE1/jbPim6YcOGJOM6Xn/99XS8+OKL0xFI23DQmXHLYYmR9vHDH/6wsjmYjjVr1gxefPHFymYwePDBBweff/75zNYNY9qg7uX9ndrKl19+mY6R//qv/xrcfPPNaVk2Qp3L28aqVavS8euvv07HaYK80G/s3bt3sHnz5tTOtKTdtrxf+izyhRdeeNg2pS79AHEOFYnD5Mo1fUHbVoXFBjJ/7bXXBq+++mqS0QMPPFC5TBaUC/rMYmBeFUsKWC+JsO+DPTX5vg+EG/fsNO21ISztHTj//POTf0G4cc+O9qDEF1WIiwaPP0CZEbhpvx7KDffU8eyzz6bjJZdcko4lUJZQPsjLb3/725k9L8RBWkgX8XCttHKOcgWkg2vI/QHhIivJFhlGucU9dphRKzDpQxk844wzKpvxkUITlZ2c73//++kYlZ8SyHIhoZ7s2rWrumoGGcIpp5ySjjkMpshY/nKIa8WKFdXVIVauXDl44403qqvD+fDDD9Px9NNPT0dx5plnps4WqCfUKQav559/PrWlxdLBmflFfQ5KTISHFZSarnvcvvrqq6QEtD3UTTqkn7altnbaaaeltjXKwxtKepxg6NIPMG6cddZZ6VzoIeCPf/xjOi4VduzYkR7agTG+SbmOegPjJWUXaXNnTNY4qzEetB+ScZt75Ac72gx9/gUXXJD6YMYA3DS+c9S4zrE05lFH0FHkJ+oGdcS8dL2nM9XM5Vhwe5uJMBU7LNg0PY/hHD+a0h8WeFoy5ghM87IEgD9BGDFcLZngVxAeS39aBmDqGz9MMxM+54oXO+LIwZ+mzDXdXIfSFNNQB+klPvyTRu19+e///u8Z+zitnedXMsn9scQk2RIm7uQBkCfXyk+Uh8jjydHSSgnJp8kQvugiL4VZui83pWWAOvsm8M99dcg9NzGNIoZFmSA/lqEou7rlPfxxTyyXCPGU4qqzh7o8lexJF/aqX5O8ZGQmE+puXhep19ipP26qr4K+jLq4GCHvpTZZB/1G7Cvr5BftCb8kvzr7xQzyk06hsTHqFAJ9gL5PspauoP66zZ3xXH03dV36jcZfDGmRvsMRu5gWrmPZatxVHIoz6khcM/5jR7ykA7s4juThEnedjtQHs56xHFZSWsgRZpiJysdB0I7RxDdt2pSm5zEsRTJ787Of/Sz5efLJJ9OMCXZo0cycoMGP+kbTr3/967T0fMIJJ6Rwrr/++mS/bdu29LSgtJ1zzjnpCZolwJx//vOfM8s5PHUOCz+dzxZmLX/5y1+mc2bleIok/vXr1w/+8Ic/JPsmSEvujzwhJ+SJXAkzplfLSRdddFE6arnl448/TscudPE7rOTFutAnlF0Me9ggKpf5I8Y/bPCVbRnqH/XwqquuSk/NzCTUbZfQstUo5dInpIv6SRqHHejELhmZyYSZl4ceeuiILSYsgbMkHJdlm2A26d133x1re8ukwoyUZp5YvaKv7AKzU0NlYupnbhcKxka2/WjLFX0cs73YxxU9QA+h7krWt912W+oHRZM7YaFz0M+rz5fewowpYwUwU6ktDBwZp5v0G3QQ9CGNGTfeeGM6fvbZZ+korrjiipRH2tjdd9+d8khbrKNJR+qDeVsK/+CDD9JRBSxoNENNO52/9957tYrqKDAwlpQc9lhEmjo6lhFuvfXWVBHoEO68887K5UiOPfbY6mw0jj766OpsdqCIxAYANAAaD1ApJUPyQp4WEsmL5a428vqSU9qTNJ8gW/ZA1aG6h0L8wgsvtOaniaZ6VufG30LVkdcZQRqpP2qXxnThjjvuGDzxxBOH1XEt+XZVjBig//M///OIvnpa0RIoyg189NFHhyknTaBgY1AUIl36gbq2DU19wmIDRQnFDeVJBv2AiZh8Xzr9XRyT6duZ9JFS1+SuLUfq76NpekDS9rm6rRHaq0s9oB7x10FdQNEkn3V01ZHGZd5f3smfEvJK3nW/Whu7d++uzsaDxrx9+/bUQFEw2RtTty/jsssuS8dXXnklHRcCKkrd3jxg/wWdG/LWfp+FQvJ66aWX0rGEZNm0b1WMOrOBcp3Xw9nQJf5nnnkmdUx0DuPCHhxm/XOww63Eueeem455x0U702BXgv1aPCkb0wXa1OrVq2cGYcHLaMyMxIGd+prvIxM33HBDmgnqOrs5idC38FDPAzz5Ry4oB/QTXfNFGIw7pQmNLv0AbTsfS9UHqE9Y7Gi8zpWnAwcOpHG9NKPXNCZBm3udgliHFNUTTzwxHXOoB+zLpC5Qj55++unKpRnNdDYxWx2piXlTLJctW5aOTAtH+HNobUTmSZfGweAblaSuG74FAqUgWEaQAsFxlEEdv8TLkoyWLUqNGfBHHngyqlM+sc870b6Qcs7sb5QbyiTXyIHO/Z133klPQOPMmqn8JM/ZIHnR+ZbkRRzIklm++OZjE9zT9IKVwB8d7lwMXE31i/jYwkC+NJNch2Sdo60MsfPSudxyGOhpD7ETQQZ79uyZUfBL/P3vf5/Z8G5ME9Rn6mzsp9WHMwOSD+y0a21piQ9ltF+USimn1NNR+/5JgDHuzTffnHkTedQ8kG9mf3kYVT9FP6n+rUs/QNumjROWoA+Iy6qLHcY9tpjlIFPKJJ+MQdmkf44vx+AuuTe5SzFkRlEv7ADlEv3nMIFCmdSNyTxoUYaqR11XOdlS2DRx0IeO1MiwcY9F0yZYGCoOyV2bQwG7YeHMbH7lGK/37Tv4/4XcF02MIw+Xe7lmMyobUAlDm2Jzo42pCqOJYceX4iUezLAgGl9mQB6kHUM8Sh9Hrtl0K7S5VvkWSjfugo3W2BGO7HN/Sh92MlyTB1AYpFHp4Rp7woJSeUWQK+55mqFp4y/h4Ub4EeyxI51KA3COHW4xLZzLPoc0Ya9wVDejHIE8UA51dRa3JhnUuSvcGF+dX5UFfrkvorqc20e4X7LBH+cx3tgeBHbUS5U/+Y9yxD3Wbe6l7tTJwRih+pwb2mod1C31TUA9U3vJTV1bXazQRmmbJVnE/rWtHwCNYaA+IPYLixmNSXV9GHLAHbmpv9U90VCPkV0Xd+Sfu2MUPueUgfyXyoQyU7lSxlwTh8pZcXAv90nvwJ/iUf+uvHPkHuyE8p+b0hg+DmMplmQiT1AUVu6GgIAMkmkEgT0Z1X0iNiz84V8CyuMF3PCP3ygUzikQ/HFUGmIY2NeBf4SvMPJGW4K0cJ/uwZC2OGjjHuOvk5vSq4qDX87r/FGpYucc5Yab0qQKSLoUZkmuJfCn+ES8L78/5lUmlrfkpfLG5PKCUji5IS+Q56Vk8saTyxRDOKLkXjLItS0siGmM8iTf5L8N6iJ1ApOXh+KPnRXE9hDrBuCXsHDDTx6mMSXqBlJMU39J/Y9tIrb/3OT1eDFD/6F2WDKxzUJTPwD415hAu14qsszHizjmQO4e/dAHq58syazNXWWCO/U6ustO/T/352MR17jhh/LjfoVH2FGRVH3ATuWMW96/Yy8T212djtQHR/EzDNiYVpjWZ2mB/UKmf9iT9fDDD3de/jfGGDMdsKd4qNgtmhfTmpj3l3fM9MKbjOzb6G0fhpmBvS7Dp1krlcYYY6YaK5ZmJHhLkRearFz2By918Tctjz76aGVjjDFmsRBfkFkKWLE0I8EbdSiWJ5988mFvJZrxQIb8NQTLI3PxproxxpiFhT8iB95E16eZFzPeY2mMMcYYY3rBM5bGGGOMMaYXrFgaY4wxxphesGJpjDHGGGN6wYqlMcYYY4zpBSuWxhhjjDGmF6ZKsdQH348//vjKZnFB/shb/Ih9ia7+ujKOXPHvvxuaPciQ/7E0xhhjRuG5555Lf1/UNIYwxvC/0/P5N0djKZYkkM8TRSMlg2Pu1tfAefvttw/uueeewRdffFHZ9Mdf//rXlNbZKmsU9Nlnnz2T9yuvvDLJBHvJaNIYRa78wSv5O+aYY9KXeJTPaATlnrvlMsjlxXleX0rh5EaNplQ36+pfl3AxpTpdMtDkt5Q3ZLh///7k1vXPcwmDhwBMXd5yqNd8MpJ00MmU4sIPbqM8YJilDfWIPk51nHP60hJ52+ThtARtSGHO52A4KdAnqq1ybOq/mvoByob2rHD6moiYVqhL1KvFAu2E8t25c2dlU+bSSy8dbNmypbqaJ/gfy3Hgw+fczofRS/CxddzzD+fPFn3APUIa8g/Njwr3E27+UfiukE/yzEfgCaP0gfhxP/JOuHNNSa4lyAsf4o9wzb35B/kBOeCW50Hyyj/kzzl2uMW6wzn28SP6grKL9qqbfIy/CdxjXiifKIN9+/alODmCyjFH/iK5X9JEnrArtRnS0pZeII2EQ5ySYVu9Qj7US7UR4snlKBmSvlIejSlBXVK94kh9pK7FtgvUV9UtGdpEDuFxP3W65D4N0KeQD/Ubo0D/R96RH0Z9Ut7GuW7rB2jPpAN3ZBn7gKVGrH953ZwkSFtpjKuD8izVjxz8jBLubJnVCNKUIQ1SfVMKl0F9tg1GFW/ccJSuus4QRaOt8EsoXXNNl/JCNrkCBarcdbLDLa/UXNPRlTpf7HCjs4xwTx6OyGVLnG3yptON4D+XAYOE8lVyF3lcJb90GtjVhdFWj1UXoiIu2ZfkKAg3KrOkA/mWHqK61ANjgLqX93eqj/nDJ22t7aEdP7T5aVUoBe2L/NPuaE+jTFbkcgP1laJLP0CcXEclij6p1H8vBahb6ttKMp4UKLe6Ma6Eyj0ff3LwM0q4s2XqX95hKnjYmKqr8Vm+fHk6sjQ5KkxJMx09rLyD7373u5Xt4bDUPCosZQwV0upq4bnrrrsGv/jFL6qr8WGpR/KS3CPY4cbnr/DbBstAt9xyS3XVnccee6w6q+e6665rrRNd42/7ZOOGDRuSjOt4/fXX0/Hiiy9ORyBtw0Fnxi2HZUnaxw9/+MPK5mA61qxZM3jxxRcrG2NGh7qX93dqK3ymVLCHm6W4hx56KLWV0jaM3/72t8kP38yv60OnBdoX/cbevXsHmzdvTu1MS9qlvEe4L+fCCy88bJtSl36AOIeKxGF9Dtf0BXVbFRYryPy1115Ln81FRg888EDlMllQLugzi4F5VSwpYL0kwr4P9pXl+z4Qbtyzg6DrGiNhae/A+eefn/wLwtXevbgHJb6oQlw0ePwByozATXuCUG7q9gPBs88+m46XXHJJOpZAWUL5IC90otrzQhykhXQRD9dKK+coV0A6uIbcHxAuspJskWGUG3ERJ26YUSsw6UMZPOOMMyqb8ZFCE5WdnO9///vp2Kb8IMuFhHqya9eu6qoZZAinnHJKOuasWrUqyVj+cohrxYoV1dUhVq5cOXjjjTeqq8P58MMP0/H0009PR3HmmWemztaYPlGfgxIjduzYkY70ZbfeeuvgtNNOS3U5ctttt6V7aM/0T7HPnmZQ+Oiv1dbIO30vkxFdQUmPEwxd+gFkfdZZZ6VzIYX9j3/8YzouFah/PLQDY3yTch31BsbLfGKjzZ06q3FWYzxoPyTjNvfIj/Qb+vwLLrggPUAwBuCm+s9R4zrH0phHHUFHkZ8ubSfmpff2Vs1cjgW3t5nIsOOY2fOB4Rw/mtJnGp8lY47ANC9LAPgThBHD1ZIjfgXhsfSnZQCmvvHDNLP2vile7OIyg8Cfpsw13VyH0hTTUAfpJT78k0btffnv//7vGfs4rZ3nVzLJ/bGEJNkSJu7kAZAn18pPlIfI48nR0koJyafJEL7oIi+FWbovN6VlgDr7JvDPfXXIPTcxjSKGRZkgP5ahKLu6pT78cU8slwjxlOKqs4e6PNXZE07J3pguUHdLdZG6TX+r+kVbUP+stk5/SF+FvcaGSV62HJdR2xiyin0l95dkHO0JnzaeU2e/mEF+0ik0NkadQlA/6Z8la+kK6q/b3Km/6rtjHdb4iyEthAMcsYtp4TqWrcZdxaE4o47ENeO/2g7pwC6OI3m4xF2nI/XBrGcsh5WUFnKEGWai8nEQtGM08U2bNqXpeQxLkUNBD372s58lP08++eTg+eefT3Zo0cxCosGP+kbTr3/967T0fMIJJ6Rwrr/++mS/bdu29LSgtJ1zzjlpZu/zzz9P15F//vOfM8s5PHUOCz+dzxZmLX/5y1+mc2bleIok/vXr1w/+8Ic/JPsmSEvujzwhJ+SJXAkzpvfrr79Ox4suuigdtdzy8ccfp2MXuvgdVvJiXegTyi6GPWwQlcv8EeMfNvjKtgz1j3p41VVXpadmZhLqlvq0bDVKuRgzKTDzwnJ3aYsJdZulW5YjhwNamjXSsu3u3bvT8e67704rO3FsmNRly1FhRkozT+SRvrILzE4NlYmZLQZmNBgb2fajLVf0vcz2Yh9X9AA9hHonWTOLPlQk0zk0uRMWOgf9vPp86S3MmDJWADOV2sLAkXG6Sb9BB6EdaMy48cYb0/Gzzz5LR3HFFVfMtB3aEXmkLdbRpCP1wbwthX/wwQfpqAIWNBo6GXjvvfdqFdVRYPAuKTl0ahEN5CVYRmDZhopAh3DnnXdWLkdy7LHHVmejcfTRR1dnswNFJDYAoAHQeIBKKRmSF/K0kEheX331VTo2kdeXnNKepPkE2bIHqg7VPRTiF154oTU/TTTVszo3/haqjrzOGDMb7rjjjrRHsq2OM/AxWLY9QMWxYVrREijKDXz00UeHKSdNsFyLQV6RLv1AU9tu6hMWGyhKKG4oTzLoB0zE5PvSqWtxTKZvZ9JHSl2Tu7Ycqb+Ppmn/vbbP1W2N0F5d6gH1iL8O6gKKJvmso6uONC7z/vJO/pSQV/Ku+9Xa0FPwuNCYt2/fnhpo3b4gcdlll6XjK6+8ko4LARWlbm8esP+Czg15L/TeOsnrpZdeSscSkmXTvlUx6os7KNd5PZwNXeJ/5plnUsdE5zAu7MFh1j8HO9xKnHvuuemYd1y0Mw12xswW2tTq1atnBuE2qHvLli1L5zrm/Rd91TQ+/NC38FDPAzwv4yEXlAP6iabJjAhhMO6UJjS69APINx9L1QeoT1jsaLzOlacDBw6kelWa0Wsak6DNvU5BrEOK6oknnpiOOdQD9mVSF6hHTz/9dOXSjGY6m5itjtTEvCmW6jy0kVvw59DaiMyTLo2DwTd2MqP+qSkCpSBYRpACwXGUQR2/xPvuu+/OLFuUGjPgjzzwZFSnfGJP5zsXSDnnCT/KDWWSa+TAtPc777yTnoDGmTVT+Umes0HyovMtyYs4kCWzfPHNxya4p+kFK4E/OtyuHfwoNNUv4mMLA/nSTHIdknWOtjLEzkvncsthoKc9xE4EGezZs2dGwTdmNlCfqbOxn8778BzcVGd1LI0No/b9kwD5ePPNN2feRB41D7RPZn95GFU/RT+p/q1LP0Dbpo0TlqAPiMuqix3GPbaY5SBTyiSfjEHZpH+OL8fgLrk3uUsxZEZRL+wA5RL95zCBQpnUjck33HBDKkPVo66rnGwpbJo46ENHamSowY9F0yZYGCoOyV2bQwG7YeHMbH7lGK/37Tv4/4XcF02MIw+Xe7lmMyobUAlDm2Jzo42pCqOJoVKT4iUezLAgGjeSIw/SjiEepY8j12y6Fdpcq3wLpRt3oZcsCEf2uT+lDzsZrskDKAzSqPRwjT1hQam8IsgV9zzN0LTxl/BwI/wI9tiRTqUBOMcOt5gWzmWfQ5qwVziqm1GOQB4oh7o6i1uTDOrcFW6Mr86vygK/3BdRXc7tI9wv2eCP8xhvbA8CO+qlyp/8l+QI2JfSbUwJ1efc0FYFfqh7QJ2l/uV9BX7iWECfwnVTW1iMICe1wdxEmbX1A6AxDNQHxH5hMaMxqa4fQw64IzfVMd0TDfU41skmd+Sfu2MUPuexjpfKhDJTuVLGXBOHyllxcC/3UV8IA3+KR/278s6Re7ATyn9uSmP4OIylWJKJPEFRWLkbAgIySKYRBPZkVPeJ2LDwh38JKI8XcMM/fqNQOKdA8MdRaYhhYF8H/hG+wsgbbQnSwn26B0PaqJAC9xh/ndyUXlUc/HJe549KJWUGE+WGm9KkCki6FGZJriXwp/hEvC+/P+ZVJpa35KXyxuTyglI4uSEvkOelZPLGk8sUQzii5F4yyLUtLIhpjPIk3+S/DeoidQKTl4fij50VxPYQ64YopTsP25hI3UCKif1lrO/0UfQ5Jahv1Gn8cU+dv8UK/YfyXzJ5m23qBwD/GhNo+3mfsFhBFlFu9G2R3D36oQ9WP1mSWZu7ygR3+vLoLju1B+7PxyKuccMP5cf9Co+wpQ/IHbBTOeOW9+/Yy3CfqNOR+uAofoYBG9MK0/osLbBfyPQPe7Iefvjhzsv/xhhjpgNeHBoqdr29IDPJzPvLO2Z64U1G9m30tg/DzMBel+HTrJVKY4wxU40VSzMSvKXIC01WLvuDl7r4m5ZHH320sjHGGLNYiC/ILAWsWJqR4I06FMuTTz75sLcSzXggQ/4aguWRuXhT3RhjzMLCH5EDb6Lr08yLGe+xNMYYY4wxveAZS2OMMcYY0wtWLI0xxhhjTC8Ul8J5Ld4YY4wxxphR8B5LY4wxxhjTC14KN8YYY4wxPTAY/P8SVN1ilffrHgAAAABJRU5ErkJggg==\"\u003e\u003c/p\u003e\n\u003cp\u003eThe model fit values have improved significantly as can be noted in Table 6. CMIN/DF have fallen below the ideal threshold of 3.0, suggesting a good model fit. Both CFI at 0.921 and TLI at 0.909 are above the threshold of 0.90, indicating an acceptable and good fit. The parsimony values, PNFI at 0.765 and PCFI at 0.798, are similar as the previous tests, above 0.5 and are confined between 0.7 to 0.8, indicating a reasonable parsimony. Both Hoelter’s critical values, 0.05 at 242, and 0.01 at 256, have exceeded the minimum threshold of 200, reflecting adequacy of sample size to ensure stability and reliability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFourth CFA – Correlation of Error Terms\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor this test, correlations of some error terms were made. Theoretical justifications include shared method variance (correlated error terms are both 4-point Likert Scale), item content overlap (a particular indicator was a duplicate of the other indicator in the same variable), and some questions have similar structures and wordings. In the case of Xiong et al. (2025), correlation of error terms was conducted in reference to the variables’ modification indices. In the context of this study, only those indicators that were similarly structured, shared method variance, and content overlap were correlated. The correlated CFA model is demonstrated in Fig.\u0026nbsp;7.\u003c/p\u003e\n\u003cp\u003eSpecified in Fig. 7, error terms representing AW2, AW3, and AW4 were correlated as these are similarly structured indicators. AW2 is structured as “I understand basic knowledge of…,” AW3 is structured as, “I understand how cryptocurrency…,” and AW4 is structured as “I understand the technology…” The correlation of error terms representing AW5 and AW6, PB2 and PB3, PR1 and PR2, AC1 and AC2, AC3 and AC4, AC5 and AC6 were also built on the same justification. PB1 and PR2 are also based on similar structure, only that PB1 is structured positive, and PR2 negative. The correlation between error terms of AC1 and AC7 is based on content overlap. That is, AC7 is a duplicate of AC1. The values reflected are the standardized estimates and the model fit values are outlined in Table 7.\u003c/p\u003e\n\u003cdiv align=\"left\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n\u003cp\u003ePer results in Table 7, the CMIN/DF has improved at 3.356 compared to the first test at 4.136 and second test at 4.754. While this may indicate a moderate fit, this is still above the ideal level of \u0026lt; 3.0. CFI at 0.889 and TLI at .0.874 resemble the first two tests, below the \u0026gt; 0.9 threshold. PNFI at 0.749 and PCFI at 0.783 are also confined between 0.7 and 0.8, indicating reasonable parsimony. For the Hoelter’s critical values, 0.01 has improved at 206 which is above \u0026gt; 200 threshold, indicating adequacy of sample to ensure reliability and stability. However, 0.05 fell shortly at 197, suggesting otherwise.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFifth CFA – Deletion of Indicators and Correlation of Error Terms\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSince the third test provided a good overall but not quite excellent fit, it was used as the basis for the fifth test, with the fourth test being the reference for the correlation. Thus, the model was tested and visualized in Fig. 8.\u003c/p\u003e\n\u003cp\u003eTo highlight Fig. 8, the indicators retained are based on the third test, while the correlation between the error terms are based on the fourth test. It is to be noted that since there were deleted indicators in the third test, the correlation of error terms representing those were also removed. Accordingly, only correlations between error terms of AW5 and AW6, PB2 and PB3, AC1 and AC2, AC1 and AC7 and AT4 and AT5 were established. The values represent their standardized estimates with the summary of model fit criteria posed in Table 8.\u003c/p\u003e\n\u003cdiv align=\"left\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n\u003cdiv align=\"left\"\u003eBased on Table 8, all the indices have significantly improved compared to the prior tests. CMIN/DF of 2.445 indicates an acceptable fit. The CFI at 0.938 and the RMSEA of 0.049 suggest good model fit. The TLI of 0.927 is also above the acceptable threshold. PNFI and PCFI at 0.765 and 0.797 respectively, are above the minimum parsimony threshold of 0.5, And finally for the critical values, both are above the acceptable limit, indicating adequacy of the sample to ensure reliability and stability. Overall, the results from the fifth test provide a good model fit. Accordingly, the structural equation model (SEM) was based on CFA from this test.\u003c/div\u003e\n\u003cp\u003e\u003cem\u003eThe Structural Equation Model\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAfter ensuring that the CFA from the fifth test is a good fit for potential modelling, the structural equation model (SEM) was established based on the conceptual framework. It is to be noted that presenting moderation in AMOS needs adding a new variable which is the interaction between the independent and the moderating variables. Thus, in this study, the interactions between financial literacy and acceptance as well as awareness and acceptance were established. The interaction is the product of the centered means of the independent and moderating variables. Although moderation can be made in a separate model, this study used a unified model to fully align with the conceptual structure. The structural equation model based on the framework is unveiled in Fig. 9.\u003c/p\u003e\n\u003cp\u003eThe structural equation model portrayed in Fig. 9 explains the directional relationships of the variables aligned with the conceptual framework. Among the highlights in the model is the existence of two new variables, Inter_FLxAC and Inter_AWxAC, which are the interactions between the independent (Accept) and the moderating variables (FinLit and Aware). Furthermore, the independent and moderating variables together with the interaction variables being exogenous, were correlated as this is necessary to run SEM in AMOS. It can also be observed that PerBen, PerRis and Attit have been assigned with error terms (e26, e27 and e28 respectively), indicating their role as endogenous variables. The values reflected are the standardized estimates and the model fit values are summarized in Table 9.\u0026nbsp;\u003c/p\u003e\n\u003cdiv\u003eTable 9 \u003cem\u003eModel Fit Indices of the Structural Equation Model\u003c/em\u003e\u003c/div\u003e\n\u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndices\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eThreshold\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInterpretation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChi-Square/Degrees of Freedom (CMIN/DF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e≤ 3.0 to 5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcceptable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComparative Fit Index (CFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt; 0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood Fit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoot Mean Square Error of Approximation (RMSEA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e≤ 0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcceptable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTucker-Lewis Index (TLI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt; .9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcceptable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParsimony-Adjusted Normed Fit Index) (PNFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt; 0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParsimonious\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParsimony-Adjusted Comparative Fit Index (PCFI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParsimonious\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHoelter's Critical (HOELTER) 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt; 200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcceptable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHoelter's Critical (HOELTER) 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt; 200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcceptable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLooking at the values of the indices in Table\u0026nbsp;9, it can be noted that overall, the SEM has a good and acceptable fit. CMIN/DF of 2.669, which is below 3.0, indicates an acceptable fit. The CFI and TLI, being above 0.90, suggest an acceptable fit. PNFI and PCFI are above 0.50, reflecting parsimony. And finally, the critical values, being above 200, proves adequacy of the sample to ensure reliability and stability.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTest of Hypotheses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn testing the hypothesis, a path analysis, based on the structural equation model, was performed. Each of the hypothesized paths were examined by analyzing their effect size, and p-values. A summary is provided in Table\u0026nbsp;10.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003eFinancial Literacy\u0026rsquo;s Influence Towards Attitude and Its Moderating Role\u003c/em\u003e\u003c/p\u003e\u003cp\u003eFinancial literacy has a small, negative and significant effect on attitude (β = -0.167, SE\u0026thinsp;=\u0026thinsp;0.081, p\u0026thinsp;=\u0026thinsp;0.039), supporting H1 but on the negative side. This implies that high financial literacy leads to adverse attitude towards cryptocurrencies. The same is found in the study of Magbitang et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As to moderation, financial literacy does not necessarily moderate the relationship between acceptance and attitude (β = -0.12, SE\u0026thinsp;=\u0026thinsp;0.081, p\u0026thinsp;=\u0026thinsp;0.051), not supporting H2. Although the relationship is not significant, the small and negative effect size supports the negative influence of financial literacy on attitude towards cryptocurrencies.\u003c/p\u003e\u003cp\u003e\u003cem\u003eAwareness\u0026rsquo;s Influence Towards Attitude and Its Moderating Role\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAlthough small in effect size, awareness has a highly significant positive influence on attitude toward cryptocurrency (β\u0026thinsp;=\u0026thinsp;0.158, SE\u0026thinsp;=\u0026thinsp;0.057, p\u0026thinsp;=\u0026thinsp;0.006), indicating that higher awareness is associated with a more favorable attitude. This supports H3. This corroborates the findings of Magbitang et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), where they also found that awareness positively and significantly influences attitude. In terms of moderation, akin to financial literacy, awareness does not necessarily moderate the relationship between acceptance and attitude (β = -0.019, SE\u0026thinsp;=\u0026thinsp;0.03, p\u0026thinsp;=\u0026thinsp;0.531), not supporting H4.\u003c/p\u003e\u003cp\u003e\u003cem\u003eAcceptance\u0026rsquo;s Influence Towards Attitude\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAcceptance\u0026rsquo;s effect size on Attitude is large. Their relationship is positive and highly significant (β\u0026thinsp;=\u0026thinsp;0.843, SE\u0026thinsp;=\u0026thinsp;0.078, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) denoting that the higher the acceptance level, the more favorable attitude towards cryptocurrency is. This supports H5. This finding is the same as the finding of Magbitang et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These direct effects of perceived benefits and perceived risks on cryptocurrency acceptance are both positive and highly significant (β\u0026thinsp;=\u0026thinsp;0.97, SE\u0026thinsp;=\u0026thinsp;0.064, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and β\u0026thinsp;=\u0026thinsp;0.515, SE\u0026thinsp;=\u0026thinsp;0.068, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively), proving their empirical inclusion in the model.\u003c/p\u003e\u003cp\u003e\u003cem\u003eContribution to Literature\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThis is the first study on cryptocurrency acceptance conducted within a state university in Eastern Visayas, encompassing both urban (Tacloban and Ormoc) and rural (nearby towns such as Tanauan, Carigara and Burauen) areas. This also adds to the limited literature of both students and educators being respondents of the studies involving cryptocurrencies. Series of comprehensive test for modification of the confirmatory factor analysis (CFA) in achieving an acceptable and good model fit based on empirical and theoretical justifications, including the deletion of indicators of a construct, and correlation of error terms representing the variables that have been employed in this study may be used as a reference for future studies that may utilize similar methods and/or variables. Finally, this study can be used for future exploratory studies to support the findings herein.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined the impact of financial literacy and cryptocurrency awareness on the acceptance towards attitude on cryptocurrencies among faculty and students at Eastern Visayas State University. Results show that the respondents despite having above-average financial literacy, generally exhibited low to moderate awareness, negative perceptions, low acceptance, and unfavorable attitudes toward cryptocurrencies. Financial literacy was found to have significant and negative effect on attitude. Both awareness and acceptance positively and significantly affect attitude towards cryptocurrency. As to moderation, both financial literacy and awareness showed a negative and not significant effect on the relationship between acceptance and attitude. These results, evidenced by an acceptable model fit for SEM, highlighted the roles of financial literacy, awareness, and acceptance in influencing the attitudes toward cryptocurrency. Finally, the combination of deletion of indicators based on high modification indices and correlation of error terms, proved to be the best method of modifying a confirmatory factor analysis (CFA) in achieving an acceptable and good model fit for potential structural equation model (SEM). Educational initiatives at Eastern Visayas State University should make a focus on increasing awareness and addressing perceived risks to improve acceptance and attitudes, while considering that financially literate individuals may require tailored strategies to align their acceptance with more favorable attitudes. For business-related programs, cryptocurrency awareness and utilization can be incorporated in financial markets and related courses. While the technology behind cryptocurrencies, blockchain, can be introduced to programs related to information technology and auditing. Future studies could examine these relationships in diverse populations to improve the general applicability of the findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is study is reviewed and approved by the Research Ethics Committe (REC) of the University of San Carlos. Approved permission to gather data is obtained from the Eastern Visayas State University President\u0026rsquo;s Office, with communications to conduct the study sent and received by the Commission on Higher Education (CHED) and Department of Education (DepEd).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors do not analyse or generate any datasets, because this work proceeds within a theoretical and mathematical approach.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbramova, S., \u0026amp; B\u0026ouml;hme, R. 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BitConnect Founder Indicted in Global $2.4 Billion Cryptocurrency Scheme. \u003cem\u003eDepartment of Justice\u003c/em\u003e. https://www.justice.gov/opa/pr/bitconnect-founder-indicted-global-24-billion-cryptocurrency-scheme\u003c/li\u003e\n\u003cli\u003eVargas-S\u0026aacute;nchez, A., Plaza-Mej\u0026iacute;a, M. \u0026Aacute;., \u0026amp; Porras-Bueno, N. (2016). Attitude. \u003cem\u003eEncyclopedia of Tourism\u003c/em\u003e, 58\u0026ndash;62. https://doi.org/10.1007/978-3-319-01384-8_11\u003c/li\u003e\n\u003cli\u003eVoskobojnikov, A., Abramova, S., Beznosov, K., \u0026amp; B\u0026ouml;hme, R. (2021). \u003cem\u003eNON-ADOPTION OF CRYPTO-ASSETS: EXPLORING THE ROLE OF TRUST, SELF-EFFICACY, AND RISK\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eXiong, Z., Xia, H., Ni, J., \u0026amp; Hu, H. (2025). Basic assumptions, core connotations, and path methods of model modification\u0026mdash;using confirmatory factor analysis as an example. \u003cem\u003eFrontiers in Education\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e. https://doi.org/10.3389/feduc.2025.1506415\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cryptocurrency awareness, acceptance, attitude, financial literacy","lastPublishedDoi":"10.21203/rs.3.rs-7160135/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7160135/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to analyze the role of financial literacy and awareness on cryptocurrency acceptance and attitude among students and faculty members of Eastern Visayas State University. The findings show that the faculty members and students have moderate cryptocurrency awareness, relatively high financial literacy, neutral cryptocurrency acceptance, and a negative attitude. The relationships among these constructs were analyzed using SEM in AMOS. The results show that financial literacy negatively and significantly influences cryptocurrency attitude. Additionally, financial literacy does not necessarily moderate the relationship between acceptance of and attitude towards cryptocurrency. The findings further showed that awareness positively and significantly influences attitude, and that it does not also moderate the relationship between acceptance and attitude towards cryptocurrency. Finally, it was found in this study that acceptance of cryptocurrency positively and significantly influences the attitude towards it.\u003c/p\u003e","manuscriptTitle":"The Moderating Role of Financial Literacy and Awareness on the Acceptance Towards Attitude on Cryptocurrency Adoption Among Faculty and Students of Eastern Visayas State University","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 12:25:49","doi":"10.21203/rs.3.rs-7160135/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b55ca834-3c39-4fd8-9c56-18d12d5d7164","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-30T09:53:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 12:25:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7160135","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7160135","identity":"rs-7160135","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}