Keywords

Clustering, Employee Performance, Performance, Market Limitation, Market Segmentation, Small and Medium Scale Enterprise, Social Networking, Social Responsibilities.

Introduction

The expanding interest in studying SME clustering around the world is based on the sector's critical contribution to the economy's value creation, through job creation, income enhancement, cost reduction, and business convenience (Jevwegaga et al., 2018; Begum & Abdin, 2015; Eniola, 2014). Additionally, clustering and its relationship to major economic sustainability have attracted a lot of attention and action in recent years. Therefore, SME clustering is essential to achieving social and economic goals, as doing so can increase their competitiveness in the global economy, generate and spread innovations, create jobs, and distribute broadly based income and welfare.

The cluster model has gained popularity and recognition over the past few decades in both theory and practice. The word “cluster” can be applied to any field. Merriam-Webster classified the word “cluster” as a noun and defined it as “a number of related things that occur together”. They went on further to state that the first known use of cluster was before the 12th century. While studying business growth, one cannot bypass clusters and clustering.

Clustering has always existed and been a part of economic history, but the focus has been greater than before during these last 15 years. According to Caven (2011), Michael Porter’s definition is the most cited definition of clusters. This idea of his was developed in 1979. In his book “On Competition”, Porter (1998) defined clusters as follows: “A cluster is a system of interconnected firms and institutions the whole of which is greater than the sum of the parts”. According to JP Morgan Chase, when discussing "an economic sector, an industry, or an integrated network of firms working in a given area”, the term "cluster" is widely used in a colloquial context. Clusters can exist at the local, national, or regional levels when applied in this method. The term "cluster" is explicitly described in the literature as "a set of directly linked and interrelated industries working in a same area", according to Delgado et al. (2012). "Industries are well-considered to be parts of a specific cluster", the authors continued, "if they are linked by externalities of various kinds (such as demand, supply, employment, institutions, technology, etc.), and each cluster includes both core industries and other industries that form a mutually beneficial business ecosystem".

A cluster can occur in an area, a State, or a country and is a geographic concentration of related businesses, organizations, and institutions in one industry (Harvard Business School)."Geographic deliberation of networked firms and institutions in a certain sector, related by their commonalities as well as their complimentary traits", is how Porter (1998) defines a cluster. They collaborate to grow enterprises in the same industry. The gathering of Small and Medium Enterprises (SMEs) in one area is known as a cluster (Navickas & Malakauskait, 2009). An excessive number of SME clusters, based on township enterprises and private enterprises, also known as "lump economies", such as "one rural community-one product" or "one town-one industry", are one of the most significant influences to encourage the fiscal progress of an undersized region (town, village), according to Wang & Meng (2007), as cited in the study of Navickas & Malakauskait (2009). In Nigeria, SMEs have been viewed as the foundation for the development of technology and the creation of new jobs. In the society we live in, SMEs have been seen to group together. In groups of five, ten, and even more, they congregate.

The primary objective of the present study is to examine the effect of effect of clusterization activities, i.e. Market location, Social Responsibilities, Employee Performance, Market Segmentation, Social Networking on the performance of small and medium scales enterprises in Nigeria; However, the study needs to look more in some factors and responsibilities that has impact in the study. The study selected a sample size from the population through a convenience sampling method in order to obtain the sample size of the study population, in which subjects are chosen for their convenience and proximity to the researchers (Sekaran & Bougie, 2017). The poll was open to all clothing vendors who owned stalls and businesses at the Akesan market. Since there were 560 clothing vendors and they could be studied in the time allotted, this study used the complete population. A sample should be appropriately representative, according to Gaye; hence a sample of 255 SMEs cloth vendors was utilized for the study.

Regarding the association between independent variables and dependent variable, the outcomes are showing support to 5 of the 5 hypotheses. The outcomes of the study shows that Market Limitation (M), and Employee Performance (EP) had insignificant negative effects on the Performance of Small and Medium Scale Enterprise (PSME), while Social Responsibilities (SR), Market Segmentation (MS), and Social Networking (SN) had significant positive effects on Performance of Small and Medium scale Enterprise (PSME).

The study provides relevant insight on Market location, Social Responsibilities, Employee Performance, Market Segmentation, Social Networking and Performance of small and medium scales enterprises The significance of the study highlights how clustering operations affect customer base, sales, capital, business size, profit, and growth of the textile business in Nigeria markets. The study aims to determine whether clusterization has a favorable or negative impact on the Performance of SMEs so that the vendors can understand how their industry is doing. It also tries to ascertain how the clustering of SMEs can impact the labour market, The study concentrated on clustering SMEs in the clothing industry because this has not been done before, and the research community will benefit from it as well by bridging knowledge gaps to identify how clustering activities affect SMEs.

Literature Review

The expanding interest in studying SME operations around the world is based on the sector's critical contribution to the economy's value creation through job creation, income enhancement, cost reduction, and business convenience (Jevwegaga et al., 2018; Jucevicius & Grumadaite, 2015; Khan & Khalique, 2014). Additionally, clustering and its relationship to major economic sustainability have attracted a lot of attention and action in recent years. Therefore, SME clustering is essential to achieving social and economic goals, as doing so can increase their competitiveness in the global economy, generate and spread innovations, create jobs, and distribute broadly based income and welfare. It is commonly acknowledged that a global economy is driven by technological advancement, and that geographic locations and concentration are crucial for regional development and competitive advantage (Braun et al., 2005).

According to new technologies and the corresponding knowledge economy business models, connecting all the stakeholders in dynamic clusters is thought to increase competition and regional innovation of SMEs. (Kim et al., 2013; Kolympiris et al., 2007; Lezgovko & Lezgovko, 2018). Currently, opinions on the geographic proximity or clustering of businesses, organizations, and institutions abound in the literature (Asheim et al., 2006; Porter, 2011). Despite the crucial roles that SMEs play and the numerous studies that have been conducted on them in the past across the globe, no specific study has been done to assess the significance of industrial clusters for entrepreneurship and economic growth. Therefore, the process that should encourage additional study is necessary to educate political choices about the achievements or failures of economic development.

A cluster, according to the Cambridge English Dictionary, is a grouping of connected things that are growing or being maintained together, as well as a grouping of nearby individuals or objects. Munir et al. (2011), on the other hand, defined a cluster as a sectorial or geographic concentration of enterprises that produce and market a range of similar goods and/or services and deal with comparable opportunities and challenges. Similar to this, industrial clustering is the gathering of companies operating in the same industry in close proximity for the purpose of cooperation and advancement (Reveiu & Dardala, 2013). The main advantage for small businesses, according to economists, is that they may benefit from the economies of scale that larger businesses frequently enjoy. Industrial clustering has advantages such as quick access to a reservoir of expertise and skilled individuals and current industry information, despite the fact that there is more rivalry. Maresova & Halek (2014) added that clusters offer the advantage of increasing production, which enables companies to compete on a national and international level. Maresova et al. (2011) the sharing of various resources, technology, knowledge, and even vision is the core tenet of clustering. The writings of Christos, Peter, and Nicholas emphasized the fact that industrial clusters develop in specific places and that characteristics of these clusters, such as growth strength, structure, degree of similarity, age, size, etc., have a significant impact on the behaviours and performances of such cluster firms, which in turn have a significant impact on the performance of the area or region, as the case may be. Among other things, the availability of industrial policy through governmental interventions to aid new or existing businesses in struggling regions was one of the proposals made by Chatterji et al. (2013). Even though it is assumed that successful businesses would eventually pay taxes to the government, in this scenario the government should nonetheless offer help up front through public subsidies and the like. In addition, technical expertise, intellectual assistance, and financial market interventions for start-ups, to name a few, should be imported (Ibidunni et al., 2017). Slaper also back the idea that industrial clustering is a magic bullet for SME performance since it boosts competitiveness by raising productivity. Additionally, a 2014 study by the JP Morgan Chase foundation concluded that more research is necessary in this field of study because it is still little understood. More research has indicated that for industrial clustering to have a greater influence and to promote economic growth, certain conditions must be met. One of these recommendations was made by Slaper, who said that a sustainable SME performance depends on having an improved business climate, infrastructure, reducing government regulations, boosting local demand, and being open to global investment and competition. According to JP Morgan Chase foundation leader should encourage small enterprises by fostering public sector coordination and leadership, opening up programs to all entrepreneurs, and offering comprehensive and long-term support?

According to JP Morgan, the term "cluster" is frequently used inadvertently to represent "an economic sector, an industry, or a linked set of enterprises functioning in a certain area". In this context, clusters may be seen in a variety of locations, from street side displays to urban settings. A cluster is viewed as an "inter-organizational" arrangement with close proximity that allows it to amass capital. Cluster is officially defined by Delgado et al. (2014) as "A group of closely related and interconnected industries operating in a particular region". Industries are well-considered to belong to a particular cluster if they are connected by externalities of various kinds (such as demand, supply, employment, institutions, technology, etc.) and each cluster includes core industries as well as other industries that form a mutually beneficial business ecosystem. According to Caven (2011) research, clusterization has played a role in economic operations throughout history, but its effects have required attraction for more than a decade. Clusters are well-designed to increase the productivity with which corporations can compete, both nationally and internationally. The word "agglomeration" is frequently used in municipal studies. It is also a very important aspect of corporate strategy. Clusters are geographically incorporated firms and linked organizations that share technological knowledge, comfort, expertise, and resources. Proper logistics, a supply of skilled labor, and other benefits are a few that local business clusters can enjoy (Navickas & Malakauskaite, 2009).

A cluster, according to Porter (1998), is defined as a "geographic concentration of networked companies and organizations in a certain industry, linked by complementary as well as comparable features". Clusters are "a pectoral and geographic core center of initiatives that manufacture and trade a variety of connected or analogous inventions and, at the end, encounter general obstacles and opportunities", according to the United Nations Industrial Development Organization. According to Navickas & Malakauskaite (2009) who proposed that networks are often mistaken as clusters, due to the fact that both forms of collaboration utilize a value chain as the mainly essential component that unites their companies. Likewise, clusters can exist in networks, whereas networks can function in clusters. Clusters, on the other hand, consist of a wider variety of organizations, such as academic, financial, and government organizations, thus their field of procedure and end product is more extensive than that of networks. According to Kulikauskas & Viselgaite (2012), there exist diverse types of clusters which can be based on the description of clusters. The authors quoted Jucevicius & Grumadaite (2015) who made available a classification, which is very all-inclusive and which pays attention on the scope.

In the cluster industry, Michael Porter is recognized as the top theorist, and his book "On Competition". Clusters have a significant impact on competition and are important for businesses, governments, colleges, and other organizations in an economy. Clusters stand for a new, unified approach to classifying economic development, setting municipal policies, and understanding an economy. Understanding where clusters are located in a region is essential for understanding the economy's dynamism and the barriers to future growth. Ironically, in a global economy, the majority of long-term competitive advantages will generally be minor. In his earlier research on the Competitive Advantage of Nations, Porter (2011) created the theory of clusters as "groups of interrelated enterprises, suppliers, related industries, and specialized organizations in certain sectors that are prevalent in specific regions". His theory holds that the competitiveness of a company is influenced by the performance of other companies as well as other elements that are linked collectively in the value-added chain, in client-customer interactions, or in local or regional contexts (Swann et al., 1998).

Having reviewed literature, it is of great notice that a number of researchers have concluded that business clusters have a positive impact on SMEs. However, there is an underlined need, in further research to identify the negative impact of clusterization on SMEs if any. Also, most of those researches were not conducted on Nigeria’s SMEs. There is a hitch in making developed countries as the standard of business cluster as majority of the research were not based on developing economies Table 1.

Table 1 Hypothesis Development
Code	Description	Path
H1	Market Location significantly affects SMEs Performance	ML → PSMEs
H2	Social Responsibilities significantly affects SMEs Performance	SR → PSMEs
H3	Employee Performance significantly affects SMEs Performance	EP → PSMEs
H4	Market Segmentation significantly affects SMEs Performance	MS → PSMEs
H5	Social Networking significantly affects SMEs Performance	SN → PSMEs

Based on the literatures reviewed, the present study proposes the following research hypotheses; Figure 1 below shows research model for this study.

Methodology

According to Kothari et al. (2009), a research design is the blueprint for data collection, measurement, and analysis. It has a significant impact on the validity of the conclusions reached and forms the solid framework for the research project. Research design, according to Njue (2010), is the blueprint and organizational framework for the research process and offers a roadmap and checkpoints to keep the researcher on track. Moreover, Zikmund et al., (2013) also described a research design as a master plan that details the techniques and steps to be taken in order to gather and analyze the required data.

The study's objectives have been met using a descriptive research approach. The qualities of things, individuals, groups, organizations, or situations are described by descriptive research. A survey is adopted for descriptive research to be used. A survey design was suitable for this study since it enables the use of questionnaires to gather data for all six variables, i.e, independent and dependent variables (Orodho, 2003).

In this study, the SMEs taken into account are those who sell fabrics. The target population was used to select the sample size. The poll was open to all clothing vendors who owned stalls and businesses and their employees at the market. Since there were 560 clothing vendors include their employees and they could be studied in the time allotted, the researcher used the complete population. A sample should be appropriately representative, according to Gaye; hence a sample of 255 SMEs cloth vendors was utilized for the study.

According to Omona (2013) recommendation that a sample of 10 to 50% is sufficient if carefully chosen, the sample size of the clothing vendors at Akesan market was 45.6% of the entire population. The respondents from the relevant small and medium scale firms were chosen using simple random selection and a stratified sample procedure. The population was divided into subgroups (strata) using stratified sampling, and respondents were then chosen at random from each stratum. Simple random sampling, according to Zikmund et al. (2013). Guarantees that every component of the population has an equal probability of being included in the sample. The study population and sample are displayed in the following Table 2 & 3.

Table 2 Population, Sampling Frame, and Respondent Selection
Population	Sampling	Respondent’s basis
560 Shop Owners & Employees	255 Shop Owners & Employees	Fabric Stores In Akesan Market

Table 3 Respondent Profile
		Frequency	Percentage	Valid Percentage	Cumulative Percentage
Gender	Male	74	29.0	29.0
	Female	181	71.0	71.0	29.0
	Total	255	100	100	100
Marital Status	Single	51	20	20	20
	Married	193	76	76	96
	Divorced	11	4	4	100
	Total	255	100	100
Age	18-25	15	5.9	5.9	5.9
	26-35	42	16.5	16.5	22.4
	36-45	102	40.0	40.0	62.4
	46 & Above	96	37.6	37.6	100
	Total	255	100	100
Market Experience	1-3 Years	29	11.4	11.4	11.4
	4-5 Years	15	5.9	5.9	17.3
	6-7 Years	45	17.6	17.6	34.9
	8 & Above	166	65.1	65.1	100
	Total	255	100	100
Position	Shop Owner	143	56.1	56.1	56.1
	Employee	112	43.9	43.9	100
	Total	255	100	100
Level of Education	Diploma	149	58.4	58.4	58.4
	Bachelor	78	30.6	30.6	89.0
	Master	28	11	11	100
	Doctoral	0	0	0
	Total	255	100	100

There is a worldwide type of simple statistics to describe the basic pattern of data used by scientists (Dickinson & Newman, 2007). Utilizing statistical tables, diagramming charts and graphs, and computing statistical parameters like measure change and central slope measures (mean, median, and method), this sort of analysis is used to characterize (summarize) the data (range, standard deviation, and variance).

In this study, quantitative data was examined. The returned copies of the surveys were examined to check for inaccuracies and to reduce their likelihood. SMEs in the market were evaluated for their performance over the period of 2011 to 2021 using the SPSS technique of data analysis, and the relationship between clustering activities and the performance of SMEs in the market was measured.

Results and Discussion

The study selected a sample size from the population through a convenience sampling method in order to obtain the sample size of the study population, in which subjects are chosen for their convenience and proximity to the researchers (Sekaran & Bougie, 2017). Sample size is a subset of the entire study population and this can be calculated using Krejcie & Morgan (1970) formulae for measuring the sample size. The target population was used to select the sample size. The poll was open to all clothing vendors who owned stalls and businesses at the Akesan market. Since there were 560 clothing vendors and they could be studied in the time allotted, this study used the complete population. A sample should be appropriately representative, according to Gaye; hence a sample of 255 SMEs cloth vendors was utilized for the study.

According to Omona (2013) recommendation, that a sample of 10 to 50% is sufficient if carefully chosen, the sample size of the clothing vendors at Akesan market was 45.6% of the entire population. The respondents from the relevant small and medium scale firms were chosen using simple random selection and a stratified sample procedure. The population was divided into subgroups (strata) using stratified sampling, and respondents were then chosen at random from each stratum. Simple random sampling, according to Zikmund et al. (2013), guarantees that every component of the population has an equal probability of being included in the sample Table 4 & 5.

Table 4 Descriptive Statistics
	N	Range	Minimum	Maximum	Mean	Std. Deviation
ML (Q1)	255	4.00	1.00	5.00	3.1294	1.17843
ML (Q2)	255	4.00	1.00	5.00	3.1137	1.12190
ML (Q3)	255	4.00	1.00	5.00	3.2275	1.20172
ML (Q4)	255	4.00	1.00	5.00	3.2588	1.11701
ML (Q5)	255	4.00	1.00	5.00	3.0784	1.18460
SR(Q1)	255	4.00	1.00	5.00	3.0902	1.18874
SR(Q2)	255	4.00	1.00	5.00	3.0902	1.14143
SR(Q3)	255	4.00	1.00	5.00	3.0039	1.08859
SR(Q4)	255	4.00	1.00	5.00	3.0353	1.17835
SR(Q5)	255	4.00	1.00	5.00	3.1686	1.38858
EP1(Q1)	255	4.00	1.00	5.00	3.0039	1.09580
EP2	255	4.00	1.00	5.00	3.1529	1.28442
EP3	255	4.00	1.00	5.00	3.2392	1.28653
EP4	255	4.00	1.00	5.00	3.1804	1.20971
EP5	255	4.00	1.00	5.00	3.0235	1.11867
MS(Q1)	255	4.00	1.00	5.00	2.9647	1.13061
MS (Q2)	255	4.00	1.00	5.00	2.9686	1.24190
MS (Q3)	255	4.00	1.00	5.00	2.9608	1.23532
MS (Q4)	255	4.00	1.00	5.00	2.9098	1.24376
MS (Q5)	255	4.00	1.00	5.00	3.0039	1.20856
SN (Q1)	255	4.00	1.00	5.00	3.0784	1.27735
SN (Q2)	255	4.00	1.00	5.00	2.9294	1.13417
SN (Q3)	255	4.00	1.00	5.00	2.8902	1.17876
SN (Q4)	255	4.00	1.00	5.00	3.1608	1.25555
SN (Q5)	255	4.00	1.00	5.00	3.0549	1.19255

Table 5 Reliability Test
Contrast	Cronbach’s Alpha	No. of items
Marketing Location	0.731	5
Social Responsibilities	0.769	5
Employee Performance	0.774	5
Market Segmentation	0.801	5
Social Networking	0.722	5
Performance of SME	0.805	5

There have been several successive reliabilities testing treatments for the six multi-variable variables in this study. Statistical results for all variables indicate that the alpha score of Cronbach is at least 0.722, which means the entire building is considered to be of reasonably high reliability. The reliability of the scales is calculated via an iterative process: if the removal of any item increases the scale's reliability, the item is omitted and examined again; however, the removal of the items resulting in significant increases was not carried out as suggested by (Nunnally, 1975). According to the degree to which a variable is consistent in what is supposed to be measured, (Hair et al., 2014). In this analysis, the alpha value of all variables is not removed, as shown in Table 5 above. The value is greater than 0.7.

Durbin-Watson statistics were used to check and verify the freedom of error assumption. In the case of a decline in the Durbin-Watson statistic values between 1.50 and 2.50 the error term independence was not infringed according to (Coakes & Ong, 2011). Table 6 below offers an overview of the Durbin-Watson value. It means that there were no problems of self-correlation between relevant values. In addition, the research was performed using the multi-recording framework for Market Limitation, Social Responsibilities, Employee Performance, Market Segmentation and Social Networking as independent variable and Performance of Small and Medium scale Enterprise as dependent variable. Tables 6 below applied to the tests.

Table 6 Multiple Regression Test
Model	R	R Square	R 2	Std. Er	R Square	F	df1	df2	Sig. F	Durbin- Watson
1	0.357a	0.127	0.110	0.87080	0.127	7.272	5	249	<.001	1.964

The determination coefficient (R2), as suggested (Hair et al., 2014), is used when the researcher wants to calculate its value as indicated by the independent’s variables or the predictor variables in respect of the fraction of the total variance of the dependent variable. When the R2

value is high, the explanatory power of the regression model is solid. In addition, the values and significance of the regression coefficients decide the variables included in the model Table 7 & 8.

Table 7 Anova Test
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	27.573	5	5.515	7.272	<0.001b
	Residual	188.817	249	0.758
	Total	216.39	254

Table 8 Research Hypothesis Summary Hypothesis
	Hypothesis	Code		Hypothesis
1	Market Location does not significantly affect the Performance of Small and Medium scale Enterprise	ML	PSME	H1 Rejected
2	Social Responsibilities significantly affect the Performance of Small and Medium scale Enterprise	SR	PSME	H2 Supported
3	Employee Performance does not significantly affect the Performance of Small and Medium scale Enterprise	EP	PSME	H3 Rejected
4	Market Segmentation significantly affects the Performance of Small and Medium scale Enterprise	MS	PSME	H4 Supported
5	Social Networking significantly affects the Performance of Small and Medium scale Enterprise	SN	PSME	H5 Supported

Regarding the association between independent variables and dependent variable, the outcomes are showing support to 5 of the 5 hypotheses. The outcomes of the study shows that Market Limitation (M), and Employee Performance (EP) had insignificant negative effects on the Performance of Small and Medium Scale Enterprise (PSME), while Social Responsibilities (SR), Market Segmentation (MS), and Social Networking (SN) had significant positive effects on Performance of Small and Medium scale Enterprise (PSME).

For the 5 study hypotheses. The first hypothesis (H₁) states that Market Location (ML) has no positive relationship with Performance of Small and Medium scale Enterprise (PSMEs). According to Pallant (2020) in a test of correlation if P value is less than 0.01, implies there is a statistically significant relationship and we accept the hypothesis, in accordance with our analysis, we notice a negative relationship between Market Location and Performance of Small and Medium scale Enterprise, with our correlation examines value, while the value of P=0.267, which is high than 0.01 level. Therefore, the hypothesis (H₁) is rejected.

The second hypothesis (H₂) states that Social Responsibilities (SR) have a positive relationship with Performance of Small and Medium scale Enterprise (PSMEs). According to Pallant (2020) in a test of correlation if P value is less than 0.01, implies there is a statistically significant relationship and we accept the hypothesis, in accordance with our analysis, we notice a positive relationship between Social Responsibilities and Performance of Small and Medium scale Enterprise, with our correlation examines value, while it was found that the value of P is <0.001, which is less than 0.01 level. Therefore, the hypothesis (H₂) is supported.

The third hypothesis (H₃) states that Employee Performance (EP) has No positive relationship with Performance of Small and Medium scale Enterprise (PSMEs). According to Pallant (2020) in a test of correlation if P value is less than 0.01, implies there is a statistically significant relationship and we accept the hypothesis, in accordance with our analysis, we notice negative positive relationship between Entrepreneur Performance and Performance of Small and Medium scale Enterprise, with our correlation examines value, while it was found that the value of P value=0.228, which is high than 0.01 level. Therefore, the hypothesis (H₃) is rejected. The fourth hypothesis (H₄) states that Market Segmentation (MS) have a positive relationship with Performance of Small and Medium scale Enterprise (PSMEs). According to Pallant (2020) in a test of correlation if P value is less than 0.01, implies there is a statistically significant relationship and we accept the hypothesis, in accordance with our analysis, we notice a positive relationship between Market Segmentation (MS) and Performance of Small and Medium scale Enterprise, with our correlation examines value, while it was found that the value of P is <0.001, which is less than 0.01 level. Therefore, the hypothesis (H₄) is supported.

The fifth hypothesis (H₅) states that Social Networking (SN) has a positive relationship with Performance of Small and Medium scale Enterprise (PSMEs). According to Pallant (2020) in a test of correlation if P value is less than 0.01, implies there is a statistically significant relationship and we accept the hypothesis, in accordance with our analysis, we notice a positive relationship between Social Networking and Performance of Small and Medium scale Enterprise, with our correlation examines value, while it was found that the value of P is <0.001, which is less than 0.01 level. Therefore, the hypothesis (H₅) is supported.

Conclusion

This study shows that Market Location, Social Responsibilities, Employee Performance, Market Segmentation, and Social Networking play different significant role to influence the Performance of SMEs in Nigeria, as empirical findings are consistent with results of this research. However, Market Location, and Employee Performance are not that important tools in Nigeria Market, while others (Social Responsibilities, Market Segmentation, and Social Networking) are important tools in the Akesan Market, for the continuation, upkeep, and Performance of businesses. However, according to the findings in the study, 2 of the clustering activities (Market Location and Employee performance) are insignificantly aided the expansion of SMEs, hence Social Responsibilities, Market Segmentation, and Social Networking factors are significantly aided the expansion of SMEs.

Given that SMEs in Nigeria are the main drivers of employment growth, it would be beneficial to conduct studies to ascertain whether there are any other factors impacting SMEs' performance in addition to clusterization. Results from these studies could be used to expand SMEs, which would increase employment in Nigeria to some extent. In order to find answers that can be put into practice for the development of Nigeria, it would also be beneficial to expand the scope of this research by increasing both the population and sample size.

References

Asheim, B., Cooke, P., & Martin, R. (2006). The rise of the cluster concept in regional analysis and policy: A critical assessment. In Clusters and Regional Development, 19-47.

Indexed at, Google Scholar, Cross Ref

Begum, A., & Abdin, M.D. (2015). Employment generation and poverty alleviation through SME cluster development in Bangladesh.

Indexed at, Google Scholar, Cross Ref

Braun, P., McRae-Williams, P., & Lowe, J. (2005). Small business clustering: Accessing knowledge through local networks. Journal of New Business Ideas and Trends, 3(2), 57-63.

Indexed at, Google Scholar

Caven, H. (2011). What is the role of a cluster in the business co-operation development?

Indexed at, Google Scholar

Chatterji, A., Glaeser, E., & Kerr, W. (2013). The origins of entrepreneurship and innovation clusters.

Google Scholar

Coakes, S.J., & Ong, C. (2011). SPSS version 18.0 for windows (version 18.0). Australia: John Wiley and Sons.

Google Scholar

Delgado, M., Ketels, C., Porter, M.E., & Stern, S. (2012). The determinants of national competitiveness. National Bureau of Economic Research.

Indexed at, Google Scholar, Cross Ref

Delgado, M., Porter, M.E., & Stern, S. (2014). Clusters, convergence, and economic performance. Research Policy, 43(10), 1785-1799.

Indexed at, Google Scholar, Cross Ref

Dickinson, D.K., & Neuman, S.B. (2007). Handbook of early literacy research.

Indexed at, Google Scholar, Cross Ref

Eniola, A.A. (2014). The role of SME firm performance in Nigeria. Oman Chapter of Arabian Journal of Business and Management Review, 34(2352), 1-15.

Indexed at, Google Scholar, Cross Ref

Hair, J.F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V.G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review.

Indexed at, Google Scholar, Cross Ref

Ibidunni, A.S., Ogunnaike, O.O., & Abiodun, A.J. (2017). Extending the knowledge strategy concept: Linking organizational knowledge with strategic orientations. Academy of Strategic Management Journal, 16(3).

Indexed at, Google Scholar

Jevwegaga, H., Ade-adeniji, O., Ibidunni, A.S., Olokundun, M.A., Borishade, T.T., Falola, H.O., & Ogunniyi, A. (2018). Role of SMEs' entrepreneurial activities and industrial clustering on SMEs' performance. Academy of Entrepreneurship Journal, 24(1), 1-7.

Indexed at, Google Scholar

Jucevicius, G., & Grumadaite, K. (2015). Patterns for cluster emergence in latecomer economies. Procedia-Social and Behavioral Sciences, 213, 198-203.

Indexed at, Google Scholar, Cross Ref

Khan, M., & Khalique, M. (2014). Strategic planning and reality of external environment of organizations in contemporary business environments. Business Management and Strategy, 5(2).

Indexed at, Google Scholar, Cross Ref

Kim, J.W., Choi, Y., Lee, D., & Jung, S.Y. (2013). Intelligent MADS with clustering and elastic net and its application to optimal design of interior PM synchronous machines. IEEE Transactions on Magnetics, 49(5), 2209-2212.

Indexed at, Google Scholar, Cross Ref

Kolympiris, C., Klein, P.G., & Kalaitzandonakes, N.G. (2007). The Entrepreneur's Choice of Location: Evidence from the Life Sciences.

Indexed at, Google Scholar

Kothari, S.P., Li, X., & Short, J.E. (2009). The effect of disclosures by management, analysts, and business press on cost of capital, return volatility, and analyst forecasts: A study using content analysis. The Accounting Review, 84(5), 1639-1670.

Indexed at, Google Scholar, Cross Ref

Krejcie, R.V., & Morgan, D.W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607-610.

Indexed at, Google Scholar, Cross Ref

Kulikauskas, D., & Viselgaite, D. (2012). Clusterization: Effects on some industry sectors of Lithuania. Ekonomika, 91(2), 79-96.

Indexed at, Google Scholar, Cross Ref

Lezgovko, M., & Lezgovko, A. (2018). The Effect of service clusters on the sustainable economic development. Economics and Culture, 15, 75-87.

Indexed at, Google Scholar, Cross Ref

Maresova, P., & Halek, V. (2014). Deployment of cloud computing in small and medium sized enterprises in the Czech Republic. Economics and Management.

Indexed at, Google Scholar, Cross Ref

Maresova, P., Jasíkova, V., & Trousil, M. (2011). Method for evaluating the performance of clusters in the Czech Republic. In Proceedings of the 2nd International Conference on Urban sustainability, cultural sustainability, green development, green structures and clean cars, Prague, Czech Republic, 30-35.

Google Scholar

Munir, A., Lim, M.K., & Knight, L. (2011). Sustaining competitive advantage in SMEs. Procedia-Social and Behavioral Sciences, 25, 408-412.

Indexed at, Google Scholar, Cross Ref

Navickas, V., & Malakauskaite, A. (2009). The impact of clusterization on the development of small and medium-sized enterprise (SME) sector. Journal of Business Economics and Management, 10(3), 255-259.

Indexed at, Google Scholar, Cross Ref

Njue, L.M. (2010). The Menengai caldera structure and its relevance to geothermal potential. In ARGEO-C3, Third East African Rift Geothermal Conference, Djibouti. 22-25.

Indexed at, Google Scholar

Nunnally, J.C. (1975). Psychometric theory-25 years ago and now. Educational Researcher, 4(10), 7-21.

Indexed at, Google Scholar, Cross Ref

Omona, J. (2013). Sampling in qualitative research: Improving the quality of research outcomes in higher education. Makerere Journal of Higher Education, 4(2), 169-185.

Indexed at, Google Scholar, Cross Ref

Orodho, A.J. (2003). Essentials of educational and social science research methods. Nairobi: Masola Publishers, 54, 71-82.

Google Scholar

Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS. Routledge.

Indexed at, Google Scholar

Porter, M.E. (1998). Clusters and the new economics of competition.

Indexed at, Google Scholar

Porter, M.E. (2011). Competitive advantage of nations: creating and sustaining superior performance.

Google Scholar

Reveiu, A., & Dardala, M. (2013). The role of universities in innovative regional clusters. Empirical Evidence from Romania. Procedia-Social and Behavioral Sciences, 93, 555-559.

Indexed at, Google Scholar, Cross Ref

Sekaran, U., & Bougie, R. (2017). Research Methods for Business: Skills Development Approach. Salemba Empat, South Jakarta.

Google Scholar

Swann, G., Prevezer, M., & Stout, D. (1998). The dynamics of industrial clustering: International comparisons in computing and biotechnology. Oxford University Press.

Indexed at, Google Scholar, Cross Ref

Wang, H., & Meng, J. (2007). Predictive modeling on multivariate linear regression. Journal of Beijing University of Aeronautics and Astronautics, 33(04), 500.

Google Scholar

Zikmund, W.G., Babin, B.J., Carr, J.C., & Griffin, M. (2013). Business research methods.

Google Scholar

Received: 12-Jan-2023, Manuscript No. BSJ-23-13127; Editor assigned: 13-Jan-2023, PreQC No. BSJ-23-13127(PQ); Reviewed: 27-Jan- 2023, QC No. BSJ-23-13127; Revised: 28-Jan-2023, Manuscript No. BSJ-23-13127(R); Published: 11-Feb-2023