Journal of Business Administration and Management Sciences Research Vol. 3(3), pp. 022-031, March, 2014 Available online athttp://www.apexjournal.org

ISSN 2315-8727© 2014 Apex Journal International

Full Length Research Paper

Determinants of growth of small and medium enterprises in Kakamega central sub-county, Kenya

James Wambua Nganda, Kadian W. Wanyonyi* and Elizabeth M. Kitili

University of Nairobi, Kenya.

Accepted 21 January, 2014

The purpose of this study was to examine the determinants of growth of Small and Medium Enterprises in Kakamega Central Sub-County, Kenya. Conceptual framework guided the study by illustrating how the various variables were interrelated. The study adopted a descriptive survey design. The target population consisted of total of 1,500 registered SMEs under Single Business Permit Registration. Simple random sampling techniques were used to select 103 SMEs entrepreneurs while purposive sampling technique was used to select registrar of single business permit. Data was collected by use of questionnaires and interview schedules. On validity of the instruments, the researcher used content validity while through pilot testing process was used to test reliability comparing with a Cronbach’s Alpha Coefficient which yielded an alpha of 0.82. The study findings indicated that there was a marginal weak association between financial factors and growth of SMEs. Correlational results between law and regulations on growth of SMEs do indicate that income taxes and collection of revenues from the government agents hamper the running of the business, thus, slowing the growth of the SMEs. Business accessibility to customers and business location did not significantly affect the growth of SMEs in Kakamega Central Sub-County. The usage and integration of internet and intranet, mobile phones and advance in technology had significant (p<0.05) influence on the growth of SMEs. The following recommendations were made: the government should ensure that the interest rates suggested by the Central Bank of Kenya are adopted by both commercial banks and micro financial institutions so as to encourage the business owners to access micro credit facilities. There is need to improve infrastructure, costs and IT training and in information relating to the business opportunities that like e-commerce and creation of e-knowledge among business owners. On the business location, the counties should subsidies the land rates and rents within the Central Business District to encourage more SMEs accessibility to customers. The researcher then analysed the data and present the results in form of frequency tables. The findings of this study may be useful to the Ministries Trade, academicians and researchers in their improvement of policies and practices on SMEs. Key words: Small and medium enterprises (SMES), determinants and growth.

INTRODUCTION Small and Medium Enterprises (SMEs) play a big role in the creation of jobs and a country’s employment rate. The most evident public benefit of small business growth is the contribution made by SMEs to employment. A large number of studies carried out in various countries have *Corresponding author. Email: kadianwanyama@gmail.com

concluded that small business plays major role in job creation (Dobbs and Hamilton, 2007). SMEs play con-siderable responsibility in providing further employment and conversion of economy. It is also implicit that sectors conquered by SMEs are better able to develop dynamic economies of scale. The roles of SMEs in the creation of productive employment are concerned with its position in the center of the range of sizes and resources intensities in a rising economy. Developing economies have started

to focus on the crucial role that SMEs can play in their development (Maad, 2008).

A majority of firms in developing countries consist of small and medium-sized firms (SMEs). Understanding what affects small firms' decisions to expand is important because small firms are important for economic growth, employment generation and poverty alleviation (Berkowitz and DeJong, 2002; McMillan and Woodruff, 2002). In fact, Beck, Naresh and Yen (2006) show that countries with larger share of SMEs in the manufacturing sector grow faster. The only way to reduce poverty in a sustainable way is to promote economic growth, through wealth and employment creation. In developing countries, SMEs are the major source of income, a breeding ground for entrepreneurs and a provider of employment (UNIDO Report, 2003).

SMEs play a crucial role to almost all economies in the world, but especially to those in developing countries. Most of the companies are micro sized enterprises (Soini and Veseli, 2011). Research done by Onugu (2005) on the growth and development of SMEs indicate that SMEs’ growth and development depends mainly on access to finance and firm size, environmental factors such as infrastructure, business legal status and legal registration. Access to finance is inferred by examining whether firms have overdraft facilities and line of credit facilities and also captured through self-reported measures of access to finance. The results showed that the most SMEs do not have access to credit facilities and are constraint by huge payments made to income tax (Ayyagari et al., 2008). This study sought to establish the influence of these factors on the growth of SMEs in Kakamega Central Sub-County.

Further findings from the study of firm the growth and development and financial constraints indicated that firm the growth depends on financial constraints, firm age and socio-cultural factors such as rules and shared values of the business, beliefs and religion, norms and business code of conduct (Beck et al., 2006). Research carried out on the financial institutions of firms indicated the growth and development is negatively related to financial constraints. The coefficient is highly significant; implying that the presence of financial constraints significantly reduces firm’s the growth and development. The positive and highly significant coefficient of 0.728 indicates that as firms get older they are able to increase their sales (Beck et al., 2006).

The factors affecting SMEs in Africa in decreasing order of intensity include: management, access to finance, infrastructure, government policy inconsistencies and bureaucracy, environmental factors, multiple taxes and levies, access to modern technology, unfair competition, marketing problems and non-availability of raw materials locally (Onugu, 2005). Therefore, this study sought to find out how these determinants affect the growth of SMEs in Kakamega Central Sub-County, Kenya.

Nganda et al 023

The following research objectives guided the study: 1. To examine how financial factors influence the growth of Small and Medium Enterprises in Kakamega Central Sub-County. 2. To establish how law and regulations influence the growth of Small and Medium Enterprises in Kakamega Central Sub-County. 3. To determine how business location influence the growth of Small and Medium Enterprises in Kakamega Central Sub-County. 4. To establish the influence of technological advance-ment on growth of Small and Medium Enterprises in Kakamega Central Sub-County. METHODOLOGY This study design adopted a descriptive survey design to establish the determinants of growth of SMEs in Kakamega Central Sub-County. The study was conducted in Kakamega Central Sub-County (Appendix 1a and 1b). The study was carried out in a total of 1,500 registered SMEs under Single Business Permit Registration in Kakamega Municipality of Kakamega Central Sub-County in Kakamega County (Ministry of Industrialization, Kakamega Central Sub-County, 2012). The sample size of 103 SMEs were obtained using coefficient of variation. Nassiuma (2000) asserts that in most surveys or experiments, a coefficient of variation in the range of 21%≤ C≤ 30% and a standard error in the range 2%≤ e ≤ 5% is usually acceptable. The researcher therefore used a coefficient variation of 21% and a standard error of 2%. The lower limit for coefficient of variation and standard error was selected so as to ensure low variability in the sample and minimize the degree or error. Questionnaire for SMEs entrepreneurs and interview schedules for Registrar of Single Businesses were used. The researcher personally filled the observation checklist. For the validation of the instrument, the researcher consulted supervisors and experts in the field of study, who assessed the validity of study instruments. Cronbach Alpha method was used and yielded an alpha of 0.82. This was to determine how items correlated among themselves. The value of alpha = 0.82 was above the threshold value which is acceptable in this study at 0.7 (Fraenkel and Wallen, 2001; Mugenda and Mugenda, 2003). The results of the plot study revealed that the research instrument was reliable and possess both content and face validity. Data was analyzed with the help of the Statistical Package for Social Sciences (SPSS) computer program. The percen-tages were used to express the degree of response to a given opinion. Cross tabulation was used to understand two different survey items and how they relate. Inferential statistics like correlational analysis (Pearson Correlation

024 J. Bus. Admin. Manage. Sci. Res.

Table 1. Age Distribution of respondents

Age distribution in years Frequency %

20-29 years 45 45.0

30-39 years 35 35.0

40-49 years 14 14.0

Above 50 years 6 6.0

Total 100 100.0

Table 2. Gender of respondents.

Gender Frequency %

Male 70 70.0

Female 30 30.0

Total 100 100.0

Coefficient) was used to show the association between the determinants and the growth of SMEs. RESULTS AND DISCUSSIONS Results in Table 1 shows that majority of respondents were in the age brackets of 20-29 years (45%), 30-39 years (35%), 40-49 years had 14% and those above 50 years had 6%. There was a significant difference among respondents in the age distribution since expected uniform distribution across age groups was not represented by 25% in each age bracket. From this statistics it is clear that majority of the respondents were in the age bracket above 20-30 years. This was an indication that the respondents had varied age distribution and therefore could have given different views on the determinants of growth of Small and Medium Enterprises in Kakamega Central Sub-County.

According to the results in Table 2, majority of the respondents were males (70%) while the rest were females (30%). The results illustrated that there was a significant (p<0.05) variation in the gender distribution among the respondents since the expected 50% was not attained because the number of males was more than that of females who participated in the study. Therefore, gender equity among the respondents who participated in this study was not achieved. It was an indication that more males participated in SMEs in Kakamega Central Sub-County compared to women.

From the results in Table 3, the respondents gave different views regarding the influence of financial factors on the growth of the SMEs in Kakamega Central Sub-County. For example, 72% of respondents indicated that their businesses had strong financial base, 7% were undecided and 20% of respondents disagreed (mean =

3.64). Majority of respondents (65%) were of the opinion that their businesses did depend on much borrowing from financial institutions, 6% of respondents were undecided, 27% disagreed and 2% strongly disagreed (mean = 3.60). Fifty nine (59%) of respondents were of the views that investments in the business are adequate, 16% were undecided, 20% of respondents disagreed while 5% strongly disagreed (mean = 3.49). Majority of the respondents (81%) observed that employees and owner of the businesses have good understanding of financial matters on running the business, 16% of respondents were undecided and 27% of respondents disagreed. Furthermore, 57% of respondents were positive that SMEs could access micro credit facilities, 16% were undecided and 27% of respondents disagreed (mean = 3.49). Therefore, it can be inferred that financial factors do determine the growth of SMEs in Kakamega Central Sub-County to some good extent. To establish the extent of the influence of financial factors on the growth of SMEs in Kakamega Central Sub-County, a correlation analysis was carried out as shown in Table 4.

Results in Table 5 indicates that though the correlation values obtained show that there was a significant (p<0.05) association between financial factors and the growth of SMEs, their coefficient values were below 0.5, an indication that there was a marginal weak association between financial factors and the growth of SMEs in Kakamega Central Sub-County. This implied that financial factors do have a significant (p<0.05) influence on the growth of SMEs in Kakamega Central Sub-County, though at varying degrees (r = 0.3462, p<0.05). The findings were in congruent with what Longenecker, Petty, Moore and Palich. (2006) noted that lack of proper financing and poor management have been posited as the main causes of failure of small enterprises. Lack of credit has also been identified as one of the most serious

Nganda et al 025 Table 3. Influence of financial factors on growth of SMEs.

Variables SA % A % U % D % SD % Mean

Business has strong financial base 16.0

56.0

7.0

15.0

5.0 3.64

Business does not depend on much borrowing from financial institutions

26.0 39.0 6.0 27.0 2.0 3.60

Investments in the business are adequate 20.0 39.0 16.0

20.0

5.0

3.49

Employees and owner of the business have good understanding of financial matters on running the business

37.0 44.0 8.0

8.0 3.0

4.04

Micro credit facilities are accessible 27.0 30.0 16.0 19.0 8.0 3.49

Key: SA = strongly agree, A = agree, U = undecided, D = disagree and SD = strongly disagree

Table 4. Financial Factors and Growth of Small and Micro Enterprises

Financial factors’ variables Pearson correlation coefficient, r

Business has strong financial base 0.469**(0.000) (s)

Investments in the business are adequate 0.226* (0.024) (s)

Good understanding of financial matters on running the business 0.463** (0.000) (s)

Micro credit facilities are accessible 0.266** (0.008) (s)

Business borrowing much from financial institutions 0.307** (0.002) (s)

Overall Correlation 0.3462, P<0.05 (s)

Constant/predictor variable: Financial Factors Dependent Variable: Growth of SMEs N= 100; s-significant; ns-not significant; ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).Levels of significance, p-value for correlation coefficients are in parentheses.

Table 5. Law and regulations on growth of SMEs.

Variables SA % A % U % D % SD %

Business meets all legal conditions for its operation 55.0

25.0

9.0

11.0

0.0

Business is registered and has all legal documents 55.0 31.0 6.0 5.0 3.0

Income taxes and collection of revenues from the government agents hamper the running of the business

39.0 33.0 14.0 9.0 5.0

Municipal council charges highly affects daily business operations

38.0 33.0 11.0

18.0 0.0

constraints facing SMEs and hindering their development (Oketch, 2000).

From the results, 80% of respondents agreed that their businesses met all legal conditions for its operation, 9% of respondents were undecided while 11% disagreed. It was also observed that 86% of respondents were of the opinion that their businesses were registered and had all legal documents, 6% were undecided and 8% of respon-dents disagreed. Furthermore, the results indicate that

income taxes and collection of revenues charged on the businesses slowed down the business growth of SMEs (65%), 14% of respondents were undecided while 21% of respondents disagreed. Majority (71%) of the respondents were positive that Municipal Council charges highly affects daily business operations through issuance of business permit, business licences. It was also noted that some of the SMEs did not have business licences and public health certifications and were only operational

026 J. Bus. Admin. Manage. Sci. Res.

Table 6. Law and regulations on growth of SMEs.

Law and regulations variables Pearson correlation coefficient, r

Business meets all legal conditions for its operation 0.374**(0.000) (s)

Business is registered and has all legal documents 0.100(p>0.05) (ns)

Income taxes and collection of revenues from the government agents hamper the running of the business

-0.018(p<0.05) (ns)

Municipal council charges highly for daily business operations 0.369**(0.000) (s)

Overall Correlation, r 0.2063, p<0.05 (s)

Constant/predictor variable: Law and Regulations Dependent Variable: Growth of Small and Micro Enterprises N= 100; s-significant (p<0.05); ns-not significant (p>0.05); ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).Levels of significance, p-value for correlation coefficients are in parentheses.

Table 7. Business location and growth of small and micro enterprises.

Variables SA % A % U % D % SD %

Business is accessible to customers 57.0

41.0

0.0

2.0

0.0

Business location affects growth and development of a business 59.0 29.0 10.0 0.0 2.0

My business is situated in town centre 41.0 29.0 4.0

13.0

13.0

Language barriers 20.0 33.0 21.0

18.0 8.0

Poor road network affect business operations 58.0 28.0 5.0 9.0 0.0

Key: SA = strongly agree, A = agree, U = undecided, D = disagree and SD = strongly disagree on certain days. This was an indication that some SMEs did operate illegally and therefore, they did not remit their taxes to the government. These SMEs if found by the government operating illegally, they are usually fined heavily, thus reducing their growth.

Correlational results in Table 6 between law and regulations on growth of SMEs do indicate that income taxes and collection of revenues from the government agents hamper the running of the business, thus, slowing the growth of the SMEs (r = -0.018, p<0.05). This was the only variables that had negative correlation on the growth of SMEs. For example, it was noted that taxes were being levied on the products which were zero rated and every transactions made by the business owners and customers were being taxed. These study findings were supported by the World Bank (2010) researchers who argued that constrains that are facing the growth of SMEs were complex tax systems. The overall results indicate that law and regulations had a profound (p<0.05) influence on growth of SMEs in Kakamega Central Sub-County (r = 0.2063, p<0.05) though, a weak association.

The findings by Wanjohi and Mugure (2008) also were consistent with the study findings that business

environment is among the key factors that affect the growth of MSEs. Unpredictable government policies coupled with ‘grand corruption,’ high taxation rates, all continue to pose great threat, not only to the sustain-ability of SMEs but also to the Kenyan economy that was gaining momentum after decades of wastage during KANU era. Therefore, it could be inferred that law and regulations to some good extent impede the growth of SMEs through high taxation rates on the products, income, bank and mobile transactions made by the business owners.

With reference to the results in Table 7, 98% of respondents were of the views that businesses were accessible to the customers and 2% of respondents disagreed. The results further revealed that 88% of respondents were of the opinion that business location affects growth of a business, 10% were undecided while 2% of respondents disagreed. On the question asked on whether the situation of the business in the town centre affects growth of SMEs, 70% of respondents agreed, 4% were undecided, 13% of respondents while 13% of res-pondents disagreed. The question on whether language barriers had an influence on the growth of SMEs, 53% of

Nganda et al 027

Table 8. Business location and growth of small and micro enterprises.

Business location variables Pearson correlation coefficient, r

Business is accessible to customers 0.498** (0.000) (s)

Business location affects growth and development of a business 0.298* (0.003) (s)

My business is situated in town centre 0.196 (0.051) (ns)

Language barriers 0.181 (0.072) (ns)

Poor road network affect business operations 0.179 (0.075) (ns)

Overall Correlation 0.2704, p>0.05 (ns)

Constant/predictor variable: Business Location Dependent Variable: Growth of Small and Micro Enterprises N= 100; s-significant; ns-not significant;

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-

tailed).Levels of significance, p-value for correlation coefficients are in parentheses.

Table 9. Technological advancement and growth of SMEs.

Variables SA % A % U % D % SD %

Change of technology has posed a great challenge to small businesses 49.0 36.0 5.0 5.0 5.0

Mobile phones have made it easier to run businesses 72.0 20.0 6.0 2.0 0.0

Internet and intranet have contributed to business growth 55.0 33.0 9.0 0.0 3.0

Advance in technology has helped business operations 59.0 36.0 3.0 0.0 2.0

Key: SA = strongly agree, A = agree, U = undecided, D = disagree and SD = strongly disagree

respondents agreed, 21% were undecided while 26% of respondents disagreed. Majority of the respondents (86%) were of the views that poor road network affect business operations, 5% of respondents were undecided and 9% of respondents disagreed. This illustrate that respondents give different views on the business location and how it affects the growth of the business.

The results from the correlation analysis in Table 8 revealed that business accessibility to customers (r = 0.498

**, p<0.01) and business location (r = 0.298*,

p<0.05) significantly affect the growth of SMEs in Kakamega Central Sub-County. It should be noted that these correlation values were below r = 0.5, an indication of a marginally weak positive association between these variables and the growth of SMEs. Other variables like situation of the business in the town centre, language barriers and poor road network did not have significant (p>0.05) association on the business growth. This could be attributed to high government charges and business rent, Kakamega Central Sub-County being cosmopolitan, therefore language barriers were not a hindrance since the study area has a developed road network.

Therefore, it was concluded that business location did not significantly (p>0.05) affect the growth of Small and Micro Enterprises in Kakamega Central Sub-County.

These study findings contradict the study by what Soini and Veseli (2011) observed that the location of your business must be accessible to the customer base and should be built to ensure efficient accessibility for future clients. According to these authors, when choosing a

business location, the business owners must take into account the costs of moving or establishing their business in the location. The labour costs, transport, proximity to suppliers, workforce disruption, language factors, and exchange rates are some of the essential location factors. According to Keith (2013), the location of a business can also affect its success and productivity by extracting financial costs. Some municipalities may have higher sales and other taxes which eat into a business's bottom line. Operational or professional licenses may cost more in some areas, adding to the cost of doing business and reducing profitability; by the same measure, some municipalities require frequent evaluations and/or inspections which further reduce a company's productivity and profit. Therefore, these affect the growth of SMEs. Results in Table 9 show that 85% of respondents agreed that change of technology has posed a great challenge to small businesses, 5% of respondents were undecided and 5% disagreed.

The results also illustrate that mobile phones made it easier to run businesses (92%), 6% of respondents were undecided while 2% of the respondents disagreed. Similarly, 88% of respondents were of the view that internet and intranet have contributed to business growth, 9% of respondents were undecided while 3% of respondents strongly disagreed. Moreover, the results further pointed out that advance in technology have helped business operations (95%), 3% of respondents were undecided while 2% of respondents strongly agreed. Therefore, the respondents gave different views

028 J. Bus. Admin. Manage. Sci. Res.

Table 10. Technological advancement and growth of SMEs.

Technological advancement variables Pearson correlation coefficient, r

Change of technology has posed a great challenge to small businesses 0.015 (0.883) (ns)

Mobile phones have made it easier to run businesses 0.542** (0.000) (s)

Internet and intranet have contributed to business growth 0.281**

(0.005) (s)

Advance in technology has helped business operations 0.497 (0.000) (s)

Overall Correlation, r 0.334, p<0.05 (s)

Constant/predictor variable: Technological Advancement Dependent Variable: Growth of Small and Micro Enterprises N= 100; s-significant; ns-not significant; ** Correlation is significant at the 0.01 level (2-tailed).* Correlation is significant at the 0.05 level (2-tailed).Levels of significance, p-value for correlation coefficients are in parentheses.

on how technological advancement and growth of SMEs in Kakamega Central Sub-County. To ascertain the strength of technological advancement on the growth of SMEs, a correlation analysis was conducted as illustrated in Table 10.

Results on correlation analysis in Table 10 show that although the change of technology has posed a great challenge to small businesses, its influence on the growth of the business in Kakamega Central Sub-County was not significant (r = 0.015, p>0.05). Usage and integration of internet and intranet (r = 0.281**, p<0.05), mobile phones (r = 0.542**, p<0.05) and advance in technology (r = 0.497**, p<0.05) had significant (p<0.05) influence on the growth of SMEs. Worth noting was that the coefficient values were still below r = 0.5, an indication of a weak relationship between advancement in technology and growth of SMEs in Kakamega Central Sub-County. The overall correlation results were, r = 0.334, p<0.05, an indication of a marginally weak positive correlation between technological advancement and growth of SMEs in Kakamega Central Sub-County. This could explain why the trend of growth of SMEs in Kakamega Central Sub-County has been worrying one for the past 10 years.

The findings were in line with what the following literature review: in most of the African nations, Kenya inclusive, the challenge of connecting indigenous small enterprises with foreign investors and speeding up technological upgrading still persists (Muteti, 2005). There is digital divide between the rural and urban Kenya. With no power supply in most of the rural areas, it is next to impossible to have internet connectivity and access to information and networks that are core in any enterprise. Lack of electricity in some of the SMEs also slowed down the growth of SMEs as observed in some of the SMEs found outskirts of Kakamega Municipality. Thus, technological change, though meant to bring about economic change even among the rural lot, does not appear to answer to the plight of the rural entrepreneurs.

The order in which determinants of growth of small and medium enterprises in Kakamega Central Sub-County was: Financial factors (r = 0.3462,) were found to have a

greater significant (p<0.05) influence than all the three determinants. The second variable in terms of the influence on the growth of SMEs was advancement in technology (r = 0.334), followed by law and regulation (r = 0.2063) which was significant and lastly business location (r = 0.270) which was insignificant. Conclusions The study findings indicated that there was a marginal weak association between financial factors and growth of SMEs. Therefore, checks on the financial factors could lead to growth of SMEs, for example, reduction in interest rates by both commercial banks and micro financial institutions. Correlational results between law and regulations on growth of SMEs do indicate that income taxes and collection of revenues from the government agents hamper the running of the business, thus, slowing the growth of the SMEs. The overall results indicate that law and regulations had a profound (p<0.05) influence on growth of SMEs in Kakamega Central Sub-County (r = 0.2063, p<0.05) though, a weak association. Business accessibility to customers and business location did not significantly (p>0.05) affect the growth of Small and Micro Enterprises in Kakamega Central Sub-County. This could be associated to the fact that most SMEs in Kakamega Sub-County were centrally placed and therefore customers’ accessing them was not a significant problem. The usage and integration of internet and intranet, mobile phones and advance in technology had significant (p<0.05) influence on the growth of SMEs. Worth noting was that the coefficient values were still below r = 0.5, an indication of a weak relationship between advancement in technology and growth of SMEs in Kakamega Central Sub-County. Recommendations The government should ensure that the interest rates

suggested by the Central Bank of Kenya are adopted by both commercial banks and micro financial institutions so as to encourage the business owners to access micro credit facilities. There should be clear loaning policies to avoid misunderstanding on expectations on repayment period and the interest rate on the borrowed loan should be subsidized by the lending institutions. The government should reduce the tax rate for SMEs to zero percent (0%) within their first three years of life and then to 20% from the fourth year and beyond. SMEs located in rural areas should enjoy 10% tax rate from their fourth year of operation. There is need to improve infrastructure, costs and IT training and in information relating to the business opportunities that like e-commerce and creation of e-knowledge among business owners. On the business location, the counties should subsidies the land rates and rents within the Central Business District to encourage more SMEs accessibility to customers. REFERENCES Ayyagari, B.A., Longman, A.D., Ajagu, A. (1998). The

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030 J. Bus. Admin. Manage. Sci. Res. APPENDIX

Appendix 1a: Map of Kenya showing administrative districts

Appendix 1a

Nganda et al 031

Appendix Ib: Map of Kakamega central sub-county.

Appendix 1b