Globally, it is evidently proven that the small and medium enterprise (SME) sector is an indispensable sector of economic growth and development. Most of the job opportunities, creation of new products and overall performance of an economy is said to be attributed to the SME sector. The challenges of SME development and employment generation are very important in today's government economic development efforts. The government of today has been making concerted efforts in ensuring the business environment is favourable for the private sector to create many jobs and this is one of the best medium in which dividends of democracy can be transferred to citizens as well reduce the challenges of insecurity and unemployment in the nation. The SMEs and entrepreneurs in general should be encouraged to drive the process of job creation as they are recognised globally as aggregate generators of employment. Entrepreneurship programmes or projects are undertaken by every government of a nation which assists its citizens to explore and achieve meaningful employment opportunities. For instance in America; the federal tax act in
Employment generation as the name implies means jobs created to respond to some sort of situation. Conceptually, it's the proactive opposite of unemployment. In the practical aspect, industries and organisations employ more workers only when it's necessary for them to do so with the aim of satisfying the demand for their products or services or when they believe they can do the required work. Most importantly they strive to minimise cost in having the work done. However, government can stimulate employment generation when it invests in projects that improve or create new services.
In most economies, the small and medium enterprises (SMEs) are considered to be the aggregate generators of employment and this is very significant in achieving the goals and objectives of the economy which includes bridging the gap between the rural and urban areas, reducing poverty and creating jobs to the teeming youths. It was highlighted by Mukhtar in
In
This study is actually a new study as hypotheses have been formulated to test the significance level of small and medium enterprises on employment generation in Kaduna state of Nigeria. The hypotheses are stated as;
In the course of the study some research gaps were identified which needs to be bridged so as to prove our alternate hypothesis which states that “SMEs have significant impact on employment generation in Kaduna state”. These research gaps included; examine the skills gap in SMEs on employment generation, slow performance of SMEs, sustainability of Job creation programmes, knowledge gap among the stakeholders and users, lack of updated Innovation and strategies used to boost employment generation and no usability of available solution to SME challenges. This present study aims at examining the correlation between the dependent and independent variables, ascertain the significance level of the variables in concern and identify new models in increasing employment generation by enterprises.
This study utilised the primary method of data collection in which the analysis of results was solely based on such data. A survey was conducted on some 5 selected SMEs from the manufacturing sector of Kaduna state namely the Arts & crafts, Furniture, Textile, Plastics and Pharmaceutical industries. The study adopted the cluster area sampling method which is a special type of sampling whereby samples are grouped and clustered on the basis of geographical locations. It is usually adopted when the research focuses on the population within a specific geographical area, like country, State, county and city blocks. The reason for adopting this sampling method is that though the sampling frame for the various clusters of Kaduna state is available and was obtained from the National population commission office, there is no available frame containing the list of micro, small and medium enterprises in the state. Hence in this situation, area sampling is one of the most suitable techniques of data collection. As argued by various scholars, that the underlying practical motivation for using area sampling is the absence of complete and accurate list of the universal elements under study since it does not depend upon the population frame. Moreover, in the cluster sampling, the full list of clusters forms the sampling frame and not the list of individual elements within the population.
Sample sizes of 1,000 respondents whom are basically the owners and staff of the SMEs with 200 in each of the 5 selected SMEs were used. The primary data was obtained through information gotten from questionnaires distributed to the entrepreneurs in the selected SMEs and this informed the variables used for the analysis where the dependent variable is employment generation
Out of the 1,000 copies of the questionnaire distributed, a total of 732 were correctly filled and returned from the different five SMEs selected which were used for the analysis. Using the Principal component analysis, what is done is simply to analyse the variables or items and see if they can be reduced into fewer components or factors which explains the relationship among the variables. However what is expected in running the factor analysis is that all these variables are correlated with each other and at the minimum significance level. If we look at the results below, where there is
unempreduc | mktexp | govintv | ppeffort | edu | nuemply | capsourc | Loc | |
---|---|---|---|---|---|---|---|---|
unempreduc | 1.000 | .794 | .248 | .097 | -.089 | -.076 | .007 | -.042 |
mktexp | .794 | 1.000 | .329 | .137 | -.093 | .011 | .001 | -.045 |
govtintv | .248 | .329 | 1.000 | .413 | -.064 | .013 | -.005 | .062 |
ppeffort | .097 | .137 | .413 | 1.000 | .036 | .068 | .016 | .080 |
edu | -.089 | -.093 | -.064 | .036 | 1.000 | .000 | .141 | -.056 |
nuemply | -.076 | .011 | .013 | .068 | .000 | 1.000 | -.008 | .157 |
capsourc | .007 | .001 | -.005 | .016 | .141 | -.008 | 1.000 | -.055 |
loc | -.042 | -.045 | .062 | .080 | -.056 | .157 | -.055 | 1.000 |
Determinant = .247
In the correlation matrix table above, figures with negative value are said to be weakly correlated while those with positive or high value are strongly correlated with each other. For instance market expansion
Another important point the researcher looked into was the issue of
In this section we have the mean and standard deviation of all the variables and the sample size is 732. Normally the sample size is expected to be above 200 samples which is acceptable as our sample used is 732 and below that is said to be poor.
Mean | Std. Deviation | Analysis N | ||
---|---|---|---|---|
unempreduc | 1.11 | .387 | 732 | |
mktexp | 1.18 | .547 | 732 | |
govtintv | 1.16 | .522 | 732 | |
ppeffort | 1.48 | .693 | 732 | |
edu | 2.75 | 1.033 | 732 | |
numemployee | 2.15 | .567 | 732 | |
capsource | 1.76 | 1.176 | 732 | |
location | 1.23 | .421 | 732 |
The KMO is expected to be a value of 0.6 but if a value of 0.5 is obtained, it’s also acceptable.
Our result shows a value of
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. | 0.547 |
Approx. Chi-Square | 1017.706 |
Bartlett's Test of Sphericity df | 28 |
Sig. | 0.00 |
As shown in
The communalities
Initial | Extraction | |
---|---|---|
unempreduc | 1.000 | .875 |
Mktexp | 1.000 | .889 |
Govtintv | 1.000 | .690 |
Ppeffort | 1.000 | .758 |
Edu | 1.000 | .546 |
numemployee | 1.000 | .690 |
capsource | 1.000 | .593 |
Location | 1.000 | .528 |
Extraction Method: Principal Component Analysis.
As shown in
This section determines or shows how many components to extract and which of the components to be retained using the Eigen value of 1. All factors with Eigen value greater than 1 are retained while those below Eigen value of 1 are removed.
Component | Initial Eigenvalues | Extraction Sums of Squared Loadings | Rotation Sums of Squared Loadings | ||||||
---|---|---|---|---|---|---|---|---|---|
Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | |
1 | 2.081 | 26.012 | 26.012 | 2.081 | 26.012 | 26.012 | 1.837 | 22.961 | 22.961 |
2 | 1.309 | 16.358 | 42.37 | 1.309 | 16.358 | 42.37 | 1.415 | 17.682 | 40.643 |
3 | 1.178 | 14.72 | 57.09 | 1.178 | 14.72 | 57.09 | 1.161 | 14.511 | 55.154 |
4 | 1.002 | 12.53 | 69.619 | 1.002 | 12.53 | 69.619 | 1.157 | 14.465 | 69.619 |
5 | 0.854 | 10.679 | 80.298 | ||||||
6 | 0.826 | 10.327 | 90.626 | ||||||
7 | 0.553 | 6.916 | 97.542 | ||||||
8 | 0.197 | 2.458 | 100 |
Extraction Method: Principal Component Analysis.
In this section (
This section shows the components that are extracted on the plot. Basically it shows the components which are above the Eigen value of 1 and all other potential factors below that. From our result we can see that there are four (4) components having Eigen value greater than 1 and this indicates that there are four (4) distinct constructs instead of eight (8) items which explains 69% of the total variance.
As shown in
Component | ||||
---|---|---|---|---|
1 | 2 | 3 | 4 | |
Mktexp | .930 | |||
unempreduc | .929 | |||
Ppeffort | .864 | |||
Govtintv | .785 | |||
numemployee | .821 | |||
location | .688 | |||
capsource | .763 | |||
edu | .719 |
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.
Based on our findings in the study that all the variables in concern are correlated with each other at the minimum significance level, and that all the variables exhibit good relationship among each other; with this we can reject the null hypothesis
An entrepreneur is known to be a risk taker and with that to identify new employment opportunities the entrepreneur is expected to take optimum advantage of his immediate environment. The entrepreneur needs to think of how to create a business venture from what is available in his close environment. Setting up a waste disposal firm or a firm which recycles waste such as plastics, scraps among others is a great way of generating employment. Other organisations that can be established to generate employment may include a cleaning services firm, online/e-commerce service firm, fruits processing factories, etc
Creating a new program or scheme will essentially boost the employment opportunities in an enterprise. In point of fact, not anything triggers employment generation in a business than initiating new program or service. A lot of human resources are available with new skills and this will give them the opportunity of getting employed in the enterprise so as to display and make effective use of their new skills.
Most of the developing nations espouse this strategy by riveting the surplus labour in prolific employment in the modern industrial sector. This model tends to introduce more employment opportunities by increasing the level of investment and capital formation. Thus, the model considers capital and labour as complementary units of employment generation. The industrialisation-led model lays much prominence on capital goods as a source of generating employment where in the absence of some capital goods such as tools, equipments and machineries it will be very difficult to generate productive employment in enterprises.
Developing inbound recruitment model simply implies that an enterprise should identify and take advantage of the proficiency of its existing personnel. Once an employee’s value proposition has been identified and is known to have a very good skill, this will give room for more employment opportunities for those outside the enterprises which have acquired the same skills. The employee value proposition has to recount with what’s unique about the enterprise and the employee needs to articulate who you are, what you do and why you do it.