chapter: 8 - inflibnetshodhganga.inflibnet.ac.in/bitstream/10603/9721/15/15...equation modeling...

CHAPTER: 8

ANALYSIS AND RESULTS

8.1 Addressing Common Method Variance

8.1.1. Harman’s Single Factor Analysis

Harman‘s one-factor test, ex post statistical tests, was conducted to test the

presence of common method effect. All the variables were entered into an exploratory factor

analysis, using unrotated principal components factor analysis, principal component analysis

with varimax rotation, and principal axis analysis with varimax rotation to determine the number

of factors that are necessary to account for the variance in the variables. If a substantial amount

of common method variance is present, either (a) a single factor will emerge from the factor

analysis, or (b) one general factor will account for the majority of the covariance among the

variables (Andersson & Bateman,1997; Aulakh & Gencturk, 2000; Greene & Organ, 1973;

Krishnan, Martin & Noorderhaven, 2006; Podsakoff et al., 2003; Podsakoff & Organ,1986;

Podsakoff , Todor, Grover & Huber, 1984; Schriesheim, 1979; Schriesheim, 1980; Steensma,

Tihanyi, Lyles, & Dhanaraj, 2005).

Results showed total 23 factors have emerged with Eigen value > 1. The first

general factor accounted for 29.42 % of the total variance. Results from independent t-test and ex

post statistical test shows no evidence of common factor, hence no common method variance is

present within the responses of participants.

8.2 Hypothesis Testing

Basic descriptive statistics for the data are shown in table 8.1 and the correlations

between the independent variables along the reliability (α) for this study are shown in table 8.2.

Cronbach‘s alpha for the nine items of knowledge sharing measure, eight item mutual trust and

seven item team performance was 0.908, 0.890 and 0.887 respectively.

8.2.1 Measures of Deviation from Normality

Skewness measures the symmetry of a distribution means up to what extent a

distribution of values deviates from symmetry around the mean (George and Mallery, 2009;

Hair, Black, Babin, Anderson & Tatham, 2006). A value of zero represents a symmetric and

evenly balanced distribution. A value between -1 to +1 is considered excellent for most

psychometric purposes (George and Mallery, 2009).

Kurtosis is a measure of the peakedness or the flatness of a distribution when

compared with a normal distribution (George and Mallery, 2009; Hair, Black, Babin, Anderson

& Tatham, 2006). Like normality, a kurtosis value near zero indicates a shape close to normal.

As with skewness, a value between -1 to +1 is considered excellent for most psychometric

purpose (George and Mallery, 2009).

Table 8.1 Descriptive statistics of the variables studied

C F LC T R TMS A TI F TV TS EI KS MT TP

N Valid 582 582 582 582 582 582 582 582 582 582 582 582 582 582 582

Missing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Skewness 0.502 -0.117 -0.529 -0.387 -0.198 -0.480 -0.454 -0.462 -0.311 -0.724 -0.647 -0.569 -0.604 -0.414 -0.702

Std. Error of Skewness 0.101 0.101 0.101 0.101 0.101 0.101 0.101 0.101 0.101 0.101 0.101 0.101 0.101 0.101 0.101

Kurtosis -0.992 0.329 -0.754 -0.247 0.355 -0.132 -0.631 -0.260 0.466 -0.065 -0.131 -0.130 0.071 0.527 -0.680

Std. Error of Kurtosis 0.202 0.202 0.202 0.202 0.202 0.202 0.202 0.202 0.202 0.202 0.202 0.202 0.202 0.202 0.202

Table 8.2 Correlation between all independent variables and cronbach‘s alpha along the diagonal

1 2 3 4 5 6 7 8 9 10 11 12

1 C (0.903)

2 F 0.085 * (0.896)

3 LC 0.009* 0.251** (0.882)

4 T 0.670** 0.105 0.340** (0.877)

5 R 0.051 0.199* 0.140* 0.029 (0.547)

6 TMS 0.055 0.306** 0.447** 0.123* 0.360** (0.857)

7 A 0.197** 0.048 0.226* 0.214** 0.089* -0.020* (0.760)

8 TI -0.161* 0.302 0.418** 0.110** 0.047* 0.202** 0.038 (0.767)

9 F -0.19 0.124 0.097* 0.033* 0.015 0.070 0.152** 0.089* (0.804)

10 TV -0.18** 0.036 0.241** 0.159** 0.210** 0.258** 0.021 0.228** 0.075 (0.688)

11 TS 0.09 0.094* 0.035* 0.210 0.075 0.005 0.065 0.145** 0.226** 0.086* (0.788)

12 EI 0.29 0.168* 0.248** 0.095* 0.303** 0.341** 0.145* 0.331* 0.117** 0.259** 0.046 (0.949)

** Correlation is significant at the 0.01 level (2-tailed)

* Correlation is significant at the 0.05 level (2-tailed)

Note: Alphas are in parenthesis along the diagonal

8.2.2 Model description

The main effects were examined using multiple regression analysis and 3 step

linear regressions for moderation interaction as suggested by Baron and Kenny (1986). Structure

Equation Modeling (SEM) was used to test the overall fit of the proposed model. Analysis has

been done using the Statistical Package of Social Sciences (SPSS) 18.0 and Analysis of Moment

Structures (AMOS) 18.0. Hypotheses have been tested using the following system of equation

as,

Hypothesis 1-5 (Organizational Characteristics)

KSCFT = α1 + β11 OSC + β12 OSF + e1 (1)

KSCFT = α2 + β21 OLC + e2 (2)

KSCFT = α3 + β31 TCFT + e3 (3)

KSCFT = α4 + β41 RCFT + e4 (4)

KSCFT = α5 + β51 TMSCFT + e5 (5)

KSCFT = α6 + β61 OSC + β62 OSF + β63 OLC + β64 TCFT + β65 RCFT + β66 TMSCFT + e6

Hypothesis 6 (Job Characteristics)

KSCFT = α7 + β71 ACFT + e7 (6)

KSCFT = α8 + β81 FCFT + e8 (7)

KSCFT = α9 + β91 TICFT + e9 (8)

KSCFT = α10 + β101 TVCFT + e10 (9)

KSCFT = α11 + β111 TSCFT + e11 (10)

KSCFT = α12 + β121 ACFT + β122 FCFT + β123 TICFT + β124 TVCFT + β125 TSCFT + e12

Hypothesis 7:

KSCFT = α13 + β131 EICFT + e13 (11)

Hypothesis 8:

TPCFT = α14 + β141 KSCFT + e14 (12)

Hypothesis 9:

TPCFT = α15 + β151 (KSCFT x MTCFT) + e15 (13)

Hypothesis 10:

KSCFT = α16 + β161 (TPCFT x OCCFT) + β162 (TPCFT x JCCFT) + β163 (TPCFT x ICCFT) + e16

Where, KSCFT stands for the knowledge sharing amongst members of cross functional teams;

OSC stands for Organizational Centralization; OSF stands for Organizational Formalization;

OLC stands for Organizational Learning Culture; TCFT stands for Training in CFTs; RCFT

stands for Rewards in CFTs; TMSCFT stands for Top Management Support in CFTs; ACFT

stands for Autonomy in CFTs; FCFT stands for Feedback in CFTs; TICFT stands for Task

Identity in CFTs; TVCFT stands for Task Variance in CFTs; TSCFT stands for Task

Significance in CFTs; EICFT stands for Emotional Intelligence in CFTs; TPCFT stands for

Team Performance in CFTs and MTCFT stands for Mutual Trust. Wherein, equation (1) directs

towards Hypothesis 1a and 1b. Equation (12) directs towards investigating the role of knowledge

sharing on performance of cross functional teams. Equation (13) is meant to explore the

moderating role of mutual trust.

8.3 Results

8.3.1 Results for relationship of organization structure with knowledge sharing

Hypothesis 1: Formal structure (1a) formalization and (1b) centralization is negatively related to

the knowledge sharing among cross functional team members.

To test hypothesis 1, the relationship of both formalization and centralization with

knowledge sharing were tested through multiple regressions. Further, bivariate regression

analysis has been performed post hoc to test the effect of formalization and centralization

separately on knowledge sharing under the hypothesis of H 1(a) and H 1(b). Analysis shows beta

coefficient value for formalization as 0.020 (0.625). It means that the formalization process does

not have any significant effect on the knowledge sharing behavior. Analysis shows beta

coefficient value for centralization as -0.105 (0.001). It means centralization is negatively related

to the knowledge sharing behavior. The results have been shown as in the table 8.6. Before

analyzing, the data has been tested against multi-collinearity. The result shows VIF values well

under the suggested range. Overall the model fit adjusted R square value is 0.009.

Table 8.3 Regression results for structure predicting knowledge sharing

Standardized coefficients Collinearity Statistics

Beta t Sig. tolerance VIF

(constant)

79.236 0.001

Centralization -0.105 -2.542 0.001 1.000 1.000

Formalization 0.020 0.489 0.625 0.987 1.013

R= 0.105, Adjusted R square = 0.009

Dependent Variable: KS, N=582

8.3.2 Results for relationship of learning culture with knowledge sharing

Hypothesis 2: Organizational learning culture is positively related to knowledge sharing across

cross functional team members.

To test hypothesis 2, the relationship of learning culture with knowledge sharing

was tested through bi-variate regression analysis. The regression results for testing this

hypothesis are shown in table 8.4. Analysis shows beta coefficient value for organizational

learning cluture as 0.563 (0.001). It means organizational learning culture is positively related to

the knowledge sharing behavior. The results have been shown as in the table 8.7. Before

analyzing, the data has been tested against multi-collinearity. The result shows VIF values well

under the suggested range. Overall the model fit adjusted R square value is 0.316.

Table 8.4 Regression results for organization learning culture predicting knowledge sharing



(constant)

31.442 0.000

Organization learning culture 0.563 16.408 0.001 1.000 1.000



8.3.3 Results for relationship of formal and regular employee training with knowledge sharing

Hypothesis 3: Formal and regular employee training is positively related to knowledge sharing

across cross functional team members

To test hypothesis 3, the relationship of employee training with knowledge

sharing has been tested through bi-variate regression analysis. The regression results for testing

this hypothesis are shown in table 8.5. Analysis shows beta coefficient value for employee

training as 0.217 (0.001). It means employee training is positively related to the knowledge

sharing behavior. The results have been shown as in the table 8.8. Before analyzing, the data has

been tested against multi-collinearity. The result shows VIF values well under the suggested

range. Overall the model fit adjusted R square value is 0.045.

Table 8.5 Regression results for employee training predicting knowledge sharing



(constant) 29.493 0.001

Employee Training 0.217 5.343 0.001 1.000 1.000.



8.3.4 Results for relationship of better reward system with knowledge sharing

Hypothesis 4: Reward system is positively related to knowledge sharing across cross functional

team members

To test hypothesis 4, the relationship of reward system with knowledge sharing

has been tested through bi-variate regression analysis. The regression results for testing this

hypothesis are shown in table 8.6. Analysis shows beta coefficient value for reward system as

0.317 (0.001). It means reward system is positively related to the knowledge sharing behavior.

The results have been shown as in the table 8.6. Before analyzing, the data has been tested

against multi-collinearity. The result shows VIF values of 2.073 i.e., well under the suggested


Table 8.6 Regression results for reward system predicting knowledge sharing


Beta T Sig. tolerance VIF

(constant)

33.406 0.001 0.482 2.073

Reward System 0.317 8.049 0.001 0.482 2.073



8.3.5 Results for relationship of top management support with knowledge sharing

Hypothesis 5: Top management support is positively related to knowledge sharing across cross

functional team members

To test hypothesis 5, the relationship of top management support with knowledge


this hypothesis are shown in table 8.7. Analysis shows beta coefficient value for top management

support as 0.441 (0.001). It means top management support is positively related to the

knowledge sharing behavior. The results have been shown as in the table 8.10. Before analyzing,

the data has been tested against multi-collinearity. The result shows VIF values well under the

suggested range. Overall the model fit adjusted R square value is 0.193.

Table 8.7 Regression results for top management support predicting knowledge sharing



(constant)

23.752 0.001

Top management support 0.441 11.828 0.001 1.000 1.000



8.3.6 Results for relationship of Job characteristics with knowledge sharing

Hypothesis 6: Job characteristics are positively related to knowledge sharing across cross

functional team members

To test hypothesis 6, the relationship of job autonomy, feedback, job identity, job

variety and job significance with knowledge sharing has been tested using step wise linear

regression analysis. Post-hoc test has been administered to test the effect of job autonomy (6a),

feedback (6b), job identity (6c), job variety (6d) and job significance (6e) on knowledge sharing.

The regression results for testing this hypothesis are shown in table 8.8. Analysis shows beta

coefficient value for autonomy as 0.399 (0.001). It means that autonomy is positively related to

the knowledge sharing behavior. The beta coefficient value for feedback, job identity, job variety

and job significance has been found 0.075 (0.026), 0.097 (0.003), 0.324 (0.001) and -0.064

(0.047) respectively. Before analyzing, the data has been tested against multi-collinearity. The

result shows VIF values well under the suggested range. Overall the model fit adjusted R square

value is 0.414. The individual model fit values for the five sub-dimensions are as in the table 8.9

– 8.13.

Table 8.8 Regression results for job characteristics predicting knowledge sharing



(constant) 6.021 0.001

Autonomy 0.399 10.860 0.001 0.747 1.339

Feedback 0.075 2.233 0.026 0.899 1.113

Identity 0.097 2.956 0.003 0.943 1.060

Variety 0.324 9.033 0.001 0.784 1.276

Significance -0.064 -1.986 0.047 0.972 1.029



Table 8.9 Regression results for job autonomy predicting knowledge sharing

Model R R

Square

Adjusted

R

Square

Std.

Error of

the

Estimate

Change Statistics Durbin-

Watson R Square

Change

Sig. F

Change

dimension 1 .568a .323 .322 .650 .323 .000 1.324

a. Predictors: (Constant), Autonomy

b. Dependent Variable: Knowledge Sharing

Table 8.10 Regression results for feedback predicting knowledge sharing

Model R R

Square

Adjusted

R

Square

Std.

Error of

the

Estimate


Watson R Square

Change

Sig. F

Change

dimension 1 .195a .038 .036 .775 .038 .000 1.261

a. Predictors: (Constant), feedback


Table 8.11 Regression results for job identity predicting knowledge sharing

Model R R

Square

Adjusted

R

Std.

Error of


Watson R Square Sig. F

Square the

Estimate

Change Change

dimension 1 .053a .003 .001 .789 .003 .202 1.230

a. Predictors: (Constant), task identity


Table 8.12 Regression results for job variety predicting knowledge sharing

Model R R

Square

Adjusted

R

Square

Std.

Error of

the

Estimate


Watson R Square

Change

Sig. F

Change

dimension 1 .515a .265 .263 .677 .265 .000 1.365

a. Predictors: (Constant), job variety


Table 8.13 Regression results for job significance predicting knowledge sharing

Model R R

Square

Adjusted

R

Square

Std.

Error of

the

Estimate


Watson R Square

Change

Sig. F

Change

dimension 1 .099a .010 .008 .786 .010 .017 1.270

a. Predictors: (Constant), task significance


8.3.7 Results for relationship of emotional intelligence with knowledge sharing

Hypothesis 7: Emotional intelligence is positively related to knowledge sharing among cross

functional team members.

To test hypothesis 7, the relationship of emotional intelligence with knowledge


this hypothesis are shown in table 8.14. Analysis shows beta coefficient value for emotional

intelligence as 0.573 (0.001). It means emotional intelligence is positively related to the

knowledge sharing behavior. The results have been shown as in the table 8.14. Before analyzing,

the data has been tested against multi-collinearity. The result shows VIF values well under the

suggested range. Overall the model fit adjusted R square value is 0.327.

Table 8.14 Regression results for Emotional Intelligence predicting knowledge sharing



(constant)

26.259 0.001

Emotional Intelligence 0.573 16.843 0.001 1.000 1.000



8.3.8 Results for relationship of knowledge sharing with team performance

Hypothesis 8: Knowledge sharing is positively related to the team performance.

To test hypothesis 8, the relationship of knowledge sharing with team

performance has been tested through bi-variate regression analysis. The regression results for

testing this hypothesis are shown in table 8.15. Analysis shows beta coefficient value for

knowledge sharing as 0.464 (0.001). It means knowledge sharing is positively related to the team

performance. The results have been shown as in the table 8.15. Before analyzing, the data has

been tested against multi-collinearity. The result shows VIF values well under the suggested


Table 8.15 Regression results for knowledge sharing predicting team performance



(constant)

1.613 0.107

Knowledge Sharing 0.464 12.611 0.001 1.000 1.000


Dependent Variable: TP, N=582

The above result establishes knowledge sharing as one of the important predictor

for team performance in cross functional work environment.

8.3.9 Results for moderator effect of mutual trust on team performance

Hypothesis 9: Mutual trust moderates the effect of knowledge sharing on team performance.

To test hypothesis 9, the moderating effect of mutual trust on the relationship of

knowledge sharing with team performance has been tested through bi-variate regression analysis.

An interaction variable has been created and its effect has been tested on the team performance.

The regression results for testing this hypothesis are shown in table 8.16. Analysis shows beta

coefficient value for interaction variable (knowledge sharing * mutual trust) as 0.326 (0.001). It

means that mutual trust moderated the effect of knowledge sharing on the team performance.

The results have been shown as in the table 8.16. Before analyzing, the data has been tested

against multi-collinearity. The result shows VIF values well under the suggested range. Overall

the model fit adjusted R square value is 0.264.

Table 8.16 Regression results for the moderating effect of mutual trust on knowledge sharing

phenomenon



(constant) 3.096 0.002

Knowledge Sharing (KS) 0.230 4.510 0.001 0.486 2.060

Mutual Trust * KS 0.326 6.375 0.001 0.486 2.060


Dependent Variable: TP, N=582

Figure 8.1 Moderating role of mutual trust in knowledge sharing process

The above figure depicts that the role of mutual trust on the knowledge sharing

process. It shows that the team performance increases with the knowledge sharing process.

However, in the presence of mutual trust the process get accelerated and team performance

increases with accelerated growth pace.

8.3.10 Results for mediator effect of knowledge sharing on team performance

Hypothesis 10: Knowledge sharing mediates the effect of, (a) organizational characteristics, (b)

job characteristics and (c) individual characteristic on team performance

In order to test hypothesis 10a, 10b and 10c, the mediated regression analysis was

performed. In order to test the mediating role of knowledge sharing, individual dimensions of

organizational, job and individual characteristics were tested against the team performance.

Analysis showed beta coefficient values for organizational, job and individual characteristics as

0.265 (0.001), 0.043 (0.001) and 0.715 (0.001) respectively. It means that knowledge sharing

mediates the overall process. Based on the extant of mediation (in comparison to the non-

mediated effects), the mediation was termed as partial, full and partial for organizational, job and

individual characteristics respectively. Before analyzing, the data was tested against multi-

collinearity. The overall model fit values were turned out to be 0.622.

Table 8.17 Regression results for mediating role of knowledge sharing on predicting team

performance

Standardized coefficients Type of mediation

Beta T Sig.

OC->TP 0.195 5.172 0.036 Partial Mediation

OC->KS->TP 0.265 6.046 0.001

JC->TP -0.058 -1.351 0.177 Full Mediation

JC->KS->TP 0.043 1.388 0.019

IC->TP 0.428 11.324 0.045 Partial Mediation

IC->KS->TP 0.715 26.130 0.001


8.4 Exploring beyond the Hypothesis

Multiple regression analysis was conducted to test the effect of all the predictor

variables considered together. Thus, the regression equation had 11 predictors. The results for

multiple regression analysis are shown in table 8.18.

Table 8.18 Model Summary

Model R R

Squar

e

Adjus

ted R

Squar

e

Std.

Error

of the

Estima

te

Change Statistics

R

Square

Change

F

Chan

ge

df1 df

2

Sig.

F

Chan

ge

dimension

0

1 .764a .583 .574 .515 .583 66.30

6

12 5

6

9

.000

a. Predictors: (Constant), AVGEI, AVGC, AVGTI, AVGFORM, AVGET, AVGTS, AVGTMS,

AVGFB, AVGR, AVGTV, AVGLC, AVGA

Table 8.19 ANOVA Table

Model Sum of

Squares

df Mean

Square

F Sig.

1 Regression 210.984 12 17.582 66.306 .000a

Residual 150.878 569 .265

Total 361.862 581

a. Predictors: (Constant), AVGEI, AVGC, AVGTI, AVGFORM, AVGET, AVGTS,

AVGTMS, AVGFB, AVGR, AVGTV, AVGLC, AVGA

b. Dependent Variable: AVGKS

For the model tested with all the predictor variables, the overall model fit turns out to be 0.574. It

means that 57.4 percent of the variance in the knowledge sharing phenomenon may be explained by

these predictor variables.

Table 8.20 Coefficientsa

Model Unstandardized Standardize t Sig. Collinearity

Coefficients d

Coefficients

Statistics

B Std.

Error

Beta Tolera

nce

VIF

1 (Constant) .330 .293 1.126 .261

AVGC -.045 .014 -.092 -3.13 .002 .858 1.165

AVGFOR

M

.010 .014 .021 .756 .450 .914 1.093

AVGLC .206 .023 .301 9.043 .000 .662 1.511

AVGET .101 .021 .137 4.829 .000 .906 1.104

AVGR .023 .020 .035 1.122 .262 .751 1.331

AVGTMS .116 .025 .149 4.594 .000 .699 1.431

AVGA .098 .039 .132 2.505 .013 .264 3.784

AVGTI .085 .022 .113 3.880 .000 .871 1.149

AVGFB .026 .023 .034 1.131 .259 .800 1.250

AVGTV .273 .033 .268 8.395 .000 .720 1.390

AVGTS .001 .021 .002 .063 .950 .906 1.103

AVGEI .090 .039 .126 2.317 .021 .248 4.030

a. Dependent Variable: AVGKS

Above results were tested using multiple regression analysis. The regression

results for testing this hypothesis are shown in table 8.20. Analysis showed standardized beta

coefficient value along with standard error. The reported values were in support to the previously

reported bi-variate correlation values. The result showed VIF values well under the suggested

range. Overall the model fit adjusted R square value is 0.574. The summary result for the overall

model has been shown as in the table 8.24. Result supported the proposed hypothesis except

hypothesis 1b and 6e wherein formalization and task significance have been found non-

significant in the proposed model.

8.5 Summary Results

Table 8.21 Summary results for the proposed hypothesis

Hypo. Construct Beta Coefficients Test Results

1a Centralization -.092

Supported

1b Formalization .021

Not Supported

2 Learning Culture .301

Supported

3 Training .137

Supported

4 Rewards .035

Supported

5 Top Management Support .149

Supported

6a Autonomy .132

Supported

6b Task Identity .113

Supported

6c Feedback .034

Supported

6d Task Variety .268

Supported

6e Task Significance .002

Not Supported

7 Emotional Intelligence .126

Supported

8 KS -> TP 0.467 Supported

9 KS*MT -> TP 0.326 Supported

10 (OC, JC,IC) -> KS -> TP 0.265,0.043,0.715 Supported

R= 0.762, Adjusted R square = 0.575 (Overall Model)

8.6 Structure Equation Modeling

The main effects model was also explored using structural equation model. The

structural model with the relevant standardized regression weights is shown in Appendix E. SEM

takes a confirmatory approach to test the dependence relationships and accounts for

measurement errors in the process of testing the model (Byrne, 2001). Further, SEM uses both

observed variables (indicator variables or in other words the items for the construct of the

measuring instrument) as well as latent variables (the underlying concepts represented by the

indicator variables or items). Assessment of model fit in SEM is done through various fit indices.

There are three major groups of goodness of fit measures. These can be classified as absolute,

incremental and parsimonious fit indices. An absolute fit index assesses how well the theoretical

model fits the data. Some of the absolute fit indices are the Chi-square or the ratio of Chi-square

to degrees of freedom and the residual based fit indices such as Root Mean Square Error of

approximation (RMSEA). The Chi-square/df ratio of 2 or 3 is taken as indicating good or

acceptable fit (Bollen, 1989; Gallagher, Ting & palmer, 2008; Schermelleh-Engel et al., 2003),

although this measure is highly sensitive to large sample sizes (Bollen, 1989). The RMSEA

measure is actually a badness of fit measure with good fitting models having low residual based

fit indices with values of 0.08 or less desirable and the value less than 0.1 is under acceptable

limit (Hu & Bentler, 1999; Schermelleh-Engel et al., 2003). Incremental fit indices or

comparative fit indices assess how well the model fits relative to an alternative baseline model or

the null model (with no measurement error). The various incremental fir indices include the

Normed Fit Index (NFI), Comparative fit index (CFI) or the Tucker-Lewis Index (TLI), with

suggestions for a cut off of 0.90 or 0.95 for a good fitting model (Hu & Bentler, 1999).

The Parsimony Goodness of Fit Index (PGFI) is adjustment to the GFI and NFI index that take

into account the parsimony of the model with acceptable cut off limits above 0.5 (Muliak et al.,

1989). Results of the structural equation modeling for main effects are shown in table 8.22.

Table 8.22 SEM model fit summary

DF CMIN CMIN/DF NFI IFI TLI CFI RMSEA

12 17.901 1.492 0.957 0.985 0.863 0.982 0.085

The fit indices indicate excellent fit for the model, with a Chi-square to degrees of

freedom ratio of 1.492 which is very less than that of the suggested cut off limit of 3 (Bollen,

1989; Gallagher, Ting & Palmer, 2008; Schermelleh-Engel et al., 2003). As this statistic is very

sensitive to sample sizes (Bollen, 1989), other fit indices have to be examined. Root mean square

error approximation is also lesser than 0.1 (RMSEA = 0.085). The incremental fit indices, NFI,

CFI are above cut off level (Hair, Anderson, Tatham and Black, 2006). The value of TLI is close

to 0.9. Thus, based on chi-square statistics and incremental fit indices, it can be concluded that

the model has a good fit. Multiple R square for the model was 0.657. The path coefficients or

standardized regression weights for the predictor relationships are shown in table 8.26.

As can be seen SEM results indicate that only centralization, learning culture,

employee training and emotional intelligence appear significant (p<0.05) in predicting

knowledge sharing. SEM results also shown that knowledge sharing has an significant positive

impact on team performance with p<0.05 again. The direction of influence is consistent with

earlier analysis in that increase in centralization would result in less knowledge sharing with

negative beta value i.e. -0.274 while increase in learning culture, employee training and

emotional intelligence would result in more knowledge sharing among the members of cross

functional teams with positive beta values i.e. 0.347, 0.344 and 0.261 respectively.

Table 8.23 Path coefficients from SEM analysis

Hypothesized Relationship Estimate P-value

Knowledge sharing Formalization -0.216 0.133

Knowledge sharing Centralization -0.274 0.005

Knowledge sharing Learning Culture 0.347 0.001

Knowledge sharing Employee Training 0.344 0.001

Knowledge sharing Rewards 0.226 0.112

Knowledge sharing Top Management Support 0.134 0.134

Knowledge sharing Job Autonomy -0.007 0.939

Knowledge sharing Job Feedback -0.062 0.431

Knowledge sharing Job Identity 0.131 0.179

Knowledge sharing Job Variety 0.151 0.117

Knowledge sharing Job Significance 0.039 0.690

Knowledge sharing Emotional Intelligence 0.261 0.005

Team performance Knowledge sharing 0.476 0.001

Figure 8.2 The path coefficient values in the studied model

8.7 Controlling for demographic variables

A control variable is any factor that remains unchanged for the study and strongly

influences relationship between independent and dependent variables in the relationship. It is

held constant to test the relative impact of an independent variable. Additionally, separate

analysis was carried out for the data controlling for the demographic variables of age, gender and

job level. This was done by creating separate data sets for the varying levels of each of the

demographic variables. The results have been shown in the table 8.24. Result shows that there is

a significant difference in the results generated for male and female respondents. Similarly,

differences have been reported for respondents for different age group and job level.

Table 8.24 Testing for the control variables

Gen

der

Ag

e

Jo

b L

evel

Ma

le

Fem

ale

< 3

0

31

-40

41

-50

>5

0

Ju

nio

r

Mid

dle

Sen

ior

N

38

5

19

7

11

6

26

9

89

10

8

17

2

28

7

12

3

Ad

j. R

2

0.5

97

.50

9

.60

3

.58

6

.50

2

.47

7

.60

9

.56

7

.49

2

Co

effi

cien

ts (

Bet

a)

wit

h

sig

nif

ica

n

ce L

evel

C

-.1

27

(.0

01)

-.0

46(.

384

)

-.1

09(.

094

)

-.1

38(.

002

)

-.0

66(.

425

)

-.0

41(.

580

)

-.1

30(.

015

)

-.1

21(.

007

)

-.0

26(.

701

)

F

.02

0 (

.560

)

.03

3(.

531

)

.00

7(.

910

)

.02

2(.

601

)

.02

9(.

717

)

.04

5(.

542

)

.02

2(.

660

)

.02

3(.

583

)

.04

8(.

480

)

LC

.33

1(.

001

)

.26

0(.

001

)

.30

2(.

001

)

.33

7(.

001

)

.26

9(.

005

)

.24

9(.

005

)

.32

1(.

001

)

.32

4(.

001

)

.25

1(.

002

)

T

.12

8(.

001

)

.14

0(.

008

)

.00

1(.

989

)

.14

5(.

001

)

.16

2(.

053

)

.11

5(.

113

)

.10

7(.

046

)

.14

6(.

001

)

.11

9(.

077

)

R

.04

0(.

290

)

.03

4(.

549

)

.01

5(.

826

)

.04

0(.

385

)

.02

6(.

765

)

.03

4(.

676

)

.02

3(.

689

)

.05

1(.

258

)

.02

9(.

694

)

TM

S

.16

6(.

001

)

.10

0(.

102

)

.14

7(.

042

)

.18

0(.

001

)

.11

0(.

228

)

.06

1(.

485

)

.18

5(.

002

)

.14

7(.

002

)

.03

9(.

631

)

A

.13

5(.

014

)

.58

0(.

001

)

.26

8(.

001

)

.56

8(.

001

)

.58

8(.

001

)

.57

3(.

001

)

.15

8(.

012

)

.57

2(.

001

)

.58

3(.

001

)

TI

.12

8(.

001

)

.12

6(.

017

)

.09

3(.

152

)

.13

5(.

003

)

.12

1(.

147

)

.18

0(.

030

)

.12

2(.

022

)

.13

3(.

003

)

.18

5(.

013

)

F

.06

4(.

088

)

-.0

22(.

688

)

.05

4(.

417

)

.07

0(.

130

)

.02

6(.

765

)

-.1

14(.

167

)

.07

2(.

189

)

.05

7(.

210

)

-.1

16(.

122

)

TV

.25

8(.

001

)

.27

9(.

001

)

.18

2(.

014

)

.28

1(.

001

)

.32

7(.

001

)

.21

9(.

013

)

.22

0(.

001

)

.29

6(.

001

)

.22

9(.

005

)

TS

.03

9(.

265

)

-.0

57(.

275

)

.05

1(.

418

)

.04

1(.

338

)

-.0

20(.

804

)

-.0

42(.

603

)

.04

3(.

394

)

.03

4(.

415

)

-.0

68(.

351

)

EI

.11

0(.

054

)

.28

5(.

001

)

.21

6(.

007

)

.21

2(.

001

)

.26

8(.

007

)

.29

5(.

002

)

.12

6(.

055

)

.23

0(.

001

)

.30

9(.

001

)

Sig

nif

ican

ce a

t 0

.05

lev

el

chapter: 8 - inflibnetshodhganga.inflibnet.ac.in/bitstream/10603/9721/15/15...equation modeling...

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