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Archives of Business Research – Vol. 10, No. 10
Publication Date: October 25, 2022
DOI:10.14738/abr.1010.13266. Sawaya, A., & Bhero, S. (2022). Financial Support, Indispensable for Smes Growth in Mozambique. Archives of Business Research,
10(10). 145-164.
Services for Science and Education – United Kingdom
Financial Support, Indispensable for Smes Growth in
Mozambique
Alen Sawaya
PhD Student with the Zimbabwe Open University (ZOU), Mozambique
Shepherd Bhero
Department of Chemical, Materials and Metallurgical Engineering
Botswana International University of Science and Technology, Botswana
ABSTRACT
Universally it is recognized that support to Small and Medium Enterprises (SMEs)
at their birth and during growth is essential to nurture them in order to avoid
premature deaths. Without formidable support to SMEs, youths would remain
unemployed and result in all sorts of economic ills such as increase in crime and
youth immigration from rural areas to urban areas, or from Mozambique to the
republic of South Africa. Support to SMEs can come in many forms; including
support from financial institutions, from government agencies, large firms and non- governmental organizations (NGO)s to mention a few. What has not been confirmed
especially in Mozambique is the fact that out of all types of support that can be
rendered to SMEs, financial support is the most important and indispensable for
SMEs survivability. The objective of the study was therefore to provide evidence
that financial support supersedes all other support that can be made available to
this important sector of the Mozambican economy. A sample of 485 SMEs was drawn
from the population of SMEs in Greater Maputo representing Mozambique as a
whole, using stratified random sampling methods. Greater Maputo, the capital of
Mozambique was chosen as the nucleus of the study, because Maputo is the main
centre of economic activities of the country. A face to face interview was conducted
using structured, close-ended questionnaires to collect the primary data. Data was
processed using multiple regression analysis, in order to isolate the one single
variable, financial support, whist keeping the other support variables constant. The
study found that of all supporting variables available for SMEs at start-up and
during growth, financial support was critical and obligatory in Mozambique. It was
recommended that financial institutions should be restructured to be more
proactive to support SMEs in Mozambique, especially aiming at the most
disadvantaged SMEs owner-managers.
Keyword: Financial support, multiple regressions, small and medium enterprises (SMEs),
support structures.
INTRODUCTION
SMEs are recognized as an engine of growth in Mozambique. However SMEs face a series of
challenges from inception, and through the process of growth. Some of the challenges of SMEs
development in Mozambique include lack of financial facilities, lack of markets, tight and rigid
government taxation systems, inadequate infrastructure, low capacity of research and
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development in technology, globalization and inadequate managerial knowledge and skills.
Other challenges may include, barrier from global sourcing, low productivity and poor
enforcement of regulatory legislations (Omar et al., 2009). Although SMEs are renowned for
their critical role in the economy of Mozambique but the number of registered SMEs and their
productivity is still low. SMEs’ contribution to growth in Mozambique is no more than 28.5
percent of GDP (PME Moçambicana, 2015). SMEs on their own cannot overcome start-up and
growth challenges in order to meet the expectations of spawning sustainable growth in the
economy and solve the critical issue of unemployment especially for the youth. Unemployed
youths roaming the street of the capital Maputo are responsible for increases in criminal
activities, and the influx of migration especially to the Republic of South Africa, in desperate
search of occupation. For the SMEs to thrive and survive they need support in different forms
and at different stages of their development. Support for SMEs can take several forms such as
finance, education and training in business management, mentoring, infrastructure and
services. Support can be provided by several bodies, including financial institutions,
government agencies, educational establishments, foreign agencies, large firms, non- governmental organizations (NGOs), religious organizations and even well-off private
individuals. In order to be more functional, the study isolated four major supporting variables
being financial support, government support; and support from large businesses and NGOs.
Similarly, the study demarcated two observable factors that were used to determine
sustainability of SMEs. The two factors are increase in sales turnover and increase in
production. The bottom line emanates from the assumption that if SMEs receive support from
the four variables, one at a time, or all of them simultaneously, SMEs will either increase sales
turnover or production or both of them at the same time. The ultimate target is to determine
whether financial support has any exceptional influence in SMEs development compared to
other types of support available in Mozambique.
REVIEW OF THE RELATED LITERATURE
The following sections will review the literature pertaining to the support system available to
SMEs in Mozambique, and thereafter highlighting the significance of support provided by
financial institutions. The review of literature will be extended to review the SMEs in
Mozambique, emphasizing on their role and challenges that they face in their quest for
contributing to economic growth and employment generation in the country.
The concept of Support structures available to SMEs
Support is an expression that is be used to insinuate assistance of any sort rendered to an
individual or to a cause with the intention of alleviating the individual or cause from an adverse
situation, and if not from an precarious situation then it is simply an action that is put forth with
the motive of encouraging that individual to better his performance or the cause to reach its
desired objectives (Heider et al., 2014). Support can be rendered in different forms and aimed
at different entities, but for this study, support will be confirmed to SMEs. Osano and
Languitone (2016) mention that support programs should be formulated to empower SMEs so
that they can be connected to the larger developmental national objectives with the major focus
being poverty reduction and employment creation, thus minimising illegal migration of the
youth to South Africa. The formation of SMEs has not been an easy task, as it is generally
perceived entrepreneurs; especially young people face a number of obstacles and negative
perception when embarking on creating SMEs. Accessible literature alerts that many SMEs do
not survive their third year of operation (Nangoli et al., 2013). This might be due to the fact
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that no structured and holistic support is provided to still young but already established SMEs
(i.e. entrepreneurs already operating for more than 2 years). Government support for SMEs
can take several forms such as education, training in business management, mentoring,
infrastructure development and services. Muritala et al., (2012) researching government
support schemes in Nigeria mentioned that the government offered tax holidays and tariff
concessions to SMEs for the first six years of operation. In Zimbabwe according to Maseko et
al., (2012) the government established institutions such as Small Enterprises Development
Corporation (SEDCO) to provide technical and financial support to the SMEs sector.
In Mozambique, the Small Enterprises Consulting and Supporting Cabinet (GAPI), offers
business and management training including courses on creating and developing own
businesses based on ILO guidelines (Nsabimana, 2010). The Mozambican government has also
made efforts at tax relief to SMEs, but as Robert (2003) argues, this policy has not been well
defined. Corporate Income tax of 30 percent is the same for all businesses regardless of size.
Import duties on consumer goods is 20 percent and between 0-7.5 percent on raw materials,
fuel, equipment and intermediate goods. Other major taxes are VAT (17 percent), withholding
tax (15 percent), dividends (18 percent) and royalties (15 percent). In short, these tax-rates do
not favour SMEs in any respect.
Support from large businesses is critical for SMEs development. Badal (2013) supports this
premise by mentioning that when SMEs work together with large corporations the SMEs
reform themselves in a manner that improve their organizational structures, management
procedures, and operations. These modifications lead SMEs to upgrade their technologies,
increase their competence, and most importantly, become financially stable. Large businesses
could support SMEs by allowing start-up SMEs use of the large firms’ offices and workspace.
SMEs face endemic problems of having their goods and services recognized and accessibility to
markets. Large firms could also support SMEs as their secure suppliers by taking the following
factors into consideration: (i) Identify support talent. (ii) Provide financial help to the SMEs.
(iii) Make the procurement process transparent. (iv) Simplify the outsourcing process (Badal,
2013). Kamunge et al., (2014) reveal that one of the most critical challenges for SMEs is the
negative perception they face especially from larger firms. Prospective consumers see small
businesses as lacking the capability to provide quality goods and services and are unable to
satisfy more than one critical project simultaneously. In Mozambique a noteworthy example of
large firms support is offered by the large aluminium processing factory (MOZAL). Currently,
MOZAL buys about 30 percent of its supplies from local Mozambican SMEs and wants to
increase that number (Mwanza, 2012). Castel-Branco and Goldin (2003) however criticise the
scheme between MOZAL and SMEs, arguing that very few SMEs have benefited. The authors
argue that many of SMEs were not prepared enough to cope with the supply chain mechanism
that was expected by MOZAL. Conversely, MOZAL itself was not experienced to deal with
Mozambican SMEs, whom according to a research by Langa and Mandlate (2013), could not be
upgraded to MOZAL’s standards of procurement.
Aponte (2013) construes that NGOs can support SME by providing them resources, knowledge
and technology. NGOs play a vital role as go-between establishments in making available links
between the business sector, development cooperation agencies, and the government. Baur and
Schmitz (2012) emphasise that NGOs are important in encouraging the development of
sustainable SMEs by connecting local and global actors claiming to be the natural vehicle of
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growth and local empowerment. Carmichael (2005) reveals that in Tanzania, NGOs support
SME programs by supplying common facilities including establishment of foundry depots as
well as manufacturing workshops. Other NGOs mainstay activities include formation in
management, book-keeping and marketing; making available of credits for working tools and
initial capital; and industrial extension services. Along the years NGOs have focussed support
into rural development reconstruction and emergency circumstances, especially following
situations of natural disasters and the resurgences of contagious diseases (Vletter et al., 2015).
Although NGOs in Mozambique have been identified by their grassroots distinctiveness, and
often represented a springboard towards the creation of viable tools for supporting businesses
especially SMEs, however, Bellucci (2002) warns that NGOs in Mozambique excessively became
defensive towards the government in order to have favourable patronage. Local NGOs have
been censured for being uncoordinated, creating parallel SMEs projects among different
establishment that put the small business sector away from their routine duties in order to
serve the self-centred expectations of the NGOs. On the other hand NGOs have been criticized
for their over dependency on foreign donors (Bellucci, 2002).
Role of Financial support to SMEs in Africa and Mozambique
In most developing countries, micro and SMEs represent over 95 percent of national activity
and employ nearly 90 percent of the workforce, yet their benefits from financial assistance is
no more than 5 percent (Gbandi & Amissah, 2014). Cálice et al., (2012) revealed that in Kenya,
Tanzania, Uganda and Zambia the lack of sufficient information ranked as the predominant
disincentive to financial institutions support to the SMEs. Conversely, financial institutions
mentioned that the lack of collateral was the main deterrent to SMEs from receiving loans.
Collateral and guarantees are not easy to come by especially to start-up SMEs in developing
countries. Gbandi and Amissah (2014) reveal that the majority of small enterprises in Nigeria
do not enjoy property rights, an aspect that dispossesses them of access to both capital and
credit. Financial institutions do not escape the blame regarding SMEs financing. Collier (2008)
argues that banks in Africa reap a lot of financial yields from lending to large firms and would
not bear the risks of lending to SMEs whose monetary rewards are far lower in comparison.
This view is shared by De Ferranti and Antony (2007) who claim that banks in developing
countries impose higher interest fees and rates on lending of an average of 22 percent
compared to an average of 12 percent in developed countries.
Financial assistance in Mozambique has been stepped up in recent times, with more banks
creating special packages aimed at start-up and establishes SMEs. Financial projects sponsored
between international agencies such as the Japanese Agency for International Development
(JICA) and Portuguese Enterprise Cooperation Fund (FECOP) with local commercial banks have
advanced large sums to be at the disposal of SMEs financing (AMB, 2014). One aspect
overlooked by these banks is the overall ability of the targeted SMEs in accessing the loans.
According to a World Bank (2003) report, short and long-term loans in Mozambique require
collateral of up to 300 per cent. Besides the exigency on collateral, eligibility to loans is subject
to presentation of viable business plans, edited accounts and ability to pay back the loan
without hitches (Noticias, 2014). The conditions made by financial institutions in Mozambique
of lending only to SMEs with bankable projects or those with ability to payback without
drawbacks, misrepresent the reality of the SMEs sector in Mozambique, where a majority of
SMEs are still young and weak. Fox and Sohnesen (2013) underpins that most SMEs in
Mozambique were formed in the last five years, and about 25 percent were less than one year
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old. Many businesses are irregular operating for only a few months of the year: implying that
nearly half of all enterprises operate only for some months of the year (Sawaya & Bhero, 2017).
To add to this, a substantial number of SMEs in Mozambique operate informally without
properly organized accounts.
According to Castelo-Branco (in Borgarello et al., 2004) land cannot be used as collateral
because in Mozambique the land and other resources belong to the state. Start-up SMEs are
therefore practically shut out of financial support from established banks or microfinance
institutions because they have no collateral or other guarantees to offer. Most entrepreneurs
would resort to obtain the start-up capital from family and friends, due to emotional factors
including love, friendship, and loyalty. High interest rates have also contributed to start-up
SMEs failure to access loans. A study in Kenya by Ong’olo and Awino (2013) revealed that nearly
three quarters of new start-up SMEs could not access loans from banks due to high interest
rates and persistent demand on collateral. Merely less than 2 percent of start-up SMEs got
assistance from banks. In Mozambique similar scenario is evidential with SMEs opting for the
so called Xitique financing schemes which involve a group of people coming together and offer
each other loans in turns, without charging interest (Cunha, 2014). Funds exchanged through
the Xitique system are frequently not enough to sustain a business operation, and in many
instances the funds are used for other family related activities, such as rehabilitation of
residences and paying college fees. This is a clear indication that limited research and literature
is available on the impact of financial support towards the sustainability of SMEs in
Mozambique.
SMEs role in the Mozambican economy
SMEs are viewed as cradles of bravery, where risk taking is supreme and hard work is a norm.
For any potential entrepreneur, SMEs usually represent the first job, the beginning of a career.
In the case of larger enterprises, SMEs signify the source from which they came from; at the
same time they symbolize the potential source from where competition will emerge in the
future (Sawaya & Bhero, 2017). To the economy as whole SMEs are foundations of new ideas,
a basis for employment creation and economic growth (Berisha & Pula, 2015). Thus SMEs are
a stepping stone to the world of entrepreneurs, and although only a few SMEs would grow to
be large enterprises, it is also true that only a few large enterprises did not begin as SMEs. It
might be doubtful on the extent to which SMEs are a force of innovation and growth in an
economy, but undoubtedly they remain critical as drivers for employment generation. Castel- Branco (2003) explains that the main reason why SMEs characterization varies particularly
from county to country; industry to industry; size to size depend on the role to which they are
expected to play. Statistical figures in almost all the countries show that SMEs are totally
predominant in the economies, representing close to 95% of all the companies, having
extensive command in generating the gross domestic product and employment creation
(Hussain et al., 2012).
SMEs are the fastest expanding economic segment and may be viewed as the prime solution for
home-grown entrepreneurship in Mozambique (Fox & Sorrenson, 2013). According to Valá
(2007) almost 78 percent of the total entrepreneurial businesses in the manufacturing and
industrial sector in Mozambique belonged to the SMEs, employing almost 67 percent of all the
workers employed in this area. It is still a matter that requires much more research to
determine the unsatisfactory performance of existing SMEs in Mozambique. Jones and Tarp,
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(2012) in analysing the performance of SME in Mozambique had a problem in concluding
whether SMEs underperformed because of the coexistence of poverty and labour market
stagnation and imperfections; a result of weaknesses in human capital formation or the result
of lack of overall support.
Challenges to SMEs in Mozambique
There is little scope for a common set of policies and logical tools to be successfully deployed
to address the SMEs issue, not to mention the challenges that face this sector (Ong’olo & Awino,
2013). Zimba (2015) adverts that apart from the presence of so many SMEs in Mozambique;
they contribute to a modest 24.1 percent of the national income due to having low levels of
production. The unfavorable contribution of SMEs into the national wealth had prompted the
Mozambican government to constitute the Institute of Promotion of Small and Medium
Enterprises (IPEME), whose main mission is to encourage the deployment, consolidation and
development of SMEs, mainly through the creation of incubators (Zimba, 2015). This strategy
does not define in details how the planned incubation course of action is formulated. The policy
instrument does not clearly outline which particular groups (such as the youth), are given
preference especially in securing finance for starting their businesses. This is a serious gap that
has been observed and the current study is intended to address this important omission
pertaining to SMEs development in the Mozambican context.
The challenge of insufficient finance to SMEs in Mozambique is overwhelming according to
Osano and Languitone (2016). The authors mentioned that there is a relationship between
awareness of funding and access to finance by SMEs and that there is a relationship between
collateral requirements and access to finance by SMEs, including a relationship between small
business support and access to finance by SMEs. Of the list of the most problematic factors for
doing business that were cited in the current World Economic Forum (WEF, 2017) report,
access to financial was ranked as the first item that hindered Mozambican competitiveness in
global grading as shown in Figure 1.
Figure 1: Most problematic factors for doing business in Mozambique
Source: World Economic Forum, Executive Opinion Survey 2017
0.5
1.3
1.5
3.5
3.7
4
4
4.3
5.7
5.7
6.3
7
8.2
11.5
14.7
18.2
0 5 10 15 20
Tax regulations
Insufficient capacity to innovate
Poor public health
Government instability/coups
Foreign currency regulations
Poor work ethic in national labor force
Crime and theft
Tax rates
Inflation
Restrictive labor regulations
Inadequate supply of infrastructure
Policy instability
Inadequately educated workforce
Inefficient government bureaucracy
Corruption
Access to financing
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Critical to Mozambique’s bleak performance, four challenges on the top of the table rankings
are important for discussion. Starting from the fourth item: the forth most profoundly
mentioned challenge was the inadequacy of educated workforce. Trained workforce is critical
for development of SMEs the world over. According to Khalique et al., (2011) the most
devastating problem to SMEs sustainability is the lack of human capital, as it is too costly to
attract and employ a competitive workforce. Entrepreneurs and a workforce with a better level
of education and training are more able to adapt their firms to the ever-changing business
circumstances (Khalique et al., 2011). Concerning the third item, prevalent bureaucracy in all
sectors of the government, makes SMEs support to be jeopardised, especially at start-up
registration and taxation ramifications (USAID/Speed,2014). According to Fox and Sohnesen
(2013) in Mozambique, the problem of bureaucracy is even worse than in many other African
countries. Accessing finance is a complicated task, mainly due to the overburdening
bureaucracy in processing financial requests and the lack of necessary guiding information.
The second and fundamentally imperative item cited is corruption, which is a scourge for
sustainable advancement of all the institutions in Mozambique and impact devastatingly on
SMEs support. According to the Centro de Integridade Pública (CIP, 2016) the impact of
corruption on the economy is so serious, that in terms of added value, corruption is responsible
for the loss of US $ 2.5 billion or above of 17% of the average annual Value Added in the
economy, between 2012 and 2014, meaning that each person out of a population of twenty five
and a half million residents, would have lost $ 98 a year to corruption. The first and most critical
factor mentioned in the WEF (2017) report was the problem of finance to all sectors of
economic activities in the country. The worse sector that would feel the lack of finance would
the SMEs. The exposure of the WEF (2017) report brings into perspective the need of this study
to determine the importance of financial support to all sectors in Mozambique and to SMEs in
particular. As earlier mentioned, strong and sustainable SMEs are the only prospects for
employment for the youth in Mozambique. Failure to support SMEs would result in the
continued problem of youth migrating out of the country to neighbouring South Africa or
youths resorting to acts that put at bay the internal stability of the country.
METHODOLOGY
The following section will review the methodology that will be used to analyse the study
question, that financing is the most important form of support compared to any other support
structures available to SMEs in Mozambique. The study was carried out on the basis of the
following methodological format.
Data selection
The research was based on a selection of 485 formally registered SMEs chosen from a
population of SMEs in the city of Maputo using the stratified random sampling method. A set of
questionnaires were prepared composed of structured, close ended format. The closed-ended
questions give quantitative researchers the ability to analyze data at a much flexible and faster
rate than the open-ended questions in order to get an in-depth analysis and not to miss on
important annotations. A face to face interview was conducted using a group of interviewers
who visited the SMEs in their respective working areas. The areas covered included all the
seven municipal districts of Greater Maputo, involving three types of SMEs categories: being
manufacturing, services and commerce based SMEs.
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The empirical strategy and assumptions
The research problem was to critically scrutinize the importance of financial support to SMEs
survivability and growth. Additional categories of support structures in the economy had to be
incorporated in order to create the comparison factors. In prospective, four main support
variables were selected for this study, including financial institutions, government agencies,
large firms and non-governmental organizations (NGOs). To complete the paradigm, it was
essential to select factors that would represent SMEs sustainability. Increase in sales turnover
and increase in production were selected to symbolize SMEs sustainability and growth. An
appropriate statistical paradigm had to be decided that incorporated the four support variables
and the two SMEs sustainability factors in a linear relationship so as to able to answer the
research problem. The multiple regression analysis was deemed the appropriate tool for such
analysis. Multiple regression analysis is a statistical technique that simultaneously develops a
mathematical relationship between several independent variables and an interval scaled
dependent variable (Fry, 2015). In this study the four support components; financial
institutions, government agencies, large firms and NGOs represented the independent
variables. The dependent variables were the increase in sales turnover and increase in
production that were investigated separately in two disconnected sets of multiple regression
analyses. The multiple regression models allow the researcher to determine the involvement
of each of the predictor (independent) variables to the total variance explained. The regression
coefficients represent the mean alteration in the dependent variable for one unit of change in
one chosen independent variable while holding constant other independent variables in the
regression model (Keith, 2006). In this regards, it was possible to analyse the critical impact of
financial support to SMEs sustainability in Mozambique using this model whilst maintaining
constant the other support variables.
The relationship between the dependent variable designated as Y and the independent
variables depicted as Xi is demonstrated in a simple relationship as shown in Equation 1 below:
Y =
+ β1X1 + β2X2+ β3X3..... βiXi + ε
Equation (1)
Where the
value depicts the constant, the βi are the unknown parameters of the regression
and ε is the error term, which captures the unexplained variation in the dependent variable
(Stevens, 2009). The constant
is the expected mean value of Y when all X (independent
values) are equal to zero (Antonakis & Dietz, 2011). A scenario where all Xs are zero rarely
happens; consequently, the value of
is insignificant and will be ignored. Although the
value is insignificant, it is imperative to be included in the multiple regression models for it to
be far-reaching and inclusive. If the constant value is not included, this will imply that the
residuals have a mean of zero. Moreover, this will suggest that the regression line has to start
at the origin implying that both the independent and dependent variables are equal to zero at
the point of origin (Antonakis & Dietz, 2011). The repercussion of this is to make the coefficients
of the predicator variables biased on determining the results of the dependent variable.
The methods used to analyse the data
There are five requirements that have to be fulfilled prior to running the multiple regression
analysis. In order to perform multiple regression, and the results to become statistically viable
the following assumptions have to be adhered to:
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i. The dependent and independent variables should have a linear relationship and
multivariate normality in the multiple regression, assuming the variables are normally
distributed. (Osborne & Waters, 2002). In this study, both the dependent variable
forming the sustainability of SMEs and the independent variables referred to as the
support variables were in a linear relationship and due to the large sample of
respondents (485 interviewees) a normal distribution pattern was assumed to exist.
This is in accordance to the attributes of the Central Limit Theorem. The central limit
theorem states that sampling distribution of the mean of any independent random
variable will be normal or almost normal, if the sample size is large enough with a finite
level of variance. The mean of all samples from the same population will be
approximately equal to the mean of the population (Dehling & Wendler, 2010).
ii. The second assumption is that two or more of the independent variables have to be
continuous variables: nominal or ratio variables. The independent variables financial,
government, large firms and NGO’s supports were set up from dichotomous questions
of either ‘yes’ or ‘no’ types. Dichotomous questions are nominal variables; as such, they
fulfil this condition.
iii. Third, data should be free from multicollinearity. An analysis of the correlation
coefficients between the independent and dependent variables were carried out to
prove the non-existence of multicollinearity within the independent variables.
iv. Data needs to prove homoscedasticity (Antonakis & Dietz, 2011). The presupposition of
homoscedasticity entails equal variance of errors amongst all the independent variables.
From this study, the analysis of correlation proved the non-existence of collinearity;
meaning that none of the independent variables were related to each other. The error
variances for each variable would therefore be independent of the other, meaning that
they are stretched out consistently among all the variables.
v. The last assumption is that there should be a linear relationship between the dependent
variable and each of the independent variables. Multiple regressions can accurately
estimate the relationship between dependent and independent variables when the
relationship is linear in nature (Keith, 2006). From the correlation analyses tables,
strong relationships are evident between the independent and dependent variables,
meaning that changes in the variances of the independent variables positively influence
changes in the variances of dependent variable. This is a proof of the existence of a linear
relationship between the dependent variable and each of the independent variables.
The relationship of multiple regression analysis basing on the four independent variables and
the two dependent variables had to be organized in a statistical format so as to carry out the
examination procedures. The first procedure was to assign appropriate symbols to all the
variables in the relationship. Beginning with the independent variables the designations were
as follows:
Financial support = Fs; Government support = Gs; Large firms support = Ls; and NGOs support
=Ns.
For the sake of this study, the variables were denominated simply as financial, government,
large firms and NGOs, respectively.
The dependent variables sales turnover and increase in production were denoted with the
symbol St and Pi respectively.
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The multiple regression models based on the construct of the two dependent variables were
separately represented as follows:
Equation 2 epitomizes the increase in sales turnover as the dependent variable and the
statistical relationship is presented as follows:
St =
β1 Fs + β2 Gs + β3 Ls + β4 Ns + ε
Equation (2)
Similarly, Equation 3 demonstrates the relationship of increase in production as the dependent
variable which can be represented as:
Pi =
β1 Fs + β2 Gs + β3 Ls + β4 Ns + ε
Equation (3)
For the multiple regression model to be viable it had to be verified with five testing and
validating mechanisms. These are the R-value, the R-Squared, the F-ratio, the β-values and the
t-test values. R-values measure the strength and direction of a linear relationship between two
variables in this case between the independent and dependent variables. The value of R is
always between +1 and –1 (Bryman & Cramer, 2011). The R-squared test determines the
measurement of the variance in the dependable variables in this case sales turnover and
production that is forecasted from the independent variables namely financial, government,
large firms and NGOs. The F-ratio derived from the ANOVA table, determines if the overall
multiple regression model is a good fit for the data results (Hair et al., 2010). The unstandadised
β-value coefficients in multiple regressions determine how much one independent variable
influences the increase in turnover or production when all the other independent variables are
held constant. On the other hand, the t-test gives the probability of two sets of values that is,
dependent and independent values coming from different groups (Yoo et al., 2014). The greater
the t-value, either positive or negative, the greater is the evidence of the existence of differences
between the variables. The p-value tells how confident one can be that each individual
independent variable has some correlation with the dependent variable (Hair et al., 2010).
Prior to carrying out the multiple regression analyses, correlation analyses were performed.
Correlation analysis was performed to prove the absence of multicollinearity within the
independent variables so as to meet one of the conditions for the use of the multiple regression
models. The second purpose was to verify the strength of the linear relationship between the
independent variables and the dependent variables, for the multiple regression exercise to be
relevant. Correlation analysis only tells the strength of the relationship between two variables
without taking into consideration the scale and direction of causality. The results of the
correlation analyses are evaluated on the premises of the correlation strengths as follows: 0 to
0.190, is regarded as very weak, 0.200 to 0.390 as weak, 0.400 to 0.590 as moderate, 0.600 to
0.790 as strong and 0.800 to 1 as very strong correlation. The Kendall’s tau-b correlation
method was the appropriate correlation test employed in the correlation analyses because data
used in this study was of non-parametric format. The Kendall’s tau is flexible and it theorizes
on few assumptions concerning the distribution of variables and the relationships amongst the
variables under examination (Rasinger, 2013). Finally, following the application of the multiple
regression analysis, it was vital to test the model for reliability in order to reaffirm the
dependency and trustworthiness of the process. This was done using the Cronbach alpha test
performed using the 6 variables used in this study (financial, government, large firms, NGOs,
increase in sales turnover and increase in production), all scaled on dichotomous nature.
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URL: http://dx.doi.org/10.14738/abr.1010.13266
RESULTS AND DISCUSSIONS
The data was processed applying the SPSS program adhering to the methodology detailed in
the previous section. The first procedure involved carrying out the correlation analysis in two
stages. The first analysis determined the correlation between the independent variables and
increase in sales turnover as the dependent variable, and the second was to determine the
correlation between the independent variables and increase in production as the dependent
variable.
Results of correlation analysis between the independent variables and sales turnover
The four independent variables (financial, government, large firms and NGOs) were paired
against sales turnover. The output of the Kendall’s tau-b correlation matrix is presented in
Table 1.
Table 1: Correlation between support structures and sales turnover
Variable Financial Government Large firm NGOs
Increase
sales
support support support support turnover
Financial support 1 0.160 0.310 0.262 0.587
Significant (2 tailed
test) - 0.001 0.001 0.001 0.001
Government support 0.160 1 0.182 0.116 0.501
Significant (2 tailed
test) 0.001 - 0.001 0.011 0.001
Large firm support 0.310 0.182 1 0.083 0.254
Significant (2 tailed
test) 0.001 0.001 - 0.067 0.001
NGOs support 0.262 0.116 0.083 1 0.190
Significant (2 tailed
test) 0.001 0.011 0.067 - 0.001
Increase sales
turnover 0.587 0.501 0.254 0.190 1
Significant (2 tailed
test) 0.001 0.001 0.001 0.001 -
Results from Table 1 show low correlation between the independent variables, the highest
being the correlation between financial support and large firms support where the R-value is a
mere 0.310. Low R–values reaffirm that correlation is not strong between the independent
variables and do not harbour the threat of multi-collinearity. These results meet one of the
conditions of applying the multiple regression models, that data should be free from
multicollinearity. The significant two tails tests (the p-value) for all the variables in the table
are less that 0.05 that is p<0.05 for the combination of variables in the table. When p-values are
less than 0.05 in a two tails test, at the 95 percent confidence level, they give a strong indication
that the results are significant, and confirm the validity of the data for use in the analysis.
Concerning the correlation between the independent variables with the dependable variable,
the strongest relationship was given by financial support and sales turnover with the R-value
of 0.587. The second in line was the influence of government support with an R-value of 0.501.
In most cases, correlation analyses with R-values that are above 0.500 are considered
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influential in inferring relationship of variables. On the other hand, correlation relationships
between large firms and NGOs to sales turnover gave low R-values of 0.254 and 0.190
respectively. Therefore the positive correlation between the independent variables (financial,
government, large firms and NGOs) and sales turnover indicate that these variables support to
SMEs is crucial for influencing sales turnover, with financial support leading the other variables.
Results of correlation analysis between the independent variables and production
The second correlation analysis involved the independent variables paired with increase in
production as the dependable variable. The output of the Kendall’s tau-b correlation matrix
between the four independent variables and increase in production is presented in Table 2.
Table 2: Correlation between support structures and increase in production
Variable Financial Government Large firm NGOs Increase in
support support support support production
Financial support 1 0.160 0.310 0.262 0.551
Significant (2 tailed
test) - 0.001 0.001 0.001 0.001
Government support 0.160 1 0.182 0.116 0.522
Significant (2 tailed
test) 0.001 - 0.001 0.011 0.001
Large firm support 0.310 0.182 1 0.083 0.188
Significant (2 tailed
test) 0.001 0.001 - 0.067 0.001
NGOs support 0.262 0.116 0.083 1 0.144
Significant (2 tailed
test) 0.001 0.011 0.067 - 0.001
Increase in production 0.551 0.522 0.188 0.144 1
Significant (2 tailed
test) 0.001 0.001 0.001 0.001 -
In this correlation matrix, the strongest relationship was given by the independent variable
financial support versus increase in production with an R-value of 0.551; followed by
government support against increase in production with the R-value of 0.522. The notable
difference with the correlation of sales turnover is that in this case the R-values were lower.
The implication is that perhaps additional eternal factors such as advanced technology and
trained manpower were needed to influence the increase in production within the SMEs. Low
results were evident in the correlations between large firms and NGOs in relationship to
increase in production, with R-values of 0.188 and 0.144 respectively. All the results were
statistically significant though. The two tails tests (the p-values) were less that 0.05 that is
p<0.05 for all the variable combinations. The positive correlation between financial,
government, large firms and NGOs to increase in production, indicate that these factors support
to SMEs, are vital for influencing increases in production. The higher correlation between
financial support to increase in production compared to government, large firms and NGOs,
gave an early signal of the impact of financial support to SMEs sustainability that exceeded the
influence wielded by the other independent variables in increase in production amongst SMEs.
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URL: http://dx.doi.org/10.14738/abr.1010.13266
Results and discussion on the multiple regression analyses
After fulfilling the requirements of the correlation analyses, the study proceeded with multiple
regression analysis to answer the research problem of whether financial support is
indispensable for SMEs growth and survivability in Mozambique. The multiple regressions
were carried out to determine the relationship between the four independent, supporting
variables on the sustainability of SMEs. To determine SMEs sustainability, increase in sales
turnover and production represented the dependable variables. Both assessments were made
using SPSS data analysis. The necessary testing parameters were applied to analyse the results
of the multiple regression model.
Multiple regression between the independent variables and increase in sales turnover
The multiple regression exercise was carried out matching the independent variables financial,
government, large firms and NGOs, against increase in sales turnover as the dependent variable.
The results are shown in Table 3.
Table 3: Multiple regression between independent variables and sales turnover
ANOVAa
Model
Sum of
Squares df
Mean
Square F Sig. R
R
Squar
e
1 Regression 212.613 4 53.153 136.089 0.001b 0.734a 0.539
Coefficientsa
Model
Unstandardized
C Coefficients
Standardiz
ed
Coefficients
t
Signa
p < .005
95.0%
Confidence
Interval for B
β Std. Error Beta
Lower
Bound
Upper
Boun
d
i
(Constant) 0.32
3 0.042 7.665 0.001 0.240 0.406
ii
Financial Support 0.89
7 0.060 0.514
14.97
7 0.001 0.780 1.015
iii
Government
support
0.79
2 0.062 0.420
12.83
1 0.001 0.671 0.914
iv
Large firms
Support
0.03
4 0.099 0.012 0.348 0.728 -0.160 0.229
v
NGOs Support 0.01
6 0.103 0.005 0.155 0.877 -0.187 0.219
a. Dependent Variable: Increase in Sales turnover
b. Predictors: (Constant), Financial support, Government support, Large firm and NGOs support.
An R-value of 0.734, as shown in Table 3, implies a decent level of prediction of sales turnover
from the independent variables, and is significant with the F-test value of 136.089 at 4 degrees
of freedom. The R-Square factor (also called the coefficient of determination) is also crucial in
multiple regressions. The R-square is 0.539, implying that the independent variables (support
variables) explain 53.9 percent of the variability of the sales turnover. However, how each
independent variable influences the increase in sales turnover will be established through the
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following analysis. The unstandadised β-value coefficients determine how much one
independent variable affects the increase in turnover when all the other independent variables
are held constant. In this respect, the multiple regression models exert automatic control of the
influence of each predictor variable to the dependent variable. From the results in Table 3, the
β-values indicators are: financial 0.897; government 0.792; large firms 0.034 and NGOs 0.016.
The constant value is 0.323. These results can be joined together in a predictive equation, using
the formula elaborated in Equation 2 to produce a value added Equation 4 below:
St =
0.897 Fs + 0.792 Gs + 0.034 Ls + 0.016Ns + ε
Equation (4)
The F-ratio is given as 136.089, at 4 degrees of freedom at significant level of 95 percent, which
can also be represented statistically as F (4, 95) = 136.089. This is a strong positive result, and
shows that the results are trustworthy in this multiple regression model. The p-value tells how
confident that each individual independent variable has some correlation with the dependent
variable. With p < 0.05, suggests that the multiple regression model is a good fit for the data in
the study. With the assurance of goodness of fit, the model gives the confidence required to
proceed and examine the influence of the independent variables upon the dependent variable.
In this analysis, the financial support (Fs) variable gave strong β-values of 0.897 with a t-test of
14.977 that is significant at the 95 percent significant level. The interpretation is that for each
single unit increase in financial support to SMEs, sales turnover would increase by 0.897 units,
keeping government, large firms and NGOs variables constant. These results offer confirmative
inference that financial support is critical for SMEs sustainability, whether at start-up or during
the growth phase. The multiple regression analysis therefore serves to reconfirm the validity
of these findings. Although the β-value of government support variable at 0.792 was
satisfactory, it was below the magnitude exerted by financial support. Large firms and NGOs
support variables, with β-values of 0.034 and 0.016 and potrayed low t-tests of 0.348 and 0.155
respectively. The tests were not even significant at the acceptable levels. Aweak influence from
large firms and NGOs support was evident on the increase in sales turnover of the SMEs in this
study, implying that there was very little support rendered by these sectors to start-up SMEs
or other stages of SMEs development in Mozambique.
Multiple regression between the independent variables and increase in production
The second multiple regression model carried out to answer the research problem, involved
the four supporting sectors to SMEs herewith defined as the independent variables, matched
against the increase in production, a second term used to represent SMEs sustainability,
expressed as the dependent variable. Table 4 presents the results of the multiple regression.
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URL: http://dx.doi.org/10.14738/abr.1010.13266
Table 4: Multiple regression between independent variables and production
ANOVAa
Model
Sum of
Squares df
Mean
Square F Sig. R
R
Squar
e
1 Regression 156.914 4 39.229 116.902 0.001b 0.707a 0.500
Coefficientsa
Model
Unstandardized
C Coefficients
Standardiz
ed
Coefficients
t
Signa
p < .005
95.0%
Confidence
Interval for B
β Std. Error Beta
Lower
Bound
Upper
Boun
d
i (Constant) 0.262 0.039 6.712 0.001 0.186 0.339
ii
Financial Support
0.776 0.057 0.461
13.58
0 0.001 0.663 0.888
iii
Government
support 0.752 0.055 0.484
13.57
3 0.001 0.643 0.861
iv
Large firms
Support
-
0.167 0.092 -0.064
-
1.824 0.069 -0.347 0.013
v
NGOs Support -
0.093 0.095 -0.033
-
0.985 0.325 -0.279 0.093
From the ANOVA table the R-value is 0.707, which indicates a strong relationship between the
variances of the independent and dependent variables and is significant with the F-test value
of 116.902 at 4 degrees of freedom. The R-squared amount is 0.500, which is also not a bad
indicator of the predictability of the model as it describes 50 percent of the variability of the
mean of the dependent variable. The unstandadised β-values coefficients are given as per the
coefficients on Table 4. Using the formula adopted in Equation 3 the relationship can be
represented in a predictive multiple regression model in Equation 5:
Pi =
0.776 Fs + 0.752Gs - 0.167 Ls - 0.093 Ns + ε
Equation (5)
Similar to the sales turnover analysis, the results of multiple regression on SMEs in
Mozambique shows how influential financial support is on the increase in production and thus
sustainability of the SMEs. The β-value for financial support of 0.776 is a strong indicator,
meaning that for one unit application of financial support, production would increase by 0.776
units. This reveals the power of financial support and its influence on SMEs start-up, growth
and sustainability when other independent variables are held constant. The results are
significant at p < 0.05 and considering the t-test results at 13.580 are also significant.
Government support also showed encouraging results, with β-value of 0.752, but still financial
support was outstanding. An interesting aspect in this model is that both large firms and NGOs
β-values are negative. In any case, these results are not even significant as they portray low t- values (negative values) compared to the t-table values at (n-1) degrees of freedom. The
Dependent Variable: Increase in Production
Predictors: (Constant), Financial support, Government support, Large firm and NGOs
support..
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interpretation of this occurrence is that the dismal results are a consequence of very limited
participation of both large firms and NGOs in supporting SMEs in Mozambique.
Test for reliability and consistency for the Multiple regression model
Following the exercise done to determine the impact of financial support to SMEs sustainability
by using the multiple regression models, the next logical step was to determine the reliability
of the process for it to be trustworthy and dependable. The Cronbach alpha test was performed
using all the 6 independent and dependent variables involved in this study. The results are
given in Table 5.
Table 5: results of the Cronbach Alpha test
Reliability Statistics
Cronbach's
Alpha
Cronbach'
s Alpha
Based on
Standardiz
ed Items
N of Items
0.771 0.755 6
Item-Total Statistics
Research
Important
variables
Scale
Mean
if Item
Delete
d
Scale
Variance
if Item
Deleted
Corrected
Item- Total
Correlati
on
Squared
Multiple
Correlati
on
Cronbach's
Alpha if
Item
Deleted
Financial Support 2.47 4.531 .603 .468 .719
Government
Support 2.58 4.799 .525 .380 .738
Large Firms
Support 2.84 5.608 .302 .142 .781
NGO Support 2.86 5.751 .228 .079 .790
Increase Turnover 1.90 2.854 .794 .663 .654
Increase
Production 2.06 3.275 .748 .636 .664
The Cronbach alpha coefficient of 0.771 for the 6 items, shown in Table 5 suggests that the items
have high internal consistency. Any result that is above 0.700 is considered dependable and
steadfast. This implies that data in this study is internally consistent and reliable, meaning that
uni-dimensionallity; convergent validity and scale reliability were verified for all 6 variables. A
much-detailed analysis is derived from the ‘item total statistics’ table. The last column depicts
Cronbach alpha statistical level when some items are excluded, meaning that if financial
support is excluded, the Cronbach alpha coefficient drops from 0.771 to 0.719. The low level of
large firms and NGOs support to SMEs in Mozambique is proven by the fact that if these two
support variables are removed, the Cronbach alpha coefficient actually goes up to 0.781 and
0.790 respectively, above the average coefficient of 0.771. From Table 5 it is evident that if the
two dependent variables; increase in sales turnover and production are excluded, the Cronbach
alpha would depict lower internal consistence results of 0.654 and 0.664, respectively,
compared to the general internal consistence result of 0.771. From the ‘corrected Item-total
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URL: http://dx.doi.org/10.14738/abr.1010.13266
correlation’ column, the high values of 0.794 and 0.748 for increase in turnover and increase
in production respectively(which insinuates the Cronbach alpha coefficient for these two
variables) signify that the two dependent variables are appropriate instruments to be used to
represent SMEs sustainability and development.
CONCLUSIONS AND RECOMMENDATIONS
This study scrutinized support structures to SMEs in Mozambique, in a purported mission of
establishing whether one particular component, financial support outperformed all other
support factors in influencing the establishment, growth and development of SMEs in the
country. The study chose and concentrated on four major supporting variables being financial
support, government support, and support from large businesses and NGOs. The motive was
primarily to study the impact and magnitude of each of the four supporting variables on SMEs
start-up and sustainability. In so doing the prime objective was to prove that financial support,
was a much more critical and obligatory variable in encouraging SMEs start-up, survivability
and growth.
Through an examination of 485 SMEs in Greater Maputo, using the process of multiple
regression analysis, it was found that although support from NGOs, large firms and government
agencies was important to SMEs development in Mozambique, financial support was the most
significant of all and an indispensable supporting element. Without financial support, young
entrepreneurs would not be empowered to launch small enterprises, and existing SMEs would
not thrive to contribute to national income, employment creation and discourage the youth
from criminality and illegal migration out of the country.
It is recommended that financial institutions should be more proactive in SMEs support in
Mozambique. Financial institutions should devise lending policies with less stringent
requirement for collateral, on audited accounts and business plans. It is understandable that
existing financial institutions have ongoing financial plans to assist SMEs, but it is their
imperative responsibility to establish institutional structures that will nurture and support
SMEs at start-up and past the formative years and ensure that they survive the vagaries of
metaphorical ‘infant mortality’. Banks should create specialised department which render in
depth assistance to the smallest SMEs and support owner-managers having difficulties in
sourcing finance. Banks should engage trained staff members to guide potential entrepreneurs
on the procedures of sourcing financial packages available to SMEs. These specialized
departments may be called Underprivileged Customers Assistance Departments (UCAD).
Consultants can be engaged to guide potentially disadvantaged SMEs clients on the procedures
of making loan application, formulating business plans, building up collateral to guarantee the
loans, and hints on how to effectively manage the loans in their businesses. Only in this manner
will financial support make a difference to SMEs development and a positive impact for the
benefit of the Mozambican economy.
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