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\title{Factors Affecting Performance of Micro and Small Enterprises in South West Ethiopia: The Case of Bench Maji, Sheka, and Kefa Zones}
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             \author[1]{Gemechu  Abdissa}

             \affil[1]{  izan-Tepi University, Mizan Teferi}

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\date{\small \em Received: 15 December 2015 Accepted: 4 January 2016 Published: 15 January 2016}

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\begin{abstract}
        


The aim of this study is to investigate the factors that affect the performance of SMEs in Bench Maji, Sheka, and Kefa zone particular to manufacturing, trade and service sector. In this study, mixed research methods were used. Stratified simple random sampling was used to select proportional number of samples from the study area. Both primary and secondary source of data were used. To obtain the primary data, questionnaires were distributed for 278 micro and small sized enterprises owners and managers to access the performance status of their enterprises and also to examine factors affecting their performance. Secondary data were collected from books, journals, past research works, official documents and the Internet.

\end{abstract}


\keywords{small and medium enterprises (SMEs); internal and external factors; and performance.}

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\section[{Introduction}]{Introduction}\par
he success of the government and a country, in regard to business development, is related to small business sustainability \hyperref[b6]{(Carrasco-Davila, 2005)}. Local and federal authorities had been developing programs that promote the creation of new jobs thru the small business (Plan Nacional de  {\ref Desarrollo, 2007)}. The small and medium business sectors are recognized as an integral component of economic development and a crucial element in the effort to lift countries out of purveys. The dynamic role of micro and small enterprises (MSEs) in developing countries as engines through which the growth objectives of developing countries can be achieved has long been recognized. Small businesses play an important role in the development of a country and serve as a means to sustain and grow economies \hyperref[b28]{(Ibrahim, Angelidis, \& Parsa, 2008)}. Due to the ease in starting and simplicity in operation, small businesses are initiated for various reasons depending upon entrepreneur motives and traits \hyperref[b33]{(Kozan et al., 2006)}. Small businesses contribute to lowering unemployment as well as generate new sources of employment.\par
Recent empirical studies show that MSEs contribute to over 60\% of GDP and over 70\% of total employment in low-income countries, while they contribute over 95\% of total employment and about 70\% of GDP in middle-income countries. Therefore, an important policy priority in developing countries is to reform the policies that divide the informal and formal sectors, so as to enable the poor to participate in markets and to engage in higher value added business activities  {\ref (Ayyagari, Beck and Demirgüc-Kunt, 2003)}.\par
Policies to promote the development of MSEs are common in both developed and developing countries  {\ref (Storey, 1994;} {\ref Levitsky, 1996;}\hyperref[b24]{Hallberg, 2000)}. In the case of developed countries, it has become commonplace for governments during the last two or three decades to implement policies or programs designed to promote aspects of micro and small-sized enterprises (MSEs). This has coincided with an increase in the importance, in terms of contribution to employment and GDP growth, of SMEs in most of the developed economies  {\ref (Storey, 1994)}. In the case of developing economies, policies designed to assist MSEs have been an important aspect of industrial policy and multilateral aid programs such as those of the United Nations since the 1950s (Levitsky, 1996). However, while there are wide variations across countries the traditional picture is one where the relative importance of SMEs tends to decline as a country moves up the developmental ladder \hyperref[b24]{(Hallberg, 2000;} {\ref and Liedholm and Meade, 1999)}.\par
In addition, they also comprise a significant proportion of the business enterprises. It may therefore be argued that, purely from the viewpoint of their significance in their economies, MSEs warrant attention from governments. Storey (1994) has argued, in the UK context, that the increased importance of MSEs means that public policies towards them cannot be considered in isolation from other influences in the economy and cannot be left to those with a particular interest in MSEs. The significance of SMEs in their economies makes it important for policymakers to ensure that these enterprises do not face impediment that hamper their ability to operate efficiently and do not face tedious administrative compliance costs. As Lattimore et al. (1998) note, while economic importance provides a strong basis for public policy consultation with small business, in itself it provides little justification for specific interventions.\par
Despite a long history of development efforts, MSEs were perceived rather as a synthetic construction mainly of "social and political" importance \hyperref[b24]{(Hallberg, 2000)}, especially throughout the 1980's and up to late 1990's. Although domestic MSEs constituted most of what could be and what are still deemed as the private business activity in most developing countries, private sector development strategies advocated for and implemented in these countries were skewed towards the needs of large-scale business, including foreign invested ones. This type of policy advice was partly motivated by the rather disappointing (Meyer-Stamer, Jörg and Frank Waltering, 2000) results achieved through extensive MSE support systems operated in developed countries since the 1970's.\par
While contributions of MSEs were recognize, many programs and policies were developed to support them, their journey in many instances is short-lived with high rate of failure mostly in Africa due to several factors (Michael and Jeffrey, 2009; \hyperref[b36]{Lussier, 1996;}\hyperref[b26]{Honjo, 2000;} {\ref ILO,2007;} {\ref Wiboonchutikula, 2001}; \hyperref[b46]{Zewde and Associates, 2002)}. There are many obstacles hindering their growth like competitions, lack of access to credit, cheap imports, insecurity, debt collection, marketing problems, lack of enough working space, identical products in the same market, change in demand and absence of market linkages, lack of raw material accessibilities  {\ref (Wiboonchutikula,2001)}.\par
Okpara \& Wynn (2007) research on smallbusiness development has shown that the rate of failure of MSEs in development countries is higher than the developed world. According to Geberhiwot and Wolday, (2006) more than 11,000 MSEs were surveyed and about 5 percent of them admitted having main constraints like lack of working space for production and marketing, shortage of credit and finance, regulatory problems (licensing, organizing, illegal business), poor production techniques, input access constraints, lack of information, inadequate management and business skill, absence of appropriate strategy, lack of skilled human resource, low level of awareness of MSEs' as job area, low level of provision and interest for trainings and workshop. These constraints confirm with other developing countries, especially poor management, corruption, lack of training and experience, poor infrastructural development, insufficient profits and low demand for product and services.\par
Shiffer and Weder (2001) clearly show that there are size-based policy biases against MSEs, and more so against smaller firms in the microeconomic environment. These biases cover all areas: legal and regulatory frameworks, governance issues, such as bureaucracy and corruption, access to finance and property rights. Governmental interventions on all fronts are required. The existence of such biases point out to either market or government failure and is closely related to the capacity of the stakeholders involved. At times, markets may correct these failures. However, in some cases, removal of failures in the business environment may require adopting structuralist (selective intervention) approaches rather than market-friendly approaches, as market forces may not be sufficient to remedy the capacity deficits in the system. The choices made will be political, but they should be based on sound analyses \hyperref[b34]{(Lall, 2001)}.\par
Even though in the past decades the focus of Ethiopian government was mainly on large organizations, particularly on manufacturing sector, the recent wave of private sector development initiatives however shifted the policy efforts to MSEs and SMEs. This new orientation has been possible because of poor performance in most state owed companies and the tension introduced by globalization and the increased need for competiveness \hyperref[b46]{(Zewde \& Associates, 2002;}\hyperref[b25]{Hamilton and Fox, 1998)}. Thus, the health of micro and small business sectors is very important for the overall economic growth potential and future strength of an economy since they utilize local resources, satisfying vital needs of large segment of the population with their products and services, serve as sprees of technological, marketing and management capacity and skill acquisition, and enable technological process via adoption technology  {\ref (FeMSEDA, 2004)}.\par
The south west region is endowed with ample natural resource. MSEs make productive use of resources and improved the efficiency of domestic markets, thus facilitating long-term economic growth. MSEs also seem to have advantages over other largescale competitors in that they are able to adapt more easily to market conditions and utilize the ample resources. The sector has the potential to contribute towards creating employment opportunities and reducing poverty. However, even if ample resource is available in the region they have not performed creditably well and hence have not played the expected vital role in the economic growth and development of the country. This situation has been of great concern to the government, citizenry, operators, practitioners and the organized private sector groups.\par
Therefore, the basis for this study is that the government formulated some policies, and established many institutions to promote the smooth functioning of SMEs. However, the sector is not performing up to the expectations of many stakeholders as it has been suffering from several problems. Therefore, the study aims at identifying the impact of the varied problems on the performance of MSEs in Bonga, Mizan-Aman, and Teppi Towns. 
\section[{II.}]{II.} 
\section[{Literature Review}]{Literature Review}\par
According to (Enock Nkonoki, 2010), the main factors/problems that limits small firm's success/growth into two groups; first is the factors that originate from within the firm (in other words they are internal to the firm) and the second group is factors that originate from outside the firm (these are external to the firm). The Internal factors limiting small firm growth are the characteristics and attitude of the entrepreneur(s) and the firm as a whole. These factors can be impacted by the decisions made in the firm either by the entrepreneur(s) or the staff in the firm. These factors are, Lack of motivation and drive, Lack of background and experience in the business, Capital constraint, Lack of a proper business plan/vision, Theft/cheating and lack of trust in doing business, Poor management, Running informal/unregistered businesses, Lack of proper record keeping, Inadequate education and training, People factor/lack of needed talent and Improper professional advice and consultation. The External factors limiting small firm growth are the factors have to do with decisions, rules and policies that affect a small firm directly, and in response the firm has not really control over the decisions made but an influence to a change of their existence is possible.   {\ref (2006)}, more than 93 percent of MSEs replied that they did not apply for bank loans for the reasons they considered themselves as discouraged potential borrowers, need credit but are discouraged from applying by the perceived or real high collateral requirement, high cost of borrowing, difficulty of processes, ineligibility, or concern about their repayment ability and uninformed (i.e. not aware of the facility, or where and how to apply, etc.). The study done by Admasu. \hyperref[b0]{Abera (2012)}, the main sources of startup and expansion finance or funds for most MSEs are personal savings followed by iqub/idir, family and friends/relatives. The formal financial institutions have not been able to meet the credit needs of the MSEs. Since there is high interest rate and collateral requirement, most MSEs have been forced to use the informal institutions for credit. But the supply of credit from the informal institutions is often so limited to meet the credit needs of the MSEs. In some cases this problems may be the inability of many operators to meet formal financial institutions requirements for example business plan, governance systems and other accountability issues which are linked to business risk. This shows that the studied operators accessed finance mainly from informal sources.\par
According to Minster of Urban Development and Construction, (2013), the study also identified a number of challenges and constraints hindering the growth of MSEs in Selected Major Cities of Ethiopia. These challenges were manifested in terms of capital, technology and employment growth trends. Enterprises from the regional cites indicated that shortage of finance (42 percent) to expand their business was their principal challenge, followed by lack of working premise (28.3 percent); and lack of access to market or absence of linkage to market. The study also showed that lack of access to land has been one of the most crucial bottlenecks (26.4 percent) in Addis Ababa, problem of finance (25.6 percent) and access to market (25.1 percent) were among the strong factors inhibiting the growth of these enterprises in the capital. The findings of Mulu (2007) also indicate that banks and MFIs do not seem to support MSEs expansion. Due to this 85 percent of the respondents have never received credit from these formal sources. The availability of other informal sources of finance, however, affects growth positively and significantly. This shows that in the absence of formal source of credit, informal networks appear more appealing for MSEs. Hence, firms with better network to borrow from informal sources such as, relatives, friends, and suppliers better loosen credit constraints, and grow faster.\par
The other major constraints identified by various studies on MSEs in Ethiopia are associated with market and finance problems. The causes of market-related problems of MSEs engaged in metal and wood work are shortage or absence of marketing skills, poor quality of products, absence of marketing research, shortage of market information, shortage of selling places, and absence of sub-contracting (FMSEDA. 2006). The product line of MSE activities in Ethiopia is relatively similar (Assegedech Woldelul. 2004 and cited in Admasu. \hyperref[b0]{Abera. 2012)}. Accordingly she states that: lack of product diversity, however, is prevalent and as a result similar products are over-crowding the market. Some micro enterprises shift from one product to another, and in doing so, capture better market opportunities. Nevertheless, as soon as the market has established itself, a multitude of further micro enterprises start off in the same business and this causes the selling price to fall immediately. According to Mulugeta (2011 and cited in Admasu. Abera. 2012 ) has identified and categorized the critical problems of MSEs in to marketrelated problems, which are caused by poor market linkage and poor promotional efforts; institution-related problems including bureaucratic bottlenecks, weak institutional capacity, lack of awareness, failure to abide policies, regulations, rules, directives, absence of training to executives, and poor monitoring and followup; operator-related shortcomings like developing a dependency tradition, extravagant and wasting behavior, and lack of vision and commitment from the side of the operators; MSE-related challenges including lack of selling place, weak accounting and record keeping, lack of experience sharing, and lack of cooperation within and among the MSEs and finally society-related problems such as its distorted attitude about the operators themselves and their products. 
\section[{III.}]{III.} 
\section[{Materials and Methods}]{Materials and Methods}\par
In order to analyze the potential impacts of factors on performance of MSEs, this study made use of a research methodology. This section provides an overview of the study's research approach which lays within the mixed methods strategies. The chapter discusses procedures and activities under taken, focusing on namely the study's research design, questionnaire design, data collection, sampling strategy, data processing and analysis and instrument development. Besides, the section deals with a discussion on the ethical issues. 
\section[{a) Research Design}]{a) Research Design}\par
Research design is the blueprint for fulfilling research objectives and answering research questions  {\ref (John A.H. et al., 2007:20-84)}. In other words, it is a master plan specifying the methods and procedures for collecting and analyzing the needed information. It ensures that the study would be relevant to the problem and that it uses economical procedures. The same authors discusses three types of research design, namely exploratory (emphasizes discovery of ideas and insights), descriptive (concerned with determining the frequency with which an event occurs or relationship between variables) and explanatory (concerned with determining the cause and effect relationships). The types of research employed under this study were descriptive and explanatory research. The major purpose of descriptive research is description of the state of affairs as it exists at present. Then this study describes and critically assesses the factors affecting the performance of MSEs in three towns of Bench Maji, Sheka and Kefa Zones. Second, the study employs explanatory in that the relationship between variables is correlated with an aim of estimating the integrated influence of the factors on performance.\par
Moreover, the study utilized cross-sectional in the sense that all relevant data was collected at a single point in time. The reason for preferring a cross-sectional study is due to the vast nature of the study and the limitation of time. And obtaining information from a cross-section of a population at a single point in time is a reasonable strategy for pursuing many descriptive researches (Janet M.  {\ref Ruane, 2006:94)}.\par
According to Mark et al. (2009:101) mixing qualitative and quantitative approaches gives the potential to cover each method's weaknesses with strengths from the other method. In this study, a combination of qualitative and quantitative approaches of doing research was employed, which has been practiced, as recommended by  {\ref Creswell (2009:203-216}). 
\section[{b) Data Collection i. Sources of Data}]{b) Data Collection i. Sources of Data}\par
The study employed both primary and secondary sources of data collection.\par
a. Primary Sources In order to realize the target, the study used well-designed questionnaire as best instrument. This was completed by the owner managers/or operators of the enterprises.\par
b. Secondary Sources Secondary data from files, pamphlets, office manuals, circulars and policy papers were used to provide additional information where appropriate. Besides, variety of books, published and/or unpublished government documents, websites, reports and newsletters were reviewed to make the study fruitful. 
\section[{c) Target Population}]{c) Target Population}\par
In this study the target populations is all MSEs operating within three twons (Mizan-Aman, Bonga and Tepi). According to Federal Micro \& Small Development Agency of Ethiopia there are 973 MSEs operating within Mizan-Aman, Bonga and Tepi (FMSAE, 2014). The study targets those enterprises within the three towns because the towns have a concentration of various MSE types and can thus be representative of most enterprise sectors in Benchi-Maji, Kaffa and Sheka zones. 
\section[{d) Sample Size Determination and Sampling}]{d) Sample Size Determination and Sampling}\par
Techniques Stratified simple random sampling was used to get information from different sectors of the MSEs. This technique is preferred because it is used to assist in minimizing bias when dealing with the population. With this technique, the sampling frame can be organized into relatively homogeneous groups (strata) before selecting elements for the sample. According to Janet (2006:94), this step increases the probability that the final sample is representative in terms of the stratified groups.\par
The strata's are sectors including: manufacturing, trade and service.\par
According to Catherine  {\ref Dawson (2009:54)}, the correct sample size in a study is dependent on the nature of the population and the purpose of the study. Although there are no general rules, the sample size usually depends on the population to be sampled.\par
According to Federal Micro \& Small Development Agency of Ethiopia (FMSAE, 2013) MSEs operating within the three twons are 973 which includes Trade (434), Manufacturing (355) and Service (184). The sample size selected here is considered as representative of trade, manufacturing and service and also large enough to allow for precision, confidence and generalibility of the research findings.\par
In order to determine sample size Yemane (1967) finite and large population sample size formula with 95\% confidence level is employed. The formula used to obtain this sample size is presented bellow. So, according to the above table, the following sample size was determined for this particular study. increases and so does the other one), or negative (when one variable increases and the other one decreases (Pallant, 2010). The test also indicates the strength of a relationship between variables by a value that can range from --1.00 to 1.00; when 0 indicates no relationship, -1.00 indicates a negative correlation, and 1.00 indicates a perfect positive correlation (Pallant, 2010). For the rest of the values is used the following guideline:- 
\section[{??}]{??}\par
? small correlation for value 0.1 to 0.29 ? medium correlation for 0.3 to 0.49 ? Large correlation for 0.50 to 1.0 (Pallant, 2010).\par
Like the demographic factors, the scale typed questionnaire entered to the SPSS software version16.00, to process correlation analysis. Based on the questionnaire which was filled by the SME members, the following correlation analysis was made. in the above, the values of correlation are also used for checking multicollinearity. The correlation between each of the independent variables is not too high, meaning that the correlation is not above value 0.5. It can be concluded that in this study there is no problem with multicollinearity. The strongest relationship between the independent variables is 0.497 between politics, entrepreneurial and marketing. 
\section[{b) Correlation Analysis of Production, Trade and Service Sector and SMEs Performance}]{b) Correlation Analysis of Production, Trade and Service Sector and SMEs Performance}\par
The Pearson correlations between independent variables management factor, social, marketing factors, infrastructural factor, political, financial factor, technological factor, land availability and the dependent variable SMEs performance are depicted in Figure  {\ref 4} The values of correlation are also used for checking multicollinearity. The correlation between each of the independent variables is not too high, meaning that the correlation is not above value 0.5. It can be concluded that in this study is no problem with multicollinearity.\par
The Pearson correlations between independent variables management factor, social, marketing factors, infrastructural factor, political, financial factor, technological factor, land availability and the dependent variable SMEs performance in the Trade sector of SME in Teppi town, Bonga town and Mizan-Aman town are depicted in Fig.  {\ref 4}.2 below.  
\section[{? Correlation Analysis between Infrastructural factor and SMEs performance}]{? Correlation Analysis between Infrastructural factor and SMEs performance}\par
Pearson correlation test was also conducted for these variables and the results are shown in Fig.  {\ref 4} The values of correlation are also used for checking multicollinearity. The correlation between each of the independent variables is not too high, meaning that the correlation is not above value 0.5. It can be concluded that in this study is no problem with multicollinearity.\par
The Pearson correlations between independent variables management factor, social, marketing factors, infrastructural factor, political, financial factor, technological factor, land availability and the dependent variable SMEs performance in the Trade sector of SME in Teppi town, Bonga town and mizan Aman town are depicted in Fig.  {\ref 4}  
\section[{c) Regression Analysis}]{c) Regression Analysis}\par
The multiple regression analysis is "an analysis of association in which the effects of two or more independent variables on a single, interval scaled dependent variable are investigated simultaneously" \hyperref[b47]{(Zikmund et al., 2010)}. The results of this analysis indicate how well a set of variables is able to predict the dependent variable. Furthermore, it shows how much unique variance in the dependent variable is explained by each of independent variables (Pallant, 2010).\par
Regression analysis was conducted to know by how much the independent variable explains the dependent variable. It is also used to understand by how much each independent variable (management factor, social, marketing factors, infrastructural factor, political, financial factor, technological factor, land availability) explains the dependent variable.\par
When a small sample is involved the Adjusted R square value in the sample tends to be a rather optimistic overestimation of the true value in the population. The adjusted R square statistic corrects this value to provide a better estimation of the true population value, rather than the normal R square value (Pallant, 2010).\par
So, for the whole regression analysis of this study the adjusted R square were considered to provide a better estimation of the true population than the normal R square. The results of the regression analysis are as following.\par
For the purposes of determining the extent to which the explanatory variables explain the variance in the explained variable, regression analysis was employed. The results of such analysis for production, trade and service sector were narrated under.\par
i 
\section[{. Regression Analysis of the Manufacturing sector and SMEs performance}]{. Regression Analysis of the Manufacturing sector and SMEs performance}\par
The model summary in table \hyperref[tab_6]{4}.1 presents how much of the variance in the dependent variable is explained by the model. The multiple coefficient of determination denoted as R square is 0.695. The value of the R square indicates that 69.5 percent of variance in the dependent variable was explained by the model. By looking at the Sig.-value in table 4.1, it is possible to interpret whether the particular independent variable has a significant relationship with the dependent variable. The relationship is significant if the Sig. value is not larger than 0.05. The results show that there is a significant relationship for political (0.000), social (0.002), land available (0.001), Technological factor (0.003), Infrastructural factor (0.002), Marketing factors (0.002), Financial factor (0.003) and Management factor (000). This means that all the variables are good predictors of the dependent variable. The multiple regression result table 4.1 indicates that, all the internal and external factors that used in this study have positive and significant influence on the explained variable. The value of (?= .640, .285, .716, .530, .844, .383, .508, and 0.174) for political, social, land availability, technology, infrastructure, marketing, financial, and management factors respectively. Furthermore, the study aims to identify which of the variables contributed the most to prediction of the dependent variable. This information can be investigated via Standardized coefficient Beta in table  {\ref table} 4.1. In this study the highest Beta value is 0.844 for infrastructure factor, and second highest is 0.716 for land availability. The independent variables management factor (.174), social (.285), technology factor (.530), financial factor (.508), and political factors (.640) are also good predictors. These results indicate that the variables infrastructure factor and political factor make the strongest unique contribution in explaining the dependent variable SMEs performance.\par
These results enable to conclude that the model explains 69.50 percent of the variance in SMEs performance. The largest unique contribution is provided by the variables infrastructure factor, Land availability, and political factor. Thus, these variables represent good predictors of the dependent variable. 
\section[{ii. Regression Analysis of Trade sector}]{ii. Regression Analysis of Trade sector}\par
The model summary in table 4.2 presents how much of the variance in the dependent variable entrepreneur performance in trade sector is explained by the model.\par
The Results of regression analysis against SMEs performance can be seen in table 4.2. The result shows that explanatory variables have the power to explain SMEs performance. In this case the results of correlation of explanatory variables and SMEs performance and adjusted R Square (.631) were taken into consideration. The regression analysis model summary indicates that an explanatory variable which is entered into the regression model on SPSS has relationship with SMEs performance with correlation coefficient of 0.471. The adjusted coefficient of determination (R 2 ) 0.631 indicates the highest effect/variability of explanatory variables on SMEs performance.\par
Therefore, it is pointed out that 63.1 percent of explanatory variables can explain the dependent variable that is SMEs performance. As it is indicated in the ANOVA table, the total explanatory variables is considered as predictors of SMEs performance and reported high level of significance p<0.01. And also the adjusted R square value of 0.631confirming that, 63.1 percent of the variation in SMEs performance is explained by explanatory variables. Explanatory variables as used for prediction were found to be significantly related to SMEs performance as the p-value is less than 0.01.\par
By looking at the Sig.-value in Table \hyperref[tab_6]{4}.2, it is possible to interpret whether the particular independent variable has a significant impact on the dependent variable. The relationship is significant if the Sig.-value is not larger than 0.05. The results show that there is a significant relationship for political (0.001), social (0.000), land availability (0.032), technological (0.027), infrastructural (0.002), market (0002), motivational (0000), management (004). This means that the variables political, social, land available, technology, infrastructure, market, financial and management factors are good predictors of the dependent variable which is SMEs performance, are a good predictors.\par
The multiple regression result table 4.13 indicates that the explanatory variables have positive and significant influence on entrepreneurial performance. The value of (?= 0.572., 0.496, 0.643, 0.542, 0.526, 0.391, 0.322, 0.158, \& 0.419,) for political, social, land availability, technology, infrastructural, marketing, financial, management and entrepreneurial factors respectively show that there is a positive direction and its influence is significant at p< 0.05 the dependent variable and these are good predictors.\par
Furthermore, the study aims at identifying which of the variables contributed the most to prediction of the dependent variable. This information can be investigated via Standardized coefficient Beta in table 4.2. The standardized coefficients mean that "values for each of the different variables have been converted to the same scale so they can be compared  {\ref (Pallant, 2010)}. In this study the highest Beta value is 0.643 for land availability, 0.572 political and 0.526 for infrastructure. Both independent variables are statistically significant since the Sig. value is less than 0.05. These results indicate that the variables land availability, political and infrastructure makes the strongest unique contribution in explaining the dependent variable SMEs performance.\par
These results enable to conclude that the model explains 63.1 percent of the variance in SMEs performance. The largest unique contribution is provided by the variables land availability, political and infrastructure. Thus, these variables represent good predictors of the dependent variable.\par
iii. Regression Analysis of Service sector Again here the researcher analyzes the conceptual framework of independent variables were entered into the multiple regression equation: (management factor, social, marketing factors, infrastructural factor, political, financial factor, technological factor, land availability and entrepreneurial). The model summary in table  {\ref 4.3} presents how much of the variance in the dependent variable is explained by the model. The multiple coefficient of determination denoted as adjusted R square is 0.562. The value of the adjusted R square indicates that 56.2 percent of variance in the dependent variable was explained by the model. By looking at the Sig.-value in table 4.3, it is possible to interpret whether the particular independent variable has a significant impact on the dependent variable. The relationship is significant if the Sig.-value is not larger than 0.05. The results show that there is a significant relationship for political (0.000), social (0.002), land available (0.001), Technological factor (0.003), Infrastructural factor (0.002), Marketing factors (0.002), Financial factor (0.003), management factor, motivational factor (000) and entrepreneurial factor (000). This means that all the variables are good predictors of the dependent variable of the service sector.\par
The multiple regression result table 4.3 indicates that the explanatory variables have positive and significant influence on SMEs performance. The value of (?= 0.405., 0.199, 0.591, 0.564, 0.349, 0.371, 0.355, 0.214, \& 0.419,) for political, social, land availability, technology, infrastructural, marketing, financial, management and entrepreneurial factors respectively show that there is a positive direction and its influence is significant at p< 0.05 the dependent variable and these are good predictors.\par
Furthermore, the study aims to identify which of the variables contributed the most to prediction of the dependent variable. This information can be investigated via Standardized coefficient Beta in table According to Minster of Urban Development and Construction, (2013), the study also identified a number of challenges and constraints hindering the growth of MSEs in Selected Major Cities of Ethiopia. These challenges were manifested in terms of capital, technology and employment growth trends. Enterprises from the regional cites indicated that shortage of finance (42 percent) to expand their business was their principal challenge, followed by lack of working premise (28.3 percent); and lack of access to market or absence of linkage to market. The study also showed that lack of access to land has been one of the most crucial bottlenecks (26.4 percent) in Addis Ababa, problem of finance (25.6 percent) and access to market (25.1 percent) were among the strong factors inhibiting the growth of these enterprises in the capital. The findings of Mulu (  {\ref 2007}) also indicate that banks and MFIs do not seem to support MSEs expansion. Due to this 85 percent of the respondents have never received credit from these formal sources. The availability of other informal sources of finance, however, affects growth positively and significantly. This shows that in the absence of formal source of credit, informal networks appear more appealing for MSEs. Hence, firms with better network to borrow from informal sources such as, relatives, friends, and suppliers better loosen credit constraints, and grow faster.\par
Generally, in line with Poor (2006), Mulu (2007), and Minster of Urban Development and Construction (2013), our finding also shows that MSEs that operate in Bench Maji, Sheka \& Kefa Zone suffer from lack of access to finance, working premises, infrastructure, social and cultural, Political and legal factors. 
\section[{V. Conclusions and Recommendations a) Conclusions}]{V. Conclusions and Recommendations a) Conclusions}\par
This research was conducted in Bench Maji, Sheka and Kefa Zone capital towns (Mizan-Aman, Tepi and Bong) respectively with the prime intent of critically assessing the factors affecting the performance of MSE operators engaged in production, trade and service activities. Specifically, the study attempted to examine the internal and external factors that affect the performance of MSEs, to describe the characteristics of small enterprises operating in the study area and to recommend possible solution to alleviate the problem of MSEs. Based on the objectives and findings of the study, the following conclusions are worth drawn.\par
According to (Enock Nkonoki, 2010), the main factors/problems that limits small firm's success/growth into two groups; first is the factors that originate from within the firm (in other words they are internal to the firm) and the second group is factors that originate from outside the firm (these are external to the firm). Lack of a proper business plan/vision, Poor management, and lack of needed talent are among the internal factors. The External factors limiting small firm growth are Corruption, Competition, Government policy, Technological barrier, in access to finances/funding, Bureaucratic processes and Unfavorable economic factors.\par
In line with the Enock, 2010 findings, the regression result of this particular study showed, all the internal and external variables (factors) included in this particular study were statistically significant and therefore, affects the performance of SMEs in the study area was affected by both variables.\par
The finding of this research shows that, most of the MSEs operators have no efficient experience and management knowhow to perform their activities effectively and efficiently. These lead to them unsuccessful because they run their business activities without having adequate knowledge about the business environment. Lack of managerial know-how places significant constraints on SME development.\par
Regarding infrastructural facilities, most of MSEs operators had no adequate infrastructural facilities at the given study area, specially insufficient and interrupted electric power and water supply. These lead to them, unable to generate adequate profit by satisfying the needs of the customers. Infrastructural problem is not only the problem of the study area problem it is a country wide problem, therefore this problem is not solved by the MSEs operators rather than by the government of the country.\par
The result of the finding shows that majority of MSEs operators in the study area does not have enough working premises. Because of this, the MSEs operators are not perform their business related activities effectively and efficiently. And also, the location of the working premises is not suitable for attracting the new customers that means, the working premises have no access to market.\par
Regarding other external environmental factors, majority of MSEs operators activities are affected by external related problem such as technological related problems i.e. the MSEs operators are did not have the opportunity to get modernized technology at the given study area which made them unsuccessful. And the other external problem is, there was a problem of market linkage with the external parties such as vendor, suppliers and customers. Because of there was a problem of marketing linkage through external parties, most of the time the MSEs operators are kept their products in the store. It is true that, finance, working place, infrastructural, marketing factors are factors that affect the performance of MSEs, this does not mean that all factors are equally affect the performance of the business enterprises. As compared with the other factors, technological factors, lack of infrastructural facilities, shortage of working premises and shortage of finances for start-up and expansion purposes are the top most factors that affect the growth and success of MSEs activities at Bench Maji, Sheka and Kefa Zone. 
\section[{b) Recommendations}]{b) Recommendations}\par
As one can observe from this study, both internal and external factors determine Small and Medium Enterprises performance in South Western part of the SNNP regional state of the country. Thus, Small and Medium Enterprise managers, directors, governmental bodies and all stakeholders should not only be concerned about internal structures and policies, but also must consider the external environment together in designing out strategies to improve their performance.\par
Regarding the internal factors (management and entrepreneurial skills), enterprises owned by individuals with previous management experience have better performance as compared with those MSEs operators who have no previous management experience. Therefore, Bench maji, Sheka and Kefa Zone micro and small enterprise agency in cooperation with Mizan Tepi University and other government bodies are better to work on preparing training programs on management issues and creating experience sharing opportunities especially to those enter into the sector without any previous business background.\par
In relation to the external factors (politico-legal, social, working premises, technology, infrastructure, marketing and finance) the following possible recommendations were forwarded; ? The MSEs operators are better to enhance their marketing skills through proper training and experience sharing with other successful medium and large scale enterprises. In addition to this marketing skills, such as setting competitive price for their products, creating good interpersonal relationship with customers and the way of promoting their outputs to the customers in an effective manner. Moreover, the government bodies such as Bench maji, Sheka and Kefa Zone micro and small enterprise agency and the other stakeholders are better to assist them by searching market for their products which is produced by the MSEs operators, by doing this, they are try to save them from losses. ? To overcome the problems related to establishing and starting the MSEs businesses, it is needed primarily to conduct market assessment. This can identify and prioritize the type of MSE business, to pin point the sources of inputs to identify the working place and other premises to establish and to start the type of business. Moreover, it is required to train and council the MSEs in developing saving culture and to generating initial capital by themselves. ? Concerning to the source of finance, the major sources of finance or funds for most of MSEs operators at the study area are by borrowing money from microfinance institutions. The reason for emphasizing on informal sector is that the requirement of collateral/guaranty is relatively rare as compared with formal sectors like banks but the informal sectors like MFIs are unable to provide/supply enough credit to them as they want. Therefore, Bench Maji, Sheka and Kefa Zone micro and small enterprise agency in cooperation with other government bodies have to develop comfortable source of finance for MSEs by organizing and supporting the performance of MFIs and other source of finance. This can be done by communicating with the banks and other credit institutions to minimize their requirements to provide fund. By doing so, the MSEs can get enough access to finance for their business activities. ? There are infrastructural facility problems in the study area, like power interruption, inadequate supply of water, and transportation problems. Therefore, the government and the other concerned body have to give attention to minimize such kind of problems to improve the performance of MSEs. ? Finally, the study sought to investigate the internal and external factors that influence performance of SMEs that operates in Bench Maji, Sheka and Kefa Zone. However, the variables used in the statistical analysis did not include all factors that can affect SMEs performance in the area. Thus, future researchers could incorporate external factors such as corruption, size of the enterprise and inflation rate.   \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.6721938775510204\textwidth}P{0.08239795918367346\textwidth}P{0.07372448979591836\textwidth}P{0.021683673469387755\textwidth}}
\tabcellsep =\tabcellsep ?? ?? + ??(??) ??\\
Where:\tabcellsep \tabcellsep \\
\multicolumn{4}{l}{n= Number of sample taken}\\
\multicolumn{3}{l}{N= Population size}\\
\multicolumn{4}{l}{e = sampling error /level of precision.}\\
\multicolumn{4}{l}{Accordingly the target population results, the}\\
following samples.\tabcellsep \tabcellsep \\
n =\tabcellsep \multicolumn{2}{l}{973 1 + 973(0.05)2}\tabcellsep = 278\end{longtable} \par
  {\small\itshape [Note: (Source: Zelalem, Issues and Challenges of Rural WaterScheme, 2005)    ]} 
\caption{\label{tab_1}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{31} \par 
\begin{longtable}{P{0.307380073800738\textwidth}P{0.153690036900369\textwidth}P{0.388929889298893\textwidth}}
IV.\tabcellsep Data Presentation, Analysis and\tabcellsep al., 2008). The Pearson correlations were calculated as\\
\tabcellsep Discussion of Results\tabcellsep measures of relationships between the independent variables and dependent variables. This test gives an\\
\multicolumn{2}{l}{a) Pearson Correlation Analysis}\tabcellsep indication of both directions, positive (when one variable\\
\tabcellsep This research is investigating the strength of\tabcellsep \\
\multicolumn{2}{l}{relationships between the studied variables. The study}\tabcellsep \\
\multicolumn{2}{l}{employs the Pearson correlation which "measures the}\tabcellsep \\
\multicolumn{2}{l}{linear association between two metric variables" (Hair et}\tabcellsep \\
\tabcellsep \tabcellsep © 2016 Global Journals Inc. (US)\end{longtable} \par
 
\caption{\label{tab_2}Table 3 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.2990118577075099\textwidth}P{0.5224308300395257\textwidth}P{0.028557312252964425\textwidth}}
shown in figure\tabcellsep \tabcellsep \\
Political Factor\tabcellsep \tabcellsep \\
\tabcellsep 0.652\tabcellsep \\
Social Factor\tabcellsep \tabcellsep \\
\tabcellsep .367\tabcellsep \\
Land Available\tabcellsep \tabcellsep \\
0.561\tabcellsep \tabcellsep \\
Technological Factor\tabcellsep .331\tabcellsep SMEs\\
\tabcellsep .889\tabcellsep Performance\\
Infrastructural Factor\tabcellsep .462\tabcellsep \\
Marketing Factors\tabcellsep .411\tabcellsep \\
Financial Factor\tabcellsep .396\tabcellsep \\
\tabcellsep .738\tabcellsep \\
Management Factor\tabcellsep \tabcellsep \\
Entrepreneurship Factor\tabcellsep \tabcellsep \\
\tabcellsep \tabcellsep .1\\
\tabcellsep above.\tabcellsep \\
\tabcellsep \multicolumn{2}{l}{? Correlation Analysis between Political factor and}\\
\tabcellsep SMEs performance\tabcellsep \\
\tabcellsep \multicolumn{2}{l}{Pearson correlation test was conducted to see}\\
\tabcellsep \multicolumn{2}{l}{the degree of relationship between the independent}\\
\tabcellsep \multicolumn{2}{l}{variable i.e. political factor and SMEs performance. The}\\
\tabcellsep \multicolumn{2}{l}{results of the correlation between these variables are}\end{longtable} \par
 
\caption{\label{tab_3}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.4824223874070835\textwidth}P{0.0014866637516397026\textwidth}P{0.010778312199387845\textwidth}P{0.3207477044162659\textwidth}P{0.03456493222562308\textwidth}}
\multicolumn{3}{l}{Political Factor ? Correlation Analysis between Marketing factors and}\tabcellsep ? Correlation Analysis between Management factor\tabcellsep \\
SMEs performance\tabcellsep \tabcellsep \tabcellsep and SMEs performance\tabcellsep \\
\multicolumn{3}{l}{Pearson correlation test was conducted to see}\tabcellsep Pearson correlation test was also conducted for\tabcellsep \\
\multicolumn{3}{l}{.356 the degree of relationship between the independent Social Factor variable i.e. marketing factor and entrepreneur .532 performance. The results of the correlation between}\tabcellsep these variables and the results are shown in Fig. 4.2 above. As it is indicated in the Fig. 4.2, there is significant positive correlation between Management\tabcellsep \\
\multicolumn{3}{l}{these variables are shown in Fig. 4.2 above. As it is}\tabcellsep factors and SMEs performance. In other words\tabcellsep \\
\multicolumn{3}{l}{indicated in the Fig. 4.2, there is significant correlation Land Available .691 between marketing factor and SMEs performance. In}\tabcellsep Management factors and SMEs performance are correlated in a moderate relationship (r=0 .386 with\tabcellsep \\
\multicolumn{3}{l}{Technological Factor Infrastructural Factor other Political factor and SMEs performance have strong relationship (r= -0.524 with p<0.01). .279 .828 ? Correlation Analysis between Financial factor and SMEs performance For these variables Pearson correlation test was conducted and the results are shown in Fig. 4.2 above.}\tabcellsep p<0.01). ? Correlation Analysis between Entrepreneurship SMEs Performance factor and SMEs performance conducted and the results are shown in Fig. 4.2 above. As it is shown in the Fig. 4.2, there is significant For these variables Pearson correlation test was\tabcellsep Year 2016\\
\multicolumn{3}{l}{As it is shown in the Fig. 4.2, there is significant .524 correlation between financial factor and SMEs}\tabcellsep correlation between Entrepreneurship factors and SMEs performance. In other words Entrepreneurship factors\tabcellsep \\
\multicolumn{3}{l}{Marketing Factors Financial Factor Management Factor performance. In other words Financial factor and SMEs .662 .386 .342 performance have high or strong relationship (r=0 .662 with p<0.03). Political Factor Social Factor .310 .876}\tabcellsep and SMEs performance have moderate relationship (r=0 .342 with p<0.02).\tabcellsep Volume XVI Issue X Version I\\
Entrepreneurship Factor Land Available Technological Factor Infrastructural Factor Marketing Factors Financial Factor Management Factor\tabcellsep .353\tabcellsep .544 .362 .595 .380 .376 .689\tabcellsep SMEs Performance\tabcellsep A ( ) Global Journal of Management and Business Research\\
Entrepreneurship Factor\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
  {\small\itshape [Note: Fig. 4.3: Correlation analysis of Service sector and SMEs performance]} 
\caption{\label{tab_4}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{41} \par 
\begin{longtable}{P{0.06115107913669064\textwidth}P{0.22167266187050358\textwidth}P{0.010701438848920863\textwidth}P{0.11771582733812949\textwidth}P{0.14217625899280575\textwidth}P{0.13911870503597124\textwidth}P{0.09631294964028778\textwidth}P{0.06115107913669064\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \tabcellsep Model Summary\tabcellsep \\
\tabcellsep Model\tabcellsep R\tabcellsep R Square\tabcellsep Adjusted R Square\tabcellsep Std. Error of the Estimate\tabcellsep Durbin-Watson\\
\tabcellsep 1\tabcellsep .892 a\tabcellsep .796\tabcellsep .695\tabcellsep .521\tabcellsep 2.158\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep Coefficients\tabcellsep \\
\tabcellsep Model\tabcellsep \tabcellsep \multicolumn{2}{l}{Unstandardized Coefficients}\tabcellsep Standardized Coefficients\tabcellsep T\tabcellsep Sig.\\
\tabcellsep \tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\\
1\tabcellsep (Constant)\tabcellsep \tabcellsep 5.815\tabcellsep 1.083\tabcellsep \tabcellsep 5.369\tabcellsep .000\\
\tabcellsep Political\tabcellsep \tabcellsep .675\tabcellsep .112\tabcellsep .640\tabcellsep .199\tabcellsep .000\\
\tabcellsep Social\tabcellsep \tabcellsep .362\tabcellsep .164\tabcellsep .285\tabcellsep 2.215\tabcellsep .002\\
\tabcellsep \multicolumn{2}{l}{Land availability}\tabcellsep .729\tabcellsep .101\tabcellsep .716\tabcellsep 1.278\tabcellsep .001\\
\tabcellsep \multicolumn{2}{l}{Technological factor}\tabcellsep .605\tabcellsep .102\tabcellsep .530\tabcellsep 3.986\tabcellsep .000\\
\tabcellsep \multicolumn{2}{l}{Infrastructural factor}\tabcellsep .982\tabcellsep .177\tabcellsep .844\tabcellsep 2.729\tabcellsep .003\\
\tabcellsep \multicolumn{2}{l}{Marketing factors}\tabcellsep .451\tabcellsep .104\tabcellsep .383\tabcellsep 1.445\tabcellsep .002\\
\tabcellsep \multicolumn{2}{l}{Financial factor}\tabcellsep .549\tabcellsep .121\tabcellsep .508\tabcellsep 2.049\tabcellsep .003\\
\tabcellsep \multicolumn{2}{l}{Management factor}\tabcellsep .207\tabcellsep .101\tabcellsep .174\tabcellsep 1.460\tabcellsep .000\\
\multicolumn{4}{l}{a. Dependent Variable: SMEs measurement}\tabcellsep \tabcellsep \end{longtable} \par
  {\small\itshape [Note: a. Predictors: (Constant), management factor, social, marketing factors, infrastructural factor, political, financial factor, technological factor, land availability b. Dependent Variable: performance measurement]} 
\caption{\label{tab_5}Table 4 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.021621621621621623\textwidth}P{0.22027027027027027\textwidth}P{0.0013513513513513514\textwidth}P{0.19594594594594594\textwidth}P{0.13108108108108107\textwidth}P{0.1283783783783784\textwidth}P{0.0918918918918919\textwidth}P{0.059459459459459456\textwidth}}
\tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{3: Regression Analysis of Service sector}\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep Model Summary\tabcellsep \\
\multicolumn{2}{l}{Model}\tabcellsep R\tabcellsep R Square\tabcellsep Adjusted R Square\tabcellsep Std. Error of the Estimate\tabcellsep Durbin-Watson\\
1\tabcellsep \multicolumn{2}{l}{.789 a}\tabcellsep .622\tabcellsep .562\tabcellsep .513\tabcellsep 2.165\\
\multicolumn{2}{l}{Dependent}\tabcellsep \tabcellsep \multicolumn{2}{l}{performance measurement.}\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep Coefficients\tabcellsep \\
\tabcellsep \multicolumn{2}{l}{Model}\tabcellsep \multicolumn{2}{l}{Unstandardized Coefficients}\tabcellsep Standardized Coefficients\tabcellsep t\tabcellsep Sig.\\
\tabcellsep \tabcellsep \tabcellsep B\tabcellsep Std. Error\tabcellsep Beta\\
1\tabcellsep (Constant)\tabcellsep \tabcellsep 5.284\tabcellsep 1.204\tabcellsep \tabcellsep 4.387\tabcellsep .000\\
\tabcellsep Poetical\tabcellsep \tabcellsep .542\tabcellsep .115\tabcellsep .405\tabcellsep .368\tabcellsep .004\\
\tabcellsep Social\tabcellsep \tabcellsep .255\tabcellsep .181\tabcellsep .199\tabcellsep 1.413\tabcellsep .005\\
\tabcellsep \multicolumn{2}{l}{Land available}\tabcellsep .647\tabcellsep .117\tabcellsep .591\tabcellsep 1.248\tabcellsep .003\\
\tabcellsep \multicolumn{2}{l}{technological factor}\tabcellsep .658\tabcellsep .111\tabcellsep .564\tabcellsep 3.578\tabcellsep .001\\
\tabcellsep \multicolumn{2}{l}{Infrastructural factor}\tabcellsep .469\tabcellsep .184\tabcellsep .349\tabcellsep 2.558\tabcellsep .002\\
\tabcellsep \multicolumn{2}{l}{Marketing factors}\tabcellsep .402\tabcellsep .105\tabcellsep .371\tabcellsep 1.336\tabcellsep .000\\
\tabcellsep \multicolumn{2}{l}{Financial factor}\tabcellsep .277\tabcellsep .136\tabcellsep .355\tabcellsep 2.038\tabcellsep .004\\
\tabcellsep \multicolumn{2}{l}{Management factor}\tabcellsep .286\tabcellsep .107\tabcellsep .214\tabcellsep 1.555\tabcellsep .003\\
\tabcellsep \multicolumn{2}{l}{Entrepreneurial factor}\tabcellsep .541\tabcellsep .173\tabcellsep .419\tabcellsep 1.413\tabcellsep .000\end{longtable} \par
 
\caption{\label{tab_6}Table 4 .}\end{figure}
 			\footnote{© 2016 Global Journals Inc. (US)} 			\footnote{Factors Affecting Performance of Micro and Small Enterprises in South West Ethiopia: The Case of Bench Maji, Sheka, and Kefa Zones} 			\footnote{© 2016 Global Journals Inc. (US) 1} 		 		\backmatter   			 
\subsection[{Acknowledgements}]{Acknowledgements}\par
Our gratitude to the many individuals who have helped shape this work cannot adequately be conveyed in a few sentences. Our heartfelt thanks go to all our friends and family who directly or indirectly contributes their unlimited initiation and facilitation in the work. Unforgettable warmest appreciation also goes to our employer Mizan-Tepi University who supports in all the finances and encourage us to finish this piece of work.\par
Last but not the least, we wish to give our thanks to all managers, owners of SMEs and employees for their willingness to participate in filling the questionnaires. 			  			 			 			  				\begin{bibitemlist}{1}
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