# Introduction he growth of Small, Medium Enterprises Sized (SMEs) is an important phenomenon in economic development of any country. SMEs play a great role in absorbing the unemployed and they contribute to the growth of Gross Domestic Product (GDP). Measuring SMEs growth is difficult due to complex nature of their existence. In most countries, sales turnover, number of employees, profitability and size of the premises are considered as important factors when measuring SMEs growth. Brush, Ceru and Blackburn. (2009) define SMEs growth as a "geographical expansion, increase in the number of branches, inclusion of new markets and clients, increase in the number of products and services, fusions and acquisitions". However, the main indicators of SMEs growth lie on sales volumes and the total number of employees (Masum and Fernandez, 2008; Achtenhagen, Naldi and Melin, 2010) The growth of SMEs is incapacitated by several factors one of them being the fear to take risks in business. SMEs owners fear business closure due to failure in their businesses' operations. Fear in their business operations limits the capacity of the individual to take risks and search for opportunities, which could eventually produce business growth. However, SMEs growth is perceived differently because SMEs owners (entrepreneurs) are heterogeneous in growth intentions (Machado, 2016). SMEs growth is important and should be sustainable for small and medium enterprises. In fact, their survival depends entirely on their abilities to access the opportunities in the market with other big companies (Davidsson, Achtenhagen and Naldi, 2010;Machado, 2016). Businesses growth enables SMEs to survive against all odds on the way through graduating from micro to small to medium and finally to big companies (Rauch and Rijskik, 2013) Most African countries have implemented several policies and reforms ranging from sectoral, institutional, microeconomic and macroeconomic aimed at ensuring sustainable economic growth and poverty reduction (Salami, Kamara and Brixiova, 2010). All the ways through, SMEs still play a great role in absorbing the unemployed population and economic growth. Although Tanzania' economic growth trend and thematically the ideas guiding economic development have been changing from time to time, the SMEs sector, both formal and informal still plays a great role in creating employment opportunities in the country. The Small Industries Development Organization (SIDO) in 2008 reported that 92 % of the school leavers (at all levels) were potentially absorbed by SME sector. This shows that SMEs is a good option for absorption of unemployed individuals, school leavers, college and university graduates in the country. Furthermore, the SME sector was estimated to occupy about 20 % of the total labour force in Tanzania (URT, 2012). The size of the SMEs sector and its role in the economy is currently significant in Tanzania (Magembe, 2017). This sector is also very important in generating income as well as contributing to GDP growth rate and foreign exchange earnings due to international trade (URT, 2010). By 2008, it was reported that 30 % of the GDP originated from SME sector. The contribution increased from 30 % to 35 % in 2011 (URT, 2011). The government of Tanzania recognizes the significant importance of SMEs in the economy. This is because; substantial support to SMEs has been always in place. This support includes, Policy Framework, Institutional Support, Private Support and International Sector Support Policy Framework. The government has been supporting SMEs since they play significant roles in the start-up and growth of businesses. They also play a vital role in simplifications of taxation, licencing and registering SMEs, technical support, researches, innovation, networking and improving access to financial services (Argidius Foundation, 2017; UK Essays, 2017 ). The government is currently working through Tanzania Revenue Authority (TRA) under the Ministry of Finance and Planning to formally license and register all SME owners who are currently doing their businesses informally. This is expected to enable the SMEs to increase their owners' access to financial services and loans from all financial institutions and other important services from the government something that will eventually influence the SMEs' growth. However, despite these efforts by the government and other stakeholders to promote SMEs growth, limited access to credit is a serious global problem that has been reported to impede SMEs growth (Yongqian, Armstrong and Clarke, 2012). Also in Tanzania, the key obstacle to SMEs growth is limited access to credit and know how, especially for younger and new entrants in businesses. Most of the young people are school leavers and graduates. They face this obstacle in businesses because they lack experience, networking and collateral to pledge to lenders for loan approval because of their short time in business (Magembe, 2017). Market imperfections lead to credit rationing and may severely limit the investment and operations of SMEs due to shortage of capital that eventually limits the growth of SMEs. The same problem is facing SMEs in Tanzania (Stiglitz and Weis, 1981). Credit constraints limit business start-up and growth something that accounts for about 70%of the problems that face SMEs in Tanzania (Olomi, 2009). Other problems that are reported to hinder SMEs' growth include: some SMEs operate in remote areas, inability of the SMEs owners to prepare and present loan applications that meet lenders' requirements, limited capacity of SMEs owners who start and operate businesses, in terms of the attitudes, exposure, skills and experiences, regulatory framework and limited access to working premises (URT, 2003; Alexander, 2003;Stein, Goland and Schiff, 2010;Maziku, 2012 andMagembe, 2017). This situation has resulted into limited contribution of the SMEs to GDP growth. For example, a Survey on SMEs done in 2012 estimated that there were 2.75 million Micro, Small and Medium Enterprise (MSME) owners and about 3.16 million Micro, Small and Medium Enterprises (MSMES) in Tanzania, contributing about 27% of the GDP and employing more than 5.2 million people ( URT, 2012). This study examined the major challenges that impede SMEs' growth and factors that influence their growth. It used cross sectional data collected through questionnaire. It provides a wide scope on the major challenges that impede SMEs growth and the factors that influence their growth. Both descriptive and logistic analyses on challenges that impede SMEs growth were done. Logistic regression analysed SMEs owners' characteristics and their businesses' characteristics that play vital roles in influencing business growth whereas descriptive. # II. # Problem Statement Many policy makers, researchers, and practitioners have drawn their attention on SMEs' growth due to their contribution in absorbing unemployed population and their pivotal position in the entire process of economic growth and development (Hamis, 2011;Magembe, 2017). However, some of SMEs start and fail to grow to prosperity. The graduation rate from Micro to small, medium and large enterprises in Tanzania is very low and most enterprises remain micro while medium sized enterprises are rare (Argidius Foundation, 2017). Now many scholars, policy makers and practitioners are of the view that limited growth of SMEs is the problem that calls for more researches (Shane and Venkataraman, 2000;McKelvie and Wiklund, 2010). The literature reviews show several studies that have been conducted to measure the companies' growth but putting less emphasis on SMEs (Machado, 2016). Therefore, this study examined the challenges and factors influencing SMEs growth. The factors examined in the logistic regression model include personal and business characteristics. # a) Purpose of the Study The main objective of this study was to investigate the challenges that impede SMEs' growth and factors that influence their growth. The specific objectives of the study were: i) To examine the main challenges that impede SMEs growth ii) To determine the factors influencing the SMEs growth. # b) Significance of the Study This study is useful to decision makers, policy makers, borrowers, lenders and entrepreneurs. It contains empirical evidences that can help decision makers to intervene the situation that affects the SMEs. To students, researchers and academicians, the study adds knowledge in the literature on the issues of challenges that impede SMEs' growth and factors influencing their growth. # III. Literature Review a) Theoretical Literature Review Trait theory guides this study. The theory is rooted in entrepreneurship. It suggests that entrepreneurs possess some in-born and exceptional characteristics. These unique characteristics include innovation, great self-confidence; need for personal control, high independence and restlessness (McClelland, 1961). Some authors, such as Shapero and Sokol (1982) advance the theory in which they said people become entrepreneurs after experiencing an entrepreneurial event. Experiencing such event makes people to alter the perception of their surroundings and subsequently modify course and follow a certain route (Maalu, Nzuve and Magutu, 2010). The process of interpretation and analysis of the entrepreneurial behaviour is determined by various factors including values, memories, cultural settings, past experiences and imaginations (Muthomi, 2017). With reference to Trait theory, enterprises can be enhanced by many factors, which range from individuals, businesses and settings. The government or other stakeholders, who can enhance SMEs growth at their exposure, may attribute the factors in settings (Machado, 2016). This theory in particular underpins this study. # b) Empirical Literature Review Achtenhagen et al (2010) conducted a study about SMEs growth in Sweden using a sample of 2,455 SMEs taken from Sweden's Statistics register that was designed to be representative of small and medium sized enterprises. In their analysis, they involved variables such as increase in sales, employees, profit, assets, and the firm's value. Other factors that were involved in the analysis were competences, SMEs' practices in efficiency and the professional sales process. The results of their findings showed that all other mentioned factors were significant to firms' growth except that the increase in the number of employees did not signify SMEs growth. Some of the variables such as increase of profits are included in logistic regression analysis of the present study. Furthermore, SMEs' growth is affected by different factors and its magnitude is different depending on the levels of SMEs growth. For example, Wiklund, Patzelt, and Shepherd (2009) conducted a study on SMEs growth using data from 413 small businesses in Sweden. They used an integrative model to explain SMEs' growth by associating the variables of human capital and attitudes of SME operators, resources, enterprising features and setting. All these factors were reported to have significance on SMEs' growth. Human capital may mean education/skills of the SME owners that play a vital role in influencing growth of the SMEs. This study includes in the logistic regression both individual and business characteristics that are thought to influence SMEs' growth. On the other hand, a study conducted in Netherlands used data from a SMEs survey, involving 1,535 SMEs. The researchers adopted a 7-point Likert scale to rate the factors contributing to SMEs growth. Factors such as motivation, internal control, locus and personal aims of entrepreneurs may have a positive effect on SMEs growth (Wakkee, Van Der Veen and Eurlings, 2015). Using database of 13,221 firms from 46 countries, based on 9-year data Brito, Brito and Vasconcelos in 2007 investigated the relationship between size of the firm and firms' growth in Portugal. The results showed a positive relationship between size and growth rate of firms. They further reported that, the relationship between size of the firm and its growth is a complex phenomenon. As it is in industries with a great number of new firms, the small firms are at a disadvantage and they are obliged to grow fast or quit from the business due to business' failure. This study employed logistic regression using cross sectional data that involved 300 SMEs owners. The size of the firm is one of the variables that were involved in the analysis. The existing Literature for SMEs growth in Tanzania show that entrepreneurs who have enough education, experiences in business management and coming entrepreneurial family their businesses grow than entrepreneurs who lacked the aforementioned attributes. In addition, factors like achievement desire, internal locus of control, risk-taking propensity, innovativeness and self-efficacy, attitude towards entrepreneurship, entrepreneurial alertness, different cognitive styles and entrepreneurial motivation play a great role on influencing SMEs growth in Tanzania (Isanga, 2012, Kirama, 2014). Moreover, the literature reveals how the SMEs growth phenomenon has been dealt with differently. Many factors that are analysed include education, experience, age, fear of failure, personal aims, internal control, growth aspirations, motivations, expectations and growth intentions. The firm's characteristics or activities that may also influence growth are size, profits, start-up capital (firms' value) and ownership. All of these variables showed positive relationship with the SMEs growth (Achtenhagen et al., 2010). Most of these attributes play great roles in influencing entrepreneurship inspiration and growth of SMEs (Muthomi, 2017). # Methodology The study was conducted in the three Municipalities of Dar es Salaam city namely; Kinondoni, Ilala and Temeke. The reasons for conducting this study in the region include among others, Dar es Salaam city is the most populated of all cities in the country and comprises of many SMEs. During the National Baseline Survey report of 2012 on SMEs, Dar es Salaam was used as a benchmarking for evaluation of other regions. In such regard, many economic activities were conducted with many operating financial institutions (URT, 2012; URT, 2013). SMEs operating in the region reflect the nature of all SMEs operating countrywide. Furthermore, the study adopted a quantitative research design, where quantitative approach helped to obtain and present data through percentages calculated to get the respondents' rating on challenges that impede their business growth. In addition, the approach enabled the researcher to analyse data using logistic regression when examining factors that influence SMEs growth. # b) Data collection The study used cross section data. The data were primary and secondary from authentic sources. Primary data were collected through questionnaire survey that contained both close and open questions which enabled the researcher to collect quantitative data. The secondary data were collected from publications such as SMEs Survey of 2012 and Bank of Tanzania (BoT). # c) Sample size The study surveyed 300 SMEs owners from the 405,902 MSMEs owners in Dar es Salaam region (URT, 2012). The sample size is determined by the nature of the study at hand. The nature of this study and the model enabled the author to select a sample of 300 SMEs owners that enabled him conduct logistic regression. However, limited resources marginalized the process of adding more respondents. # d) Model Specification Regarding the theoretical and empirical literature review, the model and variable to be estimated were specified according to Trait theory of entrepreneurship. Other variables thought to be relevant to situation prevalent in Tanzania were added to the model to make the study more precise and concise. # Determinant model (Growth of SMEs) Where, ?? ?? is the dependent variable, ???? is a vector of explanatory variables (personal and SMEs characteristics), ?"? ?? is the error term and ?? is vector of coefficients to be estimated. © 2019 Global Journals = log(?? ?? ) = ?? ?? ?? + ?"? ?? (1) available in a particular country. Given the nature of SMEs, data and policy of Tanzania, this study used the binary choice logit model with micro-level regressors. The logistic regression model assumes the probability of SMEs growth to be defined by latent variable Y* as presented by the relationship in the equation ( 2): However, in practice, it is observed that Y is defined by Y i = 1, if Y*>1 and Y i = 0 otherwise. The likelihood of the logit model is as shown here in equation (3): Where; Yi is the true dependent variable, taking two values, 1 if the SMEs or business grow and 0 if the SMEs or businesses do not. The dependent variable is assumed to depend on individual SMEs owner and business/firm's characteristics contained in the vector × ??. The explanatory variables × ?? and error term ?"? i are distributed with constant variance and zero mean. # f) Model Specification The log linear model is, Where, ?? ?? is the SMEs growth as the dependent variable and ? ?? is a vector of explanatory variables, ?? a Coefficient to be estimated and ?"? ?? is the random error term. With the logistic regression model, the dependent variable is binary, taking only two values, 1 if the SMEs or businesses grow and 0 if do not. Hence, the probability of SMEs to grow depends on the explanatory variables X i . The use of logit model is justified by the fact that the data to be used have more than one response. Moreover, explaining why some individuals' SMEs or business can grow and others cannot is best done with the logit model (Maddala, 1992;Green, 2003;Gujarati, 2004) Description of the Variables Used in the Analysis of SMEs Growth The functional form of the SMEs growth is shown below; Growth = ? 0 + ? 1 ED_CTION2 + ? 2 MARITAL2 + ? 3 experience_7 + ? 4 OWNERSHIP3 + ? 5 stupcut + ? 6 SIZE_FIRM + ? 7 BT_OVER2 + ?"? 5) The explanatory variables include individual or personal and SMEs/firm' characteristics. The variables that were estimated in equation ( 5) are constant (?) and coefficients (?i). The error term (?"?) is included in the equation to take care of any other factors that might have not been included in the model but may in fluence SMEs growth. # g) Estimation techniques This study used the logit model to estimate the factors that influence SMEs growth in the study area. Maximum likelihood was employed during the estimation procedures because it has a number of desirable statistical properties; all parameters are consistent, efficient and asymptotically normal in the analogy of the test in regression estimation (Bokosi, 2004). V. # Descriptive Analysis a) Explanatory Analysis of the Sample This study surveyed 300 SMEs owners/operators and managers in their business premises in Dar es Salaam city. Table1 depicts a summary of the statistical variables. About 77 per cent of the respondents were males whereas 23 per cent were females. The reasons behind for having a larger per cent of male respondents was that 1) Males were found at business premises and they more occupied this category of SMEs than females. 2) Most women participated in micro-enterprises, which actually require low capital, and lastly 3), Traditions sometimes-hindered women from participating in businesses. (2) The mean age was 40 years. This is because the study target was adults who were able to operate or supervise business. By grouping the respondents into age groups, it was found that respondents falling within the 18-35 years bracket were 31%, a group of age from 36-55 were 64% and 56 and those above that age category were 5% of all the respondents. This implies that the age that is between youth and elderly dominated the SMEs in the region. Possibly, this is because they engaging in these ventures had savings that they used as start-up capital. It is also likely that they accessed credit easily compared to other age groups in the region given their experience in running business and having had accumulated enough wealth and assets to pledge as collateral. ????????(???? = 1) = exp (× ?? ? ??) 1 + exp (× ?? ? ??) (3) log(?? ?? ) = ? ?? ?? + ?"? ??(4) On marital status, it was noticed that about 83% of the respondents were married, 17% were widows/widowers, divorced and separated. The reason for having large percentage of married respondents was that the study had targeted the adults that were operating or managing businesses and considering their age, they were socially expected to have been married already. and above. Small and medium businesses are easily started since they do not require high education or any specialized skills. This study classified ownership of business in the region as being owned individually and under partnership in order to get a clear picture on the nature of ownership of business in the study area. With regard to this, it was revealed that 79% of the businesses operated by the respondents were owned individually and 21% were under partnership. This implies that majority of the SME owners were the sole decision makers and for that they were rarely compelled to receive advice from other stakeholders. With this kind of ownership, the likely impact is that where decisions were made, they would have a great effect on their businesses. Generally speaking, in contrast, partnerships have well-organized operations and businesses thus run, have high probability of getting loans from banks and non-bank financial institutions as they own equipment and real estates, which they pledge as collaterals. Another feature of the sample was education where 63% of the respondents had primary education, while 37% of the respondents had secondary education Furthermore, about 43% of the SMEs had employed up to four (4) employees whereas 34% of SMEs had employed 5-49 employees and 23% had employed 50-99 employees. Majority of the SMEs had less than five (5) employees implying that most of the SMEs engaged in small enterprises. Moreover, 78% of the SMEs respondents were dealing with trading activities involving buying and selling of final products (goods). The plausible reason for such a larger number of the respondents in trade activity is that it was easier to initiate and conduct it. It did not require high skills and capital compared to other production activities such as transport, construction and manufacturing. Only 5% of the SMEs respondents were VI. # Descriptive Analysis a) Challenges that hinder SMEs' growth in the Study Area It was revealed that SMEs growth in the region was being faced by several challenges, which were affecting it. Figure 2 shows the extent of each challenge as respondents rated them in terms of percentage. This was rated as the first challenge that was hindering SMEs' operators to start and grow their businesses in the region. It made 35.13% of all of the respondents involved in the sample. The magnitude of the problem was reflected in the fin scope survey of 2012 on financial exclusion, which reported that about 68% of the SMEs were excluded from formal financial institutions (Micro Small and Medium Enterprises Survey, 2010; Fin scope Survey, 2012). Limited access to finance implies limited access to capital to start and expand business operations. Many business operators complained about the stringent conditions that were put forward by financial institutions to them, as they were perceived too risky groups that were likely to be potential loans defaulters. Even those who managed to access financial services they did not get loans due to lack of assets that they could use to pledge for such loans. It was noted that some of the SMEs owners in the region lacked knowledge on preparation of sound business plans to meet the moneylenders' requirements. With this fact, most of the SMEs had limited access to financial services and products which ultimately limited them in accessing start-up capital that eventually impeded their businesses' growth. ii. # Lack of collaterals It was noticed that, about 34.64% of the respondents in the region rated lack of collaterals as the second major challenge impeding business growth in the region. It was revealed that Collateral requirements by financial institutions were limiting SMEs owners from getting enough capital, which could eventually enhance start-up and expansion of their businesses. It was noted that most of the banks and non-bank financial institutions demanded collaterals for loans disbursement due to fear resulting from market imperfection. Asymmetric information in the financial markets obliged loan officers to demand collaterals to control defaulters. As it has already been revealed, most of the SMEs were operating informally something which was considered more risky. Therefore, to minimize collateral requirements, asymmetric information should be controlled through thorough screening and scrutiny of loan applicants to hedge risks associated with adverse selections. iii. Limited training Regarding this aspect, about 30.25% of the respondents involved in the study rated limited training as the third challenge impeding the start-up and growth of businesses in the region. It was found that limited training for SMEs operators led to lack of innovation and found to be involved in manufacturing whereas 2% were revealed to be dealing with construction. The study revealed that 7% of the SMEs owners were dealing with service provision (education and others) and 6% of the respondents were dealing with transport business. creativity in business. This is because they failed to develop new technology in doing business that eventually caused lack of good business plans, failure to keep business records, limited market networking among SMEs and failure to access and compete in local and international markets. Due to this challenge, SMEs operators in the region were doing business by just using their inborn traits such as experience therefore ending up doing business to meet subsistence needs only. Therefore, in order for SMEs to grow, training is very important for SMEs operators to be trained regularly on all aspects of doing business in region. Training events on entrepreneurship will enhance creativity and innovation in doing business. # VII. # Regression Analysis a) Multicollinearity test To test for severity of Multicollinearity, the study adopted the Variance Inflation. Factor (VIF) after regression as presented in Table 2 below. (Kutner, Nachtsheim and Neter, 2004). Table 2 shows that there is no any VIF exceeding 10 and so there is no any severe multicollinearity. # b) Correlation test The Spearman correlation test was used to check if some of the variables that were used in the logistic regression were correlated. Table 3 depicts the correlation results between variables. # Table 3: Correlation results From Table 3, if the pair-wise or zero-order correlation coefficient between two regression is high, say it is greater than 0.90 (r> 0.90), then multicollinearity is a serious problem (Gujarati, 2004;Field, 2005) . Table 3 shows that variables involved in the model, none of them violate this rule of thumb. # VIII. # Econometric Estimation Logistic Model was used to analyse the factors influencing SMEs growth in the region. In fact, several approaches could be used to dichotomous variables, including probit or discriminant functions analysis (Wright and Stigliani, 2012). However, due to the nature of data involved in this study, logistic regression was opted to be used in the analysing the data. # a) Logistic Regression The dependent variable (SMEs growth) was treated as a discrete variable with two values: 1 if the variables as indicated in Table 4 among other variables involved in the logistic regression were statistically significant thus explaining the likelihood of SMEs growth. The rest of the variables as shown in Table 4 were not statistically significant but still gave some insight on the contribution on SMEs growth. Source: Field data, (2014) Theoretically, it was expected that coefficients' signs of the variables involved in this study could to be positive signs as they appear in the above Table 4. However, Alexander (2003) noted that the problem arises when several of these characteristics are included in the regression at the same time because they may alter the results and their signs. This might have influenced the regression results in this study. The coefficient of SMEs operators' experience (Experience_7) was positive and statistically significant. The odd ratio for experience was greater than 1. Experience as depicted in Table 4 reveals that, the SMEs, which were operated by experienced individual in the sector were about 22% more likely to grow or exist longer in operations. This implies that SMEs growth is related to experience of theowners because experiences enhance SMEs' survival. In addition, experiences enable the search for all necessary information related to SMEs' survival/operations in both local and international markets. Sarwoko and Frisdiantara (2016) obtained the same results in Malang. Their study revealed that experience was important in influencing SMEs growth. Furthermore, Davidsson et al, (2010) in the review of the literature in Sweden concluded that experience of SMEs owner in the SMEs sector is very crucial for SMEs growth in advanced and developing economies. The present study and the literature cement on the fact that experience matters on SMEs growth in Tanzania and in other countries. The coefficient of SMEs start-up capital (Stupcut) was positive and statistically significant. The odd ratio for start-up capital from Size of the firm (SIZE_FIRM) was positive and statistically significant for small firms. The odd ratio from Table 4 for the size of the firm was less than 1. Hence, it was 93% less likely for the SMEs to keep their businesses growing. The reference was a small firm that had employed less than 49 employees. The odd ratio implies that small firms are not likely to access credit compared to the medium ones. Habib and Darush (2016) obtained the same findings in Sweden. Furthermore, Nham and Yoshi (2009) in Vietnam obtained the same results that size of the SMEs influences their growth. Yeboah (2015) in Ghana obtained the same results. The size of the SMEs plays a great role in influencing SMEs growth in Ghana. With regard to these findings, the size of the SMEs significantly influences their growth. Graduating from the present group to another is attributed by size. Therefore, being too small may not be favoured by the situation in the market. Ownership of the firm/business (OWNERSHIP3) was positive and statistically significant. The odd ratio for ownership from Education of SMEs operator (ED_CTION2) was positive but statistically insignificant for primary or less education implying that education matters in running business in the region. The odd ratio was less than 1. The study revealed that, 65% businesses in the region were less likely to grow. However, the factor was not statistically significant but its sign was positive implying that education influenced the growing of the business in the region. However, the results obtained by Yeboah (2015) in Ghana revealed that education was positive and statistically significant in influencing SMEs growth. Variation in findings may be influenced by the fact the number of variables involved in the model and data type alter the results. The coefficient of SMEs operators' marital status of SMEs owners (MARITAL2) was positive but statistically insignificant. The results in Table 4 show that odd ratio for marital status was less than 1. Hence, unmarried SMEs owners were about 67% less likely to grow their business operations. SMEs operators who were married had a higher possibility to succeed in business than other groups in the region. This may be because married SMEs owners are experienced enough to run businesses in the region unlike the other groups who are still young and not experienced in businesses. Regarding this, the findings by Mutoko and Kapunda (2017) in Botswana revealed that marital status has no influence on SMEs growth. The implications of these findings may be that marital status has nothing to do with SMEs growth in many places. # IX. Conclusion and Recommendations This study has addressed the challenges that impede SMEs growth in Dar es Salaam region. They include limited access to financial services, lack of collaterals and limited training. Mitigating these challenges in the region would bring about efficiency and effective business operations. These challenges hinder SMEs to graduate from small to medium sized businesses as well as to large businesses. Again, the study examined factors influencing SMEs growth using logistic regression. The results showed that experience, start-up capital, size of the firm, ownership and business turnover are statistically significant in influencing SMEs growth in the region. With these findings, the study recommends that the government should strategically adopt a holistic approach to regulate the financial market to create a window for SMEs owners to access financial services. Banks and non-bank financial institutions should not take collaterals as the only most important judgmental factor in the situation of credit rationing due to financial market imperfections. If SMEs access loans and other financial services easily, it can simplify the process of start-up and expansion of their businesses operations. They study further recommends that, the government through the Ministry of Industries, Trade and Investment, Ministry of Finance and Planning, banks and non-bank financial institutions should collaborate to conduct regular training to SMEs owners. Such training may enhance creativity and innovation which will eventually improve know how in businesses. This will offer SMEs operators opportunities to harness the possibility to grow through their engagement in local and international markets. Regular training can be done in collaboration with Institutional entities like universities, private sector, international sectors support as well as the adjustment of policy frameworks in the country to offer deliberate support to SMEs owners to start and expand their businesses. Furthermore, the results from regression analysis reveal that experience, start-up capital, size of the firm, ownership and business turnover (benefits) influence the growth of SMEs in the region. These findings imply that both personal and businesses' characteristics are crucial for growth in business. If SMEs owners are given business education through indoor training, it will enable them to maintain these attributes and have vision for success. Business education can be offered through seminars, workshops and symposium nearby their business premises. It is thus the study recommends that, the government through respective Ministries, Agencies and other stakeholders in SME subsector should deliberately be committed to offer business education. 1![Figure 1: SMEs growth process](image-2.png "Figure 1 :") ![a) Sampling Procedure Respondents were obtained through stratified random sampling because the targeted population was heterogeneous. The study covered fourteen (14) wards of the Dar-Es-salaam City's three Municipalities namely Ilala (Buguruni, Kivukoni, Ilala, Mchikichini and Mtafukoge), Kinondoni (Makuburi, Manzese, Mikocheni, Tandale and Ubungo) and Temeke (Charambe, Sandali, Tandika and Temeke).](image-3.png "") 1Source: Field data, (2014) b) Sample characteristics Independent VariablesExplanationAverageStandard Deviation.Mini mum.Maximum .AgeThe age of the SMEs' owner/manager.40.446678.8733462168GenderGender of the SMEs' owner/manager 1=Male, 0=Female0.7733333 0.419374701Education of the SMEs owners 0 = PrimaryED_CTION2education, 1 = secondary and tertiary.3663366.482601educationMARITAL2Marital status 1=Married, 0= not in marriage.8349835.371809501Experience of the SMEs owner 0 = 0 -4 years,50 Year 2019experience_7 OWNERSHIP3 stupcut1 = 5 to 14 years, and 2 = 15 years of experience Nature of ownership of the SMEs 1 = Individually, 0 = Partnership Initial capital that SMEs owner started business with.1.049505 .2145215 174193.5.8102355 .411169 349090.90 0 200002 1 1000000Volume XIX Issue II Version ISIZE_FIRM BT_OVER2 BSS_TYPEGrouped according to the number of employees The profits generated per annum by SMEs 1 = Generated profits, 0 = Generated no profits Economics activities which are conducted in the region construction, 4= transport 5 = trading, and 6 =other (specify) 1 = education, 2= manufacturing, 3 =11.2008 .1386139 4.73267318.08826 .346 1148 .95831411 0 199 1 6E )(Global Journal of Human Social Science -© 2019 Global Journals 2VariableVIF1/VIFstupcut2.640.378592BT_OVER22.640.379108SIZE_FIRM1.690.590037OWNERSHIP31.530.654547ED_CTION21.200.831935experience_71.070.938869MARITAL21.050.954956Source: Field data, (2014)If the VIF exceeds 10, then there is severeMulticollinearity 4Year 201952Volume XIX Issue II Version IE )(Global Journal of Human Social Science -© 2019 Global Journals © 2019 Global Journals ## Acknowledgement My sincere appreciation goes to my Family and my workmates who keep on encouraging me to publish. 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