The Role of Small and Medium Enterprises in Achieving Economic Goals of the Vision of Saudi Arabia 2030

Abstract

This research aims to identify the role that small and medium enterprises (SMEs) can play in achieving the economic goals of the vision of Saudi Arabia 2030. The study relied on descriptive analysis, designing a standard model, and analyzing it using the Eviews9 program. The study also adopted the questionnaire as a tool for data collection. The study area covered Alkharj and Hawtat Bani Tamim governorates. The sample size of the study was 142 participants. The study's results confirmed the existence of a significant impact of changes in independent variables (X1, X2, X3, X4), which are (GDP, non-oil exports, number of employees, and public revenues), respectively. The dependent variable (Y) represents the number of small and medium-sized businesses in the Kingdom of Saudi Arabia. Additionally, it was found that 61.3% of small and medium-sized enterprises in the governorates of Al-Kharj and Hawtat Bani Tamim operate in the commercial sector. Most study participants concur that SMEs significantly lowered the unemployment rate and helped boost the GDP rate in the Kingdom of Saudi Arabia. The obstacles and difficulties facing the establishment of these enterprises were financial problems, marketing problems, and corporate monopoly. Furthermore, most of the small and medium l enterprises faced financing problems.

1. Introduction

Small and medium enterprises are receiving great attention considering the global economic crisis. Today, SMEs are the focus of industrial policy to reduce poverty rates. Several international organizations, led by the United Nations Industrial Development Organization UNIDO and the World Bank, have started to support SMEs.

Previous studies used to ignore regional differentiation and did not reflect their role in achieving the economic goals of vision or long-term strategies. This research aimed to fill this gap. The study acquires special importance because it deals with the participation of SMEs in the GDP, and this corresponds to the vision of the Kingdom 2030. Also, the study focuses on identifying the role that medium and small enterprises can play in achieving the economic goals of the vision of the Kingdom of Saudi Arabia, and among those goals are raising the percentage of non-oil government revenues, developing the retail sector and small enterprises, reducing the unemployment rate, elevating the size of the economy and developing the proportion of non-oil exports,

Small and medium enterprises can play an essential role in achieving some of the economic goals of the Kingdom’s Vision 2030. For example, they can reduce unemployment from 11.6% to 7%. Moreover, they can raise the GDP by 15% and expand the commercial sector’s participation in the GDP from 40% to 65%. They can increase Saudi non-oil exports from 16% to 50% and increase government profits from 163 million Saudi riyals to 1 trillion Saudi riyals (Saudi Vision 2030). Jerry (1979) and Schmitz (1982) indicate that financing is one of the most important and serious problems and obstacles facing small and medium enterprises in developing countries.

The total number of (SMEs) in the Kingdom of Saudi Arabia in 2020 reached 571177 establishments. The rate of entry of new SMEs into the market was 2%. SMEs contributed 197 million, which was 90% of all businesses. Almost 85% of these are single-owner companies. According to Ministry of Economics Finance reports (2016–2019), there was an increase in the number of SMEs from (122013) in 2016 to (202297) in 2019, with an increase of (59%). With the increase in the number of (SMEs), the gross domestic product increased from (241850) in 2016 to (297362) in the year 2019, with an increase of (8%); non-oil exports increased from (177694) in 2016 to (229,184) in the year 2019, an increase of (78%); the number of workers in medium and small enterprises increased from (247080) in 2016 to (547914) in the year 2019, with an increase of (45%); public revenues increased from (514 billion) in 2016 to (975 billion riyals) in the year 2019, an increase of (53%); the gross domestic product increased from (241850) in 2016 to (297362) in the year 2019, with an increase of (8%). Almost 74% of SMEs are in commerce and building companies, followed by the industrial sector, According to The General Authority for Small and Medium Enterprises (Monshaat, 2020).

SMEs are one the most powerful means of promoting the national economy (Gerry, 1979), driving development, having a positive socio-economic impact, raising exports, creating new careers for Saudi youth, opening new labor markets, varying income origins, and increasing productivity. Therefore, it is necessary to identify the reality and obstacles of (SMEs) in the Kingdom of Saudi Arabia and attempt to find solutions. So, the main question of the research is: what is the role of medium and small enterprises in achieving the economic goals of the vision of Saudi Arabia 2030?

Small and medium-sized businesses are defined and understood differently in different countries based on their unique capabilities and economic and social conditions, such as the types of factors that go into production, the quality of traditional industries that predated modern industry, population density, the availability of labor and the level of qualification of that workforce, the average level of wages and income, and other economic and social aspects. Also, the meaning differs depending on whether it is used for statistical, financial, or other objectives.

Small and Medium Enterprises (SMEs) affiliated with the Small and Medium Enterprises Authority in Saudi Arabia are defined as companies with a separate commercial registry with fewer than 249 workers and profits of less than 200 million Saudi riyals (Monshaat, 2020).

The idea of small and medium-sized enterprises’ cluster development to direct regional economic growth and development and address the bottlenecks problem faced by small and medium-sized enterprises is of considerable practical value (Rong & Li, 2015).

SMEs (small and medium-sized firms) are widely acknowledged as essential to developed nations’ economies and the functioning of the labor market. Also, SMEs pay taxes that go toward the state budget. Domestic labor comprises most of those employed in the SME sector (Pham et al., 2021). SMEs in developing nations face various issues and barriers, such as organizational barriers, the ability to make developmental decisions, organizational weaknesses, and gaps in workers’ training and development (Hirishman, 1959; Leff, 1979; Leys, 1973; Kilde, 1979). According to the findings, Saudi Arabia’s small and medium-sized businesses contribute only 22% of the country’s GDP, compared to 70% in other economies. Moreover, they contribute only 5% of Saudi exports. In 2016, small and medium enterprises employed 34% of the Saudi workforce (Barjas, 2017).

2. Literature Review

There were several studies focused on small and medium enterprises from different aspects. When researching the economic role of medium and small enterprises, Boukedjane (2022) stated that small and medium-sized businesses are considered one of the most significant solutions to advance the wheel of development and reduce the unemployment problem of most countries world, whether developed or developing, suffer from. Through this study, the contribution of small and medium-sized enterprises to reducing the unemployment problem was highlighted from 2010 to 2017. One of the most important aspects of controlling is identifying deviations between actual and planned results and determining the causes of these deviations. Failure to pay attention to this activity can affect the company’s performance.

Dobošová et al. (2022) focused on identifying selected research findings from Slovak agricultural enterprises. The paper’s main goal was to evaluate and analyze the approach of agricultural enterprises in Slovakia to the implementation of deviation analysis. The basic technique applied for data collection was a questionnaire survey, which was supplemented by a direct interview with managers of selected agricultural entities.

Soegoto et al. (2022) examined the role of Domestic Investment (DI), Foreign Investment (FI) and Micro, Small, and Medium Enterprises (MSMEs) in reducing poverty in Indonesia. This is also because there has been no previous Indonesian research involving the four variables with data from 2010 to 2020. This study used quantitative methods to determine the impact of DI, FI, and MSMEs on the magnitude of poverty. Data combining time series and cross-section data is analyzed using the least square panel method. The study results showed that poverty decreases when there is an increase in domestic investment, foreign investment, and the number of micro, small and medium-sized enterprises in Indonesia.

Saibi (2021) referred to the magnitude of DI, FI, and MSMEs as determined using quantitative methods in this study. Small businesses and entrepreneurship were two of the most significant economic contributions. This was demonstrated in several studies. Despite this, assessing the contribution of small and medium-sized enterprises and their relationship with other factors related to economic development posed significant challenges.

So, in this article, we’ll try to explain how small and medium-sized enterprises and entrepreneurship fit into the developing economy. The study shows the positive effects of entrepreneurship and small and medium-sized businesses on the restructuring of the firms, the creation of jobs, and their prominence as a remedy for unemployment issues. Also, it promotes societal progress through equal distribution.

Nwankwo and MacDonald (2020) stated that without proper adoption of entrepreneurial marketing (EM) as a business strategy, many small and medium enterprises (SMEs) in both developed and developing economies would fail to survive. This conceptual paper aims to elaborate on the strategic role of EM in SMEs. To achieve this, the complexity of EM and the aspects of entrepreneurial marketing (EM) were discussed. The paper asserted that the strategic roles of EM embrace eight cardinal dimensions for SMEs only to ensure the business’s long-term direction and leverage resources and capability to sustain an advantage over competitors.

Xuan (2020) also showed the factors that influence the size of the investment capital used by small and medium-sized enterprises in Vietnam. A sample of 458 small and medium-sized enterprises from around the country was used in the study. The study is based on data from a time series in October 2019 in Hanoi, Bac Can, Buon Ma Thuot, and Pleiku. The factors that influence the size of investment capital in Vietnam’s medium- and small-sized businesses are also identified in this study. Software STATA 14.0 and SPSS 20.0 are used to process the data. The study’s findings suggest that factors influencing the organization’s capital contribution include (1) business lines, (2) import and export business, (3) kind of company registration, (4) business location, (5) operational time, and (6) the percentage of the organization’s capital contribution.

Mechaali and Mahrez’s (2020) described the reality of SMEs and their contribution to economic development in Algeria. The results demonstrated that despite the efforts made by the Algerian government to develop SMEs, the results are still subpar, and the contribution of this type of enterprise to economic development (employment, export, GDP, value-added) has not been achieved. This calls for increased efforts and a new strategy to overcome challenges and obstacles like funding, technology, and marketing.

Mokoena (2017) explored the role of Local Economic Development (LED) in reshaping the economy of South Africa through the promotion of Small, Medium, and Micro Enterprises (SMMEs)in local government. LED is considered a modern strategy in the local government development process, given that South Africa still has significant unemployment and poverty rates. This paper is built on the post-1994 South African government concept of grassroots initiatives and community participation. The findings indicate South Africa’s need to make the SME environment conducive and favorable for business purposes. LED does contribute positively towards the growth of the economy of South Africa.

Al-Khatib et al. (2015) stated that SMEs are crucial to economic growth. The study results revealed that (a) marketing is indeed a catalyst of economic development in developing countries; (b) developing countries, in general, exhibit unique marketing, economic, and cultural characteristics that are different from those experienced in more developed Western countries; and (c) even though marketing plays an important role in the economic development of developing nations, modern marketing technology developed in the West and transferred to developing countries should be adapted and tailored to fit and match the specific needs of these developing countries.

According to Salem (2014), SMEs have important roles to play in the economy. This paper aimed to enhance understanding of how business incubators function and their impact on national development and growth of a developing nation. First, the paper reviews the literature on the relationship between business incubators and economic development; then, the paper provides an analysis of the characteristics and objectives of business incubators in Saudi Arabia. The paper also examines the significant contribution of business incubators in promoting the economic growth and development of Saudi Arabia. The paper points out the importance of introducing business incubator strategies and programs and the role of such important moves in ensuring sustainable value creation and wealth-building.

Zamberi Ahmad (2012) stated that SMEs face obstacles, such as getting finance/capital, regulations, a shortage of financing options, and a hostile business environment. Zamberi also found unexpected policy modifications and a shortage in training. These problems differ based on business activity. Elimam (2017) found that there was a significant impact of small enterprises on the unemployment rate of Saudi Arabia. Statistically insignificant results were revealed for the amount of funding variable, and a negative relationship has been determined between the unemployment rate and the amount of funding.

Al-Nsour (2012) attempted to measure the replacement flexibility between small enterprises’ production factors in KSA, not only to measure economic and social efficiency and macro and micro productivity rate of production elements of such enterprises but also to measure the role of such enterprises in labor employment. The study concluded that the high cost of wages or interest rates by 10% led to a decline in work opportunity cost and employment levels in (SMS) enterprises. Moreover, the study found that small enterprises achieved economic efficiency that relatively exceeded macro enterprises’ economic achievements. Medium-sized enterprises achieved a higher average of the total productivity of production factors, while macro-ones achieved a higher average of partial productivity.

The Literature Review results identified the primary hypothesis: Medium and small enterprises have a statistical effect in achieving the economic goals of the vision of Saudi Arabia 2030. To verify the primary hypothesis, the following sub-hypotheses were formulated:

H1: Medium and small enterprises have a statistical effect on reducing unemployment performance evaluation.

H2: Medium and small enterprises have a statistical effect on the rate of GDP growth.

H3: Medium and small enterprises have a statistical effect on increasing government revenues.

H4: Medium and small enterprises have a statistical effect on increasing non-oil exports.

3. Research Methods and Material

This research follows the descriptive approach in the theoretical framework, based on secondary sources such as books, previous studies, periodicals, and reports, and inferential statistics can be used to answer the research questions. The researchers used several statistical methods: Spss 26 and Eviews 9 software to analyze the model questionnaire data.

The study relies on primary data, reports from the Ministry of Finance and Economy and the Central Statistical Organization in the Kingdom of Saudi Arabia, and secondary data such as books, references, and scientific journals. However, the research depends on the primary data through a questionnaire. The participants were the owners of (SMEs) or the managers in these enterprises.

The study population covers the SMEs in ALkharj and Hawtat Bani Tamim. The sampling technique followed simple random to select medium and small companies from the two governorates. The sample size reached 142 participants from different enterprises.

The model was formulated and described to clarify the linear relationship between the dependent variable (number of medium and small enterprises) and the independent variables (GDP, non-oil exports, number of employees, public revenues) as follows:

NOMSP = f (GDP, NOPGRY, NOEMP, TRVNS) (1)

When formulating a multiple linear regression model:

Y = B₀ + B₁ X₁ + B₂ X₂ + B₃ X₃ + B₄ X₄ + µ (2)

Whereas:

Y = Number of medium and small establishments (dependent variable)

X₁ = GDP (independent variable)

X₂ = non-oil exports (independent variable)

X₃ = number of employees (independent variable)

X₄ = Public Revenue (independent variable)

μ = random variable (independent variable)

4. Results

Data on the variables included in the model was collected in the form of a time series that included (4 years) representing the period (2016–2019). Due to its qualities of objectivity, consistency, and effectiveness, i.e., the least variance, the best techniques for estimating the parameters of the multiple linear regression model and the best estimates of the quality of the squares method is ordinal least squares (OLS). Estimates of the OLS parameters can be obtained by finding the least end of the sum of the squares of errors (residuals). Therefore, the regression sum of squares is explained by the variables as independently as possible, and under the conditions of the Ordinary Least Squares (OLS) method, the obtained estimates are best linear and unbiased and have the least variance.

4.1. Analysis of the Multiple Linear Regression Equation

Here are the results of the multiple linear regression equation analysis using Eviews9 software (Table 1):

Y = 7.12 + 0.94 X₁ + 1.81 X₂ + 2.08 X₃ + 0.04 X₄ + µ (3)

Table 1: Multiple Linear Regression Model Analysis

4.2. Results of the Model

After obtaining the results of the multiple linear regression equation analysis using Eviews9 software, the estimates are evaluated through the numerical signs and values of the model parameters. These estimated values show whether these estimates are significant or economically meaningful and whether they are statistically acceptable, and it is called a t-test. In addition to the availability of the conditions of the ordinary least squares method, there are a number of criteria that enable the evaluation process, including:

4.3. Testing the Suitability of Estimates to Economic Criteria

This criterion is based on the estimations’ direction (positive or negative), size, extension, limitations, estimator, or numerical value. The measured multiple linear regression model illustrates the suitability of the estimates to the economic criteria based on the analysis’s findings. The estimated parameters must match the economic criteria for the existence of a direct relationship between the dependent variable (the number of medium and small enterprises) and the independent variables (GDP, non-oil exports, number of employees, and general revenue).

4.4. Testing the Suitability of the Estimates to the Statistical Standards

The economic suitability of the obtained estimates has been confirmed (Morale test). From the analysis results, depending on the value of β for each independent variable, we found that when the gross domestic product increases by one unit, the number of medium and small enterprises increases by (0.94) units. When the value of non-oil exports increases by one unit, the number of medium and small enterprises increases by (1.81) units. When the number of employees increases by one unit, the number of medium and small establishments increases by (2.08) units. When the value of public revenues increases by one unit, the number of medium and small enterprises increases by (0.04) units

Y = 7.12 + 0.94 X₁ + 1.81 X₂ + 2.08 X₃ + 0.04 X₄ + µ (4)

4.5. Testing the Significance of the Parameter for each Independent Variable

Comparing the value of (Prob) with the significance level (0.05) at a 95% confidence level. We note that the significance level is (0.05) higher than the value of the independent variables, which confirms the significance of each independent variable.

4.6. A measure of the Explanatory Power of the Model (Multiple Determination Coefficient R²)

The multiple determination coefficient R² value is “76%”, which states that the independent variables explain 76% of changes in the dependent variable, and the rest of the percentage value represents “unexplained changes” due to variables that were not part of this study. The value of the coefficient of determination R² lies between zero and one (0 ≤ R² ≤ 1), and when the coefficient of determination is equal (R²) to zero, it indicates that the explanatory power of the model is lacking. This implies that the independent variables have failed to interpret changes in the dependent variable. When the coefficient of determination is equal (R²) to one, it indicates that the model’s explanatory power is certain. This implies that the independent variables have succeeded in explaining the total changes in the dependent variable. The explanatory power of the estimated multiple linear regression model reached a value of (0.76), which shows independent variables contributed 76% to the interpretation of the dependent variable (number of small and medium projects) and the remaining 24% represent other “unexplained” variables in the model, which are included in the random variable μ.

4.7. Test the Significance of the Parameters of the multiple linear Regression Model

To test the significance of the multiple linear regression model by measuring the variance of the estimated random errors, the variance of the regression coefficients, the variance of the stability of the regression equation, its standard error, the test of the significance of the parameters of the linear regression, and the formation of confidence limits for the parameters of the linear regression model Multiplex, and test for the significance of the linearity of the multiple regression (using F).

4.8. Multiple linear Regression Parameters Significance Tests

Used to test each parameter in the multiple linear regression model by formulating hypotheses and comparing the absolute value of the t-statistic calculated in the model with the tabular t-statistic. And then accepting or rejecting the null hypothesis that there is a relationship between each independent variable and the dependent variable.

4.9. Formulation of Hypotheses

By this, we mean the null and acceptance hypotheses. In the case of the null hypothesis, there is no relationship between the dependent variable “a number of small and medium enterprises” and the independent variables H0: β ≠ 0, while in the case of the alternative hypothesis, there is a relationship between the dependent variable “the number of small and medium enterprises” and the variables Independent H1: β = 0.

4.10. Parameters Significance Test

The multiple linear regression model was measured using the least squares method, with a confidence level of 95% and a level of significance (0.05) { then the tabular t-statistic value reached (1.96 = t), and from the results of the previous analysis, a comparison will be made between the calculated t-statistic value for each parameter with its tabular value (Table 2).

From Table 2 and by taking the absolute value of the calculated t-statistic value and comparing it with its tabular value of (1.96 = t), we find that the calculated t-statistic values of all the independent variable (X₁–X₄) exceed the tabular value (>1.96). Thus, we reject the hypothesis Nullity and accept the alternative hypothesis that there is a statistically significant relationship between the independent variable (Y) and the dependent variables (X₁, X₂, X₃, X₄).

Table 2: T-statistic Value for the Significance of Parameters

4.11. Significance Test for Collinearity of Multiple Regression (using F)

Formulation of Assumptions:

Null hypothesis: (non-significant regression H0: β = 0)

Alternative hypothesis: (significant regression H1: β ≠ 0)

The results in Table 3 showed that the calculated value of the F-statistic (9.84 = F-statistic) exceeded the 5 units, and thus we reject the null hypothesis and accept the alternative hypothesis, meaning that there is a significant relationship between the dependent variable and the independent variables in general and that the F-statistic value of the estimated model reached (0 = F-statistic), demonstrating the significance of the model.

Table 3: Testing the Significance of the Multiple Regression

4.12. Testing the Predictive Ability of the Model

After confirming the validity of the estimates according to the three criteria (economic, statistical, and standard theory), it can be used to predict the numerical values of variables in the future. Therefore it is necessary to test the ability of the standard model to predict- prediction is based on the near future being an extension of the near present. Thus, it is a description of the state of the phenomenon at a specific point or period of time in the future. The concept of scientific forecasting based on econometric studies is connected to the scientific research and analysis of the present and past phenomena and the forecasting of this phenomenon’s behavior in the future. According to the methods used in the prediction, the point prediction method was used, which is intended to give only one value to the expected event or the future event of the dependent variable.

4.13. Measurement Problems of the Multiple Linear Regression Model

With regard to the measurement problems of the multiple linear regression model, the researchers explain the following:

Autocorrelation between variables: The presence of autocorrelation between the residuals does not affect the values of the regression coefficients in the equation under study, and even in the case of autocorrelation, the regression coefficients are unbiased. The presence of autocorrelation leads to the calculation of fewer standard errors for these parameters, thus drawing a picture Too optimistic, and by using the Durban Watson test to detect autocorrelation, the researchers found that (DW = 2.018221), which is the first order, and its value exceeded (1.3), which indicates that there is no autocorrelation between the independent variables, By performing a correlation and autoregressive test, it was shown that there was no autocorrelation between the independent variables at the level of significance 0.05, as well as depending on the values of Auto-Correlation, all of which are close to zero at the number of 16 armpits.

In the face of this issue, parameter estimates using the ordinary least squares approach remain unbiased and consistent, but they lose their efficiency. Instead, parameter estimates should be made using the “Goldfield and Quant” test through the ascending order according to the values of the independent variables, followed by the third test. The total observations and their comparison by testing each variable in the model to represent both small and large values of the independent variables, and then finding the issue of the instability of the variance through the significance of F, and by applying it, it became clear the significance of the model and the absence of the issue of the instability of variance “heterogeneity.”

Collinearity problem or linear dependence: The multi-linear problem is one of the standard problems that arise as a result of an imbalance in one of the conditions of the Ordinary Least Squares (OLS) method, which is that the multiple regression model does not have a perfect linear correlation between two or more independent variables in the model (Table 4). Therefore, the existence of a linear relationship between one of the independent variables and one or more of the other independent variables leads to the emergence of the problem of multi-linearity, which results in a number of problems when analyzing the regression in terms of estimating the parameters of the model as well as the standard errors of these parameters. In the estimated model of the multiple linear regression under study, the problem of linear multiplicity was addressed by conducting a preliminary treatment for each independent variable (nonoil exports, number of employees) in the model. After the initial preliminary study, the first analysis (–1) for these two variables, and then the analysis again, it was found that the model’s high coefficient was insignificant due to its low significance.

Table 4: Autocorrelation Between Model Variables

4.14. Results of the Questionnaire

At the beginning of this section, descriptive statistics have been applied to the collected data. The frequencies adopted for the sample characteristic and the averages for the pivots or dimensions of the study. Table 5 shows 96 (67.6%) small and medium institutions in Akharj province and 46 (32.4%) in Hawtat Bani Tamim.

Table 5: Characteristics of the Variables

Regarding economic activity, it appears that 87 (61.3%) adopted the commercial activities, 24 (16.9%) for the services, 20 (14.1%) for the industrial activities, 11 (7.7%) focused on the agricultural activities.

Also, the findings revealed that 103 (72.5%) of the small and medium institutions they concerning with training for their labor, and 39 (27.5%) did not care about the training. Table 5 shows the barriers and difficulties facing establishing medium and small enterprises. There 54 (38%) explained that the obstacles and difficulties were financial problems, 49 (34.5%) for marketing problems, and 39 (27.5%) for corporate monopoly.

Descriptive statistical methods such as averages and standard deviations were used to assess the study participants toward subscales items. The questionnaire statement was assessed according to the averages and ranked them descending. Table 5 presents the results of the first pivot, which is the contribution of medium and small projects in reducing the unemployment rate. According to the study participants’ viewpoints, the statement “the facility keeps pace with developments in the Kingdom “came in first with an average of 4.21. In the second rank came the phrase” the Corona crisis negatively affected the total number of workers” with an average of 4.13, and in the third rank came the phrase “the facility absorbed an estimated number of Saudis this year compared to the previous year “ with an average of 3.83. In the fourth rank appear the phrase “the facility absorbed an estimated number of non-Saudi workers this year compared to the previous year,” with an average of 3.27.

Table 6 shows participants’ viewpoints concerning the role of SMEs in reducing the unemployment rate. The results reveal that the overall mean value of participants’ perceptions reaches (3.86). This average value indicates that most participants agree there was an effective role of SMEs in reducing the unemployment rate.

Table 6: Averages and Standard Deviation of Respondents’ Opinions

The results in Table 6 illustrate the pivot of SMEs’ contribution to the GDP growth rate. The phrase “the facility uses methods of encouraging employees such as incentives and rewards “came in the first rank with an average of 4.02. In the second rank came the phrase “there is an increase in the capital of the facility annually,” with an average of 3.87, followed by the phrase “a change occurred in the facility’s total production” in the third rank of respondents’ opinions with an average of 3.84. The phrase “total production increased from last year” came in the fourth rank of respondent’s opinions with an average of 3.76.

Also, the results show participants’ perceptions regarding the contribution of (SMEs) to the GDP growth rate. The results showed that the overall mean value of participants’ perceptions is (3.85) with an SD (of 0.71). This average value indicates that most owners agree that the small and medium contributed to increasing the rate of GDP in the Kingdom.

Table 6 shows the results of the contribution of SMEs to government revenues. The statement “the facility’s revenue increased compared to the previous year” came in the first rank with an average of 3.83. In the second rank came the statement “selling prices stimulate production,” with an average of 3.82, followed by the statement “the profits of the facility increased compared to the previous year,” which came in the third rank with an average of 3.76, and in the last rank appear the phrase “fees for establishments have been reduced” with an average 3.23.

In addition, the result shows the participants’ perceptions regarding the contribution of SMEs to government revenues. The results showed that the overall average value of participants’ perceptions reached (3.66). This average value indicates that most owners agree that the small and medium enterprises contributed to government revenues.

Table 6 illustrates the results of the contribution of SMEs to the increase in non-oil exports. In the first rank came the phrase “the facility’s production can be exported,” with an average of 3.65, followed by the statement “the Ministry encourages the export of the facility’s products” in the second rank, with an average of 3.61. In the third rank came the phrase” the facility’s products are exported,” with an average of 3.45. In the fourth rank came the phrase” the facility’s exports increased this year compared to the previous year,” with an average of 3.38. Also, the result revealed the participants’ perceptions regarding the contribution of SMEs to the increase in non-oil exports. The results showed that the overall mean value of participants’ perceptions reached (3.52). This average indicates that most owners of the enterprises agree that (SMEs) contributed to the increase in non-oil exports.

Table 6 shows the results of the financing problems facing SMEs. According to the respondent’s opinion, the phrase “ the principal source of financing the facility is the owner of the facility” came in the first rank with an average of 4.01, followed by the statement “the main source of financing for the facility is the shareholders” with an average 3.47. In the third rank came the phrase “the main source of financing for the facility is through a loan from a financing institution,” with an average of 3.35. The last rank came to the statement “the business is having difficulty obtaining loans,” with an average of 3.22. The result revealed the owners’ perceptions regarding the financing problems facing small and medium enterprises. The results showed that the overall average of participants’ perceptions was (3.51). This average value indicates that most owners of enterprises agree that small and medium enterprises face financing problems.

5. Discussion

Nowadays, SMEs play an important role in the Saudi economy in various fields such as commercial, industrial, agricultural, and services. This is in line with the 2030 vision of Saudi Arabia. Therefore, this study aimed to identify the role that (SMEs) can play in achieving the economic goals of the vision of Saudi Arabia 2030.

The results showed that 61.3% of SMEs in the governorates of Al-Kharj and Hawtat Bani Tamim operate in the commercial fields, which is the general trend of most investors. 72.5% of the small and medium enterprises mentioned that they are interested in training workers in these institutions.

According to the participants’ opinions, the obstacles and difficulties facing the establishment of these enterprises were financial problems, marketing problems, and corporate monopoly.

The results showed that most participants in the study agreed that SMEs had an important role in reducing the unemployment rate and contributed to increasing the rate of GDP in the Kingdom. In addition, the SMEs contributed to government revenues. This result agrees with the result of Elimam (2017).

Most of the participants in the study agree that (SMEs) to an increase in non-oil exports, and most of them faced financing problems. This results in the same line as Al-Nsour (2012).

6. Conclusion

This study attempted to identify the role of SMEs in achieving the economic goals of the vision of Saudi Arabia 2030. Small and medium enterprises nowadays play an important role in the Saudi economy in various fields such as commercial, industrial, agricultural, and service. This aligns with the economic objectives of the vision of the Kingdom of Saudi Arabia 2030. The results showed a significant relationship with statistical significance between the dependent variable, “number of small and medium enterprises,” and the independent variables (GDP, non-oil exports, number of employees, and public revenues). This means significant reciprocal and complementary economic relations exist in the Saudi economy.

The study revealed the important role of SMEs in reducing the unemployment rate, contributing to an increase in the rate of GDP in the Kingdom, contributing to government revenues, and contributing to the increase in non-oil exports. Most of the participants explained these enterprises faced financing problems.