Biswajit Datta PhD in BA Thesis Viva-Voce.ppt

1
Performance Evaluation
of
Knowledge Capital Management
in
Public Sector Enterprises in India
Biswajit Datta

2
Performance Evaluation
of
Knowledge Capital Management
in
Public Sector Enterprises in India
Biswajit Datta
B.E.(Mechanical)
M.Tech. (Operations Research)
Program Manager, CMC Ltd., Kolkata
External Advisor
Dr. Bijan Sarkar
Professor
Department of Production Engineering
Jadavpur University
Kolkata 700032
Internal Advisor
Dr. Mrs. Salma Ahmed
Professor
Faculty of Management Studies & Research
Aligarh Muslim University
Aligarh 202002

3
Outline
 Chapter Scheme
 Knowledge Management and Capital
 Concept of VAIC
 Literature Review
 Research Methodology
 Data Analysis
 Conclusion and Recommendation
 List of Publications
 References
 Annexure

4
Chapter Scheme
 Chapter 1 Introduction
1.1 Knowledge Management and Knowledge Capital
1.1.1 Concept of Knowledge Capital
1.1.2 Knowledge Capital / Intellectual Capital
1.1.3 Knowledge Capital Model
1.1.4 Knowledge Capital & it’s Valuation
1.1.5 Methods for Valuation of Knowledge Capital
1.1.6 Elements of Knowledge Capital
1.1.7 Models of Knowledge Capital
1.2 India and Knowledge Capital
1.3 Knowledge Capital and Indian Public Sector
1.4 Justification of Research
Chapter 2 Concept of VAIC
2.1 Introduction to Value creation
2.2 Value and Classification
2.3 Knowledge Value
2.4 Efficiency and Value Creation Process
2.5 Value Added Intellectual Coefficient
Chapter 3 Literature Review
3.1 Knowledge Management
3.2 Knowledge Capital (KC)/Intellectual Capital
3.3 Knowledge Management, Knowledge Capital and Company Performance
3.4 Value Added Intellectual Coefficient (VAIC)
3.5 Knowledge Management and Knowledge Capital in Indian Companies
3.6 Gap Identification

5
Chapter Scheme
Chapter 4 Research Methodology
4.1 Research Process
4.2 Problem Statement
4.3 Research Objectives
4.4 Hypotheses Formulation
4.5 Scope of Study
4.6 Research Variables
4.7 Sources of Data
4.8 Sample Size
4.9 Test of Reliability
4.10 Tools for Data Analysis
4.11 Limitations of the Study
Chapter 5 Data Analysis
5.1 Descriptive Statistics
5.2 Correlation Analysis
5.3 Regression Analysis
5.4 Structural Equation Model (SEM)
5.5 Panel Data Analysis
5.6 Grey Relational Analysis (GRA)
5.7 Malmquist Productivity Index (MPI)
Chapter 6 Conclusion and Recommendation
6.1 Conclusions
6.2 Recommendations
6.3 Implications of the Study
6.4 Scope of Future Research
References
Annexure

6
Introduction
 Knowledge
 Facts, information, and skills acquired through experience or education
 Knowledge is information that changes something or somebody
 The transfer of knowledge and not knowledge itself is power
 Knowledge Management
 Systematic approach to manage people, groups and organizational knowledge
 Facilitates efficient and effective process of sharing knowledge among the members of the
organization
 Knowledge Capital
 Employee’s knowledge, data and information about processes, products, customers and
competitors
 Accumulated knowledge of employees along with company’s structure produces products
or services

7
Introduction
Framework for Strategic Knowledge Management of Organizations (Curado and Bontis (2007))

8
Introduction
 At present, Performance of an organization measured on the basis
of Financial Capital only
 Intellectual Capital is the core of knowledge management
 Value and Efficiency
 Performance evaluation of an enterprise – Financial Capital and
Intellectual Capital
 The researcher has adopted VAIC Model
Old Economy New Economy
Model of
Measurement
Quantities Value
Units Prices Efficiency

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Concept of VAIC
 Value is output minus input and Efficiency is measured by the ratio
of output to input, where a larger value of these ratios indicate
better performance
 VAIC (Value Added Intellectual Coefficient) model which takes
care of both Financial Capital and Intellectual Capital
 VAIC is designed to effectively monitor and evaluate the efficiency
of value added (VA) by a firm’s total resources focusing on value
addition in an organization and not on cost control

10
Concept of VAIC
 VAIC model introduced by Ante Pulic(2000)
 VA – Value Added, HC – Human Capital, SC – Structural Capital, CE –
Capital Employed
 ICE – Intellectual Capital Efficiency, HCE – Human Capital Efficiency, SCE-
Structural Capital Efficiency, CEE – Capital Employed Efficiency
 The first step in calculating CEE, HCE and SCE is to determine a firm’s total
Value Added, VA.
VA = P + C + D + A
Where P ( Operating Profits), C (Employee Costs - the salaries and the social
expenses for the staffs) and D (Depreciation) and A (Amortisation) of assets.
 HCE = VA/HC
 SCE = SC/VA ( SC = VA – HC)
 CEE = VA/CE
 VAIC = ICE + CEE = HCE + SCE + CEE

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Concept of VAIC
Value Added Intellectual Coefficient (VAIC) Model (Pulic (2000))

12
Literature Review
 95 studies have been reviewed on
 Knowledge Management
 Knowledge Capital (KC)/Intellectual Capital
 Knowledge Management, Knowledge Capital and Company
Performance
 Value Added Intellectual Coefficient (VAIC)
 Knowledge Management and Knowledge Capital in Indian
Companies

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Literature Review
Gaps Identified are -
 No study explicitly tested relationship of
components of VAIC with Earning Per Share
 No study of additional components to VAIC
with Earning Per Share in Indian context
 No such study on Intellectual Capital has been
done for all sectors of Public Sector organization
 No study has been done on Intellectual Capital
and Earning Per Share for any Indian Public or
Private Sector organizations

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Research Methodology
Flow Chart of Research Methodology

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 Research Question 1 – What are the relationships between different
elements of Knowledge Capital
 Research Question 2 - What are the important elements of
Knowledge Capital for Public Sector Enterprises in India
 Research Question 3 – How do the different elements of Knowledge
Capital relate to performance of the Public Sector Enterprises in India
 Research Question 4 – How can the different elements of Knowledge
Capital be measured
 Research Question 5 – How can the different elements Knowledge
Capital be reported

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Research Objectives
 Evaluate performance of KC of PSE
 Establish relationship with VAIC and EPS
 Establish relationship with HCE and EPS
 Establish relationship with SCE and EPS
 Establish relationship with CEE and EPS
 Establish relationship with other variables and EPS
 Size of the Assets(ASSET)
 Frequency of Board meeting(MEETING)
 Number of Executives(NOEXE)
 Remuneration of CEO and Directors ( CEOEXDIR)
 Ratio of Non-Executive Director to Total Number of Directors (NONR)

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Research Methodology-Hypotheses
H0 1: There is no significant successful business performance for Public Sector Enterprise (PSE)
H1 1: There exists significant successful business performance for Public Sector Enterprise (PSE)
H0 2: There exists no significant relationship of Human Capital Efficiency (HCE) with Earnings Per Share
(EPS) for Public Sector Enterprise (PSE)
H1 2: There exists significant relationship of Human Capital Efficiency (HCE) with Earnings Per Share
H0 3: There exists no significant relationship of Structural Capital Efficiency (SCE) with Earnings Per Share
H1 3: There exists significant relationship of Structural Capital Efficiency (SCE) with Earnings Per Share
H0 4: There exists no significant relationship of Capital Employed Efficiency (CEE) with Earnings Per
Share (EPS) for Public Sector Enterprise (PSE)
H1 4: There exists significant relationship of Capital Employed Efficiency (CEE) with Earnings Per Share
H0 5: There exists no significant relationship of Size of Assets (ASSET) with Earnings Per Share (EPS) for
Public Sector Enterprise (PSE)
H1 5: There exists significant relationship of Size of Assets (ASSET) with Earnings Per Share (EPS) for
Public Sector Enterprise (PSE)

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Research Methodology-Hypotheses
H0 9: There exists no significant relationship of Number of Independent Board member versus Total
Number of Directors (NONR) with Earnings Per Share (EPS) for Public Sector Enterprise (PSE)
H1 9: There exists significant relationship of Number of Independent Board member versus Total
Number of Directors (NONR) with Earnings Per Share (EPS) for Public Sector Enterprise (PSE)
H0 10: The panel data has no Fixed effect
H1 10: The panel data has Fixed effect
H0 6: There exists no significant relationship of Frequency of Board Meeting (MEETING) with
Earnings Per Share (EPS) for Public Sector Enterprise (PSE)
H1 6: There exists Significant relationship of Frequency of Board Meeting (MEETING) with
Earnings Per Share (EPS) for Public Sector Enterprise (PSE)
H0 7: There exists no significant relationship of Number of Executive (NOEXE) with Earnings
Per Share (EPS) for Public Sector Enterprise (PSE)
H1 7: There exists significant relationship of Number of Executive (NOEXE) with Earnings Per
Share (EPS) for Public Sector Enterprise (PSE)
H0 8: There exists no significant relationship of Remuneration of CEO and Directors
(CEOEXDIR) with Earnings Per Share (EPS) for Public Sector Enterprise (PSE)
H1 8: There exists significant relationship of Remuneration of CEO and Directors (CEOEXDIR)
with Earnings Per Share (EPS) for Public Sector Enterprise (PSE)

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 The study is Exploratory/Descriptive in nature
 Uses secondary sources of data
 Out of 280 Public Sector Enterprises in India (including
Banks) under Central Government control, 61 are listed
in Bombay Stock Exchange
 Sample Size : 50 Nos
 Sampling Method : Random
 The period of study is financial year 2001-02 to 2010-11
 Scope of Study – 50 nos. Public Sector Enterprises for
the financial year 2001-02 to 2010-11

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 Companies in the sample cover eight sectors
1- Agriculture
2- Capital Goods
3- Finance including Banks
4- Metal & Mining
5- Oil & Gas
6- Power
7- Miscellaneous and
8- Transport

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Research Methodology -Variables
 HCE – Human Capital Efficiency
 SCE – Structural Capital Efficiency
 CEE – Capital Employed Efficiency
 VAIC – Value Added Intellectual
Coefficient
 MEETING – No. of Board Meeting in a
year
 ASSET – Fixed Asset
 NOEXE – No. of Executives
 CEOEXDIR – CEO and Directors
remuneration
 NONR – Non-Executive Directors/Total
No. of Directors
 EPS – Earning Per Share

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Research Methodology -Variables
 Earning Per Share, EPS = Net Profit after Tax available for
Equity Shareholders / Weighted Average number of Equity
Shares.
 It is a measure of return to the shareholders by dividend and
investment
 This is a market indicator of profits and the most important profit
measure for investor and other individual
 If the earnings continue to increase on a per-share basis, the firm
judged to be increasingly successful. On the other hand, a drop in
earnings per share is viewed as a symptoms of problems in the
organization

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Research Methodology-
Tools for Analysis
Descriptive Statistics- To analyze mean values
 Correlation and Regression analysis - To analyze the relationship of research
variables with Earning Per Share (EPS)
 Structural Equation Model (SEM) – It is a confirmatory analyses of the
respective regression equation in respect to covariance and correlation
 Panel data analysis – To analyse the time effect
 Grey Relational Analysis(GRA) - To measure Rank according to efficient
utilization of Intellectual Capital to generate maximum EPS
 Malmquist Productivity Index (MPI) - To find the PSE and for which
period most optimally utilized Knowledge Capital to have bench mark

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Data Analysis –
Descriptive Statistics
 As the mean value of ICE here is always > 2.5 (Pulic, 2008), we can say that the null hypothesis is rejected.
 H0 1: There is no significant successful business performance for Public Sector Enterprise (PSE) - rejected
 H1 1: There exists significant successful business performance for Public Sector Enterprise (PSE) –
accepted
Sector/
Parameters EPS
HCE
SCE
ICE
CEE
VAIC
ASSET
MEETIN
G
NOEXE
CEOEXD
IR
NONR
Consolidated 27.44 5.74 0.62 6.36 0.56 6.92 5092.23 10.80 8450.87 0.83 0.64
Finance 30.04 5.62 0.54 6.16 0.40 6.56 947.65 12.85 10060.82 0.65 0.76
Agriculture 2.71 2.98 0.64 3.62 0.35 3.97 983.56 8.80 1735.10 0.55 0.63
Capital Goods 53.25 2.82 0.58 3.40 0.66 4.06 600.56 8.13 4239.65 0.81 0.57
Metal and Mining 19.24 5.10 0.63 5.73 0.71 6.44 4362.04 8.00 15642.19 1.06 0.62
Miscellaneous 14.86 2.43 0.49 2.92 1.08 4.00 1372.02 7.92 3284.82 0.71 0.45
Oil and Gas 35.65 7.58 0.83 8.41 0.70 9.11 13541.15 11.37 7989.12 1.27 0.52
Power 5.80 9.57 0.86 10.43 0.29 10.72 35554.96 12.17 4943.00 0.98 0.57
Transport 41.06 14.77 0.87 15.64 0.36 16.00 2517.69 9.95 1077.05 0.96 0.53
Descriptive Statistics (Mean values of all the Parameters – Sector-wise)

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Data Analysis - Correlation
 No Significant relationship of HCE with EPS(r=-0.02, p > 0.05)
 SCE has a positive correlation and significant relationship with EPS(r=0.14, p < 0.01)
 No Significant relationship of CEE with EPS(r=0.06, p > 0.05)
EPS HCE SCE CEE ASSET MEETING NOEXE CEOE
XDIR
NO
NR
EPS 1
HCE -0.02 1
SCE 0.14** 0.42** 1
CEE 0.06 -0.08 0.02 1
ASSET -.09* 0.07 0.19** -0.08 1
MEETING -0.06 0.01 -0.03 -0.20** 0.16** 1
NOEXE 0.18** -0.16** -0.18** 0.08 0.13** 0.02 1
CEOEXDIR 0.32** -0.01 0.03 0.05 0.27** -0.04 0.55** 1
NONR 0.04 -0.30** -0.25** -0.15** -0.19** 0.38** 0.16** -0.09* 1
Correlation Matrix (Consolidated)

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Sl. No Hypot
hesis
Description Correlation Coefficients Result
1 H0 2 No significant relationship of Human Capital Efficiency (HCE) with
Earnings Per Share (EPS) exists for PSE
HCE vs EPS (r= -.02)
and p > 0.05
Accepted
2 H1 2 Significant relationship of Human Capital Efficiency (HCE) with
HCE vs EPS(r= -.02)
and p >0.05
Rejected
3 H0 3 No significant relationship of Structural Capital Efficiency (SCE)
with Earnings Per Share (EPS) exists for PSE
SCE vs EPS (r= 0.14)
and p < 0.01
Rejected
4 H1 3 Significant relationship of Structural Capital Efficiency (SCE) with
SCE vs EPS (r= 0.14)
and p < 0.01
Accepted
5 H0 4 No significant relationship of Capital Employed Efficiency (CEE)
with Earnings Per Share (EPS) exists for PSE.
CEE vs EPS(r=0.06)
and p > 0.05
Accepted
6 H1 4 Significant relationship of Capital Employed Efficiency (CEE)
CEE vs EPS(r=0.06)
and p > 0.05
Rejected
7 H0 5 No significant relationship of Size of Assets (ASSET) with
ASSET vs EPS (r = -
0.09) and p < 0.05
Rejected
8 H1 5 Significant relationship of Size of Assets (ASSET) with Earnings
Per Share (EPS) exists for PSE
ASSET vs EPS (r = -
0.09) and p < 0.05
Accepted

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Sl. No Hypothesi
s
Description Correlation Coefficients Result
9 H0 6 No significant relationship of Frequency of Board Meeting
(MEETING) with Earnings Per Share (EPS) exists for PSE
MEETING vs EPS(r
= -0.06) and p > 0.05
Accepted
10 H1 6 Significant relationship of Frequency of Board Meeting
(MEETING) with Earnings Per Share (EPS) exists for PSE
MEETING vs EPS(r
= -0.06) and p > 0.05
Rejected
11 H0 7 No significant relationship of Number of Executive (NOEXE)
NOEXE vs EPS
(r=0.18) and p <
0.01
Rejected
12 H1 7 Significant relationship of Number of Executive (NOEXE) with
NOEXE vs EPS
(r=0.18) and p <
0.01
Accepted
13 H0 8 No significant relationship of Remuneration of CEO and
Directors (CEOEXDIR) with Earnings Per Share (EPS) exists for
PSE
CEOEXDIR vs EPS
( r = 0.32) and p <
0.01
Rejected
14 H1 8 Significant relationship of Remuneration of CEO and Directors
(CEOEXDIR) with Earnings Per Share (EPS) exists for PSE
CEOEXDIR vs EPS
( r = 0.32) and p <
0.01
Accepted
15 H0 9 No significant relationship of Number of Independent Board
member versus Total Number of Directors (NONR) with
NONR vs EPS( r=
0.04) and p > 0.05
Accepted
16 H1 9 Significant relationship of Number of Independent Board
member versus Total Number of Directors (NONR) with
NONR vs EPS( r=
0.0437) and p > 0.05
Rejected

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Data Analysis - Regression
 For consolidated - the independent variables together account for 17.4 per cent of the variance
 The R value (0.418) indicates the multiple correlation coefficients between all the independent variables
and the dependent variable
 The Adjusted R Square (0.161) adjusts for the bias in R Square as the number of variables increases
 The Standard error of the estimate 30.14 is the measure of the variability of the multiple correlations
 Here the 17.4 percent is very low. Hence, researcher has done sector wise analysis.
Model Summary (Consolidated and Sector wise)
Sector R R Square Adjusted R Square Std. Error of the
Estimate
Durbin-Watson
Consolidated 0.418 0.174 0.161 30.14 1.630
Finance 0.809 0.654 0.640 20.75791 1.672
Agriculture 0.918 0.843 0.729 0.77112 1.404
Capital Goods 0.863 0.745 0.679 17.03994 1.821
Metal and Mining 0.624 0.389 0.309 32.21367 1.373
Miscellaneous 0.675 0.456 0.350 20.84997 1.035
Oil and Gas 0.924 0.853 0.830 9.91025 1.152
Power 0.950 0.903 0.865 0.91379 1.970
Transport 0.926 0.857 0.753 10.93995 1.969
a. Predictors: (Constant), NONR, CEOEXDIR, CEE, SCE, ASSET, MEETING, HCE, NOEXE
b. Dependent Variable: EPS

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 F is always greater than Fcritical. So, H0 null hypothesis is rejected and the equations are significant
F, Fcritical and Standard error for all sectors

30
Consolidated : EPS = - 0.53 - 0.20 HCE + 23.39 SCE + 0.78 CEE - 0.0005 ASSET –
0.29 MEETING + 0.0001 NOEXE + 11.49 CEOEXDIR + 15.58 NONR
Finance : EPS = -49.82 - 0.55 HCE + 59.51 SCE – 10.45 CEE + 0.01 ASSET +
Agriculture : EPS = -1.17 + 1.07 HCE + 0.33 SCE + 7.23 CEE – 0.000003 ASSET
- 0.01 MEETING - 0.002 NOEXE + 2.50 CEOEXDIR – 0.09 NONR
Capital Goods : EPS = 24.95 – 1.96 HCE + 123.74 SCE + 1.91 CEE + 0.01 ASSET
- 4.72 MEETING + 0.001 NOEXE + 23.08 CEOEXDIR – 50.31 NONR
Metal & Mining : EPS = 4.25 + 5.56 HCE - 2.90 SCE + 4.83 CEE – 0.0004
ASSET - 1.25 MEETING + 0.00006 NOEXE + 0.82 CEOEXDIR – 10.70 NONR

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Miscellaneous: EPS = -11.16 + 12.77 HCE + 4.70 SCE – 6.04 CEE – 0.009 ASSET
- 2.40 MEETING + 0.006 NOEXE + 6.89 CEOEXDIR + 11.99 NONR
Oil & Gas : EPS = -168.70 – 1.95 HCE + 189.15 SCE + 33.16 CEE + 0.001
ASSET - 0.55 MEETING - 0.001 NOEXE + 3.12 CEOEXDIR + 85.14 NONR
Power : EPS = -3.57 + 0.02 HCE + 3.02 SCE + 4.49 CEE - 0.00003 ASSET -
Transport : EPS = -224.30 + 0.03 HCE + 331.09 SCE – 125.71 CEE + 0.003
ASSET - 0.32 MEETING + 0.02 NOEXE – 6.72 CEOEXDIR + 8.94 NONR

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Chart with Coefficient of all sectors

33
HCE has significant influence on EPS in Finance, Metal and Mining, Miscellaneous and Oil and Gas
sector
SCE has significant influence on EPS in Consolidated , Finance, Capital Goods, Oil and Gas and
Transport sectors
CEE has significant influence on EPS in Oil and Gas sector
ASSET has significant influence on EPS in Consolidated, Finance and Oil and Gas sectors
MEETING has significant influence on EPS in Finance and Capital Goods sectors
NOEXE has significant influence on EPS in Finance , Oil & Gas and Power sectors
CEOEXDIR has significant influence on EPS in Consolidated, Finance, Agriculture , Capital Goods and
Power sectors
NONR has significant influence only on Oil and Gas sector

34
Data Analysis –
SEM(covariance) - Consolidated

35
Data Analysis –
SEM(covariance)

36
Data Analysis – SEM(correlation) -
Consolidated

37
Data Analysis –
SEM(correlation)

38
Data Analysis - SEM
Indicator Good fit Acceptable fit Model Tested
Chi-square 0<= chi-square<=2 2<=chi-square<=3 Here the chi-square
value is .000 and
hence the model is a
good fit.
CFI(Comparativ
e Fit Index)
0.97 <=CFI<=1.00 0.95<=CFI<=0.97 In the model the
value is 1.00
NFI(Normed Fit
Index)
0.95<=NFI<=1.00 0.90<=NFI<0.95 In the model the
value is 1.00
C.R(Critical
Ratio)
> 1.96 under 5% level of
significance
> 1.96 under 5%
level of significance
In the model it is
10.464 and hence
good fit
SEM Model Fit Parameters

39
Data Analysis –
Panel Data Analysis
Fixed Effect Random Effect(GLS)
Coefficient
Standard
Error
t-ratio
p-value
Coefficient
Standard
Error
t-ratio
p-value
Coefficient
Standard
Error
t-ratio
p-value
Constant -5.33859 9.83686 -0.5427 0.5876 14.8427 8.97386 1.654 0.09877 * 7.8675 11.5222 0.6828 0.49505
HCE 0.612945 0.268739 2.2808 0.02303 ** 0.34768 0.23145 1.5022 0.13369 0.55181 0.251802 2.1914 0.02889 **
SCE 14.6094 3.8653 3.7796 0.00018 ***
15.8565 3.93111 4.0336 0.00006 ***15.1942 3.76411 4.0366 0.00006 ***
CEE 1.49321 1.52599 0.9785 0.32835 1.2466 1.52924 0.8152 0.41537 1.35012 1.48151 0.9113 0.36258
ASSET -0.0005 0.0002158 -2.3145 0.02110 ** -0.0003 0.00017 -1.8556 0.06411 * -0.0003 0.000194 -1.714 0.08717 *
MEETING -0.932154 0.431747 -2.159 0.03139 ** -0.8963 0.42368 -2.1154 0.03490 ** -0.9642 0.41729 -2.3105 0.02127 **
NOEXE 0.0030262 0.0004918 6.1537 <0.00001 ***
0.00067 0.00021 3.1429 0.00177 ***0.00165 0.000344 4.7945 <0.00001 ***
CEOEXDIR 9.9509 1.30454 7.6279 <0.00001 ***
8.70751 1.28162 6.7941 <0.00001 ***
9.14765 1.25058 7.3147 <0.00001 ***
NONR -2.83814 10.6632 -0.2662 0.79024 -2.368 10.1364 -0.2336 0.81538 -4.9502 10.2043 -0.4851 0.62782
Mean dependent variable
S.D. dependent variable
Sum Squared Residual
S.E. of Regression
R-squared
Adjusted R-squared
F(49, 442)
P-value(F)
Breusch-Pagan test
Hausman test
Cross Sectional Units - 50, Time Series Length - 10, 500
Observations Dependent Variable - EPS
27.43552 27.43552 27.43552
Random Effect (GLS)
using Nerlove's
Model1 Model2 Model3
32.90774 32.90774 32.90774
164840 497194.3 723220.1
19.31168 31.78927 38.34005
0.694954
2.04E-68
0.655615
15.3902
624.421 8.17E-138 624.421 8.17E-138
Chi-square(1) p-value Chi-square(1) p-value
Chi-square(8) p-value Chi-square(8) p-value
42.5284 1.08E-06 21.3101 0.00636785
Comparison of different Fixed Effect and Random Effects Models

40
Data Analysis –
Panel Data Analysis
Random/Fixed or OLS
No.
Fixed effect
(F test)
Random effect
(B-P LM test)
Selection
1
H0 is not rejected
(No fixed effect)
H0 is not rejected
(No random effect)
Pooled OLS
2
H0 is rejected
(fixed effect)
H0 is not rejected
(No random effect)
Fixed effect model
3
H0 is not rejected
(No fixed effect)
H0 is rejected
(random effect)
Random effect model
4
H0 is rejected
(fixed effect)
H0 is rejected
(random effect)
Fixed effect model is chosen if the
null hypothesis of a
Hausman test is rejected; otherwise,
random effect model is fit.
Source : Hun Myoung Park(2011)

41
Data Analysis –
Panel Data Analysis
In Model1 , Fcritical(49,442) is 1.721 with p-value < 0.005. So,
the null hypothesis is rejected.
For both the random models, Chi-square value is very high
(624.421) for Breusch-Pagan test. So, the null hypothesis is
rejected.
Also, for Hausman test, Ch-square(8) with p=5% significance is
15.507. The values of the above two Random models are more
than this value. The null hypothesis has been rejected and
adopted Fixed effect model
H0 10: The panel data has no Fixed effect - rejected
H1 10: The panel data has Fixed effect - accepted

42
Data Analysis -
Panel Data Analysis
Fixed Effect Model with Time Dummies
F-test p-value is < 0.0001, which is lower than 1% of significance. Hence, the null hypothesis has
been rejected and most of the variables in this list are significant. Time variant also has significance.
R-squared is much improved from the OLS value of 0.174.
Coeffi
ci
ent Standard Error t-rati
o p-val
ue
Constant -3.3537 9.9547 -0.3369 0.73636
HCE 0.6579 0.2648 2.4843 0.01336**
SCE 11.1979 3.8715 2.8924 0.00402***
CEE 1.0638 1.5031 0.7077 0.4795
ASSET -0.0006 0.0002 -2.812 0.00515***
MEETING -0.948 0.4237 -2.2375 0.02576**
NOEXE 0.0024 0.0005 4.6614
<0.00001***
CEOEXDIR 6.1175 1.6938 3.6117 0.00034***
NONR -3.7688 10.5049 -0.3588 0.71994
dt_2 2.0432 3.81132 0.5361 0.59217
dt_3 5.9713 3.84402 1.5534 0.12106
dt_4 6.6894 3.84126 1.7415 0.08231
dt_5 9.8423 3.84821 2.5576 0.01088*
dt_6 13.6146 3.89789 3.4928 0.00053**
dt_7 17.7679 3.97519 4.4697 0.00001***
dt_8 12.798 4.12408 3.1032 0.00204***
dt_9 14.4875 4.51878 3.2061 0.00145***
dt_10 17.0722 4.60395 3.7082 0.00024***
Mean dependent vari
abl
e 27.43552
S.D. dependent vari
abl
e 32.90774
Sum Squared Resi
dual 153300
S.E. of Regressi
on 18.81599
R-squared 0.716309
Adjusted R-squared 0.673068
F(49, 433) 16.2972
p-val
ue 4.58E-71
Wal
d Test - Chi
-square(9) 32.5951
p-val
ue 0.000157055
Cross Sectional Units - 50, Time Series Length - 10, 500 Observations

43
Data Analysis –
Panel Data Analysis
 Fixed effect model is appropriate with different intercept with same slope
 2nd year (2002-03) and 3rd year (2003-04) have no significant effect but
from 4th year (2004-05) onwards it has a significant effect on EPS. It has
also been seen that the time coefficients are increasing only dropped in
8th year(2008-09) and again increasing from 9th year(2009-10)
 HCE, SCE, ASSET MEETING and NOEXE are having significant
effect in EPS. But CEE and NONR has no significant effect on it
 CEE is an important constituent of VAIC (Wasim-ul-Rehman (2009)) but
it is insignificant in the above analysis. Gu Lixia (2009) while studying
listed companies in China found that Board independence (the ratio of
independent director in the board) has insignificant effect performance.

44
Data Analysis - GRA
D M Us
A verage
Grey
R elat io n
Grade R ank D M Us
A verage
Grey
R elat io n
Grade R ank D M Us
A verage
Grey
R elat io n
Grade R ank
PFC 0.4740 1 PGCIL 0.4235 1
8 BOI 0.41
1
6 35
NM DC 0.4549 2 NTPC 0.4232 1
9 NFL 0.41
1
0 36
SBI 0.4520 3 ONGC 0.4232 20 AB 0.41
00 37
CCIL 0.4497 4 NALCO 0.421
4 21 EIL 0.4098 38
STC 0.4477 5 SCI 0.4204 22 CIL 0.4094 39
OIL 0.4455 6 GM DC 0.4201 23 RCFL 0.4089 40
GAIL 0.4329 7 BOB 0.41
95 24 IND 0.4089 41
REC 0.4326 8 BLWI 0.41
94 25 SAIL 0.4089 42
BEL 0.431
5 9 CAN 0.41
92 26 IOB 0.4083 43
JK 0.4303 1
0 M OIL 0.41
74 27 SB 0.4079 44
PNB 0.4280 1
1 OBC 0.41
72 28 VIJAYA 0.4059 45
BHEL 0.4277 1
2 NLC 0.41
44 29 DENA 0.4046 46
DCI 0.4274 1
3 M M TC 0.41
37 30 HCOP 0.4046 47
HPCL 0.4254 1
4 BEM L 0.41
36 31 BOM 0.4043 48
BPCL 0.4241 1
5 UNION 0.41
20 32 UCO 0.4034 49
CORP 0.4240 1
6 IDBI 0.41
1
8 33 M TNL 0.3987 50
IOCL 0.4235 1
7 ALL 0.41
1
8 34
The rank of PFC is highest with 0.4740 and MTNL is the lowest with 0.3987.

45
Data Analysis - MPI
SBI is the most efficient (all score are < 100%). This was observed that in the year
2007-08 when SBI’s score was 17.86% (most least score)
Sl.
No. DMU Score
HCE
{I}{V}
SCE
{I}{V}
CEE
{I}{V}
EPS
{O}{V} Benchmarks
{S}
HCE
{I}
{S}
SCE
{I}
{S}
CEE
{I}
{S}
EPS
{O}
28 {X} SAIL(2008-09) 272.02% 0.28 0 0 1 489 (0.68) 490 (0.32) 0 0.01 0.25 0
29 {X} SAIL(2009-10) 251.25% 0 0 0 1 489 (1.00) 0.34 0.04 0.13 0
30 {X} SAIL(2010-11) 340.99% 0.33 0 0 1 489 (0.56) 490 (0.44) 0 0.01 0.09 0
31 {X} SBI(2001-02) 51.86% 0.78 0.01 0 1 0
32 {X} SBI(2002-03) 44.47% 0.65 0.01 0 1 0
33 {X} SBI(2003-04) 37.47% 0.53 0.01 0 1 0
34 {X} SBI(2004-05) 35.82% 0.52 0.01 0 1 0
35 {X} SBI(2005-06) 31.25% 0.47 0.01 0 1 0
36 {X} SBI(2006-07) 29.27% 0.43 0.01 0 1 0
37 {X} SBI(2007-08) 17.86% 0.06 0.01 0.2 1 0
38 {X} SBI(2008-09) 23.07% 0.02 0 0 1 0
39 {X} SBI(2009-10) 20.28% 0.05 0.01 0.23 1 0
40 {X} SBI(2010-11) 19.47% 0.29 0 0 1 0
41 {X} SCI(2001-02) 479.91% 0 0 0 1 489 (1.00) 1.89 0.16 0.06 0
42 {X} SCI(2002-03) 372.13% 0 0.31 5.02 1 451 (0.01) 452 (0.06) 489 (0.93)
1.14 0 0 0
43 {X} SCI(2003-04) 184.96% 0 0 0 1 489 (1.00) 3.06 0.2 0.06 0
44 {X} SCI(2004-05) 81.67% 0 0 0 1 0

46
Conclusions
Human Capital Efficiency(HCE) is having no significance
influence on EPS performance
Structural Capital Efficiency (SCE) has significant
influence on value creation which leads to higher
performance of EPS
Capital Employed Efficiency(CEE) is not showing
significant contribution of value creation leading to higher
performance of EPS
Size of the Asset (ASSET) depicted significant negative
influence on Earning Per Share (EPS)

47
Conclusions
Frequency of the Board meeting (MEETING) has no
significance influence on Earning Per Share (EPS)
 Remuneration of CEO and Directors has significant
positive influence on Earning Per Share (EPS)
Number of Executives (NOEXE) has no significant
influence on Earning Per Share (EPS)
Ratio of Number of Non-Executives Director to Total
Number of Directors (NONR) has no significant influence on
Earning Per Share (EPS)

48
Recommendations
Value Added Intellectual Coefficient (VAIC) is recommended to use as an
indicator of performance
SCE is found to be the most important factor influencing EPS. Indian PSE should
take steps to enhance this Structural Capital like Brand Building, Knowledge
Management system implementation
Size of the Assets and CEO’s & other Director’s remuneration have highly
significant impact on Earning Per Share (EPS) and can be accommodated in Value
Added Intellectual Coefficient (VAIC) to have a better picture of Intellectual Capital
utilization
It is recommended that Public Sector Enterprises include Remuneration of CEO
and other Directors and Size of Asset as important variables of VAIC

49
Recommendations
Potential investors and portfolio managers should look after the
Knowledge Capital of companies for investment
New model of valuation of a firm based on the study can emerge
and it will help to judge the proper valuation of Public Sector
Enterprises for disinvestment
Low-ranking companies, whose GRA level is low can follow the
best practices set by other company
In the basis of MPI indicator the companies can find and benchmark
for most productive users of Intellectual Capital

50
Implications
 Established an essential link between intellectual capital and financial
performance
 Impact of intellectual capital on earnings per share
 Investors in the market place tend to demand shares of firms having higher
performance than those with average performance in respect to intellectual capital
 Establish VAIC as an aggregated, standardized measure of corporate intellectual
ability. May help in start-up valuation
 Business managers may benefit by understanding the importance of allocating
precious resources to support IC and financial return than the same investment in
physical assets

51
Implications
The performance measurement by GRA, MPI - will provide meaningful
implications of intellectual capital management. They are useful
benchmarking tools to examine the relative firms’ progress among
competitors
Intellectual capital is an essential strategic tool in sustaining in business
Measuring the operational performance of intellectual capital management
and competitiveness of these companies will enable these firms to examine
whether they have managed these vital intangible assets efficiently
VAIC measures the depth and breadth of IC efficiency based on a company’s
accounting data and produces a standardized measure that can be used for
comparison across companies, industries and nations

52
Scope of Future Research
Studies can be undertaken involving a larger numbers of input
variables and output variables; such as, number of patents, the
ratio of R&D expenditure or number of research employees
This research study focused on Public Sector Enterprises; other
high-tech industries/Private sectors can also be assessed using
the same model
The research study may include Department-wise, Project-wise
analysis to avoid non-value creators or improve less-value
creators

53
References
Baltagi B.H.. (2005). Econometric Analysis of Panel Data.(3rd ed.).Sussex: John Wiley Sons
Ltd
Bajpai, N.(2011). Business Research Methods. New Delhi: Dorling Kindersley(India) Ltd.
Banerjee, S.(2012).Measurement and Accounting for Intellectual Capital. The
Management Accountant (Journal of The Institute of Cost Accountants of India). 47(11),
1272-1280
Chu, S. K. W., Chan. K. H. , Yu, K. Y., Ng, H. T., Wong, W. K. (2011). An Empirical
Study of the Impact of Intellectual Capital on Business Performance. Journal of
Information & Knowledge Management.10(1),11-21
Halim, S.,(2010) “Statistical analysis on the intellectual capital statement”. Journal of
Intellectual Capital ,Vol. 11 ( 1), 61-73
Ho, C. A., Williams, S. M.(2009). International Comparative Analysis of the
Association between Board Structure and the efficiency of value added by a Firm
from its physical capital and intellectual capital resources. The International Journal
of Accounting. 38 (4),465-491
Pulic, A.(2008).The principles of Intellectual Capital Efficiency – A brief description
and many more

List of Publications
 Datta, B., Ahmed Salma (2015). Measuring Intellectual Capital in Indian PSEs. Metamorphosis, Journal of
Indian Institute of Management Lucknow.14(1), 48-68.
http://www.metamorphosisjournal.com/index.php/MJMR/article/view/71815
 Datta, B., Ahmed Salma (2015). Knowledge Capital Management of Indian Public Sector Enterprises – a Panel
Data Analysis. Journal of Institute of Public Enterprise, Hyderabad. 38(1 & 2), 35-51.
http://www.ipeindia.org/main/uploads/IPE/JIPE/JIPE_38_12_2.pdf
 Datta, B. (2014). Intellectual Capital Management of Public Sector Enterprises in India. IIM Shillong Journal
of Management Science. 5(1), 29-40.
http://www.indianjournals.com/ijor.aspx?target=ijor:iimsjms&volume=5&issue=1&article=003
 Datta,B. (2014). Performance of Intellectual Capital Management of Indian Public Sector Enterprises.
International Journal of Applied Operational Research (www.ijorlu.ir). 4(1) 27-38.
http://ijorlu.liau.ac.ir/files/site1/user_files_b406fb/admin-A-10-1-84-ea61f3c.pdf
 Datta, B. (2014). Performance of Intellectual Capital Management of Indian Public Sector Enterprises-
Using GRA and MPI. Indian Journal of Commerce & Management Studies. 5(1), 98-104.
 Datta,B.(2012). Intellectual Capital Performance Management of Indian Public Sector Banks. Contemporary
Issues in Business and Information Management(ISBN– 978-81-8424-744-2)(pp. 28-39). New Delhi: Allied
Publishers Pvt. Ltd.
54

List of Conferences attended
 Performance of Intellectual Capital Management of Indian PSEs in 5th Doctoral Theses Conference
held at IBS, Hyderabad on 2-3rd April 2012. Abstract paper published in the proceedings.
 Intellectual Capital Performance Management of Indian Public Sector Banks in International
Conference on Business and Information Management (ICBIM’2012) held at National Institute of
Technology, Durgapur on Jan’ 9-11th 2012. Abstract paper published in the proceedings.
 Assessment of Intellectual Capital of Maharatna and Navaratna Indian Public Sector Enterprises
:using GRA and MPI in International Conference on Operations Research for Sustainable Development
in Globalised Environment held at Calcutta Business School, Kolkata on Jan’ 6-8th 2012. Abstract paper
published in the proceedings.
 Intellectual Capital Performance of Indian Public Sector Enterprises :using GRA and MPI in
International Conference on Advance in Modeling, Optimization and Computing (AMOC 2011) held at IIT,
Roorkee on Dec 5-7’2011. Abstract paper published in the proceedings. This is also appreciated by our
company CMC Ltd and published in an article in in-house journal – Interface, Volume 18.
55

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