Job Satisfaction in AYM Company

This assignment analyzes job satisfaction levels within AYM Company. It examines the influence of factors such as age, gender, and employee position on job satisfaction. The analysis utilizes regression models to identify significant predictors of job satisfaction, focusing on variables like skill improvements and goal setting. The findings reveal a correlation between age and job satisfaction, with older employees reporting higher levels. Position within the company also impacts job satisfaction, with management roles exhibiting the highest scores. Recommendations are provided to enhance overall job satisfaction at AYM Company.

Work Aspect Preference Scale Survey Result Report
Introduction
The survey was designed to asses work aspect preferences and job satisfaction in All in Your Mind company (AYM).
AYM was established in 1994 to manufacture variety of stress-relieving squeezy toys which are readily available in
retail outlets globally. AYM has three sites in different countries Melbourne Australia, Mumbia India and
Queenstown New Zealand. It has 319 employees in total.
Table 1. AYM employees’ distribution
Australia India New Zealand
Male Female Male Female Male Female
Management 17 29 5 6 2 6
Factory
workers
0 0 0 0 62 43
Customer
care
26 13 71 39 0 0
The survey was conducted on 194 employees working for All in Your Mind, the respondent of the survey were
chosen at random to answer some set of questions. Factors that were being checked are work aspect preference
to determine the qualities of work that different employees considered important. Business problem being solved
was how to reduce staff turnover and identify which kinds of employee the company should employ.
Method
This section discusses the procedure and approach in which the survey used to collect data and analyze and report.
Participants
The respondents used in the survey were employees from AYM from the three sites that is Melbourne, Mumbai
and Queenstown. They composed of both gender, male and female. The task force includes 319 employees in
different level that is management, factory works and customer care. Mumbai and Melbourne mostly employed
customer care and management employees while in Queenstown majority are factory work.
Materials and Procedure
A sample of 194 employees was selected to be in survey. Stratified sampling method was used to select the
sample, this first subdivide the task forces into three groups depending on site and level of employee and select
each employee at random. This method of sampling ensures that the sample is representative and each employee
has equal chance to be in the sample. The survey utilized questionnaires as research tools. Each respondent was
asked to answer some set of questions concerning job satisfaction, which was self administered. Job satisfaction
scale ranged from 18-90 with high score showing greater job satisfaction. Each aspect of job was rated as 1 was job
aspect totally unimportant, 2 job aspect little important, 3 job aspect moderately important and 4 job aspect
extremely important.
Data analysis
The report made use of inferential and descriptive statistics. Descriptive statistics are mainly used to describe the
distribution of the data and visualize the data. Inferential statistics used were t-test and analysis of variance
(ANOVA) and regression analysis. They are used to check significance between variables.
Results

Description of the Sample
The data was cleaned and analyzed using IBM SPSS version 22. All variables were scale expect gender, position,
location and team which were nominal variables. As shown by screenshot below
The distribution of gender was 31.44% male and 68.56% female. There was high proportion of male in the sample
as compared to female. This is representation of population of employees in AYM; female employees are more
compared to females.
The distribution of respondents with location is shown below

Majority of respondents to the survey were from Mumbai with 40%, Queenstown with 32% and Melbourne 28%.
Mumbai has highest number of employees and Melbourne site has the lowest number of employees.
Relationship between Age and Job Satisfaction
Analysis of variance was used to check the interaction between age and job satisfaction scale and because both are
scale variable ANOVA can be used. It assumes that data set is continuous and normally distributed.
Table 2. Analysis of Variance of age and job satisfaction
ANOVA Table
Sum of
Square
s df
Mea
n
Squar
e F Sig.
Job_Sati
sf * Age
Betwe
en
Groups
(Combine
d)
15182.
55
43 353.0
8
.87 .70
Within Groups 60869.
67
15
0
405.8
0
Total 76052.
23
19
3
The p-value is 0.70 which is greater than the significance level of 0.05 therefore we fail to reject null hypothesis
and conclude that age and job satisfaction are statistically significance and age affect the scale of job satisfaction.
Relationship between Gender and Job Satisfaction
Student t test statistic is used to check whether there statically significance difference between the means of two
groups. It assumes that the data is normally distributed. We used t test to check whether job satisfaction mean
scale is different between male and females in AYM.
Independent Samples Test
Levene's
Test for
Equality of
Variances t-test for Equality of Means
F Sig. t df
Sig.
(2-
tailed
)
Mean
Differenc
e
Std.
Error
Differenc
e
95%
Confidence
Interval of
the
Difference
Lowe
r
Uppe
r
Job_Sati
sf
Equal
variance
.59 .44 -
1.7
192.0
0
0.08 -5.38 3.05 -
11.40
.64

s
assume
d
6
Equal
variance
s not
assume
d
-
1.8
1
124.2
4
0.07 -5.38 2.97 -
11.27
.51
The significance value in Levene's Test for Equality of Variances is 0.44 greater than significance level of 0.05 thus
the variance of our groups that is male and female can be treated as equal. From table above t(124.24)=-1.81,
p=0.07 which is greater than 0.05. Thus we fail to reject null hypothesis and conclude that the population mean of
job satisfaction is same for male and female. The average job satisfaction scale is not different for male and
females employees in AYM.
Relationship between Job Satisfaction and Position of Employee
There three groups of employee thus t-test can not be used instead we use ANOVA and Turkey test which test
which group are statistically significance. ANOVA test assumes equal variance for each group and the data follow
normal distribution.
ANOVA
Job_Satisf
Sum of
Squares df
Mean
Square F Sig.
Betwee
n
Groups
10426.3
7
2.00 5213.18 15.1
7
0.0
0
Within
Groups
65625.8
6
191.00 343.59
Total 76052.2
3
193.00
The p-value is 0.00 which is less than significance level of 0.05 we reject null hypothesis and conclude that there is
statistical difference in mean score of job satisfaction between employees in different position.
Multiple Comparisons
Dependent Variable: Job_Satisf
Tukey HSD
(I) Position Mean Std. Sig 95%

Differe
nce (I-J) Error .
Confidence
Interval
Low
er
Bou
nd
Upp
er
Bou
nd
Managem
ent
Factory
Workers
19.935
61*
3.66 0.
00
11.2
9
28.5
8
Customer
Care
8.4171
2*
3.31 0.
03
0.61 16.2
3
Factory
Workers
Managem
ent
-
19.935
61*
3.66 0.
00
-
28.5
8
-
11.2
9
Customer
Care
-
11.518
48*
3.17 0.
00
-
19.0
0
-
4.04
Customer
Care
Managem
ent
-
8.4171
2*
3.31 0.
03
-
16.2
3
-
0.61
Factory
Workers
11.518
48*
3.17 0.
00
4.04 19.0
0
*. The mean difference is significant at the 0.05 level.
The post hoc confirm statistically significant mean job satisfaction difference in all position of employee. The mean
difference between management employees and factory workers is highest with management average job
satisfaction score being 19.94 higher than that of factory workers. While the mean job satisfaction score of
management is 8.42 higher than that of customer care employees. Customer care employees’ job satisfaction
score is 11.52 higher than that of factory workers.
Job Satisfaction and Job Characteristics
Regression analysis was used to determine which characteristics of job improve job satisfaction.
Model Summary
Model R R Square
Adjusted
R Square
Std.
Error of
the
Estimate
1 .303a .09 .06 19.27
a. Predictors: (Constant), Q40, Q5, Q36, Q8, Q22, Q3,
Q16

Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B
Std.
Error Beta
1 (Constant) 57.83 8.67 6.67 0.00
Q3 -5.21 2.01 -0.22 -2.60 0.01
Q5 0.73 0.64 0.08 1.14 0.26
Q8 -0.12 1.59 -0.01 -0.07 0.94
Q16 1.29 1.64 0.07 0.79 0.43
Q22 3.29 1.63 0.16 2.02 0.04
Q36 -0.92 1.36 -0.05 -0.68 0.50
Q40 2.59 1.69 0.14 1.53 0.13
a. Dependent Variable: Job Satisfaction
The R-square is 0.303 which means that only 30.3% of job satisfaction variation is explained by independent
variables that is job characteristics, the model is poor model. Only two job characteristics in regression model are
significant that skill improvements and the work that set goals for worker to achieve.
Conclusion and Recommendations
Age affect the level of job satisfaction, those who are older tend to enjoy their job. This may due to high level of
experiences and awareness of their working conditions in terms of fellow workers and the environment. Gender
does not influence job satisfaction score both male and female have the same mean job satisfaction score. There is
high variation of job satisfaction level and position of employee in AYM Company. Those in management have high
level of job satisfaction score, followed by those in customer care and lastly those who work as factory workers has
low score of job satisfaction. Employees think that in order to be satisfied by their job there is need for this job to
improve their skills and set goals which they are supposed to accomplish. The study recommends the following
 There is need for the company to identify why there is high disparity of job satisfaction in different
position of employees. In order to improve the working condition of every employee irrespective of
gender, position or locations.
 AYM should ensure they improve skills of their employees and set goals that their employees are
supposed to accomplish in order to improve job satisfaction score.

References
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