Keywords

Entrepreneurship Education, Entrepreneurial Cultural, Entrepreneurial Intentions, Kingdom's Vision 2030.

JEL Classifications

L26, M1

Introduction

Researchers have recently become more interested in the field of entrepreneurship and establishing new institutions and because of its increasing importance and its strong effect on economy. Nearly 80% of the world's GDP comes from small and medium enterprises, as well as more job opportunities (Tripathi, 2019). However, Saudi youth are not so enthusiastic to work in private sectors compared to other countries, especially the fresh graduates, where most of them find themselves looking for jobs that are more stable than their tendency to create their own businesses at the time of the increasing unemployment and the increasing number of graduates every year that makes it difficult to find a job. In addition, the age group below 35 years represents about 70% of Saudi society (Kyari, 2020).

Entrepreneurship process is a phenomenon consisting of many stages starting from the entrepreneurial trending which became the theoretical basis that framed many studies. And in order to achieve the highest percentage of the entrepreneurial business we have to stand at each stage of this process and try to understand it. Studying the entrepreneurial process enables the analysis, interpretation, and understanding of the factors and obstacles that can affect and prevent individuals from choosing a business as a career. The entrepreneurial trend has received great attention, and most studies targeted class students, as this category represents the backbone of society, and the unemployment of educated people is a waste of community resources that can be spent on other developmental aspects (Bassem & Mehdi, 2018).

Are the pioneers who firstly studied the factors that affect choosing the entrepreneurship as a career and accordingly presented an example of entrepreneurial process which is based on transformation concept? In other words, "In order for the individual to initiate a significant and important change in his direction in life, such as taking the decision to establish his own institution, this decision must be preceded by an event that stops and breaks the usual routine". And according to these authors: "The process of change in the course of an individual's life can be described as a guiding force, leading the individual in a certain direction and at a given moment."

Review Literature

The literature on entrepreneurial intentions has rapidly grown since the publishing of the seminal works by (Shapero & Sokol, 1982) some 30 years ago. Since the early nineties, we have seen an explosion of research using entrepreneurial intention models as a framework, thereby confirming the applicability of the concept in various settings. Nevertheless, despite the existence of alternative models, there is some evidence of the compatibility of these intention-based models (Boyd & Vozikis, 1994).

Additionally, with the publication of an increasing number of studies based on the concept of EI, new applications, mismatches and specifications have emerged (Carsrud & Brännback, 2011). The vast majority of this research lacks systematization and categorization, with a tendency to start anew with every study. There is therefore a risk of the field stagnating and lacking robustness (Fayolle & Liñán, 2014).

Entrepreneurship education has not achieved sufficient maturity either in theory or in practice. Early courses on entrepreneurship have started in the United States in 1940s. Since those times, entrepreneurship education has increased considerably in the developed world (Khan, 2017). The number of universities and colleges with entrepreneurship courses in their curricula has clearly increased in the United States since the late 1960s.

Furthermore, as stated by (Almahdi, 2019), entrepreneurship education is not a single event, but rather a continuous process comprised of a series of events. In consequence, the role of education and training in entrepreneurship and in the identification of endowment of entrepreneurial potential at a young age, are becoming evident for students, politicians and educators (Rashid, 2019).

One of the critics in entrepreneurship courses, pointed by (Neck & Greene, 2011), is the fact that they are focused in the exploitation of opportunities assuming that the opportunity has been already identified. Thus, very little time and attention is given to creativity and idea generation process. Accordingly (Jusoh et al., 2011) in their analysis about training needs of education in entrepreneurs found that in entrepreneurial skills training there is a lack in areas such as how to enhance creativity and innovation.

Although the alleged benefits of entrepreneurship education have been much celebrated by researchers and educators, there has been little rigorous research on its effects (Peterman, N.E. & Kennedy, 2003). In fact entrepreneurship education ranks high on policy agendas in Europe and the US, but little research is available to assess its impact and their effects are still poorly understood. Several previous studies find a positive impact of entrepreneurship education courses or programs (Rodrigues et al., 2009).

Research Methodology

The author divided the transformation process into three groups:

• Negative transformation: Such as divorce, layoffs, immigration, and job dissatisfaction. They are usually outside the control of the individual and imposed from outside.

• Positive transformation: Such as family, consumers and investors.

• Special situations: such as getting out of military service, study, or prison.

These factors are the basis for the change in the individuals’ life and the dynamics of the entrepreneurial event.

In the Saudi context, studies have recently begun to emerge about the entrepreneurship, because of awareness of the academic community in addition to the ruling authority of the importance of entrepreneurship and the importance of this type of studies. This concern is also due to the high unemployment rate of the educated population. Therefore, the problem of our study will be as follows:

Why Saudi youth are reluctant to choose private work as a career? Is it due to their lack of an entrepreneurial culture or lack of awareness of the mechanisms of assistance that can help them at the beginning of the life cycle of their projects? Does the business orientation differ between students of business administration and students of accounting?

To answer this problem, the study targeted 101 students from the fields of Business Administration and Accounting (seventh and eighth level) of the Faculty of Business Administration at Al-Majmah University.

The Importance of Study

The importance of the study emerges from the fact that entrepreneurship has become very important in the progress and prosperity of countries' economies. And here our study becomes more significant because the entrepreneurship of the educated people is actually a real investment to countries. Therefore, and through studying the entrepreneurial orientation, the factors influencing the students' choice of the entrepreneurship as a professional track will be monitored.

Objectives of the Study

The objective of this study is to identify the most important variables and factors that influence the students' choice of the entrepreneurship as a professional track and to measure their entrepreneurial orientation.

Mechanism of the Study

The field study aims at identifying the main reasons and motives for not choosing independent business as a career. Field research was carried out by means of a questionnaire on a group of students; the group was selected in a non-random manner, due to the absence of a comprehensive and updated statistical community from reliable sources (government bodies). We have therefore relied on an intentional sample. Of the 150 students surveyed, only 101 were answered, representing 67%. The data were collected through the self-method in order to increase the response rate and reduce the duration and cost of the survey (Rauch & Hulsink, 2015).

The questionnaire included a question about the career track after graduation, and we chose 19 test criteria derived from theoretical work, these criteria were classified according to four main axes for reasons of students' fear to join the entrepreneurial work as a professional track (reasons of a personal nature, reasons of an academic nature, reasons of a family nature, reasons related to the labor market), The five-point significance scale was used to the degree of importance divided into five criteria (strongly agree, agree, neutral, disagree, strongly disagree). The data was processed by SPSS version 23 and a set of statistical methods were used in data analysis and descriptive analysis using the arithmetic mean to rank the test criteria in descending order by the category of respondents (students of business administration and accounting) and the standard deviation to determine the extent to which data are separated from their arithmetic mean and to order the averages in the equal case, where the best grade is given to the less standard deviation (McAdam et al., 2018).

The "t-student" test was used for the difference between the averages for the two samples to see whether there was similarity or divergence of opinion among the students of two majors about the importance of stimuli. The factor analysis was used to summarize 19 occupational choice pathways to fewer specific factors so that they could be reduced to key factors and to determine the extent of these factors. Discriminant analysis was then used to study the effect of the determined factors in the two samples in order to derive motives that contribute to the distinction between the two groups.

Outcomes of the Field Study:

The Order of the Entrepreneurial Orientations

The following table presents descriptive statistics of the criteria for choosing an entrepreneurial orientation or stable work ranked according to the importance for each sample and using t-student test for the difference between the two groups Table 1.

Table 1 Results of Descriptive Analysis
Test motives	Arithmetic mean		Standard deviation		Priority level		LEVINI test (level of significance	(t-student) (level of significance)
	Career Track		Career Track		Career Track
	Private business	Stable job	Private business	Stable job	Private business	Stable job
fear of failure	4.4833	4.8293	0.9653	0.5874	1	1	12.407 (0.001)	-2.048 (0.043)
Lack of entrepreneurial culture	3.9833	4.6585	1.4081	0.8546	8	2	11.368 (0.001)	-2.742 (0.007)
Lack of awareness of assistant mechanisms	4.3667	3.9024	0.9382	1.1358	2	4	1.038 (0.311)	2.240 (0.027)
Lack of personal desire	3.9500	4.1951	1.3074	1.0054	9	3	4.066 (0.046)	-1.013 (0.314)
Lack of awareness of importance	4.1000	3.8049	1.0845	1.0300	4	6	0.571 (0.452)	1.370 (0.174)
Long working hours	4.1000	3.8049	1.1153	0.9544	5	5	0.809 (0.371)	1.383 (0.170)
Social Responsibility	4.2500	3.4146	1.0833	1.4313	3	12	7.816 (0.006)	3.336 (0.001)
The nature of the father's job	4.000	3.6585	1.0892	1.1749	7	8	2.120 (0.149)	1.498 (0.137)
Father's opposition	3.9333	3.2439	1.0393	1.4103	10	15	9.625 (0.003)	2.828 (0.006)
Academic track	3.8167	3.5854	1.2142	1.1613	12	10	0.017 (0.895)	-1.013 (0.341)
My nature dealing with others	3.9333	3.3415	1.1625	1.3713	10	14	4.460 (0.037)	2.335 (0.022)
the father's opposition to future plans	3.6500	3.7073	1.1324	0.9285	13	7	2.237 (0.138)	-0.268 (0.789)
Possibility of getting a job	4.1000	3.3902	1.1763	1.2051	6	13	9.487 (0.003)	2.619 (0.010)
Failure / success of some experiences	3.6500	3.5610	1.1763	1.2051	13	9	0.353 (0.554)	0.370 (0.712)
High cost of private business	3.5167	3.4390	1.2821	1.2854	15	11	0.104 (0.748)	0.299 (0.766)
bank financing difficulty	3.4333	3.1220	1.0476	1.2082	16	17	0.417 (0.520)	1.388 (0.171)
Monthly salary is not rewarding	3.333	3.1951	1.4575	1.1878	17	16	0.528 (0.024)	0.503 (0.616)
Impact of family and friends	2.3833	2.1463	1.2363	1.1739	19	19	0.325 (0.570)	0.965 (0.337)
Other reasons	2.6167	2.3171	1.1213	0.9601	18	18	1.360 (0.246)	1.396 (0.166)
Source: prepared by the author

Based on the results shown in Table 1, the most important criterion in choosing stable job is fear of failure, which is the higher mean for the two samples (4.8293 for MBA students and 4.4833 for accounting students). Although "fear of failure" is rated as the most important incentive for all students, the results of Levini test and the t-student test indicate to a noticeable variation between the two samples. The value of "Livini" test is 12.407 with a significance of 0.001. This value is smaller than α=0.05 level. This makes us assume that the two samples are not equal, the value of "t-student" test is -2.048 with a significance of 0.043 which is less than α=0.05 level and therefore we can assume a significant variation between the two samples.

A part from the importance of "fear of failure", the results indicate to a variation in the classification of test criteria between the two samples. For MBA students, "lack of entrepreneurial culture" is the second criterion in terms of importance (the mean equals 4.6485) followed by “Lack of awareness about assistant mechanisms", "lack of personal desire", then “lack of understanding" and "long hours" then “Possibility of getting a job”. It is clear according to Levini test that there is a variation between the two samples in “lack of entrepreneurial culture” as well as for the mean as shown by the "t-student" test. The value of "Levini" test is 11.368 with a significance of 0.001 and the value of the t-student test is -2.742 with a significance of 0.007, which is smaller than α=0.01, which means that the variation between the two samples significant. It is therefore possible to conclude that there is a variation in the degree of importance of “lack of entrepreneurial culture” between MBA students and accounting students. As shown by the mean in the table, the average of the degree of importance for MBA students is greater than that of accounting students.

Results of the Factor Analysis

Factor Analysis is a statistical method aimed at simplifying the correlations between several variables to conclude independent classifications based on specific classification principles (Chen, Y. 2014). The Factor Analysis was carried out in order to synthesize the variables (test factors 19) in order to obtain fewer factors.

The KMO and the Bartlett test show that the correlation matrix has a level of significance (Table 1). Bartley test is significant and Kaiser Meyer Olken is 0.79, which is statistically good and responds to what is admitted in field studies (Prakoso et al., 2019).

The factor analysis summarized the 19 variables and extracted six major components with distinct values greater than one with a total variance of 67.035%. This proportion meets the criteria established in the field statistics, which recommends that a contrast ratio shouldn’t be less than 50% (Miralles et al., 2016). The first major component has the largest distinct value (component variance) and equals 5.425 and explains 28.551% of the total data of the variables. The second major component has a distinct value of 2.360 and explains 12.423% of the total data of the variables; the third main component has a distinct value of 1.478 and explains 7.787% of the total data. The fourth major component has a distinctive value of 1.268 and explains 6.766% of the total data. The fifth main component has a distinct value of 1.162 and explains 6.116% of the total data. The sixth main component has a distinct value of 1.043 and explains 5.488% of the total data (Table 2).

Table 2 Results of Factor Analysis
Selection factors	Principal Factors						Freedom digree (FD)
	1	2	3	4	5	6
Long working hours	0.806						0.687
High cost of private business	0.707						0.686
Lack of awareness of assistant mechanisms	0.594						0.561
the father's job	0.702						0.541
Social responsibility	0.582						0.575
Lack of awareness of importanceة	0.591						0.508
Academic track	0.565						0.641
fear of failure		0.770					0.646
Lack of personal desire		0.744					0.727
Lack of entrepreneurial culture		0.711					0.626
the father's opposition to future plans			0.680				0.580
bank financing difficulty			0.589				0.674
My nature dealing with others			0.672				0.731
Lack of awareness of assistant mechanisms				0.833			0.828
Father's opposition				0.873			0.835
the father's job					0.547		0.603
Monthly salary is not rewarding					0.840		0.728
Other reasons					0.619		0.694
Impact of family and friends						0.922	0.865
Discriminant values	5.425	2.360	1.478	1.268	1.162	1.043
Percentage of explained variable	28.551	12.423	7.781	6.676	6.116	5.488
Ratio of total data	28.551	40.975	48.756	55.431	61.547	67.035
Kronbach Alpha (α)	0.831	0.711	0.654	0.782	0.612	-
“KMO” Test=0.790	“Bartlett” Test		Kai square= 684.606		Freedom degree= 171	Level of significance=0.000

To measure stability of the extracted factors, Alpha-Cronbach (α) test is used. The results showed that “Alpha Kronbach” was greater than 0.7 for three factors (1, 2, 4) and greater than 0.6 for factors (3, 5), which is an acceptable result showing the correlation between the variables of each factor. It also responds to those adopted by researchers in the statistics field that "Alpha Kronbach" is between [0.7: 0.6] acceptable.

The orthogonal rotation using the varimax variance criterion is used to extract factors composed of closely related variables while not related to other factors in order to identify the other factors. The rotation of the axles aims to make the large loading larger and small loading smaller than they are before recycling.

The results shown in the following table show that the initial values of the freedom digree exceed 0.5, which is considered good and exceeds 0.8 for some variables. This is very good according to some references. Thus, we can say that the common factors explain a high variance of the variables, where we note that the lowest ratio is 0.508 for the variable "high cost of the private project." The results of the loading of components of the six matrices, which symbolize the correlation coefficient between the component (factor) and the variable, show the correlation between the factors and their variables. The most significant variables in relation to the first factor are "long working hours" (0.806) and "High cost of private business “(0.707)” nature of the father's profession" (0.702). The strongest variables associated with the fifth factor are the "monthly salary is not rewarding" (0.840). As for factors 2, 3, 4, and 6, their dependent variables are closely related to them. The variables associated with the extracted factors, the first factor was called "the high cost of private business" and the second factor was called "the influence of the family".

Results of Discriminant Analysis

Discriminant analysis is a multivariate analysis method used to study the relationship between a nominal dependent variable and a set of independent quantitative variables. There are two main objectives for the Discriminant analysis. The first objective is to classify a single or group of items into two or more groups based on variables, and the second is to identify variables that contribute to the classification process, (Shankar & Nithyananda, 2017) in order to identify any of the factors derived from the factor analysis, and a better explanation of the entrepreneurial orientation option of the students of “Business Administration” and “Accounting” and then the Discriminant analysis is performed. The objective of this analysis is to identify the factors that contribute to the classification between the students of “business administration” and the students of “accounting” according to their entrepreneurial orientation.

• Dependent variable: Student of "Business Administration"/"Accounting"

• Independent factors: (F1) fear of failure, (F2) lack of entrepreneurial culture, (F3) lack of personal desire, (F4) difficulty of getting a job, (F5) the monthly salary is not rewarding, (F6) family influence.

The Significance of the Discriminant Function Test

The Wilks test indicates the significance of the characteristic function since the value of the Wilkes is 0.823 less than 0.9 and the value of the Chi-2 box is 18.67 at a level of 0.005, which is less than the 1% (Table 3). This means that the calculated function is successful, and there is a significant difference between the two groups Table 3.

Table 3 Wilks Test and (CHI-2) Square
Function Test	The Wilkes Scale	Chi square	Freedom degree (FD)	Level of significance
1	0.823	18.670	6	0.005
Source: prepared by the author

Test of Significance of the Variables in Discriminant Function

Fisher's test (F: Table 4) shows that the discriminant function has the ability to distinguish between the two groups depending on the listed factors. However, statistical factors are "fear of failure", "lack of entrepreneurial culture" and "difficulty in getting a job". The significance of factors test indicates to the importance of the factor “the lack of entrepreneurial culture” in the function where we note that it is characterized by high significance (significance level=0.006) followed by "difficulty of getting a job" (significant level=0.017) and "fear of failure" 0.077) Table 4.

Table 4 Fisher Test for Each Variable in the Discriminated Function
Variables (factors)	Fisher	ddl1	ddl2	Significance level
(F1) fear of failure	3.198	1	1	0.077
(F2) lack of entrepreneurial culture	7.885	1	1	0.006
(F3) lack of personal desire	0.195	1	1	0.660
(F4) difficulty in getting a job	5.938	1	1	0.017
(F5) monthly salary is not rewarding	0.805	1	1	0.372
(F6) family influence	0.509	1	1	0.480
Source: prepared by the author

Table 5 shows that the factor "lack of entrepreneurial culture" has the greatest effect on the classification process. This effect is negative (-0.685) followed by "difficulty finding a job" (0.606) and "fear of failure" (0.456) the structure of the matrix in the table shows the same results Table 5.

Table 5 Discriminant Coefficients and Matrix Coefficients
Factors	Unified discriminant coefficients	Matrix coefficients
	function	function
Lack of entrepreneurial culture	0.685-	- 0.609
Difficulty of getting a job	0.606	0.529
Fear of failure	0.456	0.388
Source: prepared by the author

Table 6 shows the discriminant factors according to specialization (business management/accounting) where we note that the factors have the same classification but their signals are opposite. "Lack of entrepreneurial culture" has a significant and positive impact on the specialization of business administration and a large and negative impact on accounting specialization, while "lack of personal desire", "difficulty of finding a job" and "monthly salary is not rewarding" have the opposite effect Table 6.

Table 6 Ranking Coefficients Between the two Groups
Factors	Specialization
	Business Administration	Accounting
Lack of entrepreneurial culture	0.393	- 0.269
difficulty of finding a job	0.344-	0.235
Fear of failure	0.256-	0.175
Constant	- 0.847	- 0.765
Source: prepared by the author

Predicted Calculation of the Correct Classification

As for the correct rating ratios for the discriminant function, they are listed in the following table. The correct rating ratio for the specialization of “Business Management” category was 68.3% and "Accounting" 75% (Stroud, 2015). The correct classification rate in the Linear discriminant function was 72.3%, while 73 (28+45) out of 101 were correctly classified into the category to which they belonged, while 28 (13+15) were classified incorrectly.

The reclassification test shows that the correct classification for the “Business Management” category is 63.4% and the correct classification for the “Accounting” category is 68.3%, (Hessels et al., 2015), while Category 67 (41+26) of 101 correctly belongs to the category to which it belongs, which means that the Linear discriminant function is better Table 7.

Table 7 Predictive Classification Results
Item			Predictive Classification		Total
			Private Business	Stable Business
Original	Count	Business Administration	28	13	41
		Accounting	15	45	60
	%	Business Administration	68.3	31.7	100
		Accounting	25	75	100
Cross-valid	count	Business Administration	26	15	41
		Accounting	19	41	60
	%	Business Administration	63.4	36.6	100
		Accounting	31.7	68.3	100
Source: prepared by the author

Conclusion

The results of the discriminant analysis showed that the factors "lack of entrepreneurial culture" and "difficulty of finding a job" have a decisive impact on choosing "stable business". There is, however, a significant impact on the other factors such as "fear of failure" between "business administration" students and "accounting" students. This factor positively affects the category of "business administration" and negatively for “accounting”. This means that for business administration students the "fear of failure" factor is the main motivation for their choice of stable business, but for students of "accounting" is an obstacle for them.

The results also showed that the two factors 'difficulty of finding a job' and 'lack of entrepreneurial culture' have a significant effect but less than the ‘fear of failure' effect’ and this effect is especially evident for students of accounting.

We can say that if we want to increase the number of institutions and motivate individuals towards entrepreneurship, then we have to develop the entrepreneurial spirit in order to establish a good entrepreneurial culture as to solve the crisis of unemployment and also to boost economy through the creation of value- added.

The success of this approach depends on a real will reflected through the establishment of many mechanisms that push young people towards entrepreneurship and the need to adopt the idea of “incubators” that have proven their worth in many countries of the world. This is because of its importance through:

• Discover creative capabilities and translate them into distinct production projects.

• Assisting and supporting leading institutions and providing the necessary climate and capabilities to support these projects.

• Continuous monitoring of the institutions belonging to the incubators.

• Incubators provide advanced strategies capable of incubating ideas and developing the competitiveness of these institutions.

• Provide necessary facilities such as banking facilities and the most important financing means for incubated projects and also provide the most important procedures and guarantees needed for small and medium institutions.

Acknowledgement

The author would like to thank the deanship of scientific research and research center for engineering and applied sciences, Majmaah University, Saudi Arabia, for their support and encouragement; also the author would like express our deep thanks to our university, Majmaah University, Saudi Arabia.

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