B. Result and Discussion
3. Descriptive Analysis
We have seen the characteristics of respondents and the author wants to analyze the answers of questionnaires that have been spread out to 100 respondents who have been joining as GoJek driver as sample of this research. The analysis
66 encompasses every variable of the questionnaires which becomes the dimensions of variables that is researched, as follows:
a. People consist of several indicator, as follows:
In this research, the variable of people is measured by three indicators.
The description of the result as follows:
Table 4.9 Description of People
Interval (%) Criteria Frequency Precentage 84% < score ≤ 100% Excellent 7 7.0%
69% < score ≤ 84% Very Good 31 31.0%
52% < score ≤ 68% Good 55 55.0%
36% < score ≤ 52 % Not Good 7 7.0%
20% ≤ score ≤ 36% Bad 0 0.0%
Total 100 100%
Highest 90.0%
Lowest 43.3%
Mean 66.3%
Source: Primary Data Processed
Based on table 4.11 above, the frequency of the answer about the variable of People the majority of respondent stated excellent at 7 or 7% , 31 respondents or 31% stated very good, 55 respondents or 55% stated good, and 7 respondents or 7% stated not good. The index presentation of People is categorized as Good. The description also can be seen on the chart:
67 Figure 4.10
Chart of People
Source: Primary Data Processed Table 4.11 Indicator of People No Indicator Empirical
Score
Ideal Score
Percentage Criteria 1 Inclusive
Workforce
651 1000 65.1% Good
2 Diverse Participation among providers
659 1000 65.9% Good
3 Inclusion as a priority in capital markets
679 1000 67.9% Good
Source: Primary Data Processed
Table 4.12 above, depict that each of the indicator shows that inclusion as a priority in capital markets (67.9%), diverse participation among providers (65.9%), and Inclusive workforce (65.1%) can be preliminary conclude the
7.0%
31.0%
55.0%
7.0%
0.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
Excellent Very Good Good Not Good Bad
68 indicators are categorized as good. It shows that by promoting inclusive workforce and diversity will increase wellbeing of Gojek driver.
b. Product & Process consist of several indicators, as follows:
In this research, the variable of people is measured by three indicators.
The description of the result as follows:
Table 4.11
Description of Product & Process
Interval (%) Criteria Frequency Percentage 84% < score ≤ 100% Excellent 1 1.0%
69% < score ≤ 84% Very Good 49 49.0%
52% < score ≤ 68% Good 44 44.0%
36% < score ≤ 52 % Not Good 6 6.0%
20% ≤ score ≤ 36% Bad 0 0.0%
Total 100 100%
Highest 85.0%
Lowest 40.0%
Mean 66.6%
Source: Primary Data Processed
Based on table 4.11 above, the frequency of the answer about the variable of Product & process the majority of respondent stated excellent at 1 or 1% , 49 respondents or 49% stated very good, 44 respondents or 44% stated good, and 6 respondents or 6% stated not good. The index presentation of Product & Process is categorized as Good. The description also can be seen on the chart.
69 Figure 4.2
Chart of Product & Process
Source: Primary Data Processed
Table 4.12
Indicator of Product & Process No indicator Empirical
Score
Ideal Score
Percentage Criteria 1 Innovation
Management
986 1500 65.7% Good
2 Human
centered Design
650 1000 65.0% Good
3 Services Innovation
2360 3500 67.4% Very
Good Source: Primary Data Processed
Table 4.14 above, depict that each of the indicator shows that services innovation (67.4%)is categorized as very good while Innovation management (65.7%), and human centered design (65.0%) can be preliminary conclude the indicators are categorized as good. It shows that by fostering innovation across its entire value chain will increase wellbeing of Gojek driver.
1.0%
49.0%
44.0%
6.0%
0.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
Excellent Very Good Good Not Good Bad
70 c. Partnerships consist of several indicators, as follows:
In this research, the variable of partnerships is measured by four indicators. The description of the result as follows:
Table 4.13
Description of Partnerships
Interval (%) Criteria Frequency Percentage 84% < score ≤ 100% Excellent 19 19.0%
69% < score ≤ 84% Very Good 41 41.0%
52% < score ≤ 68% Good 21 21.0%
36% < score ≤ 52 % Not Good 15 15.0%
20% ≤ score ≤ 36% Bad 4 4.0%
Total 100 100%
Highest 95.0%
Lowest 35.0%
Mean 68.3%
Source: Primary Data Processed
Based on table 4.15 above, the frequency of the answer about the variable of Partnerships the majority of respondent stated excellent at 19 or 19% , 41 respondents or 41% stated very good, 21 respondents or 21% stated good, 15 respondents or 15% stated not good, and 4 respondents or 4% stated bad The index presentation of Partnerships is categorized as Good. The description also can be seen on the chart.
71 Figure 4.3
Chart of Partnerships
Source: Primary Data Processed
Table 4.14
Indicator of Partnerships No Indicator Empirical
Score
Ideal Score
Percentage Criteria 1 Government
and
policymakers
341 500 68.2% Very
good 2 Communities
and civil society actors
353 500 70.6% Very
good 3 Other sharing
economy companies
344 500 68.8% Very
good 4 Broader
business communities
328 500 65.6% Good
Source: Primary Data Processed
Table 4.16 above, depict that each of the indicator shows that Communities and civil society actors (70.6 %), Other sharing economy companies (68.8 %) and Government and policymakers (68.2 %) are categorized as very good while the Broader business communities (65.6%) is
19.0%
41.0%
21.0%
15.0%
4.0%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
Excellent Very Good Good Not Good Bad
72 categorized as good. It shows that by having different types of partners could help support sharing economy that will increase wellbeing of Gojek driver.
d. Wellbeing consist of several indicators, as follows:
In this research, the variable of Wellbeing is measured by four indicators. The description of the result as follows:
Table 4.15
Description of Wellbeing
Interval (%) Criteria Frequency Percentage 84% < score ≤ 100% Sangat Tinggi 1 1.0%
69% < score ≤ 84% Tinggi 39 39.0%
52% < score ≤ 68% Cukup 49 49.0%
36% < score ≤ 52 % Rendah 10 10.0%
20% ≤ score ≤ 36% Sangat Rendah 1 1.0%
Total 100 100%
Highest 84.6%
Lowest 33.8%
Mean 64.0%
Source: Primary Data Processed
Based on table 4.17 above, the frequency of the answer about the variable of Wellbeing the majority of respondent stated excellent at 1 or 1%, 39 respondents or 39% stated very good, 49 respondents or 49% stated good, 10 respondents or 10% stated not good, and 1 respondents or 1% stated bad The description also can be seen on the chart.
73 Figure 4.4
Chart of Wellbeing
Source: Primary Data Processed
Table 4.16 Indicator of Wellbeing No Indicator Empirical
Score
Ideal Score
Percentage Criteria 1 The hedonic
approach
658 1000 65.8% Good
2 The
Eudaimonic approach
1896 3000 63.2% Good
3 Quality of Life (QoL)
951 1500 63.4% Good
4 Wellness 656 1000 65.6% Good
Source: Primary Data Processed
Table 4.18 above, depict that each of the indicator shows that The hedonic approach (65.8%), Wellness (65.6%), Quality of Life (QoL) (63.4%) and The Eudaimonic approach (63.2%) are categorized as good. It shows that all the indicators proof the wellbeing of Gojek driver.
1.0%
39.0%
49.0%
10.0%
1.0%
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
Excellent Very Good Good Not Good Bad
74 4. Classic Assumption Test
a. Normality Test
Normality test is done in order to see the level of normality of data used, whether it is normal distributed or not. The level of normality of data is highly crucial because if data is normally distributed, it means the data can represent the population (Priyatno, 2012: 34). Normality data test aims to test whether the dependent variable and independent variables both have a normal distribution or not in the regression model. The result of normality data test using Kolmogorov-Smirnov test is shown in the following table:
Table 4.17
The Result of Kolmogorov Test
One-Sample Kolmogorov-Smirnov Test
Unstandardized Residual
N 100
Normal Parametersa Mean .0000000
Std. Deviation 4.46852987
Most Extreme Differences Absolute .053
Positive .046
Negative -.053
Kolmogorov-Smirnov Z .534
Asymp. Sig. (2-tailed) .938
a. Test distribution is Normal.
b. calculated from data
Source: Primary Data Processed
Based on the result of Kolmogorov-Smirnov test, the value of Asymp Sig (2-tailed) is 0,938 = 93,8 % and its value is bigger than significance level of 0,05. According to Priyatno (2012: 38) if significant value (Asym Sig 2 tailed) > 0.05, data is normally distributed and if significant value
75 (Asym Sign 2 tailed) < 0.05, data is not normally distributed. Therefore, it can be concluded that the data is normal distributed. Normality test is also can be seen on the normal P- P Plot, as following:
Figure 4.18
The result of Normal P- P Plot of Regression Standardized Residual
Source: Primary Data Processed
On the P-Plot can be seen that data distribute around diagonal line and is following towards histograph to normal distribution. Thus, it can be concluded variable Y is normally distribute.
b. Multicollinearity Test
According to Priyatno (2012: 56) multicollinearity is a condition where the relationship of perfect linear or nearly perfect among interdependent variables happens in regression model. The way to know either there is
76 sypmtomp of multicollinearity or not is by seeing the value of Variance Inflation Factor (VIF) and tolerance, if the value of VIF is less than 10 and value of tolerance is more than 0.1, thus, it is stated that multicollinearity does not occur (Ghozali in Priyatno, 2012: 56). The multicollinearity test can be seen in the following table:
Table 4.19
The Result of Multicollinearity Test
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) 1.354 3.874 .349 .728
X1 .481 .178 .225 2.708 .008 .701 1.427
X2 .597 .103 .472 5.782 .000 .727 1.376
X3 .501 .160 .234 3.127 .002 .866 1.155
a. Dependent Variable: Y
Source: Primary Data Processed
Based on the result above, the value of VIF for People (X1) is 1,427, the value of VIF for Product & Process (X2) is 1,376, and the value of VIF for Partnerships (X3) is 1,155. It can be seen that the value of Variance Inflation Factor for all independent variables is less than 10.
The value of Tolerance for People (X1) is 0,701, the value of Tolerance for Product & Process (X2) is 0,727, and the value of Tolerance for Partnerships (X3) is 0,866. The value of Tolerance for all independent variables is also bigger than 0,1. Therefore, it can be concluded that the
77 multicollinearity does not occur on People, Product &Process, and Partnerships.
c. Heteroscedasticity
Heterocedasticity test is used to indicate in a regression model whether there is variance inequality of residual on one observation to other observations (Ghozali, 2006: 105). Heterocedasticity can be indicated by seeing the resulted scatterplot. The result of heterocedasticity test can be seen in the following graph:
Figure 4.20
The Result of Heterocedasticity Test
Source : Primary data Processed
From the scatterplot graphs above, it is appeared that the data points are spread out, not only gather above or below Y axis. Then the distribution does not form a wavy pattern. Then, glejser test can also be used for ensuring the heteroscedasticity does not occur. According to Ghozali in (Priyatno,
78 2012: 62) if the significance value among independent variables with residual is more than 0,05, then, heterocedasticity does not occur. Beside the Scatterplot test, heteroscedasticity test also can be done with Glejser test.
Output of glejser test can be seen in following:
Table 4. 21
The Result of Glejser Test
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 2.922 2.212 1.321 .190
X1 -.082 .101 -.099 -.813 .418
X2 .048 .059 .096 .808 .421
X3 .033 .092 .040 .365 .716
a. Dependent Variable: Abs_res Source: Primary Data Processed
Based on the table 4.44 above, it can be seen that the significance value of People (X1) is 0,418, the significance value of Product & Process (X2) is 0,421, and the significance value of Partnerships (X3) is 0,716. Thus, the data show that all independent variables are greater than 0,05 and it means there is no independent variable which is significantly influence dependent variable Abs_res. Thus, it can be concluded that the heteroscedasticity does not occur. Therefore, it can be concluded that the multiple linier regression model is free from heteroscedasticity classical assumption so this deserves to be used in this research.
79 5. Hypothesis Test
After doing normality test, multicollinearity test, and heterocedasticity test. Thus, the regression model is deserved to be used for testing the hypothesis among the variables.
a. f-Test (Simultaneous Test)
Baroroh (2012: 2) has defined regarding F Test, this test is done in order to know the influence of independent variables towards dependent variable simultaneously. F test basically indicates whether all exogenous variable have influence endogenous variable simultaneously.
Table 4.22 The Result of F-Test
ANOVAb
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 2276.982 3 758.994 36.859 .000a
Residual 1976.808 96 20.592
Total 4253.790 99
a. Predictors: (Constant), X3, X2, X1 b. Dependent Variable: Y
Source: Primary Data Processed
Based on the table above, to test whether variable People (X1), Product
& Process (X2), and Partnerships (X3) have significant influence towards Well-being (Y1) simultaneously, there are several steps to test the hypothesis by F test as follows:
Ho : People, Product & Process and Partnership simultaneously have no influence towards Well-being significantly
80 Ha : People, Product & Process and Partnership have significant influence towards Well-being simultaneously The formula to test F test with F table are : If F count > F table, then H0 is rejected and Ha is accepted
If F count < F table, then H0 is accepted and Ha is rejected
According to table analysis of variant (ANOVA), calculation value of F test was obtained 36.859 with probabilities 0.000. F table with significant level based on 0.05, the degree of freedom (df) for df1= 3 and df2 = 96, then the number of F table = 2.70. Thus, F test (36.859) > F table (2.70) and the significant level (0.000) < (0.05). Therefore it can be conclude, H0 is rejected and Ha is accepted. It means People (X1), Product & Process (X2), and Partnerships (X3) have significant influence towards Well-being (Y1) simultaneously.
b. T-Test (Partial Test)
This test is used to know whether independent variables partially influence towards dependent variable, or not, by assuming other independent variables are constant (Levine, 2011: 326).
Hypothesis can be depicted as follows:
Ho 1: People partially do not have influence in increasing well-being Ha 1: People partially have influence in increasing well-being
Ho 2: Product & Process partially do not have influence in increasing well- being
Ha 2: Product & Process partially have influence in increasing well-being Ho 3: Partnership partially does not have influence in increasing well-being Ha 3: Partnership partially has influence in increasing well-being
81 The formula to test T test with T table:
a) T count ≥ T table or Sig. (Asym Sig 2 tailed) < α: Ho is rejected b) T count ≤ T table or Sig. (Asym Sig 2 tailed) > α: Ho is accepted
Table 4.23 The Result of t-Test
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
T Sig.
Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) 1.354 3.874 .349 .728
X1 .481 .178 .225 2.708 .008 .701 1.427
X2 .597 .103 .472 5.782 .000 .727 1.376
X3 .501 .160 .234 3.127 .002 .866 1.155
a. Dependent Variable: Y
Source: Primary Data Processed
Based on the table of coefficient above, it can be acquired that the variable of People (X1) has significance value of 0,008 which is less than 0,05 (0,008 < 0,05) and also has t count of 2,708 which is greater than t table of 1,98 (2,708 > 1,98). Thus, it means Ho is rejected and Ha is accepted because of t count is greater than t table and the value of significance is less than α (0,05), it can be concluded that the variable of people partially have influences towards wellbeing.
The variable of Product & Process (X2) has significance value of 0,000 which is less than 0,05 (0,000 < 0,05) and also has t count of 5,782 which is greater than t table of 1,98 (5,782 > 1,98). Thus, it means Ho is rejected and Ha is accepted because of t count is greater than t table and the value of
82 significance is less than α (0,05), it can be concluded that the variable of product and process partially have influences towards wellbeing.
The variable of Partnerships (X3) has significance value of 0,002 which is less than 0,05 (0,002 < 0,05) and also has t count of 3,127 which is greater than t table of 1,98 (3,127 > 1,98). Thus, it means Ho is rejected and Ha is accepted because of t count is greater than t table and the value of significance is less than α (0,05), it can be concluded that the variable of partnerships partially have influences towards wellbeing.
6. Multiple Linier Regression Test a. Regression Equation
Technique of analysis that has been used in this research is the multiple linier regressions. Analysis of multiple linier regressions is used as the analysis tools of statistics because this research has been designed to research the variables which have influence among independent variables and dependent variable. Regression equation can be determined by seeing the table 4.57 below,
83 Table 4.24
The Result of Multiple Linier Regressions
Coefficientsa
Model
Unstandardized Coefficients
Standardize d Coefficients
t Sig.
Correlations
B Std. Error Beta Zero-order Partial Part
1 (Constant) 1.354 3.874 .349 .728
X1 .481 .178 .225 2.708 .008 .544 .266 .188
X2 .597 .103 .472 5.782 .000 .654 .508 .402
X3 .501 .160 .234 3.127 .002 .447 .304 .218
a. Dependent Variable: Y
Source: Primary Data Processed
From table of coefficients above, thus the regression model reached is as follows:
Y = 1,354 + 0,481 X1 + 0,597 X2 + 0,501 X3 Where:
Y = Wellbeing X2 = Product & Process
X1 = People X3 = Partnerships
The regression equality shows that the regression coefficient of Constanta is positive and this means by assuming the inexistence of independent variables such the variables of people, product & process and partnerships. Then, the dependent variable which is wellbeing tends to increase.
The regression equality shows that the regression coefficient of variable People is positive. It means by assuming the other independent
84 variables are constant, the variable of wellbeing tends to increase when the variable of people increases.
The regression equality shows that the regression coefficient of variable Product & Process is positive. It means by assuming the other independent variables are constant, the variable of wellbeing tends to increase when the variable of product & process increases.
The regression equality shows that the regression coefficient of variable Partnerships is positive. It means by assuming the other independent variables are constant, the variable of wellbeing tends to increase when the variable of partnerships increases.
b. Determinant Coefficient (Adjusted R2)
Coefficient of determination (R2) is basically used to measure regarding how far the ability of model can define variance of dependent variable. Yet, the weakness of using the coefficient of determination is bias towards the sum of independent variables that has been put into the model.
Therefore, many researchers suggest using the value of Adjusted R2 (Ghozali, 2006: 83). Then, the result of determinant coefficient can be seen, as follows:
85 Table 4.25
The result of Determinant Coefficient Model Summary
Model R R Square Adjusted R
Square
Std. Error of the Estimate
1 .629a 0.395 0.390 5.54386
A. Predictors: (Constant), X2, X1,X3 Source: Primary Data Processed
Based on the table of 4.48 above, it shows that Adj. R2 is 0,390 or 39%. This means that the magnitude of influence from variable people, product & process, and partnerships towards wellbeing of Gojek driver is 39%. While the rest of dependent variable value is 61% can be explained by the other variables that have not been included in this research.
86