Abstract

Enterprise education and training play a pivotal role in bridging workforce skills and improving the performance of small businesses. Considering the significant role of small businesses, this study attempts to investigate how small manufacturing units improve their performance using training methods. Therefore, for this study mixed-method approach was adopted to find the relationship between both variables. In the first part of the methodology, face to face, and semi-structured interviews were conducted with business owners and managers. Furthermore, survey questionnaires were carried out to find the relationship between both variables. For a mixed-method approach, 26 owners/managers were interviewed, and 136 respondents were identified from food and furniture manufacturing units with several employees who comes on the definition of small firms. The results of the study confirm in small firms positively impacted the individual performance and overall non-financial turnover of employees. The study concludes with future recommendations for academicians and policymakers.

Key Words

Enterprise educating, Enterprise Training, Small business, Performance, United Kingdom.

Introduction

According to Dai (2012), small businesses are facing tough competition in to attract a skilled workforce to remain competitive in developed and developing economies. This view is acknowledged by Renta-Davids et al (2014) who argue that Small and Medium Enterprises (SMEs) face startup competition in business innovations and enterprise development areas, depending on the socio-economic conditions of the sector and industry. In the well-developed United Kingdom (UK) economy, policymakers eager to invest in enterprise training, which is helpful for employees’ knowledge acquisition (Ellinger et al., 2011). However, it is directly related to employees’ turnover rate (Lancaster and Di Milia, 2014), usually leads to an increase in the case of small manufacturing units. 

In the developed economies there is no uniform definition of SMEs available. The researcher's definition of small and medium enterprises varies from one country to another country, the total assets, revenues, and the total workforce. According to Malik and Nilakant (2011), the general definition which explains the SMEs domain is quite problematic, vague, and gives three main reasons. Firstly, Malik and Nilakant (2011) argue that historical asset value and the crucial point, which can play an important role and high inflation time, for example in the case of China and India. In the second case, definitions about Micro, Small, Medium Enterprise (MSMEs) do not fit into domestic units with a lower financial performance. Thirdly, Malik, and Nilakant (2011) further explain that different scales method to measure MSMEs to define total assets or number of employees; also varies from services to manufacturing sector. According to Paik et al, (2011), the most effective method to define MSMEs is the headcount in the organization. Table 1 is an official demonstrates (or the most commonly used) definition of SMEs. 

Problem Statement and Research Context

The latest studies suggest that the policymakers and practitioners have been interested in the skills development of the small and medium-size workforce. According to Sultan et al, (2016), a skilled workforce is an important component to remain competitive, though, difficult to retain. SMEs' education and training are the prevailing mode of enterprise core strength and economic growth to promote an entrepreneurial culture.

Table 1. Definition of SMEs

Location	Size	Headcount	Assets	Annual Sales	Other
USA	Micro	<10	<$100,000	<$100,000	None
	Small	10<50	<$100,000 <$3 million	<$100,000 <$3 million	None
	Medium	50<300	<$3million <$15million	<$3million <$15million	None
UK	Micro	<10	<£1.2million	<£1.2million	None
	Small	10<49	<£6.5million	<£6.5million
	Medium	50<249	<£25.9million	<£25.9million
European commission	Micro	<10	<€2million	<€2million	None
	Small	10<49	<€10million	<€10million	Balance sheet total of less than € 10 million
	Medium	50<250	<€43million	<€50million	Balance sheet total of less than € 43 million
Malaysia	Micro	<5	<RM250K	<RM250K	None
	Small	5<50	<RM10million	<RM10million
	Medium	50<150	<RM50million	<RM50million
Indonesia	Micro	<5	<50million	<5million	None
	Small	5<19	<200million	<RP1billion
	Medium	20<99	<10billion	<RP1billion
Turkey	Micro	<10	<€2million	<€2million	None
	Small	10<49	<€10million	<€10million
	Medium	50<250	<€43million	<€50million
Pakistan	Small	10<49	PKR 5million	None	None
			PKR 25million	None
	Medium	50<250

Source: Syed et al. (2015).

Furthermore, training and employee turnover are intertwined with an employment level, skills, human capital, and overall growth in the overall economy. Therefore, the competitiveness of any business, regardless of the West Midlands, to be the essential size of the business, within SMEs management consider the qualified and skilled workforce (Samra, 2009). However, in comparison to their large counterparts, SMEs have a less financial and non-financial resource to train their workforce

The prior research has existed evidence that training within SMEs leads to higher training Return on Sales (ROS) Return on Investment (ROI) and Return On Assets (ROA) in any economy of production. In these studies, the non-financial performance context, according to the researcher by Becker (1962) presented Human Capital Theory explains that training leads to increase KSAs. The researcher (Beynon et al, 2015) discuss that the training of employee has an impact on the turnover rate of retention. Lambert et al. (2007) argue that the small business employees who keep skilled workers remain competitive and enjoy the maximum profit. Similarly, Hashim and Wok (2013) explain that investment in workforce training helps to maintain employee’s commitment and increase motivation. However, Sultan et al. (2016) by critiqued has been the latter point, that the researcher examines that training enterprise is not linked with maintaining the turnover rate and increasing performance.

Hussain and Matlay, (2007) used a semi-structured interview with a small firm located in the West Midlands, interviewed by the 66 owners /managers, which they associated with the distinguished concepts of SMEs of managers’ perceptions in the training. They confirm that there is a significant relationship between SMEs and business performance, however, SMEs are reluctant to invest in a skilled workforce. Further studies confirm that the relationship between business performance an individual’ training, which is dependent on the discretion totally on business owners. Owners/Managers are reluctant is small businesses from a management perspective (Hashim, & Wok, 2013). Knudsen and Lien (2015) argue that small businesses attract more to compete in the labour market rather than to invest in skill training. This view is supported by the Market Signalling Theory (MST) of Spence (1973). Concept MST based on the Human Capital Theory (HCT), in the external labor market employees with higher skills have a high demand in the market. However, studies confirm that investment in employee training leads to poaching of the workforce (Rouditser, & McKeown, 2015). However, Market Signalling Theory (MST) explains that training is linked with the abilities of the workforce. Ciriaci (2017) concludes that the provision of training within small business units with limited resources is a challenger for owners/managers. Furthermore, in developed economies internal and external labor market issues consider as a key factor to invest in training. 

Research Methodology

This study conducted 26 interviews in the small business units in the West Midlands. Panagiotakopoulos (2011a) argues that with semi-structured interviews, the study gets the in-depth information and have the opportunity to explore better ideas and observations to analyze the data.

Saunders et al. (2012) explain that with the help of such interviews, the researcher can easily address the aim and objectives of the research. However, it is an important designing of interviews and semi-structured questionnaires which are considered as reliability such as a key issue, the validity of the finding in the form of bias which can be the impact of the result. For this purpose, purposive is non-probability sampling perhaps is the best investigation of the related training-performance choices. The research topic can be also used for the best getting information by selecting items or as a way of people most likely to provide the quality of information the experience or expertise valuable insights (Yin, 2012). In this way the researcher used, a research model that is particularly well suited for developing purposive sampling. 

Semi-Structured interviews Analysis and Discussion

The key objective of this section is two-folded. In the first part, the study goes through interviews conducted with small business managers/owners in the different areas of the West Midlands. The researcher gains to enable an approach for a small business training of a better understanding of related activities of SMEs' performance and training. The variations description of categories was also established by the owners/managers in focusing the training practices of small organizations. Several issues and techniques regarding the analysis of data, the environment in the training reveals which has been certain fluctuates identified on the dependent variable. As a small business training is explained:

… “it is certain that training is directly related to the performance of the firm and employee’s competency. However, it depends on the type, size, duration, and linked with the nature of the business. Firstly, the employee is the commitment and loyalty of any organization. Training is a time being for human resources to enhance the commitment or loyalty, however, training is not important because the wages and other relevant benefits are more concerned with the employees of small businesses. The Second key element is ethical which competition for workforce skills. However, larger firms, as well as SMEs, use poaching techniques to avoid training costs and get experienced workforce”.

Two key elements are identified from the data. Firstly, a small business has reactive nature, a positive and causal relationship of human resources development between training and employee turnover. Therefore, the key driving is the forces of career progression, compensation, and benefits. Also, there are several factors affected not only by market forces but also by the personal and psychological characteristics of employees. Hasnnon (2009) presented that the training of benefits is not confined to financial incentives and career progression only; the employability of the recipient is also the development of training, and the commitment can also encourage the organization. However, the present study of the trend findings reveals that employees are more committed and loyal only when

increase career progression, training provision, and an increase in the employees’ benefit. Therefore, in these researches assume that the logical, of the small business, has directed, trained proportionally to the investment of employees’ commitment and loyalty. 

Secondly, it has been noticed that the poaching of employees is the key issues than prevent SMEs to invest in training. Small businesses take investment in the training as an expense. This is in line with the work of Spence (1973), who presented the famous theory called Market Signalling Theory (MST). In the traditional and small business market, the MST approach increases the demand level for a workforce skilled. To compete with management for a workforce skilled which prefers small business rather than the employee invest in training. Small business general manager in the same way explained:

… “In the existing labor market, it is not easy to find a skilled employee. Formal approaches are reducing the employee’s turnover rate and effective for the long term. However, the time consuming is too expensive. In such cases, small firms prefer to give on the job training preferably apprenticeships”.

It is noticed that informal methods are respondents' invariable support (apprenticeship preferably) which is associated with the training practices and small business performance. Therefore, the importance of formal methods is the acknowledged of UK manager, but also found a lack of resources which is not feasible for the small business entities. The behavior model with such findings in line which is presented by Schuler and Jackson (1987). In the developed and developing economies, small business owners do not want to invest in training, however, the behavior model could help them to train their workforce (Thang et al., 2010).

 

Multiple Regression Analysis and Discussion

To address the research questions, statistical modeling, for example, Multiple Regression Analysis (MRA) approach was used. MRA is particularly useful to check the value of Dependent Variables (DV), workforce turnover rate on the value of Independent Variables (IVs), and training methods within small business organizations. Therefore, Multiple Linear Regression Equation (MLRE) was designed for estimating the impact of DV on IVs.

Table 2. Descriptive Information about Small Businesses

List of Semi-Structured Interviews
Numbers	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26
Firms’ Code	UK1, UK2, UK3, UK4, UK5, UK66
Participants Code	UKa1, UKa2, UKa3, UKa4, UKa5, UKa6, UKa7, UKa8, UKa9, UKa10, UKa11, UKa12, UKa13, UKa14, UKa15, UKa16, UKa17, UKa18, UKa19, UKa20, UKa21, UKa22, UKa23, UKa24, UKa25, UKa26
Size	11	32	19	13	27	33	44	48	36	12	23	35	17	19	16	14	10	14	21	28	20	18	21	24	32	12
Location	B	S1	B	D	W1	W2	S2	S2	B	S1	S1	D	D	D	W1	W1	W2	W2	W2	B	B	B	S2	B	D	B
Owner-ship	SP	P	SP	SP	SP	P	P	P	P	SP	P	P	SP	SP	SP	P	SP	SP	P	P	P	SP	SP	SP	P	SP
Education	MS	MS	HS	HS	HS	HS	HS	CG	HS	HS	CG	HS	CG	CG	HS	MS	MS	MS	UG	CG	CG	HS	CG	HS	CG	HS
Age o of business	7	12	6	8	16	11	18	14	15	5	7	9	10	11	14	9	6	5	12	16	11	10	6	17	14	8

Note: UKa1=Owner, UKa2=Manager, UKa3=Manager, UKa4=Owners. UKa5=Owner, UKa6=Owner, UKa7=Manager.UKa8=Owner, UKa9=Owner, UKa10=Manager, UKa11=Manager,

UKa12= Owner. UKa13=Owner, UKa14=Owner, UKa15=Owner, UKa16= Manager, UKa17=Manager. UKa18=Owner, UKa19=Manager, UKa20=Manager. UKa21=Owner,

UKa22=Owner, UKa23=Manager, UKa24=Manager. UKa25=Owner, UKa26=Owner,

SP= Sole proprietorship, P= Partnership

MS= Middle school, HS= High school, CG= College graduate, UG= University graduate

M= Male. F= Female

West Midlands= Birmingham, Sandwell, Dudley, Walsall, Wolverhampton, and Solihull

B=Birmingham, S1= Sandwell, S2= Solihull, W1= Wolverhampton, W2= Walsall, D= Dudley

 

Therefore, for the direction and magnitude of DV and IVs, the correlation coefficient was used. Nathans et al., (2012) explain that correlation coefficients measure and predicts the strength and direction of DV and IVs. It shows that in the case of Negative Correlation (NC), DV and IVs move in inverse. Similarly, values of Positive Correlation (PC) increase or decrease in tandem. Furthermore, Analysis of Variance (ANOVA) was used to test the Null Hypothesis ( ) and to check the relationship between variables. The F distribution and the F-ratio is adopted to find out the ratio of two mean square values. Therefore, following multiple linear Regression Equations (MLRE) is adopted to investigate the relationship between training and employee turnover rate. 

 

Training practices and Employee’s Turnover Rate= Constant+  Formal (T1.1) + Informal (T1.2) +  General (T1.3) +  Apprenticeship (T1.4) + off the job (T1.5) + on the job (T1.6) + .

Besides, IVs in an equation to check relationship DV. As shown in the following Model T1, the proposed MRA confirms the significant percentage of the variance between both variables. The results also confirm that the observed variability of IVs is (R² = 0.916, Adjusted R^{2 =} 0.912). Furthermore, the value of R^{2 supports} the Pallant (2013) argument that how much variance in the DV is explained in every individual.

Table 3. T1 Model’s Summary

Model	R	R Square	Adjusted R Square	Std.  error of the Estimate
1 UK	.913a	.909	.903	.331

As shown in the Model T1, the value of the adjusted R² value is 0.912, which shows 91.2 variances. According to Norris et al. (2014), for acceptable results larger values of R² in any model clarifies the variations in DVs. Model T1 also illustrates the results of the adjusted value of R². It is noticed that the adjusted value of R square provides a better understanding of the true population. Further results from Table 5 shows the F value is 211.711, F=211.711, P< 0.001). Furthermore, Model T1 demonstrates that the significance value is less than .001, which shows that IVs influence the DV.

Table 4. ANOVA 

Models		Sum of Squares	Df	Mean Square	F	Sig.
T1	Regression	143.511	6	21.317 .107	211.711	.000b
	Residual	13.101	129
	Total	152.658	135

From Table 6, Model T1 shows that  is rejected and the values for informal methods of training within small businesses  (B= .411, t= 4.133, p < 0.001), general training approaches (B= .263, t= 2.615, p < 0.05), apprenticeship training approach (B= .703, t= 9.101, p < 0.001), at work training approach (B= .181, t= 1.994, p < 0.05). It has also been seen that Model T1 demonstrates minimized multicollinearity between the IVs. Previous research has confirmed that the T value of less than 0.10 confirms multicollinearity between both variables (Pallant and Manual, 2010). Model T1 also confirms that for each IV, the T value is not less than 0.10. Furthermore, VIF values are under 10, confirms the reliability of Model T1.

Table 5

Model T1		Unstandardized Coefficients		SC	t	Sig.	Collinearity Statistics
		B	Std. Error	Beta			Tolerance	VIF
1	(Constant)	.246	.115		2.140	.034
	FTA	.028	.052	.030	0.535	.593	.207	4.834
	IFTA	.366	.039	.411	4.133	.000	.318	3.144
	GTA	.259	.067	.263	2.615	.007	.480	2.082
	AppT	.785	.069	.703	9.101	.000	.132	7.554
	OffJT	.012	.047	.012	0.250	.803	.286	3.498
	OnJT	.102	.053	.181	1.994	.049	.208	4.803

a. Dependent Variable: ETRP

For Model T1, P-P and the scatterplot draw a straight diagonal line and confirms no major deviations from normality. Hypothesis results of the Model T1 are summarized in Table 7.

Table 6. Hypothesis results

Hypotheses Number	Hypothesis Description	Model 1 UK
T1.1	FTA is positively associated with the ETR	Rejected
T1.2	IFTA is positively associated with the ETR	Accepted
T1.3	GTA is positively associated with the ETR	Accepted
T1.4	AppTA is positively associated with the ETR	Accepted
T1.5	OffJT is positively associated with ETR.	Rejected
T1.6	OnnJT is positively associated with ETR.	Accepted

The overall responses from the interviews and survey questionnaires confirm that the small business training in the West Midlands shows a positive association with the employee turnover rate. Results in Table – 7 show that the training approaches are positively associated with the turnover rate in the Midlands. However, in line with the discussed literature and analysis of data, the provision of training differentiates between two key elements in the West Midlands. The extensive review of literature, internal commitment is considered as a key element to control the turnover rate. It has also been observed within small business manufacturing organizations that training programs have a positive impact and significantly reduce the employee turnover rate.

However, Bakers (1962) endorsed that HCT confirms the training-commitment relationship reduces the employee turnover rate ultimately but the results in Table -7 are contrary that in West Midlands the small business entities taken initiatives towards training provisions results in higher turnover. This observation projected in Model T1 provides a clear linkage between the training provisions, financial incentives, and career progression. Dhar (2015) viewed that the monetary and non-monetary incentives lead to a high degree of employee commitment, but the small business managerial tiers are quite reluctant in investing in training. There is consensus about various researchers any incentives can enhance the staff commitment (Nawab and Bhatti, 2011) and control turnover rate (Dalziel, 2010).

Secondly, the internal and external labor market issues have identified training as a core element in the employee turnover rate. Imperfections in the local labor market are the key cause of high turnover in the small business manufacturing firms. Previous studies also confirm that the lack of a skilled workforce in developed or developing countries encourages small business managers and owners to compete rather than investing in the workforce. According to Rouditser and McKeown (2015), such approaches within SMEs increases the pooching of human resource. This view supports the MST argument that SMEs mainly focuses to get human resource from external sources (Ahmed and Chowdhury, 2009). Furthermore, MST and HCT also confirm the significant role between the skills-based training and human resource turnover rate. Therefore, data analysis confirms that within the small companies, employee training increases the turnover rate of the workforce. 

Conclusion

Findings of the current research study confirm within small business units, training for a short period boosts the employee commitment and loyalty for workforces. Therefore, employees of small business entities are more concerned with compensation and benefit the career progression of an organization. Therefore, a causal and a positive relationship has been identified in the West Midland. Besides, findings suggested that the labor market (qualitative, quantitative) of internal and external issues which also poaching and leads of small business employees within organizations. According to the Market Singling Theory (MST) of Spence (1973), has the external marketability unintended and effects of HR training is increasing the workforce. The current study examines that the skilled workforce prefers themselves and compete for small business management rather than investment in their training activities. Panagiotakopoulos (2011) endorsed that the labor and capital market imperfections to the workforce skilled to encourage small business management. 

References

• Becker, G. S. (1962). Investment in Human Capital: A theoretical analysis. The Journal of Political Economy, 70(5), 9-49.
• Beynon, M. J., Jones, P., Pickernell, D., & Packham, G. (2015). Investigating the impact of training influence on employee retention in small and medium enterprises: a regression-type classification and ranking believe simplex analysis on sparse data. Expert Systems, 32(1), 141- 154.
• Ciriaci, D. (2017). Intangible resources: the relevance of training for European firms' innovative performance. Economia Politica, 34(1), 31-54.
• Dai, Z. (2012). Toward a learning-based view of innovation. Competitiveness Review: An International Business Journal, 22(1), 18-27.
• Dalziel, P. (2010). Leveraging Training: Skills Development in SMEs. An Analysis of Canterbury Region, New Zealand.
• Ellinger, A. E., BaÅŸ, A. B. E., Ellinger, A. D., Wang, Y. L., & Bachrach, D. G. (2011). Measurement of organizational investments in social capital: The service employee perspective. Journal of Business Research, 64(6), 572-578.
• Hashim, J., & Wok, S. (2013). Who benefits from training: big guy or small fry? Development and Learning in Organizations: An International Journal, 27(3), 14-17.
• Khalid, N., Islam, D. M. Z., & Ahmed, M. R. M. (2019). Entrepreneurial Training and Organizational Performance: Implications for Future. Humanities & Social Sciences Reviews, 7(2), 590-593.
• Knudsen, E. S., & Lien, L. B. (2015). Hire, Fire, or Train: Innovation and Human Capital Responses to Recessions. Strategic Entrepreneurship Journal, 9(4), 313-330.
• Lambert, R., Leuz, C., & Verrecchia, R. E. (2007). Accounting information, disclosure, and the cost of capital. Journal of Accounting Research, 45(2), 385-420.
• Lancaster, S., & Di Milia, L. (2014). Organizational support for employee learning: An employee perspective. European Journal of Training and Development, 38(7), 642-657.
• Lee, N. (2014). What holds back high-growth firms? Evidence from UK SMEs. Small Business Economics, 43(1), 183-195.
• Long, C. S., Ajagbe, M. A., & Kowang, T. O. (2014). Addressing the Issues on Employees' Turnover Intention in the Perspective of HRM Practices in SME. Procedia-Social and Behavioural Sciences, 129 (33), 99-104.
• Malik, A., & Nilakant, V. (2011). Extending the
• Nathans, L. L., Oswald, F. L., & Nimon, K. (2012). Interpreting multiple linear regression: A guidebook of variable importance. Practical Assessment, Research & Evaluation, 17(9), 1-19.