Impact of KnowledgeManagement on Organizational Performance and Competitiveness PDF

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CHAPTER 3 IMPACT OF KNOWLEDGE MANAGEMENT ON ORGANIZATIONAL PERFORMANCE- AN ANALYSIS This chapter presents the results of reliability and validity analysis. In addition, it exhibits the results of various statistical methods namely descriptive statistics, correlation, multiple regression analysis, fa...

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CHAPTER 3 IMPACT OF KNOWLEDGE MANAGEMENT ON ORGANIZATIONAL PERFORMANCE- AN ANALYSIS This chapter presents the results of reliability and validity analysis. In addition, it exhibits the results of various statistical methods namely descriptive statistics, correlation, multiple regression analysis, factor analysis, t test and one way ANOVA. Finally, the results of structural equation modeling to PLS-PM are also presented. Reliability Reliability refers to the consistency of the measurement. That is the degree to which an instrument gives the same numeric value when the measurement is repeated under same conditions with same subjects. (Gaur and Gaur 2006).

The study has used the same common method of reliability

test namely ‘Cronbach alpha coefficient’ for assessing the reliability of the scale. Generally, Cronbach alpha level of 0.60 or above is considered to be acceptable for construct (Nunnally 1978). Reliability analysis of the constructs is presented in Table 3.1. Table 3.1: Reliability Construct IT centered KM IT Process for KM IT Support for KM Captured based KM Learning based KM Organizational performance

Number of items

Alpha value

03 02 05 10 10

0.7502 0.6540 0.7463 0.8682 0.8867

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All the constructs namely IT-process for KM ,IT support for KM, Capture based KM , Learning based KM and Organizational performance exhibit adequate reliability with internal consistency values of 0.750, 0.654, 0.746, 0.868 and 0.886 respectively which is greater than an alpha value of 0.60. Validity Validity refers to the accuracy of the research instrument. That is, the measuring instrument used in this study actually measures the property it is supposed to measure. It is believed that validity is more important than reliability, because if an instrument does not accurately measure the property, it is supposed to measure, there is no reason to use it even if it measures consistently (John Adam et al 2007) There are three types of validity which are commonly examined in research projects namely content validity, predictive validity and construct validity. (Gaur and gaur 2006). Content Validity It refers to the extent of the measurement reflects the specific intended domain of the constructs as defined conceptually. The measures included in the study have a strongly literature bases to support the content validity. Further, all the measures used in this study are well established measures on the prior research studies. Content validity of the survey instrument was established through initial pilot study involving senior professionals and academicians.

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Predictive Validity It refers to the ability of the measuring instruments to predict other measures of the same individuals with reference to a future criterion. The measures used in this study enable the researchers to predict the organizational performance. Construct Validity Construct validity tries to establish an agreement between the measuring instruments and theoretical concepts i.e. with the help of the theoretical back ground, the identified variables have a capability to predict what the researcher is supposed to measure. Construct validity assess how well the test of measure reflects the target construct (Cronbach and Meehl 1995) which can be ensured through convergent and discriminate validity. Convergent Validity Convergent validity of all the constructs was examined using the measure of Average Variance Extracted (AVE) that is the average variance shared between a construct and its items (Fornell & Larcker, 1981). A construct with an AVE of over 0.5 is expected to have adequate convergent validity. In some cases, values up to 0.40 of AVE and 0.60 of composite reliability are also considered to be acceptable if they are central to the model. (Chin 1995 and 1998, Chin et al 1999 & 2003).

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Table 3.2: Convergent Validity Variables AVE value

Composite Reliability

IT process for KM

0.678

0.863

IT support for KM

0.744

0.853

Capture based KM

0.501

0.832

Learning based KM

0.459

0.894

Organizational performance

0.489

0.905

IT centered KM

The AVE of each of the study constructs is presented in Table 3.2. The AVE of each construct was over 0.4 with the lowest AVE being 0.459 and highest at 0.744. Therefore, convergent validity of the study constructs was verified. Discriminant Validity Researchers using PLS establish the Discriminant validity of the constructs with the help of construct correlations and the measure of AVE. In order to exhibit Discriminant validity, average variance extracted should be greater than the variance shared between the construct and other constructs in the model (that is the squared correlation between two constructs). This is demonstrated in a correlation matrix which includes the correlations between the constructs in the off-diagonal elements and the square roots of the average variance extracted for each construct along the diagonal. As presented in Table 3.3 the square root of AVE for each construct is higher than its correlation with

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the remaining constructs. Therefore, the study constructs exhibit adequate Discriminant validity. Table 3.3: Discriminate Validity AVE

ITP

ITS

CBKM

ITP

0.678

ITS

0.744

0.058

CBKM

0.501

0.194

0.068

LBKM

0.459

0.203

0.063

0.325

OP

0.489

0.137

0.068

0.203

LBKM

0.442

AVE values are greater than the r squared values

Descriptive Statistics Table 3.4 Mean and Standard Deviation of IT Centered KM IT Centered KM

N

Mean

Believes IT Uses IT Internet Groupware Instant Messaging Overall

387 387 387 387 387

4.09 3.90 3.10 3.60 3.55 3.65

Std. Deviation 0.751 0.823 1.331 1.039 1.129 0.709

From the table 3.4, it is inferred that most of respondents felt that the items ‘believes IT’ (M= 4.09, SD=0.751) and ‘uses IT’ (M=3.90, SD=0.823) were most important factors in IT centered KM.

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Table 3.5 Mean and Standard Deviation of Capture based KM N

Mean

Emphasize codification

387

3.60

Std. Deviation 0.895

Emphasize capture

387

3.56

0.895

Store customer complaints

387

4.02

0.805

Believes K can be retained

387

3.81

0.912

Storage of K on intranet

387

4.03

0.847

3.80

0.613

Capture based KM

Overall

It can be seen from table 3.5, that most of respondents felt that the items ‘Customer complaints’ (M= 4.02, SD=0.805) and ‘storage of K on intranet’ (M=4.03, SD=0.847) were most important factors in capture based KM.

289

Table 3.6 Mean and Standard Deviation of Learning based KM N

Mean

Emphasizes learning

387

3.77

Std. Deviation 0.802

Solutions adopted

387

3.59

0.932

Ideas move from individual to organization

387

3.50

0.996

Policies for knowledge exchange

387

3.53

0.950

Recommendations adopted

387

3.68

0.905

Employee input to critical decisions

387

3.49

0.917

Employees knowledgeable

387

3.92

0.842

Employees share ideas and experience

387

3.56

1.017

Acquire knowledge in interactions

387

3.48

0.980

Shaping of technology and standards

387

3.71

0.969

3.62

0.631

Learning based KM

Over all

The table 3.6 brings to view that most of respondents felt that the items ‘Knowledgeable employees’ (M= 3.92, SD=0.842) and ‘emphasizes learning’ (M=3.77, SD=0.802) were found to be important factors in Learning based KM.

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Table 3.7 Mean and Standard Deviation of Organizational performance N

Mean

Adapt to unanticipated changes

387

3.48

Std. Deviation 1.031

Employees satisfied

387

3.58

0.944

Commercializes innovations

387

3.54

1.003

Employees motivated to perform

387

3.63

0.917

Potential to succeed in changes

387

3.93

0.867

Identify new opportunities

387

3.88

0.840

Meet customers' future needs

387

4.13

0.790

Capabilities for future performance

387

4.14

0.769

Capable and driven leadership

387

3.88

0.869

Loyal customers

387

3.98

0.772

3.82

0.614

Organizational performance

Over all

From the above table 3.7, it can interpreted that most of respondents felt that the items ‘Capabilities for future performance’ (M= 4.14, SD=0.769) and ‘Meet customer’s future needs’ (M=4.13, SD=0.790) were most important factors in Organizational Performance.

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3.1

Relationship between KM Strategies and Organizational Performance In order to find the extent to which knowledge management strategies

influence organizational performance, this study has employed bivariate correlations. The table 3.8 exhibits that relationship between IT centered KM and

Organizational

performance(r=0.457),

Capture

based

KM

and

Organizational performance (r=0.456) are moderate and statistically significant at 0.01 level, and Learning based KM and organizational performance are positively significant (r=0.736). Thus learning based KM is highly correlated to organizational performance. Table 3.8 Results of Correlation Analysis

Independent variables

Dependant variable

IT centered KM

0.457**

Sig. (1-tailed) 0.000

Significant

0.456**

0.000

Significant

0.736**

0.000

Significant

R value

Results

Organizational Captured based KM performance Learning based KM

** Correlation is significant at 0.01 levels (1-tailed)

Discussion of results MIS researchers argue that IT can provide performance benefits only if it is supported by organizational processes. KM is yet another IT solution for an IT-centered KM strategy. Therefore, the organizations that follow such a strategy implement IT systems as KM and make one or more organizational 292

members responsible for KM. These actions, however, do not provide any performance benefit for firms because IT-centered strategy merely focuses on making the infrastructure available and does not develop systems and processes to use the knowledge. The bivariate correlation showed that IT centered was weakly correlated to organizational performance and similar to the view. The finding is consistent with the study of Davenport (1997) and McDermott (1999). A firm that exploits its knowledge gains performance advantage. Although exploiting current knowledge is important for an organization's success and prosperity, exploitation alone will not provide a firm with long-term success because when knowledge is readily available, it cripples development of knowledge assets by hindering experimentation and exploration. The bivariate correlation showed the capture based KM also is weakly correlated to organizational performance. The finding is confirmed with the findings of March (1991) and Schulz (2001). The capacity created by learning-based KM strategy will play a vital role in yielding performance. Further, the learning processes that help in knowledge creation also aid in leveraging the knowledge, which is more important for firm performance than the knowledge itself. The bivariate correlation showed that learning based KM is highly significant and similar to the view. The finding is consistent with the study of Alavi & Leidner (2001) and Pisano, Bohmer & Edmondson (2001). 293

Multiple Regressions Analysis of Proposed Research Model Before performing PLS, it is important to verify not only the presence of multi collinearity problem but also the influence of independent variables on dependant variable. In order to prove the proposed research model, the researcher framed two sub models viz., sub model 1 and sub model 2. Therefore, an attempt has been made to validate the models through multiple regression analysis. The two sub models are as follows. IT Centered KM

Organizational Performance

Learning based KM

Figure 3.1 Sub Model 1- Complementary of IT Centered KM with Learning Based KM

Captured Based KM

Learning based KM

Organizational Performance

Figure 3.2 Sub Model 2- Complementary of Capture Based KM with Learning Based KM

Multiple Regression Analysis of Sub Model 1 294

In order to observe the influence of IT centered KM and Learning based KM on organizational performance, Multiple Regression Analysis is undertaken. The study has used organizational performance as dependant variable and IT centered KM and Learning based KM as independent variables. The result shown in table 3.9 revealed that IT centered KM significantly influence organizational performance. (Beta=0.095, t value = 2.754, p< 0.01) with 55 per cent observed variation on organizational performance. Furthermore, the result of the analysis indicate that learning based KM significantly influence organizational performance (Beta= 0.662, t value = 17.083, p...