Chapter One: Introduction
The topic of the study is to evaluate the effects of ethnic disadvantage in United Kingdom’s Small Business Ownership and Performance. The valuable contribution, therefore, is based on the use of statistical metrics to determine how different aspects of ethnic disadvantage influence performance of small business firms and the mediating role of ownership in such relationship. In this regard, the study in different chapters will keep re-evaluating the dependent and independent variables so as to end up with a focused research model that establishes a pathway for relationship justification between ethnic disadvantage and financial performance of small business firms in UK.
- Background of the study
From the survey by FSB (2019) it was held by the period 2018, there were 5.6 million established small businesses in comparison to the previous years. Howev
- What is the financial performance of small businesses in the United Kingdom in light of ownership and ethnic positioning?
er, a drop by 0.5% was reported which was commensurate to 27,000 small businesses although the employment rate increased by 2% to record at +304,000. The same trend has been linked to the number of micro non-employing firms that reduced by 1% while the employment firms grew by 2%. The SMEs contribute significantly to the economy of UK by accounting for 99.3% in terms of the private sector. In addition, an aggregate sum of 16.3 million pointing to employment was recorded in SMEs which is a base of 60% covering the private sector. On the other hand, SMEs sector in United Kingdom depicted a turnover of £2.0 trillion which represents a 52% turnover across the private sector. Further evaluation points to the dimension of location and industries whereby a greater part of the small businesses are located in southern England compared to other parts of the UK. Also, a fifth of the entire SMEs operate within the construction sector in comparison to just one per cent being in the mining, quarrying as well as utilities sector (FSB, 2019).
In the study by ERC (2018) it was noted that UK SMEs have achieved growth since the year 2015. For instance, in the period 2017, majority of entrepreneurs reported a growth performance of 36% while 19% recorded a decreased performance. The predictions were that the sector would record growth at the same time remain stable; in fact a 47% of the small business proprietors stated that they expect a growth turnover towards the year 2018. Further, a number of challenges and opportunities faced by SMEs have been cited (ERC, 2019). For instance, some of the challenges have been attributed to Brexit in that would have effects to SMEs; 54 per cent of the SMEs asserted that Brexit would not have any significant effects while 27 per cent of the proprietors indicated such occurrence would pose negative effects to the micro businesses; a 8 per cent of the small business owners regard Brexit to have positive effects to the SMEs. In light of the same, 10% of SMEs reported plans to create or rather formulate new products and services while 9 would lower their capital expenditures and spending on R&D (Balasubramanian and Lee, 2008). For instance, among the cited obstacles for small businesses in UK include: competition in the industry, red tape/regulations, taxation rates, recruitment of new staff and skills, lateness in payment, Brexit effects, pensions in the workplace, living wage at the national level, cost of premises and availability and lastly access to finance (Mazzarol, Reboud and Volery, 2010). According to ERC (2019) it is indicated that small businesses located in London have a higher turnover compared to those in South East, East Midlands, Wales, and East of England among others. In fact, this establishment anticipates one of the major analyses in the study where location of business is considered to have control effects to the relationship between ethnic disadvantage and financial performance of small firms in UK.
- Problem statement
The problem of the study rests on the fact that ethnic disadvantage may have been an impediment to the growth and sound financial performance of small firms in the United Kingdom. The challenges that come with ethnic disadvantage include lack of access to financial resources or perceptions from the customers where such may lead to negative effects such as product apathy due to preferences and considerations for products of a certain origin among others. Therefore, this study asserts that ethnic disadvantage may annihilate the growth performance of small firms in the long term. In due course, more problems and gaps confronted by small business operators are going to be explored especially in the literature review.
- Research questions
The main research questions include the following:
- What is the reality of ethnic disadvantage in the ownership of small businesses in the United Kingdom?
- What is the financial performance of small businesses in the United Kingdom in light of ownership and ethnic positioning?
- To critically analyse the effects of ethnic disadvantage towards the performance of small businesses in United Kingdom and whether there exists any mediating effects from other business-specific or industry-specific factors
- To provide suitable recommendations that may improve policies in the SMEs sector in United Kingdom on development of ethnically disadvantaged entrepreneurs
- Research aim and objectives
The aim of the study is to provide a critical analysis on the influence ethnic disadvantage of small business operators has on the performance of their enterprises in the UK context.
Further, the objectives of the study include the following:
- To explore the reality of ethnic disadvantage in the ownership of small businesses in the United Kingdom
- To analyse the financial performance of small businesses in the United Kingdom and in the context of their ownership and ethnic background of the proprietors
- To critically analyse the effects of ethnic disadvantage towards the performance of small businesses in United Kingdom and whether there exists any mediating effects from other business-specific or industry-specific factors
- To provide suitable recommendations that may improve policies governing the SMEs sector in United Kingdom on enhancement of financial performance of ethnically disadvantaged entrepreneurs
- Significance of the study
The study raised significant questions regarding the influence of ethnic positioning of the entrepreneurs that operate small businesses in United Kingdom. In specific, whether ethnic disadvantage in turn affects performance of the SMEs and how ownership further impacts on such a relationship. In the objectives, there was mention of business-specific factors and industry-specific factors which constitute the control variables in the study such as: age of the firm, industry positioning, size of the firm, location of the firm, and education of the proprietors. The benefit of including more factors in the research model was to enhance objectivity in the study’s results of the nexus between ethnic disadvantage and performance of small businesses. For that reason, it renders the study to have a objective standpoint on factors influencing performance of small businesses and from this provided solid grounds to formulate recommendations that can transform the small business sector in UK for the prosperity of all stakeholders.
- Structure of the study
The study has been developed based on five chapters as follows: Chapter one presents the background of the study, problem statement, research questions, research objectives, and significance of the study. Chapter two is the literature review where the key variables of the study have been explored: for instance, ethnic advantage in business, ownership structure in small businesses and antecedents or rather indicators of business performance in the small business sector. The gaps in knowledge have also been addressed. Chapter three consists of the methodology, data collection and analysis techniques and the scope of the econometric model. Chapter four features the data analysis especially the implementation of the research model linking ethnic disadvantage to financial performance of small business firms in the context of the United Kingdom. The analysis in this chapter also included computations aimed to confirm the mediating effects of the control variables categorised as the business-specific or industry-specific factors related to small businesses in UK. Chapter five is the final part of the study which included a statement of the key findings, recommendations, and limitations of the study. Also, a review of areas of future research has been stated in the same chapter.
Chapter Two: Literature Review
In this section the focus is to develop literature that supports the research model hence all variables are going to be explored. In anticipation there may be limitations in that past scholars had not developed their study based on similar variables as those sought in this study. The main focus is to critically analyse literature on the effects of ethnic disadvantage towards performance of small businesses and the intermediary role of other factors such as ownership, size, location, industry, age, and education of the proprietors.
2.1 Factors that affect success of small business firms
In this study, the important focus is on the effects of ethnic disadvantage on performance of small firms with specific reference to United Kingdom. Thus, the liuterature to be purused may also have focus on the effects of demographic factors on performance of small business firms.
According to Delmar and Wiklund (2008) the business environment includes both external and internal factors of an organisation which has continued influence on its successful existence. Factors that are internal to the firm are consisted to have vital impact on the growth of small business firms. In similar context, Demirguc-Kurt (2014) noted that improvement of both internal and external environment of a firm is significant for SME growth. In agreement, the importance of the business environment towards growth of SMEs was affirmed by World Bank (2006) and in the study by Van Doorn and Leeflang (2014). Fatoki and Garwe (2010) stated that the factors linked to the internal environment incorporate those that can be manipulated by the business from within. Kolstad and Wiig (2015) asserted that challenges that impact on the internal environment include competency in management, skills, poor training of the management personnel, limitations in financial understanding, poor business administration skills, and inadequate capabilities in technological adoption.
In this study, managerial competency was not directly considered among the control variables but the researcher would regard it as an important perspective of business ownership in small business firms. The justification is that ownership of a business would entail its management and administration. According to Hisrich and Drnovsek (2012) managerial competencies positively influence performance of small firms. Further, experience of the management, education, experience in the start-up, and knowledge are considered as important measurements of managerial competencies. A look at the assertions above, it can be seen that more analysis depict that education is an important measure of managerial competencies. Among the control variables in this dissertation is the aspect of education which forthwith can be interpreted under managerial competencies alongside business ownership. According to Martin and Staines (2008) the contributory role of competence in management for SMEs cannot be doubted since lack of the same was considered to be the reasons why small firms fail. In line with this, Abdel, Rowena and Robyn (2010) owner-managers of small businesses have minor understanding regarding information and use of financial and accounting hence having serious problems with literacy on financial planning. In line with the same findings, Alattar, Kothy, and Innes (2009) held that small and micro firms have the owners or managers affected due to insufficient knowledge regarding financial matters; for instance, owners with limited or inadequate skills in financial planning end up devaluing information that is got from financial statements. The assertions above have significant contributions to the ongoing study in that ownership is among the important variables that are considered central in establishing the performance of small firms in the United Kingdom. Already, a review of previous literature can depict how education align to effective management of the small firms since poor skills in understanding business operations and systems render the small firms to lose focus in their performance and the researcher would anticipate that in such context the SMEs also lack the capacity to achieve better returns.
In the study by Mazanai and Fatoki (2012) access to finance is considered as a major challenge in suppressing growth as well as survival of small start-ups. Prior to examining further on this barrier, the researcher holds the opinion that lack of access to finance can be occasioned by ethnic disadvantage of small business owners since natives may be granted priority by lenders. Also, financiers or banks to be specific may find it difficult to offer loans to owners of small firms that are considered ethnically disadvantage. Due to this reason, it becomes imperative to correlate access to finance to business owners with their ethnic locus as possible interconnected aspects of small business development. In fact, to support this point Haron et al., (2013) expressed that financial institutions involved in giving credit have created complex processes including the firms becoming much cautious about financial crises; due to this, SMEs owners find it difficult to comprehend the procedures and decision frameworks used by credit firms to advance them loans. Based on this the researcher holds the opinion that increased credit uncertainty which can link to defaulting of payments or economic disadvantage can be profiled for different ethnic groups. In the literal manner, some ethnic groups may be considered as having higher changes of defaulting such as non-Britons while the White may be given prosperity since they do not cast doubts to the loan masters. Thus, there emerge such ripple effects or connectivity between ethnic positioning and perceptions from credit firms on entrepreneurs that seek funding. For instance, Dalberg (2011) asserted that prior to the financial crises access to funds was a major concern among SMEs in developing countries, in obtaining funds that were necessary for growth and expansion. In addition, those banks are hesitant to extend funds to SMEs in order to build their capital base. The assertion is central to this study since the aspect of location is also considered a major control variable in the model. Here, it can be seen that the scholar was articulate on problem of lack of funding to SMEs in the developing countries and such is not a problem developed countries. In a literal manner, developing and developed countries are two contrasting locus that businesses can be located. Thus, such becomes their location which influences all other perceptions that financiers can consider in the decisions to accept loan application of small business firms. In a similar way, the same is the concern for having the variable of location in the research model of this study, because it has mediating effects to the relationship between ethnic disadvantage and performance of small business firms. As per the assertions by Pretorius and Shaw (2012) a majority of small business firms depend on financing from internal resources, like the contributions from the proprietors, friends, and families which often than not are insufficient for the survival and growth of SMEs. For this reason, external sources of financing are necessary to lower the challenges of cash flows for the small firms.
Arinaitwe (2011) mentioned about technical capabilities as a factor that affects performance of small firms. For instance, the author retorted that the rationale for majority of small firms to continue facing growth challenges within developing countries albeit support from the government or other firms is due to technological capabilities or lack of the same. Similarly, the author noted that small firms experience such difficulties due to poor technological implementation amidst technological advancements. On this point the researcher believes it connects to ownership factor considered as a control variable in this research. For instance, ownership of small firms points to capacities such as technological implementation by the owners of the small firms; hence, whenever owners of small firms lack such competencies the enterprise also experience low performance. In fact, the words by Arinaitwe stated that poor technological implementation creates weaknesses in terms of competitive advantage and growth capacity of the small firms. Similar sentiments are evident in the study by Singh, Garg and Deshmukh (2010) who noted that in places such as China and India, small firms experience common barriers in the efforts of the business owners to upgrade technology and enhancement of product quality (See also Mazanai and Fatoki, 2012). In the case of India, small forms suffer from low scale production which minimises the capacity to reduce costs in the transformative process including engagement in the technological upgrades which remains a major obstacle.
In a different context, competition has been considered as a barrier to performance of small firms as evident in the study by Scarborough et al., (2009). Prior to further explorations on this, the researcher links it to the control variable in the study labeled as industry. In fact, the concern for the industry of small firms in UK as modeled in chapter four of the study links to competition. In other words, industry and competition can be considered as concomitant factors that influence the operations of a firm; including the fact that rivalry manifests itself in the industry level. Xavier, Kelley, Kew, Herrington and Vorderwuibecke (2012) with similar thoughts held that firms must understand their industry positioning in order to undertake decisions that address the long-term survival or sustainability of firms and the opportunities to be optimised. Further, Ehlers and Lazenby (2010) held that the SMEs sector has continued to change and increase in a radical manner with new entrants venturing into the industry to fight for the market share. Competition trends can be tracked under variations in the trends of the market, technologies variations, and creation of new techniques for management. According to Gunasekaran, Rai and Griffin (2011) the survival of small business firms depends wholly on the industry factors whilst the same incorporates flexibility of the SMEs to rechanneling their technologies and strategies to have a better competitive advantage than their rivals. Another factor that can be considered significant at the industry level is the aspect of globalisation. As held by Longenecker (2012) small firms are required to move away from their domestic business environment and venture into globally competitive markets. In this regard, whenever small firms fail to stimulate their global positioning it in turns limits their growth capacities notwithstanding their size. In support of the same, Scarborough et al., (2009) held that the success of small firms comes whenever the management takes into consideration how they can expand across the borders. In examining the arguments around globalisation of small firms the researcher holds the opinion that they are connected to industry factors of small firms in SMEs and same aspect has influence on location. As can be seen in the research model, location and industry factors have been operationised as control variables and globalistaion defines their impact on performance of small firms.
Regulatory factors have been considered to have influence on the performance of SMEs as held by Chamberlain and Smith (2013). The author also recounts that the successful performance of small businesses continues to face threats due to the poor resources allocation as well as over-regulation. In addition, regulatory procedures around establishment of firms are also considered to be extremely intricate and jeopardising. On this position, the researcher tends to consider regulatory challenges as causing ethnic disadvantage and at the same time creating barriers within the industry environment that small firms operate in United Kingdom. Similar thoughts can be intuited in the findings by Mollentz (2012) who expressed that a number of small firms fail to comply with the regulations due to the affordability and with complexities affecting foreign owners. In the same line of though, Herrington, Kew, and Kew (2010b) stated that small firms run by non-natives are subjected to stiffer measures as opposed to natives which in the end restricts them to achieve an optimal performance. In the assertions above, the aspect of non-native can be compared to the variable on ethnic disadvantage considered central in this dissertation; already, it can be seen that such antecedents of ethnic disadvantage suppress performance of the small firms in the long-term.
More analysis next examines how key variables in this study have been debated to influence the performance of small firms. The aim is to examine the manner in which past scholars have discussed to be the impact of ethnic disadvantage in entrepreneurial activities or orientations of proprietors in different regions especially in the United Kingdom.
Chapter Three: Methodology
3.1 Introduction
The chapter describes the research methodology adopted in the present study. As secondary data is utilised in the research, the chapter only elaborates on the analysis approaches adopted; descriptive and inferential analysis. Limitations of the research methodology are further described.
3.2 Data analysis
The researcher employs both descriptive and inferential analysis in order to obtain important insights from the data.
3.2.1 Descriptive analysis
The researcher will utilise descriptive analyses such as means and standard deviation to understand the general features of the dataset. According to Anderson et al. (2018), descriptive statistics include measures of central tendency such as mean and measures of variability such as standard deviation. Descriptive statistics are important as they facilitate understanding the characteristics of data by summarising important quantitative measures.
3.2.2 Inferential analysis – regression
According to O’Donoghue (2013), inferential statistics enable a researcher to make data conclusions that extend beyond their descriptive summaries. As such, they help the researcher make inferences on the data and understand intricate relationships that exist between variables. In the current study, a linear model is developed using pooled ordinary least square (OLS) regression in order to understand the association between the different variables.
The values of the dependent variable, profit or loss, are influenced by the independent variables considered in the equation. However, control variables are included in order to minimise any external influences from other factors that are likely to influence profitability of the business. As a result, their effect has to be removed from the regression equation. The error term i also referred to as the residual, and describe the margin of error within the statistical model and help explain the differences between the model’s results and the actual results that are observed (Anderson et al., 2018).
3.3 Description of Data
3.3.1 Description of the data
The data has been derived from Merchant Savvy depicting UK SMEs Data; the methodology used is a survey approach with both longitudinal and cross-sectional time horizons coordinated by the Department for Business, Energy & Industrial Strategy in United Kingdom. The focus was to examine the trend for small businesses with less than 10 employees and that have a 60% contribution on UK job market and the overall economy in the region (Merchant Savvy, 2019). The period of consideration was 2018-2019 survey data and considered sample size was 100 small businesses.
3.3.2 Research Model
The study seeks to analyse the theme of: “Ethnic Disadvantage in United Kingdom’s Small Business Ownership and Performance. The focus is to create a succinct research model to be used to execute the entire study at the same time address the objectives set forth. Thus the key core variables for the study include the following:
Dependent Variable: Performance of small businesses
Independent Variable: Ethnic Disadvantage
Control Variable: Business Ownership
Therefore, the 1st model may be illustrated as shown below:
From the proposed model, the depiction is that the first premise is on the direct effects of ethnic disadvantage towards performance of small firms. Thus, in this study ethnic disadvantage refers to the sample of business owners or businesses as entities owned by the minority groups in the industry. The assumption for ethnic disadvantage is guided by the survey by MRG (2019) in that Asian British are the monitory groups they constitute a number of other smaller races. Therefore, Asian British may represent the ethnic group that is ethnically disadvantaged due to accessibility to resources, state rights, and representation. On the other hand, it means that Whites in the United Kingdom’s SMEs sector are the ethnically advantaged due to the fact they have more access to resources such as funding, state rights among others. Therefore, the trend for ethnic disadvantage can be represented by use of a dummy variable to connote as follows:
1 = ethnically disadvantaged
0 = Other
Business ownership in this study connotes the legal status of the SMEs or rather small business firms sampled in the study. In most incidences ownership of small business and legal status is usually a sole proprietorship. Thus, a dummy variable has been developed as shown below:
1 = Sole proprietorship
0 = Other
The envisioned effect of business ownership is that it has moderating effects to the relationship between ethnic disadvantages of the small business owners and business performance.
The notion for business performance is that it can be measured using the revenue and profitability of small firms. However, the focus shall be on profitability of small firms for 100 sample size.
3.3.3 Additional Control Variables
Education: Education of the business owner
Size of the firm: Number of Employees in the firm
Age of Firm: Number of years in the industry
Location of Firm: The region of the small firm
Industry of Firm: The type of industry the small firm extends its goods and services
The five variables above are considered as to mediate the relationship between ethnic disadvantage and performance of small firms in the United Kingdom. The modeling, therefore, would look at follows:
3.4 Further details of the study’s variables
3.4.1 Dependent variables
Total profit (Performance)
The variable reports on the business income obtained after deducting all expenses and taxes.
3.4.2 Independent variables
Ethnicity of the business owner
The variable represents the ethnic background of the business owner. A dummy variable is created to represent the different possible race status; these include: 1 for the Hispanic or Latino while 0 = otherwise; black = 1, 0 = otherwise; White = 1 and 0 = otherwise. Based on the dummy variables, the research study will also compare the performance of black-owned and Hispanic or Latino-owned businesses.
3.4.3 Control variables
Highest level of education of the business owner
Specifies the highest education achievement ranging from high school diplomas and technical degrees to associates, bachelors and masters’ degrees
Location of firm
The variable indicates the primary location where the business operated.
Legal status of the business
The variable indicates the ownership type of the business ranging from partnerships to limited liability and sole proprietorships.
Number of owners who helped run the business
The variable indicated the number of owners who facilitated running the business operations. The number of owners ranged from 1 to 10. However, the study only considered up-to four owners.
Age of firm
The variable depicts the time or period the small business has been in operation since inception.
Location
The variable shows the geographical placement of the business.
Industry
The variable indicates the core business model of the business in light of the products and or services rendered to the customers.
Business Ownership
The variable underscores the type of business ownership in the small enterprise; therefore, 1 = sole proprietorship and 0 = Other
Model Specification
The first model specification is as follows:
Profit = a +b(black) +c(Hispanic)+ d(White) + e(controls) + error
The same model can be specified as follows:
ROI = α + β1Black + β2Hispanic + β3 White + β4Age + β5Size + β6Ownership + β7Education + β8 No. of Owners + β9Industry + έ
The second model specification is as follows:
Log_ROI = α + β1Black + β2Hispanic + β3 White + β4Age + β5Size + β6Ownership + β7Education + β8 No. of Owners + β9Industry + β10 Log_invested capital + β11Log_total assets + έ
In the second model above, it can be seen that the criterion variable measuring performance has been adjusted to being log_ROI while log_invested capital and log_total assets have also been added as regressor variables.
The third and last model specification is as shown below:
Log_ROI = α + β1Black + β2Hispanic + β3 White + β4Age + β5Size + β6Ownership + β7Education + β8 No. of Owners + β9Industry + β10 Log_capital invested + β11Log_revenue + β12Log_total assets + έ
In the third model log_revenue for the small firms have been added as a predictor variable in the model.
3.5 Limitations of the methodology
The present research is challenged by several issues. First, being an econometric study, the researcher anticipates challenges in analysing the data using the selected STATA application. Existent issues with the raw data such as the presence of outliers or missing data might negatively influence the results obtained and subsequently, lead to incorrect conclusions. To avoid this, the researcher aims to extensively study available literature on using the application to minimise incorrect conclusions. Secondly, given the uniqueness of the topic of study, the researcher anticipates challenges in obtaining relevant literature to compare the study findings with the results from econometric analysis.
Results and Findings
In the current section, the specified model and further analysis is going to be implemented in this part of the study.
4.1 Descriptive Statistics
The descriptive statistics for the dataset representing the variables is as shown below:
Table 4.1: Descriptive Statistics
| Descriptive Statistics | Hispanic | Black | White | Age of Firm | Ownership | Size | Industry | No of Owners | Education | ROI |
| Mean | 1 | 1 | 1 | 3 | 1 | 3 | 1 | 2 | 1 | 0.933 |
| Standard Deviation | 0 | 1 | 1 | 1.996 | 1 | 1.767 | 1 | 1.299 | 1 | 1.945 |
The results captured the descriptive statistics for the variables: Hispanic (Mean = 1, S.D = 0) is an indication that on average the dummy variable represented the case of Hispanic as the dominant group. Further, the dummy for black race (Mean =1, S.D = 0) shows that black race dominated the trend while White (Mean = 1, S.D = 1) showing the dominant race was white type. From these results, it can be asserted that all the races dominantly presented the races with less variance. Worth noting is that Hispanic and Black dummy trends were indicative of ethnic disadvantaged race within the sampled population. On the other hand, White dummy trends were representing the ethnic advantage race. In terms of age of the firm (Mean = 3, S.D = 1.996) which indicated that on average the small business firms had been in operation for a period of 3 years. Ownership (Mean = 1, S.D = 1) indicated that majority of the sampled small business firms were sole proprietorships. In addition to this, Size (Mean = 3, S.D = 3) demonstrated that on average the small business firms were consisted for three employees and in connection to this, number of owners (Mean = 2, S.D =1.299) implied that 2 individuals had a stake in the enterprise as capital providers. The dummy trend for education (Mean = 1, S.D = 1) which depicted on average the owners of the business had achieved higher education qualifications as opposed to average qualifications. The trend for return on investment (ROI) (Mean = 0.9332, S.D = 1.9445) was considered the measure of performance for the small firms; the indication from ROI is that on average the recorded value was 93.32% which happens to be the return on investment for the majority of firms. The researcher justified this on grounds that most of them were run by the owners with an employee base of utmost 3.
4.2 Correlations
The results for linearity are as shown below:
Table 4.2: Correlation analysis test results
| Hispanic | Black | White | Age of Firm | Ownership Type | Size | Industry | No of Owners | Education | ROI | |
| Hispanic | 1.0000 | |||||||||
| Black | 0.0000 | 1.0000 | ||||||||
| White | 0.0000 | 0.0000 | 1.0000 | |||||||
| Age of Firm | 0.0871 | 0.2794 | 0.0216 | 1.0000 | ||||||
| Ownership Type | 0.0000 | 0.0000 | 1.0000 | 0.0216 | 1.0000 | |||||
| Size | -0.0371 | 0.0796 | -0.0227 | 0.0297 | -0.0227 | 1.0000 | ||||
| Industry | 0.0000 | 1.0000 | 0.0000 | 0.2794 | 0.0000 | 0.0796 | 1.0000 | |||
| No of Owners | -0.0221 | 0.2708 | 0.0696 | 0.0644 | 0.0696 | 0.1286 | 0.2708 | 1.0000 | ||
| Education | 0.0000 | -1.0000 | 0.0000 | -0.2794 | 0.0000 | -0.0796 | -1.0000 | -0.2708 | 1.0000 | |
| ROI | 0.0125 | 0.2603 | -0.0025 | 0.0323 | -0.0025 | 0.0705 | 0.2603 | -0.0637 | -0.2603 | 1.0000 |
The critical linearity trend has been highlighted with the results indicating both positive and negative effects between performance of the small business firms and the rest of the control variables. For instance, Hispanic (.0125), Black (0.2603), Age of the firm (0.0323), Size of the firm (0.0705), and Industry type (0.2603) are the variables that depict to have a positive correlation to the performance of business firms measured using return on investment. In fact, the results indicate that Hispanic and Black being the indicative trends for ethnic disadvantages have a positive correlation to ROI meaning: the higher the ethnic disadvantage the higher the nature of performance of the small business firms. However, White (-0.0025), ownership type (-0.0025), number of owners (-0.0637), and education (-0.2603) are the aspects of the small business firms have negative correlation with the performance of the small business firms.
4.3 Regressions
The trend for the regression results for the 1st, 2nd and 3rd models is as shown below:
Table 3: Performance of small business firms
| Model 1 | Model 2 | Model 3 | |
| Hispanic | 0.0626 | -0.1546 | -0.1433 |
| 0.8735 | 0.4593 | 0.4806 | |
| Black | 0.0000 | 0.0000 | 0.0274 |
| 0.0000 | 0.0000 | 0.9199 | |
| White | 0.0000 | 0.0000 | 0.0000 |
| 0.0000 | 0.0000 | 0.0000 | |
| Age of Firm | -0.0468 | -0.0385 | -0.0240 |
| 0.6427 | 0.4704 | 0.6458 | |
| Ownership Type | 0.0418 | -0.8817 | -0.8441 |
| 0.9134 | 0.0000 | 0.0001 | |
| Size | 0.0747 | -0.0417 | -0.0611 |
| 0.4982 | 0.4760 | 0.2879 | |
| Industry | 1.1994 | 0.0000 | 0.0000 |
| 0.0046 | 0.0000 | 0.0000 | |
| No. of Owners | -0.2302 | -0.0209 | -0.0280 |
| 0.1409 | 0.8017 | 0.7295 | |
| Education | 0.0000 | -0.0751 | 0.0000 |
| 0.0000 | 0.7883 | 0.0000 | |
| Log_Revenue | N/A | N/A | -0.0063 |
| N/A | N/A | 0.0143 | |
| Log_Total Assets | N/A | -0.0095 | -0.0093 |
| N/A | 0.0483 | 0.0483 | |
| Log_Capital | N/A | 0.0015 | 0.0008 |
| N/A | 0.7710 | 0.8738 | |
| Constant | 0.6531 | 4.1995 | 4.2670 |
| 0.3026 | 0.0000 | 0.0000 | |
| N | 100 | 100 | 100 |
| R Squared | 0.0939 | 0.2056 | 0.2571 |
| Adjusted R Squared | 0.0032 | 0.1028 | 0.1495 |
In the first model, the R squared indicated that only 9.39% of the cases for ROI (performance of small business firms) are explained by the trends in ethnic disadvantage, industry, size, location, education, age, among other control variables. The researcher holds the position that a 9.39% is a weak goodness-of-fit. In fact, analysis of variable (F = 1.6059, Sig. = 0.1254) means the effects of the control variables have no significant impact on the performance of small business firms. Thus, the model relationship cannot be justified and one would not proceed to affirm that the control variables affect performance of the small business firms. In this regard, the entire model is going to be non-meaningful due to the spurious nature of the outcomes. As a result model 1 is going to be rejected in entirety as non-significant.
In terms of model 2 there are indications for the improvement of goodness-of-fit and significance level of the predictor variables towards performance of small business firms. For instance, model 2 depicts that the goodness-of-fit of the relationship between the dependent and independent variables improved from 9.39% to 20.56% upon addition of total assets and capital invested. Further, improvement was evident in the Anova since the significance level led to 95% confidence interval to affirming model relationship 2 was non-spurious. In other words, all the predictor variables modeled against return on investment of the sampled small business firms was significant. Further analysis of the results in model II depicted that Black race (β = .0000, Sig. = .0000), White race (β = .0000, Sig. = .0000), Ownership Type (β = -0.8817, Sig. = 0.0000), Industry (β =.0000, Sig. = .0000), and Log_Total Assets (β = -0.0095, Sig. = 0.0483) have had significant effects to the performance of the small business firms. Most specifically it can be interpreted that race i.e. ethnic advantage is significant but only showing to have 0% increase to performance of small business firms in every of its unit increase. The same results can be evidenced under Black race which was used in the study to represent the dimension of ethnic disadvantage. In both cases, there has been similar impact although the non-significant results under Hispanic (β = -1546, Sig. = 0.4593) being non-significant cannot be ignored. For instance, it can be interpreted to mean that Hispanic race i.e. ethnic disadvantage has not meaningful implication to the performance of a business which is actually a good thing. The reason for holding this position is because if ethnic disadvantage causes a negative effect to performance of small business firms then it would mean that entrepreneurs with ethnic disadvantages cannot be successful in running their small business firms. However, the case of Black race can lead to more questions since as an aspect of ethnic disadvantage it has significant effects towards performance of the small business firms as confirmed in the regression results. Nonetheless, the beta at 0% means it does not increase or decrease the trend in performance which the researcher considers to be a positive situation; the more ethnic advantage or disadvantage has impact to performance the more it can be deduced that it is a determinant of the success of entrepreneurs, and this is not the desired future in a globalising world.
In terms of the third model it can be seen that White (β = .0000. Sig. = .0000), Ownership Type (β = -0.8441, Sig. = 0.0000), Industry (β = .0000, Sig. = .0000), Education (β = .0000, Sig. =.0000), Log_Revenue (β = -0.0063, Sig. = 0.0143) and Log_Total Assets (β = -0.0093, Sig. = 0.0483). Thus, the mentioned are the regressor variables representing the situation of small business firms that have significant effects towards performance. The goodness-of-fit also has improved from 9.39%, 20.56% to being 25.71%. Thus, the predictor variables in model have a strong explanatory power on the performance trend of the small business firms. Actually for model 2 and model 3 it can be deduced that ownership type which was established to be majorly sole proprietorship have a non-incremental effect on SMEs performance by over 80% strength. Further observations depict that for both model 2 and 3 the trend for log_total assets shows a negative effect to performance of the small business firms but with less than 1% impact. Also, in model 3 log_revenue indicates non-incremental effects to performance of small business firms by 6.3% metrics.
4.3.1 Re-evaluation of the regression results
From the regression results it was seen that different variables influenced the relationship of selected factors to the performance of small business firms in varying ways. In fact, model 1 was dropped since the analysis of variance led to the belief that further interpretation would be meaningless due to the spurious nature of the regression model. However, model 2 and 3 were feasible since the Anova significance value was below 5% margin of error. Having presented the actual results, the researcher is keen to understand how each of the control variables related to the dependent variable i.e. performance of small business firms that was measured relying return on investment (ROI). For that reason, ROI captured the trade-off between sales, cost of sales, and net profit in each of the surveyed enterprises. For instance, under model 2 it was indicated that black as a dimension of ethnic disadvantage had significant influence to performance of the small business firms but the multiplier effect was only 0%. Thus, it neither indicated to increase nor decrease performance but yet it was a significant factor. The researcher would take the position that black race by itself has even if negligible, but it has incremental effects to performance. On a different occasion it was established that performance of the business firms had both low and high scores across the SMEs but the study could not ground whether it has been declining. The position is that positive effects of ethnic disadvantage in this case black race means it confirms the proposition that ethnic disadvantage cannot be ignored as not to have effects on performance of SMEs. The same position can be confirmed under model 2 in the case of white race that gave similar results in the long run. The researcher concluded that the place of ethnic disadvantage and ethnic advantage as represented by black and white races respectively have had similar effects to performance of business firms such that they cannot be considered as contributors or hindrances to the same. Further analysis of model 2 depicted that ownership type has significant negative effects to performance of business firms i.e. a decreasing effect; in real sense, therefore, the kind of ownership that is manifest in the SMEs has negative effects and could be due to the sole proprietorship aspect of the business management. Among other variables in model 2 indicated that only total assets of the small businesses has significant negative results to business performance. Thus, it decreases performance i.e. ROI with actually 9.5% per every unit increase. In other words, a unit increase in total assets leads to a 9.5% decrease in ROI of the performance of the small business firms. Also, a look at the constant value which in this case is the alpha value in model 2 affirms that despite the consideration of ethnic disadvantage and the rest of the control variables, performance of the small business firms sampled would still record achievements metrics at 4.20 points.
In turning to model 3 only white race dimensions indicated to have significant effects to performance of the small business firms since Hispanic and Black indicated to have non-significant betas. Thus, white which represented ethnic advantage seems to have better influence than other two races representing ethnic disadvantage. Moreover, the exclusiveness of the white race impacting significantly on performance of the small business firms comes after inclusion of revenue. The results indicated that revenue and total assets have negative significant results on performance of the small business firms at 6.3% and 9.3% respectively; thus, for each of their unit increase, they lead to a decrease on performance of the small business performance. Other significant results can be justified under education, industry, and ownership type. The perception created by the results of model 3 is that revenue renders ethnic disadvantage to have no significant influence on performance of small business firms. The quest would be to establish whether such is a good or bad thing: for the researcher a situation whereby small business firms have a performance that is not influenced by the state of ethnic disadvantage is a good thing and the desired future by all entrepreneurs. There needs to be an independent relationship between ethnicity and performance since that would increase sustainable competitive advantage of the SMEs in the long-term future.
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Appendix 1: Model regression I
| SUMMARY OUTPUT | ||||||||
| Regression Statistics | ||||||||
| Multiple R | 0.3064 | |||||||
| R Square | 0.0939 | |||||||
| Adjusted R Square | 0.0032 | |||||||
| Standard Error | 1.9097 | |||||||
| Observations | 100 | |||||||
| ANOVA | ||||||||
| df | SS | MS | F | Significance F | ||||
| Regression | 9 | 35.1421 | 3.90468 | 1.60594 | 0.1254 | |||
| Residual | 93 | 339.179 | 3.64709 | |||||
| Total | 102 | 374.322 | ||||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
| Intercept | 0.65313 | 0.63001 | 1.03671 | 0.30256 | -0.5979 | 1.90421 | -0.5979 | 1.90421 |
| Hispanic | 0.06255 | 0.39178 | 0.15966 | 0.87349 | -0.7154 | 0.84056 | -0.7154 | 0.84056 |
| Black | 0 | 0 | 65535 | 0 | 0 | 0 | 0 | 0 |
| White | 0 | 0 | 65535 | 0 | 0 | 0 | 0 | 0 |
| Age of Firm | -0.0468 | 0.10062 | -0.4655 | 0.64268 | -0.2467 | 0.15298 | -0.2467 | 0.15298 |
| Ownership Type | 0.04181 | 0.38324 | 0.10909 | 0.91336 | -0.7192 | 0.80286 | -0.7192 | 0.80286 |
| Size | 0.07465 | 0.10977 | 0.68006 | 0.49816 | -0.1433 | 0.29264 | -0.1433 | 0.29264 |
| Industry | 1.19938 | 0.41296 | 2.90432 | 0.0046 | 0.37931 | 2.01944 | 0.37931 | 2.01944 |
| No of Owners | -0.2302 | 0.15497 | -1.4852 | 0.14088 | -0.5379 | 0.07758 | -0.5379 | 0.07758 |
| Education | 0 | 0 | 65535 | 0 | 0 | 0 | 0 | 0 |
Appendix 2: Model regression II
| SUMMARY OUTPUT | ||||||||
| Regression Statistics | ||||||||
| Multiple R | 0.4534 | |||||||
| R Square | 0.2056 | |||||||
| Adjusted R Square | 0.1028 | |||||||
| Standard Error | 1.0018 | |||||||
| Observations | 100 | |||||||
| ANOVA | ||||||||
| df | SS | MS | F | Significance F | ||||
| Regression | 11 | 23.6381 | 2.1489 | 2.9441 | 0.0023 | |||
| Residual | 91 | 91.3305 | 1.0036 | |||||
| Total | 102 | 114.9686 | ||||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
| Intercept | 4.1995 | 0.4539 | 9.2528 | 0.0000 | 3.2979 | 5.1010 | 3.2979 | 5.1010 |
| Hispanic | -0.1546 | 0.2080 | -0.7431 | 0.4593 | -0.5678 | 0.2586 | -0.5678 | 0.2586 |
| Black | 0.0000 | 0.0000 | ######## | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| White | 0.0000 | 0.0000 | ######## | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| Age of Firm | -0.0385 | 0.0531 | -0.7249 | 0.4704 | -0.1440 | 0.0670 | -0.1440 | 0.0670 |
| Ownership Type | -0.8817 | 0.2034 | -4.3340 | 0.0000 | -1.2857 | -0.4776 | -1.2857 | -0.4776 |
| Size | -0.0417 | 0.0583 | -0.7157 | 0.4760 | -0.1574 | 0.0740 | -0.1574 | 0.0740 |
| Industry | 0.0000 | 0.0000 | ######## | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| No of Owners | -0.0209 | 0.0829 | -0.2519 | 0.8017 | -0.1855 | 0.1438 | -0.1855 | 0.1438 |
| Education | -0.0751 | 0.2787 | -0.2693 | 0.7883 | -0.6286 | 0.4785 | -0.6286 | 0.4785 |
| Log Capital Invested | 0.0015 | 0.0051 | 0.2919 | 0.7710 | -0.0087 | 0.0117 | -0.0087 | 0.0117 |
| Log_Total Assets | -0.0095 | 0.0048 | -2.0019 | 0.0483 | -0.0190 | -0.0001 | -0.0190 | -0.0001 |
Appendix 3: Model regression 3
| SUMMARY OUTPUT | ||||||||
| Regression Statistics | ||||||||
| Multiple R | 0.5070 | |||||||
| R Square | 0.2571 | |||||||
| Adjusted R Square | 0.1495 | |||||||
| Standard Error | 0.9742 | |||||||
| Observations | 100 | |||||||
| ANOVA | ||||||||
| df | SS | MS | F | Significance F | ||||
| Regression | 12 | 29.5577 | 2.4631 | 3.4606 | 0.0003 | |||
| Residual | 90 | 85.4109 | 0.9490 | |||||
| Total | 102 | 114.9686 | ||||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
| Intercept | 4.2670 | 0.3847 | 11.0908 | 0.0000 | 3.5026 | 5.0313 | 3.5026 | 5.0313 |
| Hispanic | -0.1433 | 0.2023 | -0.7083 | 0.4806 | -0.5453 | 0.2587 | -0.5453 | 0.2587 |
| Black | 0.0274 | 0.2717 | 0.1008 | 0.9199 | -0.5123 | 0.5671 | -0.5123 | 0.5671 |
| White | 0.0000 | 0.0000 | 65535.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| Age of Firm | -0.0240 | 0.0520 | -0.4612 | 0.6458 | -0.1273 | 0.0793 | -0.1273 | 0.0793 |
| Ownership Type | -0.8441 | 0.1984 | -4.2548 | 0.0001 | -1.2382 | -0.4500 | -1.2382 | -0.4500 |
| Size | -0.0611 | 0.0572 | -1.0691 | 0.2879 | -0.1748 | 0.0525 | -0.1748 | 0.0525 |
| Industry | 0.0000 | 0.0000 | 65535.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| No of Owners | -0.0280 | 0.0807 | -0.3468 | 0.7295 | -0.1882 | 0.1323 | -0.1882 | 0.1323 |
| Education | 0.0000 | 0.0000 | 65535.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| Log Capital Invested | 0.0008 | 0.0050 | 0.1593 | 0.8738 | -0.0091 | 0.0107 | -0.0091 | 0.0107 |
| Log_Total Assets | -0.0093 | 0.0046 | -2.0016 | 0.0483 | -0.0185 | -0.0001 | -0.0185 | -0.0001 |
| Log_Revenue | -0.0063 | 0.0025 | -2.4975 | 0.0143 | -0.0113 | -0.0013 | -0.0113 | -0.0013 |
