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CHAPTER – 5
DATA ANALYSIS AND INTERPRETATION
This chapter focuses on the analysis of the data collected from
the respondents of self-financing engineering colleges. The data
collected using the well structured questionnaire were initially
grouped, classified, coded and edited. Then they are organized
accordingly based on the suitability of the application of selected
statistical tools. Perception of the respondents with regard to
knowledge management is being exploited in this chapter for the
purpose of cross examining the selected variables for this study.
The perception of respondents in self-financing engineering
colleges about the Need for Knowledge Management, Genesis of
Knowledge Management practices, Performance Appraisal, Career
Planning, Training, Job Rotation, Teacher Welfare and Reward
System, Knowledge Management Climate and Outcomes and
Organisational Effectiveness are analyzed in this chapter so as to
understand the extent of knowledge management practices
implemented in self-financing engineering colleges and its
effectiveness.
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5.1. FACTOR ANALYSIS
Factor analysis has been applied to analyze a large number of
variables by identifying common and unique sets variance that are
referred to as dimensions, factors, or components. It allows the
researcher to summarize and reduce the data. The process of
summary and reduction allows the data to be described by a much
smaller number of variables than the original. In this study, the
researcher has taken all 13 elements of knowledge management for
factor analysis and the results are discussed below:
5.1.1 KNOWLEDGE MANAGEMENT – A TOOL FOR
COMPETITIVENESS
Knowledge management as a concept requires a specific and
congenial climate to take roots. It enriches the work life in an
organisation and aims to link productivity with a sense of personal
fulfillment. Knowledge management involves all management
decisions and practices that directly, affect or influence the Human
Resources in an organisation. In recent times, increased attention
comes from the realization that an organisation’s teachers empower
an organisation to achieve its goals. Eventually the development of
these resources has been critical to an organisation’s success.
Factor analysis by the principal component method is applied
on all 13 variables of knowledge management needs. The following
results are obtained for the classification of the factors.
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Number of factors for Need for Knowledge Management
Component Initial Eigen values Rotation Sums of Squared
Loadings Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
%
1 3.443 26.488 26.488 3.053 23.483 23.483
2 2.441 18.778 45.267 2.101 16.165 39.648
3 1.535 11.811 57.077 1.828 14.062 53.709
4 1.105 8.500 65.577 1.543 11.867 65.577
5 .939 7.219 72.796
6 .775 5.964 78.760
7 .641 4.932 83.692
8 .525 4.040 87.732
9 .460 3.541 91.272
10 .403 3.101 94.374
11 .310 2.388 96.762
12 .277 2.127 98.889
13 .144 1.111 100.000
The table 5.1.1 allows inferring that 13 variables of need for
knowledge management are classified into 4 major factors. These
variables explain cumulative total variance of 65.577 per cent. The
same is depicted below as a scree plot graph.
Chart 5.1.1
Scree Plot
0
0.5
1
1.5
2
2.5
3
3.5
4
1 2 3 4 5 6 7 8 9 10 11 12 13
Component
Eig
en
Valu
es
The following table presents the variables and variable loadings
on each factor.
Variables and Variable loadings for the factors of Knowledge
Management
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Item No.
Variables/Factors Variable Loadings
Factor I – Pre-requisites
2 Increased quality awareness among students 0.820
1 New engineering colleges products/services 0.816
3 Increased Competition 0.782
5 Information Technology revolution 0.655
4 Liberalization, Privatization and Globalization 0.528
Factor II - Imperatives
10 Changed teacher expectations 0.824
7 Corporate restructuring 0.737
6 Renewed focus on students 0.592
Factor III – Change Management
11 Total Quality Management 0.858
12 Knowledge Management 0.857
13 Quality of Work Life 0.500
Factor IV - Restructuring
9 Perceived risks 0.876
8 Merger & Acquisition 0.616
It is observed that need for knowledge management practices in
the self-financing engineering colleges are determined by four factors
namely, ‘Pre-requisites,’ “Imperatives’, ‘Change Management’, and
‘Restructuring’. The factors I, II, III, and IV consist of 5, 3, 3 and 2
variables respectively. It is derived that pre-requisites of knowledge
management is the most essential factor for the implementation of
knowledge management practices.
The overall conclusion establishes that the self-financing
engineering colleges have initiated measures for the effective
implementation of knowledge management practices. They
concentrate primarily on the imperatives to join the main stream of
liberalization. The innovative changes in management have emerged in
the form of quality of service through the increased knowledge of
teachers. The process of restructuring in self-financing engineering
colleges helps to attract maximum number of students towards their
service domains.
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5.1.2 KNOWLEDGE MANAGEMENT PRACTICES
In this study, the researcher has taken 10 variables of genesis
of knowledge management practices. Factor analysis by the principal
component method is applied on all 10 variables. The following results
are obtained for the classification of factors:
Genesis of KM Practices: Factors Responsible
Component Initial Eigen values
Rotation Sums of Squared
Loadings
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
% 1 4.570 45.698 45.698 3.298 32.977 32.977
2 1.252 12.524 58.222 1.856 18.559 51.536
3 1.044 10.438 68.659 1.712 17.123 68.659
4 .757 7.572 76.231
5 .577 5.771 82.002
6 .518 5.177 87.179
7 .465 4.653 91.832
8 .380 3.795 95.627
9 .257 2.570 98.197
10 .180 1.803 100.000
It is clear from above table that all 10 variables of genesis of
knowledge management practices are classified into 3 major factors.
All 10 variables explain 68.659 per cent of total variance. The scree
plot graph below describes the same.
Chart 5.1.2
0
1
2
3
4
5
1 2 3 4 5 6 7 8 9 10
Eig
en
Va
lue
s
Component
Scree Plot
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The following table explains the variables and variable loadings
on each factor:
Variable loadings for the factors of genesis of knowledge management practices
Item No.
Variables/Factors Variable Loadings
Factor I – Work Environment
10 To develop overall health & self-renewing capabilities of teachers 0.757
5 To strengthen superior-subordinate relationships 0.729
9 To frame proactive and transparent HR policies 0.712
4 To develop & maintain a high motivational level of the teachers 0.704
8 To promote a congenial organizational climate/culture 0.633
Factor II – Personality Development
3 To develop the capabilities to handle future roles 0.790
2 To develop the individual’s capacity to perform the present job better
0.777
1 To develop the individual to realize his potential to the maximum extent
0.624
Factor III – Team Building
7 To promote inter-team collaboration 0.869
6 To foster team spirit among teachers 0.695
It is clear from the above table that factor I, II, and III consist of 5,
3, and 2 variables and are appropriately named ‘Work Environment’,
‘Personality Development’, and ‘Team Building’. The main genesis of
knowledge management practices in self-financing engineering
colleges are Individual Development, Organizational Development, and
Team Building.
In self-financing engineering colleges, the main genesis of
knowledge management practices is identified as strategies to revamp
the existing process to create a favorable work environment. It also
targets the individuals to improve their personality for their successful
discharge of duties. The top-level management teaches their teachers
for the collective team building.
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5.1.3 PERFORMANCE APPRAISAL SYSTEM
The main objectives of performance appraisal system are to
improve the performance of the organization through improved
teacher performance. The goal of performance appraisal is to allow a
teacher the opportunity to progress to his full potential so as to meet
the organizational needs as well as his personal developmental goals.
Through this process, true teamwork and maximum performance can
become a reality. The foundation of the performance appraisal has
improved the communications between the teacher and the manager.
In this study 13 variables of Performance Appraisal system are
taken into account by the researcher. The principal component
method has been applied on all 13 variables of performance appraisal
system for factor analysis:
Number of factors for Performance Appraisal System
Component
Initial Eigen values Rotation Sums of Squared
Loadings
Total % of
Variance Cumulative
% Total
% of Variance
Cumulative %
1 4.860 37.386 37.386 3.763 28.945 28.945 2 2.139 16.452 53.838 1.717 13.211 42.156 3 1.285 9.887 63.725 1.664 12.797 54.952 4 1.069 8.225 71.950 1.657 12.744 67.696 5 1.036 7.967 79.917 1.589 12.221 79.917 6 .561 4.317 84.234 7 .525 4.035 88.269 8 .428 3.295 91.564 9 .322 2.478 94.043 10 .269 2.069 96.111 11 .199 1.528 97.639 12 .170 1.307 98.947 13 .137 1.053 100.000
From the table, it is found that all 13 variables of Performance
Appraisal are grouped into five factors. All 13 variables of performance
appraisal explain 79.917 per cent of total variance. The scree plot
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graph below gives an idea as to how successive factors is accounting
for smaller and smaller amounts of total variance.
Chart 5.1.3
0
1
2
3
4
5
6
1 2 3 4 5 6 7 8 9 10 11 12 13
Eig
en
Valu
es
Component
Scree Plot
Variables and Variable loadings on each factor are presented in table
given below:
Variable loadings for the factors of Performance Appraisal System Item No.
Variables/Factors Variable Loadings
Factor I – Work Culture
6 Performance appraisal system aims at strengthening appraiser – appraisee relationships
0.856
5 Performance appraisal system has scope for correcting the bias of reporting officer
0.811
7 Performance appraisal system helps interested appraisees to gain more insights into their strength and weaknesses
0.794
4 Performance appraisal system acts as a motivating factor in improving the Work efficiency
0.780
3 Performance appraisal system provides an opportunity for self-review and reflection
0.770
Factor II – Futuristic Strategy
8 The format and procedure of Performance appraisal system keep pace with the current requirements of engineering colleges
0.906
9 Performance appraisal system helps teachers to plan their future course of action well
0.668
Factor III – Guiding Value
1 The objectives of Performance appraisal system are clear to all teachers
0.959
2 The appraisal data are used as inputs for recognition and encouragement of high performers
0.659
Factor IV- Review
10 Performance Appraisal System encourages performance review discussion
0.890
11 Discussion on Key Responsibility Area (KRA) is beneficial 0.701
Factor V - Feedback
12 The knowledge management department provides adequate feedback to functional heads
0.814
13 The knowledge management department follows up seriously the training needs identified during appraisals
0.776
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A good performance appraisal system is most crucial for
customer satisfaction because only competent teachers are compatible
to provide customer excellence. Though Performance appraisal system
exists to help everyone succeed, it can work well as long as the
teachers work with the management. Driven by this purpose, the self-
financing engineering colleges take efforts to create good work culture,
futuristic strategy, and performance planning. Performance appraisal
system in self-financing engineering colleges provides an opportunity
for self-review and it acts as a motivating factor in improving the work
efficiency.
Through performance planning, the appraisal data are used as
inputs for recognition and encouragement of high performers. The
innovative methods have been insisted in the Performance appraisal
system of self-financing engineering colleges by means of reviewing the
key responsibility areas.
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5.1.4 CAREER PLANNING AND DEVELOPMENT
Career Planning essentially means helping the teachers to plan
their career in terms of their capacities within the context of
organisational needs. Career planning is the process of enhancing a
teacher’s future value.
In this study, the researcher has taken 7 variables of Career
Planning & Development. The principal component method is applied
for all 7 variables for factor analysis. The following classification of
factor is obtained:
Number of factors for Career Planning and Development
Component Initial Eigen values Rotation Sums of Squared
Loadings
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
% 1 2.799 39.991 39.991 2.762 39.454 39.454
2 1.172 16.740 56.731 1.175 16.784 56.238
3 1.001 14.295 71.026 1.035 14.788 71.026
4 .888 12.681 83.708
5 .496 7.083 90.791
6 .354 5.060 95.851
7 .290 4.449 100.000
From the above table, it is observed that all 7 variables of Career
Planning and Development are grouped into 3 factors. All these
variables explain 71.026 per cent of total variance. The scree plot
graph given below describes the eigen value distribution.
Chart 5.1.4
Scree Plot
0
0.5
1
1.5
2
2.5
3
1 2 3 4 5 6 7
Component
Eig
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Variables and Variable loadings on each factor are presented in table
given below:
Variable loadings for the factors of Career Planning and Development Item No.
Variables/Factors Variable Loadings
Factor I – Teachers Commitment
5 Career Planning encourages teachers to remain in the organisation 0.795
4 Career Planning increases teacher’s loyalty 0.789
7 Career Planning uses human resources effectively and achieves greater productivity
0.752
3 Career Planning aims at improving teacher morale & motivation 0.698
6 Existing organisational hierarchy supports career planning 0.666
Factor II - Awareness
2 Teachers are aware of the career opportunities in the engineering colleges
0.852
Factor III – Career Advancement
1 Career Planning helps the teachers to plan their career 0.961
It is clear from the above table that factor I, II and III emerge
with 5, 1 and 1 variables respectively. Career planning is important
because the consequences of success or failure are closely linked with
each individual’s self-concept, identity and satisfaction with work and
life. Although career planning is the primary responsibility of the
individuals, the self-financing engineering colleges do take an active
interest in a teacher’s career planning by means of creating awareness
about career opportunities in the engineering colleges.
The Career planning and development policies of self-financing
engineering colleges signify its future employment needs and the
related career opportunities. Career advancement in self-financing
engineering colleges, helps the teachers latch on to their commitment
to their organization. With career planning, the self-financing
engineering colleges will be able to develop the teachers so that when
situation demands there will be an adequate supply of the right types
of skills and abilities to empower the engineering colleges to achieve
its desired objectives.
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5.1.5 TRAINING AND DEVELOPMENT
Training and Development programmes are necessary in any
college for improving the quality of work of the teachers at all levels,
particularly in the backdrop of fast changing technology, changing
values and environment.
Training tries to improve a specific skill related to a job, whereas
development aims at improving the total personality of an individual.
Training is a one- shot deal, whereas development is an ongoing,
continuous process.
Factor analysis by the principal component method is applied
on all 14 variables of Training and Development considered in the
study. The following results are obtained for the classification of the
factors:
Number of factors for Training and Development
Component Initial Eigen values
Rotation Sums of Squared Loadings
Total % of
Variance Cumulative
% Total
% of Variance
Cumulative %
1 7.529 53.777 53.777 5.169 36.923 36.923 2 1.891 13.505 67.283 2.836 20.256 57.179 3 1.188 8.485 75.767 2.602 18.589 75.767 4 .754 5.382 81.450 5 .577 4.119 85.269 6 .409 2.920 88.188 7 .345 2.463 90.652 8 .297 2.120 92.772 9 .264 1.886 94.658 10 .220 1.569 96.228 11 .186 1.328 97.556 12 .139 .993 98.549 13 .120 .855 99.404 14 .083 .596 100.000
From the table above, it is found that all 14 variables of Training
and Development are grouped into three factors. All 14 variables of
Training and Development explain 75.767 per cent of total variance.
The same is depicted in the scree plot graph given below:
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Chart 5.1.5
Scree Plot
0
1
2
3
4
5
6
7
8
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Component
Eig
en
Valu
es
Variable loadings on each factor are presented in table given below:
Variable loadings for the factors of Training and Development
Item No.
Variables/Factors Variable Loadings
Factor I – Training Mechanisms
2 Objectives and scope of training programme are well defined 0.892
1 There is a well-designed and widely shared training policy 0.850
4 The in-house programmes are handled by competent faculty 0.849
3 Engineering colleges is providing adequate training to teachers to deliver better customer service
0.819
13 Engineering colleges is ready to spend considerable amount of money for training programme
0.817
14 Training programmes conducted by the engineering colleges are suitable to meet current business requirement.
0.709
Factor II – Training Infrastructure
10 There is a visible link between training and performance level of teachers (before & after training)
0.801
12 External training programmes are carefully chosen after collecting adequate information
0.765
11 Engineering colleges assesses the training needs periodically 0.712
9 Teachers returning from training are given adequate time to reflect 0.693
Factor III – Inductive Training
6 Values and Norms are clearly explained to new teachers 0.861
8 Teachers are helped to acquire technical knowledge and skills through training
0.789
7 New recruits find induction training very useful 0.758
5 Induction training is of adequate duration 0.742
It is clear from the above table that factors I, II and III emerge
with 6, 4 and 4 variables respectively. In fact a systematic and
effective training is an invaluable investment in the Human Resources
of a college. It is evident from the factor analysis that the self-
financing engineering colleges are creating a conducive training
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infrastructure for the betterment of their teachers. To add more to
this, the self-financing engineering colleges assess the training needs
periodically; and the teachers returning from training are given
adequate time to reflect. It is therefore concluded that Training and
Development are needed to facilitate life-long learning of teachers that
will characterize the business in future.
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5.1.6 JOB ROTATION
Job rotation is an effective tool for on-the-job training and is not
new to the engineering colleges. However, it seems that the concept of
job rotation that sounds so interesting and acceptable in principle
does not turn out to be that simple in practice. Perhaps that is why
one could observe varying degrees of success among private
engineering colleges in implementing this concept in practice.
Factor analysis by principal component method is applied on all
8 variables of job rotation considered in the study. The following
results are obtained for the classification of the factors:
Number of factors for Job Rotation
Component Initial Eigen values Rotation Sums of Squared
Loadings Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
% 1 1.942 24.277 24.277 1.919 23.990 23.990
2 1.668 20.851 45.128 1.661 20.759 44.749
3 1.244 15.555 60.683 1.275 15.934 60.683
4 .963 12.041 72.724
5 .795 9.939 82.663
6 .691 8.633 91.296
7 .391 4.886 96.183
8 .305 3.817 100.000
From the table above, it is found that all eight variables of job
rotation are grouped into three factors. All 8 variables of job rotation
explain 60.683 per cent of total variance. The scree plot graph below
gives an idea as to how successive factors are accounting for total
varaiance.
Chart 5.1.6
Scree Plot
0
0.5
1
1.5
2
2.5
1 2 3 4 5 6 7 8
Component
Eig
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Variables and Variable loadings on each factor are presented in
table given below:
Variable loadings for the factors of Job Rotation
Item No.
Variables/Factors Variable Loadings
Factor I - Orientation
6 Job Rotation helps to reduce monotony and boredom by providing variety of work
0.808
7 Job Rotation facilitates interdepartmental cooperation and coordination
0.718
4 Takes time to learn new work due to job rotation 0.653
1 Job Rotation aim at expanding skills, knowledge of teachers of your engineering colleges
0.639
3 Job Rotation infuses new concepts and ideas 0.501
Factor II – Accommodative Approach
5 Job Rotation is practiced in all departments of your engineering college
0.850
2 The teachers are consulted (formally/informally) while their posting decision is made
0.708
Factor III – Internal Check
8 Job Rotation acts as preventive vigilance measure against frauds, mistakes, & procedural lapses.
0.802
Since job rotation permits a greater understanding of other
activities within the college, teachers are prepared more rapidly to
assume greater responsibility, especially at the upper strata. Job
rotation in self-financing engineering colleges aims at expanding
skills, knowledge of teachers and infuses new concepts and ideas
(Orientation). Job rotation in self-financing engineering colleges acts
as an Internal Check i.e. preventive vigilance measure against frauds,
mistakes, and procedural lapses like non-maintenance of course file,
attendance record of students etc. The accommodative approach
adopted by self-financing engineering colleges is more beneficial in
this regard.
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5.1.7 TEACHER WELFARE & REWARD SYSTEM
Teacher Welfare is a comprehensive term including various
services, benefits, and facilities offered to teachers by the employer.
Such generous fringe benefits make the life worth living for teachers.
The welfare amenities are extended in addition to normal wages and
other economic rewards available to teachers as per legal provisions.
Welfare measures may also be provided by the Govt. An incentive or a
reward can be anything that attracts the worker’s attention and
stimulates him to work. In the words of Burack and Boldsmith, “An
incentive scheme is a plan or programmes to motivate individual or
group performance.
In this study, the researcher has taken all 12 variables of
Teacher Welfare and Reward System into account for factor analysis
by the principal component method. The following classification of
factor is obtained:
Number of factors for Teacher Welfare and Reward System
Component Initial Eigen values Rotation Sums of Squared
Loadings Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
% 1 5.890 49.081 49.081 3.728 31.067 31.067
2 1.231 10.256 59.336 3.390 28.248 59.315
3 1.157 9.639 68.975 1.159 9.660 68.975
4 .968 8.068 77.043
5 .590 4.917 81.960
6 .493 4.110 86.070
7 .426 3.548 89.618
8 .330 2.752 92.370
9 .325 2.706 95.076
10 .277 2.306 97.382
11 .197 1.642 99.024
12 .117 .976 100.000
It is observed from the above table that all 12 variables of Teacher
Welfare & Reward System are emerged as three factors. All 12
variables explain 68.975 per cent of total variance. The same is
depicted in the scree plot graph below:
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Chart 5.1.7
Scree Plot
0
1
2
3
4
5
6
7
1 2 3 4 5 6 7 8 9 10 11 12
Component
Eig
en
Valu
es
The following table gives the variables and variable loadings on each
factor.
Variable loadings for the factors of Teacher Welfare and Reward
System Item No.
Variables/Factors Variable Loadings
Factor I – Ideal Welfare Measures
6 The working conditions of your engineering college are good for teachers
0.888
7 Teachers are rewarded for their contribution 0.816
5 Welfare measures help to improve the public image of your engineering college
0.741
4 Teacher Welfare activities undertaken by the college are very effective
0.710
11 The Reward System is always timely and of the right magnitude 0.636
Factor II - Accountability
10 Reward system makes teachers more loyal to the engineering colleges
0.837
3 Teachers welfare helps to increase teacher productivity 0.789
2 Teachers welfare reduce turnover (Leaving the engineering colleges) of staff
0.678
12 Creative ideas are encourage and rewarded 0.599
9 Rewards and recognition in your engineering colleges improve motivation and morale of teachers
0.598
8 Reward system in your engineering colleges is sufficiently flexible 0.589
Factor III - Loyalty
1 Teachers welfare activities improve the morale and loyalty of teachers
0.820
It is clear from the table above that factor I, II and III are
emerged with 5,6 and 1 variables respectively and are named Ideal
welfare measures, Accountability and Loyalty. Welfare measures and
Reward system have motivational value for those who are good
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performers. Both play an important role in setting up a development-
oriented climate. Self-financing engineering colleges are providing
ideal welfare measures and reward systems to their teachers.
Teachers are also accountable to their organization. For the self-
financing engineering colleges, welfare measures and reward system
lead to higher morale and productivity.
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5.1.8 OTHER PRACTICES
Other practices consist of 7 variables, which are taken into
account by the researcher in this study. The principal component
method is applied for all 7 variables of other knowledge management
practices for factor analysis. The following results are obtained with
regard to classification of factors.
Number of factors for other practices
Component Initial Eigen values Rotation Sums of Squared
Loadings
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
% 1 3.595 51.354 51.354 2.444 34.915 34.915
2 1.099 15.698 67.052 2.250 32.137 67.052
3 .795 11.356 78.408
4 .487 6.958 85.367
5 .471 6.723 92.090
6 .406 5.801 97.891
7 .148 2.109 100.000
From the above table, it is inferred that all 7 variables have
emerged with two factors which are named Quality management and
Participative management respectively. These variables explain 67.052
per cent of total variance. The given scree plot graph below explains
the successive factor accounting of the total variance.
Chart 5.1.8
Scree plot
0
0.5
1
1.5
2
2.5
3
3.5
4
1 2 3 4 5 6 7
Component
Eig
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Valu
es
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The table below gives the variables and variable loadings on each
factor.
Variable loadings for the factors of other practices Item No.
Variables/Factors Variable Loadings
Factor I – Quality Management
7 The desired organizational changes are carried out by your engineering colleges to improve Quality of Work Life
0.837
6 Competency Mapping followed in your engineering colleges is used for recruitment, promotions & training needs identification
0.815
5 Total Quality Management practiced in your engineering colleges is very
effective
0.760
1 Human Resource Information System supplies up-to-date information at a reasonable cost
0.602
Factor II – Participative Management
3 Teachers’ Participation in Management encourages teachers to think and take initiative
0.892
2 Teachers’ Participation in Management brings about a change in the attitude
of teachers
0.818
4 Staff Meetings of your engineering colleges aim at inculcating open-culture, family feelings, group synergy and talent recognition
0.626
It is identified that the self-financing engineering colleges are
flexible in imbibing relevant knowledge management sub-systems to
improve the quality of work life of their teachers. In fact the top
management has special concentration on creating competency
among the teachers. It is also realized that the administration of self-
financing engineering colleges employs an unblemished management
techniques for perfect transparency in their administration.
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5.1.9 PROBLEMS AND DIFFICULTIES IN IMPLEMENTING
KNOWLEDGE MANAGEMENT PRACTICES
All 12 variables of problems and difficulties in implementing
knowledge management practices are taken into account for factor
analysis by the principal component method. The following
classification of factors is obtained and presented in table below.
Number of factors for Problems and Difficulties in implementing
knowledge management practices
Component Initial Eigen values Rotation Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative % 1 6.738 56.152 56.152 4.808 40.065 40.065 2 1.802 15.017 71.169 3.733 31.104 71.169 3 .871 7.258 78.427 4 .570 4.749 83.176 5 .508 4.232 87.408 6 .408 3.403 90.811 7 .314 2.616 93.428 8 .246 2.046 95.474 9 .224 1.863 97.337 10 .131 1.089 98.426 11 .120 1.003 99.429 12 .068 .571 100.000
It is observed from the above table that all 12 factors are
classified into two major factors. All these variables explain 71.169 per
cent of total variance. The following graph explains the distribution of
factors.
Chart 5.1.9
Scree Plot
0
1
2
3
4
5
6
7
8
1 2 3 4 5 6 7 8 9 10 11 12
Component
Eig
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Valu
es
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The variables and variable loadings are presented in table given
below:
Variable loadings for the factors of Problems and Difficulties in implementing knowledge management practices
Item No.
Variables/Factors Variable Loadings
Factor I – Internal Defects
12 No uniform guidelines on certain issues 0.911
10 Lack of transparency 0.860
9 Lack of management support 0.855
11 Lack of proper Human Resource Information System 0.840
3 Lack of trust on teachers 0.753
8 Stereotyped functioning in the departments 0.720
6 Improper/inadequate training methods 0.629
5 Absence of effective Performance Appraisal System 0.599
7 Size of work force 0.566
Factor II - Resistance
2 Resistance to take risks 0.918
1 Resistance from the teachers 0.732
4 Lack of willingness of teachers to accept change 0.698
From the table above, it is clear that factor I and II is labeled as
“Internal Defects” and “Resistance”. On the part of the employer,
resistance to accept change and resistance to take risk are some of the
problems and difficulties in implementing of knowledge management
practices in the case of self-financing engineering colleges. On the part
of the teacher, the engineering colleges’ Internal Defects i.e. lack of
transparency, lack of management support & lack of proper Human
Resource Information Systems are some of the problems and
difficulties in implementing knowledge management practices.
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5.1.10 SUGGESTIONS FOR EFFECTIVE IMPLEMENTATION OF
KNOWLEDGE MANAGEMENT PRACTICES
Knowledge management progammes need the commitment of
top management to grow and develop. Knowledge management will be
effective if healthy atmosphere exists in day-to-day administration
which facilitates a development-oriented environment for the teachers.
It is a prime responsibility of the management of a college.
All the 11 variables are taken into account for factor analysis by
the principal component method. The following classification of factor
is obtained.
Number of factors for suggestions for effective implementation of
knowledge management practices
Component Initial Eigen values Rotation Sums of Squared
Loadings
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
% 1 4.095 37.230 37.230 2.946 26.782 26.782
2 1.277 11.609 48.839 2.331 21.195 47.977
3 1.160 10.548 59.387 1.255 11.409 59.387
4 .973 8.843 68.230
5 .869 7.900 76.130
6 .732 6.657 82.787
7 .597 5.424 88.211
8 .461 4.189 92.400
9 .356 3.237 95.636
10 .312 2.840 98.476
11 .168 1.524 100.000
From table above, it is inferred that all 11 variables of
suggestions for effective implementation of knowledge management
practices are grouped into three major factors. These variables explain
59.387 per cent of total variance. The graph below depicts the eigen
values against the factor number.
Chart 5.1.10
0
1
2
3
4
5
1 2 3 4 5 6 7 8 9 10 11
Eig
en
Va
lue
s
Component
Scree Plot
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Table below explains the variables and variable loadings on each
factor.
Variable loadings for the factors of suggestions for
effective implementation of knowledge management practices ITEM NO.
VARIABLES/FACTORS VARIABLE LOADINGS
Factor I – Internal Orientation
6 A need to develop accountability among teachers regarding their work
0.834
10 Knowledge management process should be planned properly and treated as investment
0.722
8 Better communication amongst the teachers & between teachers and management
0.700
4 There should be step-by-step implementation of knowledge management
0.424
Factor II – Modern Techniques
7 Refresher/Orientation programme should be conducted from time to time in the light of emerging new engineering colleges
0.863
2 A need for a systematic and modern appraisal of performance of teachers
0.722
3 Knowledge management measures should be linked with changing engineering colleges scenario
0.716
1 There should be a periodic review of knowledge management practices
0.690
5 There should be a core knowledge management department headed by knowledge management experts
0.554
Factor III – External Learning
9 Taking associations into confidence 0.927
11 Bench marking with other private engineering colleges 0.454
From the above table, it is found that factor I, II and III consists
of 4, 5 and 2 variables respectively. Knowledge management is the
true engine of the new economy as it is capable of bringing enormous
value to an engineering college.
Internal Orientation, Modern Techniques, and External
Learning are some of the suggestions adopted by the self-financing
engineering colleges for the effective implementation of knowledge
management practices. It unravels that the teachers in self-financing
engineering colleges demand technological augmentation and more
unique learning’s to meet the various exigencies in their
administration.
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5.1.11 KNOWLEDGE MANAGEMENT CLIMATE SURVEY
In this study, 21 variables of knowledge management climate
are taken into account. All 21 factors are considered for factor
analysis by the Principal Component Method. The following
classification of factors is obtained:
Number of factors for knowledge management climate survey
Component Initial Eigen values Rotation Sums of Squared
Loadings
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
% 1 7.497 35.701 35.701 4.132 19.678 19.678
2 3.242 15.437 51.138 3.769 17.945 37.623
3 1.671 7.956 59.095 2.745 13.071 50.694
4 1.468 6.990 66.085 2.245 10.689 61.382
5 1.015 4.832 70.917 2.002 9.535 70.917
6 .926 4.411 75.328
7 .863 4.108 79.436
8 .769 3.663 83.098
9 .519 2.469 85.568
10 .458 2.181 87.749
11 .418 1.988 89.737
12 .406 1.935 91.672
13 .326 1.553 93.225
14 .294 1.402 94.627
15 .261 1.241 95.868
16 .219 1.042 96.910
17 .174 .828 97.738
18 .168 .801 98.539
19 .125 .595 99.134
20 .108 .513 99.647
21 .074 .353 100.000
From table above, it is found that all 21 variables of knowledge
management climate are grouped into five major factors. These
variables explain 70.917 per cent of total variance.
Chart 5.1.11
0
2
4
6
8
1 3 5 7 9 11 13 15 17 19 21
Eig
en
Va
lue
s
Component
Scree Plot
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The above graph clearly indicates how successive factors are
accounting for smaller and smaller amounts of the total variance.
Table given below clearly explains the variable loadings on each
factor:
Variable loadings for the factors of knowledge management climate
survey Item No.
Variables/Factors Variable Loadings
Factor I – Cultural Changes
18 Pro-action (tendency to think ahead of the problem) 0.885
19 Authenticity (Teachers say what they mean and mean what they say)
0.879
17 Autonomy (degree of freedom enjoyed by the teachers) 0.843
16 Trust (extent of faith teachers have for one another) 0.744
20 Collaboration (teacher’s team spirit) 0.644
14 Openness (freedom of expression) 0.528
Factor II – General Climate
5 The top management of your engineering college makes efforts to identify and utilize the potentials of the teachers
0.801
7 There are mechanisms to reward good work done or any contribution made by teachers
0.764
4 The psychological climate in your engineering colleges is very conducive and teachers enjoy performing their duties
0.747
13 Welfare orientation is prevalent in your engineering colleges 0.679
3 The personnel policies of your engineering colleges facilitate teachers’ development in career
0.575
Factor III – Knowledge Management Mechanism Climate
6 Promotions are merit based 0.880
12 There is equal distribution of work and this helps in Job enrichment
0.767
10 Training is taken seriously and used for organisational and individual developments
0.665
8 Performance appraisal reports are based on objective assessment and actual performance evaluation
0.553
11 The engineering colleges has programmatic policies and procedures
0.535
Factor IV - Empowerment
1 The top management believes that human resources are extremely important
0.722
9 There is encouragement for innovation & flow of ideas 0.682
2 Development of the subordinates is seen as an important part of their job by superiors
0.497
Factor V – Risk Management
21 Experimentation (existence of supporting environment to take risk and innovate)
0.772
15 Confrontation (willingness to face and solve problems rather than to avoid)
0.643
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Knowledge management climate refers to the tendency and
environment where the development of individuals and teams gains
the highest priority and human resources become the most important
resources. It is derived analytically that the knowledge management
practices inflate the domain of working environment with positive
cultural changes. It also enlightens their teachers to have constructive
potentiality to meet the risks involved in their official path. An optimal
level of “Developmental Climate” is essential for facilitating knowledge
management. Such a developmental climate can be characterized as
consisting of the following tendencies on the part of the self-financing
engineering colleges:
Cultural Change (Pro-action, Autonomy, Trust, Collaboration
& Openness)
General Climate (Welfare orientation, Personnel policies,
Psychological climate, Mechanism to reward good work done
by the teachers)
Knowledge Management Mechanism Climate (Promotions,
Equal distribution of work, Performance appraisal, Training
needs)
Empowerment (Encouragement for innovation & flow of
ideas, Human resources are extremely important,
Development of the subordinates)
Risk Management (Experimentation and Confrontation)
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5.1.12 KNOWLEDGE MANAGEMENT OUTCOMES
Good knowledge management practices do make a difference on
many counts. They brace the internal capabilities of a college to deal
with the current or future challenges. Good knowledge management
practices also energize people. The commitment and motivation built
through good knowledge management practices can lead to hard work
and can have a multiplier effect in transforming of human capital to
organisational capital. The culture so built up can help to create
sustainable and lasting capability of the college to manage itself. It
can cope with the external turbulence and even encash on the
opportunities offered by the changing environment.
Factor analysis by the principal component method is applied
on all 14 variables of knowledge management outcomes variables
considered in this study. The following results are obtained with
regard to classification of the factors.
Number of factors for knowledge management outcomes
Component Initial Eigen values Rotation Sums of Squared
Loadings
Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
% 1 7.696 54.972 54.972 6.098 43.559 43.559
2 1.675 11.965 66.937 3.273 23.378 66.937
3 .998 7.129 74.067
4 .815 5.821 79.888
5 .594 4.241 84.129
6 .481 3.436 87.565
7 .321 2.295 89.860
8 .311 2.224 92.085
9 .253 1.809 93.894
10 .244 1.740 95.634
11 .215 1.539 97.173
12 .176 1.260 98.433
13 .139 .991 99.423
14 .081 .577 100.000
It is evident from the table above that, all 14 variables of
knowledge management outcomes are classified into two major factors
which are named knowledge management outcomes and
Accomplishment respectively. These variables explain 66.937 per cent
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of total variance. The graph below clearly depicts the relationship
between eigen values and factors.
Chart 5.1.12
Scree plot
0
2
4
6
8
10
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Component
Eig
en
Valu
es
Table below gives the variables and variable loadings on each factor.
Variable loadings for the factors of knowledge management outcomes
Item No.
Variables/Factors Variable Loadings
Factor I – Knowledge Management Outcomes
3 Achieve higher job satisfaction & work motivation 0.858
6 Enable retention of best performing teachers 0.856
5 Improve efficacy of teachers 0.853
7 Enhance the individual role and contribution 0.797
2 Get more competent teachers 0.788
4 Achieve more problem solving strategies 0.782
14 Achieve better organizational health 0.766
9 Attain more team work, synergy, and respect for each other 0.595
1 Increase teacher loyalty and retention 0.580
8 Achieve higher work accomplishment and greater job involvement 0.514
Factor II - Accomplishment
10 Improve productivity of engineering colleges 0.890
12 Improve profitability 0849
11 Enhance customer service 0.683
13 Utilize human resources fully and properly 0.587
Improvement in profitability and productivity of engineering
colleges and enhancing customer service are some of the
accomplishments achieved by self-financing engineering colleges.
Finally it is observed that utilizing human resources properly and to
the maximum is the final outcome of knowledge management
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mechanism. It is also inferred that the knowledge management
practices in self-financing engineering colleges have achieved a casket
of useful administrative results and college goals.
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5.1.13 ORGANISATIONAL EFFECTIVENESS
Factor analysis by the principal component method is applied
on all 12 variable of Organization Effectiveness. The following results
are obtained:
Number of factors for Organizational Effectiveness
Component Initial Eigen values Rotation Sums of Squared
Loadings Total % of
Variance
Cumulative
%
Total % of
Variance
Cumulative
% 1 6.093 50.778 50.778 4.706 39.216 39.216
2 1.645 13.705 64.483 3.032 25.267 64.483
3 .853 7.107 71.590
4 .772 6.437 78.027
5 .598 4.983 83.009
6 .476 3.968 86.977
7 .414 3.453 90.431
8 .348 2.901 93.331
9 .270 2.247 95.578
10 .227 1.890 97.468
11 .191 1.594 99.063
12 .112 .937 100.000
It is clear from the above table that 12 variables of
organizational effectiveness have emerged as only two factors labeled
as Perceptive advantages and Reengineering respectively. These
variables explain 64.483 per cent of total variance. The graph below
shows the eigen values against the factor number.
Chart 5.1.13
Scree Plot
0
1
2
3
4
5
6
7
1 2 3 4 5 6 7 8 9 10 11 12
Component
Eig
en
Valu
es
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Table given below clearly explains the variables and variable
loadings on each factor.
Variable loadings for the factors of Organizational Effectiveness Item No.
Variables/Factors Variable Loadings
Factor I – Perceptive Advantages
11 Working environment becomes conducive 0.810
12 Improves the public image 0.797
5 Improves the profitability 0.779
8 Better customer service 0.749
7 Overall College efficiency increased 0.747
1 Increases efficiency of the teachers 0.747
9 Effective communication channel at all levels 0.668
2 Helps improve motivation and commitment level of teachers 0.649
Factor II - Reengineering
4 Teachers are capable of adoption of new technology 0.862
6 Leads to cost reduction in engineering colleges operations 0.835
3 Gives higher level of job satisfaction 0.754
10 Organizational climate is redefined 0.738
Profitability, Public image, customer service, working
environment, communication channel, motivation and commitment
level of teachers are some of the perceptive advantages to measure the
impact of knowledge management practices and knowledge
management climate on Organizational Effectiveness. Self-financing
engineering colleges are capable of reengineering their operations and
capable of adoption of new technology.
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5.1.14 KNOWLEDGE MANAGEMENT PROCESS CAPABILITY
In this study, 26 variables of KM climate are taken into account.
All 26 factors are considered for factor analysis by the Principal
Component Method. The following classification of factors is obtained:
Number of factors for KM Process Capability
Component
Initial Eigen values Rotation Sums of Squared
Loadings
Total % of
Variance Cumulative
% Total % of
Variance Cumulative
%
1 6.356 31.891 31.891 4.375 20.976 20.976
2 2.242 14.937 46.828 3.896 18.495 39.471
3 1.254 8.556 55.384 2.575 14.701 54.172
4 1.349 6.99 62.374 2.126 11.869 66.041
5 1.002 4.832 67.206
6 0.875 4.411 71.617
7 0.836 4.308 75.925
8 0.697 3.663 79.588
9 0.501 2.469 82.057
10 0.418 2.181 84.238
11 0.402 1.988 86.226
12 0.397 1.935 88.161
13 0.362 1.753 89.914
14 0.298 1.692 91.606
15 0.252 1.541 93.147
16 0.209 1.425 94.572
17 0.183 0.928 95.5
18 0.179 0.909 96.409
19 0.167 0.815 97.224
20 0.127 0.717 97.941
21 0.097 0.557 98.498
22 0.083 0.453 98.951
23 0.073 0.354 99.305
24 0.063 0.253 99.558
25 0.058 0.233 99.791
26 0.054 0.209 100
From table above, it is found that all 26 variables of KM process
capability are grouped into four major factors. These variables explain
70.917 per cent of total variance. The graphical representation of the
same is given below:
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Chart 5.1.14
Scree Plot
0
1
2
3
4
5
6
7
1 3 5 7 9 11 13 15 17 19 21 23 25
Component
Eig
en
Valu
es
The table below clearly explains the variable loadings on each
factor:
Number of factors for KM Process Capability Item No.
Variables/factors Variable Loadings
Factor I – Knowledge Acquisition
1 Our college has internal processes for generating new knowledge from
existing knowledge. 0.885
2 Our college has processes for distributing knowledge throughout the organization
0.879
3 Our college has processes for exchanging knowledge with external partners
0.843
4 Our college has processes for acquiring knowledge about new products and services within our industry
0.744
5 Our college has processes for acquiring knowledge about competitors within our industry
0.644
6 My college has a culture of mentoring new staff and sharing knowledge with them
0.545
Factor II – Knowledge Conversion
7 Our college has processes for filtering knowledge. 0.801
8 Our college has processes for transferring institutional knowledge to employees.
0.764
9 Our college has processes for absorbing knowledge from employees into the institution.
0.747
10 Our college has processes for integrating different sources and types of knowledge
0.679
11 Our college has processes for organizing knowledge. 0.575
12 Our college has processes for replacing outdated knowledge. 0.502
13 Our college has processes for converting knowledge into the design of new products/services.
0.476
Factor III – Knowledge Application
14 Has processes to apply knowledge learned from mistakes. 0.880
15 Has processes for applying knowledge learned from experience. 0.767
16 Has a process for using knowledge to solve new problems. 0.665
17 Makes knowledge accessible to those who need it. 0.553
18 Uses knowledge to improve efficiency. 0.535
19 Is able to locate and apply knowledge to changing competitive 0.422
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conditions
Factor IV – Knowledge Protection
20 Has a process to protect knowledge from inappropriate use inside the institution.
0.722
21 Has a process to protect knowledge from inappropriate use outside the institution.
0.682
22 Has a process to protect knowledge from theft from within the institution.
0.497
23 Has a process to protect knowledge from theft from outside the institution.
0.772
24 Have incentives that encourage the protection of knowledge. 0.643
25 Has technology that restricts access to some sources of knowledge. 0.531
26 Have extensive policies and procedures for protecting trade secrets. 0.474
The 26-item Knowledge Management Process Capability Scale
consisted of four dimensions: knowledge acquisition, knowledge
protection, knowledge conversion, and knowledge application. The
self-financing engineering colleges though have the potential for
building and using effectively the knowledge management, it looks like
they are little skeptical to the same. In the case of knowledge
acquisition, they score on the availability of the processes but not
monitored and tracked. With regard to knowledge conversion, it is
only happening with a select category of elite teachers within the
respective colleges. As far as the knowledge application is concerned,
the colleges come good. When it comes to protection of the knowledge
few colleges have the right mechanisms in place and also use the
same to track and monitor loss of knowledge.
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5.2 ONE SAMPLE T-TEST
So far the application of factor analysis has reduced the
numerous variables of knowledge management practices in self-
financing engineering colleges. It has also facilitated to ascertain the
pioneering factors of knowledge management practices in the context
of functioning of self-financing engineering colleges. Now it becomes
necessary to analyze the contribution of every factor derived in the
factor analysis. The One Sample t-test is found appropriate for
microscopic parametric analysis of the major factors. In this, test
mean, mean values and their significance with the test values yield a
host of useful results furnished below. In this t-test, the test value is
taken as 3 and 4, which is score of ‘Neutral’ and ‘Agree’ respectively
i.e. the option given in the questionnaire. The significance of the
factors is tested through one sample t-test.
5.2.1 ONE SAMPLE-T TEST FOR THE FACTORS OF NEED FOR
KNOWLEDGE MANAGEMENT
In self-financing engineering colleges, factors such as,
‘Prerequisites’, ‘Imperatives’, ‘Change Management’ and
‘Restructuring’ determine the need for knowledge management. All
these factors are mostly practiced by the self-financing engineering
colleges; it is necessary to identify the major factor, which is the basis
for knowledge management need.
The four factors obtained from the variables of ‘Need for
knowledge management’ are tested with the parametric one sample t-
test. The values of mean, t-test value and t-value and significance
from the test are enunciated in the table given below:
One Sample Test for Need for Knowledge Management Factors N Mean Test Value t-value Significance
Prerequisites 507 4.31 4 6.575 .000
Imperatives 507 4.13 4 2.710 .008 *
Change Management 507 3.98 3 22.093 .000
Restructuring 507 3.65 3 14.405 .000
* Insignificant
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From the table above, the mean values 4.31, 4.13, 3.98, and
3.65 are obtained for the factors ‘Prerequisites’, ‘Imperatives’, ‘Change
Management’, and ‘Restructuring respectively. The t-test value for the
test value 4 confirms that the mean value of prerequisites is
significantly greater than 4 at 5 percent level of significance. This
again confirms that the respondents strongly agree to the factor
‘Prerequisites’. However, the t-test value for mean value of Imperatives
with the test value 4 is insignificant at 5 percent level of significance.
This shows that the teachers in self-financing engineering colleges
have agreed to the Imperatives like corporate restructuring, changed
teacher expectations, and renewed focus on students.
It is also inferred from the above table that the t-test value for
the test value 3 reveals that the mean values of Change management
and Restructuring are significantly greater than 3 at 5 percent level of
significance. This shows that the teachers in self-financing
engineering colleges have agreed that the Change management and
Restructuring are important for knowledge management need. The
comparison of the mean values of these four factors reveals that
prerequisites have emerged as ‘predominant factor’ of knowledge
management need. It is concluded that the Prerequisites like new
engineering college’s products/services; Quality awareness, increased
competition, IT revolution, and Liberalization, Privatization, and
Globalization are predominant variables among other variables of
knowledge management need. It is also observed that the imperatives
accelerate the knowledge management practices towards constructive
Change management techniques and Revitalizations of their teachers’
potential.
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5.2.2 ONE SAMPLE T-TEST FOR FACTORS OF GENESIS OF
KNOWLEDGE MANAGEMENT PRACTICES
The genesis of knowledge management practices in the self-
financing engineering colleges are determined by three factors namely;
‘Work Environment’, ‘Personality Development’ and ‘Team Building’
and they contribute significantly to the self-financing engineering
colleges. It is necessary to identify the major factors contributing to
the welfare of the self-financing engineering colleges. This object can
be realized by comparing the mean values of these two factors. The
three factors obtained from the variables of ‘Genesis of knowledge
management practices’ are tested with parametric one sample t-test to
find out the significance. The values of mean, t-test value and t-value
and significance from the test are exhibited in the table given below:
One Sample T-Test for Genesis of Knowledge Management practices
Factors N Mean Test
Value t-value Significance
Work Environment 507 4.19 4 4.006 .000
Personality
Development 507 4.58 4 15.674 .000
Team Building 507 4.17 4 4.445 .000
From the table above, the mean values 4.19, 4.58, and 4.17 are
obtained for ‘Work Environment’, ‘Personality Development’, and
‘Team Building’ respectively. The t-test value for the test value 4
shows that the mean values of Work Environment, Personality
Development and Team Building are significantly greater than 4 at 5
percent level of significance. This shows that the teachers in self-
financing engineering colleges have strongly agreed that all three
factors are very much important for the betterment of the college.
The comparison of mean values of Work Environment,
Personality Development, and Team Building reveals that the teachers
in self-financing engineering colleges have given greater emphasis on
Personality Development than the other two factors. It is very clear
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from the above table that the mean value of Personality Development
is more than the mean value of other two factors. Hence, it is
concluded that the teachers in self-financing engineering colleges
achieve the college development by way of developing their individual
personality and team building.
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5.2.3 ONE SAMPLE T-TEST FOR FACTORS OF PERFORMANCE
APPRAISAL SYSTEM
The Performance appraisal system in the self-financing
engineering colleges will continue to be linked with the Promotion
System. However, it will be looked upon as an information source for
many other kinds of decisions including training and placement. In
that context, Performance appraisal system will become a continuous
process of getting information and providing feedback.
Performance Appraisal helps the appraisee to set directions for his
work, identify the areas of significant contributions and avoid wastage
of time. Performance reviews facilitate sharing of one’s perspectives,
difficulties and expectations to the seniors and get more support for
improving one’s performance. Performance appraisal should be viewed
as instruments of development for every teacher in self-financing
engineering colleges. Based on the performance appraisal data, the
following categories of development decisions are taken by the self-
financing engineering colleges. They are organizing training
programmes for teachers, sponsoring teachers for external training,
job rotation, career development, potential development and
delegation.
Work Culture, Futuristic Strategy, Guiding Value, Review and
Feedback are the factors that emerge out of 14 variables in appraising
the performance of teachers in self-financing engineering colleges.
Even though these five factors are the basis for appraising the
performance of teachers in self-financing engineering colleges, it is
better to know the predominant factor ensuring effective
implementation of Performance appraisal system. The five factors,
which are obtained from the variables of Performance appraisal
system, are tested with parametric one sample t-test to find out the
significance. The values of mean, t-test value and t-value and
significance from the test are exhibited in the table below:
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One Sample T-Test for Performance Appraisal
Factors N Mean Test Value t-value Significance
Work Culture 507 3.86 3 15.880 .000
Futuristic Strategy 507 3.83 3 14.354 .000
Guiding Value 507 4.36 4 6.736 .000
Review 507 3.55 3 10.626 .000
Feedback 507 3.72 3 13.542 .000
From the table above, the mean values 3.86, 3.83, 4.36, 3.55,
and 3.72 are obtained for the factors ‘Work culture’, ‘Futuristic
strategy’, ‘Guiding value’, ‘Review’, and ‘Feedback’. The t-test value for
the test value 3 reveals that the mean values of Work Culture,
Futuristic Strategy, Review and Feedback are significantly greater
than 3 at 5 percent level of significance. Obviously the teachers in
self-financing engineering colleges are of the firm opinion about the
factors i.e. Work Culture, Futuristic Strategy, Review, and Feedback
for appraising their performance.
However, the t-test value for mean value of the factor Guiding
Value with the test value 4 is significantly greater than 4 at 5 percent
level of significance. This shows that the teachers in self-financing
engineering colleges have strongly agreed with the factor ‘Guiding
Value’ as inputs for recognition and encouragement of high
performers. By comparing the mean values of all the factors of
Performance Appraisal, it is concluded that the third factor ‘Guiding
Value’ is a predominant factor among the five factors of Performance
Appraisal System. The self-financing engineering colleges adopt an
appraisal system to guide their teachers to reach the goals within a
short span of time.
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5.2.4 ONE SAMPLE T-TEST FOR FACTORS OF CAREER PLANNING
Every teacher sets his heart on the career advancement and
better opportunities to use his talents. In fact, for most teachers,
career advancement is the most motivating factor in self-financing
engineering colleges. From the engineering colleges’ perception, it is
necessary to develop in teachers some expectation of opportunities for
the future in order to keep their motivation high. Career planning does
not mean predicting which higher jobs will be available for each
person. It essentially means helping the teacher plan his career in
terms of his capabilities within the context of college needs. Career
planning need not imply any specific commitment on the part of the
management to promote a teacher. It only implies that an individual,
after becoming aware of some of his capabilities and career
development opportunities, tends to develop himself in a direction that
improves his chances of being able to handle new responsibilities.
‘Teachers Commitment’, ‘Awareness’ and ‘Career Advancement’
are the three factors emerged out of 7 variables of ‘Career Planning
and Development’. The three factors, which are obtained from the
variables of ‘Career Planning and Development’, are tested with
parametric one sample t-test to find out the significance. The values of
mean, t-test value and t-value and significance from the test are
exhibited in the table given below:
One Sample T-Test for Career Planning and Development
Factors N Mean Test
Value
t-value Significance
Teachers
Commitment
507 3.65 3 15.092 .000
Awareness 507 3.59 3 6.881 .000
Career
Advancement
507 4.41 4 8.706 .000
From the t-test values shown in the table above, the mean
values 3.65, 3.59 and 4.41 are obtained for the factors ‘Teachers
Commitment’ ‘Awareness’ and ‘Career Advancement’ respectively. The
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t-test value for the test value 3 shows that the mean values of
Teachers Commitment and Awareness are significantly greater than 3
at 5 percent level of significance. This shows that the teachers in self-
financing engineering colleges have agreed with the factors Teachers
Commitment and Awareness for their personal and college
development.
However, the t-test value for mean value of ‘Career
Advancement’ with test value 4 is significantly greater than 4 at 5
percent level of significance. This shows that teachers in self-financing
engineering colleges have strongly agreed with the factor ‘Career
Advancement’ for their personal development. By comparing the mean
values of all three factors of career planning and development, it is
inferred that the factor Career Advancement is predominant factor.
Career Planning helps the teachers to plan their career – it is the only
variable included in the factor Career Advancement. The teachers as
well as the management of self-financing engineering colleges have
parallel views convenient to the teacher’s advancement in their official
process.
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5.2.5 ONE SAMPLE T-TEST FOR FACTORS OF TRAINING AND
DEVELOPMENT
Training is the most important function in private engineering
colleges that directly contributes to the development of human
resources. If human resources have to be developed, the engineering
colleges should create conditions in which people acquire new
knowledge and skills and develop healthy patterns of behaviour and
styles. Training becomes integral in the self-financing engineering
colleges because technology is developing continuously and
constantly. Systems and practices become obsolete with the new
discoveries in technology, including technical, managerial and
behavioural aspects.
In self-financing engineering colleges, Training and
Development are determined by the factors namely, ‘Training
Mechanisms’, ‘Training Infrastructure’, and ‘Inductive Training’.
These three factors are the foundation for training and development
in the self-financing engineering colleges. It is necessary to identify
the predominant factor among these three factors by resorting to
comparing the mean values of these factors. The three factors, which
are obtained from the variables of ‘Training and Development’, are
tested with parametric one sample t-test to find out the significance.
The level of significance is tested and the result of the t- test is
shown in the table given below:
One Sample T-Test for Training and Development
Factors N Mean Test
Value t-value Significance
Training Mechanism 507 4.01 4 0.186 0.852 *
Training
Infrastructure 507 3.84 3 16.087 .000
Inductive Training 507 3.96 3 19.962 .000
* Insignificant
From the above table, the mean values 4.01, 3.84, and 3.96 are
obtained for the factors ‘Training Mechanism’, ‘Training
Infrastructure’, and ‘Inductive Training’ respectively. The t-test value
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for the test value 4 reveals that the mean value of Training Mechanism
is insignificant at 5 percent level of significance. This means that the
respondents in the self-financing engineering colleges have agreed
with Training Mechanism as one of the instruments of Training and
Development.
However, the t-test value for mean values of ‘Training
Infrastructure’ and ‘Inductive Training’ with the test value 3 is
significantly greater than 3. It is inferred that the respondents in self-
financing engineering colleges are of the opinion that both Training
Infrastructure and Inductive Training are indispensable for Training
and Development. When the mean values of all three factors of
Training and Development are compared, the inference is that the
mean value of the factor ‘Training Mechanism’ is greater than the
mean values of other two factors and anchoring itself as a
predominant factor. The teachers in self-financing engineering colleges
are more concerned with the Training Mechanism, since it increases
knowledge in handling latest technological innovations in the
educational sector. It is found that a powerful training mechanism is
indispensable to impart training to their teachers.
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5.2.6 ONE SAMPLE T-TEST FOR FACTORS OF JOB ROTATION
Working in the same job continuously for several years without
much change may have demotivating effects. Some private engineering
colleges plan job rotation like asking Mathematics teacher to handle
Quantitative subjects in MBA program as a mechanism of maintaining
the motivation of teachers. Training is crucial in preparing the
teachers before placing them in a new job. Job rotations can be
planned on the basis of potential appraisal. Job rotation opens up
opportunities for an individual to test and develop his potential.
‘Orientation’, ‘Accommodative Approach’ and ‘Internal Check’
are the three factors emerged from 8 variables of job rotation in self-
financing engineering colleges. These three factors are widely
practised in self-financing engineering colleges and it is necessary to
figure out the predominant one which contributes much to the
private engineering colleges. The three factors obtained from the
variables of ‘Job Rotation’, are tested with parametric one sample t-
test to find out the significance. The level of significance is tested
and the result of the t- test is shown in the table given below:
One Sample Test for Job Rotation
Factors N Mean Test Value
t-value Significance
Orientation 507 3.98 3 26.773 .000
Accommodative Approach
507 3.29 3 4.419 .000
Internal Check 507 4.43 4 8.787 .000
From the above table, the mean values 3.98, 3.29, and 4.43 are
obtained for the factors ‘Orientation’, ‘Accommodative Approach’, and
‘Internal Check’ respectively. The t-test value for the test value 3
shows that the mean values of ‘Orientation’ and ‘Accommodative
Approach’ are significantly greater than 3 at 5 percent level of
significance. Hence, the teachers in self-financing engineering colleges
concede the essential of ‘Orientation’ and ‘Accommodative Approach’
for job rotation in their college. However, the t-test value for mean
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value of ‘Internal Check’ with the test value 4 being significantly
greater than 4. It is concluded that the teachers in self-financing
engineering colleges have also agreed with the factor ‘Internal Check’
for job rotation.
A microscopic analysis on the variables of each factor of job
rotation reveals that the variable consultation for posting decision
(Mean 2.99; t= -0.078) is not significant with respect to the test value
3 (t-test significance value is more than (0. 05). In particular, it has
been highlighted that the teachers in self-financing engineering
colleges are not eloquent about the consultation for posting decisions.
The comparison of the mean values of these three factors,
allows that the mean value of ‘Internal Check’ is more than the other
two factors and endorses that job rotation acts as a preventive
vigilance measure against frauds, mistakes and procedural lapses.
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5.2.7 ONE SAMPLE T-TEST FOR FACTORS OF TEACHER WELFARE
AND REWARD SYSTEM
Teachers are likely to give the best of them, if there is a
guarantee of recognizing their effort and they be rewarded. Rewarding
a teacher means giving him something more than what is usually
given to teachers at his level. When reward comes as a mark of
recognition for the job done well, the teacher feels motivated. This
recognition may be in terms of tangible benefits or non-tangible ones
such as a certificate.
All 12 variables of ‘Teacher Welfare and Reward system’ in self-
financing engineering colleges are emerged with three factors namely
‘Ideal Welfare Measures’, ‘Accountability’ and ‘Loyalty’. To identify the
major factor contributing to ideal welfare and reward system in self-
financing engineering colleges, the comparison of the mean values of
these factors becomes imperative.
One sample t-test is applied on these two factors of ‘Teacher
Welfare and Reward System’ in self-financing engineering colleges and
the following mean values and t-values are obtained.
One Sample Test for Teacher Welfare and Reward System
Factors N Mean Test
Value t-value Significance
Ideal Welfare
Measures 507 3.87 3 16.914 .000
Accountability 507 3.82 3 18.737 .000
Loyalty 507 4.23 4 4.778 .000
From the table above, the mean values 3.87, 3.82, and 4.23 are
obtained for the factors ‘Ideal Welfare Measures’, ‘Accountability’, and
‘Loyalty’ respectively. It is also found that the factors ‘Ideal Welfare
Measures’ and “Accountability’ are proved to be significantly greater
than 3 at 5 percent level of significance. This makes it certain that the
respondents have agreed with these factors. The factor ‘Loyalty’ is
proved to be significantly greater than 4 at 5 percent level of
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significance and that makes it clear that the respondents strongly
agree with the factor ‘Loyalty’.
When the mean values of all the three factors are compared, the
mean value of ‘Loyalty’ is higher than the other two factors. This leads
to the inference that the factor ‘Loyalty’ is a dominant factor among
the three factors of teacher welfare and reward system. It is concluded
that the teachers in self-financing engineering colleges feel that their
loyalty is inseparable from welfare and reward system. It is concluded
that the top-level management in self-financing engineering colleges
has realized the loyal and trustworthy nature of their teachers. In fact,
they accrue maximum benefits by announcing suitable rewards to
their teachers.
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5.2.8 ONE SAMPLE T-TEST FOR FACTORS OF OTHER PRACTICES
‘Quality Management’ and ‘Participative Management’ are the two
factors formed out of 7 variables of other mechanisms in self-financing
engineering colleges. One sample t-test is applied on these two factors
of other mechanisms in self-financing engineering colleges and the
following mean values and t-values are obtained.
One Sample T-Test for other Mechanisms/ Sub-Systems
Factors N Mean Test
Value
t-value Significance
Quality Management
507 3.60 3 12.932 .000
Participative
Management
507 3.54 3 11.132 .000
From the table above, the mean values 3.60 and 3.54 are
obtained for the factors ‘Quality Management’ and ‘Participative
Management’ respectively. The t-test values for the test value 3 reveal
that the mean values of Quality Management and Participative
Management are significantly greater than 3 at 5 percent level of
significance. This shows that the respondents agree with these factors.
Hence, it is inferred that the respondents in self-financing engineering
colleges have accepted both factors of other mechanisms for individual
and college development.
The comparison of mean values of the two factors leads to the
findings that the mean value of the factor ‘Quality Management’ is
more than the factor ‘Participative Management’. It is concluded that
the respondents in self-financing engineering colleges are more
concerned about ‘Quality Management’ than ‘Participative
Management’
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5.2.9 ONE SAMPLE T-TEST FOR FACTORS OF PROBLEMS AND
DIFFICULTIES IN IMPLEMENTING KNOWLEDGE MANAGEMENT PRACTICES
Problems and difficulties in implementing the knowledge
management practices in self-financing engineering colleges are
determined by the factors namely, ‘Internal Defects’ and ‘Resistance’.
As they are hurdles in the effective implementation of knowledge
management practices in self-financing engineering colleges, it is
beneficial to know the vital factor among them. This becomes possible
by comparing the mean values of these factors. These two factors are
obtained from the variables of ‘Problems and Difficulties in
implementing knowledge management practices’ are tested with
parametric one sample t-test. The values of mean, test value and t-
value and significance from the test are exhibited in the table given
below:
One Sample T-Test for Problems and Difficulties in Implementing
Knowledge Management practices
Factors N Mean Test Value t-value Significance
Internal Defects 507 3.00 3 0.066 .947 *
Resistance 507 2.65 3 -5.829 .000
* Insignificant
From the above table, the mean values 3.00 and 2.65 are
ascertained for the factors ‘Internal Defects’ and ‘Resistance’
respectively. The t-test value for the test value 3 clearly reveals that
the mean value of ‘Internal Defects’ is insignificant at 5 percent level of
significance. This makes it clear that the respondents neither agree
nor disagree with this factor. However, the t-test value for the mean
value of ‘Resistance’ with test value 3 is significantly greater than 3 at
5 percent level of significance. This makes it clear that the
respondents disagree with this factor.
A microscopic analysis on the variables of each factor reveals
that the variable ‘Absence of effective Performance Appraisal System’
which has obtained the mean score less than 3 is not significantly
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proved because the t-test value is more than 0.05 at 5% level of
significance. This implies that the respondents in self-financing
engineering colleges neither agree nor disagree with respect to the
above variable.
The following other variables which have obtained the mean
score less than 3 has significantly proved because the t-test value is
less than 0.05 at 5% level of significance.
3. Lack of trust on teachers (Mean = 2.56 ; t-test significance
value = .000)
6. Improper/Inadequate training methods (Mean = 2.80; t-test
significance value = .002)
2. Resistance to take risks (Mean = 2.47 ; t-test significance
value = .000)
1. Resistance from the teachers/trade union (Mean = 2.75; t-
test significance value = .001)
4. Lack of willingness of teachers to accept change (Mean =
2.73; t-test significance value = .001)
It is concluded that the respondents in self-financing
engineering colleges have disagreed with respect to the above
variables. By comparing the mean values of these two factors, it is
inferred that ‘Internal Defects’ is a dominant factor. It is concluded
that the teachers in self-financing engineering colleges are more
worried about the internal defects, which are prevailing, in the
engineering college system while implementing the knowledge
management practices.
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5.2.10 ONE SAMPLE T-TEST FOR FACTORS OF SUGGESTIONS FOR
EFFECTIVE IMPLEMENTATION OF KNOWLEDGE MANAGEMENT PRACTICES
It is obvious that the Indian educational sector needs an
effective and well-knitted human resource development programme to
tackle the newly emerging situation. The engineering college system
has significantly assumed the character of need-based and purpose-
oriented activity from that of profit-oriented and security-based
activity.
Conducive knowledge management climate should be developed
for the effective implementation of knowledge management practices
in self-financing engineering colleges. ‘Internal Orientation’, ‘Modern
Techniques’ and ‘External Learning’ are the three factors have
emerged from the 11 variables of suggestions for the effective
implementation of knowledge management practices. Though these
factors are widely practiced/accepted by the teachers in their private
engineering colleges, it is beneficial for them to identify the
predominant factor providing optimistic suggestions.
These three factors, which are obtained from the variables of
‘Suggestions for Effective Implementation of knowledge management
practices’, are tested with parametric one sample t-test. The values of
mean, t-test value and t-value and significance from the test are
exhibited in the table given below:
One Sample T-Test for Suggestions for Effective Implementation of
Knowledge Management practices
Factors N Mean Test Value t-value Significance
Internal Orientation 507 4.42 4 11.525 .000
Modern Techniques 507 4.30 4 10.004 .000
External Learning 507 4.01 4 .0157 .876 *
* Insignificant
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From the table above, the mean values 4.42, 4.30, and 4.01 are
obtained for the factors ‘Internal Orientation’, ‘Modern Techniques’,
and ‘External Learning’ respectively. The t-test values for the test
value 4 clearly show that the mean values of Internal Orientation and
Modern Techniques are significantly greater than 4 at 5 percent level
of significance. This means that the respondents have strongly agreed
with the factors Internal Orientation and Modern Techniques as
suggestions for the effective implementation of knowledge
management practices in self-financing engineering colleges. However,
the t-test value for the test value 4 shows that the mean value of
External Learning is insignificant. This implies that the respondents
have agreed with the factor External Learning for the above purpose.
The comparison of the mean values of the three factors of
suggestions for effective implementation of knowledge management
practices discovers that the mean value of ‘Internal Orientation’ is
more than the other two factors. It is inferred that the respondents in
self-financing engineering colleges give much emphasis to ‘Internal
Orientation’. It is concluded that Accountability, Planned knowledge
management process, Better Communication, and Step-by-step
Implementation of knowledge management are some of the variables
included in the factor ‘Internal Orientation’. It is concluded that
accountability, planned knowledge management process,
communication and predefined implementation of knowledge
management practices have levered the teachers to perform their
duties with good mental vigour.
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5.2.11 ONE SAMPLE T-TEST FOR FACTORS OF KNOWLEDGE
MANAGEMENT CLIMATE SURVEY
The development climate of a college assumes a greater
importance in fostering its human resources. Private engineering
colleges should periodically survey its psychological climate and take
corrective action wherever needed. For any improvement in the
knowledge management climate of an engineering college, emphasis
should be given to the knowledge management policies and systems.
Cultural Change, General Climate, Knowledge Management
Mechanism Climate, Empowerment, Risk Management is the five
factors that have emerged from 21 variables of knowledge
management Climate Survey in self-financing engineering colleges.
These five factors obtained from the variables of ‘knowledge
management Climate Survey’ are tested with parametric one sample t-
test. The values of mean, t-test value and t-value and significance
from the test are exhibited in the table given below:
One Sample T-Test for Knowledge Management Climate Survey
Factors N Mean Test
Value t-value Significance
Cultural Changes 507 3.55 3 12.235 .000
General Climate 507 3.88 3 20.831 .000
Knowledge Management
Mechanism Climate 507 3.77 3 17.518 .000
Empowerment 507 4.19 4 5.565 .000
Risk Management 507 3.76 3 17.747 .000
From the above table, the mean values 3.55, 3.88, 3.77, 4.19,
and 3.76 are obtained for the factors ‘Cultural Change’, ‘General
Climate’, ‘Knowledge Management Mechanism Climate’,
‘Empowerment’, and ‘Risk Management’ respectively. The t-test values
for the test value 3 reveals that the mean values of Cultural Change,
General Climate, knowledge management Mechanism Climate and
Risk Management are significantly greater than 3 at 5 percent level of
significance. This means that the respondents have agreed with the
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factors Cultural Change, General Climate, knowledge management
Mechanism Climate, Empowerment and Risk Management as the
basis for creating conducive knowledge management climate in their
private engineering colleges.
However, the t-test value for the mean value of ‘Empowerment’
with the test value 4 is significantly greater than 4 at 5 percent level of
significance. This implies that the respondents have strongly agreed
with the factor ‘Empowerment’. The inference drawn is that
experimentation and confrontation are the foundations for
empowerment in the self-financing engineering colleges.
The comparison of the mean values of all the five factors of
knowledge management Climate Survey confirms that the mean value
of the factor ‘Empowerment’ is more than the other four factors. It is
concluded that the self-financing engineering colleges emphasize a
process replete with positive notions to trigger a powerful dynamism
among the teachers.
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5.2.12 ONE SAMPLE T-TEST FOR FACTORS OF KNOWLEDGE
MANAGEMENT OUTCOMES
Out of 14 variables of the knowledge management outcomes,
two factors have emerged in the self-financing engineering colleges i.e.
‘Knowledge Management Outcomes’ and ‘Accomplishment’. These two
factors obtained from the variables of ‘knowledge management
Outcomes’ are tested with parametric one sample t-test. The values of
mean, t-test value and t-value and significance from the test are
tabulated in the table given below:
One Sample T-Test for Knowledge Management Outcomes
Factors N Mean Test
Value
t-
value
Significance
Knowledge Management Outcomes
507 3.95 4 -1.110
.269 *
Accomplishment 507 3.91 4 -
1.985
.049
* Insignificant
From the table above, the mean values 3.95 and 3.91 are
obtained for the factors ‘Knowledge Management Outcomes’ and
‘Accomplishment’ respectively. When the mean values of the two
factors are compared the mean value of the factor ‘knowledge
management Outcomes’ becomes more than the factor
‘Accomplishment’. It is concluded that the respondents in the self-
financing engineering colleges have laid more emphasis on ‘knowledge
management Outcomes’ than ‘Accomplishment’. The factor ‘knowledge
management Outcomes’ which has obtained the mean score less than
4 is not significantly proved because the t-test value is more than 0.05
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at 5% level of significance. This makes it clear that the respondents
strongly agree with this factor.
The factor ‘Accomplishment’ which has obtained the mean
score less than 4 is significantly proved because the t-test value is
less than 0.05 at 5% level of significance. This makes it obvious that
the respondents in self-financing engineering colleges agree with this
factor. Hence, it is inferred that the respondents in self-financing
engineering colleges have accepted both the factors of knowledge
management Outcomes as the impact of proper implementation of
knowledge management practices.
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5.2.13 ONE SAMPLE T-TEST FOR FACTORS OF ORGANISATIONAL
EFFECTIVENESS
‘Perceptive Advantages’ and ‘Reengineering’ are the two major
factors that emerge from the 12 variables of ‘Impact of knowledge
management Mechanisms and knowledge management Climate on
Organisational Effectiveness’ in the self-financing engineering colleges.
These two factors obtained from the variables of ‘Impact of Knowledge
Management practices and Knowledge Management Climate on
Organizational Effectiveness’ are tested with parametric one sample t-
test. The values of mean, t-test value and t-value and significance
from the test are exhibited in the table given below:
One Sample Test for Impact of Knowledge Management practices and
Knowledge Management Climate on Organizational Effectiveness
Factors N Mean Test
Value t-
value Significance
Perceptive
Advantages 507 4.05 4 1.189 .236 *
Reengineering 507 4.09 4 1.589 .114 *
* Insignificant
From the table above, the mean values 4.05 and 4.09 are
obtained for the factors ‘Perceptive Advantages’ and ‘Reengineering’
respectively. The t-test values for the test value 4 make it known that
the mean values of Perceptive Advantages and Reengineering are
insignificant. This implies that the respondents have agreed that both
‘Perceptive Advantages’ and ‘Reengineering’ are the outcomes of
knowledge management practices and knowledge management
Climate in the self-financing engineering colleges.
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5.2.14 ONE SAMPLE T-TEST FOR FACTORS OF KNOWLEDGE
MANAGEMENT PROCESS CAPABILITY
The self-financing engineering colleges should take additional
measures in terms of acquiring knowledge, conversion of the same,
application and protection. The main focus should be on creating a
solid platform on which knowledge management activities can be
built.
Knowledge acquisition, Knowledge conversion, Knowledge
application and Knowledge protection are the four factors that have
emerged from the twenty six variables of Knowledge Management
Process capability in self-financing engineering colleges.
These four factors obtained are tested with parametric one
sample t-test. The values of mean, t-test value and t-value and
significance from the test are exhibited in the table given below:
One Sample T-Test for Knowledge Management Climate Survey
Factors N Mean Test Value t-value Significance
Knowledge Acquisition 507 4.85 3 11.352 .000
Knowledge Conversion 507 3.78 3 18.318 .000
Knowledge Application 507 3.59 3 15.718 .000
Knowledge Protection 507 3.15 4 6.655 .000
From the above table, the mean values 4.85, 3.78, 3.59, 3.15,
are obtained for the factors respectively. The t-test values for the test
value 3 reveals that the mean values are significantly greater than 3 at
5 percent level of significance. This means that the respondents have
agreed with the factors as the basis for establishing a knowledge
management process capability in their engineering colleges.
The comparison of the mean values of all the four factors of
Knowledge Management Process Capability confirms that the mean
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value of the factor Knowledge acquisition is more than the other
factors. It is concluded that the self-financing engineering colleges
emphasize a process replete with positive notions to trigger a powerful
dynamism among the teachers towards acquiring knowledge.
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5.3 CLUSTER AND DISCRIMINANT ANALYSIS - CLASSIFICATION OF
SELF-FINANCING ENGINEERING COLLEGES
Hither to, the application of one sample t-test has been useful to
test the significance of the factors of knowledge management
practices; knowledge management Climate and Outcomes and
Organizational effectiveness in self-financing engineering colleges.
Cluster analysis is yet another measure to group similar objects
together. Some measure of similarity is used to do this. It is a method
for classifying the variables into clusters. The use of Cluster analysis
is exploited in this perspective to classify the self-financing
engineering colleges into various heterogeneous groups. Each
heterogeneous group is homogeneous with in itself based on the
independent variables.
In this study, the researcher aims at classifying the colleges
based on their perception and approach towards knowledge
management practices. The knowledge management implementation
process is studied in three stages namely identifying the needs and
objectives of knowledge management, and the teachers’ knowledge of
the knowledge management practices and their ultimate effectiveness.
The multivariate cluster analysis is applied on the factors of three
stages of knowledge management implementation in the self-financing
engineering colleges. The hierarchical cluster is taken in as a support
to identify the number of classifications (clusters). The agglomeration
schedule and the emergence of the coefficients foretell the existence of
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heterogeneous groups of teachers determined by the perception level
of knowledge management implementation.
5.3.1 CLASSIFICATION OF RESPONDENTS BASED ON THEIR
PERCEPTION OF KNOWLEDGE MANAGEMENT NEEDS & GENESIS
The factor analysis manifests that the knowledge management
needs & objectives in self-financing engineering colleges are aimed at
‘Pre-requisites’, ‘Imperatives’, ‘Change Management’, ‘Restructuring,
‘Work Environment’, ‘Personality Development’ and ‘Team Building’.
Now an attempt is made to classify the teachers’ perception relating to
the factors of knowledge management needs and objectives. The K-
Means Cluster Analysis is applied on these factors by identifying the
coefficients of hierarchical clusters. The results are spaced out in the
following tables.
Final Cluster Centers
Factors Cluster 1 2
Prerequisites 4.51 3.77 Imperatives 4.37 3.51 Change Management 3.99 3.96 Restructurisation 3.76 3.38 Work Environment 4.41 3.60 Personality Development 4.76 4.11 Team Building 4.29 3.87
From the table above, it is understood that there are two groups
of teachers and they are formed based on their perception of the
factors of ‘Knowledge Management Needs and Objectives’. The
frequency obtained for each of these two groups is presented in the
table.
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Number of Cases
Cluster 1 365.000
2 142.000
Valid 507.000
From the above tables, it is found that two different
heterogeneous clusters towards knowledge management needs and
objectives exist among the teachers of self-financing engineering
colleges. It is observed that 72 percent of the teachers are found in
cluster one, and the remaining 28 percent of teachers in the second
cluster. It is further inferred that first cluster teachers posses a clear
understanding about the factors - Pre-requisites, Imperatives, Change
Management, Restructuring, Work Environment, Personality
Development, and Team Building. As far the factor ‘Change
Management’ is concerned the two clusters have an identical
perception.
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5.3.2 JUSTIFICATION OF THE CLUSTERS OF KNOWLEDGE
MANAGEMENT NEEDS AND GENESIS
The multivariate discriminant analysis is brought to bear on the
problem of justifying the number of clusters. In this analysis, the
cluster classification is considered as a grouping variable and the
factors of knowledge management needs and genesis are considered
as independent variables. The following results explain the profound
justification for heterogeneous clusters. The canonical correlation and
their values are presented in the table given below.
Eigen values
Function Eigen value % of Variance Cumulative % Canonical Correlation
1 4.498(a) 100.0 100.0 .904
Wilks' Lambda
Test of Function(s) Wilks' Lambda Chi-square df Sig.
1 .182 246.280 7 .000
From the Eigen values table, it is found that one discriminant
function has been formed with canonical correlation co-efficient 0.904.
The significance of discriminant function is established in the Wilks
Lambda table. The significance justifies the emergence of different
heterogeneous clusters from the cluster analysis.
The discriminant function is formed as follows to obtain the
perception level of teachers of self-financing engineering colleges about
Knowledge Management Needs and Objectives.
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Standardized Canonical Discriminant Function Coefficients
Factors Function
1 Y1 - Prerequisites .860 Y2 - Imperatives .620 Y3 - Change Management .025 Y4 - Restructurisation .049 Y5 - Work Environment .610 Y6 - Personality Development .359 Y7 - Team Building .237
Z1 = 0.860 x Y1 + 0.620 x Y2 + 0.025 x Y3 + 0.049 x Y4 + 0.610
x Y5 + 0.359 x Y6 + 0.237 x Y7.
The structure matrix is presented in the table 5.47 to explain
the significant factors present in the discriminant function.
Structure Matrix
Factors Function
1 Imperatives .433 Personality
Development
.400 Work Environment .391 Prerequisites .341 Team Building .205 Restructurisation .152 Change Management .012
This discriminant function and structure matrix prompts a
conclusion that the teachers in self-financing engineering colleges in
the awareness of all the factors of knowledge management needs and
objectives. From the Structure Matrix Table 5.47, it is found that the
top-level management in self-financing engineering colleges
distributes their importance equally to restructuring their
management, creating a conducive environment by developing an
optimistic mental outlook among teachers. They also concentrate
more on team building abilities to bring out the ardent enthusiasm of
their teachers.
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5.3.3 Classification of Colleges Based on Their Perception towards
Knowledge Management Mechanisms/Sub-Systems
The application of factor analysis of knowledge management
mechanisms in self-financing engineering colleges gives in the
emergence of 19 factors. Now an attempt is made to classify the
perception about the factors of knowledge management Mechanisms.
The K-Means Cluster Analysis is applied on these factors by
identifying the coefficients of hierarchical clusters. The mean scores
presenting the classification are obtained and the final cluster centers
of the factors are exhibited in the table given below.
Final Cluster Centers
Factors Cluster 1 2
Work Culture 3.99 2.81 Futuristic Strategy 3.95 2.84 Guiding Value 4.41 3.94 Review 3.62 2.94 Feedback 3.80 3.09 Teachers Commitment 3.73 2.96 Awareness 3.63 3.25 Career Advancement 4.49 3.81 Training Mechanism 4.11 3.22 Training Infrastructure 3.97 2.80 Inductive Training 4.04 3.31 Orientation 4.03 3.59 Accommodative Approach 3.37 2.63 Internal Check 4.48 4.00 Ideal Welfare Measures 3.98 2.96 Accountability 3.92 2.98 Loyalty 4.26 3.94 Quality Management 3.66 3.16 Participative Management 3.60 3.02
From the table above, it is found that there exist two groups of
clusters based on the perception of self-financing engineering colleges
about the factors of ‘knowledge management practices’. The frequency
obtained for each group is given in the below table .
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Number of Cases
Cluster 1 452.000
2 055.000
Valid 507.000
From the above tables, it is found that the first cluster of
respondents (452, 89.33 percent) strongly agrees with the factors of
‘Guiding Value’, ‘Career Advancement’, ‘Training Mechanisms’,
‘Inductive Training’, ‘Orientation’, ‘Internal Check’ and ‘Loyalty’. They
also agree to the factors ‘Work Culture’, ‘Futuristic Strategy’, ‘Review’,
‘Feedback’, ‘Teachers Commitment’, ‘Awareness’, ‘Training
Infrastructure, ‘Accommodative Approach, ‘Ideal Welfare Measures’,
‘Accountability’, ‘Quality Management’ and ‘Participative
Management’.
The second cluster of respondents in self-financing engineering
colleges (55, 10.67 percent) strongly agrees with one factor of ‘Internal
Check’ in knowledge management practices. They agree to the factors
of ‘Guiding Value’, ‘Feedback’, ‘Awareness’, ‘Career Advancement’,
‘Training Mechanisms’, ‘Inductive Training’, ‘Orientation’, ‘Loyalty’,
‘Quality Management’ and ‘Participative Management’. They are
indecisive about the factors ‘Review’, ‘Teachers Commitment’, ‘Ideal
Welfare Measures’ and ‘Accountability’. Finally, they disagree with the
factors of ‘Work Culture’, ‘Futuristic Strategy’, ‘Training
Infrastructure’, and ‘Accommodative Approach’. Finally, it is
concluded that the respondents in the first cluster are strongly in
favour of implementation of knowledge management practices in their
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private engineering colleges. Now some steps should be taken by the
self-financing engineering colleges to improve the morale of the
respondents in the second cluster.
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5.3.4 Justification of the Clusters of Knowledge Management Practices
The discriminant analysis is carried out to justify the cluster
classification. The cluster classification is considered as a grouping of
variable and the factors of ‘knowledge management practices’ are
considered as independent variable. The following results explained
the profound justification for heterogeneous clusters.
Eigen values
Function Eigen value % of Variance Cumulative % Canonical Correlation
1 2.195(a) 100.0 100.0 .829
Wilks' Lambda
Test of Function(s) Wilks' Lambda Chi-square df Sig.
1 .313 160.870 19 .000
From the Eigen values table, it is found that only one
discriminant function has been formed with canonical correlation co-
efficient 0.829. The significance of discriminant function is established
in the Wilks Lambda table. The significance justifies the emergence of
different heterogeneous clusters from the cluster analysis.
The standardized canonical discriminant function is formed as
follows to obtain the perception level of teachers of self-financing
engineering colleges about knowledge management practices.
Standardized Canonical Discriminant Function Coefficients
Factors Function
1 Y8 - Work Culture .411 Y9 - Futuristic Strategy .862 Y10 - Guiding Value -.271 Y11 - Review .046 Y12 - Feedback .301 Y13 - Teachers Commitment .113
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Y14 - Awareness -.356 Y15 - Career Advancement .651 Y16 - Training Mechanism .343 Y17 - Training Infrastructure .313
Y18 - Inductive Training .186 Y19 - Orientation .062 Y20 - Accommodative Approach .097 Y21 - Internal Check .232 Y22 - Ideal Welfare Measures -.591 Y23 - Accountability .391 Y24 - Loyalty -.289 Y25 - Quality Management -.425 Y26 - Participative Management .459
Z1 = 0.411 x Y8 + 0.862 x Y9 - 0.271 x Y10 + 0.046 x Y11 +
0.301 x Y12 + 0.113 x Y13 - 0.356 x Y14 + 0.651 x Y15 + 0.343
x Y16 + 0.313 x Y17 + 0.186 x Y18 + 0.062 x Y19 + 0.097 x Y20
+ 0.232 x Y21 - 0.591 x Y22 + 0.391 x Y23 - 0.289 x Y24 - 0.425
x Y25 + 0.459 x Y26.
The structure matrix is presented in the Table 5.53 to explain
the significant factors present in the only one function.
Structure Matrix
Factors Function
1 Training Infrastructure .463 Work Culture .442 Accountability .435 Ideal Welfare Measures .389 Futuristic Strategy .372 Teachers Commitment .342 Inductive Training .279 Career Advancement .259 Training Mechanism .246 Review .240 Feedback .239 Orientation .216 Participative Management
.215
Quality Management .190 Accommodative Approach
.190
Internal Check .173 Guiding Value .155 Loyalty .118 Awareness .077
From the above tables, it is clear that the teachers of self-
financing engineering colleges have learned to adopt the existing
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system of performance appraisal techniques and welfare and reward
system. They find that innovation in their career advancement and
their capabilities of performing in all the hierarchical positions are on
the equal footing.
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5.3.5 Classification of colleges based on the perception of respondents
The factors of ‘Knowledge Management Climate, Problems and
Difficulties in implementation of knowledge management practices,
Suggestions for effective implementation, knowledge management
Outcomes and Organisational Effectiveness’ in self-financing
engineering colleges namely ‘Cultural Changes’, ‘General Climate’,
‘knowledge management Mechanism Climate’, ‘Empowerment’, ‘Risk
Management’, ‘Internal Defects’, ‘Resistance’, ‘Internal Orientation’,
‘Modern Techniques’, ‘External Learning’, ‘knowledge management
Outcomes’, ‘Accomplishments’, ‘Perceptive Advantages’ and
‘Reengineering’ are considered as independent variables and the
cluster analysis is applied. The cluster classification is based on their
mean scores. The final cluster centers for the factors are presented in
table given below:
Final Cluster Centers
Factors Cluster 1 2
Knowledge Management Outcomes 3.26 4.22 Accomplishments 3.55 4.06 Internal Defects 3.06 2.98 Resistance 2.83 2.58 Internal Orientation 4.03 4.57 Modern Techniques 4.07 4.39 External Learning 4.00 4.01 Cultural Changes 3.25 3.67 General Climate 3.37 4.09 knowledge management Mechanism Climate 3.31 3.96 Empowerment 3.84 4.34 Risk Management 3.38 3.92 Perceptive Advantages 3.63 4.22 Reengineering 3.30 4.40
From the table above, it is found that there exist two groups of
colleges based on the perception of respondents in self-financing
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engineering colleges about the factors of ‘knowledge management
Climate, Problems and Difficulties in implementation of knowledge
management practices, Suggestions for effective implementation,
knowledge management Outcomes and Organisational Effectiveness’.
The frequency obtained for each group is exhibited in table 5.55.
Number of Cases in each Cluster
Cluster 1 145.000
2 362.000
Valid 507.000
From the above tables, it is learnt that two different
heterogeneous clusters towards knowledge management Climate,
Problems and Difficulties in implementation of knowledge
management practices, Suggestions for effective implementation,
knowledge management Outcomes and Organisational Effectiveness
exist among the teachers of self-financing engineering colleges. It is
inferred that 28.67 percent of the colleges fall in cluster one and 71.33
percent of the colleges make their way to second cluster. The
respondents in the first cluster strongly agree with the factors
‘Internal Orientation’, ‘Modern Techniques’, and ‘External Learning’ of
suggestions for effective implementation of knowledge management
practices. They disagree with the factor ‘Resistance’. They agree with
the other factors of ‘knowledge management Climate, Problems and
Difficulties in the implementation of knowledge management
practices, Suggestions for effective implementation, knowledge
management Outcomes and Organisational Effectiveness’.
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The respondents in the second cluster strongly agree with the
factors ‘knowledge management outcomes’, ‘Accomplishments’,
‘Internal Orientation’, ‘Modern Techniques’ ‘External Learning’,
‘General Climate’, ‘Empowerment’, ‘Perceptive Advantages’ and
‘Reengineering’. They agree with the factors ‘Cultural Changes’,
‘knowledge management Mechanisms Climate’, and ‘Risk
Management’. They are not in a position to decide about the factor
‘Internal Defects’. They disagree with the factor ‘Resistance’. Finally it
is concluded that the teachers in the second cluster have sound
knowledge about ‘knowledge management Climate, Problems and
Difficulties in implementation of knowledge management practices,
Suggestions for effective implementation, knowledge management
Outcomes and Organisational Effectiveness’ in self-financing
engineering colleges.
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5.3.6 Justification of the clusters
The significance of the cluster classification is tested by using
discriminant analysis. The multivariate discriminant analysis is
brought to bear on the problem of justifying the number of clusters. In
this analysis, the cluster classification is considered as a grouping
variable and the factors of knowledge management Climate, Problems
and Difficulties in implementation of knowledge management
practices, Suggestions for effective implementation, knowledge
management Outcomes and Organisational Effectiveness are
considered as independent variables. The following results explain the
profound justification for heterogeneous clusters.
Eigen values
Function Eigen value % of Variance Cumulative % Canonical
Correlation
1 2.951(a) 100.0 100.0 .864
From the table 5.56 the canonical correlation co- efficient proves
that the only one discriminant function formed for the heterogeneous
factors is highly significant. The significance is explained with the help
of the Chi-square values presented in the table below.
Wilks' Lambda
Test of Function(s) Wilks' Lambda Chi-square df Sig.
1 .253 193.723 14 .000
From the table 5.57, the Chi-square value is proved to be highly
significant in supporting the formation of discriminant function.
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The discriminant function is formed as follows to obtain the
perception level of the teachers of self-financing engineering colleges
about Knowledge Management Climate, Problems and Difficulties in
implementation of Knowledge Management Practices, Suggestions for
effective implementation, Knowledge Management Outcomes and
Organisational Effectiveness.
Standardized Canonical Discriminant Function Coefficients
Factors Function
1 Y27 - Knowledge Management Outcomes .651 Y28 - Accomplishments -.609 Y29 - Internal Defects -.185 Y30 - Resistance .009 Y31 - Internal Orientation .389 Y32 - Modern Techniques -.104 Y33 - External Learning -.197 Y34 - Cultural Changes .690 Y35 - General Climate .342 Y36 - Knowledge Management Mechanism Climate -.035 Y37 - Empowerment .119 Y38 - Risk Management -.064 Y39 - Perceptive Advantages .209 Y40 - Reengineering .354
Z1 = 0.651 x Y27 - 0.609 x Y28 - 0.185 x Y29 + 0.009 x Y30 +
0.389 x Y31 - 0.104 x Y32 - 0.197 x Y33 + 0.690 x Y34 + 0.342 x Y35
- 0.035 x Y36 + 0.119 x Y37- 0.064 x Y38 + 0.209 x Y39 + 0.354 x
Y40.
Table below explains the significant factors present in the one
function with the help of Structure Matrix.
Structure Matrix
Factors Function
1 Reengineering .654 knowledge
management outcomes
.634 General Climate .473 Internal Orientation .390
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knowledge
management
Mechanism Climate
.377 Perceptive Advantages .367 Empowerment .353 Risk Management .300 Accomplishment .282 Modern Techniques .248 Cultural Changes .215 Resistance -.091 Internal Defects -.030 External Learning .005
The teachers in self-financing engineering college’s posses a
congruent opinion about the emergence of knowledge management
climate through optimistic knowledge management practices. It is also
deduced from the structural matrix that they have certain resistance
forces like their internal orientation and defects. They are equally
enthusiastic in giving possible suggestions to implement knowledge
management practices for the deriving of fruitful outcomes and
positive organizational effectiveness.
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5.4 CORRELATION ANALYSIS
Correlation analysis attempts to determine the degree of
relationship between the variables. The correlation could be positive or
negative. Whether the correlation is positive or negative depends very
much upon the direction of change of the variables. In this segment,
the Karlpearson’s Co-efficient of correlation is applied to measure the
degree of association between the variables.
5.4.1 RELATIONSHIP BETWEEN KNOWLEDGE MANAGEMENT
PRACTICES AND KNOWLEDGE MANAGEMENT CLIMATE
In this analysis, the knowledge management practices namely
Performance Appraisal, Career Planning and Development, Training
and Development, Job Rotation, Teacher Welfare and Reward System,
Other knowledge management practices and knowledge management
climate are considered for the statistical analysis.
According to T.V.Rao, there is linkage between knowledge
management practices and knowledge management climate. The
factor analysis on the above-mentioned knowledge management
practices and the knowledge management Climate ascertained the
existence of 19 and 5 factors respectively. The Karl Pearson’s co-
efficient of Correlation is applied on the two blocks of knowledge
management practices and knowledge management climate to
establish their individual relationships.
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Relationship between knowledge management practices and
knowledge management climate
Factors Cultural Changes
General Climate
Knowledge Management Mechanism
Climate
Empowerment Risk
Management
Work Culture
Pearson Correlation
.318(**) .129 .426(**) .322(**) .218(**)
Sig. (2-tailed)
.000 .116 .000 .000 .007
N 507 507 507 507 507
Futuristic Strategy
Pearson Correlation
.003 .458(**) .404(**) .290(**) .449
Sig. (2-tailed)
.972 .000 .000 .000 .068
N 507 507 507 507 507
Guiding Value
Pearson Correlation
-.024 .350(**) .322(**) .145 .126
Sig. (2-tailed)
.775 .000 .000 .076 .125
N 507 507 507 507 507
Review
Pearson Correlation
.317(**) .227(**) .307(**) -.007 .056
Sig. (2-tailed)
.000 .005 .000 .929 .498
N 507 507 507 507 507
Feedback
Pearson Correlation
.294(**) .524(**) .348(**) .205(*) .320(**)
Sig. (2-tailed)
.000 .000 .000 .012 .000
N 507 507 507 507 507
Teachers Commitment
Pearson Correlation
.210(**) .257(**) .394(**) .130 .076
Sig. (2-tailed)
.010 .002 .000 .113 .357
N 507 507 507 507 507
Awareness
Pearson Correlation
-.220(**) -.018 .261(**) .171(*) -.060
Sig. (2-tailed)
.007 .830 .001 .036 .469
N 507 507 507 507 507
Career Advancement
Pearson Correlation
.273(**) .235(**) .255(**) .256(**) .365(**)
Sig. (2-tailed)
.001 .004 .002 .002 .000
N 507 507 507 507 507
Training Mechanism
Pearson Correlation
-.021 .290(**) .440(**) .474(**) .271(**)
Sig. (2-tailed)
.800 .000 .000 .000 .001
N 507 507 507 507 507
Training Infrastructure
Pearson Correlation
.280(**) .303(**) .453(**) .194(*) .293(**)
Sig. (2-tailed)
.001 .000 .000 .017 .000
N 507 507 507 507 507 Inductive Training
Pearson Correlation
.135 .128 .441(**) .402(**) .214(**)
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Sig. (2-tailed)
.099 .118 .000 .000 .008
N 507 507 507 507 507
Orientation
Pearson Correlation
.449 .137 .142 -.092 .120
Sig. (2-tailed)
.068 .095 .084 .262 .143
N 507 507 507 507 507
Accommodative Approach
Pearson Correlation
-.085 .118 .306(**) .168(*) -.086
Sig. (2-tailed)
.303 .449 .000 .040 .298
N 507 507 507 507 507
Internal Check
Pearson Correlation
.069 .213(**) .034 .292(**) .217(**)
Sig. (2-tailed)
.401 .009 .681 .000 .008
N 507 507 507 507 507
Ideal Welfare Measures
Pearson Correlation
.450(**) .462(**) .569(**) .409(**) .400(**)
Sig. (2-tailed)
.000 .000 .000 .000 .000
N 507 507 507 507 507
Accountability
Pearson Correlation
.460(**) .339(**) .468(**) .164(*) .282(**)
Sig. (2-tailed)
.000 .000 .000 .045 .000
N 507 507 507 507 507
Loyalty
Pearson Correlation
.125 .228(**) .202(*) .081 .352(**)
Sig. (2-tailed)
.128 .005 .013 .324 .000
N 507 507 507 507 507
Quality Management
Pearson Correlation
.458(**) .356(**) .473(**) .333(**) .500(**)
Sig. (2-tailed)
.000 .000 .000 .000 .000
N 507 507 507 507 507
Participative Management
Pearson Correlation
.349(**) -.100 .140 -.022 .118
Sig. (2-tailed)
.000 .223 .087 .789 .450
N 507 507 507 507 507
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
It is inferred from the above table 5.60, that the factor ‘Work
Culture’ of Performance Appraisal asserts a significant
relationship with the factors ‘Cultural Changes’, ‘Knowledge
Management Mechanism Climate, ‘Empowerment’ and ‘Risk
Management’. This prompts a conclusion that the evaluation of
Performance Appraisal with more promptitude and sharpness
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would induce the authenticated responsibility among the
teachers to meet various exigencies of administration in future,
whereas it does not have significant relationship with ‘General
Climate’. This shows that the teachers in self-financing
engineering colleges are highly responsible and open minded in
realizing their responsibilities without expecting any
appreciation from the top management. They have the nerve for
sustained hard work to meet out the risk involved in their
assignments.
‘Futuristic Strategy’ has positive correlation with ‘General
Climate’, ‘Knowledge Management Mechanism Climate’ and
‘Empowerment’. The prudential strategies of top-level
management in self-financing engineering colleges ensure a
favorable atmosphere among the teachers to create an
optimistic environment to fight against the dire consequences of
negations of the service. The knowledge management practices
are dedicated to extract a maximum performance from the
teachers within the short span of time. But, the factor
‘Futuristic Strategy’ does not have significant relationship
(significance value is more than 0.05) with ‘Cultural Changes’
and ‘Risk Management’. This implies that the futuristic
strategies are not massive enough in self-financing engineering
colleges to make radical changes in the culture of the teachers
and their risk taking abilities.
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‘Guiding value’ has positive correlation with ‘General Climate’
and ‘Knowledge Management Mechanism Climate’. This shows
that a transparent Performance Appraisal exercised diligently in
self-financing engineering colleges enlightened the teachers in
accepting the proper and constructive knowledge management
climate in the self-financing engineering colleges. This leads to
the conclusion that the Performance Appraisal System and its
implementation strengthen the knowledge management policies
of self-financing engineering colleges to produce an optimistic
climate. The ‘Guiding Values’ and their influences
conspicuously create a mechanism visibly workable for
promotions and pragmatic approaches of top-level management,
whereas it does not have significant relationship (significance
value is more than 0.05) with the factors ‘Cultural Changes’,
‘Empowerment’, and ‘Risk Management’. The teachers of self-
financing engineering colleges possess an opinion that a strict
Performance Appraisal leads to dejections and uninteresting
approaches in the work environment.
The factor ‘Review’ is positively correlated (significance value is
less than 0.05) with the factors ‘Cultural Changes’, ‘General
Climate’, and ‘knowledge management Mechanism Climate’ of
knowledge management Climate survey. The frequent reviews of
Performance Appraisal make the teachers more responsible,
loyal, and trustworthy in the formation of the conducive
knowledge management Climate. The relationship between the
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factor ‘Review’ with ‘Empowerment’ and ‘Risk Management’ of
knowledge management Climate survey is not significantly
correlated (significance value is more than 0.05).
‘Feedback’ is positively correlated (Significance value is less
than 0.05) with all the factors of knowledge management
Climate Survey. Therefore, it is concluded that the identification
of training needs relating the measure of performance of
teachers are useful in creating a good work environment in self-
financing engineering colleges.
The factor ‘Teachers Commitment’ has positive relationship with
the factors ‘Cultural Changes’, ‘General Climate’ and ‘Knowledge
Management Mechanism Climate’ of knowledge management
Climate Survey. Therefore, it is concluded that the unstinted
devotion of teachers towards their responsibilities is useful for
the self-financing engineering colleges to implement the
measures towards goal achievement and ultimatum. There is no
significant relationship (significance value is more than 0.05)
between ‘Teachers Commitment’ and the factors ‘Empowerment’
and ‘Risk Management’ of Knowledge Management Climate
Survey. Since the teachers are adequately committed in self-
financing engineering colleges they do not require the
empowerment and they are not accessible to any risk-taking
processes.
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‘Awareness’ is positively correlated (Significance value is less
than 0.05) with the factors ‘knowledge management Mechanism
Climate’ and ‘Empowerment’. It is also inferred that the factor
‘Awareness’ of Career Planning and Development is not
significantly correlated with factors ‘General Climate’ and ‘Risk
Management’. The teachers in self-financing engineering
colleges can ascertain their career opportunities and they have
positive impact on the customer service and other important
responsibilities of the engineering colleges. They also
understand that their career advancement does not have any
influence in an environment free from risk and knowledge
management practices. There is negative correlation between
‘Awareness’ and ‘Cultural Changes’. Therefore, it is concluded
that the teachers in self-financing engineering colleges have
certain amount of doubt that the cultural changes in their
environment would hamper their career advancement.
‘Career Advancement’ is significantly correlated (Significance
value is less than 0.05) with all factors of knowledge
management Climate. There is a positive correlation between
‘career advancement’ of Career Planning and Development and
‘Cultural Changes’, ‘General Climate’, ‘knowledge management
Mechanism Climate’, ‘Empowerment’ and ‘Risk Management’ of
knowledge management Climate Survey. The top-level
management of the self-financing engineering colleges is
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enthusiastic in materializing the theoretical aspects of
knowledge management practices to create an optimistic
knowledge management climate to attract their teachers
magnetically to join the torrent of career advancement.
‘Training Mechanism’ is not at all correlated with the factor
‘Cultural Changes’, whereas it is positively correlated with the
factors ‘General Climate’, ‘knowledge management Mechanism
Climate’, ‘Empowerment’, and ‘Risk Management’ of knowledge
management Climate Survey. Therefore, it is concluded that the
main objectives of Training Mechanism in self-financing
engineering colleges are to improve the climate rather than
making any significant radical changes in their culture. They
are very firm in their ideology of extracting maximum potential
from their teachers.
All the factors of knowledge management Climate Survey are
having significant relationship (Significance value is less than
0.05) with the factor ‘Training Infrastructure’. This shows that
the training infrastructure is well planned in the self-financing
engineering colleges to employ multifarious strategies in making
their teachers suitable elements to catalyze the knowledge
management practices in their working environment.
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One of the factors of ‘Training & Development’ is ‘Inductive
Training’ in self-financing engineering colleges. It has positive
relationship with the factors ‘Knowledge Management
Mechanism Climate’, ‘Empowerment’, and ‘Risk Management’ of
knowledge management Climate Survey. However, it has no
correlation with ‘Cultural Changes’ and ‘General Climate’. The
exposure of the teachers to the inception of training makes
them experience their personal strength to fight against
indomitable administrative errors and risk involved in their day-
to-day proceedings.
‘Orientation’ is the first factor emerged from 7 variables of job
rotation. It does not have any correlation with all the factors of
knowledge management Climate Survey. This shows that Job
Rotation is incompatible with the improvement of knowledge
management climate. It is considered as a tool of innovation to
produce increased knowledge among the teachers of self-
financing engineering colleges.
The factor ‘Accommodative Approach’ is significantly correlated
(Significance value is less than 0.05) with ‘Knowledge
Management Mechanism Climate’ and ‘Empowerment’.
Moreover, there is no correlation between ‘Accommodative
Approach’ and ‘Cultural Change’, ‘General Climate’ and ‘Risk
Management’. The propensities of the top-level management of
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self-financing engineering colleges for consulting their teachers
during postings increase their responsibilities to practice
knowledge management to increase the college efficiency and
the individual efficiency.
‘Internal Check’ is positively correlated (Significance value is
less than 0.05) with the factors ‘General Climate’,
‘Empowerment’, and ‘Risk Management’. Job rotation creates
awareness among the teachers about their limitations and made
them think twice in materializing their administrative decisions.
In fact, this is considered as a climate with decency and
decorum to suppress corruptions and malpractices at the work
place. However, there is no correlation (Significance value is
more than 0.05) between ‘Cultural Change’ and ‘knowledge
management Mechanism Climate’ with the factor ‘Internal
Check’. The internal check amplifies the administrative
capacities of the teachers and in no case it clears the way to
create a collective situation for the betterment of the
organization.
‘Ideal Welfare Measures’ and ‘Accountability’ are the two factors
which emerge from 12 variables of ‘Teacher Welfare and Reward
System’ in self-financing engineering colleges. These two factors
are positively correlated with all the factors of knowledge
management Climate Survey. It is found that the management
of the self-financing engineering colleges identifies that both
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welfare measures and accountability energize the teachers to
have more internal strength to create an optimistic climate for
the development of their private engineering colleges.
Another factor ‘Loyalty’ is positively correlated (Significance
value is less than 0.05) with the factors ‘General Climate’,
‘knowledge management Mechanism Climate’ and ‘Risk
Management’, but it has no significant (Significance value is
more than 0.05) relationship with the factors ‘Cultural Changes’
and ‘Empowerment’ of knowledge management climate survey.
It is also understood that loyalty and trustworthiness of the
teachers are imperative to create an honest climate in the
college. It leads to more pious and holistic approach of the
teachers towards the administration of the private engineering
colleges. The teachers of the self-financing engineering colleges
know too well of the indispensability of their loyalty and
honesty.
‘Quality Management’ is having significant relationship
(Significance value is less than 0.05) with all the factors of
knowledge management Climate Survey. The management of
the self-financing engineering colleges desires quality of service
in the midst of tangible practices of knowledge management.
‘Participative Management’ has positive correlation with
‘Cultural Changes’ and does not have any correlation with other
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factors of knowledge management Climate Survey. This shows
that the teachers’ participation compose them to obtain the
responsibility of renewing the errors and mistakes in their
proceedings.
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5.4.2 RELATIONSHIP BETWEEN KNOWLEDGE MANAGEMENT
PRACTICES, KNOWLEDGE MANAGEMENT CLIMATE, AND KNOWLEDGE MANAGEMENT OUTCOMES
In this analysis, the knowledge management practices namely
Performance Appraisal, Career Planning and Development, Training
and Development, Job Rotation, Teacher Welfare and Reward System
and certain other knowledge management practices, knowledge
management Climate and knowledge management Outcomes are
considered for the correlation analysis. The Karl Pearson’s co-efficient
of correlation is adopted on knowledge management practices,
knowledge management Climate and knowledge management
Outcomes to establish their individual relationship.
Relationship between knowledge management practices, knowledge
management climate and knowledge management outcomes
Factors Knowledge Management
Outcomes Accomplishment
Work Culture
Pearson Correlation
.327(**) .190(*)
Sig. (2-tailed) .000 .020
N 507 507
Futuristic Strategy
Pearson Correlation
.629(**) .235(**)
Sig. (2-tailed) .000 .004
N 507 507
Guiding Value
Pearson Correlation
.416(**) .174(*)
Sig. (2-tailed) .000 .033
N 507 507
Review
Pearson Correlation
.072 .179(*)
Sig. (2-tailed) .381 .029
N 507 507
Feedback
Pearson Correlation
.430(**) .288(**)
Sig. (2-tailed) .000 .000
N 507 507
Teachers Commitment
Pearson Correlation
.310(**) .164(*)
Sig. (2-tailed) .000 .045
N 507 507
Awareness Pearson
Correlation .422(**) .157
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Sig. (2-tailed) .000 .055
N 507 507
Career Advancement
Pearson Correlation
.164(*) .353(**)
Sig. (2-tailed) .045 .000
N 507 507
Training Mechanism
Pearson Correlation
.661(**) .317(**)
Sig. (2-tailed) .000 .000
N 507 507
Training Infrastructure
Pearson Correlation
.441(**) .346(**)
Sig. (2-tailed) .000 .000
N 507 507
Inductive Training
Pearson Correlation
.460(**) .233(**)
Sig. (2-tailed) .000 .004
N 507 507
Orientation
Pearson Correlation
.193(*) .091
Sig. (2-tailed) .018 .268
N 507 507
Accommodative Approach
Pearson Correlation
.348(**) .054
Sig. (2-tailed) .000 .513
N 507 507
Internal Check
Pearson Correlation
.247(**) .048
Sig. (2-tailed) .002 .556
N 507 507
Ideal Welfare Measures
Pearson Correlation
.606(**) .407(**)
Sig. (2-tailed) .000 .000
N 507 507
Accountability
Pearson Correlation
.315(**) .392(**)
Sig. (2-tailed) .000 .000
N 507 507
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Factors Knowledge
Management Outcomes
Accomplishment
Loyalty
Pearson Correlation
.193(*) .231(**)
Sig. (2-tailed) .018 .004 N 507 507
Quality Management
Pearson Correlation
.422(**) .495(**)
Sig. (2-tailed) .000 .000 N 507 507
Participative Management
Pearson Correlation
.049 .242(**)
Sig. (2-tailed) .548 .003 N 507 507
Cultural Changes
Pearson Correlation
.137 .541(**)
Sig. (2-tailed) .095 .000 N 507 507
General Climate
Pearson Correlation
.543(**) .481(**)
Sig. (2-tailed) .000 .000 N 507 507
knowledge management Mechanism Climate
Pearson Correlation
.686(**) .668(**)
Sig. (2-tailed) .000 .000 N 507 507
Empowerment
Pearson Correlation
.575(**) .337(**)
Sig. (2-tailed) .000 .000 N 507 507
Risk Management
Pearson Correlation
.415(**) .448(**)
Sig. (2-tailed) .000 .000 N 507 507
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
It is very clear from the above table that the factors ‘Work
Culture’, ‘Futuristic Strategy’, ‘Guiding Value’, ‘Feedback’ of
Performance Appraisal System, ‘Teachers Commitment’, ‘Career
Advancement’ of Career Planning and Development, ‘Training
Mechanisms’, ‘Training Infrastructure’, ‘Inductive Training’ of Training
and Development, ‘Ideal Welfare Measures’, ‘Accountability’, ‘Loyalty’
of Teacher Welfare and Reward System, ‘Quality Management’ of
Other knowledge management practices, ‘General Climate’, ‘knowledge
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management Mechanism Climate’, ‘Empowerment’ and ‘Risk
Management’ of knowledge management Climate survey are positively
correlated with knowledge management Outcomes. This implies that
the knowledge management practices & knowledge management
Climate in self-financing engineering colleges produce positive
knowledge management outcomes in the form of productivity and
profitability.
The factor ‘Review’ of Performance Appraisal System is
significantly correlated (significance value is less than 0.05) with
the factor ‘Accomplishment’. It is concluded that the periodic
reviews about the performance of the teachers is one of the
strategies to gauge the accomplishment of the administration
functions, whereas there is no correlation (significance value is
more than 0.05) between the factors ‘Review’ and ‘knowledge
management Outcomes’. The competency of the teachers of self-
financing engineering colleges cannot be achieved by the
periodic reviews alone. It does require more efficient planning
and prompt executions.
‘Awareness’ is positively correlated (significance value is less
than 0.05) with ‘knowledge management Outcomes’, whereas it
has no significant relationship with the factor ‘Accomplishment’.
It is ascertained that this profound awareness among teachers
subscribes to the fruitful results of development. But, the
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awareness is not the only track for the administration to
accomplish several of their objectives.
The factors ‘Orientation’, ‘Accommodative Approach’ and
‘Internal Check’ of Job Rotation are positively correlated
(significance value is less than 0.05) with ‘knowledge
management Outcomes’. But, there is no such kind of
relationship (significance value is more than 0.05) in the case of
‘Accomplishment’. Exposing the teachers to different
administrative capacities leads to higher skills, adventurous
spirits and innovation.
‘Participative Management’ of other knowledge management
practices is significantly correlated (significance value is less
than 0.05) with ‘Accomplishment, whereas it is insignificant
(significance value is more than 0.05) in the case of ‘knowledge
management Outcomes’. The teachers’ participation and the
initiation of the management empower the management to
ensure its productivity and profitability. But, it alone cannot
increase their potentiality for personality development.
‘Cultural Changes’ of knowledge management Climate survey
are significantly correlated (significance value is less than 0.05)
with ‘Accomplishment’, whereas they are insignificant
(significance value is more than 0.05) in the case of ‘knowledge
management Outcomes’. The radical changes in the culture of
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self-financing engineering colleges subscribe to the achievement
clear to the top-level management. But the fact remains that the
radical changes in the self-financing engineering colleges do not
aim at the complete individual developments of the teachers.
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5.4.3 RELATIONSHIP BETWEEN KNOWLEDGE MANAGEMENT
PRACTICES AND SELECT OTHER VARIABLES
In this study, the knowledge management practices namely
Performance Appraisal, Career Planning and Development, Training
and Development, Job Rotation, Teacher Welfare and Reward System
and certain other knowledge management practices, knowledge
management Climate, knowledge management Outcomes and
Organisational Effectiveness are considered for the correlation
analysis. The Karl Pearson’s co-efficient of correlation is adopted on
knowledge management practices, knowledge management Climate
and knowledge management Outcomes to establish their individual
relationship.
Relationship between knowledge management practices, knowledge management Climate, knowledge management Outcomes and
Organisational Effectiveness
Factors Perceptive Advantages
Reengineering
Work Culture
Pearson Correlation
.121 .402(**)
Sig. (2-tailed) .141 .000
N 507 507
Futuristic Strategy
Pearson Correlation
.228(**) .682(**)
Sig. (2-tailed) .005 .000
N 507 507
Guiding Value
Pearson Correlation
.042 .315(**)
Sig. (2-tailed) .607 .000
N 507 507
Review
Pearson Correlation
.449 .263(**)
Sig. (2-tailed) .069 .001
N 507 507
Feedback
Pearson Correlation
.250(**) .268(**)
Sig. (2-tailed) .002 .001
N 507 507
Teachers Commitment
Pearson Correlation
.076 .313(**)
Sig. (2-tailed) .357 .000
N 507 507
Awareness Pearson
Correlation .159 .397(**)
Sig. (2-tailed) .052 .000
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N 507 507
Career Advancement
Pearson Correlation
.325(**) .192(*)
Sig. (2-tailed) .000 .018
N 507 507
Training Mechanism
Pearson Correlation
.409(**) .627(**)
Sig. (2-tailed) .000 .000
N 507 507
Training Infrastructure
Pearson Correlation
.314(**) .521(**)
Sig. (2-tailed) .000 .000
N 507 507
Inductive Training
Pearson Correlation
.288(**) .551(**)
Sig. (2-tailed) .000 .000
N 507 507
Orientation
Pearson Correlation
.227(**) .268(**)
Sig. (2-tailed) .005 .001
N 507 507
Accommodative Approach
Pearson Correlation
-.070 .325(**)
Sig. (2-tailed) .394 .000
N 507 507
Internal Check
Pearson Correlation
.185(*) .244(**)
Sig. (2-tailed) .024 .003
N 507 507
Ideal Welfare Measures
Pearson Correlation
.368(**) .709(**)
Sig. (2-tailed) .000 .000
N 507 507
Accountability
Pearson Correlation
.230(**) .497(**)
Sig. (2-tailed) .005 .000
N 507 507
Loyalty
Pearson Correlation
.327(**) .135
Sig. (2-tailed) .000 .100
N 507 507
Quality Management
Pearson Correlation
.500(**) .618(**)
Sig. (2-tailed) .000 .000
N 507 507
Participative Management
Pearson Correlation
.166(*) .173(*)
Sig. (2-tailed) .042 .034
N 507 507
Cultural Changes
Pearson Correlation
.597(**) .218(**)
Sig. (2-tailed) .000 .007
N 507 507
General Climate
Pearson Correlation
.452(**) .559(**)
Sig. (2-tailed) .000 .000
N 507 507
knowledge management
Mechanism Climate
Pearson Correlation
.525(**) .607(**)
Sig. (2-tailed) .000 .000
N 507 507
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Empowerment
Pearson Correlation
.381(**) .456(**)
Sig. (2-tailed) .000 .000
N 507 507
Risk Management
Pearson Correlation
.577(**) .440(**)
Sig. (2-tailed) .000 .000
N 507 507
knowledge management
Outcomes
Pearson Correlation
.500(**) .782(**)
Sig. (2-tailed) .000 .000
N 507 507
Accomplishment
Pearson Correlation
.728(**) .485(**)
Sig. (2-tailed) .000 .000
N 507 507
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
It is very clear from the above table that all the factors (except
‘Loyalty’) of ‘knowledge management practices’, ‘knowledge
management Climate’ and ‘knowledge management Outcomes’
are significantly correlated (significance value less than 0.05 at
5 percent level) with the factor ‘Reengineering’ of organisational
effectiveness. This implies that all the factors of ‘knowledge
management practices’, ‘knowledge management Climate’ and
‘knowledge management Outcomes’ wheel the process of
reengineering; they eventually give organisational effectiveness
to self-financing engineering colleges. However, the factor
‘Loyalty’ is not having any correlation with ‘Reengineering’. The
loyalty of the teachers helps the management to implement all
the required processes sanity its time but it may not help them
to transform their entire culture. The reshaping of their
organisational optimistic culture depends on assistance of
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suitable knowledge management practices and favourable
outcomes.
There is no significant relationship (significance value more
than 0.05 at 5 percent level of significance) between the factors
‘Work Culture’, ‘Guiding Value’, ‘Review’, ‘Teachers
Commitment’, ‘Awareness’, and ‘Perceptive Advantage’ of
organisational effectiveness. This shows that the knowledge
management practices have no immediate effect on the
developmental processes of self-financing engineering colleges.
They eliminate the administrative errors in the passage of time.
The ‘Accommodative Approach’ is not at all correlated with the
‘Perceptive Advantage’, whereas it is correlated with
‘Reengineering’. This shows that the top-level management of
self-financing engineering colleges is trying to fit in the teachers
in a situation to make a radical change for the efficient
administration.
There is no significant relationship (significance value more
than 0.05 at 5 percent level of significance) between the factors
‘Loyalty’ and ‘Reengineering’. It is already found that loyalty is
philosophical but not a phenomenal. This shows that the loyalty
among the teachers would not help the administration to
revamp their qualitative approach. The variables included in the
factor ‘Loyalty’ are not active to bring the process of
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reengineering in self-financing engineering colleges to
organisational effectiveness.
There is significant relationship between the factors such as
‘Futuristic Strategy’, ‘Feedback’ of Performance Appraisal
System, ‘Career Advancement’ of Career Planning and
Development, ‘Training Mechanisms’, ‘Training Infrastructure’,
‘Inductive Training’ of Training and Development, ‘Orientation’,
‘Internal Check’ of Job Rotation, ‘Ideal Welfare Measures’,
‘Accountability’ of Teacher Welfare and Reward System, ‘Quality
Management’, ‘Participative Management’ of Other knowledge
management practices, ‘Cultural Change’, ‘General Climate’,
‘knowledge management Mechanism Climate’, ‘Empowerment’
and ‘Risk Management’ of knowledge management Climate
survey, and ‘knowledge management Outcomes’ and
‘Accomplishment’ of knowledge management Outcomes, with
‘Perceptive Advantage’ of Organisational effectiveness. This
shows that the knowledge management sub-systems and the
climate derived through its effects aim at removing the
difficulties of the administration. The main notion of the
management of the self-financing engineering colleges is to
maximize the possible outcomes from the teachers for their
desired benefits.
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5.4.4 RELATIONSHIP BETWEEN INTENT AND OUTCOMES OF
KNOWLEDGE MANAGEMENT PRACTICES
In this correlation analysis, the objectives of knowledge
management practices and knowledge management outcomes are
taken into account. The factor analysis of knowledge management
practices and knowledge management Outcomes ascertained the
existence of ‘Work Environment’, ‘Personality Development’, ‘Team
Building’, ‘knowledge management Outcomes’, and ‘Accomplishment’.
The Karl Pearson’s co-efficient of correlation is adopted on Objectives
of knowledge management practices and knowledge management
Outcomes to establish their individual relationship.
Relationship between objectives of knowledge management and knowledge management outcomes
Factors Knowledge Management
Outcomes Accomplishment
Work Environment
Pearson Correlation
.221(**) -.154
Sig. (2-tailed) .007 .059
N 507 507
Personality Development
Pearson Correlation
.267(**) -.118
Sig. (2-tailed) .001 .151
N 507 507
Team Building
Pearson Correlation
.019 -.128
Sig. (2-tailed) .818 .119
N 507 507
** Correlation is significant at the 0.01 level (2-tailed).
It is inferred from the above table that the factors namely ‘Work
Environment’ and ‘Personality Development’ of objectives of
knowledge management practices are having significant
relationship (significance value less than 0.05 at 5 percent level)
with ‘Knowledge Management Outcomes’. It is concluded that
the teachers in the self-financing engineering colleges are
sufficiently aware of the objectives of knowledge management
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practices and their consequences. They are able to ascertain
that the practices of knowledge management sub-system would
enable them to achieve better performance and efficiency. This
implies that ‘Work Environment’ and ‘Personality Development’
are contributing much to the self-financing engineering colleges
in the form of knowledge management Outcomes.
However, the factors ‘Work Environment’, ‘Personality
Development’ and ‘Team Building’ are not at all correlated with
the factor ‘Accomplishment’. The work environment in self-
financing engineering colleges is not much advanced to reach
their desired accomplishments. The team building abilities and
personality development of the teachers are also not directly
related to the accomplishments of the self-financing engineering
colleges. This clearly shows that these factors do not have any
kind of direct relationship with accomplishing improved
customer service in self-financing engineering colleges,
improving productivity and profitability of the engineering
colleges and proper utilization of human resources.
There is no significant correlation (significance value is more
than 0.05) between the factors ‘Team Building’ of objectives of
knowledge management practices and ‘knowledge management
Outcomes’. The Team Building actually helps the teachers to
coordinate themselves to enjoy a pleasant atmosphere of
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cooperation but it has limited effects on the real outcomes of
knowledge management practices. This clearly shows that the
factor ‘Team Building’ is not contributing much to the
development of the college in the form of higher work
accomplishment and greater job involvement.
In this chapter, forty four major factors are explored from the
variables of knowledge management elements and the respondents are
clustered into different groups based on the major factors. The
significance of the factors of knowledge management practices,
Climate, Outcomes and Organisational Effectiveness are also tested.
The degree of association between the factors is also measured.
