3.6 Research Instrument
The most likely research instrument is used in carrying the research is questionnaires. The reason why we use questionnaire survey is simply because; we can get the direct response and feedback from the respondents with low cost over a short period of time. Questionnaires are easier to conduct as compared to observations and interview technique. Respondents can take less time to understand and answer it. For the convenience, the respondents were requested to select their respective opinion and given limited alternative responses.
A simple and systematic manner was used to design the questionnaires. Thus, it did motivate the respondents to give their opinion. The respondents were only required to select one answer that best fit from the fixed range of given answers for each of the questions. Individual opinions and comments were not demanded from the respondents. All questions were close-ended questions with the use of a five-point Likert scale consisting of Strongly Disagree, Disagree, Know Nothing About This, Agree and Strongly Agree. This scale evidently enabled the respondents to answer the questions based on their own ranking.
There are three different sections in these questionnaires that are A, B and C. Section A consists of 10 questions. It was used to obtain each of the respondents’ personal information, such as demographic data. In this section, each respondent was required to fill in their personal data, such as their gender, age, ethnic group, education level, period of working and monthly salary. Section B consists of 22 questions regarding the Independent Variables of the relationship between organization support on job performance based on the three different Independent Variables which are facilities, support from top management and training exposures. Meanwhile, Section C consists of 10 questions that require the respondents to provide their views on the impact from the organization support towards their job performance.
3.7 Measurement of Variables
Our Independent Variables are facilities, support from top management and training exposures. Meanwhile, the Dependent Variable is job performance. Statistical Package for Social Sciences (SPSS) version 2.2 was used to analyse data collected. For the initial stage, the measurement of variables was done in the pilot study, and the test of reliability was used for measuring analysed data for the scale measurement. Reliability refers to the consistency of a measure. Although no exact reliability can be calculated in this stage, it was still able to estimate the different tradition numbers like Cronbach’s Alpha that measures internal consistency (Tavakol & Dennick, 2011). This form of reliability will evaluate the consistency of results across items on the same or identical test. In Cronbach’s Alpha reliability test was completed in pilot testing stage. The intention of Pilot Test is to verify that the entire questions are suitable and correlated before the actual administration of questionnaires distributed to the respondents. By testing an idea or hypothesis, we can minimize the wasting of money and timing on this research. Pilot test shall be carried out before large scale quantitative research to verify the scope. Meanwhile, we managed to minimize the number of the errors and repeated works when the data were being analysed, coded, and interpreted and summarized.
3.8 The Pilot Study
In the pilot study, we distributed 35 sets of questionnaires and subsequently collected by hand after three days. After that, a brief scanning was performed on the 35 completed questionnaires and all the responses were keyed in by using the Statistical Package for Social Sciences (SPSS) version 2.2.
Table 3.1: Reliability Table on Cronbach’s
No Variable Cronbach Alpha (α) N of Items
1. Job Performance (DV) .942 10
2. Training (Factor 1 – IV) .924 9
3. Facilities (Factor 2 – IV) .871 7
4. Support (Factor 3 – IV) .886 6
Cronbach's alpha Internal consistency
α ≥ 0.9 Excellent
0.9 > α ≥ 0.8 Good
0.8 > α ≥ 0.7 Acceptable
0.7 > α ≥ 0.6 Questionable
0.6 > α ≥ 0.5 Poor
0.5 > α Unacceptable
The table 3.1 shows 3 factors to be measured and categorized as Independent Variables. Those 3 independent variables indicate a fair, good and very good reliability. Among all the variables, the Cronbach’s Alpha for training exposures scored the highest value (0.924) which is more than the range of 0.70 to 0.90. The 9 items been used to measure this variable are considered good reliability. This is followed by facilities; the Cronbach’s Alpha is 0.871 that fits the range of 0.90 to 0.70. The 7 items measuring this variable are considered good reliability. Support from top management shared scored the Cronbach’s Alpha at 0.886 that fit within 0.70 to 0.90. The 6 items measuring this variable are considered good reliability.
3.9 Method of Analysis
The Statistical Package for Social Sciences (SPSS) version 2.2 was used to analyse the data collected. A total of 235 questionnaires were distributed to respondents, with the percentage of response rate was 100%. The data analysis technique was used to convert data into information in order to explore the relationship between independent variables that included facilities, support from top management and training exposures and the dependent variable that was job performance.
3.10 Summary
In conclusion, the fundamental way to gather the related information is by using the primary data from the questionnaires. Furthermore, the sampling design was completed in the beginning of this chapter and the questionnaires were completely distributed to the respondents.
Chapter 4
RESEARCH RESULTS
4.0 Introduction
All the collected questionnaires will be analyzed in this chapter by interpreting the received data. The data gathered from the respondents will be analyzed using the IBM Statistical Package for the Social Sciences (SPSS) statistics 22. The result will be analyzed and divided into several parts, such as demographic analysis, reliability test, and Pearson Correlation Analysis.
4.1 Description Analysis
4.1.1 Respondent Demographic Profile
4.1.1.1 Gender
Table 4.1: Gender of Respondents
Frequency Percent
Valid Male 215 91.5
Female 20 8.5
Total 235 100.0
Table 4.1 indicates proportions of both male and female respondents who took part in the survey. This involved a total of 235 respondents, consisting of 215 male respondents (91.5%) and 20 female respondents (8.5%).
4.1.1.2 Age
Table 4.2: Age of Respondents
Frequency Percent
Valid 25-29 24 10.2
30-34 57 24.3
35-39 80 34.0
40-44 61 26.0
45-49 13 5.5
Total 235 100.0
Table 4.2 specifies different age range of respondents in the RMN Engineering Department. Ages of respondents are classified into five categories which are 25-29, 30-34, 35-39, 40-44 and 45-49. Majority of respondents are between the ages of 35-39 which consist of 34.0% (80 people) of the total amount of respondents. Followed by the category of age 40-44 which has occupied 26.0% (61 people), category of age which is 30-34 has occupied 24.3% (57 people), category of age 25-29 which has occupied 10.2% (24 people), and category of age which is 45-49 has occupied 5.5% (13 people).
4.1.1.3 Marital Status
Table 4.3: Marital Status of Respondents
Frequency Percent
Valid Single 13 5.5
Married 220 93.6
Others 2 .9
Total 235 100.0
Table 4.3 indicates the number of marital status of people who participates in the conducted survey. Out of the 235 chosen sample, 220 of them (93.6%) are married, 13 respondents (3. 3%) are single and 2 respondents are others.
4.1.1.4 Races
Table 4.4: Races of Respondents
Frequency Percent
Valid Malay 217 92.3
Indian 8 3.4
Chinese 5 2.1
Others 5 2.1
Total 235 100.0
Table 4.4 indicates the number of different races of people who participated in the conducted survey. Out of the 235 sample chosen, 217 of them (92.3%) are Malays, 8 respondents (3.4%) are Indians, 5 respondents (2.1%) are Chinese and 5 respondents (2.1%) represent other races.
4.1.1.5 Branch
Table 4.5: Branch of Respondents
Frequency Percent
Valid Supply 72 30.6
Technical 72 30.6
Executive 91 38.7
Total 235 100.0
Table 4.5 indicates the breakdown of respondents representing the 3 branches in the RMN who participated in the survey. Out of the 235 sample chosen, 72 of them (30.6%) were from Supply branch, another 72 respondents (30.6%) were from Technical branch and 91 respondents (38.7%) were from Executive branch.
4.1.1.6 Level of Education
Table 4.6: Level of Education of Respondents
Frequency Percent
Valid High school 98 41.7
Diploma 84 35.7
Degree 31 13.2
Professional course 1 .4
Postgraduate 9 3.8
Others 12 5.1
Total 235 100.0
Table 4.6 indicates the education level of respondents. The highest number of respondents is those who completed high school, which contributes 41.7% (98 respondents). It is followed by 35.7% of diploma holder (84 respondents), 13.2% degree holders (31 respondents), 0.4% professional courses, 3.8% postgraduates (9 respondents) and 5.1% representing other qualifications (12 respondents).
4.1.1.7 Current Job
Table 4.7: Current Job of Respondents
Frequency Percent
Valid Staff 81 34.5
Supervisor 114 48.5
Trainer 4 1.7
Managerial position 27 11.5
Others 9 3.8
Total 235 100.0
Table 4.7 indicates the current job of respondents. The highest number of respondents is those appointed as supervisors, which is 48.5% (114 respondents). It is followed by 34.5% who are staff (81 respondents), 11.5% from managerial (27 respondents), 1.7% are trainers (4 respondents) and 3.8% representing others (9 respondents).
4.1.1.8 Working Period Current Organization
Table 4.8: Working Period Current Unit of Respondents
Frequency Percent
Valid Less than one years 55 23.4
1-2 years 102 43.4
3-5 years 78 33.2
Total 235 100.0
Table 4.8 shows the respondents’ working period in their current unit. The majority of respondents have been working for 1-2 years (102 respondents or 23.4%), followed by 3-5 years (78 respondents or 33.2%), and less than one year (55 respondents or 23.4%).
4.1.1.9 Working Experience in the Service
Table 4.9: Working Experience in the Service of Respondents
Frequency Percent
Valid Less than 10 years 33 14.0
11-15 years 52 22.1
16-20 years 103 43.8
More than 20 years 47 20.0
Total 235 100.0
Table 4.9 shows the respondents’ length of service in the RMN. The majority of them have been working for 16-20 years (103 people or 43.8%), followed by those working between 11-15 years (52 people or 22.1%), more than 20 years (47 people or 20.0%), and less than 10 years (33 people or 14.0%).
4.1.1.10 Monthly Salary
Table 4.10: Monthly Salary of Respondents
Frequency Percent
Valid RM1500-RM3000 29 12.3
RM3001-RM5000 109 46.4
RM5001-RM7000 92 39.1
<RM7000 5 2.1
Total 235 100.0
Table 4.10 indicates the monthly income levels of all respondents. The majority of the respondents’ salary are between RM3,001 to RM 5,000 (109 people or 46.4%), followed by those earning between RM5,001 to RM7,000 (92 people or 39.1%), those drawing between RM1,500 to RM3,000 (29 people or 12.3%), and those getting above RM7,000 (5 people or 2.1%).
4.2 Scale Measurement
The IBM SPSS statistics 22 will be used to identify the value of mean and standard deviation of each question, while the Likert scale is used to evaluate the respondents’ responses. The Likert scale’ options are (1) Strongly Disagree, (2) Slightly Disagree, (3) Know Nothing About This, (4) Slightly Agree, and (5) Strongly Agree.
SD = Strongly Disagree
D = Slightly Disagree
N = Know Nothing About This
A = Slightly Agree
SA = Strongly Agree
For the purpose of this study, a measurement scale was chosen to develop the questionnaires which are the nominal scale and summated rating scales (Likert). According to the questionnaires setup, demographic profiles are required as stated under section A. It is designed by using nominal scale which concerns the race, education and gender. For questions regarding age, experience and salary, they are designed using the ordinal scale. On the contrary, the questionnaires design under Section B uses Likert Scale to investigate the effect of compensation packages on job performance among RMN personnel. The Likert Scale is applying a five-point scale which consists of Strongly Disagree, Slightly Disagree, Know Nothing About This, Slightly Agree and Strongly Agree.
4.3 Reliability Statistics
We used Cronbach’s Alpha to test the reliability test for every item in the questionnaires in order to ensure its consistency and stability.
Table 4.11: Reliability Test
No Variable Cronbach Alpha (α) N of Items
1. Job Performance (DV) .906 10
2. Training (Factor 1 – IV) .876 9
3. Facilities (Factor 2 – IV) .602 7
4. Support (Factor 3 – IV) .818 6
Table 4.12: Reliability Reference
Cronbach's alpha Internal consistency
α ≥ 0.9 Excellent
0.9 > α ≥ 0.8 Good
0.8 > α ≥ 0.7 Acceptable
0.7 > α ≥ 0.6 Questionable
0.6 > α ≥ 0.5 Poor
0.5 > α Unacceptable
4.3.1 Reliability Analysis (Table 4.11)
Referring to Sekaran and Bougie (2010), reliability test was used to check the data collected and investigate the trustworthy in condition of generating an accurate result without errors. We used Cronbach’s Alpha to conduct the reliability test for every item in the questionnaires in order to ensure its consistency and stability.
Among all the variables, the Cronbach’s Alpha for training exposures scored the highest value (0.876) which is in the range of 0.70 to 0.90. The 9 items used to measure this variable are considered good reliability. This is followed by facilities; the Cronbach’s Alpha is 0.602 that fits the range of 0.60 to 0.70. The 7 items measuring this variable are considered as moderate reliability but are still acceptable. Management support shared scored the Cronbach’s Alpha at 0.818 that fits within 0.70 to 0.90. The 6 items used to measure this variable are considered as good reliability.
4.4 Pearson Correlation Coefficient Table
The Pearson Correlation signifies the relationship between two variables based on the degree of covariance between them. The Pearson’s Correlation Coefficient shows the result of correlation, significance of relationship, direction and strength between the independent variables and dependent variable. Basically, the higher the value of the correlation coefficient, the stronger is the relationship between two variables.
According to Deborah J. Rumsey in statistics, the correlation coefficient r measures the strength and direction of a linear relationship between two variables on a scatterplot. The value of r is always between +1 and –1. To interpret its value, see the following values the correlation r is closest to:
Table 4.13: References of Correlation Table
S/N Correlation Coefficient
Description
1. Exactly –1 A perfect downhill (negative) linear relationship
2. –0.70. A strong downhill (negative) linear relationship
3. –0.50. A moderate downhill (negative) relationship
4. –0.30 A weak downhill (negative) linear relationship
5. 0 No linear relationship
6. +0.30 A weak uphill (positive) linear relationship
7. +0.50 A moderate uphill (positive) relationship
8. +0.70 A strong uphill (positive) linear relationship
9. Exactly +1 A perfect uphill (positive) linear relationship
Table 4.14: Correlation Table
Correlations
DV_235JP IV_235F1_Facilities IV_235F2_Support IV_235F3_Training
DV_235JP Pearson Correlation 1 .683** .640** .752**
Sig. (2-tailed) .000 .000 .000
N 235 235 235 235
IV_235F1_Facilities Pearson Correlation .683** 1 .698** .678**
Sig. (2-tailed) .000 .000 .000
N 235 235 235 235
IV_235F2_Support Pearson Correlation .640** .698** 1 .770**
Sig. (2-tailed) .000 .000 .000
N 235 235 235 235
IV_235F3_Training Pearson Correlation .752** .678** .770** 1
Sig. (2-tailed) .000 .000 .000
N 235 235 235 235
**. Correlation is significant at the 0.01 level (2-tailed).
4.4.1 Correlation between Training Exposures and Job Performance
H1: There is a significant relationship between training exposures and job performance
According to the result stated in table 4.14, the relationship between training exposures and job performance is significant since the p-value (0.000) is less than 0.05, thus there is a significant relationship between training and job performance. This first IV, facilities has a 0.752 correlation with the variable of job performance. The Pearson correlation coefficient is +0.752, falling within the coefficient range of ±0.70 to ±0.90 that indicates a strong positive correlation between training exposures and job performance. This value indicates a strong uphill (positive) linear relationship.
4.4.2 Correlation between Support from Top Management and Job Performance
H2: There is a significant relationship between support from top management and job performance.
According to the data stated in table above, the relationship between support from top management and job performance is significant since the p-value (0.000) is less than 0.05; hence there is a significant relationship between support from top management and job performance. Besides that, support from top management has a 0.640 correlation with the variable of job performance. The figure of Pearson correlation coefficient +0.640 falls within the coefficient range of ±0.50 to ±0.70, indicating a strong positive correlation between support from top management and job performance. This value indicates a strong uphill (positive) linear relationship.
4.4.3 Correlation Facilities and Job Performance
H3: There is a significant relationship between facilities and job performance
According to the result stated in table 4.14, the relationship between facilities and job performance is significant since the p-value (0.000) is less than 0.05, thus there is a significant relationship between facilities and job performance. This third IV, facilities has a 0.683 correlation with the variable of job performance. The figure of Pearson Correlation coefficient +0.683 falls within the coefficient range of ±0.50 to ±0.70, indicating a strong positive correlation between facilities and job performance. This value indicates a strong uphill (positive) linear relationship.
Conclusion
Training is significant to estimate the dependent variable that is job performance for this survey. Apparently, the p-value for training (0.000) is less than the alpha value 0.05, with the Pearson Correlation coefficient +0.640 showing a strong positive correlation between training exposures and job performance.
Support from top management is significant to forecast the dependent variable which is job performance for this survey. As the p-value for support (0.000) is more than the alpha value 0.05, the Pearson Correlation coefficient +0.640 yield a strong positive correlation between support from top management and job performance.
Facilities are significant to forecast the dependent variable which is job satisfaction for this survey. As to the p-value for facility (0.000) is less than the alpha value 0.05, the Pearson Correlation coefficient +0.683 points out a strong positive correlation between facilities and job performance.
Chapter 5
CONCLUSION
5.0 Introduction
Career development supported by organization support, for instance training exposures, facilities and support from top management are very important because it can generate the Navy People who are progressive, educated, dynamic and competitive. As afore-mentioned, descriptive, reliability, and inferential statistics have been applied in Chapter 4 to correlate and interpret the data collected. In this a critical, we will summarize the statistical analyses and further discuss the results. Apart from that, the implications and the disadvantages of the study and suggestions on improvisation for the future study will be included as well. The general conclusion of the entire project will be drawn at the end of this study.
5.1 Summary of Statistical Analyses
5.1.1 Respondents Demographic Profile
This study has involved a total of 235 respondents, with the breakdown of 215 male respondents (91.5%) and 20 female respondents (8.5%). The age range of respondents is classified into five categories, which are 25-29, 30-34, 35-39, 40-44 and 45-49. The majority of respondents are between the age of 35-39 (34.0% or 80 people), 40-44 (26.0% or 61 people), 25-29 (10.2% or 24 people), 30-34 (3.3% or 2 people), and 45-49 (5.5% or 13 people). The breakdown of the respondents’ marital status denotes 220 of them are married (93.6%), 13 are single (5.5%) and 2 representing other status (0.9%). The number of different races consists of 217 Malays (92.3%), 8 Indians (3.4%), 5 Chinese (2.1%), and 5 other races (2.1%).
The education level of respondent was also taken into account in this survey. 98 respondents completed high school (41.7%), 84 diploma holders (35.7%), 31 degree holders (13.2%), 1 with professional course (0.4%), 9 postgraduates (3.8%), and 12 respondents possessing other qualifications (5.1%). In terms of job position, 114 respondents are supervisor (48.5%), 81 staff (34.5%), 27 in managerial posts (11.5%), 5 trainers (1.7%), and 9 other positions (3.8%).
Meanwhile, 102 respondents have been working in their current unit between 1-2 years (23.4%), followed by 78 respondents serving between 3-5 years (33.2%), and 55 respondents serving less than one year (23.4%), whereas the respondents’ length of service in the RMN shows the main group serving between 16-20 years (103 people or 43.8%), followed by 11-15 years (52 people or 22.1%), more than 20 years (47 people or 20.0%), and less than 10 years (33 people or 14.0%). The majority of respondents’ salary are between RM3,001 – RM 5,000 (109 people or 46.4%), followed by between RM5,001 – RM7,000 (92 people or 39.1%), RM1,500 – RM3,000 (29 people or 12.3%), and above RM 7,000 (5 people or 2.1%).
5.1.2 Scale Measurement
The Reliability Test and Cronbach’s alpha were applied to examine 22 items used to evaluate the core reliabilities of the five constructs in the questionnaires. The related table shows 3 factors to be measured and categorized as Independent Variables, denoting fair, good and very good reliability. Among all the variables, the Cronbach’s Alpha for training exposures scores the highest value 0.876 which is within the range of 0.70 to 0.90. The 9 items used to measure this variable are considered as good reliability. This is followed by support from top management, where the Cronbach’s Alpha scores 0.818, fitting the range of 0.70 to 0.90. The 6 items measuring this variable are considered as high reliability. Facilities scored the Cronbach’s Alpha at 0.602, fitting within 0.60 to 0.70. The 7 items used to measure this variable are considered as good reliability. As a result, all the constructs used in this research were found to have internal consistency reliability. Hence, the related paradigm shows Cronbach’s Alpha value that exceeds 0.6, indicating the right reliability and stability of the measurement in this research.
5.1.3 Summary of Inferential Analyses
Table 5.1: Summary of Inferential Analyses
No Hypotheses Results
1. H1: There is a significant relationship between training exposures and job performance. Accepted
2. H2: There is a significant relationship between support from top management and job performance. Accepted
3. H3: There is significant relationship between facilities and job performance. Accepted
5.2 Implications of the Study
This study has developed a model that integrates the variables, which will influence the RMN personnel job performance. This involvement is useful as there is a limited study in the relationship of this three independent variables and job performance, especially in the RMN. This finding has managed to identify the career development consist of training exposures, facilities and support from top management which have positive relationship towards personnel job performance. Generally, it should give the impact on future planning and new direction of the human resource development. Those three elements of IV will be considered as the key factors to boost the RMN job performance in achieving its mission. Thus, we conclude that similar enforcement agencies, like the police, customs and Maritime Enforcement Agency may have the same influence of this three IV over their personnel’s job performance.
5.3 Recommendation for Future Research
For future related research, we would like to propose for an extension of the same IV and DV across all the three Services in the Malaysian Armed Forces (MAF). This extension would provide big population for the sampling and collective result of MAF job performance and further clarify a significant relation between training exposures, support from top management and facilities on job performance. Notwithstanding, there is a possibility that the result might be slightly different compared to this study which is merely limited to RMN personnel due to different working environment and Standard Operating Procedure (SOP).
5.4 Conclusion
This study provides an improved perception about the factors affecting job performance in the RMN. Based on the findings, the three factors identified as training exposures, support from top management and facilities indicate a strong relationship with job performance in the RMN. This study also shows a significant relationship between training exposures, support from top management, and facilities (independent variables) and job performance (dependent variable). Hence, this signifies that if IV gets higher, it will give more positive effects on DV value in the RMN organization.
Training exposures has become the most significant element; hence the corrective and related actions need to be taken to enhance the training support in the RMN. This study proves that a high budget for training exposures purposes is a good investment for a higher productivity and efficiency in the RMN. New skills and techniques, better workplace behavior, and good leadership skills could be achieved via correct training method and resources. In order to make the training more effective in improving organizational, as well as individual performance, it is important that the perception regarding effectiveness of training must be made positive. With the efforts to strengthen its human capital, the RMN has collaborated with the University Management & Science (MSU) for training chef, stewards and logistic personnel and with Boustead Heavy Industry Corporations Berhad (BHIC), also Department of Skills and Development (DSD) for training technicians.
Support from top management indicates a significant relation to the Job performance in the RMN. The support from top management has been translated as how the top and intermediate level plan their future career and carry out leadership functions. Leaders not only set objectives and goals, but also act pro-actively in training activities, managing tasks, and giving moral support. The awards, rewards and recognition in the RMN show the relationship of support from top management in the RMN and personnel job performance. This incentive conforms to the study done by Eisenberger et al (1986) which stressed that organizational support will be more effectively enhanced if employees view organizational rewards.
Facilities strongly affect the working atmosphere. This significant relation to job performance strengthens the perspective about how facilities, such as building as a whole, the workplace environment, IT, furniture, cleaning services would affect productivity. Satisfaction with the facilities has proven to have a significant influence on job performance, especially in psychological aspects. In short, this study has further shown that facilities can significantly contribute toward its personnel’s moral, effectiveness and efficiency.