Validity And Reliability Analysis Commerce Essay

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A Confirmatory Factor Analysis was conducted by using Amos 17.0 to assess the measurement model. To assess the overall fitness of model eight- model fitness measures have been used which are : Chi- Square (CMIN), Degree of Freedom (DF), probability level (P-Value) the ratio of chi- square to degree of freedom (CMIN/DF), goodness of fit index (GFI), normed fit index (NFI), and root mean square error of approximation (RMSEA). As getting recommendation from the previous literature such as (Fitzgerald, Drasgow, Hulin, Gelfand, & Magley, 1997; Robert, Probst, Martocchio, Drasgow, & Lawler, 2000), these formative indicators were created from original measuring items for each of their conceptualized latent constructs on the basis of items psychometric properties and substantive contents. This process increases the degree to which each measure share the difference. The result of model from the confirmatory factory analysis described an adequate fit of CMIN (Chi-Square) = 2624.028, Degree of freedom (DF) = 791, Probability level (p-value) = .000, CMIN/DF = 3.32. Various statisticians suggested that value of CMIN/DF provide the good fit for model fitness. It has been evaluated that the ratio of chi-square and degree of freedom should be neared to one for adequate fit model. For describing this aspect, different authors recommended their suggestions for goodness of model fit. For instance, Wheaton, Muthen, Alwin, and Summers, (1977) and Marsh and Hocevar, (1985) discussed for CMIN/DF should be < 5 and between 5 and 2 for adequate model fitness.

To test the model fitness other ratios have been calculated. For instance, Goodness of Fit Index (GFI) examines the fitness of model in relation to the another model (J. J. Hair, Anderson, Tatham, & Black, 2003) is observed as 0.698; Normed Fit Index (NFI) estimated the propotionate of model,that how model can increase its fitness in comparison with null model (J. J. Hair, et al., 2003) is 0.602; and for the overall fitness of model, Comparative Fit Index (CFI) measure used by Gerbing & Anderson, (1992) is 0.680. All of these rations have the larger value than the threshold values and indicating adequate model fitness. In addition,the value of Root Mean Square Error of Approximation (RMSEA) is 0.095, that estimations showed the model fitness in CFA. Browne and Cudeck, (1993) recommended the value of RMSEA for a good fitness model must be less than 1, that is used for data analysis. The above mentioned ratios of model estimated through CFA reflects that this model is better fit for the data and can be used for examining the factor loading.

4.1.2 Factor Loading

Convergent validity was estimated by testing the Factor loadings of each indicator in its related scale as mentioned in Figure 3 and Table 5. Hair et al, 's (1998) suggested, Item statements which have factor loading greater than 0.50 are considered to be highly significant and kept in the scale. The item statements which were negatively insignificant or indicating insignificant factor loading (< 0.50), removed from the scale. CFA shows that all items of organizational culture (Involvement and Adaptability), IT infrastructure, Managerial Support, Knowledge Creation, Knowledge Sharing, Knowledge Application and Corporate Performance are significantly loaded on their respective scales and have factor loading more than 0.50; Hence no item statement was removed from its respective measurement scale. The Factor Loading of each item has also been mentioned in Table 5.

Table : Factor Loading and Reliability Testing

Variable Name

Item

Factor Loading

Cronbach Alpha

Organizational Culture

Everyone in my organization believes that he or she can have a positive impact.

1.00

0.84

Business planning is an ongoing process and involves everyone in this process to some degree.

0.83

Cooperation across different departments of the organization is highly encouraged.

0.96

Work is organized so that each employee can see the relationship between his or her job and the goals of the organization.

1.10

Authority is delegated so that the employee can act on their own.

1.03

Employees' capabilities are viewed as an important source of competitive advantage.

1.11

We respond well to competitors and other changes in the business environment.

1.32

New and improved ways to do work are continually adopted.

1.61

All members are encouraged to contact directly with customers.

0.68

Customer comments and recommendations often lead to change.

1.21

Innovation and risk taking are encouraged and rewarded.

0.94

We view failure as an opportunity for learning and improvement.

0.73

Management Support

Senior managers provide funding and other resources for knowledge infrastructure.

1.000

0.84

Top management encourages employees to find new methods for performing a task.

1.082

Top management encourages employees to suggest ideas for new opportunities.

1.088

Reward systems are provided to induce knowledge management.

.856

Senior managers emphasize the importance of knowledge management for company's success.

.685

Senior managers emphasize the importance of organizational learning for company's success.

.668

Confirmatory Factor Analysis and Reliability Testing

Variable Name

Item

Factor Loading

Cronbach Alpha

Information Technology

Our company provides IT support for employee communication.

1.000

0.82

Our company provides IT support for searching and accessing necessary information.

1.414

Our company uses IT support for on the job learning.

1.293

Our company provides IT support for collaborative works regardless of time and place.

1.223

My organization provides technology that allow employees to search and retrieve stored knowledge.

1.197

Knowledge Management- Creation

Employees constantly generate new ideas.

1.000

0.70

Employees adapt their work to meet customer requirements.

.645

Teams regularly create innovative processes.

.873

Research and development is conducted with true spirit.

.944

Project feedback is used for subsequent project improvement.

1.108

Knowledge management- Sharing

My team members actively talk with each other and share knowledge.

1.000

0.71

My team members regularly share knowledge with other teams.

1.222

There is process to share knowledge between employees and management.

2.087

Knowledge sharing is encouraged by offering incentives.

2.365

Knowledge Management- Application

There is culture of applying knowledge learned from mistakes.

1.000

0.77

There is culture of applying knowledge learned from experiences.

1.222

Knowledge is utilized in new product development.

2.087

Knowledge is utilized in new product development.

2.365

Quickly knowledge application is done for critical competitive needs.

2.420

Corporate Performance

Your Organization quickly response to market demand and environmental changes.

1.000

0.84

Organization anticipates potential market opportunities for new product/services.

.882

The strategic position of my organization in the industry is very strong.

.838

My organization has competitive edge over its major competitors

.863

My organization has substantial market share relative to its major competitors.

.699

4.2 Descriptive Statistics

In this section, comparison of means on the basis of bank types, banks, number of employees in a branch and age of branches are being presented. One Way ANOVA has been applied to compute comparison of means of all studied variables on the basis of these demographics.

4.2.1 Comparison of Means on the Basis of Bank Types

As reflected in the Table 6, comparison of means of all studied variables is being made on the basis difference in bank types. It is reported that means of organizational culture, management support, IT infrastructure, Knowledge management practices and organizational performance are highest in foreign banks as compare to the means of these variables in local private and local nationalized banks. P-value reflects that there is significant difference between means of these variables as p-value is less than 0.05 in case of all comparisons. Higher and lower means are also reflected by the dark and light shaded boxes.

Table : Comparison of Means on the Basis of Bank Types

Bank Types

OC

MS

IT Infra.

KC

KS

KA

OP

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

Local Pvt

3.55

0.57

3.40

0.74

3.63

0.76

3.34

0.72

3.44

0.74

3.51

0.61

3.68

0.78

Local Nationalized

3.74

0.61

3.60

0.67

3.62

0.68

3.31

0.82

3.47

0.74

3.51

0.66

3.90

0.58

Foreign

4.17

0.47

4.24

0.69

3.78

0.59

3.78

0.50

4.02

0.48

4.05

0.63

3.90

0.56

F-test

7.45

7.51

0.23

1.95

3.29

4.05

1.76

P-value

0.00

0.00

0.90

0.14

0.04

0.01

0.17

Higher Mean

Lower Mean

4.2.2 Comparison of Means on the Basis of Bank

Table 7 shows the comparisons of means of all studied variables on the basis of bank difference. It is reported that culture of Citi bank is better than all other banks whereas culture of Summit bank is weak as compare to that of other banks. Similarly, share of

Table : Comparison of Means on the Basis of Bank

Bank

OC

MS

IT Infra.

KC

KS

KA

OP

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

MCB

3.39

0.56

3.38

0.78

3.64

0.72

3.48

0.82

3.36

0.84

3.48

0.74

3.59

0.77

UBL

3.43

0.44

3.05

0.63

3.49

0.57

3.26

0.73

3.29

0.68

3.60

0.57

3.77

0.60

Al-Falah

3.20

0.67

2.99

0.81

3.26

0.84

3.11

0.93

3.10

1.03

3.38

0.85

3.12

0.72

ABL

3.36

0.49

3.12

0.65

3.61

0.60

3.18

0.69

3.34

0.68

3.54

0.60

3.41

0.63

SCB

4.01

0.38

4.00

0.59

4.33

0.53

3.73

0.45

3.93

0.57

3.71

0.34

4.42

0.56

Al-Habib

3.90

0.34

3.76

0.90

3.92

0.76

3.37

0.95

3.94

0.61

3.72

0.82

4.15

0.81

HBL

3.56

0.61

3.44

0.73

3.64

0.84

3.46

0.64

3.44

0.66

3.40

0.45

3.80

0.84

Faisal

3.87

0.13

3.69

0.44

3.95

0.60

3.63

0.54

3.78

0.28

3.91

0.51

4.05

0.50

JS

4.00

0.59

3.79

0.46

3.95

0.53

3.75

0.68

4.31

0.63

4.20

0.63

4.00

0.83

NIB

3.58

0.58

3.61

0.50

3.91

0.52

3.58

0.48

3.69

0.60

3.40

0.52

3.20

0.57

Askari

3.47

0.41

3.08

0.88

3.60

0.31

3.13

0.79

3.50

0.63

3.57

0.46

3.40

0.40

Meezan

3.35

0.63

3.27

0.94

3.40

0.78

2.93

0.76

3.03

0.57

3.23

0.49

3.93

0.68

Silk

3.32

0.68

3.07

0.28

3.20

0.81

2.44

0.43

3.20

1.01

3.00

0.00

3.92

0.44

KASB

3.28

0.83

2.78

0.67

3.00

1.91

2.73

0.23

3.75

0.87

3.13

0.81

3.20

1.44

Soneri

3.26

0.21

2.81

0.61

3.33

0.39

3.40

0.83

3.21

0.95

3.47

0.43

3.77

0.61

Al-Baraka

3.31

0.61

3.54

0.79

3.80

0.85

3.55

0.87

2.69

0.52

3.70

0.35

3.75

1.06

HMPB

3.36

0.39

3.21

0.65

3.05

0.78

3.03

0.47

2.84

0.91

3.25

0.64

3.03

0.70

Bank Islami

4.11

0.43

3.89

0.25

3.13

1.03

3.47

0.31

3.67

0.14

3.87

0.31

3.73

0.31

My Bank

3.42

0.94

3.75

1.30

4.40

0.85

4.00

1.13

3.13

0.18

3.90

0.14

3.70

1.84

Samba

3.58

0.12

3.92

0.12

3.60

0.00

3.40

0.28

3.38

0.18

2.30

0.42

2.40

0.57

Summit

2.54

0.65

3.17

0.00

2.70

0.14

3.20

0.85

3.13

0.18

3.30

0.42

2.70

0.99

FWBL

4.20

0.58

4.19

0.65

3.76

0.97

3.38

0.67

3.61

0.71

3.47

0.53

3.93

0.79

NBP

3.71

0.18

4.25

0.12

4.10

0.14

4.00

0.28

4.13

0.18

3.60

0.00

4.30

0.71

BOP

3.74

0.75

3.56

0.80

3.69

0.86

3.22

0.98

3.35

0.87

3.57

0.85

4.00

0.57

Khyber

3.76

0.50

3.61

0.48

3.51

0.43

3.37

0.64

3.57

0.56

3.51

0.40

3.75

0.60

Dubai Islamic

3.71

0.06

3.08

0.82

3.40

0.85

3.00

0.85

3.25

1.06

2.90

0.42

3.80

0.28

HSBC

4.21

0.29

3.67

0.47

3.90

0.71

3.90

0.14

4.38

0.18

4.30

0.42

4.00

0.28

Barclay

3.79

0.88

3.92

0.59

4.00

0.57

3.60

1.13

4.25

1.06

4.10

0.99

4.00

1.41

Citi

4.39

0.35

4.89

0.10

3.40

0.69

4.00

0.20

4.00

0.25

4.20

0.53

3.67

0.61

Deutsche

4.00

0.43

3.94

0.77

3.80

0.69

3.53

0.58

3.75

0.43

3.73

0.92

3.93

0.12

F-test

2.77

2.72

1.44

2.00

1.79

1.57

2.29

P-value

0.00

0.00

0.72

0.23

0.00

0.04

0.00

Higher Mean

Lower Mean

management support for knowledge management practices is higher in Citi banks while it is lower in KASB banks as compare to the other banks. IT infrastructure is better in Standard Chartered Bank and poor in Summit bank relative to that of other banks. Knowledge is created on extensive basis in NBP, Citi bank and My bank whereas its ratio is very poor in Meezan bank. HSBC bank is on the top in case of knowledge sharing while Al-Baraka is poor as compare to the other banks. Citi bank is on the top in case of knowledge application whereas Samba is poor than all other banks. In case of performance, respondents of SCB reported that their performance is very good while Samba is on the lower position in case of organizational performance.

4.2.3 Comparison of Means on the Basis of Branch Age

Table 8 reflected the comparison of means of all studied variables on the basis branch groups constituted on the basis of their ages. Bank branches with less than 1 years of age are very good in organizational culture, management support for knowledge management and knowledge sharing whereas poor in knowledge creation and application. Likewise, bank branches with greater than 10 years of age are higher in case of knowledge creation and organizational performance while poor in organizational culture and knowledge sharing. Other differences have also been reflected in the table with dark and light shaded boxes.

Table : Comparison of Means on the Basis of Branch Age

Branch Age

OC

MS

IT Infra.

KC

KS

KA

OP

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

<1 year

3.76

0.74

3.72

0.65

3.60

0.83

3.23

0.71

3.59

0.76

3.37

0.66

3.73

0.86

1-5 years

3.60

0.60

3.47

0.82

3.56

0.89

3.37

0.75

3.50

0.79

3.61

0.61

3.65

0.82

5-10 years

3.67

0.5

3.39

0.75

3.68

0.65

3.35

0.72

3.48

0.70

3.46

0.61

3.66

0.75

>10 years

3.58

0.59

3.45

0.72

3.66

0.68

3.39

0.75

3.42

0.74

3.58

0.63

3.81

0.68

F-test

0.65

1.14

0.34

0.32

0.40

1.36

0.81

P-value

0.58

0.33

0.80

0.81

0.76

0.26

0.50

Higher Mean

Lower Mean

4.2.4 Comparison of Means on the Basis of No. of Employees in a Branch

Table 9 shows the comparisons of means on the basis of branches with different number of employees. As per statistics, it is reported that branches in which number of employees are greater than 20 are better in top management support for KM, IT infrastructure, Knowledge creation, sharing and application and organizational performance whereas organizational culture is better for KM in branch where number of employees are 10 to 15. Other differences have also been reflected in the table with dark and light shaded boxes.

Table : Comparison of Means on the Basis of No. of Employees in a Branch

No. of Employees

OC

MS

IT Infra.

KC

KS

KA

OP

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

M

SD

10-15

3.61

0.62

3.47

0.79

3.62

0.74

3.34

0.66

3.46

0.71

3.47

0.63

3.64

0.78

15-20

3.57

0.37

3.29

0.65

3.23

0.75

3.36

0.75

3.35

0.66

3.36

0.54

3.71

0.67

> 20

3.60

0.58

3.50

0.73

3.71

0.73

3.38

0.80

3.50

0.79

3.61

0.62

3.80

0.74

F-test

0.04

0.64

2.46

0.07

3.73

2.80

1.47

P-value

0.96

0.53

0.09

0.93

0.69

0.06

0.23

Higher Mean

Lower Mean

4.3 Mean, Standard Deviation and Correlation among Variables

Table 10 shows the means and standard deviation of research variables and coefficient of correlations among these variables. Spearman correlation (two tailed) has been used for this analysis through SPSS 17. It is reported that almost all variables are significantly correlated on 99% significance level with each other. However, the inter-relationship of management support with knowledge sharing and organizational performance are positive significant on 90% confidence level. It is further reported that means of all variables is around 3.50 like means scores of organizational culture, management support for knowledge management, IT infrastructure, knowledge creation, knowledge sharing, knowledge application and organizational strategic performance are 3.60, 3.47, 3.63, 3.36, 3.47, 3.54, and 3.72 respectively. It indicates that positive change in one variable will cause the significant increase in other variables. The plausible justification for positive correlation is that all of these variables are positive and favorable for the organization. If organizational culture, top management support, IT infrastructure, knowledge management practices will be good, the strategic performance of organization will be higher.

Table : Mean, Standard Deviation and Correlation Matrix

Sr. No.

Variable

M

SD

1

2

3

4

5

6

1

Organizational Culture

3.60

0.59

-

2

Management Support

3.47

0.75

0.64***

-

3

IT Infrastructure

3.63

0.74

0.44***

0.55***

-

4

Knowledge Creation

3.36

0.73

0.51***

0.60***

0.43***

-

5

Knowledge Sharing

3.47

0.74

0.54***

0.15*

0.45***

0.60***

-

6

Knowledge Application

3.54

0.63

0.48***

0.48***

0.35***

0.63***

0.58***

-

7

Organizational Performance

3.72

0.75

0.57***

0.12*

0.52***

0.49***

0.48***

0.50***

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

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

*. Correlation is significant at the 0.10 level (2-tailed).

4.4 Hypotheses Testing

The Structural Equation Modeling (SEM) model was performed using AMOS 17 and SPSS 17. SEM model has widely been used in different studies related to knowledge management (Gold et al, 2001; Yang, 2008) and found to be reliable in predicting the practices of knowledge management with organizational performance. Moreover, multiple linear regression was also applied to confirm the results of SEM model. The decisions of hypotheses are also shown in Table 12.

4.4.1 Structural Equation Model

After removing these paths, SEM was computed again and presented in Figure 4. The model B found to be good fit as it yielded a good fit of CMIN = 15.476, DF = 4, CMIN/DF = 3.869. Several statisticians recommended the value of CMIN/DF as measure of model fitness. For instance, Wheaton, et al.,(1977), viewed that the ratio must be five or less indicate a good model fitness. Marsh and Hocevar, (1985) suggested that the ratio must be between 5 and 2 whereas, Carmines and McIver, (1981) are of the view that it should be between 1 and 3 to indicated a good model fitness. Thus, it is concluded that the model B is reasonably fit and can be used for data analysis. Other model fitness ratios are presented in Table 11 and proved SEM B as a good fit for estimation. For example, CFI is 0.967, NFI is 0.974, GFI is 0.989, and RMSEA is 0.084. Values of NFI, CFI and GFI close to 1 show a good fit of the model (J. J. Hair, et al., 2003). Browne and Cudeck, (1993) reported that the RMSEA value must be less than 1 for a model to be used for data analysis therefore, RMSEA is found to be reasonably fit as it is 0.084 and can be used for data analysis.

Table : Fitness Ratio of Structural Equation Model

CMIN

DF

CMIN/DF

GFI

NFI

CFI

RMSEA

Model B

15.476

4

3.869

0.989

0.974

0.967

0.084

Figure : Structural Equation Model

Organizational Culture

Top management support

IT Infrastructure

1a:0.21***

1b:0.42***

1c:0.31***

Knowledge Creation

Knowledge Sharing

Knowledge Application

Organizational Performance

2a:0.11*

2b:0.08

2c:0.21***

3a:0.12**

3b:0.27***

3c:0.11*

4a:0.11*

4b:0.10*

4c:0.21***

6:0: -0.10

5: 0.43***

7: 0.28***

***. Significant at 0.01 level; **. Significant at 0.05 level; *. Significant at 0.10 level

Table : Decisions of Hypotheses

Hypothesis No.

Independent Variable

Mediating Variable

Dependent Variable

Regression Co-efficient

Decision

1a

Organizational Culture

--

Knowledge Creation

0.21***

Accepted

1b

Organizational Culture

--

Knowledge Sharing

0.42***

Accepted

1c

Organizational Culture

--

Knowledge Application

0.31***

Accepted

2a

Top Mgt. Support

--

Knowledge Creation

0.11*

Accepted

2b

Top Mgt. Support

--

Knowledge Sharing

0.08

Rejected

2c

Top Mgt. Support

--

Knowledge Application

0.21***

Accepted

3a

Technological Infrastructure

--

Knowledge Creation

0.12**

Accepted

3b

Technological Infrastructure

--

Knowledge Sharing

0.27***

Accepted

3c

Technological Infrastructure

--

Knowledge Application

0.10*

Accepted

4a

Organizational Culture

--

Organizational Performance

0.43***

Accepted

4b

Top Mgt. Support

--

Organizational Performance

-0.10

Rejected

4c

IT Infrastructure

--

Organizational Performance

0.28***

Accepted

4d

Knowledge Creation

--

Organizational Performance

0.11*

Accepted

4e

Knowledge Sharing

--

Organizational Performance

0.12*

Accepted

4f

Knowledge Application

--

Organizational Performance

0.21***

Accepted

5a

Organizational Culture

Knowledge Creation

Organizational Performance

0.21***

0.11**

0.11

0.11

Accepted

5b

Organizational Culture

Knowledge Sharing

Organizational Performance

0.42***

0.12*

Accepted

5c

Organizational Culture

Knowledge Application

Organizational Performance

0.31***

0.21***

Accepted

6a

Top management support

Knowledge Creation

Organizational Performance

0.11*

0.11*

Accepted

6b

Top management support

Knowledge Sharing

Organizational Performance

0.08

0.12*

Rejected

6c

Top management support

Knowledge Application

Organizational Performance

0.21***

0.21***

Accepted

7a

Technological Infrastructure

Knowledge Creation

Organizational Performance

0.12*

0.11*

Accepted

7b

Technological Infrastructure

Knowledge Sharing

Organizational Performance

0.27***

0.12*

0.10*

Accepted

7c

Technological Infrastructure

Knowledge Application

Organizational Performance

0.10*

0.21***

Accepted

***. Significant at the 0.01 level.

**. Significant at the 0.05 level.

*. Significant at the 0.10 level.

4.4.2 Multiple Regression Model of Knowledge Creation

In this model the dependent variable is knowledge creation and there are three independent variables organizational culture, top management support and IT infrastructure.

KC = β0 + β1 OC + β2 MS + β3 IT + €

KC = β0 + β1 0.21*** + β2 0.41*** + β3 0.13**

Table : Multiple Regression Model of Knowledge Creation

Model

Un-standardized Coefficients

T

Sig.

Collinearity Statistics

B

Std. Error

Tolerance

VIF

1

(Constant)

.754

.239

3.158

.002

Org. Culture

.210

.080

2.609

.010

.576

1.735

Mgt Support

.408

.067

6.093

.000

.503

1.987

Info Tech

.125

.058

2.155

.032

.686

1.457

Adjusted R2 = 0.39, F-value = 54.18, P-value of F-test = 0.00.

a. Dependent Variable: Knowledge Creation

The determination of coefficient (R2) of this model is 0.39 which means independent variables of this model cause 39% changes in the dependent variable knowledge creation. Furthermore, the F value of this model is 54.18 which is significant at (p< 0.001) which proved the overall fitness of present model. In order to check the multicolinearity among the independent variables two tests are used one is the Tolerance test and other one is the Variance Inflation Factor (VIF). In the existing model Tolerance Index for three independent variables are 0.58, 0.50 and 0.69 which demonstrate that there is no problem of multicolinearity among them as these values are not closer to 0. Another test is the VIF whose values are ranging from 1.46 to 1.99 which also provides additional evidence about the absence of multicolinearity among the independent variables.

Structural Equation Model (SEM) and Multiple Regression analysis has been used to compute the predictive role of culture on knowledge creation, sharing and application. It is examined that organizational culture became a most significant predictor for knowledge creation {β= .21, p-value < .01}. Thus hypothesis 1a was supported. It was also explained by the proposed model that 25% of the variance in knowledge management creation is due to the organizational cultural practices. This finding suggested that perceived level of culture is contributed to higher level of knowledge creation. It indicated that organizational cultural characteristics like adaptability and involvement more favorable integrate the activities of knowledge creation. In any organization, knowledge creation played the prominent role for the growth of the organization, as same in the case of banking sector. It is related with the employees that how they generate the new ides to meet the customer requirements. The organizations have the culture to make the teams in related areas to innovative the process. The employees are engaged in the activities to support the research and development culture. Creation of the knowledge in the banks is highly valued. Knowledge creation is used in the collection of information to solve the existing issues more effectively and efficiently. It promotes innovation and proficiently solves the problems from individual level to organizational level. The bankers perceives that if the organizational culture is supportive than it is easy for the employees to create the knowledge. The management support emphasis on the activities of research and development. Creation of knowledge is coming from inside the organization and outside sources.

Many researchers have shown organizational culture to be the main determinant of successful knowledge creation (Gold et al, Lee and Choi, 2003). The researchers focus of the direct role of culture on knowledge creation. Zheng et al, (2010), discussed the role culture with knowledge creation which is playing the mediating role in organization. Saeed et al, (2010), put on the light on the significant role of culture with knowledge creation in corporate sector of Pakistan. Von Krogh et al, (1998) describe knowledge as an individual and social practice that relies on the interaction among society members and human developments. The phenomenon is that knowledge is created by people who are working together. Knowledge creation is influenced by people interaction and cultural factors.

Structural Equation Model (SEM) and Multiple Regression analysis reported that top management support is found to be the most significant predictor of knowledge creation {β= 0.41, p-value < .01}. The SEM model reveals that top management support has 41% positive variance in knowledge creation. Thus the proposed hypothesis 1b is accepted. As top management are expected to be knowledgeable about the standing of KM activities and its integration at different levels of the organization. The role of the top management in the organizations is to clarify the vision, defining the goals of different department, development the strategies for organizational growth, implementation of those strategies and evaluate the progress. Senior managers encourage the employees to go for innovation in products, processes and dealing with customers. To accomplish these activities senior managers provide the resources and funding. Top management support employees for experimentation and encourage them to perform the task in new ways. Top management appreciates the ideas which shared by the employees and grant them new opportunities for growth. Reward systems are provided to induce knowledge management. Senior managers know the importance of knowledge management for organizational success. Learning is the important aspect of the knowledge management to grasp new market opportunities. Top management makes the decisions towards organizational interest. Senior management played a significant role for the creation of knowledge, generation of new ideas, and development of employee's attitude toward innovation.

The result of current study is consistent with the study of Lin (2007), that top management support facilitates the process of change and learning in knowledge creation. Employees are found to be involved in knowledge creation practices. Hassan & Lou (2009), reported the positive and significant relationship in top management and knowledge creation. This study conducted in Chinese context.

Informational technology is noted to be the significant predictor for organizational knowledge creation {β= .13, p-value =< .05}. Both SEM and regression models describe that information technology showed a positive relationship with knowledge creation. The result indicates the 13% of the variance in the knowledge creation. Hypothesis 3a was accepted. Technical infrastructure is considered as necessary component of any organization to implement the information system as it makes the employees technically capable to create, transmit and apply knowledge. Computing abilities, simple workable methods and networks, collectively named as "common operating environment" can be utilized to make the employees able for codification and exchange of knowledge (Gold et al., 2001). The IT support provides the new knowledge creation through collaboration in team members. When they expose their knowledge, opinion, beliefs and thought, they are getting the feedback that providing the valuable information regarding the systems of the organization. Previous findings prove that IT infrastructure has ability as a critical enabler for knowledge management processes. Alavi and Leinder, (2001) described that although IT has different perspectives in different studies but it is still playing a significant role in creation of knowledge. IT infrastructure provides the support for searching and assessing important information. It is the most influential part in organizational learning. IT integrates the employee activities and support the interaction and communication among employees. It also provides the support system for collaborative work regardless of time and place.

Western countries are more advanced in technologies than the developing countries so that the technical infrastructure is very important for influencing knowledge and value creation. The report of World Bank (1998) emphasized the requirement of established technical infrastructure in developing countries for knowledge management. The analysis of the studies, conducted in US and Taiwan by (Gold et al, 2001), Yang (2008) indicated that role of IT is limited in creation of knowledge. The reason is that if someone is not willing to share what he knows than it cannot be helpful for knowledge creation.

4.4.3 Multiple Regression Model of Knowledge Sharing

This model investigates the effects of organizational culture, top management support and IT infrastructure on knowledge sharing practices.

KS = β0 + β1 OC + β2 MS + β3 IT + €

KS = β0 + β1 0.42*** + β2 0.08 + β3 0.24***

Table : Multiple Regression Model of Knowledge Sharing

Model

Un-standardized Coefficients

T

Sig.

Collinearity Statistics

B

Std. Error

Tolerance

VIF

1

(Constant)

.563

.247

2.281

.023

Org. Culture

.421

.083

5.076

.000

.576

1.735

Mgt Support

.08

.069

1.988

.138

.503

1.987

Info Tech

.26

.060

4.265

.000

.686

1.457

Adjusted R2 = 0.36, F-value = 49.49, P-value of F-test = 0.00.

a. Dependent Variable: Knowledge Sharing

Results reveal that organizational culture and IT infrastructure strongly and significantly influence the knowledge sharing practices of banks as (p<0.001). Adjusted R2 of this model is 0.36; it means 36% variation in the dependant variable knowledge sharing is brought about by the independent variables. For good model fitness of any classic linear regression model, F-value provides good model fitness. According to the results of this model, co-efficient of F-Test is 49.49 which is significant at p<0.01.These evidences indicate that model is fit and we can use this for the analysis of data. Moreover, Tolerance Test is used to identify the linear relations among the independent variables. According to (Neter, Wasserman, Kutner, & Li, 1996), Tolerance test exhibits that amount of variance of any particular variable which is not accounted by other independent variable present in the same model. If the value of Tolerance Test is closer to 0 then there is no problem of multicolinearity among the independent variables of the model. In this model the value of Tolerance Test is ranging from 0.50 to 0.69 which is highly satisfactory and reveals the absence of multicolinearity. Further, an additional test VIF is also used to identify the problem of multicolinearity among the independent variables of linear regression model. The coefficients of VIF Test for three independent variables are 1.74, 1.99, and 1.46 which nullify the presence of multicolinearity problem between the independent variables of present model. .

Hypothesis 2a investigates the influence of organizational culture on knowledge sharing practices. As the results described that {β = 0.42, p- value < .01}. It showed the strong association of organizational culture and knowledge sharing. The hypothesis 2a was supported. The proposed model explained 42% of the variance in organizational knowledge sharing practices. This analysis explained that perceived organizational culture has a positive effect on knowledge sharing practices. Organizational culture is considered to be worked as to manage the element of renewal and change. There is a need of social settings to share the knowledge in the organization. The values of organizational culture lead towards effective knowledge sharing. An involvement culture helped in sharing knowledge in organizations. Sharing of knowledge include the exchange or transfer of knowledge. Knowledge sharing practices linked external knowledge sources with employees who internally worked in the organization. Supportive organizational culture becomes back bone of organization in knowledge sharing activities. Knowledge sharing helped the team members to enhance and exchange their existing knowledge. Organizations can become innovative and increase their learning with strong networks of knowledge sharing. Organizations encouraged employees to share their knowledge to made organizations more competitive. Management offered incentives to employee to encourage sharing of knowledge. Knowledge sharing helped in pass down of knowledge from firm to individuals and from individuals to individuals who worked on different levels in organization. It brings huge benefits to organizations as well as to employees. According to Lee and Choi (2003) organizational cultural practices influenced the knowledge management creation process.

Knowledge sharing practices make possible arrangements to facilitate the activities that necessary for the learning in the organizations. They are encouraged and implemented disperse knowledge for learning practices. To increase the interaction between employees with one another social interaction is playing an important role. By creating the social interaction in individuals companies can achieve best possible gain of knowledge sharing. Different researchers discussed the social exchange theory. Social exchange theory described that people give the favor to others by having the expectations of some future gain. The focused on future gain but they don't know the specific time (Kankanhalli, Tan, & Wei, 2005)). Knowledge sharing can contribute in the organizations through the improvement in human capital. Islam et al (2011) contributed their valuable feedback in the service sector of Bangladesh regarding culture and knowledge sharing. It concluded in the study that organizational culture with trust and communication among organizational employees impact positively on knowledge sharing.

Different researcher discussed the possible mechanism of by which organizational culture and knowledge management practices have the relationship with each other. Gold et al (2011) have discussed that organizational culture and knowledge sharing have the significant relationship with each other. Organizational culture has a direct and significant impact on knowledge sharing (Zheng, et al., 2010). Lodhi (2005), considered the importance of organizational policies and practices and communication channels in organizational culture in academic sector of Pakistan. Management focused that the attitude of sharing knowledge at individual level or at group level is highly profound because of the organizational culture and policies. O'Dell and Grayson (1998) studied that supportive culture is helpful and plying a significant role in sharing of knowledge. Culture is worked as an enabler and has significant and direct impact on knowledge sharing. The respondents of the research were the 191 knowledge practitioner in different industries of Hong Kong (Khalifa & Liu, 2003). Islam et al (2011) reported that in service sector of Banglades organizational culture with the variable of trust and communication in staff members has significant and positive impact on knowledge sharing.

Top management support is reported to have insignificant effects on knowledge sharing {β= .080, p-value >= .1}. Result of the variables both by SEM and regression models investigate that top management support explained 0.8% of the variance in organizational knowledge practices. The hypothesis 2b was not supported. It is the responsibility of the top management to develop the networks where employee can share their existing knowledge. They need sufficient communication channel for social interaction among them. Top management connections with employees are important for knowledge sharing.

The result of the current study is different from the previous studies and confirmed the notion of Luo and Hassan (2009). This study has discussed the social network theory based on Chinese organizations. That networking gaps among top management create the discrepancy in knowledge sharing. The study on the Korean firms revealed the fact that top management support has no significant effect on knowledge sharing Cho and Lee (2004). This is a surprising result for the management because it is concerned with the commitment they have with their employees. If employees of organization are not eager to share their knowledge, a knowledge management idea is fated.

Szulanski (1996), noted that sharing of knowledge is practically proved to be a difficult challenge. Employees don't want to share the knowledge because they think that it is their unique competitive advantage. If the top management takes the sharing of knowledge seriously, it is easy for the employees to act accordingly.

Information technology proved to be the strong predictor for the knowledge sharing {β= .24, p-value =< .01}. Both SEM and regression models describe that information technology showed a positive relationship with knowledge sharing. The result indicates the 24% of the variance in the knowledge creation. Hypothesis 3a was accepted. When knowledge is shared with help of IT, its value increased. IT is the most important antecedents for the effective sharing of knowledge. It provides the proper mechanism for the sharing and communication of ideas within the organization. IT provides the strong network for connectivity among individuals who are becoming the part of knowledge sharing. Sharing of knowledge with information technology network support the transfer of knowledge from one location of the other where it needed. IT integrates the activities between the individuals in a department and who worked in the other departments. It enhances the capability of sharing and interaction between individuals. It supports to increase the memory of the organization which ultimately promotes the organizational learning.

Different studies proved that IT has a prominent and greater impact on knowledge sharing practices. The result of the studies conducted in US by Gold et al, (2001), consistent with the result of proposed hypothesis. Yang (2008), provided the same results in analysis of Taiwanese firms that knowledge sharing is influenced by proper IT infrastructure. Walsham, (2001), described in his study that IT improves the capability of knowledge management and knowledge management activities in organizations. Hung et al, (2005) studied this concept in pharmaceutical sector of Taiwan, the impact of IT capability on knowledge sharing. Al- Busaidi and Olfman (2005) conducted the study in different service and manufacturing sectors of Oman. The result of the study showed that technological infrastructure has significant impact on km practices. Tanriverdi (2005) conducted the study to investigate the impact IT on knowledge management on 1000 Fortune firms in US. The author discussed that IT capability improve the KM capability of firms. IT resources have directly influence the KM processes (Zack, McKeen, & Singh, 2009; Zaim, Tatoglu, & Zaim, 2007). Alavi and Leinder (2001) suggested in his research that proper investment in information technology helped firm to achieve competitive advantage and sharing and acquisition of knowledge. Rhodes et al., (2008) conducted the study on high tech organizations in Taiwan. The findings of the study are consistent with the results of current study as IT infrastructure has significant on knowledge sharing. IT can support knowledge application by helping it to make the part of the organization routine. It can have a directive and positive influence on knowledge application. IT improved the integration and application knowledge by capturing, assessing and upgrading it. The IT infrastructure creates the environment which helpful for the companies to reinforce the knowledge for organizational processes.

4.4.4 Multiple Regression Model of Knowledge Application

In this model effect of organizational culture, top management and IT infrastructure has been checked on the knowledge application practices of banking sector.

KA = β0 + β1 OC + β2 MS + β3 IT + €

KA = β0 + β1 0.31***+ β2 0.21*** + β3 0.11*

Table : Multiple Regression Model of Knowledge Application

Model

Un-standardized Coefficients

T

Sig.

Collinearity Statistics

B

Std. Error

Tolerance

VIF

1

(Constant)

1.443

.221

6.536

.000

Org. Culture

.309

.074

4.160

.000

.576

1.735

Mgt Support

.207

.062

3.342

.001

.503

1.987

Info Tech

.111

.054

2.350

.072

.686

1.457

Adjusted R2 = 0.28, F-value = 37.76, P-value of F-test = 0.00.

a. Dependent Variable: Knowledge Application

There are three basic things which are used to check the overall model fitness of any classic linear regression model. These are adjusted R2, F statistics and lastly Durbin Watson statistics. As per the results of this model, adjusted R2 is 0.28, F-value is 37.76 significant at (p<0.01) level. F-value is significant and described that this model is fit for further analysis of data. Moreover, there is no problem of multicolinearity among the independent variables of this model as the coefficients of Tolerance Test for any independent variable is not closer to one. The Tolerance coefficient of Organizational Culture is 0.58, for Top Management is 0.50 and for IT infrastructure is 0.69. Similarly, Variance Inflation Factor test (VIF) values are also highly satisfactory for each independent variable which further confirms that there is no multicolinearity between independent variables. VIF for Organizational Culture is 1.74, for Top Management is 1.99 and for IT infrastructure is1.46.

The hypothesis 3a measured the influence of organizational culture on knowledge application. It is found that organizational culture is the important predictor on knowledge application. Organizational culture has significant and positive impact on knowledge application practices {β= .31, p-value < .01}. Thus, proposed hypothesis 3a is accepted. It is noted that organizational culture has 30 % positive variance in knowledge application. It means that organizational culture contributed in the application of knowledge for the development of the organization and developed the strategies for gaining the competitive advantage. Involvement of the top management encourages the employees for application of knowledge. Empowerment in the organizations creates the involvement with organization and employees. Supportive culture in organizations actively encourages the cooperation and team orientation in employees. Authority was delegated to the employees so that they can act to perform the responsibilities and can improve their skills. Knowledge application is that how employees applied the knowledge which they learned from the capability development. Management developed the culture of application of knowledge which they have learned from experience. Employees utilized the knowledge for new product development. Knowledge application is done for critical competitive needs.

Different researchers examined the relationship of organizational culture and knowledge application in different industries of various countries. Mixed findings are reported with respect to knowledge management. Gold et al (2001) noted that a positive and significant relationship has exist between knowledge application and organizational culture in United States context. The researchers focused on the capabilities development and knowledge management process of the organizations. They conducted the analysis on the basis of social capital theory for creating intellectual capital. Through the development of key capabilities knowledge activities can be enhanced. The role of knowledge management process was also proved by the integration of knowledge. Zheng et al (2010) reported the mediating role of knowledge management with culture and organizational effectiveness. The study focused on the significant role of knowledge application with organizational culture in service, manufacturing and agricultural sector of China. In contrast of these studies different researches reported the knowledge management practices as the combined construct with culture who worked as an enabler.

Hypothesis 3b investigates the path from top management support to organizational knowledge application practices. The analysis suggested that management support {β= .21, p-value < .01} has a strong, positive association with organizational knowledge application. Hypothesis 3c was supported. The proposed model explained 21% positive variance in knowledge application practices. Both model SEM and regression provided the same result. The senior management clearly supports the application of knowledge management. Top management provides the social support for the implementation of knowledge. Lin (2007) examined this relationship in large Taiwan firms. The results of this study support the hypothesis of current study. The top management should focused on proper formulation and implementation of knowledge management strategies

Information technology proved to be the strong predictor for the knowledge application {β= .110, p-value =< .10}. Both SEM and regression models describe that information technology showed a positive relationship with knowledge application. The result indicates the 10% of the variance in the knowledge application. Hypothesis 3a was supported. Knowledge application is the actual use of knowledge with the help of different technological resources for the creation of value and competitive advantage in the organizations. Effective application of knowledge in organization enhanced the efficiency and decreases the cost in operations. Identification of right knowledge for the use of different activities is very necessary. Effective utilization of knowledge resources increases the collaboration within the employees working indifferent department of organizations. Information technology impact the knowledge application for creating the road map and it provide the direction to the organization to excel in the business world and proper implementation of strategies.

Previous studies indicated that IT playing the significant role if implementation and utilization of knowledge. Yang (2008), discussed that IT effect on knowledge application. The results of the study are consistent with the results of current study and these results are also consistent with the study of Gold et al, (2001). This study conducted in US on manufacturing and service sector. Hung et al, (2010), reported that IT impact on knowledge application in pharmaceutical sector. Hussain et al., (2004), Al-Mabrouk (2006), Stenmark (2002) and Wong and Aspinwall (2006) proved that IT impact on Knowledge application in different sectors. The results of these studies showed the same as to the current study. Alavi and Leinder (2001) suggested that proper investment in IT infrastructure and optimized use of organizational resources impact in greater knowledge application. It is true that appropriate support of IT has positive impact on Knowledge sharing, Creation securing and dissemination. These findings verified that these results are consistent with outcome of studies of Davenport et al, (1998) and O'Dell and Garyson, (1998).

Multiple Regression Models of Organizational Performance

In this model effect of three dimensions of Organizational Culture, Top Management Support, and IT Infrastructure Knowledge Management (Knowledge Creation, Sharing and Application) on organizational performance were checked.

OP = β0 + β1 OC + β2 MS + β3 IT + β4 KC + β5 KS + β6 KA + €

OP = β0 + β1 0.43*** + β2 -.10 + β3 0.28*** + β4 0.11* + β5 0.12* + β6 0.21***

Table : Multiple Regression Model of Organizational Performance

Model

Un-standardized Coefficients

T

Sig.

Collinearity Statistics

B

Std. Error

Tolerance

VIF

1

(Constant)

.081

.249

.327

.744

Org. Culture

.426

.082

5.196

.000

.512

1.953

Mgt Support

-.103

.069

-1.486

.139

..512

2.288

Info Tech

.275

.058

4.738

.000

.640

1.564

Knowledge Creation

.114

.069

1.662

.098

.501

2.135

Knowledge Sharing

.121

.065

1.459

.068

.503

1.988

Knowledge Application

.210

.074

2.854

.005

.558

1.791

Adjusted R2 = 0.46, F-value = 37.87, P-value of F-test = 0.00.

a. Dependent Variable: Organizational Performance

In order to identify the overall fitness of linear regression model, values of adjusted R2, F-statistics and Durbin Watson statistics plays an important role. Overall results of these values are highly satisfactory and indicate that model is overall fit. Value of adjusted R2 is 0.46, it means about 46% change in organization performance is due to the three dimensions of knowledge management practices of banks. F- Value is 37.87 which is significant at (p< 0.01) level. Furthermore, in order to check the multicolinearity among the independent variables two tests are used one is the Tolerance test and other one is the Variance Inflation Factor (VIF). Tolerance Test is used to identify the linear relations among the independent variables. According to (Neter, et al., 1996), Tolerance test exhibits that amount of variance of any particular variable which is not accounted by other independent variable present in the same model. If the value of Tolerance Test is closer to 0 then there is no problem of multicolinearity among the independent variables of the model. The Tolerance coefficient of all independent variable ranged from 0.50 to 0.64. Similarly, Variance Inflation Factor test (VIF) values are also highly satisfactory for each independent variable which further confirms that there is no multicolineaity between independent variables.

The SEM and regression analysis proves that organizational culture is the most significant predictor of organizational performance {β= .43, p-value < .01}. The findings of the study are affirmed the findings of similar studies (Denison, 2000; Yilmaz & Ergun, 2008). Thus, hypothesis 4a is supported. As far as the effect of top management support on organization performance is concerned, it is reported that top management support does not contribute significantly in enhancement of organizational performance {β= .10, p-value > 1} that lead to the rejection of hypothesis 4b. Moreover, it is reported that IT infrastructure has positive and significant impact on organizational performance {β= .28, p-value < .01}. It means that if an organization focuses on its IT infrastructure, provides necessary technology to employees, the performance of the organization improved significantly. Hence, hypothesis 4c is supported. Furthermore, it is noted that the findings of the study aligned with the findings of previous studies (Mithas, Ramasubbu, & Sambamurthy, 2011)

The knowledge management practices have a significant effect on organizational performance. The study investigates that organizational knowledge creation has influence the organizational performance {β= .11, p-value < = .10}. As the result supported the hypothesis 4d, and showed the strong and positive association between knowledge creation and organizational success. The proposed model explained 11% positive variance in organizational knowledge creation practices. These findings verified that knowledge creation practices and corporate performance has strong association with each other. Knowledge creation mechanism in organizations encourages the employees to generate new ideas. Adaptation of new work practices for customer satisfaction increase the customer ratio. When employees link km practice to corporate performance, it formulates a strong situation for espousing and producing new ideas for implementing and demonstrating knowledge management and its advantages (P. Carrillo & Chinowsky, 2006). The firms that have potential and teamwork to create innovative processes grasped the potential market opportunities in their true spirit. Succes

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