SPSS

CHAPTER 4

DATA ANALYSIS & INTERPRETATION

This section addresses findings of the study, their interpretations and results extracted from the study. It covers all the objectives set in the study and all the questions that made researchers investigate and conduct this study.

Table 4.1: Demographical Features of respondents

VARIABLES	F	%	TOTAL
			F	%
Gender	Male	71	56.8	125	100
	Female	53	42.4
Age	less than 20	1	.8	125	100
	21-23	28	22.4
	23-25	64	51.2
	26 and above	31	24.8
Degree	Bachelors	65	52.8	125	100
	Masters	59	47.2
Semester	8th (Bachelors)	95	75	125	100
	4th (Masters)	30	25
Specialization	EE (Electrical Engineering)	43	34.4	125	100
	ME (Mechanical Engineering)	44	34.6
	CE (Chemical Engineering)	39	31.2
Entrepreneur in Family	Yes	26	20.8	125	100
	No	99	79.2
Personal Entrepreneurial Experience	Yes	4	3.2	125	100
	No	121	96.8
Entrepreneurial Course	Yes	0	0	125	100
	No	125	100

Table 4.1 represents the data on the entrepreneurial intentions of the technical graduates from electrical, chemical and mechanical engineering of Lahore.

According to the data in the table 4.1, 56.8% of the respondents were male and 42.4% are female. When I distributed the population by age, then majority of the respondents were in between 20-26 and above years in which 23-25 represent the majority of the students and represent 51.2%. The majority of the respondents are part of bachelors programs 52.8% and 47.2% were belongs to masters programs. If we distribute the students according to the semester than 75% belongs to bachelors program 8th semester and 25% belongs to Masters and 4th semester. The respondents from the Electrical engineering are 34.4%, 34.6% belongs to mechanical engineering and 31.2% belongs to chemical engineering. Only 20.8% students claim that they have entrepreneurs in family whereas 79.2% said no to this requirement. Only 3.2% students have personal entrepreneurial experience and the left have no entrepreneurial experience yet. The response to the question about entrepreneurial course is negative from all students.

Table 4.2 : Descriptive Statistics

Descriptive Statistics
	N	Mean	Std. Deviation
Behavioral Factors	125	3.5947	.26550
Structural and Financial Factors	125	2.8160	.79954
Economic Factors	125	2.3093	.98572
Cultural Factors	125	3.1080	1.47982
Entrepreneurial Intentions	125	3.1580	1.55073
Self Efficacy	125	3.3467	2.11119
Locus of Control	125	4.0112	2.64859
Risk Taking	125	3.0740	3.12557
Valid N (listwise)	125

Descriptive statistics are given in the table 4.2; it consists of the mean and standard deviation of all the factors that affect the entrepreneurial intentions of the students. The instrument used for the data collection comprises five point likert scale from strongly disagree to strongly agreed. The mean score of behavioral factors is 3.5947 and mean score of locus of control is 4.0112 that are near to agree. Structural and financial factors and cultural factors are near to neutral as their mean score are 2.8160 and 3.1080. The economic factors have the mean score is 2.3093 that is near to disagree. Entrepreneurial intentions, Self efficacy and risk taking are also neutral 3.1580, 3.3467, 3.0740.

In crux we can say that most of the respondents are neutral to factors such as entrepreneurial intentions, self efficacy, risk taking, cultural factors and structural and financial factors.

Table 4.3: Antecedents of motivation, linear regression test

Variables	R	R Square	B	P
Working Condition	0.662	0.439	0.505	0.000
Training	0.377	0.142	0.407	0.000
Recognition	0.429	0.184	0.525	0.000
Compensation	0.617	0.381	0.935	0.000

Independent variables = Working condition, Training, Recognition, Compensation

Dependant variable = Motivation

The table 4.3 indicates those factors which helps employees to motivate. There is a highly significant relationship between working condition and employees motivation (r= 0.662, p< 0.01) the relationship is strong and it has positive impact on the motivation level. This finding verifies our hypothesis H4 that “good working conditions motivate employees”.

Table 3 also indicates that there is a positive and significant relationship between training and motivation where (r= 0.377, p<0.01) here the relationship is moderately strong and has positive impact on the motivation. This verifies our hypothesis H3 that “appropriate training motivate the employees”.

Table 4.3 showed that there is a positive and significance relationship between recognition and motivation of employees that is (r= 0.429, p<0.01) the results shows moderately significant but positive relationship which certify our hypothesis H2 that “recognition is a good motivator for the employees” .

Table 4.3 also showed that there is a positive and highly significant relationship between compensation and motivation of employees that is (r= 0.617, p<0.01). This result showed strong, significant and positive relationship which certify our hypothesis H1 that “Appropriate compensation plans motivate employees” .

Table 4.4: Consequences of motivation, linear regression test

Variables	R	R Square	B	P
Commitment	0.641	0.411	0.632	0.000
perceived performance	0.527	0.278	0.438	0.000
Job satisfaction	0.552	0.305	0.446	0.000

Independent variable = Motivation, Dependent variable = Commitment, perceived performance, job satisfaction

Table 4.4 represents the outcomes of motivated employees. Table 4 indicates that there is a positive and highly significant relationship between motivation and employees commitment where (r= 0.641, p<0.01).This verifies our hypothesis H6 that “motivation increase the commitment level of the employees”.

Table 4.4 also indicates that there is a positive and significant relationship between motivation and perceived performance where (r= 0.527, p<0.01).This verifies our Hypothesis H5 that “Motivation increase the performance of the employees”.

Table 4.4 also indicates that there is a positive and significant relationship between motivation and job satisfaction where (r= 0.552, p<0.01).This verifies our hypothesis H7 that “Motivation provide job satisfaction to the employees”.

Table 4.5: Simple T-test

Variables	Mean	S.D	t	Sig.
Gender	Male	2.912	0.581	-1.926	0.05
	Female	3.062	0.613
Designation	Faculty	3.00	0.591	1.532	0.12
	Administrative	2.89	0.608
nature of job	Permanent	2.92	0.628	-1.194	0.05
	Contractual	3.06	0.525

T-test on dependent variable “Motivation”

Table 4.5 represents the results of simple t-test on motivation. These results show that motivation level varies according to the gender, designation and nature of the job. It is concluded that females are more motivated as compared to the male ones. On the basis of designation of the employees faculty members are more motivated while administrative members are less motivated. According to the nature of job it is observed that the employees working on contractual basis are more motivated as compared to the employees who are working on permanent basis.

Table 4.6: One way ANOVA test

Variables	Mean	S.D	F	sig.
Age	less then 25	2.91	0.487	1.62	0.2
	25-40	2.94	0.592
	above 40	3.09	0.672
Qualification	Below Bachelor	2.93	0.826	2.796	0.012
	Bachelor	2.76	0.572
	Masters	2.94	0.578
	M.Phil	3.19	0.621
	PhD	3.47	0.115
Current Organizations Experience	0-2 Years	2.90	0.667	2.984	0.032
	2-5 Years	2.91	0.559
	5-10 Years	3.03	0.502
	above 10 Years	3.28	0.707
Total Job Experience	0-2 Years	2.80	0.646	2.284	0.07
	2-5 Years	2.89	0.586
	5-10 Years	3.00	0.585
	above 10 Years	3.10	0.597

One way ANOVA test on dependent variable “Motivation”

Table 4.6 represents the results of one way ANOVA test on the dependent variable motivation. The results of the table shows that the motivation level of the employees increases according to the age i.e. the employees having age more then 40-years are most motivated and employees in between 25-40 years of their age are slightly less motivated. Table also indicates that motivation level also increases with the increase in qualification of the employees i.e. PhD employees are more motivated and then M.Phil’s, Masters and bachelors respectively. The values of the table also shows some important figures that motivation level of the workers also increased with their current job experience and total job experience. Here we can say that employees having more experience are more motivated and employees having less experience are less motivated.