1. NOCIONES BÁSICAS
1.2. La experiencia perceptiva
After the outline of an adequate measurement model, the researcher proceeded to test the proposed hypothesis with AMOS SEM. In this analysis, the structural paths and the R-
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square scores of endogenous variables are examined to assess the explanatory power of the structural model. Structural Equation modelling is a multivariate analysis technique that is used to analyse structural relationships. This technique is the combination of factor analysis and multiple regression analysis and is used to analyse the structural relationship between the measured variables and latent constructs (Byrne, B. M., 2001).
There is a need to check for multicollinearity issues before going ahead for testing the hypothesis. Multicollinearity occurs when the model includes multiple factors that are correlated not just to the response variable, but also to each other. In other words, it results when there are factors that are a bit redundant(Costello & Osborne, 2005).
Multicollinearity increases the standard errors of the coefficients. Increased standard errors in turn mean that coefficients for some independent variables may be found not to be significantly different from 0. In other words, by overinflating the standard errors, multicollinearity makes some variables statistically insignificant when they should be significant. Without multicollinearity (and thus, with lower standard errors), those coefficients might be significant.
One way to measure multicollinearity is the variance inflation factor (VIF), which assesses how much the variance of an estimated regression coefficient increases if the predictors are correlated. If no factors are correlated, the VIFs will all be 1. The general rule of thumb is that VIFs exceeding 4 warrant further investigation, while VIFs exceeding 10 are signs of serious multicollinearity requiring correction(Schumacker & Lomax, 2010).
The multicollinearity for our model is checked and found no issues of it as can be seen in table 6.26 by checking the VIF values.
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Table 6.26: VIF VALUES
Model Collinearity Statistics Tolerance VIF 1 (Constant) cohesion .551 1.784 communication .575 1.740 diversity .873 1.146 teammemchar .560 1.786 conflict .423 2.366 orgcul .251 3.680
a. Dependent Variable: trust
Hypothesis Testing:
i) Checking Direct Effects without Mediator: The initial structural model is drawn as shown in figure 6.22. The control variables are added and made to covary with all independent variables.
Figure 6.22: Initial structural model
In order to check for direct effects, the mediators are removed as shown in figure 6.23 and then checked for standard regression weights and squared multiple correlations as shown in table 6.27 to table 6.29.
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Figure 6.23: Structural model without mediators
Table 6.27: Regression Weights: (Group number 1 - Default model)
Estimate S.E. C.R. P Label trust <--- orgcul .393 .065 6.019 ***
trust <--- diversity .063 .023 2.754 .006 trust <--- communication .121 .052 2.325 .020 trust <--- teammemchar .142 .030 4.670 *** This table can be analysed as follows:
a) The probability of getting a critical ratio as large as 6.019 in absolute value is less than 0.001. In other words, the regression weight for orgcul in the prediction of
trust is significantly different from zero at the 0.001 level (two-tailed).
b) The probability of getting a critical ratio as large as 2.754 in absolute value is .006. In other words, the regression weight for diversity in the prediction of trust
is significantly different from zero at the 0.01 level (two-tailed).
c) The probability of getting a critical ratio as large as 2.325 in absolute value is 0.020. In other words, the regression weight for communication in the prediction of trust is significantly different from zero at the 0.05 level (two-tailed).
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d) The probability of getting a critical ratio as large as 4.67 in absolute value is less than 0.001. In other words, the regression weight for teammemchar in the prediction of trust is significantly different from zero at the 0.001 level (two- tailed).
Table 6.28: Standardized Regression Weights: (Group number 1 - Default model)
Estimate trust <--- Orgcul .36 trust <--- Diversity .132 trust <--- communication .138 trust <--- teammemchar .23 This table can be analysed as follows:
a) When orgcul goes up by 1 standard deviation, trust goes up by 0.358 standard deviations.
b) When diversity goes up by 1 standard deviation, trust goes up by 0.132 standard deviations.
c) When communication goes up by 1 standard deviation, trust goes up by 0.138 standard deviations.
d) When teammemchar goes up by 1 standard deviation, trust goes up by 0.227 standard deviations.
Table 6.29: Squared Multiple Correlations: (Group number 1 - Default model)
Estimate
trust .35
This means that it is estimated that the predictors of trust explain 34.5 percent of its variance. In other words, the error variance of trust is approximately 65.5 percent of the variance of trust itself.
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Table 6.30: Results of direct effects
Hypothesis Number
Statement of Hypothesis Results
H1 A positive relationship exists between organizational
culture and trust in virtual project teams.
Supported
H4 A negative relationship exists between diversity of
team members and trust in virtual project teams.
Not supported; came out to be positive
relationship
H7 A positive relationship exists between communication
of team members and trust in virtual project teams.
Supported
H9 A positive relationship exists between characteristics
of team member on trust in virtual project teams.
Supported
H12 A positive relationship exists between leadership
skills of the manager and trust in virtual project teams.
Not supported, no effect
H13 A positive relationship exists between task-
technology fit on trust in virtual project teams.
Not supported, not required
ii) Checking mediator effects with multiple mediators by using Sobel test:
In order for a hypothesis to be supported, many criteria must be met. These criteria can be classified as global or local tests. In order for a hypothesis to be supported, the local test must be met, but in order for a local test to have meaning, all global tests must be met. Global tests of model fit are the first necessity. If a hypothesized relationship has a significant p-value, but the model has poor fit, we cannot have confidence in that p-value. Next is the global test of variance explained or R-squared. We might observe significant p-values and good model fit, but if R-square is only 0.025, then the relationships we are testing are not very meaningful because they do not explain sufficient variance in the dependent variable (Hayes & Andrew. F., 2013)
The model fit is achieved after adding controls. Next the R-squared values of dependent variables are checked as shown in Table 6.31. The R-squared values are good to go. Therefore two global tests- model fit and R-Squared are passed.
Table 6.31: Squared Multiple Correlations: (Group number 1 - Default model)
Estimate
conflict .172
Cohesion .847
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There are many ways of doing mediation. Baron and Karon approach is dead, therefore sobel test is followed. In case of two mediators connected to independent variable, the sobel test is used because independent variable has multiple indirect paths to dependent variables. This means that the estimate calculated includes both the indirect paths. So the p-value for either indirect paths cannot be determined individually.
First of all, the direct effect of independent variable on dependent variable is significant. When the mediator variable M enters the model, the direct effect would be reduced since some of the effect has shifted through the mediator. If it is reduced but still significant, the mediation effect here is called “partial mediation”. However, if the direct effect is reduced and no longer significant, then the mediation is called “complete mediation”.
When analyzing the mediator, there are two effects involved namely direct effect and
indirect effect. The direct effect is the effect from independent variable directly to dependent variable, while the indirect effect is the effect from independent variable to dependent variable that goes indirectly through the mediating variable. The significance of indirect effects indicates the mediation exists and the significance or insignificance of direct effects indicates the type of mediation (Zainudin, 2012)
.
For indirect effects, bootstrapping is always required.H2: Conflict mediates the positive effect of organizational culture on trust.
As organizational culture has two mediating variables, in order to verify these hypothesis individually, we have to first remove the path from organizational culture to cohesion and calculate the data for this hypothesis as this independent variable has multiple indirect paths to the dependent variable. This means that the estimate calculated includes both indirect paths and it’s not possible to determine the p- values for either indirect path individually.
The direct effect of organizational culture to trust has been calculated without mediator in table 6.28. Now the direct effect of organizational culture to conflict and then from
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conflict to trust is calculated and is shown in table 6.32. Also, the indirect effect of organizational culture to trust via conflict through bootstrapping is shown in table 6.32.
Table 6.32: Standardized Indirect and Direct Effects (Org. Cult to trust with conflict as mediator) Standardized Indirect Effects (Group number 1 - Default model)
Avgsize Age teamm emchar
commu
nication diversity orgcul conf
lict
cohes ion
trust -.002 .069 .182 -.037 -.003 .243 .308 .000
Standardized Indirect Effects - Two Tailed Significance (BC) (p-value)
trust .888 .019 .001 .298 .960 .001 .001 ...
Standardized Direct Effects (Group number 1 - Default model)
trust -.017 -.011 .050 .170 .154 -.104 .235 .485
Standardized Direct Effects - Two Tailed Significance (BC) (p-value)
trust .613 .764 .371 .004 .001 .202 .001 .001
Table 6.33: Summary of Indirect and Direct effects (Org. Cult to trust with conflict as mediator) Indirect Effect Direct Effect
Bootstrapping Results .243 -.104 (.358)
Bootstrapping P-value .001 .202
Result Significant Non – Significant
The results in table 6.33 shows that indirect effect is significant at 99.9% confidence level. The significance of this indirect effect says that mediation is there. From the table 6.33, we can see that the direct effect is reduced to - 0.104 from 0.358 and is non- significant. The mediation is full as the direct effect got reduced and is non-significant, and hence hypothesis H2 is supported.
H3: Cohesion increases the positive effect of organizational culture on trust.
As organizational culture has two mediating variables, in order to verify these hypothesis individually, we have to first remove the path from organizational culture to conflict and calculate the data for this hypothesis as this independent variable has multiple indirect paths to the dependent variable. This means that the estimate calculated includes both indirect paths and it’s not possible to determine the p- values for either indirect path individually.
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The direct effect of organizational culture to trust has been calculated without mediator in table 6.28. Now the direct effect of organizational culture to cohesion and then from cohesion to trust is calculated and is shown in table 6.34. Also, the indirect effect of organizational culture to trust via cohesion through bootstrapping is shown in table 6.34.
Table 6.34: Standardized Indirect and Direct Effects (Org. Cult to trust with cohesion as mediator)
Standardized Indirect Effects (Group number 1 - Default model)
Avgsize Age teamm emchar
commu
nication diversity orgcul conf
lict
cohes ion
trust -.015 .062 .152 .098 .000 .275 .208 .000
Standardized Indirect Effects - Two Tailed Significance (BC) (p-value)
trust .123 .033 .001 .001 .991 .001 .001 ...
Standardized Direct Effects (Group number 1 - Default model)
trust -.017 -.012 .050 .173 .156 -.106 .239 .464
Standardized Direct Effects - Two Tailed Significance (BC) (p-value)
trust .615 .766 .372 .004 .001 .202 .001 .001
Table 6.35: Summary of Indirect and Direct effects (Org. Cult to trust with cohesion as mediator) Indirect Effect Direct Effect
Bootstrapping Results .275 -.106(.358)
Bootstrapping P-value .001 .202
Result Significant Non - Significant
The results in table 6.35 shows that indirect effect is significant at 99.9% confidence level. The significance of this indirect effect says that mediation is there. From the table 6.35, we can see that the direct effect is reduced to - 0.106 from 0.358 and is non- significant. The mediation is full as the direct effect got reduced and is non-significant, and hence hypothesis H3 is supported.
H5: Conflict mediates the negative effect of diversity on trust in virtual project teams. As Diversity has two mediating variables, in order to verify these hypothesis individually, we have to first remove the path from Diversity to cohesion and calculate the data for this hypothesis as this independent variable has multiple indirect paths to the dependent variable. This means that the estimate calculated includes both indirect paths and it’s not possible to determine the p- values for either indirect path individually.
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The direct effect of Diversity to trust has been calculated without mediator in table 6.28. Now the direct effect of Diversity to conflict and then from conflict to trust is calculated and is shown in table 6.36. Also, the indirect effect Diversity to trust via conflict through bootstrapping is shown in table 6.36.
Table 6.36: Standardized Indirect and Direct Effects (Diversity to trust with conflict as mediator)
Standardized Indirect Effects (Group number 1 - Default model)
Avgsize Age teamm emchar
commu
nication diversity orgcul conf
lict
cohes ion
trust -.014 .039 .157 -.030 -.032 .467 .201 .000
Standardized Indirect Effects - Two Tailed Significance (BC) (p-value)
trust .148 .141 .001 .298 .118 .001 .001 ...
Standardized Direct Effects (Group number 1 - Default model)
trust -.017 -.011 .049 .170 .154 -.104 .234 .487
Standardized Direct Effects - Two Tailed Significance (BC) (p-value)
trust .613 .766 .376 .004 .002 .201 .001 .001
Table 6.37: Summary of Indirect and Direct effects (Diversity to trust with conflict as mediator) Indirect Effect Direct Effect
Bootstrapping Results -.032 .154 (0.132)
Bootstrapping P-value .118 .002
Result Non- Significant Significant
The results in table 6.37 shows that indirect effect is non-significant. Therefore there is no mediation of conflict as mediator from diversity to trust, and hence hypothesis H5 is not supported.
H6: Cohesion mediates the negative effect of diversity on trust in virtual project teams. As Diversity has two mediating variables, in order to verify these hypothesis individually, we have to first remove the path from Diversity to conflict and calculate the data for this hypothesis as this independent variable has multiple indirect paths to the dependent variable. This means that the estimate calculated includes both indirect paths and it’s not possible to determine the p- values for either indirect path individually.
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The direct effect of Diversity to trust has been calculated without mediator in table 6.28. Now the direct effect of Diversity to cohesion and then from cohesion to trust is calculated and is shown in table 6.38. Also, the indirect effect Diversity to trust via cohesion through bootstrapping is shown in table 6.38.
Table 6.38: Standardized Indirect and Direct Effects (Diversity to trust with cohesion as mediator)
Standardized Indirect Effects (Group number 1 - Default model)
Avgsize Age teamm emchar
commu
nication diversity orgcul conf
lict
cohes ion
trust -.014 .042 .148 -.029 .022 .459 .201 .000
Standardized Indirect Effects - Two Tailed Significance (BC) (p-value)
trust .122 .109 .001 .298 .025 .001 .001 ...
Standardized Direct Effects (Group number 1 - Default model)
trust -.017 -.011 .049 .168 .122 -.103 .232 .484
Standardized Direct Effects - Two Tailed Significance (BC) (p-value)
trust .617 .766 .378 .004 .002 .204 .001 .001
Table 6.39: Summary of Indirect and Direct effects (Diversity to trust with cohesion as mediator) Indirect Effect Direct Effect
Bootstrapping Results .022 .122 ( 0.132)
Bootstrapping P-value .025 0.002
Result Significant Significant
The results in table 6.39 shows that indirect effect is significant. The significance of this indirect effect says that mediation is there. From the table 6.39, we can see that the direct effect is reduced to 0.122 from 0.132 and is significant. The mediation is partial as the direct effect got reduced and is significant. And now for partial mediation, we need to check that absolute value of (div -> cohesion) X (cohesion->trust) > absolute value of (diversity ->trust) that is .046 X .484 = 0.022264 which is not greater than 0.122, and hence hypothesis H6 is supported and has partial mediation.
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H8: Conflict mediates the positive effect of communication on trust in -virtual project teams
The direct effect of Communication to trust has been calculated without mediator in table 6.28. Now the direct effect of Communication to conflict and then from conflict to trust is calculated and is shown in table 6.40. Also, the indirect effect Communication to trust via cohesion through bootstrapping is shown in table 6.40.
Table 6.40: Standardized Indirect and Direct Effects (Communication to trust with conflict as mediator)
Standardized Indirect Effects (Group number 1 - Default model)
Avgsize Age teamm emchar
commu
nication diversity orgcul conf
lict
cohes ion
trust -.014 .040 .149 -.030 -.010 .465 .203 .000
Standardized Indirect Effects - Two Tailed Significance (BC) (p-value)
trust .123 .129 .001 .298 .670 .001 .001 ...
Standardized Direct Effects (Group number 1 - Default model)
trust -.017 -.011 .049 .169 .153 -.104 .234 .485
Standardized Direct Effects - Two Tailed Significance (BC) (p-value)
trust .615 .766 .376 .004 .002 .201 .001 .001
Table 6.41: Summary of Indirect and Direct effects (Communication to trust with conflict as mediator) Indirect Effect Direct Effect
Bootstrapping Results -.030 .169(0.138)
Bootstrapping P-value .298 .004
Result Non-significant Significant
The results in table 6.41 shows that indirect effect is non-significant. Therefore there is no mediation of conflict as mediator from communication to trust, and hence hypothesis H8 is not supported.
H10: Cohesion increases the positive effect of team member characteristics on trust. The direct effect of team member characteristics to trust has been calculated without mediator in table 6.28. Now the direct effect of team member characteristics to cohesion and then from cohesion to trust is calculated and is shown in table 6.42. Also, the indirect