knowledge management system and learning organization

7/31/2019 Knowledge Management System and Learning Organization

http://slidepdf.com/reader/full/knowledge-management-system-and-learning-organization 1/19

The IUP Journal of Knowledge Management, Vol. IX, No. 2, 201126

and Learning Organization: An Empirical

Study in an Engineering Organization

© 2011 IUP. All Rights Reserved.

* Associate Professor, Jamal Institute of Management, Jamal Mohamed College, Tiruchirappalli, Tamil Nadu,India. E-mail: [email protected]

** Assistant Professor, Jamal Institute of Management, Jamal Mohamed College, Tiruchirappalli, Tamil Nadu,India. E-mail: [email protected]

U Syed Aktharsha* and H Anisa**

Introduction

Knowledge Management (KM) comprises a range of strategies and practices that deal withhow knowledge is acquired, transferred, and shared with all the members of the organization.

Such strategies and practices seek to achieve the organization’s objectives.

Knowledge Management System (KMS) refers to a comprehensive information and

communication technology platform used for managing knowledge in organizations for

supporting creation, capture, storage and dissemination of information.

Review of Literature

Sense (2008) examined how people can conceive learning and KM processes within project

teams and provided conceptual guidance on the most effective way to managerially approach

these important and often neglected project issues. The conceptual paper by Andrew draws on

and dissects a very broad and relevant literature on learning and KM. In this paper, he puts

The purpose of this paper is to analyze the impact of Knowledge Management System (KMS) on

learning organization. This paper also attempts to investigate the relationship between demographic

profile and KMS and the relationship between demographic profile and learning organizations. A private

engineering concern in a district has been chosen for conducting this study and a sample of 65 managers

and engineers were chosen from the population of 180 managers and engineers together.

A survey-based instrument is used to gather the responses from managers and engineers. Some variations

were observed on KMS due to the factors such as innovation, different market entry and market share.

Some variations were observed on the properties of learning organization due to factors such as knowledgeapplication, KM process and shared vision. The study is limited to one particular organization. The

results may not be applicable to other business organizations. KMS is an IT based system developed for

managing knowledge in organizations which supports the creation, capture, storage and dissemination

of information. Nowadays, many organizations especially knowledge-based organizations have started

realizing the importance and benefits of KMS and also the contribution of KMS in learning organizations

is well understood by the organizations.

Keywords: Knowledge, Knowledge Management System (KMS), Learning organization

Knowledge Management System



Knowledge Management System and Learning Organization:An Empirical Study in an Engineering Organization

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forward a conceptually grounded argument for a greater practical emphasis to be placed on the

social systems in learning and KM processes in projects. Here, he also provided a foundation for

project practitioners to critically reflect on their current learning and KM attitudes and practices,

and also encouraged their attention towards the management of their social system projects

(Merx-Chermin and Nijhof, 2005). Through this study, we can gain a better understanding of

the factors that influence the innovative power of organizations. They examined the concept

of innovation and innovative power by analyzing the relationship between the construct of the

learning organization, knowledge organization and innovative organization. The innovative

process model drawn out by Mireille consists of three processes—knowledge creation, innovation

and learning to learn. He conducted an exploratory study on Oce Technologies, The Netherlands.

Armstrong and Foley (2003) outline the results of current research carried out at Victoria

University, Australia, into what is a learning organization, how organizations learn, and how to

develop a learning organization. The objective of the study by Anona was to identify the

components that underpin the development and operation of a learning organization, i.e., the

foundations, or organizational learning mechanisms that support the development and

maintenance of a learning organization. This research provided an instrument for systematically

measuring and monitoring progress towards achieving a learning organization.

Loermans (2002) had briefly looked at the overlaps and synergies between various knowledge

concepts. He argued that the discipline of KM at a corporate level and the phenomenon of the

learning organization are inextricably linked and should always be analyzed and discussed in

concert. Rowley (2000) had established a clear link between learning and knowledge, and

proposes a simple model, which made this relationship explicit. In the research paper he argued

that indiscriminate knowledge creation will not lead to organizational learning, and that

knowledge is not something that can be viewed as a neutral tool in the learning process.

Pemberton and Stonehouse (2000) revealed that competitive success is governed by an

organization’s ability to develop new knowledge assets that create core competencies. The author

stressed that organizational learning is an integral feature of any learning organization that exploits

its knowledge resources to generate superior performance. In his research paper he also explored

the ideas and links between organizational learning and KM, making reference to a number of

sectors and companies, and specifically the airline industry, arguing that the culture, structure and

infrastructure of an organization are essential elements that facilitate and nurture learning.

Simonin (1997) revealed that experience alone is not sufficient for the achievement of

greater results from collaboration. He had also emphasized the need and importance of

internalizing experience in the view of developing collaborative know how for the purpose of

contribution towards future collaborative benefits.

Finnegan and Willcocks (2006) attempted to apply a processual analysis to the

implementation of a Customer Relationship Management (CRM) system from a knowledgemanagement perspective to a contemporary (1999-2004) situation within a UK city council.

This paper seeks to place a specific focus on the neglected areas in previous CRM studies such

as sub-cultures, psychological contracts, how tacit knowledge can be surfaced and transferred,

and with what will be the effect on implementation. The major findings of the study showed




that a rich picture emerges of sub-cultural silos of knowledge linked with psychological

contracts and power-based relationships influencing and inhibiting adoption and acceptance

of the CRM system.

Klein and Heuser (2008) presented an expanded socialization content typology. Besides,two other components are added to this typology to reflect the fact that—(a) each of those

content dimensions needs to be learned relative to different organizational levels (e.g., job,

work group, unit, organization) and (b) socialization occurs over several months and there

are temporal considerations relating to the different socialization content dimensions.

The conceptual measurement and research needs suggested by these extensions to the

socialization literature, are identified.

Dechant et al. (2000) presented a model of team learning. Two dissertation studies that

affirm the model and provide additional insight into the nature of team learning in corporate

settings were mainly highlighted by the authors.

Fink and Ploder (2009) has proposed a theoretical framework as a layer concept to describe

the special situation of knowledge management in SMEs. Based on this framework, empiricalstudies were conducted in German-speaking countries to find out the relevant methods and

tools supporting knowledge management in SMEs. The major findings revealed that there

are spime methods of knowledge management that support the four key knowledge processes

in SMEs, i.e. knowledge identification, knowledge acquisition, knowledge distribution and

knowledge preservation. The results are explained in the developed ‘Technical Social Social

Technical Model’ (TSST Model), which is a balanced system for technical and social knowledge

applications.

Crossan and Guatto (1996) analyzed the results of a keyword search of three databases

using the terms ‘organizational learning’ and ‘learning organization’ to uncover patterns

relating to—(a) amount of publishing activity by year; (b) influential authors; (c) journals

publishing organizational learning research; and (d) type of research published.Kyobe (2010) presented a multi-theoretical model that can be used to identify knowledge

transfer impediments contributing to the crises in the IS discipline in a university. The

literature on crisis management and crises in the IS discipline revealed that many crises are

caused due to lack of appropriate knowledge development and sharing in research and

education. Knowledge management research was reviewed and synthesized to create a

comprehensive framework for identifying impediments to knowledge transfer in a university

setting.

Belsis et al. (2005) did a survey with five organizations (public and private) and five

security experts and consultants. A model to illustrate the structure of IS security knowledge

in an organization is then proposed. The major findings of the study revealed that—successful

security management largely depends on the involvement of users and other stakeholders insecurity analysis, design, and implementation as well as in actively defending the Information

Systems (IS). However, there is a lack of lack required knowledge of IS security issues that

would allow them to play an important role in IS security management among most

stakeholders.




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Yahya and Goh (2002) examined the linkages between four areas of human resource

management (training, decision-making, performance appraisal, and compensation and

reward) with five areas of knowledge management (knowledge acquisition, knowledge

documentation, knowledge transfer, knowledge creation, knowledge application). The analysis

suggest that a knowledge organization requires a different management approach than the

non-knowledge organization. Hence, the role of human resource management is also unique.

In terms of employee development, there must be a focus on achieving quality, creativity,

leadership, and problem solving skill. Compensation and reward system should be designed

on promoting group performance, knowledge sharing, and innovative thinking. They have

also mentioned that the performance appraisal must be the base of evaluation of employee's

knowledge management practices and an input for directing knowledge management efforts.

Appelbaum and Goransson (1997) stated that organizational learning is currently a

fashionable concept, and this is due to an attempt by many large organizations to develop

structures and systems that are more adaptable and responsive to change. While reviewing

the framework for organizational learning, they also examined learning organization with

regard to twofold nature of organizational learning. Literatures were developed and presentedby considering the learning organization from generative or transformational perspective

and incremental or adaptive perspective. Conclusions were drawn by integrating the two

perspectives on the learning organization into the reviewed framework for congruence.

The aim of the research conducted by Buckler (1998) was to synthesize a learning process

model from relevant learning theory, and to derive a practical model, which can be used by

organizations to facilitate individual, team and organizational learning, resulting in

continuous improvement and innovation in business processes.

Research Methodology

The present study is undertaken to find out the following:

• To investigate the relationship between demographic profile and KMS.

• To investigate the relationship between demographic profile and learning organization.

• To identify the variables and their grouping into factors that influence the KMS and

learning organization.

Sampling DesignA private engineering concern was chosen for conducting this study. The study has takeninto account the various aspects of KMS and its contribution to learning organization. Thedecision to choose this particular private company was taken because the senior administratorsof the concern permitted to conduct this study on KM and learning organization. A sampleof 65 managers and engineers has been chosen from the population of 180 managers andengineers together using stratified random sampling method. The tabulated description of

demographic details of the sample is presented in Table 1.

Data Collection

The data was collected from the managers and engineers of the selected engineering enterprise

through a questionnaire which had 3 major parts, namely;




Table 1: Frequency Distribution of Sample Demographics

S. No. Variables Number Frequency (%)

1. Age

Below 30 23 35

30-40 18 28

41-50 15 23

Above 50 9 14

2. Educational Qualification

Diploma 35 54

UG 25 38

PG 5 8

3. Designation

Engineer 45 69

Manager 20 31

4. Department

Engineering 21 33

Production 21 32

Quality Control 23 35

5. Experience

Below 10 29 45

10-20 23 35

20-30 6 9

Above 30 7 11

6. Income Level

Below 10,000 7 11

10,000-20,000 25 39

20,000-30,000 16 24

Above 30,000 17 26

1. Demographic characteristics.

2. Effects of KMS.

3. Learning organization characteristics.

Measurement ScaleThe questionnaire consisted of a series of statements, where the engineers and managers were

requested to provide answers in the form of agreement or disagreement to express their perceptions




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towards KMS and learning organization. A Likert scale was used so that the respondent can select

a numerical score ranging from 1 to 5 for each statements where 1, 2, 3, 4 and 5 denote ‘strongly

disagree’, ‘disagree’, ‘neutral’, ‘agree’ and ‘strongly agree’, respectively in part 2 and 3.

Data Analysis

Reliability Analysis

Pre-testing techniques, namely, Cronbach’s Alpha and Hoteling’s t-square test were used to

check the reliability and equivalence of the variables used for the research. The results of this

analysis are presented in Table 2.

Table 2: Reliability Analysis

Dimension Name No. of Cronbach’s Hoteling’s df

Items Alpha t-Square Test

Effects of Knowledge Management System (Part II) 13 0.798 491.263* 12.53

Learning Organization (Part III) 11 0.695 256.390* 10.55Note: * means differs at 1% level of significance.

The above results of Cronbach’s Alpha indicate that the two dimensions, namely, effects

of KMS (Part II) and characteristics of learning organization (Part III) achieved a high

internal consistency of 79.8% and 69.5% respectively. Similarly, Hoteling’s t-squared test

exhibits that the mean of items under all dimensions were significantly different at 1%

level. Thus, it is clear that all items in the questionnaire conveyed different meaning to the

respondents.

Chi-Square Analysis

Chi-Square Test of Significance (Age and KMS)H

0:There is no significant relation between age and KMS.

H1:

There is a significant relation between age and KMS.

Chi-Square Test of Significance (Qualifications and KMS)

H0:

There is no significant relation between qualifications and KMS.

H1:

There is a significant relation between qualifications and KMS.

Chi-Square Test of Significance (Department and KMS)

H0:

There is no significant relation between department and KMS.

H1: There is a significant relation between department and KMS.Chi-Square Test of Significance (Designation and KMS)

H0: There is no significant relation between designation and KMS.

H1:

There is a significant relation between designation and KMS.




Chi-Square Test of Significance (Experience and KMS)

H0:

There is no significant relation between experience and KMS.

H1:

There is a significant relation between experience and KMS.

Chi-Square Test of Significance (Income Level and KMS)

H0:

There is no significant relation between income level and KMS.

H1:

There is a significant relation between income level system and KMS.

The values of chi-square statistics obtained from chi-square distribution table for all six

combinations are 12.59, 9.49, 9.49, 5.99 , 12.59 and 12.59 in that order and the calculated

chi-square statistics values are 5.484, 2.421, 3.853, 2.596, 4.975 and 5.983 in that order which

lies in the acceptance region. Thus, the null hypotheses cannot be rejected, whereas alternative

hypotheses are rejected. So, it can be concluded that demographic characteristics of managers

and engineers and effects and usage of KMS are independent on the basis of statistical evidence

at 5% level of significance. Results of chi-square are presented in Table 3.

Table 3: Results of Chi-Square Analysis

S. No. Variables Chi-Square Statistic

1. Age and KMS 5.484 < 12.59 (Not Significant)

2. Qualifications and KMS 2.421 < 9.49 (Not Significant)

3. Department and KMS 3.853 < 9.49 (Not Significant)

4. Designation and KMS 2.596 < 5.99 (Not Significant)

5. Experience and KMS 4.975 < 12.59 (Not Significant)

6. Income Level and KMS 5.983 < 12.59 (Not Significant)

Chi-Square Test of Significance (Age and Learning Organization)

H0:

There is no significant relation between age and learning organization.

H1:

There is a significant relation between age and learning organization.

Chi-Square Test of Significance (Qualifications and Learning Organization)

H0:

There is no significant relation between qualifications and learning organization.

H1:

There is a significant relation between qualifications and learning organization.

Chi-Square Test of Significance (Department and Learning Organization)

H0:

There is no significant relation between department and learning organization.

H1: There is a significant relation between department and learning organization.

Chi-Square Test of Significance (Designation and Learning Organization)

H0: There is no significant relation between designation and learning organization.

H1:

There is a significant relation between designation and learning organization.




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Chi-Square Test of Significance (Experience and Learning Organization)

H0:

There is no significant relation between experience and learning organization.

H1:

There is a significant relation between experience and learning organization.

Chi-Square Test of Significance (Income Level and Learning Organization)

H0:

There is no significant relation between income level and learning organization.

H1:

There is a significant relation between income level and learning organization.

The values of chi-square statistics obtained from chi-square distribution table for all

five combinations are 7.82, 5.99, 5.99, 3.84 , 7.82 and 7.82 in that order and the calculated

chi-square statistics values are 2.554, 2.696, 3.436, 0.685, 1.099 and 3.235 in that order

which lies in the acceptance region. Thus, the null hypotheses are accepted where as

alternative hypotheses are rejected. So, it can be concluded that demographic

characteristics of managers and engineers and learning organization are independent on

the basis of statistical evidence at 5% level of significance. Results of chi-square are

presented in Table 4.

Table 4: Results of Chi-Square Analysis

S. No. Variables Chi-Square Statistic

1. Age and Learning Organization 2.554 < 7.82 (Not Significant)

2. Qualifications and Learning Organization 2.696 < 5.99 (Not Significant)

3. Department and Learning Organization 3.436 < 5.99 (Not Significant)

4. Designation and Learning Organization 0.685 < 3.84 (Not Significant)

5. Experience and Learning Organization 1.099 < 7.82 (Not Significant)

6. Income Level and Learning Organization 3.235 < 7.82 (Not Significant)

Table 5: KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.777Bartlett’s Test of Sphericity Approx. Chi-Square 299.589

df 78.000

Sig. 0

Factor Analysis

Dimensions: Effect of KM

Data validity for factor analysis was calculated using Kaiser-Meyer-Olkin (KMO) measure of

sampling adequacy. The minimum acceptable level is 0.5. Since calculated KMO (0.777) is

greater than 0.5, so it is appropriate to do factor analysis. Hence, Bartlett’s test of sphericity

value is 299.589, which is also a kind of chi-square and it is significant. The results of KMO

and Bartlett’s test of sphericity are shown in Table 5.




Extraction Method: Principal Component Analysis

Table 6 reveals that four factors have been extracted out of 13 variables that exceed the

Eigenvalue of one. The variables less than the Eigenvalue of one are not considered during

extraction method.Table 6: Total Variance Explained

ComponentInitial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1. 4.610 35.460 35.460 4.610 35.460 35.460

2. 1.927 14.826 50.286 1.927 14.826 50.286

3. 1.187 9.130 59.416 1.187 9.130 59.416

4. 1.000 7.693 67.108 1.000 7.693 67.108

5. 0.755 5.811 72.919

6. 0.695 5.348 78.2687. 0.661 5.082 83.350

8. 0.557 4.285 87.635

9. 0.428 3.289 90.924

10. 0.366 2.816 93.740

11. 0.328 2.522 96.262

12. 0.260 2.003 98.265

13. 0.226 1.735 100.000

Table 7: Rotation Sums of Squared Loadings

Total % of Variance Cumulative %

2.833 21.793 21.793

2.433 18.715 40.508

1.866 14.353 54.861

1.592 12.247 67.108

Table 7 shows that factor 1, factor 2,

factor 3 and factor 4 explain a variation of 21.793, 18.715, 14.353, and 12.247,

respectively and together show the variance

of 67.108. It is inferred that Factor 1 consists

of five variables, of which collaboration and

innovation are found to be significant with

a variation of 21.793%. Factor 2 consists of

three variables of which different market types is significant with a variation of 18.715%.

Factor 3 consists of three variables of which delegation of authority and accountability is

significant with a variation of 14.353%. Factor 4 consists of two variables of which better

staff attraction is significant with a variation of 12.247%. Based on the results of factor

loading (Table 8), the factors are named which is given in Table 9.

Dimensions: Learning Organization

Data validity for factor analysis was calculated using KMO measure of sampling adequacy.

The minimum acceptable level is 0.5. Since calculated KMO (0.670) is greater than 0.5, so it

is appropriate to do factor analysis. Hence Bartlett’s test of sphericity value is 117.040 it is




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Table 8: Rotated Component Matrix

Component

1 2 3 4

Collaboration 0.761 0.063 0.379 –0.108

Innovation 0.731 0.131 0.185 0.031

Adaptation Capability 0.680 0.213 0.291 0.041

Addressing of Communication Gap 0.655 0.033 0.242 0.474

Better ROI 0.627 0.073 0.415 0.165

Entry of Different Market Types –0.129 0.852 0.084 0.242

Enhanced Productivity or Service Quality 0.147 0.748 0.332 0.171

Sharing of Best Practices 0.410 0.704 –0.015 –0.062

Delegation of Authority and Accountability 0.247 0.261 0.793 0.065

Transformation of Individual Learning 0.018 –0.032 0.612 0.581

Fast and Better Decision Making 0.215 0.487 0.495 0.025

Better Staff Attraction 0.343 0.153 0.012 0.727

Increased Market Share –0.052 0.499 0.074 0.602

Table 9: Naming of Factors

Factor 1 Factor 2 Factor 3 Factor 4

Innovation Through Different Market Better Decision Increase Market Share

Collaboration Entry Through Making Through

Enhanced Product ivity Delegation

Collaboration Entry of Different Delegation of Better Staff Attraction

Market Types Authority andAccountability

Innovation Enhanced Productivity Transformation of Increased Marketor Service Quality Individual Learning Share

Adaptation Capability Sharing of Best Practices Fast and BetterDecision Making

Addressing of Communication Gap

Better ROI

also a kind of chi-square and it is significant. The results of KMO and Bartlett’s test of

sphericity are shown in Table 10.

Extraction Method: Principal Component Analysis

Table 11 reveals that 4 factors have been extracted out of 11 variables that exceed the

Eigenvalue of one. The variables less than the Eigenvalue of one are not considered during

the extraction method.




Table 12 shows that factor 1, factor 2,

factor 3 and factor 4 explain a variation of

16.597%, 16.372%, 15.268%, and 12.388%,

respectively and together show the variance

of 60.625%.

It is also inferred that factor 1 consists

of two variables of which easy uploading into

database is found to be significant with a

variation of 16.597%. Factor 2 consists of three variables of which sharing and acting upon

knowledge is significant with a variation of 16.372%. Factor 3 consists of three variables of which sharing of experience and information is significant with a variation of 15.268%.

Factor 4 consists of two variables of which sharing best practices are significant with a variation

of 12.388%. Based on the results of factor loading (Table 13), the factors are named which is

given in Table 14.

Table 10: KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.670

Approx. Chi-Square 117.040

Bartlett’s Test of Sphericity df 55.000

Sig. 0

Table 11: Total Variance Explained

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1. 2.905 26.406 26.406 2.905 26.406 26.406

2. 1.377 12.516 38.921 1.377 12.516 38.921

3. 1.254 11.400 50.321 1.254 11.400 50.321

4. 1.133 10.304 60.625 1.133 10.304 60.625

5. 0.880 8.002 68.626

6. 0.748 6.803 75.429

7. 0.714 6.490 81.919

8. 0.646 5.876 87.796

9. 0.564 5.130 92.926

10. 0.478 4.345 97.271

11. 0.300 2.729 100.000

Component

Table 12: Rotation Sums of Squared Loadings

Total % of Variance Cumulative %

1.826 16.597 16.597

1.801 16.372 32.969

1.680 15.268 48.237

1.363 12.388 60.625




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Table 13: Rotated Component Matrix

Component

1 2 3 4

Easy Uploading into database 0.844 0.240 –0.101 –0.015

Ready Availability of Information 0.796 0.011 0.369 0.033

Sharing and Acting upon Knowledge 0.014 0.766 0.033 0.218

Incentives for Learning 0.239 0.676 –0.025 0.155

Continuous Learning 0.037 0.674 0.192 –0.232

Sharing of Experience and Information –0.176 0.048 0.715 0.281

Technologically Enabled Learning 0.234 –0.009 0.706 –0.024

Well-defined KM Process 0.283 0.338 0.525 –0.194

Sharing Best Practices 0.028 0.162 0.067 0.784

Learning Through Communication 0.270 0.312 0.401 0.514

Sharing Powerful Vision of the Organization Acrossthe Workforce 0.427 0.076 0.211 0.490

Table 14: Naming of Factors

Factor 1 Factor 2 Factor 3 Factor 4

Better Information Application Knowledge Shared Vision

Collaboration of Knowledge Management

Process

Easy Uploading into Sharing and Acting Sharing of Sharing Best PracticesDatabase upon Knowledge Experience and

Information

Ready Availability Incentives for Learning Technologically Learning ThroughInformation Enabled Learning Communication

Continuous Learning Well-defined KM Sharing Powerful VisionProcess of the Organization

Across the Workforce

Conclusion

The conclusions derived in empirical analysis are summarized below:

• Most of respondents are aware of what KM is.

• The KM activities of an organization are greatly influenced by the demographic

characteristic of employees.

• The ability of an organization to learn mainly depends on the individualcharacteristic of an employee.

• The factors like innovation through collaboration, different market entry through

enhanced productivity, better decision making through delegation, increased

market share causes variance in KMS.




• The factors like better information, application of knowledge, KM process, and

shared vision contributes greatly to the properties of learning organization.

• Knowledge management in the organization enables better staff retention.

• Knowledge management in the organization strengthens the workers to accomplishthe task quickly.

• Knowledge management leads the business into different market types.

• Knowledge management in the organization reduces the communication gap

between employees.

• Knowledge management in the organization raises the adaptation capability among

the employees.

• Knowledge management in the organization smoothens the progress of learning.

• Knowledge management in the organization augments the continuous

transformation of individual learning.• Knowledge management in the organization affords readymade information to the

employees.

• Knowledge management in the organization strengthens the collaboration among

employees within the organization.

• Knowledge management makes every effort for learning and re-learning through

training modules in the organization.

• The practice of KM in the organization makes way for sharing the best practices

among employees which results in enhanced collaboration among employees.

Based on the findings, few suggestions are offered by the authors which are summarized

below:

• This study should be made every year to evaluate the new practices that can bring

in changes in the organization.

• Attention should be given to those people who are innovative and are always

ready to offer new ideas.

• There should be coordination among employees such that they think they are

working for the same goals and objectives.

• Management should care more about the staff’s communication by giving time for

sharing informally and give a high priority to KM on the agenda.

• There should be exchanges of experiences and knowledge among people of differentorganizations by creating online communities for the purpose.

It is concluded that the KMS helps the organization in improving its performance in

terms of innovation and better decision making. Also it paves the pathway for an organization




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to transform into a learning organization. So the organization should continuously focus its

efforts on KM.

Limitations: The results obtained in this study could be subject to some limitations. The

study is restricted only to a particular engineering firm in a district. The population belongsto only managers and engineers and samples were drawn from particular departments of a

selected organization. Identifying managers and engineers who are really familiar and

experienced with KMS was found to be difficult.

Some avenues for further research are as follows: The relationship between KMS and

organizational culture; the relationship between KMS and knowledge sharing; the relationship

between KMS and knowledge seeking practices; the relationship between KMS and intellectual

capital; the relationship between KMS and task characteristics.

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in the Project-Based Environment”, International Journal of Managing Projects in Business,

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41

Part 1: Demographic Profile

Gender: Male Female

Age: Below 30 Years 30-40 Years 41-50 Years Above 50 Years

Educational Qualifications: Diploma UG PG

Designation: Engineer Manager

Department: Engineering Production Quality Control

Experience: Below 10 Years 10-20 Years 21-30 Years Above 30 Years

Income Level: Below 10,000 10,000-20,000

20,000-30,000 Above 30,000

Part 2: The Effects of Knowledge Management System (KMS)

Please put tick mark in the appropriate box matching your opinion

Q. Questions Strongly Agree Neutral Disagree Strongly

No. Agree Disagree

1. The KMS helps in fast and

better decision making.

2. KM helps in enhanced productivity

or service quality.

3. Implementing KM results in sharing

best practices.

4. KM makes it easy to enter different

market types.

5. KM helps in increased innovation by

the employees.

6. Application of KMS results in

increased market share.

7. KM increases the learning/adaptation

capability of employees.

8. KM helps in better staff attraction/

retention.

9. KM results in enhanced collaboration

within the organization.

10. KM helps to address the communica-

tion gap in the organization.

Appendix

Questionnaire




11. KM helps in constant and continuous

transformation of individual learning

to organizational learning and

vice versa.

12. KM results in increased delegation of

authority and accountability to

individuals.

13. KM helps to achieve better ROI.

Part 3: Learning Organization Characteristics

Please put tick mark in the appropriate box matching your opinion


No. Agree Disagree

1. Information is readily available

on required topics from current

publications to industry specific

processes.

2. Information regarding process

description can be uploaded in

organization’s database.

3. Personal best practices can be

shared with other employees.

4. Enabling hardware and software

technologies are available to support

learning rather than control it.

5. There are well defined processes for

creation, capture, and acquisition

of knowledge.

6. Useful knowledge can be easily

shared and acted upon.

7. A cohering and powerful vision of the organization is shared across the

workforce to promote need for

strategic thinking at all levels.

Appendix (Cont.)


No. Agree Disagree




43

Appendix (Cont.)


No. Agree Disagree

8. There are enabling structures in

terms of hierarchy and

communication flows that facilitates

learning.

9. There are cohesive teams in

organization which facilitates

sharing of experiences and

information among employees.

10. The organization provides incentives

to motivate users to learn from

experiences and use KM system.11. The organization continuously

strives for learning, unlearning and

re-learning for its employees.

Reference # 29J-2011-04-02-01



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