The closer the correlation between these two variables to 1, the stronger the linear correlation (Hoang Trong & Chu Nguyen Mong Ngoc, 2005).
In this research model, the author will test the correlation between each dependent variable and the independent variables and between the independent variables with each other. The author expects a strong linear correlation between the independent variables and the dependent variables.
b. Regression analysis
The multiple regression analysis method used here is the Enter method: all variables are entered once and the statistical results related to the variables entered into the model are examined.
In this study, the author will analyze the regression of 03 main models, which are the impacts of 07 independent variables (job characteristics, training and promotion, income, superiors, colleagues, brand, benefits) on 03 component variables of organizational commitment (emotional commitment, maintenance commitment, and moral commitment). Through that, the author hopes to provide answers to the two questions (1) and (2) in the research purpose section.
c. Testing hypotheses
Assessment of regression model fit: R2, adjusted R2.
Test the research hypothesis by accepting or rejecting the research hypothesis presented.
Determine the level of influence of independent variables on 03 component variables (dependent variables) of organizational commitment: the factor with a larger β coefficient can be said to have a higher level of influence than other factors. If the correlation coefficient is negative, it means that they have an inverse relationship, and if it is positive, it means that they have a positive relationship.
d. Analyze the impact of qualitative variables using Dummy variables
The assessment of the influence of qualitative variables on organizational commitment here is done by using Dummy variables and then running multiple regression using SPSS software. In fact, we can recode all qualitative variables to conduct multiple regression analysis using Dummy variables. However, we can analyze ANOVA first to determine whether the qualitative variables affect the employee's organizational commitment factor or not, if not, we will not need to use Dummy variables to run regression, but if we find that the qualitative variable has an influence, we will use Dummy variables to test by running multiple regression. The purpose of this analysis is to find out whether there is a significant difference (statistically significant) in the factors affecting the organizational commitment factor between different groups of employees.
The qualitative factors analyzed in this research are gender, age, education level, seniority, department/job, and average monthly income of Military Bank employees in Ho Chi Minh City area.
3.5. SUMMARY
In this chapter, the author has presented an overview of the research process to achieve the set objectives. The research process consists of two main steps: qualitative research to discuss and exchange to supplement and correct scales and variables; and quantitative research to survey, collect data, process and analyze data using SPSS software.
CHAPTER 4: RESEARCH RESULTS
After collecting and processing the data, the author will present a basic description of the research sample and the results of the analyses performed.
4.1. SAMPLE DESCRIPTION
4.1.1. Data collection method
As presented in the above chapter, the sample was selected by convenience sampling method with a minimum sample size of 240 samples.
The collection method was to send questionnaires directly to the respondents who were employees of the Military Commercial Joint Stock Bank in Ho Chi Minh City. The total number of questionnaires sent was 500. The number of responses was 287, however, after re-checking, it was determined that 255 questionnaires were valid and were used for analysis. Thus, the study met the minimum sample size requirement.
4.1.2. Sample information description
o The ratio of male and female interviewees was quite similar (49.4 and 50.6%).
o More than 66% of the respondents were between 25 and 30 years old. The number of people over 40 years old accounted for less than 4%.
o Most of the interviewees were university graduates (83.5%).
o The majority of interviewees were people with less than 5 years of experience (32.9% for less than 3 years and 46.7% for 3 to 5 years).
o As many as 43.9% of the respondents worked in the sales department, 33.3% came from customer service and the rest came from the support department.
o Regarding average income, the majority is from 5 to 10 million VND (69%), from 10 to 15 million VND accounts for 28.2%.
The author would like to summarize and describe detailed sample information in the following table:
Table 4.1. Sample description
STT
Information | Frequency | Rate (%) | ||
1 | Sex | Male | 126 | 49.4% |
Female | 129 | 50.6% | ||
2 | Age | Under 25 years old | 33 | 12.9% |
25 to 30 years old | 169 | 66.3% | ||
From 30 to 40 years old | 43 | 16.9% | ||
Over 40 years old | 10 | 3.9% | ||
3 | Education level | Secondary/College | 17 | 6.7% |
University | 213 | 83.5% | ||
Above University | 25 | 9.8% | ||
Other | 0 | 0% | ||
4 | Years of service | Under 3 years | 84 | 32.9% |
From 3 years to 5 years | 119 | 46.7% | ||
From 5 years to 10 years | 27 | 10.6% | ||
Over 10 years | 25 | 9.8% | ||
5 | Function/Department | Customer service | 85 | 33.3% |
Business | 112 | 43.9% | ||
Support | 58 | 22.7% | ||
Other | 0 | 0% | ||
6 | Average income | Under 5 million VND | 1 | 0.4% |
From 5 to 10 million VND | 176 | 69.0% | ||
From 10 to 15 million VND | 72 | 28.2% | ||
Over 15 million VND | 6 | 2.4% | ||
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4.2. TESTING AND EVALUATING THE SCALE
4.2.1. Cronbach's Alpha Analysis
Table 4.2. Results of Cronbach's Alpha analysis
STT
Observation variable | Correlation coefficient variable – sum | Cronbach's Alpha coefficient If the variable is removed | |
1 | Job Characteristics – CV: Cronbach's Alpha = .744 | ||
CV1 | .593 | .658 | |
CV2 | .582 | .664 | |
CV3 | .504 | .721 | |
CV4 | .506 | .703 | |
2 | Training and Advancement – DT: Cronbach's Alpha = .930 | ||
DT5 | .712 | .927 | |
DT6 | .803 | .916 | |
DT7 | .857 | .909 | |
DT8 | .756 | .922 | |
DT9 | .788 | .918 | |
DT10 | .857 | .909 | |
3 | Income – TN: Cronbach's Alpha = .828 | ||
TN11 | .668 | .780 | |
TN12 | .698 | .766 | |
TN13 | .660 | .783 | |
TN14 | .608 | .804 | |
4 | Superior – CT: Cronbach's Alpha = .860 | ||
CT15 | .659 | .837 | |
CT16 | .694 | .830 | |
CT17 | .686 | .830 | |
CT18 | .697 | .828 | |
CT19 | .635 | .840 | |
CT20 | .574 | .854 | |
5 | Colleagues – Enterprises: Cronbach's Alpha = .842 | ||
DN21 | .687 | .797 | |
DN22 | .642 | .817 | |
DN23 | .731 | .776 | |
DN24 | .656 | .810 | |
6 | Brand – TH: Cronbach's Alpha = .877 | ||
TH25 | .737 | .848 | |
TH26 | .761 | .832 | |
TH27 | .763 | .832 | |
TH28 | .704 | .857 | |
7 | Welfare – PL: Cronbach's Alpha = .810 | ||
PL29 | .593 | .806 | |
PL30 | .728 | .664 | |
PL31 | .670 | .730 | |
8 | Emotional attachment – GBTC: Cronbach's Alpha = .898 | ||
GBTC32 | .738 | .878 | |
GBTC33 | .816 | .867 | |
GBTC34 | .702 | .883 | |
GBTC35 | .837 | .865 | |
GBTC36 | .741 | .877 | |
GBTC37 | .558 | .909 | |
9 | Maintenance Engagement – GBDT: Cronbach's Alpha = .926 | ||
GBDT38 | .833 | .905 | |
GBDT39 | .839 | .902 | |
GBDT40 | .896 | .891 | |
GBDT41 | .840 | .902 | |
GBDT42 | .642 | .941 | |
10 | Moral Commitment – GBDD: Cronbach's Alpha = .901 | ||
GBDD43 | .743 | .882 | |
GBDD44 | .824 | .870 | |
GBDD45 | .726 | .885 | |
GBDD46 | .822 | .872 | |
GBDD47 | .731 | .884 | |
GBDD48 | .587 | .909 | |
After performing Cronbach's Alpha analysis of 10 scales, the results obtained were quite good. All Cronbach's Alpha indexes of the scales met the given requirements.
≥ 6. Regarding the variable-total correlation coefficient, all indexes are quite high (>0.5), higher than the minimum requirement (<0.3)
Thus, after analyzing Cronbach's Alpha, all variables give parameters that meet the research requirements. Therefore, the author will continue to use all of this data to perform the next steps, more specifically the next EFA analysis.
4.2.2. Exploratory factor analysis (EFA)
When analyzing EFA, to evaluate the reliability of the scale, the best strategy is to use EFA analysis for all scales at the same time (Nguyen Dinh Tho, 2011). In this research topic, the author will analyze EFA in general for independent variables, and the dependent variables will be analyzed separately 3 times according to 3 components of organizational commitment.
4.2.2.1. Factor analysis for independent variables
The author conducted an exploratory factor analysis (EFA) of the following 7 scales: Job characteristics; Training and promotion; Income; Superiors; Colleagues; Brand; and Benefits.
The author uses the Principal components factor extraction method with Varimax rotation and stops when extracting factors with Eigenvalues greater than or equal to 1 to perform the analysis. The analysis results are shown in the following table (the author has eliminated factor loadings with small values < 0.3 for easy observation) (please see details in Appendix 5) .
Table 4.3. Results of exploratory factor analysis for independent variables
STT
Variable | Element | |||||||
1 | 2 | 3 | 4 | 5 | 6 | 7 | ||
1 | CV1 | .319 | .642 | |||||
2 | CV2 | .724 | ||||||
3 | CV3 | .636 | ||||||
4 | CV4 | .667 | ||||||
5 | DT5 | .724 | ||||||
6 | DT6 | .751 | ||||||
7 | DT7 | .864 | ||||||
8 | DT8 | .764 | ||||||
9 | DT9 | .748 | ||||||
10 | DT10 | .862 | ||||||
11 | TN11 | .754 | ||||||
12 | TN12 | .773 | ||||||
13 | TN13 | .746 | ||||||
14 | TN14 | .704 | ||||||
15 | CT15 | .705 | ||||||
16 | CT16 | .687 | ||||||
17 | CT17 | .635 | ||||||
18 | CT18 | .677 | .305 | |||||
19 | CT19 | .635 | ||||||
20 | CT20 | .689 | ||||||
21 | DN21 | .748 | ||||||
22
DN22 | .713 | |||||||
23 | DN23 | .771 | ||||||
24 | DN24 | .741 | ||||||
25 | TH25 | .851 | ||||||
26 | TH26 | .821 | ||||||
27 | TH27 | .800 | ||||||
28 | TH28 | .749 | ||||||
29 | PL29 | .713 | ||||||
30 | PL30 | .747 | ||||||
31 | PL31 | .765 | ||||||
Eigenvalues | 11,052 | 2,532 | 2.109 | 1,681 | 1,448 | 1,322 | 1,254 | |
Variance (%) | 35,652 | 8,169 | 6.804 | 5,423 | 4,672 | 4,265 | 4,047 | |
Sig. | 0.000 | |||||||
KMO | 0.878 | |||||||
There are 2 small notes on the data after analysis:
o The observed variable CV1 has a simultaneous loading factor of 0.319 on factor 1 and 0.642 on factor 6. The difference is 0.323, satisfying the requirement ≥ 0.3.
o Observed variable CT18, has a simultaneous loading factor on factor 2 of 0.677 and factor 6 of
0.305. The difference is 0.372, satisfying the requirement ≥ 0.3.
The analysis results show:
The number of extracted factors is 07 factors.
KMO coefficient reached 0.878: Factor analysis is suitable for research data.
Bartlett test: Meets requirements (Sig = 0.000 < 0.05). Proves that the observed variables in factor analysis are correlated with each other in the population.
Total variance extracted: 69.031% (greater than 50%). It shows that the above 7 factors explain 69.031% of the variation in the data.
Eigenvalue coefficient values of all factors meet the requirements (>1).
Through the results of factor analysis, we see that all variables have satisfactory factor loading coefficients (factor loading > 0.5).