<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article  PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article"><front><journal-meta><journal-id journal-id-type="publisher-id">OALibJ</journal-id><journal-title-group><journal-title>Open Access Library Journal</journal-title></journal-title-group><issn pub-type="epub">2333-9705</issn><publisher><publisher-name>Scientific Research Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.4236/oalib.1105146</article-id><article-id pub-id-type="publisher-id">OALibJ-94642</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Biomedical&amp;Life Sciences</subject><subject> Business&amp;Economics</subject><subject> Chemistry&amp;Materials Science</subject><subject> Computer Science&amp;Communications</subject><subject> Earth&amp;Environmental Sciences</subject><subject> Engineering</subject><subject> Medicine&amp;Healthcare</subject><subject> Physics&amp;Mathematics</subject><subject> Social Sciences&amp;Humanities</subject></subj-group></article-categories><title-group><article-title>
 
 
  The Impact of Banking Finance on Financial Performance of Egyptian Small Businesses in Period from 2013 to 2016
 
</article-title></title-group><contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Eslam</surname><given-names>Mahmoud Saadallah</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Ashraf</surname><given-names>Salah</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib></contrib-group><aff id="aff1"><addr-line>The Arab Academy for Science and Technology &amp;amp; Maritime Transport, Alexandria, Egypt</addr-line></aff><pub-date pub-type="epub"><day>02</day><month>08</month><year>2019</year></pub-date><volume>06</volume><issue>08</issue><fpage>1</fpage><lpage>14</lpage><history><date date-type="received"><day>26,</day>	<month>December</month>	<year>2018</year></date><date date-type="rev-recd"><day>24,</day>	<month>August</month>	<year>2019</year>	</date><date date-type="accepted"><day>27,</day>	<month>August</month>	<year>2019</year></date></history><permissions><copyright-statement>&#169; Copyright  2014 by authors and Scientific Research Publishing Inc. </copyright-statement><copyright-year>2014</copyright-year><license><license-p>This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/</license-p></license></permissions><abstract><p>
 
 
  Small business becomes important for the economic development as it acts as an engine of employment. This research focuses on the impact of banking fi-nance at a normal interest rate on small business financial performance. The research approach adopted in this dissertation includes quantitative secondary data from annual reports of 90 small business firms in Egypt in period from 2013 to 2016 (2013 and 2014 
  without loan, 2015 and 2016 with a loan). Data was collected for the research variables; Loan Volume, Return on Assets, Re-turn on Equity and Net Profit Margin. Loans Volume at a normal interest rate represents the independent variable, Firm Leverage and firm age represent the control variable, and Financial Performance Indicators (ROE, ROA and NPM) represent the dependent variable. Results showed that loan volume has a nega-tive significant impact to financial performance of small business, firm lever-age has a negative significant impact to financial performance of small business and firm age has insignificant impact to financial performance of small business.
 
</p></abstract><kwd-group><kwd>Banking Finance</kwd><kwd> Loan Volume</kwd><kwd> Financial Performance</kwd><kwd> Return on Assets</kwd><kwd> Return on Equity</kwd><kwd> Net Profit Margins</kwd></kwd-group></article-meta></front><body><sec id="s1"><title>1. Introduction</title><p>The economy in Egypt, socially, entrepreneurship empowers citizens, generates innovation and changes mindsets, economically, entrepreneurship stimulates markets. Thus, entrepreneurship is described as a potential driver to support the economic growth, but the ability of small business access to finance has always been considered a major obstacle facing many small businesses [<xref ref-type="bibr" rid="scirp.94642-ref1">1</xref>] . Small Businesses in Egypt provide almost 60 percent of jobs and three quarters of the national value added but access to external finance is an important barrier to small entrepreneurs development in Egypt similarly to many other countries so small business needs to provide a business environment in terms of regulations and the way they are implemented and the government needs to improve infrastructure and access to markets to open opportunities for small entrepreneurs [<xref ref-type="bibr" rid="scirp.94642-ref2">2</xref>] . Supporting of small business is a complex phenomenon. Although it has gained academic interest, there is no single model that has been developed to date that can adequately explain why some small business grows and others do not. On 6/12/2015, the Central Bank of Egypt made a decision (the initiative of the Central Bank of Egypt to support the small business) which includes the following:</p><p>− Increasing the portfolio of loans and facilities granted to small and medium enterprises to not less than 20% of the total portfolio of the bank within 4 years from the date of issuing these instructions.</p><p>− Targeting 200 billion pounds to finance small projects within 4 years.</p><p>− The lending rate for small and very small businesses does not exceed 5% (decline simple rate).</p><p>− Care of small industrial companies, small labor-intensive companies, small business that have innovative ideas and small companies that target export.</p><p>− New credit facilities are not approved to repay the existing credit facilities to take advantage of the new pricing [<xref ref-type="bibr" rid="scirp.94642-ref3">3</xref>] .</p><p>The following questions have to be answered during this study:</p><p>1) To what extent could the banking finance at a normal interest rate effect on the financial performance of small business enterprises in Egypt?</p><p>2) What is the role of firm leverage and firm age in the impact on the financial performance of small business enterprises in Egypt?</p><p>3) What is the role of CBE to support small business compared to other developing countries?</p><p>4) Are there any problems faced by Small Businesses after obtaining bank financing?</p><p>5) What are small business enterprises in Egypt need to improve their financial performance?</p><p>The overall aim of this research is to measure the impact of banking finance at a normal interest rate on the financial performance of small business enterprises in Egypt and clarify the importance of banking finance for small business where small businesses suffer from many problems whether before obtaining funding or after funding.</p><p>There are three objectives aiming to achieve which are:</p><p>1) Measuring the impact of banking finance on the financial performance of small business enterprises in Egypt.</p><p>2) Measuring the role of Loans Volume and Firm Leverage on the financial performance.</p><p>3) Measuring the role of Loans Volume and Firm Age on the financial performance.</p><p>Two main research hypotheses are developed. Each hypothesis is tested once against the whole sample of Small Businesses and another time against Small Businesses with loans only. Hypotheses are stated as follows:</p><p>H<sub>1</sub>: banking finance has a significant impact on small business Financial Performance</p><p>Sub H<sub>1</sub>: banking finance has a significant impact of ROA.</p><p>Sub H<sub>1</sub>: banking finance has a significant impact of ROE.</p><p>Sub H<sub>1</sub>: banking finance has a significant impact of NPM.</p><p>H0: banking finance has an insignificant impact on small business Financial Performance</p><p>Sub H0: banking finance has an insignificant impact of ROA.</p><p>Sub H0: banking finance has an insignificant impact of ROE.</p><p>Sub H0: banking finance has an insignificant impact of NPM.</p><p>The research variables were defined and adopted according to the study of [<xref ref-type="bibr" rid="scirp.94642-ref4">4</xref>] , [<xref ref-type="bibr" rid="scirp.94642-ref5">5</xref>] and [<xref ref-type="bibr" rid="scirp.94642-ref6">6</xref>] Research variables of this study are Banking Finance, representing the independent variable, Firm Leverage representing the control, and finally, Financial Performance Indicators (ROE, ROA and NPM), representing the dependent variable. Banking Finance is defined as the loans volume acquired by Small Businesses in Egypt in the year 2017. Firm Leverage is defined as the percentage of debt to asset presented in million EGP for the year 2017.</p></sec><sec id="s2"><title>2. Literature Review</title><p>In this section, the researcher tries to make use of the literature review that is related with current study, explaining the impact of banking finance on financial performance of the Egyptian small business to figure out the gaps in these researches. This helps the researcher to develop the hypotheses relevant to the findings and recommendations and it provides details about the Egyptian experiences to support the small business enterprises as well as an overview of the Egyptian economy. Besides, an overview of the small business experiences in both developing countries. There are different points of views on the impact of banking finance on the financial performance of small businesses that it was an incentive for us to take care of this topic to fill these gaps. The majority of literature review showed that banking finance has a positive impact on the financial performance of small enterprises. The governments and lending institutions be supportive to the small enterprises and also advice small business on how to estimate their plan for viability to ensure that they make smart decisions when investing in projects. The small businesses be faced with difficulties when accessing finance from different sources. The majority of literature review was used sales growth, ROA, ROE, NPM and working capital as indicators of financial performance.</p><sec id="s2_1"><title>2.1. The Importance of Banking Finance</title><p>Working capital management (WCM) in small businesses is essential to sustaining the life of the business. A steady flow of cash is essential to maintain a business, and efficient working capital will maximize profitability, while poor WCM is one of the primary reasons for business failure. In this study, a sample of 200 Vietnamese Small Businesses was used to test the impact of WCM on the profitability in the period from 2010 to 2012. It was shown that that WCM is a significant area of financial management; the efficient WCM can significantly impact on the profitability and liquidity of the business. In this study, we find a significant negative relationship between gross operating income and the number of days of accounts receivable, accounts inventories and cash conversion cycle. These results are consistent with those found in previous studies in large firms, particularly in Small Businesses.</p></sec><sec id="s2_2"><title>2.2. The Impact of Banking Finance on Small Business</title><p>[<xref ref-type="bibr" rid="scirp.94642-ref7">7</xref>] used secondary data that significant number of the small scale benefited from the microfinance institution credit facilities even though only few of them were suitable to secure the required amount needed. Interestingly the microfinance institution has grown phenomenally in the last 10 years. Majority of the small scale acknowledged positive contributions of microfinance institutions credit facilities towards promoting their market excellence and overall economic company competitive advantage like tax incentives and financial supports, the research recommend that the government should supply adequate infrastructural facilities such as electricity, good road network, and training institutions to help small scale in Nigeria.</p><p>Moreover, [<xref ref-type="bibr" rid="scirp.94642-ref8">8</xref>] investigated the small business experiences in Malaysia, using a secondary data that the key lessons that may benefit the Egyptian experience in the development of small business growth and access to finance can be summarized in the following points which are; top management commitment and setting up a committee engaged in policy and planning management and coordination, real employee participation, employee rewards and skills development. small business database will support bankers toward better planning to boost Small Businesses access to finance and introduction of new programs, the monitoring of existing programs being implemented in the areas of enhancing access to financing and developing financing products to support the small business different activities. Strengthening enabling business infrastructure, and boosting the capacity and capability of small business to able to produce favorable results in terms of productivity and performance across all sectors, modernization of equipment is necessary ingredients to enhance small business productivity and to accelerate the movement up the value chain.</p><p>The government should play a big role in learning small business practitioners on the incentives available to them and how to access them and delivering these incentives through many agencies. Small business should not totally rely on government institutes; they should try to find their own path of progress by relying on strategies. The government institutes have to invest more in marketing researches and innovation to support small business philosophy and its competitiveness have to establish credit guarantee schemes to support banks in increasing lending and decrease risks. To conclude Malaysia’s success in achieving sustained growth can be summed up as “getting the basics right,” through long-term planning and visionary leaders, development policies, good economic management, support private investment, develop human resource as well as develop good physical and institutional infrastructure [<xref ref-type="bibr" rid="scirp.94642-ref8">8</xref>] .</p></sec><sec id="s2_3"><title>2.3. Firm Leverage, Firm Age and Financial Performance</title><p>In general leverage has a negative impact on performance. However, it was also claimed that the leverage-performance relationship is significantly moderated by product diversity. In other words, leverage could be beneficial or detrimental to the financial performance of general insurance firms, contingent on the level of their product diversity. Firms with high leverage and high product diversity perform significantly better than firms with high leverage but low product diversity. Firms with low leverage are largely the smaller general insurers and these firms should focus on their respective niche product segments. Empirical evidence was provided that the leverage-performance relationship is a function of the product-mix strategy of the firm, and contributes to the existing literature by proving further insights on impact of leverage on firm performance and the role of product diversity in influencing the leverage-performance relationship in a highly regulated general insurance industry [<xref ref-type="bibr" rid="scirp.94642-ref9">9</xref>] .</p></sec></sec><sec id="s3"><title>3. Theoretical Background</title><sec id="s3_1"><title>3.1. Small Businesses Experiences in Egypt</title><p>On 28/2/2017 a decision was issued by the Central Bank of Egypt stated as the new definition of Small business as follows: The medium companies, capital is from 5 million pounds to 15 million to industrial companies and from 3 million to 5 million to another company, work force is from 10 to 200 workers, sales are from 50 million pounds to 200 million pounds. In the small companies, capital is from 50 thousand pounds to 5 million to industrial companies and Less than 3 million to another company, work force is from 10 to 200 workers, and sales are from 1 million pounds to 50 million pounds. Allowing banks to finance small businesses whose sales range from 1 million to 10 million without obtaining audited financial statements and the lending rate for small businesses does not exceed 5% (decline simple rate).</p><p>The Social Fund Initiative</p><p>At the end of 2014, the Social Fund for Development (SFD) launched an initiative to support small businesses at an interest rate of 10% (decline simple rate)</p><table-wrap id="table1" ><label><xref ref-type="table" rid="table1">Table 1</xref></label><caption><title> Financial indicators for the Egyptian economy</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >List</th><th align="center" valign="middle" >2015/2016</th><th align="center" valign="middle" >2016/2017</th></tr></thead><tr><td align="center" valign="middle" >Real GDP growth rate</td><td align="center" valign="middle" >4.3%</td><td align="center" valign="middle" >4.2%</td></tr><tr><td align="center" valign="middle" >Average annual inflation rate</td><td align="center" valign="middle" >13.97%</td><td align="center" valign="middle" >23.3%</td></tr><tr><td align="center" valign="middle" >Budget deficit/GDP</td><td align="center" valign="middle" >12.3%</td><td align="center" valign="middle" >10.9%</td></tr><tr><td align="center" valign="middle" >Average monthly exchange rate per year (LE - USD)</td><td align="center" valign="middle" >8.85 LE</td><td align="center" valign="middle" >14.7 LE</td></tr><tr><td align="center" valign="middle" >Domestic debt/GDP</td><td align="center" valign="middle" >96.7%</td><td align="center" valign="middle" >91.1%</td></tr><tr><td align="center" valign="middle" >Unemployment rate</td><td align="center" valign="middle" >12.5%</td><td align="center" valign="middle" >11.9%</td></tr><tr><td align="center" valign="middle" >Net International Reserves</td><td align="center" valign="middle" >17.5 billion dollars</td><td align="center" valign="middle" >31.3 billion dollars</td></tr></tbody></table></table-wrap><p>through more than 10 banks operating in Egypt Note that the mid corridor at this time was about 10.25% and the mid corridor rate at April 2018 about 18.25%.</p><p>On 24/4/2017 a decree was issued by the Prime Minister of Egypt to establish the Small and Medium Enterprises Development Authority to replace the Social Fund for Development to include, training programs, financing programs, investment ideas and investment opportunities.</p><p>On May 2019 the Small and Medium Enterprises Development Authority launched a new initiative to support small businesses at an interest rate of 13% (decline simple rate) through more than 10 banks operating in Egypt Note that the mid corridor at this time was about 16.25%.</p><p>The Egyptian economy is one of the diversified economies in the Middle Eastinion―agriculture, tourism, manufacturing and services sectors. Due to recent structural reform, the Egyptian economy is achieving high growth rates―and an attractive investment climate has evolved thanks to positive developments in transportation, infrastructure, communication, skilled labor, energy, modern industrial cities, free zones, banking and stock markets. The following (<xref ref-type="table" rid="table1">Table 1</xref>) are the most important financial indicators for the Egyptian economy last two years.</p></sec><sec id="s3_2"><title>3.2. Banking Finance Experiences for Small Business in Some Developing Countries</title><p>Small business experiences in Malaysia</p><p>Malaysia is an economy depending on natural resources and exploitation of the land, oil, gas, tin, timber, palm oil and rubber. However, after independence, industrialization led to the transformation of the Malaysian economy from resource and agriculture to industry [<xref ref-type="bibr" rid="scirp.94642-ref8">8</xref>] .</p><p>Gross national savings is 38.2% of GNP, the GDP growth rate is about 5.8% and the unemployment rate is 3.5%. Employment for about 56% of the total labor force and represent 99.2% of the total business establishments that provide as in 2009. the challenges faced by small business in Malaysia that the lack of a comprehensive framework in terms of policies towards small business development, inadequate data, too many agencies lacking effective coordination, limited access to finance and capital, difficulties in accessing loans, inability to survive in the mainstream of industrial development, underutilization of technical assistance, advisory services, lack of skilled and talented workers, and concentrated global competition from other producers. All of the mentioned challenges are almost similar to the situation of Egyptian small business. Malaysia is an economy depending on natural resources and exploitation of the land, oil, gas, tin, timber, palm oil and rubber. However, after independence, industrialization led to the transformation of the Malaysian economy from resource and agriculture to industry [<xref ref-type="bibr" rid="scirp.94642-ref8">8</xref>] .</p><p>The objective was to develop capable Malaysian small business to become competitive in the global market. On the other hand, BNM offers financial advisory services. Banking institutions and other avenues of financing small business are specialized financial institutions, which are established by the government that aims at accelerating the growth of strategic sectors identified by the government.</p><p>In addition to Development Financial Institutions, there are other avenues of financing small business including small business Credit Bureau, venture capital, Agro-bank, leasing and factoring companies, all of which undertook a diverse range of financial initiatives To conclude Malaysia’s success in achieving sustained growth can be summed up as “getting the basics right,” through long-term planning and visionary leaders, development policies, good economic management, support private investment, develop human resource as well as develop good physical and institutional infrastructure [<xref ref-type="bibr" rid="scirp.94642-ref8">8</xref>] .</p><p>Small business experiences in India</p><p>The small businesses have been the development driver of the Indian economy. The Indian economy, being one of the largest economies in the world as measured by the purchasing power parity (PPP) with a GDP growth rate about 7.2% which makes it the third-biggest economy after China and the United States. The MSME sector contributes almost 40% to the entire output of India, 35% of the industrial exports and occupies 90% of the industry and the employment in the MSMEs sector in India increases to reach 732.24 lakh persons (73,200 mm) in 2010-2011 [<xref ref-type="bibr" rid="scirp.94642-ref10">10</xref>] . The challenges facing MSME sector in India; lack of finance, planning and marketing assistance, technology are main ones that hurdle SMEs from access to success. Reserve Bank of India is raising directions to all Banks to be followed in terms of allocating 40% of the finance at banks to SME annually. In case of not covering the allocated percentage, the reserve bank direct the remaining to finance one of the priority sectors like textiles and agriculture with interest rate never exceed 4%.</p></sec></sec><sec id="s4"><title>4. Research Methodology and Sampling</title><p>The researcher has mentioned the Philosophy of the study which is Positivism Philosophy. Then, it was mentioned the followed approach which is deductive Approach. Also, the quantitative design was the design research in this study. The practitioner has collected historical secondary data collected from official periodicals. The unit of analysis was organizations where the data used is that of year 2017. All of this information will be mentioned in <xref ref-type="table" rid="table2">Table 2</xref> as follows.</p></sec><sec id="s5"><title>5. Data Analysis and Findings</title><p>In this chapter empirical study is illustrated for the purpose of test hypotheses. First, descriptive analysis was progressed to interpret the models' variables. Second, verification of the normality assumptions and third, testing regression assumptions will be presented with details in this chapter. Data analysis was conducted using correlation and regression.</p><p>Descriptive Analysis</p><p><xref ref-type="table" rid="table3">Table 3</xref> shows the measures of central tendency and dispersion of the research variables where the ROE was found to have the mean of 0.3803, the ROA was found to have the mean of 0.2889, the NPM was found to have the mean of 0.1450, the Loans Volume was found to have the mean of 1.4891, and the Firm Leverage was found to have the mean of 0.3941, while Firm Age has a mean of 4.5000.</p><table-wrap id="table2" ><label><xref ref-type="table" rid="table2">Table 2</xref></label><caption><title> Research methodology summary</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Philosophy</th><th align="center" valign="middle" >Positivism</th></tr></thead><tr><td align="center" valign="middle" >Approach</td><td align="center" valign="middle" >Deductive Approach</td></tr><tr><td align="center" valign="middle" >Design</td><td align="center" valign="middle" >Quantitative Research Design</td></tr><tr><td align="center" valign="middle" >Population</td><td align="center" valign="middle" >Small Enterprise in Egypt</td></tr><tr><td align="center" valign="middle" >Sample Size</td><td align="center" valign="middle" >90 small business firms in Egypt in period from 2013 to 2016 (2013 and 2014 without loan, 2015 and 2016 with a loan)</td></tr><tr><td align="center" valign="middle" >Data collection</td><td align="center" valign="middle" >Historical Secondary Data collected from official periodicals</td></tr><tr><td align="center" valign="middle" >Statistical Packages</td><td align="center" valign="middle" >SPSS</td></tr><tr><td align="center" valign="middle" >Unit of analysis</td><td align="center" valign="middle" >Organizations unit of analysis where the data used is that of year 2017</td></tr><tr><td align="center" valign="middle" >Time Horizon</td><td align="center" valign="middle" >CNPMs Sectional</td></tr></tbody></table></table-wrap><table-wrap id="table3" ><label><xref ref-type="table" rid="table3">Table 3</xref></label><caption><title> Descriptive analysis for the research variables</title></caption><table><tbody><thead><tr><th align="center" valign="middle" ></th><th align="center" valign="middle" >Range</th><th align="center" valign="middle" >Minimum</th><th align="center" valign="middle" >Maximum</th><th align="center" valign="middle" >Mean</th><th align="center" valign="middle" >Std. Deviation</th><th align="center" valign="middle" >Variance</th></tr></thead><tr><td align="center" valign="middle" >ROE</td><td align="center" valign="middle" >1.24</td><td align="center" valign="middle" >0.01</td><td align="center" valign="middle" >1.25</td><td align="center" valign="middle" >0.3803</td><td align="center" valign="middle" >0.27134</td><td align="center" valign="middle" >0.074</td></tr><tr><td align="center" valign="middle" >ROA</td><td align="center" valign="middle" >0.98</td><td align="center" valign="middle" >0.01</td><td align="center" valign="middle" >0.99</td><td align="center" valign="middle" >0.2889</td><td align="center" valign="middle" >0.22456</td><td align="center" valign="middle" >0.050</td></tr><tr><td align="center" valign="middle" >NPM</td><td align="center" valign="middle" >0.40</td><td align="center" valign="middle" >0.00</td><td align="center" valign="middle" >0.40</td><td align="center" valign="middle" >0.1450</td><td align="center" valign="middle" >0.08124</td><td align="center" valign="middle" >0.007</td></tr><tr><td align="center" valign="middle" >Log Loans Volume</td><td align="center" valign="middle" >4.08</td><td align="center" valign="middle" >0.00</td><td align="center" valign="middle" >4.08</td><td align="center" valign="middle" >1.4891</td><td align="center" valign="middle" >1.53543</td><td align="center" valign="middle" >2.358</td></tr><tr><td align="center" valign="middle" >Firm Leverage</td><td align="center" valign="middle" >5.77</td><td align="center" valign="middle" >0.00</td><td align="center" valign="middle" >5.77</td><td align="center" valign="middle" >0.3941</td><td align="center" valign="middle" >0.59421</td><td align="center" valign="middle" >0.353</td></tr><tr><td align="center" valign="middle" >Firm Age</td><td align="center" valign="middle" >3.00</td><td align="center" valign="middle" >3.00</td><td align="center" valign="middle" >6.00</td><td align="center" valign="middle" >4.5000</td><td align="center" valign="middle" >1.11959</td><td align="center" valign="middle" >1.253</td></tr></tbody></table></table-wrap><p>Normality Test</p><p><xref ref-type="table" rid="table4">Table 4</xref> illustrates the formal test of normality assumption for the research variables, where it could be seen that the research variables are not normally distributed as the corresponding P-values are less than 0.05.</p><p>The Relationship between Loans Volume and Financial Performance Indicators</p><p>Spearman correlation coefficients used to describe the relationships between the variables. <xref ref-type="table" rid="table5">Table 5</xref> shows the correlation matrix for the relationship between Loans Volume and ROE. It was found that the relationship is insignificant, as the corresponding P-value is more than 0.05 (=0.135) and the correlation coefficient is −0.079.</p><p><xref ref-type="table" rid="table6">Table 6</xref> shows the correlation matrix for the relationship between Loans Volume and ROA. It was found that there is significant negative relationship, as the corresponding P-value is less than 0.05 and correlation coefficient is −0.212.</p><table-wrap id="table4" ><label><xref ref-type="table" rid="table4">Table 4</xref></label><caption><title> Formal testing of normality</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  rowspan="2"  ></th><th align="center" valign="middle"  colspan="3"  >Kolmogorov-Smirnov<sup>a</sup></th><th align="center" valign="middle"  colspan="3"  >Shapiro-Wilk</th></tr></thead><tr><td align="center" valign="middle" >Statistic</td><td align="center" valign="middle" >df</td><td align="center" valign="middle" >Sig.</td><td align="center" valign="middle" >Statistic</td><td align="center" valign="middle" >df</td><td align="center" valign="middle" >Sig.</td></tr><tr><td align="center" valign="middle" >ROE</td><td align="center" valign="middle" >0.118</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" >0.000</td><td align="center" valign="middle" >0.922</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" >0.000</td></tr><tr><td align="center" valign="middle" >ROA</td><td align="center" valign="middle" >0.119</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" >0.000</td><td align="center" valign="middle" >0.899</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" >0.000</td></tr><tr><td align="center" valign="middle" >NPM</td><td align="center" valign="middle" >0.066</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" >0.001</td><td align="center" valign="middle" >0.972</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" >0.000</td></tr><tr><td align="center" valign="middle" >Loans Volume</td><td align="center" valign="middle" >0.334</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" >0.000</td><td align="center" valign="middle" >0.761</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" >0.000</td></tr><tr><td align="center" valign="middle" >Firm Leverage</td><td align="center" valign="middle" >0.254</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" >0.000</td><td align="center" valign="middle" >0.617</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" >0.000</td></tr><tr><td align="center" valign="middle" >Firm Age</td><td align="center" valign="middle" >0.172</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" >0.000</td><td align="center" valign="middle" >0.856</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" >0.000</td></tr></tbody></table></table-wrap><table-wrap id="table5" ><label><xref ref-type="table" rid="table5">Table 5</xref></label><caption><title> Correlation matrix between loans volume and ROE</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  colspan="3"  ></th><th align="center" valign="middle" >Loans Volume</th><th align="center" valign="middle" >ROE</th></tr></thead><tr><td align="center" valign="middle"  rowspan="6"  >Spearman's rho</td><td align="center" valign="middle"  rowspan="3"  >Loans Volume</td><td align="center" valign="middle" >Correlation Coefficient</td><td align="center" valign="middle" >1.000</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Sig. (2-tailed)</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >N</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle"  rowspan="3"  >ROE</td><td align="center" valign="middle" >Correlation Coefficient</td><td align="center" valign="middle" >−0.079</td><td align="center" valign="middle" >1.000</td></tr><tr><td align="center" valign="middle" >Sig. (2-tailed)</td><td align="center" valign="middle" >0.135</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >N</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" >360</td></tr></tbody></table></table-wrap><table-wrap id="table6" ><label><xref ref-type="table" rid="table6">Table 6</xref></label><caption><title> Correlation matrix between loans volume and ROA</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  colspan="3"  ></th><th align="center" valign="middle" >Loans Volume</th><th align="center" valign="middle" >ROA</th></tr></thead><tr><td align="center" valign="middle"  rowspan="6"  >Spearman's rho</td><td align="center" valign="middle"  rowspan="3"  >Loans Volume</td><td align="center" valign="middle" >Correlation Coefficient</td><td align="center" valign="middle" >1.000</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Sig. (2-tailed)</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >N</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle"  rowspan="3"  >ROA</td><td align="center" valign="middle" >Correlation Coefficient</td><td align="center" valign="middle" >−0.212**</td><td align="center" valign="middle" >1.000</td></tr><tr><td align="center" valign="middle" >Sig. (2-tailed)</td><td align="center" valign="middle" >0.000</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >N</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" >90</td></tr></tbody></table></table-wrap><p><xref ref-type="table" rid="table7">Table 7</xref> shows the correlation matrix for the relationship between Loans Volume and NPM. It was found that there is significant negative relationship, as the corresponding P-value is less than 0.05 and correlation coefficient is −0.110.</p><p>The effect of Loans Volume, and Firm Leverage on Financial Performance Indicators</p><p><xref ref-type="table" rid="table8">Table 8</xref> shows the data for the regression model of the effect of Loans Volume and Firm Leverage on ROE. It could be noted that there is a significant negative effect of Loans Volume on ROE as the corresponding P-value is 0.005 with a coefficient of −0.028, while, there is a significant negative impact of Firm Leverage on ROA as the corresponding P-value is 0.025 with a coefficient of 0.057. Also, coefficient of determination (R Square) is 0.027 which means that Independent Variables explain 2.7% of the variation in ROE.</p><p><xref ref-type="table" rid="table9">Table 9</xref> shows the data for the regression model of the effect of Loans Volume and Firm Leverage on ROA. It could be noted that there is a significant negative effect of Loans Volume and Firm Leverage on ROA as the corresponding P-values for both variables are 0.001 with a coefficient of −0.026, and −0.069. Also, coefficient of determination (R Square) is 0.086 which means that Independent Variables explain 8.6% of the variation in ROA.</p><table-wrap id="table7" ><label><xref ref-type="table" rid="table7">Table 7</xref></label><caption><title> Correlation matrix between loans volume and NPM</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  colspan="3"  ></th><th align="center" valign="middle" >Loans Volume</th><th align="center" valign="middle" >NPM</th></tr></thead><tr><td align="center" valign="middle"  rowspan="6"  >Spearman’s rho</td><td align="center" valign="middle"  rowspan="3"  >Loans Volume</td><td align="center" valign="middle" >Correlation Coefficient</td><td align="center" valign="middle" >1.000</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Sig. (2-tailed)</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >N</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle"  rowspan="3"  >NPM</td><td align="center" valign="middle" >Correlation Coefficient</td><td align="center" valign="middle" >−0.110*</td><td align="center" valign="middle" >1.000</td></tr><tr><td align="center" valign="middle" >Sig. (2-tailed)</td><td align="center" valign="middle" >0.036</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >N</td><td align="center" valign="middle" >90</td><td align="center" valign="middle" >90</td></tr></tbody></table></table-wrap><table-wrap id="table8" ><label><xref ref-type="table" rid="table8">Table 8</xref></label><caption><title> Regression model of loans volume and firm leverage on ROE</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  rowspan="2"  >Model</th><th align="center" valign="middle"  colspan="2"  >Unstandardized Coefficients</th><th align="center" valign="middle" >Standardized Coefficients</th><th align="center" valign="middle"  rowspan="2"  >t</th><th align="center" valign="middle"  rowspan="2"  >Sig.</th><th align="center" valign="middle"  rowspan="2"  >R-square</th></tr></thead><tr><td align="center" valign="middle" >B</td><td align="center" valign="middle" >Std. Error</td><td align="center" valign="middle" >Beta</td></tr><tr><td align="center" valign="middle" >(Constant)</td><td align="center" valign="middle" >0.399</td><td align="center" valign="middle" >0.020</td><td align="center" valign="middle" ></td><td align="center" valign="middle" >19.638</td><td align="center" valign="middle" >0.000</td><td align="center" valign="middle" >0.027</td></tr><tr><td align="center" valign="middle" >Loans Volume</td><td align="center" valign="middle" >−0.028</td><td align="center" valign="middle" >0.010</td><td align="center" valign="middle" >−0.158</td><td align="center" valign="middle" >−2.851</td><td align="center" valign="middle" >0.005</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Firm Leverage</td><td align="center" valign="middle" >0.057</td><td align="center" valign="middle" >0.025</td><td align="center" valign="middle" >0.125</td><td align="center" valign="middle" >2.255</td><td align="center" valign="middle" >0.025</td><td align="center" valign="middle" ></td></tr></tbody></table></table-wrap><p>a. Dependent Variable: ROE.</p><table-wrap id="table9" ><label><xref ref-type="table" rid="table9">Table 9</xref></label><caption><title> Regression model of loans volume and firm leverage on ROA</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  rowspan="2"  >Model</th><th align="center" valign="middle"  colspan="2"  >Unstandardized Coefficients</th><th align="center" valign="middle" >Standardized Coefficients</th><th align="center" valign="middle"  rowspan="2"  >t</th><th align="center" valign="middle"  rowspan="2"  >Sig.</th><th align="center" valign="middle"  rowspan="2"  >R-square</th></tr></thead><tr><td align="center" valign="middle" >B</td><td align="center" valign="middle" >Std. Error</td><td align="center" valign="middle" >Beta</td></tr><tr><td align="center" valign="middle" >(Constant)</td><td align="center" valign="middle" >0.354</td><td align="center" valign="middle" >0.016</td><td align="center" valign="middle" ></td><td align="center" valign="middle" >21.699</td><td align="center" valign="middle" >0.000</td><td align="center" valign="middle" >0.086</td></tr><tr><td align="center" valign="middle" >Loans Volume</td><td align="center" valign="middle" >−0.026</td><td align="center" valign="middle" >0.008</td><td align="center" valign="middle" >−0.175</td><td align="center" valign="middle" >−3.253</td><td align="center" valign="middle" >0.001</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Firm Leverage</td><td align="center" valign="middle" >−0.069</td><td align="center" valign="middle" >0.020</td><td align="center" valign="middle" >−0.182</td><td align="center" valign="middle" >−3.376</td><td align="center" valign="middle" >0.001</td><td align="center" valign="middle" ></td></tr></tbody></table></table-wrap><p>a. Dependent Variable: ROA.</p><p><xref ref-type="table" rid="table1">Table 1</xref>0 shows the data for the regression model of the effect of Loans Volume and Firm Leverage on NPM. It could be noted that there is an insignificant effect of Loans Volume on NPM as the corresponding P-value is 0.848 which is more than 0.05, while, there is a significant negative impact of Firm Leverage on NPM as the corresponding P-value is 0.000 with a coefficient of −0.039. Also, coefficient of determination (R Square) is 0.083 which means that Independent Variables explain 8.3% of the variation in NPM.</p><p>The effect of Loans Volume, and Firm Age on Financial Performance Indicators</p><p><xref ref-type="table" rid="table1">Table 1</xref>1 shows the data for the regression model of the effect of Loans Volume and Firm Age on ROE. It could be noted that there is an insignificant effect of Loans Volume and Firm Age on ROE as the corresponding P-values are more than 0.05 (0.413, and 0.786).</p><p><xref ref-type="table" rid="table1">Table 1</xref>2 shows the data for the regression model of the effect of Loans Volume and Firm Age on ROA. It could be noted that there is an insignificant effect of Loans Volume and Firm Age on ROA as the corresponding P-values are more than 0.05 (0.153, and 0.368).</p><p><xref ref-type="table" rid="table1">Table 1</xref>3 shows the data for the regression model of the effect of Loans Volume and Firm Age on NPM. It could be noted that there is an insignificant effect of Loans Volume and Firm Age on NPM as the corresponding P-values are more than 0.05 (0.192, and 0.716).</p><p>Analysis Conclusion</p><p>In this chapter, the descriptive, correlation and regression analysis were conducted to respond to the research hypotheses. <xref ref-type="table" rid="table1">Table 1</xref>4 shows the results of the hypotheses based on the analysis.</p><table-wrap id="table10" ><label><xref ref-type="table" rid="table1">Table 1</xref>0</label><caption><title> Regression model of loans volume and firm leverage on NPM</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  rowspan="2"  >Model</th><th align="center" valign="middle"  colspan="2"  >Unstandardized Coefficients</th><th align="center" valign="middle" >Standardized Coefficients</th><th align="center" valign="middle"  rowspan="2"  >t</th><th align="center" valign="middle"  rowspan="2"  >Sig.</th><th align="center" valign="middle"  rowspan="2"  >R-square</th></tr></thead><tr><td align="center" valign="middle" >B</td><td align="center" valign="middle" >Std. Error</td><td align="center" valign="middle" >Beta</td></tr><tr><td align="center" valign="middle" >(Constant)</td><td align="center" valign="middle" >0.161</td><td align="center" valign="middle" >0.006</td><td align="center" valign="middle" ></td><td align="center" valign="middle" >27.267</td><td align="center" valign="middle" >0.000</td><td align="center" valign="middle" >0.083</td></tr><tr><td align="center" valign="middle" >Loans Volume</td><td align="center" valign="middle" >−0.001</td><td align="center" valign="middle" >0.003</td><td align="center" valign="middle" >−0.010</td><td align="center" valign="middle" >−.192</td><td align="center" valign="middle" >0.848</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Firm Leverage</td><td align="center" valign="middle" >−0.039</td><td align="center" valign="middle" >0.007</td><td align="center" valign="middle" >−0.285</td><td align="center" valign="middle" >−5.282</td><td align="center" valign="middle" >0.000</td><td align="center" valign="middle" ></td></tr></tbody></table></table-wrap><p>a. Dependent Variable: NPM.</p><table-wrap id="table11" ><label><xref ref-type="table" rid="table1">Table 1</xref>1</label><caption><title> Regression model of loans volume and firm age on ROE</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  rowspan="2"  >Model</th><th align="center" valign="middle"  colspan="2"  >Unstandardized Coefficients</th><th align="center" valign="middle" >Standardized Coefficients</th><th align="center" valign="middle"  rowspan="2"  >t</th><th align="center" valign="middle"  rowspan="2"  >Sig.</th><th align="center" valign="middle"  rowspan="2"  >R-square</th></tr></thead><tr><td align="center" valign="middle" >B</td><td align="center" valign="middle" >Std. Error</td><td align="center" valign="middle" >Beta</td></tr><tr><td align="center" valign="middle" >(Constant)</td><td align="center" valign="middle" >0.436</td><td align="center" valign="middle" >0.096</td><td align="center" valign="middle" ></td><td align="center" valign="middle" >4.543</td><td align="center" valign="middle" >0.000</td><td align="center" valign="middle" >0.014</td></tr><tr><td align="center" valign="middle" >Loans Volume</td><td align="center" valign="middle" >−0.016</td><td align="center" valign="middle" >0.019</td><td align="center" valign="middle" >−0.089</td><td align="center" valign="middle" >−0.819</td><td align="center" valign="middle" >0.413</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Firm Age</td><td align="center" valign="middle" >−0.007</td><td align="center" valign="middle" >0.026</td><td align="center" valign="middle" >−0.030</td><td align="center" valign="middle" >−0.272</td><td align="center" valign="middle" >0.786</td><td align="center" valign="middle" ></td></tr></tbody></table></table-wrap><p>a. Dependent Variable: ROE.</p><table-wrap id="table12" ><label><xref ref-type="table" rid="table1">Table 1</xref>2</label><caption><title> Regression model of loans volume and firm age on ROA</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  rowspan="2"  >Model</th><th align="center" valign="middle"  colspan="2"  >Unstandardized Coefficients</th><th align="center" valign="middle" >Standardized Coefficients</th><th align="center" valign="middle"  rowspan="2"  >t</th><th align="center" valign="middle"  rowspan="2"  >Sig.</th><th align="center" valign="middle"  rowspan="2"  >R-square</th></tr></thead><tr><td align="center" valign="middle" >B</td><td align="center" valign="middle" >Std. Error</td><td align="center" valign="middle" >Beta</td></tr><tr><td align="center" valign="middle" >(Constant)</td><td align="center" valign="middle" >0.409</td><td align="center" valign="middle" >0.078</td><td align="center" valign="middle" ></td><td align="center" valign="middle" >5.270</td><td align="center" valign="middle" >0.000</td><td align="center" valign="middle" >0.059</td></tr><tr><td align="center" valign="middle" >Loans Volume</td><td align="center" valign="middle" >−0.022</td><td align="center" valign="middle" >0.016</td><td align="center" valign="middle" >−0.153</td><td align="center" valign="middle" >−1.434</td><td align="center" valign="middle" >0.153</td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Firm Age</td><td align="center" valign="middle" >−0.019</td><td align="center" valign="middle" >0.021</td><td align="center" valign="middle" >−0.096</td><td align="center" valign="middle" >−0.902</td><td align="center" valign="middle" >0.368</td><td align="center" valign="middle" ></td></tr></tbody></table></table-wrap><p>a. Dependent Variable: ROA.</p><table-wrap id="table13" ><label><xref ref-type="table" rid="table1">Table 1</xref>3</label><caption><title> Regression model of loans volume and firm age on NPM</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  rowspan="2"  >Model</th><th align="center" valign="middle"  colspan="2"  >Unstandardized Coefficients</th><th align="center" valign="middle" >Standardized Coefficients</th><th align="center" valign="middle"  rowspan="2"  >T</th><th align="center" valign="middle"  rowspan="2"  >Sig.</th><th align="center" valign="middle"  rowspan="2"  >R-square</th></tr></thead><tr><td align="center" valign="middle" >B</td><td align="center" valign="middle" >Std. Error</td><td align="center" valign="middle" >Beta</td></tr><tr><td align="center" valign="middle" >(Constant)</td><td align="center" valign="middle" >0.143</td><td align="center" valign="middle" >0.029</td><td align="center" valign="middle" ></td><td align="center" valign="middle" >4.979</td><td align="center" valign="middle" >0.000</td><td align="center" valign="middle"  rowspan="3"  >0.012</td></tr><tr><td align="center" valign="middle" >Loans Volume</td><td align="center" valign="middle" >−0.008</td><td align="center" valign="middle" >0.006</td><td align="center" valign="middle" >−0.143</td><td align="center" valign="middle" >−1.306</td><td align="center" valign="middle" >0.192</td></tr><tr><td align="center" valign="middle" >Firm Age</td><td align="center" valign="middle" >0.003</td><td align="center" valign="middle" >0.008</td><td align="center" valign="middle" >0.040</td><td align="center" valign="middle" >0.365</td><td align="center" valign="middle" >0.716</td></tr></tbody></table></table-wrap><p>a. Dependent Variable: NPM.</p><table-wrap id="table14" ><label><xref ref-type="table" rid="table1">Table 1</xref>4</label><caption><title> Summary of research hypotheses</title></caption><table><tbody><thead><tr><th align="center" valign="middle" >Hypothesis</th><th align="center" valign="middle" >Description</th><th align="center" valign="middle" >Results</th></tr></thead><tr><td align="center" valign="middle" >H<sub>1</sub></td><td align="center" valign="middle" >There is a significant negative role of Loans Volume on Financial Performance Indicators</td><td align="center" valign="middle" >Fully Supported</td></tr><tr><td align="center" valign="middle" >H<sub>2</sub></td><td align="center" valign="middle" >There is a significant negative role of Loans Volume and Firm Leverage on Financial Performance Indicators</td><td align="center" valign="middle" >Partially Supported</td></tr><tr><td align="center" valign="middle" >H<sub>3</sub></td><td align="center" valign="middle" >There is a significant negative role of Loans Volume and Firm Age on Financial Performance Indicators</td><td align="center" valign="middle" >Not Supported</td></tr></tbody></table></table-wrap></sec><sec id="s6"><title>6. Conclusions</title><p>This study focuses on three main objectives, the first objective which is measuring the impact of banking finance on the financial performance of small business in Egypt is attained by formulating the first hypothesis and three sub hypotheses. The results showed that loan volume has a negative significant impact to financial performance of small business, firm leverage has a negative significant impact to financial performance of small business and firm age has insignificant impact to financial performance of small business.</p><p>There must be a financial management within each small company that is important to help to use bank financing in a timely manner and for the appropriate purpose. To achieve a positive effect of bank financing, the banking finance should help the assets turnover to increase net profit, provide the awareness of the use of short-term financing in short-term investments and the use of long-term funds in long-term investments and the use of financing for the purpose specified for it and not for any other purpose. Finally, the initiative of the Central Bank of Egypt to support small enterprises issued in January 2016 at a rate of 5% is very good compared to the market interest rates prevailing in the market and the MSME Development Agency initiatives issued at the end of 2014 with a 10% and 2019 with a 13% return rate. The State has to support small enterprises while offering tax and guarantee benefits to small enterprises. The Chambers of Commerce and Small Business Development Agency must provide training programs for small businesses related to capital management, human capital management and the extraction of export card and training programs related to customs’ procedures and international trade agreements with the same degree of interest to senior firms and set database that includes the number of small projects in Egypt and the number of Beneficiaries of initiatives and the value of funds provided support.</p><sec id="s6_1"><title>6.1. Contribution</title><p>This research is considered useful for entrepreneurs and decision makers and anyone interested in small businesses. It helps understand the relationship between loan volume and financial performance as well as understanding the role of firm leverage as a moderator in this relationship. It also helps people who could benefit from the comparative study done between companies with loans and companies without loans in their field, in order to have more information to decide the best way to finance their business.</p></sec><sec id="s6_2"><title>6.2. Implications</title><p>This study sheds the light on important points, which could be beneficial for policy makers in setting interest rate and facilitating the process of providing finance to small businesses to encourage economic development and boost entrepreneurship in Egypt.</p></sec></sec><sec id="s7"><title>Conflicts of Interest</title><p>The authors declare no conflicts of interest regarding the publication of this paper.</p></sec><sec id="s8"><title>Cite this paper</title><p>Saadallah, E.M. and Salah, A. (2019) The Impact of Banking Finance on Financial Performance of Egyptian Small Businesses in Period from 2013 to 2016. Open Access Library Journal, 6: e5146. https://doi.org/10.4236/oalib.1105146</p></sec></body><back><ref-list><title>References</title><ref id="scirp.94642-ref1"><label>1</label><mixed-citation publication-type="other" xlink:type="simple">Elsaid, A., Dawood, M., Seracino, R. and Bobko, C. (2011) Mechanical Properties of Kenaf Fiber Reinforced Concrete. Construction and Building Materials, 25, 1991-2001. https://doi.org/10.1016/j.conbuildmat.2010.11.052</mixed-citation></ref><ref id="scirp.94642-ref2"><label>2</label><mixed-citation publication-type="other" xlink:type="simple">Selim, M.S., Shenashen, M.A., El-Safty, S.A., Higazy, S.A., Selim, M.M., Isago, H. and Elmarakbi, A. (2017) Recent Progress in Marine Foul-Release Polymeric Nanocomposite Coatings. 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