Can the e-Naira Foster Financial Inclusion in Nigeria? Evidence from Structural Equation Model

Can the e-Naira Foster Financial Inclusion in Nigeria? Evidence from Structural Equation Model | 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 Can the e-Naira Foster Financial Inclusion in Nigeria? Evidence from Structural Equation Model Usenobong Akpan, PhD, Aminu Umaru, PhD This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3861545/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 In this paper, we evaluate the extent to which the e-Naira can bridge the financial inclusion gap in Nigeria. From a survey design of a well-structured questionnaire, we subject the field-dataset to the Structural Equation Model (SEM), which combines factor analysis with multiple regression. The choice of SEM also follows from its capacity to handle complex and difficult data that may be non-normal and incomplete. Our results indicate that access, quality and usage of eNaira could have a positive and significant influence on financial inclusion in Nigeria. In particular, we found that a unit increase in e-Naira’s access, usage and quality could, respectively, lead to a 0.18, 0.32 and 0.38 per cents increase in financial inclusion in Nigeria. The results were robust to various standard diagnostic tests. Consequently, the paper provides strong support for the need to further promote access, usage and quality of the e-Naira in Nigeria as part of the strategies for enhancing financial inclusion in the country. Development Economics Financial Inclusion CBDC eNaira SEM Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Fostering financial inclusion has become a priority for policymakers and financial regulators, with an increasing number of countries introducing a number of measures to improve access to and usage of tailored financial products and services. The increasing interest in delivering financial inclusion derives from its potential for engendering economic development, especially in terms of employment creation, poverty reduction, wealth creation and general improvement in the living standards of the people. Usually, there are, at least, three aspects to financial inclusion: access to financial services and products; usage of financial services and products; and quality of financial services and products, defined by consumer ability to benefit from new financial services and products (see World Bank, 2012 ). In Nigeria, the apex financial regulatory authority, the Central Bank of Nigeria (CBN), has placed a huge premium on driving financial inclusion through its various policy framework. On 23rd October 2012, the Bank in collaboration with other stakeholders launched the National Financial Inclusion Strategy intending to improve access to payment services across the country. The major tools used in driving the strategy include agent banking, mobile money operation, financial literacy, consumer protection, know-your-customer (KYC) requirements as well as the implementation of Medium Small and Micro Enterprises (MSME) Development Fund and other credit enhancement programmes. What is more? A recent survey report by a development finance organization, the Enhancing Financial Innovation and Access (EFInA) revealed that these measures have yielded positive results, with the percentage of financially excluded adults in Nigeria decreasing from 53 per cent in 2008 to 42 per cent in 2016, and further to 36 per cent in 2020. However, despite this appreciative milestone, the report indicated that the actual number of excluded adults have increased from 36.6 million in 2018 to 38.1 million in 2020, as population growth outpaces the rate of financial inclusion growth in the country. More ever, it was also shown that at 36 per cent in 2020, Nigeria still has a higher rate of financial exclusion than many other countries in sub-Saharan Africa such as South Africa (7 per cent), Rwanda (7 per cent), Kenya (11 per cent), Namibia (22 per cent) and Tanzania (28 per cent). Perhaps, in recognition of this challenge, the Bank took another bold and innovative step by launching the e-Naira on 25th October 2021, with the aim to achieve 95 per cent of financial inclusion in Nigeria by 2024. By the design and philosophy, it is envisioned that the e-Naira would usher in a monetary system that is more inclusive of people who have been historically excluded from financial products and services. In other words, it is expected that the e-Naira would improve access to digital financial services, enhance the efficiency of payments and lower the costs of financial products and services. The focus of this study, therefore, is to evaluate the extent to which the e-Naira can enhance financial inclusion in Nigeria. Can the e-Naira bridge the financial inclusion gap in Nigeria, especially with regard to access, usage, quality and adoption of the innovation? If not so, what are the limitations and what are the policy options for improvement? In this paper, we address these concerns using descriptive statistics and the Structural Equation Model (SEM), which is capable of combining factor analysis and multiple regression analysis. It can be used to test the proposed causal relationships between variables of a model which the regression method cannot do. It also allows a set of relationships between one or more independent variables (IVs), either continuous or discrete, and one or more dependent variables (DVs). In addition, it uses confirmatory factor analysis to reduce measurement error by having multiple indicators per latent variable, testing the model overall rather than coefficients individually, test the model with multiple dependents. SEM has the capacity to handle complex and difficult data that may be non-normal and incomplete (Tabachnick and Fidell, 2014 ). Our results show that access, quality and usage of eNaira could influence financial inclusion positively and significantly in Nigeria. The remainder of this paper is organized as follows. Section 2 presents an overview of the literature and some stylized facts, while section 3 describes the method of our analysis. The results of the study and the underlying discussion are contained in section 4 while the conclusion and policy options are offered in section 5. 2. The Literature and Some Stylized Facts Research on central bank digital currency (CBDC) is rapidly building up in the literature, signifying the growing global interest in trying to understand the macroeconomic and financial market implications of the new digital currency when or if adopted. Among others, the potential of CBDCs in addressing the barriers to financial inclusion has been discussed in several papers (Auer, et al, 2022 ; Maniff, 2020 ; Cooper, et al, 2019 ; Gjefle, et al, 2021 ; Alliance for Financial Inclusion, 2022 ; Ozili, 2021 and 2022 ). However, due to the novelty of CBDC projects, resulting in limited adoption rates across the world, a number of the studies on CBDC are hypothetical or explorative in nature (Foster, et al, 2021 ; Ozili, 2022 ). In most instances, the potential of CBDC in fostering financial inclusion, especially in emerging markets and developing economies (EMDEs), rest on the assumption that it could enhance payment systems efficiency by removing unnecessary third-party intermediaries from the payment network, and hence, quicken payment settlement and clearance. In this wise, CBDC is argued to possess the ability to enhance the speed, and affordability and ensure the convenience of users’ experience with the payment system. Cooper, et al, ( 2019 ) examines how retail CBDC could enhance financial inclusion through mobile money. They found that the adoption of retail CBDC via mobile money has the potential to “foster greater interoperability, improve payment efficiency, facilitate cost-saving gains by minimizing reconciliation complexity and notional costs, and reduce the key payment risks typically associated with mobile money”. In addition, they added that if properly implemented, CBDC could enhance trust in mobile financial services and ease the liquidity constraints of mobile-money agents. But if wrongly implemented, it has the potential to exacerbate financial disparities and expose agents to cyber-security threats. Cooper, et al, ( 2019 )’s views were consistent with the points made by Sahu ( 2021 ), that from an inclusion perspective, retail CBDCs can enable peer-to-peer (P2P) transfers and business transactions and thus, improve the penetration of the formal economy. In another paper, Ozili ( 2021 ) presents a number of arguments for and against the ability of CBDC to increase financial inclusion. On a positive note, the paper argued that CBDC has the potential to improve access to the digital financial services, enlarge the digital economy and enhance payment efficiency. However, the high cost to acquire digital devices, non-interest-bearing nature of some CBDCs, high preference for physical cash and the burden of satisfying the identification requirements, were listed as factors that may inhibit CBDCs’ financial inclusion potential. In the views of Andolfatto ( 2021 ), only an interest-bearing CBDC could reduce the demand for cash and encourage financial inclusion. This view was shared by Mancini-Griffoli et al. ( 2018 ) when they maintain that CBDC can encourage financial inclusion only if it is attractive as an alternative form of money. Didnenko and Buckley (2021), on the other hand, placed emphasis on the design features of CBDC. In their argument, a well-designed CBDC can offer a viable solution to financial inclusion problems in the Pacific region. The emerging picture from the literature, on the ability of CBDC to promote financial inclusion, appears to centred on its specific design features that would encourage the financially excluded to switch from holding cash to CBDC. In the emerging markets and developing economies (EMDEs), with large informal sectors, it may also depend on the level of financial literacy of economic agents (see Maniff, 2020 ; Mancini-Griffoli et al., 2018 ). Low digital literacy and awareness could foster mistrust and low acceptance of CBDC as a viable means of exchange as opposed to cash usage (Cooper, et al, 2019 ; BSP, 2019). In a survey of CBDC adoption, Boar and Wehril (2021) found that while about 86 per cent of central banks were actively researching the possibility of issuing a CBDC, 60 per cent were experimenting the technology while 14 per cent were at the development and pilot phases of CBDC. On the whole, EMDEs are leading the path in CBDC implementation or experimentation, while developed countries focused more on research. At the time of writing this paper, only 11 countries have fully launched the CBDC, namely 8 countries in the Eastern Caribbean, the Bahamas, Jamaica and Nigeria. Nigeria’s eNaira, Africa’s first central bank digital currency, was launched in October 2021. The country adopted a phased rollout approach. At the initial phase, five hundred million eNaira was minted and made accessible to only holders of bank accounts. In the next phase, which commenced on August 2022, the Bank announced plans to expand access of eNaira to the unbanked via unstructured supplementary service data and offline payments, by simply dialling *997# from their mobile phones. Despite its successful launch, the adoption rate of eNaira is still lower than expected. According to the Central Bank of Nigeria , as of August 2022, there are about 270, 000 active eNaira wallets (comprising 252,000 consumer wallets and 17,000 merchant wallets), with more than 200,000 volume of transactions carried out. This could be regarded as some kind of remarkable progress, coming barely eleven months after its introduction. But considering Nigeria’s population of over 200 million with about 55 million bank account holders, the number of active users of eNaira raises concerns on its acceptability among Nigerians. In a recent survey, Akpan and Nwanja ( 2022 ), show that eNaira is facing significant challenges with regard to its adoption as an alternative form of payment transactions. Their results show that while over 90 per cent of the respondents were aware of eNaira and have an android phone, only a small fraction (36.7%) expressed willingness to use the digital currency by installing eNaira wallet on their phones (see Fig. 1 ). Furthermore, the survey also showed that, of those who have installed the eNaira wallet, very few had actually used it to conduct financial transactions. The majority of the respondents (76.4%) were yet to perform any transactions using the eNaira (see Fig. 2 ). The authors attributed the low level of acceptability to the availability of alternative robust digital payment platforms such as internet banking and mobile banking apps as well as the scepticism about additional incentives to derive from adopting the central bank digital currency. 3. Method of Analysis In this paper, we administered a structured questionnaire on a random sample of 206 respondents. The questionnaire contains, amongst others, questions that relate to the three components of financial inclusion – usage, quality and access to eNaira. Thereafter, we subject the field-data set to a structural equation model (SEM) to examine the relationship between eNaira and financial inclusion in Nigeria. The application of SEM is considered a more robust approach to test the proposed relationships as against other traditional methods because of its capacity to combine factor analysis and multiple regression analysis. It allows a set of relationships between one or more independent variables (IVs), either continuous or discrete, and one or more dependent variables (DVs). SEM also has the capacity to handle complex and difficult data that may be non-normal and incomplete (Tabachnick and Fidell, 2014 ). However, before considering the structural model for identifying the impact of latent constructs, each of the variables was obtained through the Confirmatory Factor Analysis (CFA) on the measurement model. The latent unobserved variables are developed from the observed latent indicators extracted from the set of items in the questionnaire. These sets of items are trimmed through the test of uni-dimensionality, validity and reliability on the data and therefore appropriate latent construct representing each of the variables of the model through which the relationship between eNaira and financial inclusion in Nigeria was investigated. In this study, eNaira and financial inclusion are considered as latent unobserved whose indicators or measurements are extracted from the questionnaire administered in the course of this study. Generally, the compact form of our structural equation model is specified as follows: Based on theory, institutional knowledge and empirical evidence, it is expected that eNaira access, usage and quality can enhance financial inclusion. Therefore, it is expected that β 1 , β 2 , β 3 > 0 3.1 Diagnostic Tests 3.1.1 Goodness of Fit Test In SEM, a series of the goodness of fit indices that reflect the fitness of the model to the data at hand must be conducted. Table 1 presents the fitness indices adopted in this study. The most commonly reported model fit is RMSEA and CFI, although, the choice of indices to be reported is a matter of personal preference and perhaps, the preference of the journal editor (Hu and Bentler, 1999 ). Table 1 Test of Model Fitness Fit Indices Accepted value Authors (CMIN/DF) =0.90 Bentler ( 1990 ) TLI RMSEA >=0.90 (between 0–1) < 0.08 Tucker and Lewis ( 1973 ) Byrne ( 2001 ) Note : CMIN/DF = Chi-Square/Degree of Freedom; CFI = Comparative Fit Index; TLI = Tucker-Lewis Index; and RMSEA = Root Mean Square Error of Approximation. Source Authors’ compilation. 3.1.2 Assessing the Measurement Model The measurement Model is considered part of data preparation because it is used to measure the construct validity by assessing the factor loading, and access normality of the measurement instruments. It is also used to test the relationships between the latent variables and their observed measures. The measurement model in this sense portrayed the links between the latent variables and their observed measures (Byrne, 2001 and 2010 ). In this study, we used the Chi-square (CMIN), Relative χ 2 (CMIN/DF), CFI, TLI and RMSEA to assess the measurement model. 4. Results and Discussion 4.1 Reliability Test Table 2 presents the reliability test results. It was revealed that the alpha Cronbach coefficients for all the variables were between 0.70 to 0.89. This implies that all the items of the study were reliable given the alpha Cronbach coefficients benchmark of > = 0.7. The overall alpha Cronbach coefficient of 0.89 implies that all the items that made up the model are reliable. Table 2 Reliability Test Final test (N = 206) Variables No. Items Alpha (α) Financial inclusion 7 0.70 eAcess 6 0.70 eUsage 7 0.89 eQuality 7 0.752 Overall 27 0.89 Source : Authors’ Computation using SPSS Version 21 4.2 The Measurement Model Figure 3 represents the proposed measurement model of the study. After several adjustments, the final measurement model is presented in Fig. 4 . The results indicate that our model passed the goodness-of-fit tests, suggesting that the measurement model fits the data used. As reported, all the relevant test statistics for the modified measurement model are satisfactory. For instance, the Chi-Square (CMIN) = 271.091, DF = 126, p = .000, Relative χ 2 (CMIN/DF) = 2.152, CFI = 0.921, TLI = 0.904, RMSEA = .075 4.3 Convergent Validity Test We test the convergent validity of the individual constructs in the research questionnaire by assessing the factor loadings, Average Variance Extracted (AVE) and Modification Index (MI). Table 3 presents the first and second-order CFAs of the construct’s items in which all the items that did not meet the cut-off point of 0.5 for factor loadings and AVE, as well as MI < 15 were deleted from the path diagrams. As shown in Table 3 , nine items were deleted based on these criteria. Since all the factor loadings from the second order CFA were greater than 0.50 and the AVE were also found to be greater than 0.5, we conclude that the model passed the convergent validity test, hence is reliable for this study. Table 3 Convergent Validity and Construct Reliability Factor Loading ≥ 0.5 Constructs Items 1st Order CFA 2nd Order CFA AVE ≥ .5 MI > 15 Financial inclusion (FIN) 0.50 FIN 1 0.71 0.74 FIN 2 -0.24 Deleted FIN 3 0.66 0.67 FIN 4 0.67 0.67 FIN 5 0.67 Deleted (MI > 15) FIN 6 0.75 0.77 FIN 7 0.42 Deleted eNaira Access 0.54 eAcess 1 0.57 0.57 eAcess 2 0.82 0.81 eAcess 3 0.79 0.79 eAcess 4 -0.12 Deleted eAcess 5 0.69 0.69 eAcess 6 0.79 0.79 eNaira Usage 0.54 eUsage 1 0.67 0.59 eUsage 2 0.86 0.93 eUsage 3 0.82 0.89 eUsage 4 0.74 0.64 eUsage 5 0.73 Deleted (MI > 15) eUsage 6 0.67 Deleted (MI > 15) eUsage 7 0.64 0.54 eNaira Quality 0.57 eQuality 1 0.91 0.93 eQuality 2 0.86 0.87 eQuality 3 0.57 0.54 eQuality 4 0.63 Deleted (MI > 15) eQuality 5 0.65 0.61 eQuality 6 0.16 Deleted (MI > 15) eQuality 7 0.34 Deleted (MI > 15) Note : AVE is Average Variance Extracted and MI is Modification Index Source: Authors’ Computation. 4.4 Discriminant Validity Test Table 4 presents the construct reliability (CR), Average Variance Extracted and the square root of Average Variance Extracted in parenthesis, as well as the correlation coefficients in curly braces. We compare the square root of AVEs and the correlations. In line with Byrne ( 2010 ), the square root of AVEs of the construct were greater than the correlations of the constructs, therefore the items of all the latent constructs explained significantly more variables of the latent constructs used in this study. We conclude that the latent constructs show adequate discriminant validity. Table 4 Discriminant Validity Test Variables/Factors CR AVE Fin Access Usage Quality Financial incl. 0.831 0.497 (0.705) eAccess 0.853 0.542 {0.491} (0.736) eUsage 0.849 0.542 {0.519} {0.327} (0.736) eQuality 0.834 0.569 {0.588} {0.450} {0.400} (0.754) Note : () represent the AVEs and {} represent the correlation coefficients. Source: Authors’ Computation. 4.4 Normality Test Data is considered normally distributed if skewness is between − 2 and + 2 (Tabachnick and Fidell, 2007 ), while kurtosis is between − 7 and + 7 (Byrne, 2010 ). The skewness values in Table 5 range between − 1.58 and − 0.11 and the kurtosis values range from a maximum of 2.44 to a minimum of -1.09 which all fall within the cut-off point implying that the data used in this study are normally distributed. Table 5 Normality test Variable Skew C.R. Kurtosis C.R. M-dsquared P1 P2 Obs FIN1 − .593 -3.396 − .704 -2.016 86.96 0.000 0.000 21 FIN3 − .322 -1.846 − .599 -1.717 67.28 0.000 0.000 23 FIN4 − .441 -2.528 .019 .055 56.73 0.000 0.000 19 FIN6 − .725 -4.155 1.041 2.981 51.39 0.000 0.000 50 eQuality 1 − .636 -3.644 − .147 − .421 50.19 0.000 0.000 98 eQuality 2 − .571 -3.275 − .635 -1.820 47.73 0.000 0.000 28 eQuality 3 − .108 − .618 -1.085 -3.108 47.42 0.000 0.000 80 eQuality 5 − .572 -3.280 .522 1.494 46.67 0.000 0.000 201 eUsage 1 − .652 -3.737 .467 1.338 45.83 0.000 0.000 162 eUsage 2 − .747 -4.282 − .087 − .250 43.44 0.001 0.000 NO eUsage 3 − .968 -5.548 .787 2.255 42.91 0.001 0.000 NO eUsage 4 − .821 -4.702 .268 .767 42.88 0.001 0.000 NO eUsage 7 − .624 -3.575 .154 .440 41.98 0.001 0.000 NO eAcess 1 − .948 -5.433 .340 .975 41.27 0.001 0.000 NO eAcess 2 -1.277 -7.317 .971 2.783 40.87 0.002 0.000 NO eAcess 3 − .732 -4.193 .285 .817 40.03 0.002 0.000 NO eAcess 5 -1.581 -9.058 2.436 6.979 38.64 0.003 0.000 NO eAcess 6 − .738 -4.229 .068 .194 37.15 0.005 0.000 NO Multivariate 87.396 22.858 36.17 0.007 0.000 NO Note : C.R. = Critical Ratio and NO = not an outlier. Source : Authors’ computation. Also, the overall multivariate Kurtosis of 87.4 confirms that the data is normally distributed as the multivariate Kurtosis is not large. Certainly, if the data is normally distributed then, it is a clear indication that there are no outliers in the data set. Even though, the Mahalanobis d-squared for the measurement model shows the presence of 9 outliers, indicated about 9 cases of observations farthest from the centroid (p1 = 0.000 and p2 = 0.000). We, therefore, deleted the outliers in the final analysis. 4.5 Unique Predictors to the Dependent Variable Figure 5 shows our final SEM results while Table 6 contains the coefficients of structural equation model. The results in Fig. 3 are robust as confirmed by the following goodness-of-fit indices: Chi-Square χ 2 (CMIN) = 264.143 (DF = 126), Relative χ 2 (CMIN/DF) = 2.096, p = .000, CFI = .924, TLI = .907, RMSEA = .075. Since all the fitness indices are within their threshold, then the entire model is fit. The results shown in Table 6 are also robust. The standardized path coefficients were consistent with the hypotheses, indicating a significant relationship between the predictors and criterion variables. The standardized coefficients of eAcess , eUsage and eQuality of 0.181, 0.319 and 0.382 suggest that a 1.0 per cent increase in eNaira access, usage and quality may likely lead to 0.181, 0.319 and 0.382 per cents increase in financial inclusion in Nigeria respectively. This is consistent with the expectation of our study. It was also revealing that eNaira access, usage and quality has a positive and significant influence on financial inclusion in Nigeria as indicated by their probability values of 0.021, 0.000 and 0.000 respectively. Table 6 Unstandardized and Standardized Regression Weight Hypothesized relationships b S. E Β CR p FIN <--- eAcess 0.181 0.078 0.181 2.317 0.021* FIN <--- eUsage 0.496 0.128 0.319 3.883 0.000** FIN <--- eQuality 0.530 0.127 0.382 4.180 0.000** R 2 = 0.48 Note : b is unstandardized regression coefficient, B is standardized regression coefficient, S.E is the standard error, CR is the critical ratio, ** and * represent 1% and 5% significance level. Source Author’s computation. The result indicates that about 48% of variances in the dependent variable (financial inclusion) was explained by the predictor variables entered into the structural equation modeling (eNaira access, eNaira usage and eNaira quality). The R 2 value of 0.48 was found to be significantly different from zero as proposed by Cohen ( 1988 ) and supported by Lohmoller (1989). 5. Conclusion and policy recommendations The paper investigates whether eNaira can foster financial inclusion in Nigeria using structural equation model. A survey design was employed by administering questionnaires to a random sample of 206 respondents. The study concludes, based on its findings, that increase in access to eNaira, usage of eNaira and improvement in the quality of the eNaira could affect financial inclusion positively and significantly in Nigeria. Therefore, we recommend the need to improve eNaira access, usage and quality through targeted digital and financial literacy campaigns to raise awareness, and encourage uptake and usage of eNaira, particularly in the rural areas where the majority of the unbanked and financially excluded people lives. There is a need to also apply a simplified due diligence process to enroll special groups with limitations on the use of eNaira. The CBN should equally come up with a plan to speed up the development of rural agent networks across the country and explore ways how the eNaira could be accessed via different user interfaces, aside from unstructured supplementary service data (USSD). This would entail deepening offline functionality to enable eNaira transactions to be carried out in locations with no internet, electricity or mobile network coverage. Statements and Declarations The participates were duly informed about the purpose of the study in the questionnaire and they consented to participate in the survey. The Authors did not receive any funding from any organization to conduct this study, and they have no competing interests to declare that are relevant to the content of this article. References Akpan, U. and T. F. Nwanja (2022). Central Banks’s Digital Currency and the Challenge of Monetary Policy in Nigeria, forthcoming Alliance for Financial Inclusion (2022). Central Bank Digital Currency – An Opportunity for Financial Inclusion in Developing and Emerging Economies? Digital Financial Services (DFS) Working Group, Special Report, AFI. Aminu, U., I. B. Iya, and O. A. Adewusi (2021). Causes of farmer-herders conflict in Taraba State, Nigeria. ADSU Journal of Applied Economics, Finance & Management (AJAEFM), 6 (1),11-21 Andolfatto, D. (2021). Assessing the Impact of Central Bank Digital Currency on Private Banks, The Economic Journal , 131(634): 525-540. Auer, R., H. Banka, N. Y. Boakye-Adjei, A. Faragallah, J. Frost, H. Natarajan and J. Prenio (2022). Central Bank Digital Currencies: A New Tool in the Financial Inclusion Toolkit? FSI Insights on Policy Implementation No. 41, Bank for International Settlements. Bagozzi, R. P. and Y. Yi (2011). Specification, Evaluation, and Interpretation of Structural Equation Models, Journal of the Academy of Marketing Science, 40: 8-34. Bangko Sentral Ng Phlipinas (BSP) (2019). Financial Inclusion Survey , BSP. https://www.bsp.gov.ph/Inclusive%20Finance/Financial%20Inclusion%20Reports%20and%20Publications/2019/2019FISToplineReport.pdf?amp Bentler, P.M. (1990) Comparative Fit Indexes in Structural Models. Psychological Bulletin, 107, 238-246. http://dx.doi.org/10.1037/0033-2909.107.2.238 Boar, C. and A. Wehrli (2021). Ready, Steady, Go? – Results of the third BIS Survey on Central Bank Digital Currencies, BIS Working Papers, No. 114. Byrne, B. M. (2001). Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. Mahwah, NJ: Lawrence Erlbaum Associates. Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming. 2nd Edition. Routledge Taylor & Francis Group. Cohen, J. (1988). Statistical power analysis for behavioral sciences (2 nd ed.) , Hillsdale, Lawrence Erlbaun Associates, NJ. Cooper, B., A. Esser, M. Allen, (2019). Central bank digital currency (CBDC) for Financial Inclusion: A case for Mobile Money, Cenfri, June. Foster, K., S. Blakstad, S. Gazi, and M. Bos (2021). “Digital currencies and CBDC impacts on least developed countries (LDCs). Dialogue on global digital finance governance”, Technical Paper 1.2, United Nations Development Programme, New York, NY. Gjefle, E., Z. Herring, C. Kubli, B. O’Rea and G. Rakusen (2021). Centering users in the Design of Digital Currency: The Future of our Money, MIT Digital Currency Institute and Maiden Labs. Hair, J. F., M. Sarstedt, T. M. Pieper, and C. M. Ringle (2012). The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications, Long Range Planning, 45(5), 320-340 Hu, L. T., and P. M. Bentler (1999). Cutoff criteria for fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives. Structural Equation Modeling , 6 , 1–55. Lohmöller, J. B. (1989) Latent Variable Path Modeling with Partial Least Squares, Physica, Heidelberg. Mancini-Griffoli, T., M.S.M. Peria, I. Agur, A. Ari, J. Kiff, A. Popescu and C. Rochon (2018). Casting Light on Central Bank Digital Currency, IMF Staff Discussion Note, 8, Washington, DC. Maniff, J. (2020). “Inclusion by Design: Crafting a Central Bank Digital Currency to Reach all Americans”, Federal Reserve Bank of Kansas City Payments System Research Briefing, December. Ozili, P. K. (2021). Can Central Bank Digital Currency Increase Financial Inclusion? Arguments for and Against. Available at SSRN: http://dx.doi.org/10.2139/ssrn.3963041 Ozili, P. K. (2022). Central Bank Digital Currency Research Around the World; A Review of Literature, Journal of Money Laundering Control, 1368-5201. Sahu, P. (2021). CBDCs and Financial Inclusion: A Developing Story, CBDC Insider, July, https://cbdcinsider.com/download/cbdcs-and-financial-inclusion/ Tabachnick, B. G. and L. S. Fidell (6 th ed) (2014). Pearson New International Edition, Using Multivariate Statistics. England and Associated Computer Throughout the World, 770-780 Tabachnick, B. G., and L. S. Fidell (2007). Using Multivariate Statistics (5th ed.). New York: Allyn and Bacon. Tucker, L. R., and C. Lewis (1973). A Reliability Coefficient for Maximum Likelihood Factor Analysis. Psychometrika, 38, 1-10. http://dx.doi.org/10.1007/BF02291170 World Bank (2012). Financial Inclusion Strategies Reference Framework , Washington, DC, NW, June. Footnotes Governor’s Speech at the Flag-off of the Second Phase of the eNaira Project, retrieved from https://www.cbn.gov.ng/FeaturedArticles/2022/articles/eNairaHackathonStory.pdf The questionnaire can be assessed online at e-Naira and Financial Inclusion in Nigeria (office.com) Bagozzi and Yi ( 2011 ) provide a comprehensive and user-friendly compendium of the standards for the use and interpretation of SEMs. A factor loading is an inferred parameter estimated from empirical associations among observed variables Additional Declarations The authors declare no competing interests. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3861545","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266925956,"identity":"9a8dbef9-b12a-4b42-9a0b-b9cacd16c76c","order_by":0,"name":"Usenobong Akpan, PhD","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYFACNhBhw2BAqpY00rUcJkGLwfFjaQ9+7jgvby7dwPziYxtDnrwDIS1n0o4b9p65bbhzzgE2y5ltDMWGBwhpOZDeJsHbdjvB4EYCmzHPGYbEjQ2EtJx/3ib5t+0cKVpupB2T5m07ANLC/JingiFxPgEdDJI3nqUby7YlG+6ckdjGOKNCInEDIS1859PMHr5ts5M3l0g+/OGDgU3ifEIOY4BGDBAwtkkwMEgAA4R4LQzMH0CkPBG2jIJRMApGwcgCAD5iQu6Dy9zvAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-6866-5397","institution":"Central Bank of Nigeria","correspondingAuthor":true,"prefix":"","firstName":"Usenobong","middleName":"","lastName":"Akpan","suffix":"PhD"},{"id":266925957,"identity":"ad53c647-1c27-46d6-adc5-0e6f25885626","order_by":1,"name":"Aminu Umaru, PhD","email":"","orcid":"","institution":"Central Bank of Nigeria","correspondingAuthor":false,"prefix":"","firstName":"Aminu","middleName":"","lastName":"Umaru","suffix":"PhD"}],"badges":[],"createdAt":"2024-01-14 00:06:28","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3861545/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3861545/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49822905,"identity":"81f910ac-d047-4544-8c09-4d20d731de52","added_by":"auto","created_at":"2024-01-18 15:28:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":16332,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAwareness of the e-Naira and Readiness to Use It\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: \u003c/strong\u003eAkpan and Nwanja (2022).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3861545/v1/cb8e12096557e84237372132.png"},{"id":49822906,"identity":"c51fd580-496f-4ebc-b7c8-150b8bfd8208","added_by":"auto","created_at":"2024-01-18 15:28:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19670,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eUsage of the e-Naira by Respondents\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: \u003c/strong\u003eAkpan and Nwanja (2022).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3861545/v1/542c09d4f18bcc6b81769e2e.png"},{"id":49824461,"identity":"d6fd714a-6e7a-4ab2-add8-d4816346f81f","added_by":"auto","created_at":"2024-01-18 15:36:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":173212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProposed Measurement Model for Financial Inclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Designed by Authors.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3861545/v1/4b816eabec4f4bc7ff22cb4e.png"},{"id":49822907,"identity":"17a58aba-8ef4-48a0-9a8d-f2e3635fb05b","added_by":"auto","created_at":"2024-01-18 15:28:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":120386,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModified Measurement Model for Financial Inclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: \u003c/strong\u003eDesigned by Authors.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3861545/v1/aaa55724d40720fa5f45402b.png"},{"id":49824460,"identity":"42a33998-c0b9-4e01-b81e-39d9c0d5716d","added_by":"auto","created_at":"2024-01-18 15:36:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":153873,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFinal Structural Equation Model for Financial Inclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Designed by Authors.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3861545/v1/2f7e7c25bed0ac494aa1a067.png"},{"id":49825613,"identity":"29bea892-6599-43a0-ba85-637bb3522763","added_by":"auto","created_at":"2024-01-18 15:44:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":904352,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3861545/v1/12b6dee6-3750-4b8d-9905-8fa026d38699.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eCan the e-Naira Foster Financial Inclusion in Nigeria? Evidence from Structural Equation Model\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFostering financial inclusion has become a priority for policymakers and financial regulators, with an increasing number of countries introducing a number of measures to improve access to and usage of tailored financial products and services. The increasing interest in delivering financial inclusion derives from its potential for engendering economic development, especially in terms of employment creation, poverty reduction, wealth creation and general improvement in the living standards of the people. Usually, there are, at least, three aspects to financial inclusion: access to financial services and products; usage of financial services and products; and quality of financial services and products, defined by consumer ability to benefit from new financial services and products (see World Bank, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Nigeria, the apex financial regulatory authority, the Central Bank of Nigeria (CBN), has placed a huge premium on driving financial inclusion through its various policy framework. On 23rd October 2012, the Bank in collaboration with other stakeholders launched the National Financial Inclusion Strategy intending to improve access to payment services across the country. The major tools used in driving the strategy include agent banking, mobile money operation, financial literacy, consumer protection, know-your-customer (KYC) requirements as well as the implementation of Medium Small and Micro Enterprises (MSME) Development Fund and other credit enhancement programmes.\u003c/p\u003e \u003cp\u003eWhat is more? A recent survey report by a development finance organization, the Enhancing Financial Innovation and Access (EFInA) revealed that these measures have yielded positive results, with the percentage of financially excluded adults in Nigeria decreasing from 53 per cent in 2008 to 42 per cent in 2016, and further to 36 per cent in 2020. However, despite this appreciative milestone, the report indicated that the actual number of excluded adults have increased from 36.6\u0026nbsp;million in 2018 to 38.1\u0026nbsp;million in 2020, as population growth outpaces the rate of financial inclusion growth in the country. More ever, it was also shown that at 36 per cent in 2020, Nigeria still has a higher rate of financial exclusion than many other countries in sub-Saharan Africa such as South Africa (7 per cent), Rwanda (7 per cent), Kenya (11 per cent), Namibia (22 per cent) and Tanzania (28 per cent).\u003c/p\u003e \u003cp\u003ePerhaps, in recognition of this challenge, the Bank took another bold and innovative step by launching the e-Naira on 25th October 2021, with the aim to achieve 95 per cent of financial inclusion in Nigeria by 2024. By the design and philosophy, it is envisioned that the e-Naira would usher in a monetary system that is more inclusive of people who have been historically excluded from financial products and services. In other words, it is expected that the e-Naira would improve access to digital financial services, enhance the efficiency of payments and lower the costs of financial products and services.\u003c/p\u003e \u003cp\u003eThe focus of this study, therefore, is to evaluate the extent to which the e-Naira can enhance financial inclusion in Nigeria. Can the e-Naira bridge the financial inclusion gap in Nigeria, especially with regard to access, usage, quality and adoption of the innovation? If not so, what are the limitations and what are the policy options for improvement?\u003c/p\u003e \u003cp\u003eIn this paper, we address these concerns using descriptive statistics and the Structural Equation Model (SEM), which is capable of combining factor analysis and multiple regression analysis. It can be used to test the proposed causal relationships between variables of a model which the regression method cannot do. It also allows a set of relationships between one or more independent variables (IVs), either continuous or discrete, and one or more dependent variables (DVs). In addition, it uses confirmatory factor analysis to reduce measurement error by having multiple indicators per latent variable, testing the model overall rather than coefficients individually, test the model with multiple dependents. SEM has the capacity to handle complex and difficult data that may be non-normal and incomplete (Tabachnick and Fidell, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Our results show that access, quality and usage of eNaira could influence financial inclusion positively and significantly in Nigeria.\u003c/p\u003e \u003cp\u003eThe remainder of this paper is organized as follows. Section 2 presents an overview of the literature and some stylized facts, while section 3 describes the method of our analysis. The results of the study and the underlying discussion are contained in section 4 while the conclusion and policy options are offered in section 5.\u003c/p\u003e"},{"header":"2. The Literature and Some Stylized Facts","content":"\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eResearch on central bank digital currency (CBDC) is rapidly building up in the literature, signifying the growing global interest in trying to understand the macroeconomic and financial market implications of the new digital currency when or if adopted. Among others, the potential of CBDCs in addressing the barriers to financial inclusion has been discussed in several papers (Auer, et al, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Maniff, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cooper, et al, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gjefle, et al, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Alliance for Financial Inclusion, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ozili, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e and \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, due to the novelty of CBDC projects, resulting in limited adoption rates across the world, a number of the studies on CBDC are hypothetical or explorative in nature (Foster, et al, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ozili, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). In most instances, the potential of CBDC in fostering financial inclusion, especially in emerging markets and developing economies (EMDEs), rest on the assumption that it could enhance payment systems efficiency by removing unnecessary third-party intermediaries from the payment network, and hence, quicken payment settlement and clearance. In this wise, CBDC is argued to possess the ability to enhance the speed, and affordability and ensure the convenience of users\u0026rsquo; experience with the payment system.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eCooper, et al, (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) examines how retail CBDC could enhance financial inclusion through mobile money. They found that the adoption of retail CBDC via mobile money has the potential to \u0026ldquo;foster greater interoperability, improve payment efficiency, facilitate cost-saving gains by minimizing reconciliation complexity and notional costs, and reduce the key payment risks typically associated with mobile money\u0026rdquo;. In addition, they added that if properly implemented, CBDC could enhance trust in mobile financial services and ease the liquidity constraints of mobile-money agents. But if wrongly implemented, it has the potential to exacerbate financial disparities and expose agents to cyber-security threats. Cooper, et al, (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u0026rsquo;s views were consistent with the points made by Sahu (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), that from an inclusion perspective, retail CBDCs can enable peer-to-peer (P2P) transfers and business transactions and thus, improve the penetration of the formal economy. In another paper, Ozili (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) presents a number of arguments for and against the ability of CBDC to increase financial inclusion. On a positive note, the paper argued that CBDC has the potential to improve access to the digital financial services, enlarge the digital economy and enhance payment efficiency. However, the high cost to acquire digital devices, non-interest-bearing nature of some CBDCs, high preference for physical cash and the burden of satisfying the identification requirements, were listed as factors that may inhibit CBDCs\u0026rsquo; financial inclusion potential. In the views of Andolfatto (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), only an interest-bearing CBDC could reduce the demand for cash and encourage financial inclusion. This view was shared by Mancini-Griffoli et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) when they maintain that CBDC can encourage financial inclusion only if it is attractive as an alternative form of money. Didnenko and Buckley (2021), on the other hand, placed emphasis on the design features of CBDC. In their argument, a well-designed CBDC can offer a viable solution to financial inclusion problems in the Pacific region.\u003c/p\u003e\n\u003cp\u003eThe emerging picture from the literature, on the ability of CBDC to promote financial inclusion, appears to centred on its specific design features that would encourage the financially excluded to switch from holding cash to CBDC. In the emerging markets and developing economies (EMDEs), with large informal sectors, it may also depend on the level of financial literacy of economic agents (see Maniff, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mancini-Griffoli et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Low digital literacy and awareness could foster mistrust and low acceptance of CBDC as a viable means of exchange as opposed to cash usage (Cooper, et al, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; BSP, 2019).\u003c/p\u003e\n\u003cp\u003eIn a survey of CBDC adoption, Boar and Wehril (2021) found that while about 86 per cent of central banks were actively researching the possibility of issuing a CBDC, 60 per cent were experimenting the technology while 14 per cent were at the development and pilot phases of CBDC. On the whole, EMDEs are leading the path in CBDC implementation or experimentation, while developed countries focused more on research. At the time of writing this paper, only 11 countries have fully launched the CBDC, namely 8 countries in the Eastern Caribbean, the Bahamas, Jamaica and Nigeria.\u003c/p\u003e\n\u003cp\u003eNigeria\u0026rsquo;s eNaira, Africa\u0026rsquo;s first central bank digital currency, was launched in October 2021. The country adopted a phased rollout approach. At the initial phase, five hundred million eNaira was minted and made accessible to only holders of bank accounts. In the next phase, which commenced on August 2022, the Bank announced plans to expand access of eNaira to the unbanked via unstructured supplementary service data and offline payments, by simply dialling *997# from their mobile phones.\u003c/p\u003e\n\u003cp\u003eDespite its successful launch, the adoption rate of eNaira is still lower than expected. According to the Central Bank of Nigeria\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e, as of August 2022, there are about 270, 000 active eNaira wallets (comprising 252,000 consumer wallets and 17,000 merchant wallets), with more than 200,000 volume of transactions carried out. This could be regarded as some kind of remarkable progress, coming barely eleven months after its introduction. But considering Nigeria\u0026rsquo;s population of over 200\u0026nbsp;million with about 55\u0026nbsp;million bank account holders, the number of active users of eNaira raises concerns on its acceptability among Nigerians.\u003c/p\u003e\n\u003cp\u003eIn a recent survey, Akpan and Nwanja (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), show that eNaira is facing significant challenges with regard to its adoption as an alternative form of payment transactions. Their results show that while over 90 per cent of the respondents were aware of eNaira and have an android phone, only a small fraction (36.7%) expressed willingness to use the digital currency by installing eNaira wallet on their phones (see Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFurthermore, the survey also showed that, of those who have installed the eNaira wallet, very few had actually used it to conduct financial transactions. The majority of the respondents (76.4%) were yet to perform any transactions using the eNaira (see Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The authors attributed the low level of acceptability to the availability of alternative robust digital payment platforms such as internet banking and mobile banking apps as well as the scepticism about additional incentives to derive from adopting the central bank digital currency.\u003c/p\u003e"},{"header":"3. Method of Analysis","content":"\u003cp\u003eIn this paper, we administered a structured questionnaire on a random sample of 206 respondents. The questionnaire contains, amongst others, questions that relate to the three components of financial inclusion \u0026ndash; usage, quality and access to eNaira. Thereafter, we subject the field-data set to a structural equation model (SEM) to examine the relationship between eNaira and financial inclusion in Nigeria.\u003c/p\u003e\n\u003cp\u003eThe application of SEM is considered a more robust approach to test the proposed relationships as against other traditional methods because of its capacity to combine factor analysis and multiple regression analysis. It allows a set of relationships between one or more independent variables (IVs), either continuous or discrete, and one or more dependent variables (DVs). SEM also has the capacity to handle complex and difficult data that may be non-normal and incomplete (Tabachnick and Fidell, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eHowever, before considering the structural model for identifying the impact of latent constructs, each of the variables was obtained through the Confirmatory Factor Analysis (CFA) on the measurement model. The latent unobserved variables are developed from the observed latent indicators extracted from the set of items in the questionnaire. These sets of items are trimmed through the test of uni-dimensionality, validity and reliability on the data and therefore appropriate latent construct representing each of the variables of the model through which the relationship between eNaira and financial inclusion in Nigeria was investigated. In this study, eNaira and financial inclusion are considered as latent unobserved whose indicators or measurements are extracted from the questionnaire administered in the course of this study.\u003c/p\u003e\n\u003cp\u003eGenerally, the compact form of our structural equation model is specified as follows:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAi8AAAKvCAYAAACiZC+cAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAABhaVRYdFNuaXBNZXRhZGF0YQAAAAAAeyJjbGlwUG9pbnRzIjpbeyJ4IjowLCJ5IjowfSx7IngiOjU1OSwieSI6MH0seyJ4Ijo1NTksInkiOjY4N30seyJ4IjowLCJ5Ijo2ODd9XX1wKYA6AADT8UlEQVR4Xuz9768V2XXnjx++zx2nu/0IMQgBI8VyLEd2G1ttQDKSgThRlGjwNJBRhJQIm/bE0mQ6kOnGikfGntBDEiVKoN2KJRQlQCtInyhKdwBL2IIOsvEPdSu2HCmAEEE86naPnT+A73kV9b6su9hVp86559x76vJ+SZuq/WvttddetWvXrjqXNfeHDIwxxhhjesL/rz4aY4wxxvQCL16MMcYY0yu8eDHGGGNMr/DixRhjjDG9wosXY4wxxvQKL16MMcYY0yu8eDHGGGNMr/DixRhjjDG9wosXY4wxxvQKL16MMcYY0yu8eDHGGGNMr/DixRhjjDG9wosXY4wxxvQKL16MMcYY0yu8eDHGGGNMr/DixRhjjDG9wosXY4wxxvQKL16MMcYY0yu8eDHGGGNMr/DixRhjjDG9wosXY4wxxvQKL16MMcYscOvWrcFzzz03OHfuXJ3SzBtvvFGVfeutt+oUY5YHL17MY8mTTz45WLNmzeCdd96pU7rzta99rar74osv1inLAzeTzZs3V22jv28aZtrgTzt37hx89rOfHezdu7dK02JG18xHP/rRBb/bunXr4Mtf/vLgd37ndwavv/56lWbMcuDFi3nsYBHw7rvvVuff+MY3quM47NmzZ3Do0KHBV7/61WoyJ7z00kt1bjO7d+/uVK4JbiBPPPHE4P79+4N/+Id/qPrx4x//uM41ZumwCDl9+vTgQx/6UJ3ywO82bNgw+MlPfjJ4++23q7T/8l/+S3WEp556avDqq68O/tt/+28TPQwYMwlevJjHDibnTZs2Ved///d/Xx3H4ac//Wl1fPbZZwdnz54dvPnmm4PDhw9XabOEJ+GbN29WT7088XIz0dOxMUuFnRMW9fhW5MKFCwv+zULlM5/5TOWHkY0bN1a++PWvf71OMWa2ePFilhVetWjrGZgw2Y5erlcwbIFfv359cOzYsSrOE+O4T4tPP/304NSpU9XOCxN2fEqdJb/6q79a3Vw++clP+nWRmTr/+I//WC3IR8HiXYv/yPbt2wd/93d/V8eMmS1evJhlg9ccH/zgB6tXLt/73veqVyhsNXNDXr9+fV1qMXot0xbG4cSJE9WCg8ArGPjOd75THbvyN3/zN4Ndu3ZVEzjt89HirGHrHlux08NijwWMt+jNNPnud7/beB0KfI6F+5//+Z/XKQ9Zt25ddV0bsyzcN2aZGd6A7+N6w8XD/ZdffrlOnT1vv/121ebVq1er+PAps9JjuJiq4l24efNmVR45L7zwwv0333yzznmU4QKnkt8WpEsbtENZ2er48eNVnHRjpsUof+T6efrpp6vrtwR1kWHMcuCdF7Ps/NzP/Vx11K8algs+zmXXQu/0f/3Xf706dvlJKPDKiVdGly5dGvzbv/3b4Ctf+UrrKyO+FRheYwuB3ZrhwmNRWv6+IEObvJ5ilyjb6vvf/359Zszs+fznPz/4n//zf/o7KzMXePFilhW+cfnCF75QnXfZYs6viEqhK3/8x3+86J3+li1bqiOvrbr8zPP8+fNVWX6uzIeLy8G9e/eqo3QFfTDMR5LGTAsW5j/60Y/q2GJ4bfnhD3/YCxczN3jxYpYFvgthofErv/Ir1ceyTJT8YkF/Q6KJuEvRFLrA4oTFUnyn/973vrc+Gwz++q//uj4bVB8Ts0MD+sAYnnnmmep48eLF6nud5fjm5P3vf3+163Ljxo2qPT7U5ZsD0p5//vm6lDFLB7+/c+dOHXsI1w6/bIu/qGMRk/3/7t271XVtzLIwnPyNmTn6TkPvy1977bUqvmnTptbvRqYFbSnovX7+JgUd+aYFfcjj2xb0jd/EoDc6U57vXsbRHZm0MS60ybcGapNvddDTmGki387I33PIcJ1M4t/GTMIa/hk6ojEmwI4Lr4hOnjxZpyyGv7L7uc99rvqGZTn+xosxywG7L//7f//vwac//ek6pRv6HoxvwZbrlap5vPFrI2MCbIfrA14+zGVrvOmn0Ly64a/tGrNa+Ku/+qvqm7Rx/o4Q1wjfkvEnBLxwMcuFFy/GBPijdf/yL/9S/ZKIxQl/0p/vTiJM1v/f//f/Db75zW/6o1mzquDXcyza2Vns8is8FvZf/OIXq0XPuLs1xiwFvzYyxhhjTK/wzosxxhhjeoUXL8YYY4zpFV68GGOMMaZXePFijDHGmF7hxYsxxhhjeoUXL8YYY4zpFV68GGOMMaZXePFijDHGmF7hxYsxxhhjeoUXL8YYY4zpFV68GGOMMaZXePFijDHGmF7hxYsxxhhjeoUXL8YYY4zpFV68GGOMMaZXePFijDHGmF7hxYsxxhhjeoUXL8YYY4zpFV68GGOMMaZXePFijDHGmF7hxYsxxhhjeoUXL8YYY4zpFV68GGOMMaZXePFijDHGmF7hxYsxxhhjeoUXL8YYY4zpFV68GGOMMaZXePFijDHGmF7hxYsxxhhjeoUXL8YYY4zpFV68GGOMMaZXePFijDHGmF7hxYsxxhhjeoUXL8YYY4zpFat68XLr1q3B5s2b65gp8cYbbwx2795dx8wo1qxZU/lVX+F66LP+8Nxzzw1eeumlOlZmHq97dB6l90py7ty5yrYrCdfXtHjc5rZ5GL/lZK4XLzgezqwQIc5gCc5jWcKmTZvqXFMCG23btm2wcePGOsU0waSAvaCP9pL+N2/enDv9dZ2PgpsR5U6dOjVYv359nboYFi1dZM0aFim6cUrvI0eOVPF5g8Us+u3bt69OWX66+oD8eNQCnDLMbdOG9tH1nXfeqVNWnjh+GzZsqFNXP3O9eLlw4cLg+PHj1SLk/v37deqDyQBOnz5dHWHv3r3VxAwcKX/o0KEqbspgo127dtUx08bJkycrX+wr6H/27Nk6Nl9wncfru4mtW7cuXONN3LhxYy6u+8uXLy8sEtG7S/9WCvREv5V82NNcPwr8GF1HLcBnNbc9//zz1fF973tftWDQAnUlmYfxWwnm/rXRM888U01YcaV95syZaoK6ePHiovTr169X6fP2ZGlWB01P+2b58LVtVpJ79+4NPvKRjwxee+21wZtvvlktuszKMPeLl7Vr11ZHFiaCBQsrcIjp7MTs37+/jj1E22qE/C5cW7oKcTHEOeX1Skrvq6M8gnaCxoWtx49+9KODJ598spJJnJV80/v6Ue1STzo3lckylsJbb71V6U4fBDZC7osvvlinjIe2jwn5/S19IV9t6LVh2xiOS2yf0DS2sUwkprflxVeeQJr6Qb/jGMoOUTf1UbYglPwm1ok7lePSVR/ylEZ+RL6Zryf6nXWP7alcJraV7Zlp0itfDyUbjgvyebDi9VZuD+KY5b5RVn4QdYl12uoJ2TqS+9qExoegsR4H6a8gXTlqnEa1EetH1IdIlEXIvhD1yWMxDrTNq6jf/u3fHnz6058efOhDH6pzxifbCNkR0nRsKhNlLNVvGQPdh7Af54rPLfd7wKZNm+4fP368Oj979mwV4NChQ/d37dpVnQPlIuTTRaVfvXq1ikdZsU6MI5eyBORQl3zJEDk+Dujx9ttv33/66afvv/zyy/dfeOGF+8PVfJ37KFFX6irOuXQl3Lx5s0onv6kOSHf6NwnoS1vIQJYg3taPJmRnyHLRmziBfmgs2sZwXKiHbCH/kQ7Ilg6COvJBjpQR0k990bjEuNogUJ806YDsPDbR38mL+TGuNqQ7II+0SRmlj8ZF0JbilFM/kRHHjrQoR3YAypGvOEiOZNNuLIN85ArkxXGNccpFGyEzxicBPWgj20p6q23ypTftKp+gfB2jfVRW9lYd6S2bRhvIjkJtE9SO6ikuu05iD/pEXY0RID/2A7lqC9QeASRDZahLPPYr25lz6avyalO2ijqNCzo+8cQT1ZytcRwX+YdAZ8U5R0cFEfsBsQ5EnxgX6jJf0yfkci/ivoS8pdhq1kw+ky0jcaDigOmCxBnyhQDEo6MDcQ0wsuJg62KR85OX6yMzt7PUQWYRwAXRRQY60Z6CkC2kO2T9cz5gg9yfcYkXMk4fx2hcNAYK0SbomWWPGsOu0A71MthP9imViWmULY0h+mW9ox82ta0xpU+KR/nkxX5GOSU/bWqnK6P0EZRRiGPDeb6eoDSuaksh9pN4bjemIU/tyB+kM0Q9suxpQX+y/Wkr2iPbU7pGfUppUOpjLBPzIds4tw2Ujzo3td2VaANkcB7bxBaxfeLkR7Id49ipDxF8QPpSN/YZKF/y2S4g99lnnx35kNkV2YMQxwr9SIu2oaz6onGJ+YCM6F/jgsxYn/ikY78c9OKn0tu3b1/4viW+8+ZDuOGADc6fP199B1N6ZdQGH/fxCwBtvSEL7t69Wx1LoIO2gxXgzp071XESPvGJT1RHPjpugnZpiw8Bh+M29seXs9z+47XR7du3q/Ovf/3rg6NHj1bn48KWLmMwvCirPnZhkjEch3G2Y9GFvksX1cU2+K/SCfRRNmsi+jfg4/IRjSfb2JKpX4uQN4vxbtMH9GpmOOFV46exGBfsRr+QgZ2WAt8oALrITviL5NJGtOFSXissJ+P+qoTy8RvBa9euVTaZ5TdEO3bsGFy6dKk6x1d41R/9J37UPAmlaxx/xE+nzeuvvz74tV/7tcFf/uVfDr7yla9M5ZUR4H/DBUN13hX59DRBJ+CVGMhPZmHLadGLxcuWLVuqI5NjXqAcPHhw8Morr1QXySSGxnFwoBjihFxiuAp+pM7hw4fr3PHgO5d//Md/HLz77rutN5wTJ05UX88v9QOxfMFzw10qXMjoTl9++tOfTjQOXDxMrtxUxp3QJhnDJnQRR9puFCxaGReBPaUDN2G9f6dM1I+g77bakH9DSQ8tFGKQ/bI/LWWBLZr0oS0W9eizlAkPe42zeAX1U/NECcmMQcQ0xq/kAysNi43MOAtr5icWDlrEsYCbxrXfxp49eyq7Y0/5ivyHcZ70ISezHOP113/915V/PfXUU3XK5Bw7dqy6h3S5/tvIixgtyCfhRz/6UTVHqX98S/r0009X5/NKLxYvTMZcdFxseWLUr5HGuZAFFxIXcZzk88demQMHDlSTdLxgOM83ii5ws//iF784+OxnP1s5Ck8kX/va1xplxclm3L/JgA1xzlhvVF+78oEPfKBaeNCX3/u936tTJ0MXZNcPBScZwxJa7DC+gnGlX3lhqrGnTdrWgjJ/REnddevWVQtuzqNe1O0y6dI2/o09uBkIjWfUF9QG6blNLTqWQpM+QotjbDHOZJpvpBrPJj+4cuVKffagDDuRpUUv8wVzR5Yju8Qxo0101o8E2IWZZF4R0ScnReOcfZx4vvlrgUM55qjMzp07q/7lxduskO7sbOka0oKGj8cnXeTKr3TNIj8yyfU/ig9/+MPVDb3LNdsFjSVHxnIc5NPx2sePSZuUb37zm9WvqATXF/4y1wyduBfwzo9QYjhoj7zrI43uKQwdflG6ZFEvllP68KJblK76oHeSCpQdF2TwnYvenfKRFLJyPwTtxzajDkPnX5QnGbFvuf8E+qp+kr4UkPHaa6/VscmINo/9ox95PCNNYzgJUU62Ce1EvXJ+9pn4vpjzmKe66BrTYx2hcSqR20Q/ke0SdS+105UmfWJf4pipvPIIJX8k5LSoM+fUy/JiX7K8pnZUp6k8xHLjghzJzG3IdshW2pe+9KVFZbBfpMl+IubTXhwLysa2YqBs1FVpENOjX40D/UCXCLpmebl/srvSOeYyskFMIzSlgeLZvl2gDnM2eizlm5c4FtgauYrHcSPITjFdxHLIkJ+h37jQrzh/8x3jUufzWbOGf4YdNmZieB/8z//8z9W7YGNWAzy98/S51K39eYEnfHaf4mtndhHYtRjeTJf0qu9xgt3yz3/+89X5LHZ45gH6yB/hGy4Oi7uZ80IvXhuZ+YWFC9/seOFiVhO8klktCxcYPpU/8opRr8fM+PAaabXyne98p/KXeV64gBcvZiJYtPDhHzsuX/7yl+tUY1YHs/6YdbnhZpS/keGbhl27dnnXZQz4ru+Tn/zkI9/BrSZ++MMfzv3HuuDXRsYY8xign+eK48ePr+qbsFndePFijDHGmF7h10bGGGOM6RVevBhjjDGmV3jxYowxxphe4cWLMcYYY3qFFy/GGGOM6RVevBhjjDGmV3jxYowxxphe4cWLMcYYY3qFFy/GGGOM6RVevBhjjDGmV3jxYowxxphe4cWLMcYYY3pFbxYv586dq4J5lN27dw9u3bpVx9p57rnnOpc10+ell16qwizZvHnz2GOMTr6+jDGCe8Ubb7xRx+aPXixeMOCVK1cGe/furVP6A/8NfXQAbhCkTeNGgXMh6+LFi3XKaE6ePDnYuXNnHTPLBT7AWB05cmSwfv36OnW6yB9u3rxZp5RhoSK/lD+iV99Qf8dZDL711lvV4o56H/3oR+dqcpZe86STeXi9xBDHiIfHWT+QgOaQWaP+njp1qk6ZU+73gJ6o+QhXr16tdB/eTOqU6XP27Nmx26DOrl276phZThgr7D8rJvEHys5ar1mB3sePH69jo8HvX3jhheqcetR/8803q/hyorkh8vbbb1dp5K1GSn2ed/CX0ly5adOmhWuMPi3HtYO/0u5yoLGaZ1+c+50XVoGHDh2qY2YasIN148YNP+GZx46jR48u7EwdPnx4MLwZDD70oQ9V8eXk7t279dlDnnrqqfpsdVLq8zzDq1d2tdmtzhw8eHCwcePGOmZWgrlfvLzyyiuD7du317GHaOuOI9vHgrhCfDWj7XGFWIdz8uP2IFuBkZjH9m5GeaqLbtu2bavymCBJ12KB+pxLfwXiXDCKRx1iuaZXTrmPyIptRHm8Ojpz5kwde5SoB0G6C9lMW91CW/mEbMMMNiXEOm06Q5te4+TFMaQf0R9AYyg4J01+oDGIuhJoJxLHZJQ9Illf6Yc8dIDYdkm2xoaQ9SqlRXL70RYleB3Doljlsz2F7EiZJ598cvD6669X6bITeaRJd9lZEFfeOPaMsHiRfiziS7zzzjuVndExt6X20VN90ZhgB15HkUY59S9CH9Q+5QjRvj/72c8W5HJEF9FFvnjxxRcX9Kc9yWnSnzI6py7n6jd14/hyji4Qx45ztRlp63MeU8lt0ie2Rx9oj0A6dSWLOiLrTxm1M4oTJ07UZw9h4QvIgn379lXn8gN0j/0SxON1p+shQpr0lEwCr3bxXaWrbK6vtgXt0a7sJh1zO23zwVxS78DMJdrOjltXhw4dqtIIbOdRhu00leUIMa7zCHK0Naag7WelK05ZgohxbdML6pJHm5IjnbRNHdNUhqPgXG1Ld5XP8fyaQPlRHmWi/oD8ti3ImBfLSl8F2pNsxkN6Q46LLENbrpQnzhHUN+WrnsjxJp2B82yTqIf6QD2lqTx1lUa+6nKMbeQ4/Yh2l2z1pwtqUyAztoH9ZS9AtnSVT3CuMuijfCGfkV7Zrjme4bXLE088cf/ll1+uXn00QTl0pz3KPfvss4t0p5+SA7zeIS6kR9Yz2qcN5Gk8aLttHMiXXLXDURBHd/qBTMpyjr6qRz+Il2xCmZJNo1zsRVx6jis/2ppz1YOS/kAaZWmbdjU+HFWe8PTTTy/yQ+qjC3XkTxnK5PRs2+wTTfqoPfkK9SinV4KkI1e2oXzWP45nE/F6iYE+EoC4xkj9ieVk35gGisf+UjbGqSebch5tznmsTznJlD7kKQ3Z6Cd5be3mcZlHmmekOQADlwyoQYrIOSMMLukaiCbI02CLOJhZh9g+bZTqgtqVs0IpLTuO6kNbv0C6RHmUjzKIx3zIF0IJyiBbIUI89ltjFdsZ1Qbl1Q9Qe5HYDn2K/YKsR5POnGc/EtleGqNYnvw8DsSj/rIB9SQjQ1r2lzZiu8gnHnVDVtSTOPlxHLLuKiOkt/SiPCHSpne2Q1eoE/XK8WzD3A8gv0vb6J5lc/PrCu1EO+c40EaWWSoH6Bz7JnJ5brLq3zjyue50Y4ds26Z6lMn2jH4tOCeNRQVk+SVKfWZMWXgIyRUlfSC3l+spLp2xBTZRfFIkN85rxPO1UUpT3Xht0gf1Q3aO+dSRHPqc59N8TeRrGUr1BPUpT4hlpOtS7TVLVs3febl9+3b1fjJugw0HskrfunXrYDgIi/K0dTYKbaXxCkh12SIE8mgjU3pH2sb+/fsr3Uvbdm39aoL3+vpSXDLHeT9LHdq5fPkys8FgeCHUOc3cu3evOg4vgAU9tc05LdCLfkk+Ae7cuTNSZ9LiGOat1knglQN9lEz6Drzbn9b7fV6Z6tdkbF/jW8PJauGV3+nTpyv/niZtds6wHY9+0Q5sUTfB1jW2Z5t/3F84yZcngWviwIEDdewB77777sKriwxtsf2uVzRdwD7IlB1U79q1a9VxEuJ3MOPI57r73Oc+t1AOW8uPxkXXdon/+I//qM8mAzu/+uqrC3rqVXvTuEzKZz/72eo7lV/7tV+r/LPtdVsbXGvMLdOc10TJzrTH665po1dGwNw4XOBU531i1SxegEmdgYhBCwmcQGk43qiJk4tqx44ddWxQLX6iXIIWBPyMeymgGze+8+fPV5P7888/X+c8oK1fJSQPWchk4h4HbpK0eeHChTqlO9g26zpNhk8Kj8jn/XMXnWOdaX2wzEUf5RLiZBNvuJPcfCUr6spNmMUFafmGPC2a7Jz58Y9/XB1juabvSPBHFgTo/L3vfW9ZJ0z8ct26dXXs4cej73//+6tjhm/CfvKTnwz+9E//tOpTF9773vdW1120BaFkN8qOyzjyn3jiieomm8tOwnve857qyLc4mbVr19Znoyn1+ed//ueLvsYcNm2wE2P6+7//+4Nf+ZVf6XQ9Nj3k6kFlFly/fr0+mx3Hjh2r7D7ug/Y8MdeLFxYHOEmXp1jtXjBBCpyTCZ5jTMc5svPxBCtUFmdHB26K+SahMjgAN5IoP55DaUWd03AmPk7m6SrukrT1qw2eMugTMksTATs3bX/vJd6AtNPUhhZM3Jwi2RZLId60hcYX2nSOkxDluZnFiffSpUv12YOx6AI2ZhGs9kH91aIj2oOFZAa9eAKKMjL4GE+kWtRKNvaIC6VxaWpzlJ0jsuEofwR8Gx+Rzj/4wQ8WfYz605/+dFFcN0ul/cZv/EZ1LcQPRbveRCin3Srqs6DHrk2/8ME/fuu3fqvya9phMZD1yTfzT33qU1W9r33ta1Wccox/yW4f+MAHqiO6IJ9j7LuIaePIx8Z//Md/vJDH+OgD1ib9gTzGIcKvsbDfH/7hH1b5hL/4i7+o5kXNVXnsSpT6/Ou//uvVufwHfaNPl/SBJl9pQh/3wsc//vHqqF0jrr+mRUp+yEU/5pZ4v4DSrmQTWpzQ57gbpjkU+XFMsY9g/DNRRunj4ibUBsdxd0HnguEqd67hfd1wkqlji9/REeI7Ob2nUxg6QpU+HPBiuoh5hNieGF6oi8rEd4pZp6a8oYM0loNSGjT1q61d9Rn7lUBGtF0k2wu5On/11VcX5WVbITfml9rI/cG22b6qF8tA1CWmt+lMXtaLNJFlYrN4nutGYllCtEebTiqH/k1jJKinfgrq53pN/qB0+pHLfOlLX1oUl17ZJrn9iD4cpRzfaLz22mt1zmL0wS7l+LBSfkCb+jiVoG81JDN+ExH7Qh3ao5y+vWgitk152tfHnCXIj+3Qf7WDPpKT26Xvaodjky1oG5mU00emsR7oo1OC2hlHfhxrztXfJv1je/IdIRsoHxmSR57S5T8lSn0G/BhdSCdfOjXpE9vLvqL2sy1pT2U4xvZJa7oG83VAyMQyTdeTkF4E+hptKmIZQpyrcr/yXBrnI9qO8gmSFeshK9aL54rPI2v4Z6jgXMPKeGj0RTsS0wT5Qwdc0lPsPMLTBtuC2W6s+NldmOS1kJkePPGw+9X0msUYY0yZXnzzwsJinO0w82CBwqKltODjtYAXLisPW/7xdZUxxphu9GLnBfTebxa7I6tx54Uv6ktP9Nww+XZiVrtYxhhjzKzpzeJlFrA7oZ/mwaGef30NLMRgNb4GM8YYY+CxXrwYY4wxpn+sqr/zYowxxpjVjxcvxhhjjOkVXrwYY4wxpld48WKMMcaYXuHFizHGGGN6hRcvxhhjjOkVXrwYY4wxpld48WKMMcaYXuHFizHGGGN6hRcvxhhjjOkVXrwYY4wxpld48WKMMcaYXuHFi1kRNm/eXJ+ZNvhfwm/dulXHHuXcuXODl156qY4tH/rfy+cZfKzNdqsV+s3/mN8Eec8991wd6xe7d++uz1YnXM+EDOO13NfcvF8/XryMgMHLNxBuFqS1TRCmDBfEuBchF+60J1smweW+6cuXuvQlTlYbN26sjhF8j/x9+/bVKcsDdqPdTZs21Snzh2x38+bNou0mZSV8BuhLl5u25iX6vXbt2jr1IfK/bdu21Sn9AhucPHmyjq1O9u7dO7hy5coj9xbG7vjx43Vstszq+pk6900rZ8+evW8zTZdDhw7dH9786thoKDu8cOvYdGBMGdt5hj6P8j3yp22bUdDeOOO3Esziuu2Dz6jfwxtPnfIojB3XYJ/A55bbz1cKxi777rR9eRR9uO9558WYOWX9+vX1mTHdWLduXX22ujhy5Mhgz549dWx1w27HcHG5sMvHTsjVq1erc/OQXixe9KqBkLdttRWqkL8BYODzO0TKRciPMgTp2pZXnl4fxXPIMmg3wpYnW4HadidEvfQaQKFpmziXizqA2pHNOIK2lAnZhqN0lw05NsnI4zAu0S6xffRn+5LJK+a12QHdFJQfZRIHxpbz2Jcsl3jsWxyXWC6OJah99Uv6oUdbe3k8RSwzilg26hXtQZAeSm8jXoOXL1+uUx+SfSgi38l9jSxlPEX0odOnT9epD2lrQzrGfuSxhpLPRP8gyOaK6xqEaMcoA6QDgXz0BcrF/ub2VC4T22ryK9GkV7QHoWT3yDvvvDP46Ec/WgV48cUXq3qKQ5SXQ0k+OvCaMr7CkF6vv/764K233lrQn/hKgz5PPvlk1XfsIb8k3pXt27cPXnnlleqcV2Vbt26tzpuIvp9tyHgqNJWBUdfP3FHvwMwtqDhcddaxxfH8+kHb7OQTOCdoq3fXrl0LaYJycQuVMoqzfZe3z8jLMvI2utqmrOorSHfpIjiPW73IzFu/xKknkK+4+q6guopry1X6K79N92hDguyYZaicUD6B8wxpsU21BcikXoxTVvoDesT6iquugurINsiUzsTVn4z6Ix2A86hDbp/ykHWgnvqnNLVL2abxBMklCNqNZciTXmpbfYxx2o86g8ZG9ikhvSUTiEdZ1I86xThlFbLeimv8hOLSX0H9lL7oBiqnOFCfNNHUBkg+QTaR7Tmq74qXkA4xnzTJA/orWdmunCsggz5+61vfWkhT3yH2g/QYl1yCZNNuLMN51Iuy0XaKq0+RWK8E+qjeCy+8UMl57bXXFukzLrQZfQc5L7/8ciX/2WefrY5vv/32/aeffnqkfssB+tBn6YO+pMUxGEX2jzZoQ+Mn28fxU5APcVQZiHUEupI2z8y1dlzEecBxYg0Wxo2Dq7Q4CMTjhKKByyCXdEK8UKiby+c0zmMbENuRc8QyUYbyY1/aoP+UJ0T7lPpPX2J/st04b9MdOJfjQ5aBPm1tZCifx1U2UIg6UTa2T1sxrrrqN+VpQ+R8yG1kcp+iPKExVIj9zTqLUrvIloxoF8mP5DTO1Q7HqDNID+pF2V3JtgbiURbnsU95/JGR7Rf7sdTxJK9NPoxqg3xChPzYrxzPZLsQjz4Bso2C2oeSnQCZUXcgHuUIyY/t5rRoT/oTdQbpoXrjonrqW5bDeVMo9Z+0PDaQx4zzbKeVAj2eeOKJalGleKkPTeQxG4X8WSH66TSun3lkrl8b3blzp3ploK0swsWLF6st0Lt371Zllvo1tLYfd+zYwUhV7xqnwTjfK9AH+jl0soV+lrb1tO0N6Dq8IKrzaTPutxYbNmxYGBe4du3aI9u8bbBdSXlsQL9GcePGjYXXSATqgnxiGuzfv39RnzK0e/To0Urf4SRQp47HtMfz9u3blc6yCwGbks6vGI4dO7YoD98fBbYeBW3odQpBv2a5d+9edRzFUsdz1CsRWA6f4ZsMbKHXONg9XgO82sA2jDXlJoG+oj+v7pAzvMnUOZPRNsfyqgLfjnnxlVITXP9PP/30wqsObDy8cVfngN5NYZxfE6HnF77whTr2YIyfeeaZOvZgbsfmKwHjw73kqaeequL4AvcY0BhyTU6DcefPTJfrZx6Z+29eGJTs4BcuXKhzl254bkDcNA4fPlynTAY/b8toguwCk1zs46lTpx7pGzcfLohp/1xwqbpjO8oTuCi5SXS56QETPZMQF944C1HGLNqLMK3JAJh46c/58+erSfD555+vcx68Qyavax+bmGQ8udnEG0GGvGwXycc+SuOmhO93gYk3kuPATTS2SWh7T5/7sdTxzNcK8jOz9hn8lz6dOXOm8msWwAIfmvTmEjlx4kTVRpwDR8HiAX9tur7Iy3aRfMZQaejPtT2KH/zgB4PPfOYzdezB/PKRj3ykjk0HviuBT3/609WR8Ue/97///VVciyzSVoLr168PfvmXf7mODQaXLl0a/OIv/mJ1zjjgi5/85Cer+FKYdP7MdLl+5o25XrzoSSav9pkINOnEHQomjRLx5ly6+DQZ4wgsGkqUFklK4wZEPT1xATcGbk5diX2UnJIzqk2OXSaSUUxDd9i5c2c1VproxkVP6aUdJ4g3zIMHD1Z9j2PSZRchM+oCxQZ8NEe5PBZxUjxw4EB91o3Ybpfx1NioTNONS7tF0RbUob6C4OlYT6X4HovOEtg6+gfymIgj+FD+ezN5POJ1lfux1PHE/rnf+thRLJfPcO3QV+xbWryp/SY/byL6f1w0Z7sLbp6CMnnMRNsci67RRshkoTOKeKMG4p/4xCfq2Pjw8Wp+UPj2t79d7e4IdCOunQ4eqEoLU/wYX+dD2gw7GAQRd5qarhHK5w9xWVi9++67Cwsp7IiNP/axj1VxYIH38Y9/fEGf2C6MWnBmRs2fbXS5fuaS4Y1mrhkOevXuLQaR84aTaHXUuzxQmsJwxbtwTjm92yMMn2gWyg8dp5bw4F2qymR5aqspPconUC6nv/rqq1V7sVwJZCqf8rEv8bwUJ2QZlIEm3WNZAnbI6fQjl1OINhS5n4xhtG+0i/Qr6Zz7Rx+yP6j9mK76sR3Om2jKV13l65yxjHklmxHQN6bl8QTiUXa0Z5bX1I7qNJUHtdtE9I+sJ7bNZQiyM8TxJcR+iCiTMO545vrRbvS9VIY2IKYRhOIqF2WWfELIRhnSY/3SOaHUTwJ2zGmxLnnEszzRxZ4KpXSVb+PNN9+syupbD44xPinIQB+hD3UF5zEuqBfRdRBlCewbrwvKZf/KUF7+IfiYmI91hT7ejWBLbEKefD1CWsmHSsTrK479cLG+cE4ojXfun0KUo+tn3nh0NHqMHHNejb1a4WKIFz14LIwoTfDm8YCbc5dFzyi4ubb5EIsDbrgZ5qAIZfJctRTo26g5jkVV1F1zI6G04IKst3kU/5E6s2SGF/Ajr05Kf57cGPN48c///M/VK+WlwmsgXj/F136CV0Df+973Blu2bKlTHqDvYuIror//+78f/O3f/m0dWxq8ZuHVctu3XYDevPoSvFIcLmYGw4Vd9YqRPsVXurz6GS5w6phpwosXs2RYvOSPP5mwhk84Iy9sY8zq5fvf//7gQx/6UB1bGnz3Uvqm48c//vHgiSeeWPR9CN+Q/NIv/VJ1/r73vW/h2xUWHPouZqnwTU2XD75ZWOn7F+CXSL/6q79afWzM3Pnss88u5KMfCx3Pm6NZw/ZLfd5rcE4+yBPHp/ALItOd/DGb7W8g+gUT9VJ/oWWMMbBqFi/GGGOMeTzwayNjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTK7x4McYYY0yveGwWL/yX4/xPo/MM/zX65s2b65gxs4PrYZSv8Z+d8r/c9oV5v76nAePG/6z81ltv1SnLB/alfWPmgblfvDCB8j/TLuWiof62bdsW/Zfp8wY3Ev7X3WkvXpZqO9MPGGPGWv/1fxv4GNdDk6+xYEFW/F/apwnyn3zyyWqxPi24sZ48ebKOPYD+vfjii3VsfliKXlu3bh18+ctfHvzO7/zO4PXXX69Ty2gcJ1mAYk/5kubgixcvVnFj5gL+V+nHgV27dt0/dOhQHZtP0A89p8XVq1f5H8Pv37x5s04x5gH4WZuv4TP4ztmzZ+uU6YKvI19hKRw/frwKGfx/+ECw0EapzEowDb0YnyeeeOL+22+/XadMF/SKY6+5hKMx84C/eTHmMWQldyHZcRneeAfDm+NgeDNk5VLnTAY7RHv27KljD7l79+7g4MGDg9dee20wvNkPDh8+XOesLNPQi/Hbu3fv4Otf/3qdYszjRS8WL2xZxi1mtl31fQh5hPxqhHzlEUrw7lj5+X05eWy3asu0VCbm5S14vR/mqDKl7VvlEUrb6G1tSMfYD2310javBoDXUeQ1vT6KdiRkPcjXa4lsA5HtHdvKcXSMfcl1cz+h1MdItDNlI8ijjdjPbItYv5QvGYL8aAvisX7JTtrGV0BerCe9s6zYLiBbZaKtSI8+1uY7IrZV0jnSplccH0LJ18XOnTsHH/zgB6ubL69BRNS3FLIdgHbw77wYI/3o0aPVwuDTn/703LwynqZe27dvH/zd3/1dHSvDuDNuItu0hNL37dtXnefrDT9R/Sgbskz6G69H4qpLUJ7SS2NsTJEHGzDzSdxaBrZXFSfodQhbsARBuRjXlmd8bcSWedyuVVxlFVRG6YojK8pTnK3WWF/brMgnLlROaJs+buU3tZF11PYuecSRRVA52alErA/Skzr0lXOC7Bn1EWpHxLjkqX5T2ahjLA8aG6Ac+QrEKRv7oHjUX2WVH+VzLvkgO6JblKH6isexivlAvRgXGveYR5r0Jz37gOKqqwDkx3R0BtJin5r6KNm0S1xlFJdeHKPNYpzzqDOU/ES89tprVd2nn376kXrjEu0T4XUKebxaoS3ZZaWZpl66dkqU/BY7aTyB+jEOKhvHHtQWQTqjv3xA/kQA+Q9BvqC0iPJoizy1b8woyp4/R8iphS4iXUDAhaqLCHI+cOHmiyheKCUZ+cKmvibK3EbUU/Jj/dwP2mqTD21tAOfxhiTbqF85nsl9FlHuKBmA3rKtoE7sH+1QhvRIHBehNjnKlpyLWEdlI+TJjlGWiP3ONhXSFyQj2gD5akM6ttkokvuc+w+kIZMQx6hkD2hKp67k5Dalv4hpkqcxJD36WmyP9CyrCerRDoFFTAQ50rUUqJsp9QPQm/SXX365WG+lmKZe8ssmst/iC/GabIN6saxkRf+K1xFQPutTuo6MmQar7puXLtuO9+7dq47DC2th+5L35sOLvEofhdrgtYzqs8UKXbc9R7U1jTYmBbuMA/qcOnVqQU8C3LlzpzrCpUuXqjLDCa5OaWbt2rX12cNvM86cOVMdgV89sGUOfD8AsW3auXHjRpU+KU2vWUqgI+MZ/Sm/uoocOHCg0hGw3YYNG6pz0KsZGF6fg+ENojofF72COXbsWCVneMOscyYDe3KNqH/yEezPK5AdO3Ys5BHy6wSgr08//fRg//791S+DeG0SQQ66NoWur1j4Jc/p06cHFy5cGHz2s599pB6vKMYZ32kxSq9ZwxjyykpjtNw24BXhcAGzyE9Kr4CN6cKq/WBXNzVRuplxw8kTZBtMvkzSggsx1x9nQoo3dygtSpbaxiiwQand9evX12fdGD5dPaJn/BCR7xwowwIst8fCpoQWMSx44uKIGzrfS0Ry2+MuXko327ioGAVjEttH35JdQbpzEz1//vwiO7HYwE75Z7/jwkIDu2U7tZH9O4PdYx8Jkh8XHrRLPzLf+MY3Bu++++6i71xmAbZv6odull0fVKZJm17LBdeFxonFCz64nDD2ap8xmNXP8c3qpzeLl6YbQYabCE+Z2qWAfIFyAfHkmJ+OczmekoTymKTVBk/QkXEmAm5QXLjqFzfPeMOdRhtCO00Z/UIjf1AH49z0tJMQFwCcq2984MfNjBsy/WYhI6jLJBafwE6cOFGV0yJty5YtVVyTXrzZS89RY9mE6kc7ozu7O7EduH79enVUfiTqLztI/xIsBKJ/RWQ3juNO7nHRrsUxtij9jY6YJnvlPgt+HRP9FVSHY0y/cuVKse8f+MAHquPXvva16jgN2IHLC1X8hQ9Zo06C/pV8mzFjYfzOO+/UKQ+JOwQqpzHGt0sfOlMm/x2WJr1oExn87Rv++ByLCs6pz3lsL8JYs5M1Cl3/eZeD8V+3bl0de5T8cNUV6YpfxEUi/ZbPANeTdvBIp5+lMTOmyPBmMLcMb1jVO1KFHOedK3BU2vBiqdKGF8VCGvWGC4HqnHQRyxDi+9yYTkBGRjIVeOdLiGmqF9PVTqzPeeyfKLVB/ZhGGYjplIMoU7aJkKZ8gmRBtCtBMkvkfksOxygTZHfZIdfVuArqx3yFqE/Oo19Z/zZ/ieWij4joK+qT4sjIvjQK2T36HMQxRGbUtclOpfRYL/q/xoJjrBf9O8tTXpQZ05vKl9DHuoT8zcuk0KbGEfRRrHQp/S0U8iKye5QjSJetVU7jJl/IUAa7RJr0Qpb84YUXXqjS+ZiZwDnyS7ZChvTK5LFCF+TENPWhRBzT7NtqM7YhYjn0U12O6qMCaULtlexvTIm5XrysJFxIefIxKwOTYJ5om24aph9w4+MXN9MAWfhIhhshN0g+js1wfUd0c58GtNv2B+RKer355puLbubop/qkZ1mj2jBmteM/UmfmGra6+SYmfyfR9krG9ANeo0wDXgXhI02vHPS6Suj/BYqviP7+7/9+8Ld/+7d1bGnwh+O++c1vDp566qk6pUzU69vf/vbg2Wefrc557TJcSFX19RqJV0TqH3pT9m/+5m9GtmHMqqVexJgEpvHOy8qj7eQ4Fkpr2/Y28ws7Dno9Mk3yzgmvXUo7dviOgl6BLCclvYaLkYVXQ9hGerEjg56kAfXYZSLdmMeZNfwzvDhMDU89+su0MJwolvzLD7M02H3JH67abY0x5vHFixdjjDHG9Ap/82KMMcaYXuHFizHGGGN6hRcvxhhjjOkVXrwYY4wxpld48WKMMcaYXuHFizHGGGN6hRcvxhhjjOkVXrwYY4wxpld48WKMMcaYXuHFizHGGGN6hRcvxhhjjOkVXrwYY4wxpld48WKMMcaYXuHFizHGGGN6hRcvxhhjjOkVXrwYY4wxpld48WKMMcaYXuHFizHGGGN6hRcvxhhjjOkVXrwYY4wxpld48WKMMcaYXuHFizHGGGN6hRcvxhhjjOkVXrwYY4wxpld48WKMMcaYXuHFizHGmEWcO3dusHv37jrWzosvvjh46aWX6pgxy4MXL+ax4Z133hl89KMfHaxZs2ZRePLJJwfPPfdclb+cvPHGG4/oEkMEHQm3bt2qU4yZDVwLV65cGVy4cKFOebCY0bWj60V85StfGaxfv77KN2a58OLFPDY89dRTg507d1bnhw4dGty/f39w8+bNajI+derU4Pz581XeUuEptMtT69atWwcvvPBCHRtU+hDQLfM3f/M3lZ6bNm0qLm5KdNXDGPH6669XC+STJ0/WKQ8W2X/8x388ePXVVyv/xBe5XljQiL1791aLF+/AmOXCixfzWPH973+/Om7fvr06bty4cfDEE09U5yvBe9/73vrsIZ/97Gfrs4f87Gc/G2zevLla7Fy9enXw9ttv1znGTI8//MM/HBw4cKCOPYBF9ne/+93qWoGPfexj1XHdunXVUTz//PODP/qjP1r2HUzzeOLFi1kWeEqLrz4UZwchPsHNmosXL1bHLVu2VMevfe1rg+9973uDp59+erBnz54qjXf46KVtcMXR96233qrSZsmHPvSh6glXoOO+ffuqmwdb9NxM2EWaNvSNJ2jt7LBYWo7+mvmA65Jr4VOf+lSd8ijswnz+858fvPzyy5UfRvBPrpHvfOc7dYoxs8OLFzNzmBTv3Lkz+IM/+IPBu+++O/j6178++Lmf+7lq+xlOnz5dHTOjvgkhjPNaBHlCr18+97nPVbsZPFmyIGAh9cu//MuDXbt2VRM5C4f/+l//a1UG3bX4WU5YVNE+uo3b566wSPmlX/qlqs+8Sjt79mx1zE/XZvXyr//6r9UuZNPCGN/btm1b5Rf/6T/9pzp1MSx4f/jDH9YxY2aHFy9m5vBEdvjw4cEPfvCDKs6rkk9/+tODf//3f6/iP//zP18dMzzZ6TuQphA/KhzFj370o+r47LPPVnWZhFnEfPWrX11Y2LDzQLvXr1+v4h/4wAeqnRDKQuk1D+/5tZgiHDlypFrkxLSlfAvwjW98o/regN0iFhV/+7d/W+csZil6UBb+/M//vBov7ICNZrHDY+YTFh3akSyBP/C6ku/GfuVXfmXRw4Axy40XL2bZuHTpUnXU6xm9kvjkJz9ZHWeN2vvwhz9cHblJ86QI165dq45AOXZZWNhoa5wdCfj4xz9eHSMszLSYIhw/frzauYlplJkEXlnxyuj3f//3q4Uai4qmBcVS9NCOEotKY5rA93h1ybURrxljlhsvXsyyoAUB29IsGkCLGXY3Skz7tZHae+aZZ6oj6OPCuKPy7W9/uzryHQzw2ks7L+zCLCfsCkGTjYyZFr/4i7+4sOM4ipX8yN0Y8OLFLAtaEOinynFBkD/8E9N8bRTbe//7318tWvSxLhOxdoPgm9/8ZnXUjpAmdHYxps1Pf/rT+uzhQiqiNv/H//gfM92mVzvs7GAr8/jxC7/wC9UDRvZDXjXy8brSdd3EhwBx48aNahFkzKzx4sUsC3lBwMeBMIsFQQm+cxHve9/7qvB//+//rf6mChNxfBWjHRq9IvqXf/mX6rhjx47qOC1YjGhnBX7zN3+zPnsI37dIRz6WjH8cbJrQDmPBtzW8EljOX4CZ+YAdUXYb+cYqwsKexct//s//udrt/Ku/+qvBa6+99shDB4ven/zkJws/pTZmlqwZPr0+/E2mMcsE33Jw4+a7jEm/B1kumLhZPPD3VZp2iWYNT73cFNg9muUly4KKRRILmXE+hjarA/5IHX/rhV/fjQsL6w0bNsz99WxWB955MSuCdjfmfYuZRQMLF+B100oT/yLvLHjPe95THb/whS9UR/N4wQfbLNbH3eFjp44FjxcuZrnw4sUsO3FBMO9bzPqDW2ynr+TPhvnbOAcPHqx+6TFL/s//+T/VKwH/6ujxhf8agL9A3fVjeHZR+TtOk+zWGDMpfm1kjDHGmF7hnRdjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXqbMuXPnBs8991wdM31g8+bNgzfeeKOOldm9e3d9tjKsdPvzzq1btwZr1qypxrIrs7hWx2nfLB8el9VH7xYvTDZMOvOGJs99+/YNNmzYUKc+mCBJn7XOyF/KBSo9Y4jkvFE3e1iOvsvuXfTJvPTSS1XdmzdvDrZu3VqnPgo+d/To0Tr2YCKUHUqLiphfujlKZ46RPAaxTydPnpxoAUMd+rlUSv6FbPRcabDTzp07B/fv3x/cuHGjTl1M1D9eq9NCYz4OjMu0F6VLGW/suFzjOS2/HMUk47ISZHvIR2f9IEybtDPJ/LniDC/4XoHKmzZtqmPzB7odP368ji0fhw4dur9r1646NjnYt0l/2pg321+9erXSeVKo32a3s2fPVv0WnA8XO9U5R9qO+ciS/ZSvOOcxSA5wntvJZUgb17eQQR+WCm1Pw79mQbR5EyX98eVo86WCrHGuD/SZZvuwlPHGhst1fU/LL7sw7risBMtpj9VCr3ZeWCUOL7DqSXmWT/OPM8OLvD7rB3fv3l2SztTfuHFjHXsUdlz2799fxwbVrprKc8QfL126VMV5erl48eJgz549VZz84cQ5eOWVV6r48HqrwnDBVMUj9+7dW9QOOy1w/fr16gjPP//84MiRI3XMiKbdFmPMKqZawvQErZ5HraTplkJ82mJlG/PiShd5MS8+8QL5esqPMpVGoAwhPwWqrqC+nvhVN6+6s9w2sIfK5vIxnTCKkv6iZPds00zuO7IJ6nu2cxtqAz0EsmK8CfRoqt/0xKMxaIP6sglysn3IzzIkd1TfKZN1Q36Tvhnqx4AuIJ0IWd9YngBN/kU/Yn36Q5xjLJ+J8qRTG/FaIMTrL15DhJK8Jv05khd9OPoGxDxCG9SN9oCoX5Sd9Vafcl+jj6BL1jfaItYjNNk2t02b0ScUQGOqNiWTOrFtkG6R3BbEOEEyKYtcobYjaoN0yQPSJa+kV5aTiTaNckWUz3lso6Qnfcr2j/KjnWI6QfVop00GIaIxiT40qt9AOflZrEvItpwnHh2lOYVB1IBjaAwbHR3kgIJ8HAAYBJ0D56pPnehMlCWNdmiXc4IcQXI4xsFV+3K4WBdZyldQ+2pPUC46nfIJ2ZkB2VmXko2QGdspoTJNIeoV7Qu0r3juu/RRyHXbiOPKufqZdaWNEjGPutEmxJvq0YfY3xLUj+Ody0fdBe236Qsqk4k2bkOykYEOgrqxfowjO5ZFB+LIogz5QrZXGkfiBNmAesRje5STvZSvULJHtp/qqF3itCeZJSiT9Qf1Qfohg7j8g3isk+MZ5MTxJy5Z0jvGkRVtQ19j/RinroLqyDYaI1C8CfoQ2wTi1Ccvto9+sU35g+wmW0iP2HbuL1CvpGesr/JqQ/qQrjIE5Kgf6C1ZQH6MUy72K9M2zupHlq982aikJ3JEbF/9hZI9ogylNdlTcnROkG6SE/WIYBfVEZxLJ6BujM8Tj86Oc0p2PgwfHQ4oE50MdNHFAQLKkc7gZNkQB1314yDKmfLAIis6S66relFPzkkT9EsXJuT8EtkeJfuU2s5k/SPILNmKdpBLiG2W7NYmvwn1HTm5feJt/aGt2J5kiRyPUK/UX0H/Yn6pfGnsSnbJYMdSv0iPvjEK2olyiNO+iPq12bLkTzmN/ue+Rn3lf7H9Uf1Bpzh+IJ1lv1KZTEl/6sW2s37ZHqPGDVnUiUimQpSX+0489iPrQ37uQ5aZ4xnai21GSv5bSgNkRF2kq9outRPjWc/cV6B8brutf+QpRDuW5ETIizLjOOcxgjwOJRtlHYA2oo7IF8Rzv2IaOuSxL9k860E86xGRTiB5Ua95phffvPCty9Cg1VfRCqdOnaq+L4hfSVMmw7cDfNeQ2bt3b+svTIaDXp+V4RuFWcG3EvRPXLlyZTB03Do2OW3fdkyCfh2zY8cOvL/6vmPa0AZjQTv8oiR/38CYM5ZN8L3JM888U50jK9oR31mKXQ8cOFDUJzPKlzL4O7Lb+jUJ+nXTtm3bFq4j/eKGPPrCNz7Ky78uWiryvzNnzlRH4Brevn17HevGunXr6rPZwlhiH9kDu0HXa59fkDD2yOH6GAX255smtSe/Kc1fk6JvqdQGIf/qbRqUZKrtacMvcujHcNFR2Xnc661tnBmT+OvRSUEu1xb6oec0mPZ8jjxsofmWMOtfOy2FXixeuKAZ9By48Rw7dqwu9QBu9E00/RyMAStdbOvXr6/PmskTGbKWSp5g+CD0woULVdo4cGMoMa3Jn4txuKofHD58uE6ZPnfu3Gn8GeyoxQdjynhokcoExUJLjPpYtw1u7FkfLZKiL+GP6B9puxnpQ/RpL1wimuRjkB3ok9Lo47Q/jB8+6VULc/k2/jOqr5cvX67PFjPtybsE+kY7EdoeeoQ+3sb/xtETe+T2pu0LzC+STXsnTpyoc6aLPmSfJVxr+BM+3WVcmmgb59u3b1fHSeFhhAVBni/GZdbzOeCr0QbYtnRvnAfmfvHCwDc90fN0GndfKIex44TLuS5+ragFefplSFxhdrmB4Ng4JDoIOWmJcXdquCnLgbo6fSynX67EfqEfctsucibbcS5WlWUM4m5RZFTfuTi4kaFfiXjxUE7jfe3atepiI16qS7saD+rhHyxIVZaFBU/9xEsXKIuR0mIUWaWJWT4RbwbYJP6KqA30QKe2xSBjHHcq0GXU0xELQMBWjH/0WZC/ZxtybcWJseSH407IW7ZsqcZBvj1q4Xvw4MFKj3hN88DCTVd0fWAYV1f0zH8LJurRBfl+0xhFv6OvPKjFtHHbA413iTzGLAzjzkJXW0K8meYFED6GrNK8Kkp6ck0DNmiaT0rogYD+jdMHaBtnxgQ9NOegV2kRQZsat2xjiDrl60+U7KG0SefzrpR0V5+X4yFhIoYTyNwyHJjqHRxheFOoUx8wdLiFPEJ871dKh5hOEEPHWpROu2I4SS7Ki/Ig5lEWPTlHRq77u7/7u4vi6ArIVBrvW2M8hqhXiVwOWbG+2itRajOS87KetCnbY4Pc9z/7sz9bFI99QU4eX5HHJqL2muwS6wLlOEd30FihaxOUUfmsSwySkcuoLuTxUIDstzEIyRaS10YcI85BdhiVHnUHpVNOtotpMa66Stf45nIK0qFE9iWVLY1HGyqTdZduUZ7ayOPS5CtZJrJiX5Gn85Kv6NrMfVV6TCMIxVUutlOyaZaf+6N+cMxjhb4i+3KUW9IlpkNJz9ge7VNe8VdffXXhnBBlxXLooT6QXhqXElEGIdol5skuBBHHUXV1rnIxP/Zd10lM+9KXvrRwTlBfs82VDk39VHrUV+Q+Y+MsZ55Zwz9DJc0cwYqX7wLiO2KeBHg6GDrl/K6EJ4RvA3gFNY0niGmD3U+fPj3Ra7tpo6ci7VbwFMYuzCxfMU0bdOYpMo414w/zYGNjRmF/nQ96998DrHbYvuP1Vn7VsFwfKS43LA74DmUeFy6ghYG2UFcK/IItfi1cpE+fFi4svnjdlsd6tS3GjTGzxzsvcwjfMezatWvRyl7fbMzqi33TDk9bK/mktdLtTwPtHp49e3Zh0aW0q0v84NKY5cI7L/OBFy9zCE/Zm9KHv3HCN6avsPuS/4sDT0GmL/AQGbHvrhxevBhjjDGmV/ibF2OMMcb0Ci9ejDHGGNMrvHgxxhhjTK/w4sUYY4wxvcKLF2OMMcb0Ci9ejDHGGNMrvHgxxhhjTK/w4sUYY4wxvcKLF2OMMcb0Ci9ejDHGGNMrvHgxxhhjTK/w4sUYY4wxvaKXi5c33nhj4b8lX21s3ry5PusG/wP1uHX6CmPO2E+L/D/EzhL+N+Vz587VsTKMI+M5Cdjlueeeq2OmDew0TT9aLvD/Sf2jL9BHrss+js8o6Ffb+DE/ME9MAvUmrdvEUuaj5WDuFy84cbzJcL5t27Y6Nh0YpHEXALrIBIOcnRNnIq3LhUj7UV6J3AZ1Nm3aVJ2vZmTHixcv1illuPgpN2qRkMeuK5PUk05HjhypUx4QZXEz5fzmzZtVfBzkE9O+JuaBScepCfnRqVOn6pR+IP8Y5f/TRPNuvCGiR9sCWb7YVqYN5rOjR48O7t+/P9i6detY82cTo3SeFK7rrvcM2qcfsHHjxuoYka337dtXpzxA9eI9JaO6eX5ZCmqX+aik79wwdJS55vjx4/eHN+g69oBdu3ZVYZ44e/bs/aWa89ChQ4/0NVJqY1Sd1cLVq1ervnOcBvjVcrn/cBKo2mL8mtDYUnYS8AF8wbQzbT9aLpbqH9MAH+O6mQX0bxbz2Kx05lob5x7UZb4hf1Jdl1K3ROleM2/4mxdjjDEryp07d+qz1cn69evrMzMt5nrxwrYh22FsX7GNRYhoy4zAFnMk5hHatt7YAoz1qUs8yshbhOQpjfra8svt5ba1JUfIOrfR1gZwrvSsa8wjoHuE8jE/ygXV4Yj+sTxx0Ba/yrYR60tX2oz2UD4h09YW8mJa7rv0jcT8NpArfUGvhBRKspvIsjLSu629JqijMuOMD2WkF+XUtrbvCfE1AsS83J82fekf5fP4RLKNutQB+qy8rG+JNj2j3Qiy3ThjL7sK9SOifnHMbTXRZvsoh4BsiHUIbfZBb/oJyGMeZj6mXlN/SY8ym+wXoY04z8e+EJfugHzKq3+ATMkn0CZ01bnNjkpXAOTw6pHXeLFO1iPqHYll2qB+LNPUz0jsSx7btn5CHKvTp0/XqXNMvQMzl7BFylZY3k5kuw7VtW2nLS6Oisc6bVuS1CNoy51ySpN8bTVrW05llI+eeZsNeZIjqC8dgbwYb3sFVGoD1I7qZV0VFznOedRBtqW92AfSNR5Anvov0IEyXaBurI9ctSuiLaQ3gXMgX2VUP8ogLcqgn9I/lgfqcE6/SiCHfOms8pGmuiorO2dZoLGlbC4P6JvtFePIjO2PMz7RFtIBFJfNkBnziec2FWeMYh66KM65ZGt81GeVyTbqUgcoJ32Vr0BcfiQfarMr51E2EJfcSC4HGtPYnvqlPtCGyhCQDeSrDET/ANqLbcY4R9kAqEMcHaJMiDJESW9kUDfKjciuBPltk/0y0i/qRjnJgyifQB3JUlwgi/gonQEZUacYxw/iNYgOxNV29BvSYjznR5sKdItlyJOupBOPNmnqJ0i26tN+LB/7BTFOGcpqrIF2SZtn5lu7IQxGHEBgYOOgA4aWo5EXHbY0OBHK54HNbRKPMikTdZBzRkppQJpClpnbjZTkjdKVfEIEGcgq2RaiXk190GSii4N4bqeNLFf9ULvIjbZRe3EMs/5Zp1w+yuQY24fsBxnyNOZqqwvyP/osoiyQPTiWxoS0WD/3lfyoe84n3tY3leco0C/qmGXm8nlMgfqkEaKsLvbPNhpVR3bOfSjZRWXa7Eq9WFeoTBdKOiGz5LexTPZt2baL7emz/DxCmSizjZLe1C3JjVBH9myyX4ncX4h9ElE+SE/ZJTNK59zH2CZ1Y1sR+hV9U5BOfULsT6kvOY3zqGu0yah+5rryKZXnvKmfpXEq6TtvrMpvXm7cuLGwVUgYOkCVfvfu3eq4UrDdiD5DJ8IrluWXQmw9ssUpWxCg7R1zF734NcDw4h2cP3++ip85c2awf//+6rwLW7ZsqY5shbINTN2DBw8OXnnllSr9xIkTgz179lTnk1DasuXL+cOHD9expUH/Gcdo17xNOwm8GhxOOI/oTxp5aku/Lrp37151zCx1fEYh/dBDOum1Jnl6rbJjx47K14eTY5U3S/TLCPoq2Nrfvn17HXuUNruePHmyOlcegb7Nauy7Msr2Fy5cGFy+fHmRfrB3797BsWPHFqXrtdAsaLLfNGHMGUPmLLXR9HooM8qO3Ef49ZPySq9ahF7pAP4+XEhU59Nilv2c9pgsF6v2g12cByeKgYt3pcBBWEQw6TH5LSfcOLItdBMv3SihywdmBw4cWFhsIGOcfnExcnO9du3a4MqVK1VdFivow0SgMksF+bMCnWVP9GbBvFSQg+8ySeVxGT4NLRpDQpvNlzI+XdFCPAbGjUmffkxrsdgVbBQX6+gw6rpvsys3YKUhi0U1zGLsx6XJ9sACRmlc/1pcYQulU59xmiVN9psm9FltEBj/0pzWRJsdWcAojcVL02KPRSF21oKtCzxAMgd2ZZb9zHL68AF1LxYvTA7jwBM8k0kckFFPGOM4gcCxMyU5MU27P0wm4/ZLjKMrNzCcXAsC4BwZ2tmIK3jZqctCjzL0gQ+9aCeiJ5E2u1OHcdKTMRcSFzNPCM8//3yVNgk8NUsW8mPfl/qkqTHHflHW9evXx95JK/kPcMPXAka6MzHqaUmM6kvb+CwV2TfLjTrdvn27OtIHfHASmmzUBDt6cbE+avHUZte8m8JuxoYNGyYaey2iqTupLcQo22e9L126VD2MMA7xWkCntt0EyLvVGtM2dONrsl+JNrmj5rvYjvqnmzI0yR7XjuzirVu3ro496pvSk2PTYlb6qQyLzDbiPWJUP5sY1U/S6Vv0aT30zDXDC3zuGU4M1fs3HWMQig8n/SrOMZYbTlJVeiaWQX5uY+g8VTmlD53gkTKCPKXRXiwzXPUuSkM/ySG9qd1MbGOUruqz3l8qIENQpymv1IeMymToX5RVQm1H0DXXy2NZGuNcBjkQ7UWgD6W0WLakd8nWMU5+iWz70lhnO0t39UnxXI78UXo0jU8k2y7HCYCdFKcMZFtK19hvykgP9Btlf8pkG3WpE+M5oE+pn1CyKzSV7zr2IupE2dje8Aa2cE4otf2bv/mbi8rIxrmvTX6i9Dh+BOqXiGNHkE6l8RdZNjrEPhByHVHqR+7DX/7lXy6Kkw+MRfaVSJvOosmOOV0+J5ROudgO+sS+A/Fo1+gz2XbIy21/61vfKvazVDenS+8sU/2EPFZRV9WfN9bwz1BBYyaCJwC+McjbpTzR8cTX5cnAzI6m8VmtsIvItz3xFZl+UjrqKdcY0x/8R+rMkuBdb37Fw/Ym6V64rDyl8Vmt4HcsmPO3PfZDY1YfXryYieEdKb8myTcHvjPo8s2MmS1N47Na4buOmzdvLnp3zznfmMz6w1RjzPLi10ZmbHjC5WOzTZs2jf1BpZk9j/P4qO8RT3HGrD68eDHGGGNMr/BrI2OMMcb0Ci9ejDHGGNMrvHgxxhhjTK/w4sUYY4wxvcKLF2OMMcb0Ci9ejDHGGNMrvHgxxhhjTK/w4sUYY4wxvcKLF2OMMcb0Ci9ejDHGGNMrvHgxxhhjTK/w4sUYY4wxvaKXi5c33nhjsHv37jq2PKxEm6afrFmzpj4bza1bt1r9ahxZTWzevLk+K4MOo8qsRugzfZ81jO8s20E+85MxbZw7d27w3HPP1bHRUHae/WruFy8YL07gnG/btq2OTQYTCXKaJpRZtGlmy6gxXQ64iaDDpk2b6pR22spK1lKQjJs3b9Ypj8INHB2mvXhhHOZ1QcSkLLts3LixTp2OzSNq5+LFi3XK0kHHl156qTrnOG35ZvowTozbSqG5cd++fYMNGzbUqc3Ir06dOlWnzCdzv3i5du3aogn+/v37g127dtWxybh+/Xp1jBNXZBZtmtkyakyXgwsXLgyOHz9ex0bT5lfjyirRRcaNGzcGhw4dqmPT4/z589XigKe9eePkyZODs2fP1rGHYC/GZFo0tbMUWKisX7++Oj98+PDg6tWr1bmZXy5fvryi8xJt49ddH6r64lf+5sUYM3VeeeWValF09OjROsUYY6bHXC9e2L46cuRI9QTHNhYhotc7hLwtxxOf8mI90tk+A+XFVw2zaDODDOrG8npCbasf81ResEWvPPoQiXkE9Vd6RNjqzrKpo37Hd6ZRZqmOtisJqkd7SkNmRNvshKwXfdCriFyfttvGVChdZbropC1UhWxbiDrxlJVBvyhjEtrqx7xSfolYvslWMYwDNjp27Njg+eefb919ybaNto9jQhDyQ4XsJ7TFuMqXmmSePn26Tn0IZRlLQZw6sc2YL6Lcpr5C9gPsHmXnvmQoA/g659kXox6x3xDz5Pcl1F/5tPobxyq32+bf4+RFvTjPtsz10YOgvsmPo655vJCJ7FiG/sZ5oTTGyiNEvagrm5Xqk8duGa9gyCOemYZOhEyTThHZjtDmF3PJ/TlmOPHdP378+P1NmzbVKQ/YtWsXe7vVEc6ePVvFOcLVq1fvD5/6qnOgnOLIVPkSk7ZJHeVBjgvqKEgnyioN0CHmK86xFKcdndP3mIcM5AvS1T/K5f4Q1CfqKk1tUJcjaWojximnOrKhdCJwDuRFG1Mv6qk4QXUJajPWJ61tTCfVif5TV6gN6ZltDbENoGyUkeOcx3gk9h1k5+wX0h9oW+VBMkS2k2RkneQDgPwYH0WbDQVlZEegDLo09QlG6c65AmWR3yaTNKG4ZCke0zTeUW/KyTbKVwDpjA5Q0oUyGtMmVJ+6ag9im5KJTgQRdYQch+hrhNgeQX3WnKB80uP4xjj6xH6Rnn03Ql7sj3SkntJAdRViOxxjm4rnOrk/spfaV77qqb8xTh3OCU0+QrnY74hkKYyrE+lCughkSCfAllEGZD+IcbUZ25g3FnvPHIKxNYiCQYkDAxg6X5CUIZ1QGsgmJmkzO4IGH0fLZFklRyFfDo8+uW3ai44oGQqSRb1YLpIdXBdF7EfJVqP04ZjrEI96cC4bq91oq5hfsk/Mhy5jOolOsU3AZipTsm3Wi/M2v0BGtqUo6UxZ+UUeP8hjmGVkfSDLKZXpCu3FuiU7kh9tBNSjLLqof0Jx6lAugqxsz1L9nKZ6EcpEOxDPekYd8lhC1jHrB1lH4jG/DWRF+0qHaF/al97Kj+R+ipIsysWyKiN9aaekT+4vaQTJKukVIS/KpU+5fLY1UCbqn8eZOtH2pfGJcjlmW8X8UT4C1I9tZsbVqTR+lKUOdXUe60OUUbJ/lKv8aMt5Y1V+86LtyB07djA6M/kgMTN0lIXtXIJ+nXTv3r3quBRu375dbT1KNoH2SAe2BGmPvpIe4SNEXmXEuktllD7jIhsNL64FeXp1N2/Er/X52HUUs/SLEqM+DOxiU/rFtyrSuWnLuQTjFvvLmAKvkcSdO3fqs4fwkSC6l15h8eFrE+vWravPminJnAZr166tjnycDLSDfZ955pkq3gS21S85pNusPui8e/duddR4EGi7i+92oc2/2+bhrVu3Vh+Fqh6B1yZLQbZEB8lEN5jUB6Y9182K6D9d5pZZ+8VysCoXL0wOwxVmNSEuJ8NVb3WRxsBFOg2GK+JHZDOpM0FwMRFvQr+iIDCBLHWSgCZ9loL6EcNKwy/PMvFmniex0qQ2S79gYi3RdlPPi4fSxM4kJl3pb3zP3wR+xXUX+0ngJoWevIMXjHXTDeXSpUv12aOUvimCUTf/3FZpATUutEl/WbAx+bNQY6xHjS35lMWmLHyW46PmPCbTvEk1+feoeZgyKo8/YMdpgL9FXQhLWRzOYq5bKl2u+7yIwcaZ3C8vXqZMyeij0E2ECbPp9+ptq/Fx22RRoFW+6DLhd2H//v2Vs0Z56B5vBupL/ugqL1S4MeinlhAvghMnTtRn7XTRZxw0mWfdJ7Ff25iOA5MdkxYTapRJXDebgwcPVr6lflMu33hn7RcQ7cZ4o3fTDRR9Yp/QPU9Y2WcYayZF6nCTbtIfuXv27KljD9H4avdFZXbu3FkdAdnocuDAgerai31Se9g7+x0yuUG2gcxcj19DlRh38maXJS7Y9u7dW+e0Q1/4aBg98ljxEWXbbtc4Cy/pM41rq8Qo/26ahxnvWI4/daBdOnHlypX67IFvjULXLOMdWUpfpzXX6XqbBqOue11v0Q7kR/vO2i+WheEFN/cMjV69f9MxBqH4cCJZeGdIGA5o9S6Pc+oL0lWmxLhtgtrJ6ZGYrzI5Dlk/vYNUiH2JOsa+c551Ik1kmVEX6uW6lBdN+kS9Cbl/5ENMl9xsa9Kb7BPThze7Kq1pTJeiU66rtkS0EfqX9Gryi5hGiOR2sz7SOY8DbYlRMgicR/1yPkH16A/x6EOQdYj5qqMgP2lKh+jDhNinPHaxrZge5UGpns7RP/tejmsslS7753IKyMzjXrKLfCGi+iWi3rltyYp9FbEcQf0R2T45ToA41mqvyb+jrtHP0Ltt/KEkU+evvvrqojyNhcj+ix6l9prGR3aVTtm/la5yCk0+EtumTTFNnaJcEfOxX9YLYhkCOuWx13jOG2v4Z6igMcaYMWGXhFcI8bUEuybsKo16tVCqy5MvOw4r/VrCmHnHf6TOGGMmQFvu+XuKLh8489qBerkuryS9cDFmNF68GGPMBPDLM76HiN8/8G0BafyBvjb4HqG0SOnTB5PGrCR+bWSMMRPC7kv8EHXTpk2tCxA+eIazZ892/rjXGPMoXrwYY4wxplf4tZExxhhjeoUXL8YYY4zpFV68GGOMMaZXePFijDHGmF7hxYsxxhhjeoUXL8YYY4zpFV68GGOMMaZXePFijDHGmF7hxYsxxhhjeoUXL8YYY4zpFV68GGOMMaZXePGyBPgfZPmP1jguF7t37x6cO3eujq0epm1L/sO85bYTui+nLyyFW7duDTZv3lzHRoMt8b2lgAzGmLEx3dF/5mjGYyXmgNUE9ov/Y/q84cVLAQZt1ASrfP5fy8OHD1eDvNSbLxda0w1FEz//3f66devq1P6B3egHN08xTVvKTvxPv8tlJ+l75MiRwfr16+vUR6E/S10AALbLNhwHfIz//bjr4oW29u3bN9i4cWOdMj70/cqVK9UYnzx5sk41beAr2J6xijBu4yw8J2EavtplHu2Crq82YltxDlgOsNNS5v2udLHDNKAvy2m/SfHipcClS5cGGzZsqGOPwk2Dgd2zZ0+dMhhs3bp14eY7KUzuTZMS/33+1atX61h/4caFnXQjnLYtsdPNmzfr2PIgfUdx+fLlJS0AxPXr16vjpLJu3LgxOHToUB0bDX3LN9BxYWG3f//+Oma6cOHChcHx48fr2EMYP8IsmYavjppHu3Lt2rWR/hfbWu45gAfKtoeWadHFDtOAebcP9xovXibg3r179dnysnbt2vps9bBStjTjsZQn/Ul3iIwxppHhU9Xcg5rDlXR1JAyfGqv0Xbt2LaQNV4pVmhiuUKs6gnzKC+KqS1Ae9WK62hJRDwW1g4yzZ89W58A59QmUk465PqiMAnpk1Lb6orKx71m/mJeJMnJZ2S+WGT4F1rkPiLbKefRdeQTZRfrF8xhIg2xLdCuVE7SvPOnc1vesXwTZ6n9TGYgy1GbUOZJtTRza+pV9AtlZb4LqtI0HxDq0Lx26QFn0ie0Tj8Q+xjylKWhccl+yPPpAkFz1M4516TqJSOfsHxGVkf1ErEMgHsn+EfVvsgUQV17WBeI4kp/7iK6xHnH1oavctmsj6h5l5f622T62RZANoo6ESK5De3kMCJlSW9JVtlEe8ciosRBZb/UnphHkI+RTR7oJ4vJjYBxyu3lOQGaTHUr11bagPdpVH6RjbifrRRrHeeVRT5gj4kWE8SEaXIYlT/lxkDUYisdBjvlAPeIaaA1whnzpoPrRsaWT4gTyJR8domNRnjh5OF12xAhlcju6+EB6iRyPoI8uQEE82o+gdpSueDyHGKcPUTbn5Gk8NVZdbUma6kCO5/GSTaJ+EdKjflFf6UiIehJvqgOyTxzbCDJynbZ+cU75CHWRQx5tRYjH/sZ4Lq/+ZPltyC7SH9nEkQ3orXNQnLbUXtQPe8W+S576yLkCeWqXo84hx4XkKcg/lE48l6Hd2E60j2woOVl/8pXXZAtApspBjEsf9BDEYzvSVXoqHtOka9SBOPJB+YQoW9A+ekW7dtEtQjnyYl85jzaNcdqKZalPXMemdqDUFmnqY7Q9cRFtDzkuJCuCvqRDbKPJp5Ab00DxaBPKxjj1kE0dzqMdOI/147jmPhOQjX6S19au+iGfmUfKd7Y5QoMeIR6dLA9q6UJjYDQ4pOf8SL4QMiX5khkHm/Zwigiy5ViZ7ECZUhsxjfq5PfJK7ZXKilL/QHZBXrQ3qK+qG6G8dO4yVrmfyI7jEfNLuuT6TSCXcoRod9oiLaL+gXTOkNY0thBlQFu/SI86RWgjtt82HkBe1ou8Jvklsu6gtJI9ovzSeJRsle2O3tE+kOVkW2RK7eS+5zJN/kM92TnnUwdd22whuRwFdSSTMrm/MV/I7oJ47A/EPpVsFPNLlNqIcVBfs51EHj/isU3Vxx7Iz30XJRtkcluydWwv2mHUWERKYxrJ7UApLfZXxLEr6UQdySnpJ98SkhHbbrMf9SlPiGWkK8d55bH85oUP0YaDXH38xFfVhGl8Fd8FPrQ7evToQrvT/NWAPn6VbALcuXOnOkb0i49Ytuu3CcjDfrEuH61R/+7du3Wph/ABHR+1Tgo244NPtcW4AW2V+jYK/Rphx44dzEhjfbwKpT5OQlu/+GgO/ZRH4NcGJdrGA8ibJbJHbB8/HPej0lEfPao/27ZtW2iHX0FBV99dCvogtNQWcwpj1mYLfd8V5x3GX+Mzrr26ol/d4fcgP9qyZUt1nJRxv8Gjn4yX+s44Anbh42Q+ElYeYZaMGosIc9fwJr5It1n8uqj0/R9tM39Om/jLJebA4QKnOu8Tj+0Hu0w2DJoCE8xyTIDAJKV2WbxoUpkEObwWB9yIY78ITb/a0S9/CDjviRMn6pwyXNjPPPNMdc5FH9sgMAGJphvtpKBfbk8XdZ5wSpNAhMUj8ib5NVMk+sukvtPWL/RT2vBJanDs2LEqvcSo8ciLvGn4On68ffv2OvZgEoxh1M2YX9dltIBrgxtJbmucX8bQdxaGo+DXHZn4sFHKF1m/aAv8NeeL27dv12cPyPFJYG5gXtDCgUUDNhz310T8oqfEOIsY/Dj3XXMX/qo09F2Onx+3jUUEHZVPHRY6s0K/JpwlzCXYuM9/tmBVL17kBNxIeRKNxAtDN9p4MbdNGm1P3qOeyvMFmf9uS5enrzhpHjhwYOFnbZyzCIsLB85LN6qsB089+WeN58+fr88e/B0FbrRcxPysmQs4y2ARphuvnqpEXKDFxUYXWx48eLCaLGI/JE8/sY47Z2fOnKnPmtH4Yh9sNg7qY2wz2qqN2Ie2fnGM6dzo881G+W3jAUxSsR36nP2MvFFPlPHmRbmdO3dWtijZA9R+aTGJTtlXWVS2LdDo/65duyo/j6idJk6fPl2fPSzbtnBVO3lsiKNjzI/6I7vNFlw7LM6a7IQ/RJvQdtOCIerVBRaZceGgBUMbsQ1snn2Mhx3GMftlJM6jlNVOmVDfs9/R77gTF+eMJsZZ6I0aiwh2iOncV/Iie5wd4Kb7knTCRtH2se2SHaKMUQ+gEbXBEV/uHUNHnlt4l4eKCsOb56K43vXFdL2jGzrBonJR1tABFuUTIshQOrIjWYfhBbnwLlUhl6EtkfskfYXS1bcIZZEddacvkaxLSQ5kHWM/Y/8VkBuh3VwmUsrLfR9eMIviTbaErC9lRdY3yoj9EjEfnZDFOXZtGh+layxz/6PMqFsk1lGZpn5lO2SZUU8YNR6xfOyzytFe9NMMdaKuJbsqTwGdcj+QI6IOBNk69yXWgdgXAm00EcsRoh2z37TZmIBekZwv/SGmE2LdeP0SYr1oE8pFm2e7ZDkEobj6FMvEkPskYluSkcey5AORaF+Vjf1rS89jqr42+WhsK9ulpL/6ncvGsRAlu0ei3C996UuLyqptEdvDf6IPiaxTHCPlSYfYb0L0F9pu8uFsr1gvnis+j6zhn6GCxizAEwE7J0NHb32qMqsH/koouwpdnsj7BLtJw5vLwo7I4wqvu/JuG7YZ3piW/PrUmJXAf6TOmMcctqX5BmS1LVzMA1iY8oovM3zirs+M6R9evBjzmMOuhJ++Vy/snuYfJPCtBzurHnfTV7x4MYvgwzl9bMuTWekDNmPmHV596qegfACZP8x8nOAXJbt27aquZ2xCYCHjLwZMn/E3L8YYY4zpFd55McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTK7x4WUXwP+nu3r27jhkzPfhfiPnfiDdv3lynzB/oOM/6mfmC/zF/nP9tnLLMsW3gg11kUoayy8m4/Z13vHgZgSbt6GgvvfRSlTbKkaeB2m8LOCTHbdu21bVWJ1x89JPjaoc+dr0RU26WN238fOfOnQP+A/obN27UqZODPMaR62hSJEPQ/02bNtUxM6/kcVsJNKfu27dvsGHDhjq1Gc33p06dqlOa4To5efJkHXtA6VqmDGVHQbuTPJDGNmN/VxNevIzg+vXr1XHjxo3VEQ4fPlxN5Fu3bq1TZgftHjp0qGqPcPz48Spd8Zs3b1YXIOe7du2q8lYre/furfrJcbVz5cqVzgsSFhTTWFQ0cezYscHBgwfr2NLhumEcuY4m5dq1a4sWK/Sf68TMN3ncVgLmVPyvqx746dWrV+tYMywyTp8+XZ1rkda2aOC6GrUwQc8LFy7Use7E+WPc/vYFL156QJenA2NmxSwXRsasBliscJ3ogVYLdIIeODM8hFGHumZ8erF4YQWpVWzeas6vVdgui2V4pZJfM1AuQn6UIUjXqll5en0UzyHLyO8WWWHjpBxVJuvVRNsTKqvqnB9X/Xll39TXElEOIdo19oOgC1BbrAoQ25SMUXow5mo/9kHpQq/McjmgDY1/UxlQXs5vk51Br+iLKh9loEekST7pbFFfvHixytMTFP2mnGwseRxjfcrHttT/JvI4R1mcs7t35MiRKi/6gEDfJ598suo/bXGueBPIjToig3i0SaktIB190EtlI3EcZDuR+9qmI+0Qok6UjzKirSC2TYi+Ok5e1Ft2idBurM85aehLfdl2VJu0Q5ryZXPSlUa5CO0oL+tFmo4Kqj9q3MQkdgflKWRi/ewXoq1vbZw5c6bTa6AMdajbhOwgok0I8RoSlCnNH4I6qp/7GPMIc81wZTjXoOLVq1fr2OL4oUOH7m/atKk6h+EKdyGfwDnh7NmzVf6uXbsW0gTlkCMoo/jwIqvqxvLkZRm0G/VQ25RVfQXpLl3GRX0sIZkcQW2r/9RVHuR4BN1zWcmhr8SFbKK+lXQkTfltNlddgmxKXkxnXIA06QTkKa6yBMnO9lBcRL3QCfkixwXlY1uAfoqrLeorH9p0pz5yo/2xhWSqLvoqTXpHfUC6yGaZbAOVV9vE83hHqP/mm2/ef/rpp6u2X3755ftvv/12JSP2T6g9AvrHPsQ66kdJb9LQJ15zoDpKl2zpjuxYJ8dFk04aQ9lGfVG+6okcj21l/TmnvEBm1EPjSz2lqTx1lUa+6ub2YzzWyf0hyO4xH6in/oLi0e8IItZvGjcR+0tQO6PsjlzisgeofwL9Yj8kQ74BTX0D6RbbiFA2yoq09bkpT32KOqJL7APILyLUzf0F2kFe9iX1iXisk+PzxsPRnUMYrDywGBPjy5l0kUHJwYhHh9SAZZBLOiEOGHVz+ZzGeWwDYjtyxFimJLcLTfoDemdni+1iy6hDyYYgfbNtqdukN7LjhUQcXUXpImuyeZNeTemSQYhtjmsPQM9S/5GbfVHIJrF8tkeT3UhTyPbKupfSgLRsX/UdPbK9InmcIPenVCaT9Scer8OI7Bvzc/2msRaUzeOBDXJa1B17xDZKekSyTpyTFiEuH6L9PA4xHyRDQXDepEceX9kmliefcpFR+pAX7SV7RH2jXLUbifnZbyDmA/3PY5RBxrh2z32PfdF51Auib4zqW8nmkSgr09bntjyIcjnmfjZRsgmySBeyi/pEfhx79TnbbV6Y69dGd+7cWbTFSGArjC3Eu3fvVmXih7SToG2yHTt24LlT++hv/fr19dn8gC15DSZb6tdJ9+7dq44ix4F3uG0fyuatST7wfOWVV6pztmu3b99encO0bK4t1OFFVskZXnx1TjewR4ZfAaj/yJOttN09LZaq+yj4yI9rhXEY9xpZt25dfdYNvYr47d/+7eqo1wTL8UH7OPB9gV5/EWRzzSVLhX6zXS/5BGAeI4/45cuXq/Ee3iSqPEEa16PqlV6LjEubPpMgO0V5yJ/Hb6Kiz5fms0wf+sbnAcyZUUdde9Og6/1hXpj7b16YYLiwY4hfX2uinJSjR49WH1Qt5ZcPwNfdmWnfkKYBk2a2Z9NNRr+0KlG6aOKHxXv27KkuBsrxC4O48JmGzTUxc/Nfyk2yNG4C/bOtpsG0dG+Dmx+LQm7WoyY4bqglui56fvSjH1W/dHvqqaeqOH7z9NNPV+fzBn6Xx3Sav17D5lk+fn7ixInKRm2/HIl1pvUhZ5M+SyHLm4cbPAv1EnEhnm/CpYeRWfTt9u3b9dnSYeykG3M5v1iaJuPcH1aauV686Aaoj8gET+6acOIHR00fPsUbFJN5Rs7FZMFNpURpkaQ0JgjqxcmGG/Q4jkU/mj4imxbomX+2hy0zOCsLL8rGfke7HzhwoDoC/WbyiJMiNz7aaxqTLjZvIk5CemLCR0qTURsat2gDztX/0sds02SU7uNMnHGc0JsnNHaRuFm3/f0fdmYYu9g3/JZ6YpRdv/nNbw4+8pGP1LEH11uXjxeXuuMx7njTV67/7NPTgmsizwOcq704nvk6jHMc5enb2rVr65TB4NKlS/XZg/Hpwih9xqU058K4Nhx33Eaxf//+6hj1wp4sFrmWdT3HOYv8+HC51L7h75MsUqgz6lqRXHSJY8d11vaAMe7Cq+v9YW4YrqzmmqGjV+/dYhA5b2j86si7OqE0heGkvHBOOd7xKc47QpUfOnYt4cE7Z5XJ8tRWU3qUT6BcTqcs6bHNEuRHWcgQMZ0gFKffkPVUeoncHvYWMb1Jb/qV60GbzeP4ENTHUnrsC/nSN/eRIBSnDOSy0aa5/xrTSK6f9ZRtYp9H6S6Uj42yLiKmUYb6alPIf2PfIk02b7v2Ik888cT91157rY7drz7ejfFItAMh2w9dQb5DaNJbNuGY7SOfU7rsmvsa7S1i2wR0kg0V5AuxDOT+KT3bMpYjr0l/yDJjH6LvKESa9Ml1muwSx0c6xXJKz+OoMYvpQm1zjCzF7rluaVxjfrSbZEAsQ6Bv2TbEM7Sf+5PrEWJbQB31KUK7uV4ey1IfIyqnfsY4xDZK40Uo9XVeWMM/QyVXBTxV8JQ5dIa53eoyZjXzzjvvDN73vvdVT9fjfmtjTJ/hVS27O11fQ3K/Yhdtkj9CZ/xH6owxU+Q73/lOtR3vhYt53OA1LZ8LdIWFjhcuk+PFizFmavzwhz+c2491jZklLNj5Nil/N1OCMvE7JjM+q+a1ER9gxY9xj0/hF0TGGGOMmT9W1TcvxhhjjFn9+LWRMcYYY3qFFy/GGGOM6RVevBhjjDGmV3jxYowxxphe4cWLMcYYY3qFFy/GGGOM6RVevBhjjDGmV3jxYowxxphe4cWLMcYYY3qFFy/GGGOM6RVevBhjjDGmV3jxYowxxphe4cXLHLJ79+7BG2+8UceMmR7nzp0brFmzptN/22+MGQzeeeedwUsvvTR48cUX65R2uMa4vqhnZocXLysMCxUuDODIjeXixYtVfLVCn+nn40Ac3zZu3bo180UFely5cmXAfyR/8uTJOnV8WFg/LuO3VDZv3lzZqi8PI/gIPttH3nrrrcGTTz7Z2Te51ijbdn2yAMEe69evH3zlK19ZSKOOxpbj1772tSoP9u7dO/jsZz87+NjHPlZd12Y2ePGywrBQ4cKAw4cPD65evVqdr2YuXLhQ3UAfB+L4trFx48YlLypGceTIkcH+/fvr2HyyXDf65WrnO9/5Tn02f/R5oVLiQx/60OAP/uAP6thouNaefvrpOlbmT/7kTwaf+cxnqgWJ+PrXvz64fft2NbZcs88+++zgc5/73KKFCrocO3bMO5wzxIsXYx4DpvkEuHXr1pksPpfrKXU5n4afeuqp+mz+4Aac4QGKh4vHhbbxYYflq1/96mDPnj11ygOwEQsf1f3EJz4xeOKJJwbvfe97q7hgwXPjxg1/AjAj5n7xwtMBT0kKcYtP236E/ATBBMV2nra4Y11t9xHyRBbzYlslVI7Ae85IzCOUUPq+ffuq89wefVL9fAHEvFGr+2gnQuxzTCeIaAfJj21KRpcx0HcW6h99IT3SZnfa0HvkpjJqQyGOR1N6Zto+Qxzy+Ko/qidIVxnaIC+2RX62cSTahxD7umnTpuq4bdu2Kq9pQmX7O/aHrfhIaWte5V9//fUFHT760Y82vvOPfSfwLQFyd+7cWeVLR2wRZcv/SFc7shcyiUf70H60CbL+7u/+rtjOKHkxznm0gV4jKK1tjDL0G1tRD/3op8j6YyfKQNs4xLGNtuYof0LWqVOnqp1ByW+yITdhleFcPhHLo7faIb1EtDGBc+ogTzYl5Proqr5iq2wj6UcZxjeDPOmGrtJ/FN/4xjeqnRl2RUvQNrr8+Z//+eAf/uEfigshfO2f/umf6piZKsMnqLnl6tWr94eTbh17wKFDh6rj8ePH7589e7Y6B7qiOHWIE3bt2lWlkae0mzdvVmkxH4jTpsjxSNRLsgUyo27IiHGIOuSypMW2aSu2x3msk+ORLF9yaD+2AeSRJojL3iLaq20MKEecgAzZQG1EOZzLHuo/8WgLgmQjT2WA+lFPtac+qlyOR6QXQbrRntJUJ+ZDk+6xvPTO/aGMdFWaymY7yXZNZBswNpInfYjTVhPUGT5BLpQZTtwL/YhI34h0VVvIefnll+vcxVD2tddeq87RT32U3KyjZL/99ttVH9FT6ToHykgWcE4fqCedkN3WTps82Uc2pTw8++yzC/VKskttAXohT3WxF3HSIepPoB21CdSL8dw28qij/uM/bf0D8mOaykgH9Il+SPk41i+88EIVbwJZ1EdH5HGOTPk9+hIXko/+wPjTR8WR12ajbBPyc/+iTSL0hfIlJJdAmTfffLPOWUy2p5kei2egOWPUhA1yIEJ0Qpw81tVko4sEKCPHKrVFXrwhZKgT29cFhZzYThtZp3yxAf2SbsqPxH5ESheO+lOqk22kttQv4qV+UUYhjkHUO1JqW20pxP5nuVEvnUfQkfRS/9EnyoqgV9R3lM+IUbpnm5XSoKls1isT7RFBT+mqvkTdIkz8TXpl1F4ky6bdJjtzM+LGQJsRyc06NulNemyja3/b2mmSByV/KpFlN+mBrfONXmVL+tM+aSLHKdvUFqB7W/8g9rGkg9rQzTrbRPlNZB1yfeKxful6JZ/FUhcbce3EBUjWL+sTacsT6EA5xjH7M1A/9s9Mj7l+bcR2IB89sd2noC1FbUEOnRFPXNgWn5Q7d+4Mho64qC22VPMrAkH+0aNHq7bRIcJ7TvIkhy3LaXH37t3qKNkEtn9pM1N6p932QWjeHuXbBux6/vz5Kn7mzJlqTMS0xgD7sIWPDMZgHGSPCDqiO/2P2+IE5JfsMilL0b0L2JbxPX36dJ3Snabt7hI//vGPq2Mc31nxzW9+c/D9739/8L73va/yIbbfp829e/fqs9nDHEE/9PqnK8w577777iL/hGvXrk1Ff+zK6xlelSCba2Ec2nT4j//4j/pstjRdUz/96U872YixefXVVxfsy7UKTa9Nx4VrjDmVcdQ1ZJaHuf/mhcmUG4MWCSwKcEgmdOLcpKYFN1+1pVD6eI0JgbKlBYMgTzK4wTW9B56UqCOhSZe2Caspb926dfXZYHDw4MHBK6+8Up1v2LChOsK0xgC7MEHRh6XQNBkNn3oesdW0ftEzLd3bYLIdPj0uTLptXL9+vT57yDgLmOWCX2JwXWG77373u4MvfvGLVfp73vOe6tiV4dNuffYobbKa8trktcF3DT/5yU8Gf/qnfzqWL/CBZ2nO4YPQLuQPRDPYlW9AvvCFLwzefvvt6lqI/PzP/3x9VkZ2+tnPflYdI2vXrq3PZgtjwkIl0+UXfEAfuX6yjbvMWR/5yEcGP/jBD+pYM6PGwcyGuV68cEOKNyWeSOIuhp66WUws9amXL8qRgaxI06IjtnfgwIH67AFZBouEuCDI8ATWFT0Z86QXKempn8XGD/CwJwsP5UU56M0EFy9sJlL6Srn81T1MOgZ5sYVOkPs1Ctkj39yxB33E9tE2tDOtpy4xSvdxxjeWxddZHLLYYlziOEYYL26CfBgstLh8/vnnq/iop9T3v//91Y2CPozaCdHNTOV0jDc50ko3HWxPP8hnYYXe3PiBRQ386Ec/WviIsyRbbNmyZeHmkscUWcj+i7/4i0oGgb5hl1I70CYP6I/0ieD3v/Vbv1WNA7KwY7ZRiU996lNVXf2NkKgjspCD/kDa5cuXq3PxgQ98oDrSB+rSnwh1WFh9+tOfrnYFKBPH5MMf/nB1HZKuP8AW+ygb/uEf/mGVRkAffFGL4myTUf0mPeoQz0twfePH+siWeQa7YLuSjfKu6q//+q9XY6LxpEzcXWzSEz74wQ8W5zT8V9c69VkkogfXUAZ9WAQBumt3zUyB4Sp0bhlO3NX7SYXhRVPnPHiXqXTeKw4vsupc3wcoT/kxTpksY+ikC+9QY2giluHdtc7RGT1jPmlNxLolvSHqL2I5ArqXaLNhzpNdMqTHeiLaL49BtoH0y33MadEeX/rSlxbOCdIh6q1vNGI5gsh9pK0SJdvHuGwT+0yfmnSXXm39kczSOCBX+UL5TWOd+yCiDoTSWAJyaXM4ES/oX4J85OhbAo7ESQe+R1Bb+UNG4nxgqXzOYxm+hSEdHeMHmMjOsvjol3TpqzGT7rEtbKOPhCG3A23ykMU5IY+LZNEG5ZApfTUmxEvQpspkHfEL5dGG9InQFmnqh+QAejf1B6Sf8mMf9QGu+qN0xkP2KpVHlsplsl/E+mpf9WVj2uJc5bK/ZBthv1gf6Lfk0hfVj3KjTCF75uuNNuWXBM5L9QHdKA+l8TOTs4Z/hgY1xpgKnlT5xuZx+nsffYAnd/7QoKfs5UM7UvrruuPg62i2+I/UGWMWwX8hsGPHjjpmzOPL7/3e7w0uXbq08GqxK7zm4vvMaX1fZx7FixdjzAI83fOtQ9ePRs3ywM3wj/7oj6rzpm+rzPThD8+xc8K3aNqFGQULHb5j4r8PmMcP5lcLfm1kjDHGmF7hnRdjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTK7x4GYM33nhjsHv37jrWD9asWVOfzZ7nnntu8NJLL9UxI/AZxgH/6QLlu5adRzZv3jy4detWHVtd4OPnzp2rYytLH+3M/DDOHIG9l3ot0B7Xn+em1cVcL1406beF5YK2tm3bVseaYUIhrAToqAtdtlsOaJO2Tp06NVi/fn2duhgmWcowGS2V2E/gZkLavNxUIvjC0aNHB/fv3x9s3bq1Tn0I46RJVZPsxYsXq3gTKjfNBU7UY1IYW/S6efPmYOPGjXXq6kA+ho+vW7euTp0Oun7awI/k39HOfUF9PHLkSOMcEZGPY++loPmG6+/w4cPVeRujxmESkMn1tVzk++Y05ty5ZDioc8uhQ4fqs/v3hxfqfdQ9e/ZsnbI4fznYtWtXFeaRq1evVvbBTuL48eNV2nJQGp9ZUOrnvIItNm3aVMfKZJupfxyXk2mNHTKQtRqRj097bLhO2/yk1K7s3IfrIDKOny31WpDdutpINmU8+k5f/WMc5nrnZcOGDfWZmXdW25P2NLhz5059ZoxZbu7du1efdYMd0uHCpdod6jvT3h2cR+Z68TJqm+/kyZP12cOtVIW2VwjawlTI7421RayQZcX6eTuQsjmNLV+V19Z81FevmbRVSlCbo3QFyuiV1vAJripHWiTKyMS8NrtluzRtR8a+RXn0L76aiP0tvWpTHgGbtvWT+pwTYj3iemVFiGMTy0U9u/YT4tgSND7IYBIcPvlU6U39g3379lXn0TYQt3/VT0Ga2or9I5Tagmwb2YJzyHrI5oJ2SrKjnqdPn65TH20vj0WTniLKJYhYj3Plyx65XaWXiG2Uxjn6aJMfRB0IuT3SIJaTvyE/+onKCmTh64Dvx7qiZAOh9FK9CHoQ4rWLrGhL+UtEeQqZeC2V6sOoMWgj+4j8Fd3zXJFtE0HPgwcPLtxzdA1kaI82ZHOOEP0k15VdBW0Rb7MNdbIc2mrrQxtqJ+pX8puYltufO+odmLlHW4ClLUde5cRXSHpdUiqLnPjqh3oxnmVxrm1L8pCr8tqaUzucE2J94nHbM8Ypl7eLSdNWH3Jjfo4LymuLVXVBdiCAbCj9FFedHI/krW21l/tKkD3iOCiPNKBerBvjKi9oizz0yv2MfVSaynAUnKvt3M8Y13kk6hmhnPoK8g/JyTaLkA9ZhnQnSH9kSA66KF+QF/uKvBgX1FG7gH7f+ta3qnPypIfsT4g6EI/9ka1iWyoXIZ77GK+5NuhvbFO6xTaRpX6Rn8vHeATZktPUF/kMyPaxTO5b9IE4VkoDpRMnjPITAuVju7JDlMu57Ko6ystxgUzJIagv6ofkqT3lS162F2mCuvRV0M8oA6jTFJdusY1I2/ign+rnPpeI9i+Nh3RXkEzFpUce25yvOEG2ibYt1ZFMQpe+QKnveTyAMoJ8lR/HdivF4llmjtGgRkeHJiMzELrwSkSHkKNKVoT2SIeSzKwT+dEp80UQ8yHXj3mUlQODbCB9IiU76IKLxPbJz/3JE4LIekKWXyoT0ziXbM5jPygjWeiQ5UjnUj9LaZSPfYt2beu3ZI2CsnlsgbrqY1OZCOVjX9V+tE2WE20FuXwJ+U60USTrofJRLjaMehCPdoWsG5TGYpS+QvaQ3pIV243n5Mn+UOpHRPkKsgHHPHZZVhcfKNkDYluj/KTUB8mVXSDaGZlNPl4i6gycZ72jzrEtIT0po3HLRBmlMlGu8mO/hfqfoY/yB9WPNiqBrGyXqKco6YOu0Q6lNrPdcx3I7eU66u+ovoiSHtlmpX6rnkLs67yxan8q3fQNhrZBYdj/6h2nuHv3bn32kL179xZ/JdIFvnkYOs/CNhyBX5LErb/hhbaw1c424v79+6tzuHHjRrWlrLpDh67SS3pOwu3btyt9on7oS3oXuvxqoIT6r21wAq8sgDx0yMRXhF3AjtnWoq3fjPXwgl2UN872qcZoOcGPoy1L2/NcD/QR/VRu3C36TMm2JZ5//vlFY3Hp0qXO15TKXb9+vbp2t2/fPjhw4MDCr1AYm0mvGexEPnbBhpGlfK+0Ej6QWeq1PQlxzu0yR6lM1JFxZQwnRa9xxoFvXaLPEJS+muBehm9ynwHuO3v27KnOAdsxj3AtlObgeWPVLF6Y3DKlBcyxY8eqBUPbzVDvTacBzoIzxHDhwoU6d/FNlgkzT+osrnJ9nHBaDJ8AHpHfZJsrV67UZw9pm6h1s9qyZUt1zLBIyG1rzEptjQN2RLfz589XFys30Ehbv6mrNC5iJrYS5JVu4JMu6pZC7AeTf8mHsW0sx42i6wKkiVy/dNOnXeytseAaHAeuV/zhzJkzle/L/5GlBWekyzWDfbjuGMPSPAF5Ai99ADqJD4y6LqbFONf2JGC/EvFj0WibJl/LOo6zeCn5+Tg/9MCHdu7c+YgOjCtBN/rVAt/1sGhhLPB7+T79pL/0vS/0fvGim5Se3IGBYWLONyyhi4hjvDFpgtOHXmJcB5Z8VrU4RH5yj/LQn0nmxIkTj0x4OBr6SR6M0mWcL+y1cIoyaas0IXADwaYxjyeTfCOKiw6e7M+ePfvIzUE3M56gI9JDbUW9cr9L/cxp6PbKK69UN9SoQ1u/OcZ0FsWlBZqeWPKHeCA/6vqUO+lTvvwi+hdp+NzatWvrlIfEchrHaJeSHteuXauOyGVMIoxftiP2LkFZ8vCZvJDAhnraLcF40Xa8KeEjXPNx1wXGvWbkM3kXqjS+LJ4iXXxARB0on68LxmwU4+y4jnNtT4LsHvuOf3FdM6ep/zGfxWukVAbaxkuobpxD6Bt91ke3XeyFP2YfAs1R09p9mWTH6/Lly/VZeReInZLSLiuo73lOxGexETYv9VvXTR6TuWS40pp7eC+HqgpDp6pzHjK8wSwq00R8p0edKFsoHtNLaaB41hHZMJyUFqUTMvldZCTLHU7adc6jkKdy2R56d4ntiMuG0R4E6V0iyidIJtBP8iU/5wNp9EfEsgTsIHJbTXnDG1VjOSilQVO/83i12SOXjX7Z1rcI6SqTx0y2ij5QGoNcD70ypOVykaiHdI19oG5sW21k/4xySuOPjAyym+wjqBv7pX6X6HrNxP5FvWX37COlMm0+ALGOQrYLaGy69EllFWQ7jY9kZP1LsnMZ9M++K31jGch1s52zbaItYtlYhkC9PIaydyaWif3L9UfpluXHPOW3xQkQbZLnpmxXglBcera1hx2lP8dMbif3nfySL0S/imMl/5o31vDPUEFjZg5P18ML4ZGnUvN4oKe9+JqHp2V2yOKr1NUEuwjsEHmaNdNEu6hd/mpwBp9kl3WSuvPEqv1g18wX2o58HP54knkUxp+Qv09h23+1LlyMmQVcR7xSmnTxwSuovi9cwIsXsyzo25F88zKPB+y6lD4WXcovS4x5HOF7nEkX/OzYjPvB/LzixYuZOdy42Dr3jerxQ3+xc8eOHYs+UH0ckN8DNpjWx7LGjAu+iA+yY7NaXtv7mxdjjDHG9ArvvBhjjDGmV3jxYowxxphe4cWLMcYYY3qFFy/GGGOM6RVevBhjjDGmV3jxYowxxphe4cWLMcYYY3qFFy/GGGOM6RVevBhjjDGmV3jxYowxxphe4cWLMcYYY3qFFy/GGGOM6RWP5eLl1q1b1f92u9zwv3quNrAl/2PpSrHS7Wf4L+cJ8wh2OnfuXB1rBv3x1XnthzGzhGtkkjmF/zWc62b37t11Sr/BBvP8P6E/dosXFi2bNm1a8uKF+tw4u4Az49S0O69MetHt3LlzcPLkyeoceyBHId/8cn68kY6qK7B7rLdx48bB9u3bV/xGq4nryJEjg/Xr19ep8wH2QrdTp04N1q1bV6c+/G/yox9r0uY/mz98+HB1vlS0GIoTYY53Ad0muamsFPLpaN95hGsKPQklNH5tZTLIXOocu9xovPbt2zfYsGFDndoNrrFjx45V182FCxeqNGTN00Kmqz4ab+aLuWZo7MeOQ4cO3d+1a1cdG5+rV6/ex3TI6crx48fvDxcvdWx1gA2xhYg2PXv2bGUjjoK4yitf8ba65BFXiDIFto26rBRN+q00N2/erHRrs5HKcJwl6DBJO4wx11FfkB/3AeYydG2aoxirPtl+KUziZ9SZx+t+UnSNzsOc2oS/eZkAVthD565WpvP+VDUreGq+cePGYOvWrXXKYLBjx476bDDYu3fvYLjoGFy5cqWKs5ofXuAL5cknfubMmSreVpcnmaGvVqEJxoRgJufevXv1mXncYKdhuECpQml3ix1O0wx2M8tLLxYvbGEpxFcGwDYYN1K9mimVgShjKQsO2uJCZkudm++JEyfqnEeJ27GXL1+uUx+i7XwF0NalgiYSyurVCDooP24DEo9QJ05EsV6ui+xxtuRZdPDKKNL2muH27duPlMc+GoulvqLYsmXL4OLFi2ONbcn+AjnSr6kMRBldtmRLYHfkRB+GKDuPTdartEUft/tLYysZOt+2bVt1jl+THvWR/ChT/tgFytMGPpjbIW0UtM8Ngtdy1FF/2ny6K8iKMtAT2vwD5B/RTrIJdXn9AMqjLCGPFXWyLSmjvsU+kZ7b6sqo/jCvDZ+0q4eyLrLbdJE/RyinPmn8sIdkKH8c8vhr7ARpOjaViTLy2HRBdRhvZNB3wCbxuiOdeByHbKO2MZLvZJtl4rjE/mR9Rtlu7qn2X+aY4QRXny3ehtW5gra39IpB5K1btj+Jx9cU40A9ZEDTtnBpW5x47AvbklGHHKd83LokL9ZHtspry1e6qI+E+GqLeNQJ+d/61rcWysb2RoEuo8ojM45Lfs1GPPY5EutGSG/anm3LyyA76hP14xxZBNlcNm2qA9hjHB3kPwrqL23GtiRX+RyJt/lXHh9kRRnqY+xfSW7Jx5HVtY9qVzKa2hkFZXOfsm6UId7kUyUoH/sie6BjHNs41hoPBdk0jhO6ZP2yzWUHgvoVZauc2iVNbUGOt4HcaJdSXEjvPMaxDHU1fuqH4pwTpHf0AdWTLPURSIvxUSAn9oF2FI9tEoR0ELEOqO+xr6NAD+rIXopHOYoTZJdsZ+yoPEAvxTlXfdlI7ahMHgfZp6SP8kS2g2R19a+VYPGsNKdokBXi4BDX4IPKCgY65kMeqK5k5wLkZ0dHdk4jHi/MrFd2vKgjacTJlzNRNzpW7jfQhvSVrSQ/U+pHG6PKkxdtRV+y7WIfI7luhD7k8RRteU3QPvUIURd0IC0S+6DxyoyrQ8mHaSP6ispovEu2lD4ckRXrQ5YB9DGWk4zsI1m/LHsU1EWGaGpnFNnnchzUVhfZ1M3+l+3a5B9NfYg65X5DtjlQJvajJLs0piU/aIK6cQxzG9mOxMmPdXIZyVAgLrJuJVsItaUwLrSjutFGajPakbIaR9JzPsQx7Apyoq0gy6Hd6ENQqkcZ0gmxvOwUIV925jzrHceg1K8m22ls45jOG3P/2ojtrKNHjzJi1ZbmuAwdsz5bOnxTwZZq3GpDPlvZEb4FGQX1tM1I0Fa6vjvgFzS8BgFeTfGLnqFzLnwjcvr06UXfm4yCLWHaHDroQptxC3GasB3JayL9CkmUtiXzu/SmutNG27N8a4NvDS/iOqcbd+/erc/mg7Vr19Zng8GdO3fqs+kwnPAqfwPsdvDgwep8Hom/phoFfpaR3y3VP6YNY8r1q2uXMM5r0lHzTYbXuPSZOlyTGV5HIAPbIHsS9PqDV+rIGd7E65xu6LUHUB8/HYd5+8ZrqT5Xuu80zaNLtd08MNeLF97RcbPtshhoI0/mk7zb0wXMQOcA+Z1vnhhLEyUXa5YVP2iFOHEcOHCgWjyRxvm4sFCIbc3ig2PkscjLFw0XZB7HS5cuVYs00VRXTFNXFsRcsEv91ibqNG1btoHtSmgRk28oS5mo9+zZs3CjZBGzVJtNk9K3ZJAXxU3oASEzqX9g92eeeaaOTRfmwnj9EvSz3C60zTcluA55YGKREn2bmyz9pP5S4KEM+eP0IcJcwQ2+ab7oSr42Jl2MLZVpzElN10NmWrZbSeZ+5yU60iQ3bAaInRFdfNz4J1kMMdhN7asNwZOpFhlA2/lmQx190CeYFCKUYeJ4/vnnq7gWNOih84za1AQTiQsslYuTfGmB1QQf3+byyCS9NBlxA0Qf9ZGyxOMiraluFzS+fLjbFelP24zXOEjvuHt1/vz5+my2MP7YLo4nNwL8hfHE1hB1045dJvpI024SMrnJIK/roqBEvAHCJAuq6HNcZyw+4nXDddr1KXL//v3VMX40iS9Izy7+Eccc+9B2XhDkfmNzpeWHniZ0/eTyec5oYtR8Q1+znsD1yKKJkFH5ph3ckrxMnIuzfl1QGxzzDvgoGCf6Fed17Fvqaxtt/cxz5CiWMifpeog+kv0j6rMU280Fw9XzXIOKCrwb1PmXvvSlRXnDi7MqH8vofR3vApXGOWUV74LKEtSOGDr6ovzhBFOlxzYoM5zUWssQKBOhL+gboU4uJ6Isykk3jrSZdQXSY1purwlsizwR7Z6DxiGXEV3qtuVByVZtxDajT9An4sojZD9Sv7PtokzkjSL3O46ZAm3EdiQ3180+gc4xP5anbO7jcPJaFM/6q360eRdkVwXVj+n0rwuxT+ovR6UR0HMcsp3kQ9Fe2T8g1yOU2o52huwzUX/KtvUn1yWMQx4L2bCUlqHf0Sein0ZbxXOC6sU0+UDuT6zbxSfiGNBOtF1uU3aM6SKWQ4b6Jl9oI/sBdXO/og8oCMXRK/Y/+1yWIRsqnTKQ/Yd4SZ8225VkzCNr+GeooDFjwxNr2y7QcoIubLuO8x2QGQ++c1jqK9zVAk/G7IoObwxL2o0yxkyG/0idmRjel7JgWGnYGuWbGi9cZgdb0bySMcaYecCLFzMxPHHyLU/TO+/lgPe1/CXeefqIdLWBjfkQcB522IwxBvzayBhTRK9GwK9HHsIuVPzA8ezZs17YGbPMePFijDHGmF7h10bGGGOM6RVevBhjjDGmV3jxYowxxphe4cWLMcYYY3qFFy/GGGOM6RVevBhjjDGmV3jxYowxxphe4cWLMcYYY3qFFy/GGGOM6RVevBhjjDGmV3jxYowxxphe8dgtXvgfcnfv3l3HTFew25o1a+pYGf7DunPnztWx+UN92Lx5c53STslXiJNujDFm5Zj7xQs3RG44/A+3SwU5mzZtqmPTAZl9XAxhT3THvqOgf9ityXYsWJAV/6fdeYP+7ty5c8D/Q3rjxo06tZnsK88991yVdvHixTrFGGPMSvHY/a/SWmhcuHChOppucPO+dOlS442f3Qhu9mfPnh3s3bu3Tp0fGPcdO3YMDh8+XKeMJvsKi7R9+/YNbt68Odi4cWOVZowxZvnxNy+mExs2bKjP+kmX3RZjjDH9oBeLF7br43cGfLNAnCN5hPxaSa+bmvIj5Ed4wmanQei1iELMo50Yh1iWEEEPnuj12obQ9A1Gbpc+x3pql/RYLveVcsiSvQR6kC5yeyViW6O+HWnTK49P2+urWC7qCzGPUIJ+slvCay3KqK2s36j+lIjjQdBuTU5XmxzVh2hvjWUeg5yOzsYY89jDa6N55dChQ7zSqgIcP358IU4Y3pCq9E2bNlVBcE5ZsWvXroWynBMgy6eM4uTFtAh5sazaUtrVq1erOKCL6uucIB0oG2Vkzp49W+VLfyCNIGLfkaO4ZCsgA90lkyBdOarPgH4xLtvHtohnO0kvtS1inPMoB2JbkVhOegt0jHZAbowLdENOtLH0iXYlHtuLvgJqX3WyPrKB6mSbAPKjTPTINowoL7dtjDGPM4tnyjkk3yB00+Eo4g2b8vEGBORr0m+6IUWor5uG2muCsropUifKhnwDo0zWL8oogUzpA/FcUJ92FCKxfSG9oh2BtiQj9iXaWMS0Uj+znsonZFltUF46ETSWyMj9aiLbONsUsm9RJtpAesT287jlMrEd5HIe86mv81G+Zowx5gGr7puXO3fu1GcP4SPNST+w3Lp162B4U1m0ld/2iiMzjQ87Dxw4MDh16lR1zmuD+P2JXn1cvnyZu171wewk6LUEH7UiZ3iTrXMmA73QOdoNGB8+6D127NiiPL1KyZB39OjRSifGIcJ3LORJxiSvfSJr166tzyZn3bp19dkDsCcfOsOZM2cGJ0+erD5sPn/+fJXGuMlHluprxhjzuLAqP9gdPslO9dsAbircPAnIbvtJcNNPafNNbRz06x1u8Nz04i9mTpw4MRg+3S/511MsAo4fPz7Wr3Fu375d/fy4CRZAspuC5NMnpXHDpv0MN25u9G0f25InOSxemhZBJbSoyIyziGHxUUILkj179lQ+wzcwWnQePHhw8Morr1S65n6P42vGGPO40pvFS9fFCDcLiDdV6uaPWDPK54bCTUNQN94Qr1+//sjfO+EmDvv376+O8QNebsAsLrgpLQUWFqdPn65ji4k3d37KOw53796tzx72A1topycSF4WU4ebPTkIJ7RZFu3OusYjp165da9w1iWOBzEjelWDh2LRIjHIAWaRFGSwEWXB13S1jEUKb0T/YUWKsBLIY/23bti0s3LSgYTyjX7T5GunsxMj+xhjzWDN8wptb9H2AQo7rewOOShveFKoQy/FtAsQ0gohptEF5zjk2ycrp+jZC3y0oIE9IrgIyYrpklFB7yI9kPeL3Ia+++uqiPOkSyxCwX0xDD9la/SWey4gsr6mdJhu19TuWi/KQQb2Yn20D2T4EkfWTP0FMJ8geCtSF6HsxPUIZ2USgey6bdZXtQbrKZ4wx5nHmsfsjdcYYY4zpN/4jdcYYY4zpFV68GGOMMaZXePFijDHGmF7hxYsxxhhjeoUXL8YYY4zpFV68GGOMMaZXePFijDHGmF7hxYsxxhhjeoUXL8YYY4zpFV68GGOMMaZXePFijDHGmF7hxYsxxhhjeoUXL8YYY4zpFV68GGOMMaZXePFijDHGmF7hxYsxxhhjeoUXL8YYY4zpFV68GGOMMaZXePFijDHGmF7hxYsxxhhjeoUXL8YYY4zpFV68GGOMMaZXePFijDHGmF7hxYsxxhhjeoUXL8YYY4zpFV68GGOMMaZXePFijDHGmF7hxYsxxhhjeoUXL8YYY4zpFat28XLr1q3Bk08+OVizZs0jgTxBfPfu3XVsvhhXt7feemuhjy+++GKdupiXXnppoQz2mRay9Uc/+tE6ZTogc17Hp8Trr78+2Lx5c6U3R8ZkGrzzzjsLNn7jjTfq1NUDdmKc5Zfnzp2rcyZDsgh79+6t0qLvr0YbzsL3sBP2k924vqfl05Gvfe1rC23QD0H7pDF25sF9jbk92gg0P8iviTNuudyq4v4qZteuXffp4muvvVbFOW7atKk6h6tXr1b5x48fr1Pmh0l1e+GFFxb1OfP2229X+QTamBa0h0zanxbzPD4l0PeJJ55YsOuzzz47Nd0PHTo0k3GbJlxvk+iGT2K3l19+uYpzRNZSkD8iF/kgf2Jc+gC6dmUWvnf27NlKB+bMmzdvLowTYRY8/fTTVXsaL6APpEW/QhfSpjXXTOq3y82bb75Z2Yj+Zxhvxin3g3mDcVyNrOrXRhcvXqyOH/vYx6rjpz/96cGNGzeqc9i6dSuzw+Dw4cN1yvwwqW5Dx66Ov/ALv1AdMz/96U+r43ACqtqYFv/+7/9eHT/4wQ9Wx2kwz+NT4tixY4M/+IM/WLArT0LTgKctdiKGE1Gdsrr4+te/PtiyZcvgs5/9bBV/73vfWx2Xws9+9rPqyNPnU089VZ0r7b//9/9eHVcT0/Y9fO65556rzk+fPj3YuHFjZUfG6d13353JzhVz1/AGvDBewLXPHBDnqhMnTlTHac41feDIkSODP/3TP63GIsJYfPjDH6522zJf/vKXB0ePHl30tmG1sGoXL9raHK5Uq4uBCzFuRcctZJUln4seJ2Dbje058onHwScPeZQln3Jsp+JE5GnLmq1QZGsrN299sqVHPfIImixKugHn2sKlbcrlSerSpUvVBJAdXPzrv/5rddy5c2d1BPUT2VH//Lomtk+IF8s3v/nN6sjkBpIZXyPRP9LiViZ9kH0I1FO60qINsu3RUWPTdfxmAe2xWN6zZ091jv7f/e53B7/9279dlyiDz8RXHOp/ZPiEWd2YNmzYUKcsJtsw+nmb/Ox/caza8qbN3/3d3w0OHDhQnaM7k+3wibuKt0FZ9Ztxj/2+cuVKddy+fXt1hH/+53+uju9///urI7T1M/satiM/37iJSwb6aDyE/EFyyIs+vVQm9b02vVhQskjZtWtX60OOrrFot9J1PmruIp/24rwkv43l0PHUqVPV+b59+xbajXrQLyBOmCbIk40Y96jbLKEtHrzzWNBXFq5NY82979lnn63Gc9UxXNWuSth6pnsx5O020rQFSh7bkNqmZBuObTq9honbcdreZDsuvobhnPLIIk454ioTt8K1rU075KusIC9uzyJTW7bopfrUE6RLZhPqj7aU6QOy0I106as+CrWvLWTKxbalGyCTIJmynWRSH6QvW5vAsc0GgAzZIPZXOnUZv4h0bApxzNpAvrZtqSf7tqExpN/YHJ2JR5BHf8lX3+I2sPpHnvwMuTBKPnKxJ3nIzOPZlNcEtmqycxvoxPhxREfaHIX6rVdN6BvHCn3JxwcE+bkfbf2UDNLJ55wQ9ZMPSob0kk8DebSDbVS+i39QrguT+B606RX7HqE86dQhj6BriDRQXV3nHKlHoJ3S3KX5WuMJ6g8+ESENWUK6SA/pzFjENpro6rfoHtvFVlm3WUFb9CdDmq53dCv1gzT8Y7WxahcvTB7RkfPgaTKKEx7kerogcFwgnXicnIjHi0QTsdosXYTkkaYLPFLSLeslmdGhSxNARv3JTp714TxeqLn9iCa+fCFLpuA8joPqYbusT8kGTbZvs1Mev1nB5CK9sCH9zH3KUAYb01cmIM7zZIv+uqFwpC+KZz/LjJJPnFAa07Y8IX3awiiwkfRHT3xI/WtC/cY21JHf61qQ76B/hLToO9DUzyZfK40P6Rpr2UTXoOSQjt7yT91wIpLVFMgvgexxfW+UXmozXjeyOwEbC9ojTXAefVKyZWN0Ix7nLsadtNgeupAW/UF1S4sG+YHKUyaPK5BPubZQAllxDLr46rSg3dwWtoh2QG/Sos+CbLbaWH09qtEF1XTTKl0YkC9ETW4iX+SlCzFODJAXFfGmXaKkm/TSpEEecekB0g2dmiA/yoF4MwDpFy8MbJDridxfyDJlp3xhYTfSCXGiKdkg2156xgll1PjNCnSI+qN37mtEuivgC9gi2ld2zUE2yTftSFf5Gtesa1teE9igzfdKZDtRP974SqjfCrSrawvka9EvZI9YDpr6yTlpbdc5kBb1VT3aA64h4gTaIR6v2Tao04VxfQ9G6aW8CHJJi/NCl+u8y9yVr1vQ3BB9SnXzOEKcD9CraX7NUD620QT60L5A5y71pgE6xrYhjmEOEY3JamNVfvPCe8Ch81bnH/rQh6pj5h//8R+r4zPPPFMdge8iqDd0+ipeeg+rbyf0QeyZM2eq4yc+8YnqCHrf/ou/+IvVUd+DfPzjH6+O//Ef/1Edo9xISTf0Gl4sCx+zvfLKK9VRHyMD37tA0ztqvaulf/GjuOvXr1fHj3zkI9Xx29/+dnX85Cc/WR0BO+R6Qv2N+krmjh07quM//dM/Vcc8Hl/5ylcGw0mn+oCYd9iiaXxAfdYH2b/xG79RHbuMXyZ+D1IK5HeB/up7H7h8+XLVJ8AfeSfPe34hHxhOSMwq1TcK2CLaV99+kE/gPKKPr6PviS7y+Qbh3/7t3yp78R1B/JajLW+a/OAHP1j0Xcq1a9cW2U3fT8TvMdTv4aRc9e3ChQsLH/tG5Hsgn/7Upz5VHUVTP7tc5yqr71vQV9/dyM//3//7f9URPX/yk59U+fxwYJq0+V7+ZkeMqxd9+6M/+qPqPH7w3OU6HzV36bod3qCruHj11VerY5zP6Bt84AMfqI6R2Cb95uPWacL8qvkIe6Fz1I025aPLATowfgqga+JxYFUuXs6fP18duWCa0OT0nve8Z+Gmoo9Z9RGYbgB8yc3Ht6ojcB4ufMFECNwoQBOfJop169ZVba1du7aKf//736+OTII4vijpFiFNk9Pdu3cXJiXduLmA4sdyQhN4/LgO/uVf/qU66ut93RyYICQbWyKfSQz94k1d+tIv7AR37typjoCdtLBCJn2lDpMq8rALxPEq2UAfIf/4xz+u+shkSn/5UBHGHT/gxhcngBzIH4UWSffu3avitMXC6pd/+ZerOP5IfkQ+oMlYNtXkh92xt/oGGhexfv366vjDH/6wOtKu7D9KPjdc/I4bim4qqtOWN23wC/kKbTKmn/nMZ6o4Hxni54wD46zFqvrNQgeoR9/wJcBngL7LX//X//pf1YeL8UP2rv1sus5VljYIf/Inf1KNc7wJqz3aAcYnXutLZZTvYV8WZzwgfPWrX63SYJReL7zwQnUkn759/vOfr9ohPd6wR13nmdLcJd15eEKGrlP8nzmBPpIe4aNr5MufBban/8hqeoiblO9973vVL9Zok19gAefMtejC4veXfumXFs2N04LF4e3bt+tYM/pFXabtXthbhhPDqoNuKeStNqEtSbbjtMWrNG3BDi+eakuVoDS2OpXGOYE6w8m1ksPWKPGhs1TlIbaVt04JbP9JByjppvJqR2WoK5nkkUZefD0A6gv56BbzVY8yoD5RTu2r36RTnriI+koG9WQnbKe4ZLKVyTn1JFNtQckG6Excddiajv1QnbbxmwXIRn/pRr9ye4xf9kW2vtGNOtGm2uaNfdGYEKgjO6tN0rI9muRThrjkRX3b8kaBLujeFY2PXrVwzhiWIF19BtUh5OsH0Fn+VbLNqH7K3wmcy/7UiW1JD/pOfc5jH9C5bYzaoM4oaBOd2nwP8IXof130orxsyFH+E8EWshPtKk552WnU3CU/IMTXQRqfqBv50jlfT0BZ2u5qY+jit+RTjnalN+e0B/QBfWeF5ssm0EN2yWBv6bmaWJWLF2OWEyaH0kQaIX9UmccNbobcEEbBxB0XLvOAblbxJqmbcukmPyu6+B46ceObxP9YbFBf40R74ywMlhP0ioumaZIXfxkWbthmluBv4zwcgBZZ83b9TINV/UfqjFkO4rvwJnjlk1/7PO7wd1fidykleJ3BKwL+cF38ZmOl4VUgrxF49QG81uC1zPAGM/VvWtoY5Xu8GhvewKpy/JGz0qvTNviOZHhTrl7FIItvfvSKbV7Q61BebfE3T/J3ddMgv57K8Ood2+AHen05bf7qr/6q+ntI44zhF7/4xcHJkycb/+5Xr6kXMcaYCeEyansa1fa8gnkAdmnbpdBrGIV52rni6Z7XB9JNrxCWe1eCttvaxGboRpj1zsBKwY4E/Su9LpsW+Gp8pZXBzozFrG3MDgptjNrdwyfwz+XcBVxu1vDP0OjGGGOMMb3Ar42MMcYY0yu8eDHGGGNMr/DixRhjjDG9wosXY4wxxvQKL16MMcYY0yu8eDHGGGNMr/DixRhjjDG9wosXY4wxxvQKL16MMcYY0yu8eDHGGGNMr/DixRhjjDG9wosXY4wxxvQKL16MMcYY0yu8eDHGGGNMr/DixRhjjDG9wosXY4wxxvQKL16MMcYY0yu8eDHGGGNMr/DixRhjjDG9wosXY4wxxvQKL16MMcYY0yu8eDHGGGNMr/DixRhjjDG9wosXY4wxxvQKL156yHPPPTe4detWHStDPuXMaLDTuXPn6tho3njjjcGaNWsGu3fvrlP6Df2nTyvB5s2b6zOzVPBH/HKlxnJeeemllyq7cBwX5gXPo/PJXC9e5HRNYZwbzlLRDSuCU5M2aiExTbDJ9u3bBxs3bqxTHiD9NHGRT7lJLtjHBfwHm506dWqwbt26OrUd6hw7dmxw//79wYULF6qxR8a8THDj6KPri/53QfaaxnXHogVZ0wSZj+tiiH4fPXq08sutW7fWqbOFNqMvaA6apzlH1wF2OXz4cHXeBH2R/+g62rdvXxU3c8hwUOeamzdv3kfNs2fP1ikPIH716tU6NnuOHz9+f9OmTXVsZaC/u3btqmOLIR07ZZug83LaqW/Iv7raCHtmX+wz9Huc/k+TQ4cOrfg1tRrAH5fbjuNeNyuBdOTYBfwxz6/YlXQzf8z9a6O8w/A4wxP/gQMH6thDeLrgqasEdQhmOgwnwvrMmPngzp079ZmJ3Lt3rz4zq5J6ETPXoGZ82uU8r6a1ylagDLslgtVzfmLO3ae86scnmZiuAGozkstGHYCVPU8r2ikhZL1KqK3cb2TJHuTnJ6Gmek1IJ0LUK+84cE4ZoT6pPYXYrsrIRpJHelMdxi3mqY7aV4hPRzkvE8dI45DtVgIbqB5ButB2HGfOFVQ2P73FvPzULDtFu+QyEPWJ+VmfNvsqr0v/gXZiWekpOxJkF5HHI9ot94t4yWeE9FWIeciNcYh6EaLutKP2YpmutI1hzCPE8Wgi6xF1bSLbVnqobxF0yH6BfaJNcx0gLeZnPQkaU+TpHHLZJp0IKpOvlTYoG+Wr7ZKOpDWR5UhPjuRFO2f98hh0Jfpmqc/Z7iJfA3FMY5081rFOvE5iOqEv9ELTbFxCdEQGPg6uLgQGLA5avMCUJsjLTqA4bXEe25CMmIYe0Snk1NTNDo5eIDmjUP2MnF4Xq+RGSFffm1B92TXGaYNzQs6DeMFEe3CueCwje6Arx1gnxjmP9gR0UfsR2QHZsU6OI5s0ob6V7JZRu7Jl9i3lK6gdjsTVBmWjDlGnaCfprXZURnHZnyNlsz4xT9DfGFcd6daE+qB2ka94rI9s4oK4xgY4V1nONdZZPige9Y35QD3iKhvbahtr6UmQDpITZTTRNobIl0zRRWasg6wsowQ657Lqm9LQR32VjuQpTfZVudgP4rI3R8pylK0oA9EflJbrA3HaVn0FtcmRuGS0gS7RrqqLLsgvtd8EZZAlWwjZSe1k/YjHOjneBPIkQ7ZQPOvNUTKzjrSnaz3KgBiP8oB6kqv6QPkYn2dG3zXnAAwfDSrnhDzQoLQ8kFGGnDATL+p8YWgyEDGN9nObgIxcJvelpEemVE4OCE3tQ26zBLLyRYfepAvyCaRHe0O+qCCPTakM8dhG7EdJJ5DcEugW+xp1IF1jIdrsVoKy2ZY5jTai7zS1QTnSCbF89BlBXHbKNoNYv6Qj+WorypZ9uvQ/2hLUr9gW56SBykfIV1u5n1k+0Ff5gNqL+RHKyQ5RjwjtqQw2zGWijC4gDxmE2Hbs1zhIJ4UuUCe3V0pDXvSbbH8grjJtfqaxiH6T00q21BirDO3FMiW5JSQn+wJtEqCpTBPoobpilH7kR/8fp03JUpCcJrurfJRNe9Qr+ZzsX6onsv59opc/ld67d+/CtzB3796tjkv9Nka/vNAvSYYDX+csjQ0bNtRn04Wv4Y8cOTIYOl+lN0fYtm3bRL+4uH379uDixYuVLIWhs1fpgl/XUObgwYNT+xbpxo0bVT/UpvrBuPLrgB07dizSiV808GuK4UW7KF2/cEBnfiGgdOwBvP+el28Dlupr2Cxz8uTJ+mwx8VdytDWcFKvz5UDXZoRrd9Jfw+BzjK98njDur7ym9WuktjGkj6RLR8KoX2jp1y2XL1+u5A1vKHXOyjGOn3Vl7dq19dlsWO5vJNvmmzb4WTt+TH3GO9Jk95JMriX8jbkNWdKDwFyNX7VdN7TF95JKn9b1sRz0+u+8xJ/kMUhLgRsoEwaOsBSuXbtWnz1kFg6BQ+L0CjgncFMvOX8XmICjTEKcrLjghiv5ylb6SXYb3Ly4YEZNKNxQc7saBxYwSmN89PExF63S6Ts6CcopT0E3TNlJrMRHfdPwNW5yXcBejNlSbzpLoYuvdCX7PT/zbrv2S21P44Fi1BiSLh25Jps+qBcnTpyorj8eEOaJrn7WxKVLl+qzxUxrEXP9+vX67CHLvYBpm29K4JMsLJiLmnRts3upz8Bcm/WQP7VdN9wvlM69ahp/CmE5mPvFS9PEpHRNHvEJ7MyZM/XZYq5cuVKfPZh8MnoyZ/Bwrky+8YHScA4mH+RGnYmPmri6sGXLlurYNlGXUHnVb2L//v1Vn6PjUleTP/ZlF4SbIIsNPWFEtNIXPJE0TV6CXZxsM+nAMaYzftiZtKgnFzMXLnCjzn+bQWX37NlTHbv4SomoS2bcXZ1RvtYGNqNOXLxHe0DUR3pzLPn9uHRd8OnazL6Sdc1octYkH4l9lm/GG4D6qrbjr/Mkb9Tf++hK0xjSjnQDHmi6PMDEh45x/r5I3B2NMDfJHtFuXeniZ6XdNaVhe3SI9VmkcY023bS7wuKAaz7aib5yU37++eereEm3UYz74Nc234xC11HePWyye+yzxhXIY27LtgbpEtPjdZPL067+5hV6sRsztwxXW3ML7/1QsSnoveBw0BalDx2qOvI+UChNIcqmXIxTdrgQqc45iqHjVGkcla+ADtCUPlydL0qnjZwe9S2BbMpnolwC+gnKxz60QfslOegaZYL6KX2yfQkR2a6Ul8e5ZJuYnsc765Z1kZ9A7mNsI5bLlGyT037zN39zUVx6RX1po83Xsp3kP0rXWGabEc/6IDumISPWK8loIpf93d/93UXx0pjRNsRyBBjVTwJ9lW1UJtcTMY0yoik9yiVIV6XHsiWiPfIY5nEgbRTZp6MdZZsSuR+6HrO8qC91sh2z/aVzm4/EvC996UvFcvkaVnrWT/aO6bGtJnI/RNSNIP8chcpnuSX9ZGtkx7Jd9I7jFm2kuln/KDPrprHLNiUovclO2X90HQB11Md5ZA3/DJVeVbCy5GlvOBCt23d9g37xNDPO0wGvetj5mbUdWKXzNDBv297GGGPGg3sNr5zneT7v9TcvjxssQHbu3Nl5W5JyvOpZTQs4Y4wxs4WH5Hl/EPXipWfwzQnffsR3niXIp9y03u8bY4x5PJj0Rx/Lyap7bcQHSPGjxOPHj/sGPmP4GPFm+Jh5Fb6JNMYYM0esym9ejDHGGLN68WsjY4wxxvQKL16MMcYY0yu8eDHGGGNMr/DixRhjjDG9wosXY4wxxvQKL16MMcYY0yu8eDHGGGNMr/DixRhjjDG9wosXY4wxxvQKL16MMcYY0yu8eDHGGGNMr/DixRhjjDG9wouXOWf37t2DN954o46tHPzP0fPCc889N1izZs3g3LlzdUo72A87rjS3bt3qpAf/Mzr947gamCffMeZxhXlzHu4l08KLlzlFN7CLFy8O1q5dW6cuP9x40CPCBZBvrtyUl+NmSzvbt28f8J+h7927t05tBj23bds22LhxY53yYBFBOhdzVzQe8eLHNl0XUNTdtGlTHWtGOtG/w4cPF229ktBf9OnSb8aKsjdv3qxTVp5Jxn6plHxnHFS/ye7qUy6Df8564ThNe8q3CG3+nvu7mpmGfeU/p06dqlNWCcNJ0swpV69evc8QDSf/OmVlOHTo0P3hjbeOlUHPs2fP1rHZIHuMy65du6o+TBPGBF3QqSvoQWhCMld6vKfJ8ePHJxozsxj5fpvPTdvHVwrmmjafoZ/kr5b+Lgfyn3Hmq3nHOy+mN9y9e7c+W53cu3evPjNmMey+Dh8OBsPFb7WTWGLDhg31Wb85ePBgdWzaZbp06VKnXUyzupn7xQvOmrc+2QaL24pxW5UQ89huU3rpe4OYH+sJbX0TRm3dRT2yzmwZI0uvAUplIOpz7NixOrWdKJPARR8vfLUrSjYdZacMZdQGdWDfvn3VObIli0B7Ucc2+aW6QH3kg/KaoE6UUYL+5vGObUd7CdKRTdDkyY2EdNmCvknGKH+JIFM3JWSrLYi2BuLoF9uK+dCmB/1WaCqTbRjzsVO0T5vfjyK3E+vnvDwmSuOIfnH8pG+0g+xJXh57lSFQR7T1LdYhtEG+2gdkEY86l3wuc+HChco/si4l8InYF2gbcyBN+aW6BNm0yZ4aE4XY764cOnRocPTo0Tr2kBMnTjTOi7FvhKgToCc2kc1FrDfKrtRHTu5jRGVkS40r6U11IPpC1CPbN/o0IfpNlJH7L7LuxBkjxUfZYG6od2DmErbYUZFtRIhbp2xHK035QtuJlImvMqgX48iXHG3ZKxBHbiyf4xHSJQsoqzjnkqvXBupLrEM86yddmqCMZIK2VKmDLM4J2i6ULhzFKDshU+WzTOmW68ieahfIb9vqzTJi/2N/2qAvsW+ys9pVPLdFXLqqHQKyZFMCSJ9YByineCk/j1WE8tKNc4h6kBfjSgPZSTTpoXMF+R5HlVE82pB2SVM5QtRRcoB6Ma46TVBe7QLyiBNivRiP40Hf0UVtarwisrlkEGgHZFNBGdXnGGXFODIlA6gX44LyahOiDQmyI3pHm2coF+VTN/tStLvkN+kPKkNAf+RFGYpnv5GNSvakbNSLclnPUcQ2Y59Jk43yOOd2qEd9ZEU9CchRXY5ZToxHoozYVozHMuigvnCMdWJc+lEOOJJXsi/1sn6KU446IsYlK+dH+5IXdZx3mmeVOYHByhc1RicdMH7bRQ+UV1A9HIR4HEwGTo6gwY6QN2pw0UVtRSfjPOtJXPpwzLKlg5w6ows0orR4IRCP/SzpApRTkF6Qy5dkEo8XAkR7AvGmvtBek07SpdTfTNYLsh4Q9S3JjfmQy5RsAEpXiDLQI49xpDTeuR3F23SDNj2wc7RHbiOeA/kag5KOgEy1FWVTj7QmclsCGeOOGWT9iMe+Q5SD3jlf7WbdYpulek3EeiAdo+wm/xf0J7anMYv+pDES2e8pH2WQp/qSF8c168R5bgOyXEA26YS2fpVQG8iIdYnLZqSrb9I92hNy/ZKeuV4eq0y2KeQ6pTLoEduWD6A75bNdY/2oN+lZNpCf7Rz1UHuxr+Rp/IF4tuE80/tvXvjFCduIcRuM7TnQtt1wQPCsRe9J9euTM2fOVEfglz38kgX0fUWUy9faN27cqNIz2npEF9oaOkWd043bt2/XZ925c+fOVN79ttlpKbDtqy/ctXUcf/XThXF0mWR7GtatW1cd5Tfaht2yZUt17Arbueg7nJAqO64US9GjZEPGjF8+lViq31NHr94IelWBHviO0gmAz7exdevWqu/nz5+v4lzfbb9Kw0aZkydPLtgh6qbXluQxD+DfylvurXbGhOuVOYvrtwvY5fTp03VsUH07smPHjupc31tRRn06cuTI2L8U0ysJYGyHN+XqfBKef/75qn2uS2xOYHy7MuoboFFjPE3oh16rE/SKGLuX7in4YAmlSw4BXbkuaCOm4xtt/cC+sQz+MI59V5pV8cEukxMXCoELmkmFAWHyI940IMPV6qIJkgstT3SSq9C0eOFCR17bRDmKLLvLB6rjTi6ZLnaaFN1ImHy4mZTeYUfoS+liW79+fX3WjWy3pjET6Dl86liYXJhYsMc4Cy0mbSYC+jDuAm2aTEuPa9eu1WftTMPv8/WlxSNjEvMITYuoCB98vvLKK9V5l49Yr1y5Up89Cn6QdZBd0VVpLF60+F0u8FvNYU3fN0RYuOAbmu927tz5iD3xm9hXwjiwiGXcmm6+44CdkcW8wbcuo+aPks92WVS2jXEXWDh0WbQzVrkdzbmXL1+ujl3AtqrPPQvbAHNtlE3gG6km6CN6Mzfju03fEs0rvVi8xJtavkiZ6DTZAQ4cHVY3MuohJ8KTdZwg44WsyTg/1bRNUHoqpAyTxDjs37+/0i/2Lz4lldizZ091jDo21dGFrcVKps1OTeRFQumpmBsJOnEzaVsclfoiW2ssRj1162LU0xN0GS9gxy1OLk265sVVtoGeYLs+DUeyrEhbXolJ9ZANWZTE6yrbMf4yail+H/0d2+J7/LLmwIEDlZ9GHTgvLW4zXMfIoe/yq4x05vqnndg/zmUH9IioXJ6H6Ld28JroonsT2Lvk/1wb3MAYLy3YIrFNfJo+ycfjAoM8bn7ZX/K4NxF1U5sc0SuC/FELingj1+4Lspquyeizsb/E2xY8o8a4iTh/qo9tiwTAz+K8BGqHORL/iT6VdZB9s99hKxbo+Hm+f8CovtB3/AY75QcQxopF7twydOK5Zjgg1bs6Bd4N6nzoeAvv8mKaGDrMQjr1hhdndU46UFb5MQxvYlU+5Dz0KRH1Qr5kc1S7CpKhdOlMu7FcjEedIrn/6nPUM/aTNqNdKNdmp6z7n/3Zny2KUx6adNX4qVwbKqsgu0DUkdAmL+pMPfWf9JK9IKbFkO1DQAbEMSct2jnaI5ZTyOQytBllEIYL3EVx6R7LtekxnGQXzgnYA6LdZdcog4DcrCOyY1q0NceSjEz2L3QRuf/Ig6bxiEiXCOViPdkvy6NdkfugvC59gyw7x2XvaMdoA4h1NGYZ1RcqH+vkthWkA+TxoF9N12XJnjENWbFfQJmmPuTxlh0kF3KbBJHHRPVLekaaxrhELpv70paX7R/tHu2kvJLepXIijxMB2uoAadkmgP5ttlhpHp1FHyPiRSFwTl2cfUQTQJ4AVxLsOU/6lChNqKUL3Zi+gi/nmxFzYNNiwjwKc1npRt9nSvdB4vN+H3xs/0gd22ulD5TYSjTTg61+bDrPduUjUd7/Z4aTen1mTL/h9QGvN/KrgdXyh+3MZPDaq/RKjtdJo16FrTSP7eKFj0Bv3nzwJbvgnPeZoz4MM93hIpjGx3uzhIUV4x7fl/O+F//o8oGoMfOOvseJ30TwYMGCpm8faprpwTxXmp9H/chhLqh3YB5L8rvAvpuD7b/Yl5V8XzkPOowDW6TRdn1+dWhMCb1SjmHeX+fOE9FufX/Vhv70o8+vxdfwz7ATxhhjjDG94LF9bWSMMcaYfuLFizHGGGN6hRcvxhhjjOkVXrwYY4wxpld48WKMMcaYXuHFizHGGGN6hRcvxhhjjOkVXrwYY4wxpld48WKMMcaYXuHFizHGGGN6hRcvxhhjjOkVXrwYY4wxpld48WJWDP57/vhf9BvzOHLr1q3B5s2b65gRzz333GDNmjWDc+fO1SnGPGRVLV58M5xfGJfdu3dX52+88UY1KR05cqSKP24wGc/iZqXJnoCNM7J7W5kM49a17DRRuwRu7pHYz1L+ckHb8umuoGvUGT/YtGlTdW4egl23b98+uH///mDv3r11ajvRZ0oLHtm+rUyGMl3LmmVm6ByrhuEkcH+VdWnVsGvXrvuHDh2qYw9grI4fP17HHh+wA/aYBWfPnq3sSrh582adupg8DvMK+nNNl6CfTXnzjMYnwnj0sS+z4urVqxPP46pL4LxEX/zftLNqdl5YGR88eLA69+6LeVxZt27dYDhpV0/zO3furFMXs2HDhvpsvrl3717jDtWdO3ca+2f6zd27d+uz8Vm7du1guEAcDB8OBtu2batTF9MX/zft9GLxwgSmrb6mhcnRo0cHhw8fHgyf5FtfR7AdKVkEoe3BnA6xfYK2fOM2JaFJN9pk211yNCHH+rluzMsTeJuuOY8tdiE9BP3IsmmXIDuV+prrKF0hg6yLFy8OTp06VeUTj0TZnEfa2o3EVwlZPqg/CiJvJUd7NcnMdaJeuZ38ugWZ2AF75Lqcx7qyfYlYrrSlfePGjcHNmzeLtsiMsjHpURf6QJvSV8S+Rzt2oVTv2rVrgx07dtSxxdy+fbt6rRCJ9ov6Rr2iPRgbpZf6Hce/BHaL+sbyhDwuxPft21edq0zUM/pV1ifqmutFaCPbnj5HP4z2IMS8aMN8LULMz74NyiOU/FI06cAx26jEKD0vXLhQLeBL45pBT8kqtYeM2FfaI6gPGos4/tHPMshSuVhfaLzUR/WBNlUn9zn3IfsAcflGk4zof4S5p96BmVtQMW7/5TiwFRtfP1CGtAhb0LmutmqpG7fxY5wtxigbOcSRk7d683Yk5WhTQdv4iksu9WI+8Sgrxmk35sXXMepjhDxtVRPUf3Qnrj6obiwnuRxjmzFO+9HW1CvZPuop1NYkdohQP7aJDMXVL/Ub4rjHMaSOdEFfnUOMUyfKox5x8rN+OY4+pCEvEnUG8qMthPqj9BxHD+mmvKxD7FfsM9A3xalHfQIgV3EC8iWbY25X8TZoX/I5j3bhvElGzpM+kPujcugoG5MWx159VX84V9koG9Q/gtrKuoNkCerF/gq1LX3QLcuOuua4kE5ql/pKkw1Iy3opjv7qp3SI/Y5y1A8CusgmKp/jEcrHMVL/kU35ko0iKitiXPUFeXlcYtvUi/agbMl+yFWfFGJdyuZ2Y1wgI+pDfcVjewTKguLSW/ZSPunRH9CLfMrpXEE6ZRkqJ5RP4HweafaQOQBD54s0OpfIZfJgAnVyPcUpGx1NAylHk9NESrqVkCyOApnRgWN7kMvTFmkR6pNGkCzJKYHsLJf+5z4Qz/3N9aI+2XZNoGO2PzJiW5PYIUKeguS2jXuWT7vUk62kB5AuW+V6otRWCcrE8Y+yI7EfgnisC3HM0Cvqxjlyol5ZJiCDcrlsyebES2Muuyl08QvJpm62AfE4BpGYp3Zze8RjX2Id0mN5bKKyMQ9bl8YakCdblsalBHLVZ0F7pb5LNnLjmKm/Jb0oG/us8VdZ8mJ+RuUVVK+kN3HZqdT/2AdRkgOUlV5NZYC8bKvYZ2wjnUC2irplnYB8yuWysof8Bkr9EpJBaCoD6KtysT95vAB92nTiPPYZaJt0wXnUJ8tAn1H9njfm+rUR77WHxlu0lcV2e9xmYyssl+G1EWlx27K0zXry5MnqSFm2KlVf70p558724+XLlxfJB76AP3bs2KL02N6kSE90kFxto5Kn7UG204fjNxg6XZUHW7durb53UD1C3hocl1H68HqCV3bK67JN24VR7Ua0FUrfsclwMqhzHi0LjHspfePGjdWrR8YdkKO25VNAG1EvbRHLn5ROKLXTldgPwesSvXJSQC/SS+ATw4mtelVV8gVtRePL9Gs4gdU544EN0BddkNMFfFk25vsVfElgN2QxJhnZVHkchxPzwjUsHzx9+vTCqyVt+6sOMphfBOO7f//+6hxbyac5YsNR4Ddck9RRiK8algJ2QT/JlV9M8m1Im49iN/ya8ZOvC76lAs1x6tuWLVuq47h+mek6b3S5J0QYb+YFyuRXKdA2n45D2xwU0SsjoBx+OwvWr19fn3WD74CiHXllSx9K19+8MPffvGBABjkGFhSCyUUTZgw4IXmRS5cu1WePwgSfZWjSoj2lIVc3ARYwSsdpc3tLQRdBDDgSbeDwTJYl0FnlsUvb9z/j0KQPMLkqjUloGos40dYucLFxs6Fc002mbdy5SJso+ZWIafRfkzk3B6UzTidOnKjSR0FbpQm4NAmxwIjtE3RTKoGfogu+kMeGNHy/689RS9B3Jj76MM5kp49u0T8uXIAFZNMNoPQhL9cDctCBAOikm+uBAwcWfeDLdRQXBNTBfzQG8ukuCxchHQjYlAXhtGD8JFth0jEr+Sh+gQ1IK4EdmPu0QGSRwzUXx3scvywt7Lp+SDvqnpBB96YF/Kj5tAtd5iCBT2DHtut1Eq5cuVKfPaTp+ilB/ylPYHy5NvI1OW/M9eJlz5491QWVHU4TMEcmsdKEyVMUdVWWyYt4XH0rTxdlRHm5bW6E3FC4+OIFyE2w69NDG/SFSQB9I9IH9DRD+1w0gosolrt+/fojDqybtS64UYzSJ9uHG4ae0jKlm3MTXewQ0VMo+jDOomncJZ+LNI4jeUxA2C0/qZX6TJ+Qz68csi3YsWuakOPEgJ9DyTfzDQq/xsbRDuigPmCH0hM5k5P8HL0i2oFAJrJLdBk77VhluwE7M6XrI8pl0lQ/sCd2jWMDyOHGGa957C45+Dz9VJxytMvChbHQGDHG3PR0w43yQPXpS6k/Qtcitot94WaSZUZi2VHwK0r8NNaJ45+Ji/W8gBrlo7HfGXax4kNevFGP8kshf47XNWWoqwVE3BHLjLon4IOl+nEB/8orr9SpD2iaTyPy7Taa5qCMbMwRfZYK/o7e0dYsysZdPHONoLfGd+4ZKjnXDI1ZvXuLAYYD9kiaGDrpojziMLzwFqUjQ2R5qpPTkQHDVfai9OEkWKVHSnrEOAGiLLWLvFhO7cY+UEb6DW+2j9iKtEiUSV7s27e+9a2Fc0LuT5M+OZ2+lIi60Xaso7aiHSSnqd1I7Af2k3zSIdospkOb/llP5eV0+gal8W5CZdT3PHZKLxHtREAfiH1pqk/fVR6izuRJBsdoV8Jf/uVfLoo32THaWzbgPNoWcp8zedwIyJDOpX5HnZRGO+pL9B/J1/iJ3JdMaayyrlGPTJTf5EtKl5zsW03ysx6xHudZDnERdYlyZAPFc5DOTX5Zoqmcxkkh6iey/QkQ6za1rf6L2E/GRTKon231Z3/2Z4vilBexberJlnmcoo1yG7m9HCdkGZSBbDdda3lMpHNMxwa5nEKTHeeBNfwzVNIYY2YCT8XsREx7q3xSeOIdTsoL2/fszPCUupRXZ48D2Cm/SmC3bHgDXdJrF7PycE2w2xZfv7GTo9eDo16HrQSr5o/UGWPmE7aw52XhAtoel07ckNExv+IwD+F1XfxmSLAINP2Hccyv6Hl1O8948WKMmSnz9uEfCxe+jREsWkib98l6JeH7Hb6r0PcawJM6dvOuS/9h8cICPsJiddeuXXO56wJ+bWSMeazgNVb+QN/T4GjYfYkfdHNja/uVj+kXvAKMzPvrQC9ejDHGGNMr/NrIGGOMMb3CixdjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTK7x4McYYY0yv8OLFGGOMMb3CixdjjDHG9AovXowxxhjTKx6bxQv/cyz/sdhykP+Dq2mwefPm+szMmpdeeqkK805f9Jw2/G/G/OeKfYC5IP5PzPMOdsW+fQOdmePnBfRh7Pvip31k7hcvTM44wVIck/rbtm2r/lt3gTzSx5n8R+nC4oh8/nvxaSGZ/NfzZrbIJ44cOTJYv359nTp/RD1nDZPwvNzMuBHQ71OnTg3WrVtXp06GbBjDNB9udPNiLojzDg8hcTFDmaXc4JaiN+3qoQidkJX/t+15R3MyPhGhX/GBT+O9HDAe27dvr/6n8r1799ap3ZDfEEr3mey3pTIZyezTIroT/K/SjwO7du26f+jQoTo2O44fP35/OGHVsemAzMdoqFYcbH327Nk6Nr+gJ74xS/DlWbcxDsNFfNXvq1ev1injw1xAyCB3mtcuPtQ256gvHFcCdMt2oP/LMU9OE3xhlE/MYl4uIV2WAn6DjDbf6NsYzQJ/82KMeWzgKXTHjh2DCxcu1CkPYXeTMK2t/jt37gw2bNhQxx7l3r171THuzJh+c/fu3fpscthVHC6Cql27nTt31qmLafOrx4VeLF7ylpe2Xjk2bZ9pG1ShBNt7TFS5rCYvxeP2I/G8/Rb1uHz5cp36ECbMPCFSNkK+ZOS8TNY36teFuPVYqstWLEGvrNTfvGWZt6tVL4L8aC/6iT2yrEi0JyHbO5LlxLKcq/1YJhNt33ULnnblP6qrMVackIl9K9lK/Yl6xK1kQrYHcpSXZca6uW8ai6b+ow83c15PkUdZUFmFNqJuyIvIhnEMcxmIMqRDF5AdZWK3S5cuDQ4fPlzFMywiuGGw6AC1q3EFjVFEuhGi/W/fvj145plnqnPJinW50e3atauOPRyrWIa0PG4R5EabSIZC1D1COV61XLx4sSqX7R59IsqHmEfoAvJVnvqxT8RzG/ILEX2EkK+BTGwDG+HD+HKUoaAxizq2yY/lYll01Gs35TVBnSgj9lXcuHGj0rlt/EUc91xebUVkX9Lj2Me+RV+eS+odmLmErTFUlJp6faIwHNgqne3AuCWYtwi1laettrgtp61GZBGPrwtIU52sC0iu9ADialv5BMllm1ZpgnJxG5AyMa5+C+RLb0B2jLdB2WibqM/wAl84J0S9ZDMhe1Ff5wS9Yoj2kn0UJ1BPxDj1JAOoG+MR8qIc6irOUW2pv9Iz2pZyMS5bRz/ISG6UpXoEKLVFPI6T4rGudFU90qIucexUR/ahDnHZm/RYN8pSXYLa0hirDHJoT/IBe0WZ6B/jEdJj3SiLc7WvMUMW8aY6oD5GO2Zke8roHJATZZVQe9KduOwJkgVRNuSyxEGyKKv86J8E+kU56sS+5bhQ2wT1ifqypdDYZqhPXi5Pe9IHpLd0IB7r5HgGnaivfiMn6sx5qT2C2qRubCPrLZkqn2VSH7kaD4EMlRHUa4N8+iSQQRptENTfNqSvKMXVF2SSl/WU/YDzqBPlFZd+0ReJK4BkU5b2QDopPo+0W3kOyM4go2pwITtmzofsqHKKNjnE4+BlXZAZnQiyDKBOdC7KRDlCjkbgXOTynOf+dSXbIfcJ0D/3q5SmurJRLpPzIbcPUYeSTUeBPOoT8vhJrojty5cypMXxKoEcgpCsOC6xLeRlvyjpEm2F/rENiLajfLRVSQaQphDL5z4AZWLf85gSH2WbDHXUftSf82yT2F7JZvQNOdHOGfqkfOrLHqSP0j3Kpl5sn7rRXugvebnsqDggK9oWor4c0acNyksGxzyebaB/Lo+8OEbSIdoz2rDJ5wR52eakxX6jQ2xTMtWmoAzphGjLUvksk/ZiHUAv6gnieTwiJRmADNXLMkugV9QNqCM70Y/YF/Uv9ydDGYWYn/XOYxpRWwqlMvPCqvvmZdR2Yht79uyptum0hce2b9v7aLb1poG2YXkXPxyTwdBJ65wylOHXU9re67KtKOhP/DL/ypUri7aux2Gpv/gowbcIvHpT3whNaNsTsMnwIq3OuzKN99Nd4VUEvhX7xZZ9m7/if5mTJ0/WZ6PRVvJwAqrsM5zA6pzJweePHj260Ie45ZzRq5Jjx45V7Y/rZ3p9Mw7YE7seOHCgapvXRF2/KeE6xEZbt26t4tevX1/0zcHp06era1RwHW3ZsqU6P3/+/KKyuW6OA7ZkzmkCmaPmggivw9BPY0MovY5YCvgwr0Ykn3kI9P1ORL49rV+GwSTXeRP8EojxZtyB8W0bjybGva6wC74jG6pvTf6OPw4XNlWd0qucaV3nXMuMJzIY53ln1X6wm29MXRYaTHJMsGfOnKkumP3799c5zeQbTOmGMwpuBlyQTe/iS+BgCvSt6ySlm58uGib30seLJUrf80DXm0MTXLTxxoY+6huTd9O7V26K5I9zQy8RFxBti4mlwqQSx40wyvbciCdBEyQTmm7G0wJ/k/5MeJr8M3xnwKQ77s9FI3kSLd0kI+RjZ+kYfZNzFutNcFPGpwRl+ckr4IOMhb5h4XqjHWRia/qqsoBPx48quTHGfPlZvnbob5PMLjCHaGywfezPtECu2lBo87GlPiRM6zovcfDgwWpssDd2z+ORYXw0dpFx/7QC/ck2bJv/uYa4R+AT8XpDl2lc58ikb+jRF3qzeCk5TAmcjxth/HsFTZMr5AuLhQTOcO3atUZnkC44PmW1cCCdxUCJOGnigBktepCFzDbizZw2cbq1a9dWcXZh2p6GAfvogumyqAP6yuQdbcmkkp+C4gIHW5aI/UN/7KGbeF6oYM+2iUFjITnjoJtq/FiQp91ZoF293L8239TiOe6s4R/qcxfk37RL+5MQF+RZf3yi7claT5P0c9yFmJ6C4/jwYDGK2E9sJ51ZCOB7UR5gTxby+HJcaOF79E3lWaxwnRHHrlxn1GVHhWuKsmqL64CFjurSd3ZplM9OjOqrTBxXpXVZ+Gl8sHGUwZwz6mbc9foX3HTz34Jp8uFx5uI4b5YWXOoXx67XebQFlPwfH2NssPeoh9WSP6o/Gqcuu4XsCsb7BsTrGt8qLfhY3Mj++UFy1HXe9dqXDrGPc8vwBja3DAdq0fu3HB9ONlU5jkobDlKVNpxoFtKo9/9v7+xVpGiiMDxegqmIiKORgYloooIGrhcg+BMZCZuLghiarJhsImJk5hUIamCgYCAm5i6IiJEm3sB88/T2u9+ZY/XM9O7OzvTM+0DtdP2dOvUz1aerqneGX6LqmnD2FhWHkxxBmhyWy9ZeYAxXPvmlS0lvXSMn6oOeSo886S1H+lg3nMoBpWki112OcpCTwyJRbxyyIjk++pU214e6RHJb5TIi1DPKieWV2g0UrnJznWP7oEuJmB5XqjdEHSCXpfCcP9Y51hGHzFJYTst1bEvK0LjJbYwT8qvuUSYymtq1RKwX8pSXz6YxrPBSnXCxf9TOmSg765fbWq6E5Kg/YlrpRRpQWtVDackb+13x0kP5RcynNiiRxxJpY9vgmsZvRGljm8kPsRy1Qx4/Tf0gomzljXmy3rGPuI5jAFk5Xtclf2xf6RHDQONxGkrtLtq0S66z5PCZwzKUE/WN5VKm6kl4lIfb3Nwc8UcdlQ8X9VO/LxqH+DNU0CwBPAXwtDVueRULn6fXmIZ8WPPDL+bEJ7W9wpMwZcxiCdiYaWAMahVoUac/nnxZKZpm5aVraJWrzTb5LGH+Y8VkUfQx0+F/UrdEsE0zzihgSZADWXl5dBYHb41ZRDAK+J5gtAyfKHfOfolFWS5nG2UZDZdFhPFgw6V72HhZIibtX2tVJe8pY9Csr6/PfNXFmHmDca+zbBgH2Yhpe0B2v8Bo0vkJrjlEamYPZ0RmcajZzB5vG60YrL7006t0TN4H8ZQXn3DRoe1hQWOWFW3dwkF9Hw8atqz1ajWsra1N/abjfoOByKHZeepg9oaNF2OMMcZ0Cm8bGWOMMaZT2HgxxhhjTKew8WKMMcaYTmHjxRhjjDGdwsaLMcYYYzqFjRdjjDHGdAobL8YYY4zpFDZejDHGGNMpbLwYY4wxplPYeDHGGGNMp7DxYowxxphOYePFGGOMMZ3Cxsuc4NedT548Wfu6Afryy9DoflDEX6JeBfi1W35huA38Wi/5lpEnT55UzhhjIjZe5gBGQL/fHzFeMAi4US/qTQjd3r171+NHyE+cOFGHzo5r166tlOGCwUJ9+Zn+cWCokI4busbMxYsX69j5E/VrC3nod5CcBw8eVP5lRX14kA8ExiwDNl7mwLdv33rr6+u1bxsMAgyDZ8+e1SGLAzeVtbW1AzFaxJs3b3obGxu1b/m5efNmb2trq/Y1c+HChWqc3L9/f2fMYAgvClG/trx//35njEnOsvP58+fq8yC/W8YsAzZezES+f/9eXxljjDHzpxPGC8uqcvk8QIzDNaFl+UnpgCVrlq9jHpU7TobOhOBKy+Yxb2mZmC2jmI8y8zYSeqGf2K2uES1dy8XtLPRhK+Pt27dVnJb1M1rml1P9Ssvi1CnKyeWXts5ifCTnjW0D1IU0sW9yGuqouFLZMT62zThiecqDHrHeisdl0ENx6k+h/h5HLL80FsWfP3+qNOoftaf8uyHrF3Vpkk0exhhjjTSxnSD2Qa5Pm/6J7ZrLIA69pa9oCo/l4rJeTfkEcbdu3aquJUNtE9ssyyVO37dYB+VXPsoH0igsj31jOstgwen3+/XVYPDq1SvWkWvfYLC2tlaFiY8fP474BeHr6+u1bztf9EeQL6c0GxsbO2GwtbU1Eg/4KUdEf9Zb+dEDSIcfJ/3lL+kgufI3pYOSrhGVTTqBX+1OOHmlawl0zv0kP5/IU/6cFn1zXsJgmro0yYp5caof8TEPsqK8mIe2yfHZPw7qHNtNOsW2jrqofjiNA/LjB8IUrzEAyMh1iPHZH3n9+vXg8OHDte9ffxtK+qGX+hOoY/QLwqlrblvJUx7i8asN8cc82R9BfixbfnRVOZKNjKZw4DP2reo+Tl6GcOWL4I/9JT+yJVPjRnrk8KiDZBEXx5sxXWb0W7OgxEkRp4mLLyJx0xK/5HHiyRAX4zURxAmFeE1I6JAnhRhf0jNPfkAZMV2UAU16RDmTdM2U4rKMkq4R4uJNgf7JOuBHTm6nnI68kqXJOlLSN07qMX2uB5A26kB8bPNc15yftLGMceS0qr/qF+sKareoT5ZRaltkqk1In9u41GaC8m/cuFH7BoOHDx+O+NuS9ctjYxwlPZEV86tPKQdyWzT1j/RSPkDuuLEgcnipD0D9K5rkRbK++Mf1X66/oC5RDuCPbZfra0yXWfhtI5Y6Hz16xLeyN/zi1qHbcPCVOC2JspxaguVZ4q9cuVLJGU4Edcz+8OPHj+qwpfTAsQSuJWDiusSRI0fqq+mgH3grRHUfTpBV+M+fP6tPoO/YEnj58mUdsr1FkeHg4rSHPbVEzkFP+nU48dcx04OuUSfeqGKcgPTjbR7VTcv8Jd0z586dqz5ZqmcM3r59u3f37t3eixcvqvCnT5/2rl+/Xl3vF5PGYoa2u3z5cu3brn/07xUOXlNG1Ge/aNM/v379qj7pb6VlzO7nd/P48eP11e5p23/GrCoLbbyw18tkw82xCeK4ceEwXrhJZDBwhk8du3oDYlrQU3rIMXELJqXIIk1G3LBKtDFiaN9cf96gEdxgMBpLr/V++vSpvmoHN//hU+lIO7cFw0XneXBXr179Z5xgeOW6TfN2CGnQj/p9+PCheoMGY4Wbk84ezOItk0ljMULdT58+XV2j05cvX3b8wJmYpoeCaaFs6cEYyGc49kqb/qHtc9rdUhq3e20raNN/xqwqC7/yEp+M7ty5U19tkydBJuKjR4/WvlH0xgwT9KT/pdEW3ZCyPjKkmLB5ypPBgg5NBlk2cqJh8fjx4/pq/6BNs+4YBeisG8AkQ4vVhFg/iEYkEzo3GF4D52auQ4a6uZNXN3MoGaBNxHbUU3cbMCjQQTeJ+Kq69MvjLuqHwaODkSXIS/0uXbpU+SUTI+7evXtV2G6Iq1qRSWMx8vXr1+rz79+/1bVuxhit5Cfs/PnzlbxJ9cxIv6wH4/nYsWO171/aGPXT9I+gnzEKch3ajDURx23UFz8PSrtBctr0316hLfbD2DJmLgwn7IUGFeW0P4wb3gyrveAYT1iJmI88wxtzdT2czOoU/6N0ctpLjn6IZcNwwhlJp3AR00cdcOitaxxxEPXGRV2yXqWwJl0zpXIEbRTjmto4l606kF/XQmloM4j64cb1rcL5zG0e6zG8kYzEqU5RT/LHfogutkHWhXJA/TYO6RghPzIjuQ9KY2Bzc3MkTa6jxvOksSieP38+OHv2bBXPOZffv39XfhzXSsM5mGnIdaANc/uq7UpEvfO4U3vF70oeE3LjyiiN5ygTp7ZvChe5XPSHSfkyUQ409V/+jqmeWY+cTm0Xw9ERvTRmjOkah/gzHMzGrCQ84bISELe4eCJlhWDcdiWQjhWVmLdLoD/ncFiVaIJVsuFNr3fmzJk6xBhj5o//SZ1ZWViKZ6k/Gx/THLzUNldXDRdoOusU0VZs3sYwxph5YuPFrCw6HxVvzBglGDSTzhexWrGIP+XQhq2tyW/a9Pv93qlTp8aeUzHGmIPG20ZmpWH1JR/05abOoUxjjDGLiY0XY4wxxnQKbxsZY4wxplPYeDHGGGNMp7DxYowxxphOYePFGGOMMZ3CxosxxhhjOoWNF2OMMcZ0ChsvxhhjjOkUNl6MMcYY0ylsvBhjjDGmU9h4McYYY0ynsPFijDHGmA7R6/0HvXrFyrY+DQUAAAAASUVORK5CYII=\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003cp\u003eBased on theory, institutional knowledge and empirical evidence, it is expected that eNaira access, usage and quality can enhance financial inclusion. Therefore, it is expected that \u0026beta;\u003csub\u003e1\u003c/sub\u003e, \u0026beta;\u003csub\u003e2\u003c/sub\u003e, \u0026beta;\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Diagnostic Tests\u003c/h2\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1 Goodness of Fit Test\u003c/h2\u003e\n \u003cp\u003eIn SEM, a series of the goodness of fit indices that reflect the fitness of the model to the data at hand must be conducted. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the fitness indices adopted in this study. The most commonly reported model fit is RMSEA and CFI, although, the choice of indices to be reported is a matter of personal preference and perhaps, the preference of the journal editor (Hu and Bentler, \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTest of Model Fitness\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFit Indices\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAccepted value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAuthors\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\u003e(CMIN/DF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBentler (\u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;=0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBentler (\u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;=0.90 (between 0\u0026ndash;1)\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTucker and Lewis (\u003cspan class=\"CitationRef\"\u003e1973\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eByrne (\u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: CMIN/DF\u0026thinsp;=\u0026thinsp;Chi-Square/Degree of Freedom; CFI\u0026thinsp;=\u0026thinsp;Comparative Fit Index; TLI\u0026thinsp;=\u0026thinsp;Tucker-Lewis Index; and RMSEA\u0026thinsp;=\u0026thinsp;Root Mean Square Error of Approximation.\u003cbr\u003e\u003cstrong\u003eSource\u0026nbsp;\u003c/strong\u003eAuthors\u0026rsquo; compilation.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2 Assessing the Measurement Model\u003c/h2\u003e\n \u003cp\u003eThe measurement Model is considered part of data preparation because it is used to measure the construct validity by assessing the factor loading, and access normality of the measurement instruments. It is also used to test the relationships between the latent variables and their observed measures. The measurement model in this sense portrayed the links between the latent variables and their observed measures (Byrne, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e and \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). In this study, we used the Chi-square (CMIN), Relative \u0026chi;\u003csup\u003e2\u003c/sup\u003e (CMIN/DF), CFI, TLI and RMSEA to assess the measurement model.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Reliability Test\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the reliability test results. It was revealed that the alpha Cronbach coefficients for all the variables were between 0.70 to 0.89. This implies that all the items of the study were reliable given the alpha Cronbach coefficients benchmark of \u0026gt;\u0026thinsp;=\u0026thinsp;0.7. The overall alpha Cronbach coefficient of 0.89 implies that all the items that made up the model are reliable.\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eReliability Test\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\" colspan=\"2\"\u003e\n \u003cp\u003eFinal test (N\u0026thinsp;=\u0026thinsp;206)\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\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo. Items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlpha (\u0026alpha;)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFinancial inclusion\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eeAcess\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eeUsage\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eeQuality\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOverall\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e27\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.89\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Authors\u0026rsquo; Computation using SPSS Version 21\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 The Measurement Model\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e represents the proposed measurement model of the study. After several adjustments, the final measurement model is presented in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The results indicate that our model passed the goodness-of-fit tests, suggesting that the measurement model fits the data used. As reported, all the relevant test statistics for the modified measurement model are satisfactory. For instance, the Chi-Square (CMIN)\u0026thinsp;=\u0026thinsp;271.091, DF\u0026thinsp;=\u0026thinsp;126, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.000, Relative \u0026chi;\u003csup\u003e2\u003c/sup\u003e (CMIN/DF)\u0026thinsp;=\u0026thinsp;2.152, CFI\u0026thinsp;=\u0026thinsp;0.921, TLI\u0026thinsp;=\u0026thinsp;0.904, RMSEA\u0026thinsp;=\u0026thinsp;.075\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Convergent Validity Test\u003c/h2\u003e\n \u003cp\u003eWe test the convergent validity of the individual constructs in the research questionnaire by assessing the factor loadings, Average Variance Extracted (AVE) and Modification Index (MI). Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the first and second-order CFAs of the construct\u0026rsquo;s items in which all the items that did not meet the cut-off point of 0.5 for factor loadings and AVE, as well as MI\u0026thinsp;\u0026lt;\u0026thinsp;15 were deleted from the path diagrams. As shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, nine items were deleted based on these criteria. Since all the factor loadings from the second order CFA were greater than 0.50 and the AVE were also found to be greater than 0.5, we conclude that the model passed the convergent validity test, hence is reliable for this study.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eConvergent Validity and Construct Reliability\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\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eFactor Loading \u0026ge; 0.5\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstructs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1st Order CFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2nd Order CFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAVE\u0026thinsp;\u0026ge;\u0026thinsp;.5\u003c/p\u003e\n \u003cp\u003eMI\u0026thinsp;\u0026gt;\u0026thinsp;15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eFinancial inclusion (FIN)\u003c/em\u003e\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\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIN 1\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\u003e0.74\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIN 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeleted\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIN 3\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\u003e0.67\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIN 4\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\u003e0.67\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIN 5\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\u003eDeleted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(MI\u0026thinsp;\u0026gt;\u0026thinsp;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIN 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIN 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeleted\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\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eeNaira Access\u003c/em\u003e\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\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeAcess 1\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\u003e0.57\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeAcess 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeAcess 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeAcess 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeleted\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeAcess 5\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\u003e0.69\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeAcess 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\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\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eeNaira Usage\u003c/em\u003e\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\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeUsage 1\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\u003e0.59\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeUsage 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeUsage 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeUsage 4\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\u003e0.64\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeUsage 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeleted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(MI\u0026thinsp;\u0026gt;\u0026thinsp;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeUsage 6\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\u003eDeleted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(MI\u0026thinsp;\u0026gt;\u0026thinsp;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeUsage 7\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\u003e0.54\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\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eeNaira Quality\u003c/em\u003e\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\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeQuality 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeQuality 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\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\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeQuality 3\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\u003e0.54\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeQuality 4\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\u003eDeleted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(MI\u0026thinsp;\u0026gt;\u0026thinsp;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeQuality 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeQuality 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeleted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(MI\u0026thinsp;\u0026gt;\u0026thinsp;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeQuality 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeleted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(MI\u0026thinsp;\u0026gt;\u0026thinsp;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: \u003cem\u003eAVE is Average Variance Extracted and MI is Modification Index\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eSource: Authors\u0026rsquo; Computation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Discriminant Validity Test\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the construct reliability (CR), Average Variance Extracted and the square root of Average Variance Extracted in parenthesis, as well as the correlation coefficients in curly braces. We compare the square root of AVEs and the correlations. In line with Byrne (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e), the square root of AVEs of the construct were greater than the correlations of the constructs, therefore the items of all the latent constructs explained significantly more variables of the latent constructs used in this study. We conclude that the latent constructs show adequate discriminant validity.\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDiscriminant Validity Test\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables/Factors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAVE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAccess\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUsage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQuality\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinancial incl.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e(0.705)\u003c/strong\u003e\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\u003eeAccess\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.853\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=\"char\"\u003e\n \u003cp\u003e{0.491}\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e(0.736)\u003c/strong\u003e\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\u003eeUsage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.849\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=\"char\"\u003e\n \u003cp\u003e{0.519}\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e{0.327}\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e(0.736)\u003c/strong\u003e\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\u003eeQuality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e{0.588}\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e{0.450}\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e{0.400}\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e(0.754)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: () represent the AVEs and {} represent the correlation coefficients.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eSource: Authors\u0026rsquo; Computation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 Normality Test\u003c/h2\u003e\n \u003cp\u003eData is considered normally distributed if skewness is between \u0026minus;\u0026thinsp;2 and +\u0026thinsp;2 (Tabachnick and Fidell, \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e), while kurtosis is between \u0026minus;\u0026thinsp;7 and +\u0026thinsp;7 (Byrne, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). The skewness values in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e range between \u0026minus;\u0026thinsp;1.58 and \u0026minus;\u0026thinsp;0.11 and the kurtosis values range from a maximum of 2.44 to a minimum of -1.09 which all fall within the cut-off point implying that the data used in this study are normally distributed.\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNormality test\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkew\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC.R.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC.R.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eM-dsquared\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObs\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\u003eFIN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIN4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIN6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeQuality 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeQuality 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeQuality 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeQuality 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeUsage 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeUsage 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeUsage 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.548\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeUsage 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.821\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeUsage 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeAcess 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeAcess 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeAcess 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeAcess 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeAcess 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultivariate\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\n \u003cp\u003e\u003cstrong\u003e87.396\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e22.858\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e36.17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: C.R. = Critical Ratio and NO\u0026thinsp;=\u0026thinsp;not an outlier. \u003cstrong\u003eSource\u003c/strong\u003e: Authors\u0026rsquo; computation.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eAlso, the overall multivariate Kurtosis of 87.4 confirms that the data is normally distributed as the multivariate Kurtosis is not large. Certainly, if the data is normally distributed then, it is a clear indication that there are no outliers in the data set. Even though, the Mahalanobis d-squared for the measurement model shows the presence of 9 outliers, indicated about 9 cases of observations farthest from the centroid (p1\u0026thinsp;=\u0026thinsp;0.000 and p2\u0026thinsp;=\u0026thinsp;0.000). We, therefore, deleted the outliers in the final analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.5 Unique Predictors to the Dependent Variable\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows our final SEM results while Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e contains the coefficients of structural equation model. The results in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e are robust as confirmed by the following goodness-of-fit indices: Chi-Square \u0026chi;\u003csup\u003e2\u003c/sup\u003e (CMIN)\u0026thinsp;=\u0026thinsp;264.143 (DF\u0026thinsp;=\u0026thinsp;126), Relative \u0026chi;\u003csup\u003e2\u003c/sup\u003e (CMIN/DF)\u0026thinsp;=\u0026thinsp;2.096, p\u0026thinsp;=\u0026thinsp;.000, CFI\u0026thinsp;=\u0026thinsp;.924, TLI\u0026thinsp;=\u0026thinsp;.907, RMSEA\u0026thinsp;=\u0026thinsp;.075. Since all the fitness indices are within their threshold, then the entire model is fit.\u003c/p\u003e\n \u003cp\u003eThe results shown in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e are also robust. The standardized path coefficients were consistent with the hypotheses, indicating a significant relationship between the predictors and criterion variables. The standardized coefficients of \u003cem\u003eeAcess\u003c/em\u003e, \u003cem\u003eeUsage\u003c/em\u003e and \u003cem\u003eeQuality\u003c/em\u003e of 0.181, 0.319 and 0.382 suggest that a 1.0 per cent increase in eNaira access, usage and quality may likely lead to 0.181, 0.319 and 0.382 per cents increase in financial inclusion in Nigeria respectively. This is consistent with the expectation of our study. It was also revealing that eNaira access, usage and quality has a positive and significant influence on financial inclusion in Nigeria as indicated by their probability values of 0.021, 0.000 and 0.000 respectively.\u003c/p\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnstandardized and Standardized Regression Weight\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003eHypothesized relationships\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eb\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eS. E\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026Beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCR\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\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\u003eFIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeAcess\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeUsage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeQuality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.530\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: b is unstandardized regression coefficient, B is standardized regression coefficient, S.E is the standard error, CR is the critical ratio, ** and * represent 1% and 5% significance level.\u003cbr\u003e\u003cstrong\u003eSource\u0026nbsp;\u003c/strong\u003eAuthor\u0026rsquo;s computation.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe result indicates that about 48% of variances in the dependent variable (financial inclusion) was explained by the predictor variables entered into the structural equation modeling (eNaira access, eNaira usage and eNaira quality). The \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e value of 0.48 was found to be significantly different from zero as proposed by Cohen (\u003cspan class=\"CitationRef\"\u003e1988\u003c/span\u003e) and supported by Lohmoller (1989).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Conclusion and policy recommendations","content":"\u003cp\u003eThe paper investigates whether eNaira can foster financial inclusion in Nigeria using structural equation model. A survey design was employed by administering questionnaires to a random sample of 206 respondents. The study concludes, based on its findings, that increase in access to eNaira, usage of eNaira and improvement in the quality of the eNaira could affect financial inclusion positively and significantly in Nigeria. Therefore, we recommend the need to improve eNaira access, usage and quality through targeted digital and financial literacy campaigns to raise awareness, and encourage uptake and usage of eNaira, particularly in the rural areas where the majority of the unbanked and financially excluded people lives. There is a need to also apply a simplified due diligence process to enroll special groups with limitations on the use of eNaira. The CBN should equally come up with a plan to speed up the development of rural agent networks across the country and explore ways how the eNaira could be accessed via different user interfaces, aside from unstructured supplementary service data (USSD). This would entail deepening offline functionality to enable eNaira transactions to be carried out in locations with no internet, electricity or mobile network coverage.\u003c/p\u003e"},{"header":"Statements and Declarations ","content":"\u003cp\u003eThe participates were duly informed about the purpose of the study in the questionnaire and they consented to participate in the survey.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe Authors did not receive any funding from any organization to conduct this study, and they have no competing interests to declare that are relevant to the content of this article.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkpan, U. and T. F. Nwanja (2022). Central Banks\u0026rsquo;s Digital Currency and the Challenge of Monetary Policy in Nigeria, \u003cem\u003eforthcoming \u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eAlliance for Financial Inclusion (2022). \u003cem\u003eCentral Bank Digital Currency \u0026ndash; An Opportunity for Financial Inclusion in Developing and Emerging Economies? \u003c/em\u003eDigital Financial Services (DFS) Working Group, Special Report, AFI. \u003c/li\u003e\n\u003cli\u003eAminu, U., I. B. Iya, and O. A. Adewusi (2021). Causes of farmer-herders conflict in Taraba State, Nigeria. \u003cem\u003eADSU Journal of Applied Economics, Finance \u0026amp; Management (AJAEFM), 6\u003c/em\u003e(1),11-21\u003c/li\u003e\n\u003cli\u003eAndolfatto, D. (2021). Assessing the Impact of Central Bank Digital Currency on Private Banks, \u003cem\u003eThe Economic Journal\u003c/em\u003e, 131(634): 525-540.\u003c/li\u003e\n\u003cli\u003eAuer, R., H. Banka, N. Y. Boakye-Adjei, A. Faragallah, J. Frost, H. Natarajan and J. Prenio (2022). Central Bank Digital Currencies: A New Tool in the Financial Inclusion Toolkit? FSI Insights on Policy Implementation No. 41, Bank for International Settlements. \u003c/li\u003e\n\u003cli\u003eBagozzi, R. P. and Y. Yi (2011). Specification, Evaluation, and Interpretation of Structural Equation Models, \u003cem\u003eJournal of the Academy of Marketing Science, \u003c/em\u003e40: 8-34. \u003c/li\u003e\n\u003cli\u003eBangko Sentral Ng Phlipinas (BSP) (2019). \u003cem\u003eFinancial Inclusion Survey\u003c/em\u003e, BSP. https://www.bsp.gov.ph/Inclusive%20Finance/Financial%20Inclusion%20Reports%20and%20Publications/2019/2019FISToplineReport.pdf?amp\u003c/li\u003e\n\u003cli\u003eBentler, P.M. (1990) Comparative Fit Indexes in Structural Models. Psychological Bulletin, 107, 238-246. http://dx.doi.org/10.1037/0033-2909.107.2.238\u003c/li\u003e\n\u003cli\u003eBoar, C. and A. Wehrli (2021). Ready, Steady, Go? \u0026ndash; Results of the third BIS Survey on Central Bank Digital Currencies, \u003cem\u003eBIS Working Papers, \u003c/em\u003eNo. 114.\u003c/li\u003e\n\u003cli\u003eByrne, B. M. (2001). Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. Mahwah, NJ: Lawrence Erlbaum Associates.\u003c/li\u003e\n\u003cli\u003eByrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming. 2nd Edition. Routledge Taylor \u0026amp; Francis Group.\u003c/li\u003e\n\u003cli\u003eCohen, J. (1988). \u003cem\u003eStatistical power analysis for behavioral sciences (2\u003csup\u003end\u003c/sup\u003e ed.)\u003c/em\u003e, Hillsdale, Lawrence Erlbaun Associates, NJ.\u003c/li\u003e\n\u003cli\u003eCooper, B., A. Esser, M. Allen, (2019). Central bank digital currency (CBDC) for Financial Inclusion: A case for Mobile Money, Cenfri, June.\u003c/li\u003e\n\u003cli\u003eFoster, K., S. Blakstad, S. Gazi, and M. Bos (2021). \u0026ldquo;Digital currencies and CBDC impacts on least developed countries (LDCs). Dialogue on global digital finance governance\u0026rdquo;, Technical Paper 1.2, United Nations Development Programme, New York, NY.\u003c/li\u003e\n\u003cli\u003eGjefle, E., Z. Herring, C. Kubli, B. O\u0026rsquo;Rea and G. Rakusen (2021). Centering users in the Design of Digital Currency: The Future of our Money, MIT Digital Currency Institute and Maiden Labs. \u003c/li\u003e\n\u003cli\u003eHair, J. F., M. Sarstedt, T. M. Pieper, and C. M. Ringle (2012). The use of partial least squares structural equation modeling in strategic management research: a review of past practices and recommendations for future applications, Long Range Planning, 45(5), 320-340\u003c/li\u003e\n\u003cli\u003eHu, L. T., and P. M. Bentler (1999). Cutoff criteria for fit Indexes in Covariance Structure Analysis: Conventional Criteria Versus New Alternatives. \u003cem\u003eStructural Equation\u003c/em\u003e \u003cem\u003eModeling\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, 1\u0026ndash;55.\u003c/li\u003e\n\u003cli\u003eLohm\u0026ouml;ller, J. B. (1989) Latent Variable Path Modeling with Partial Least Squares, Physica, Heidelberg. \u003c/li\u003e\n\u003cli\u003eMancini-Griffoli, T., M.S.M. Peria, I. Agur, A. Ari, J. Kiff, A. Popescu and C. Rochon (2018). Casting Light on Central Bank Digital Currency, IMF Staff Discussion Note, 8, Washington, DC.\u003c/li\u003e\n\u003cli\u003eManiff, J. (2020). \u0026ldquo;Inclusion by Design: Crafting a Central Bank Digital Currency to Reach all Americans\u0026rdquo;, Federal Reserve Bank of Kansas City Payments System Research Briefing, December.\u003c/li\u003e\n\u003cli\u003eOzili, P. K. (2021). Can Central Bank Digital Currency Increase Financial Inclusion? Arguments for and Against. Available at SSRN: http://dx.doi.org/10.2139/ssrn.3963041\u003c/li\u003e\n\u003cli\u003eOzili, P. K. (2022). Central Bank Digital Currency Research Around the World; A Review of Literature, \u003cem\u003eJournal of Money Laundering Control, \u003c/em\u003e1368-5201. \u003c/li\u003e\n\u003cli\u003eSahu, P. (2021). CBDCs and Financial Inclusion: A Developing Story, \u003cem\u003eCBDC Insider, \u003c/em\u003eJuly, https://cbdcinsider.com/download/cbdcs-and-financial-inclusion/\u003c/li\u003e\n\u003cli\u003eTabachnick, B. G. and L. S. Fidell (6\u003csup\u003eth\u003c/sup\u003e ed) (2014). \u003cem\u003ePearson New International Edition, Using Multivariate Statistics.\u003c/em\u003e England and Associated Computer Throughout the World, 770-780\u003c/li\u003e\n\u003cli\u003eTabachnick, B. G., and L. S. Fidell (2007). Using Multivariate Statistics (5th ed.). New York: Allyn and Bacon.\u003c/li\u003e\n\u003cli\u003eTucker, L. R., and C. Lewis (1973). A Reliability Coefficient for Maximum Likelihood Factor Analysis. Psychometrika, 38, 1-10. http://dx.doi.org/10.1007/BF02291170\u003c/li\u003e\n\u003cli\u003eWorld Bank (2012). \u003cem\u003eFinancial Inclusion Strategies Reference Framework\u003c/em\u003e, Washington, DC, NW, June.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Governor\u0026rsquo;s Speech at the Flag-off of the Second Phase of the eNaira Project, retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbn.gov.ng/FeaturedArticles/2022/articles/eNairaHackathonStory.pdf\u003c/span\u003e\u003cspan address=\"https://www.cbn.gov.ng/FeaturedArticles/2022/articles/eNairaHackathonStory.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The questionnaire can be assessed online at e-Naira and Financial Inclusion in Nigeria (office.com)\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Bagozzi and Yi (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) provide a comprehensive and user-friendly compendium of the standards for the use and interpretation of SEMs.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e A factor loading is an inferred parameter estimated from empirical associations among observed variables\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Financial Inclusion, CBDC, eNaira, SEM ","lastPublishedDoi":"10.21203/rs.3.rs-3861545/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3861545/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eIn this paper, we evaluate the extent to which the e-Naira can bridge the financial inclusion gap in Nigeria. From a survey design of a well-structured questionnaire, we subject the field-dataset to the Structural Equation Model (SEM), which combines factor analysis with multiple regression. The choice of SEM also follows from its capacity to handle complex and difficult data that may be non-normal and incomplete. Our results indicate that access, quality and usage of eNaira could have a positive and significant influence on financial inclusion in Nigeria. In particular, we found that a unit increase in e-Naira’s access, usage and quality could, respectively, lead to a 0.18, 0.32 and 0.38 per cents increase in financial inclusion in Nigeria. The results were robust to various standard diagnostic tests. Consequently, the paper provides strong support for the need to further promote access, usage and quality of the e-Naira in Nigeria as part of the strategies for enhancing financial inclusion in the country.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Can the e-Naira Foster Financial Inclusion in Nigeria? Evidence from Structural Equation Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-18 15:28:48","doi":"10.21203/rs.3.rs-3861545/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":"62fc98b2-0129-423e-8cb1-49404cb56a57","owner":[],"postedDate":"January 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28125583,"name":"Development Economics"}],"tags":[],"updatedAt":"2024-01-18T15:28:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-18 15:28:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3861545","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3861545","identity":"rs-3861545","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}