ASSESSMENT OF TECHNICAL EFFICIENCY OF INDIAN B-SCHOOLS

1
ASSESSMENT OF TECHNICAL EFFICIENCY OF INDIAN
B-SCHOOLS A COMPARISON BETWEEN THE
CROSS-SECTIONAL AND TIME-SERIES ANALYSIS

• Presented by
• Gokulananda Patel
• Birla Institute of Management Technology
• India
• gn.patel_at_bimtech.ac.in

2
Brief Introduction

• PRE Liberalization - the government had a very a
  vital role in the development of Indian economy.
• Most of the industries were in the hands
  Governments, Only a handful was open for the
  private sector. And as these private sector units
  were operating in a monopolistic environment they
  were still profitable despite producing low
  quality products and providing poor customers
  service.

3
Brief Introduction

• POST Liberalization - Economy open for private
  and foreign companies.
• Entry of multinationals - large operations,
  optimum capacity utilization, accompanied with
  massive advertisement and effective sales
  promotions, backed by quality product and along
  with good customer relationship.
• This has compelled the Indian corporate sector to
  give more stress on quality of products,
  reduction in the cost of production, provision
  for better after sales service, adoption of
  modern technology, to engage efficient human
  resources and providing a fair rate of return to
  the investors
• In this background the responsibilities of
  management institutes have gone up manifold and
  has necessitated the efficiency measurement of
  these schools.

4
Some Indian magazines doing the B-School Rankings

• Outlook Cfore
• Business World COSMODE
• Indian Management IMRB
• The parameters covered are
• Intellectual capital, Infrastructure, Admission,
  Placements, Research and Publications etc.
• Why these rankings are important
• Enhances the prestige of individual MBA
  programmes
• Influences the starting salary of students
  (placement performance)
• The Stakeholders may use these ranking to choose
  the School.

5
Ranking of B-Schools may differ depending on the
target audience

• The Ranking criterion of Students may be
  different from the criterions of Employers.
• For Program Administrator all the criteria as
  considered by Students and Employers may be
  important.

6
Ranking of B-Schools may differ depending on the
target audience (Contd.)

• students may be more interested in the placement
  performance of the school, the average salary at
  the initial placement etc.
• The employers may be more interested in the
  intellectual capital, infrastructure facilities
  etc.

7
Comparison of Outlook Cfore Survey Business
World COSMODE Survey
Outlook-Cfore Survey Outlook-Cfore Survey Outlook-Cfore Survey Business World-COSMODE Business World-COSMODE Business World-COSMODE
Sl.No. Parameters Weightage Sl.No. Parameters Weightage
1 Placement Performance 24 1 Infrastructure both physical and academic 100 (10)
2 Intellectual Capital and Faculty 15 2 Faculty, Research Consultancy, MDP and Other programme 250 (25)
3 Industry Interface 14 3 Admission System, Curriculum and Delivery 250 (25)
4 Infrastructure Facilities 13 4 Networking and Innovation 100 (10)
5 Perception Score 20 5 Placement and Industry Interface 300 (30)
6 Extra Curricular Activities 5 6
7 International Linkages 9 7
8
Interpretation from Comparison

• On comparison of the above table above it is
  clear that
• both the survey uses different set of parameters
  and assign different weights to them.
• The weight given to placement performance and
  industry interface by Cfore survey is 38 whereas
  the weight given to these parameters by COSMODE
  is 30.
• Similarly the sub parameters considered in
  Intellectual Capital Faculty and extra
  curricular activities in C fore survey is same as
  the sub parameters considered in Faculty,
  Research Consultancy, MDP and other programmes
  in COSMODE,
• the weightage given by Cfore survey to this
  parameter is 20 whereas the weightage given by
  COSMODE is 25.

9
Interpretation from Comparison (Contd.)

• The methodology of converting the raw data into
  scores on various parameters across these two
  surveys also varies.
• The COSMODE survey uses both absolute and
  relative scale to assign the scores based on the
  nature of the parameters. Wherever the absolute
  score is used it is based on the slabs or
  intervals, the relative score is assigned based
  on the comparison with the highest in the
  category.
• In the Cfore the marks are allotted in a
  particular category by normalizing against the
  topper in that category.
• The number of institute participating in these
  two surveys also varies, the C fore survey
  invited 950 B-School out of them 236
  participated, whereas for the COSMODE 384
  B-Schools were given with questionnaire out of
  which 130 B-Schools submitted the completely
  filled in questionnaire and they ranked 100
  B-schools.

10
Comparison of Ranks
Sl.No. Name of the Institute Rank (COSMODE) Rank (Cfore) Absolute Deviation(d)
1 Indian Institute of Management, Ahmedabad 1 1 0
2 Indian Institute of Management, Bangalore 2 3 1
3 Management Development Institute, Gurgaon 3 4 1
4 Indian Institute of Management, Calcutta 4 2 2
5 Xaviers Labour Relation Institute, Jamshedpur 5 6 1
6 Indian Institute of Foreign Trade, New Delhi, 6 9 3
7 National Institute Of Industrial Engineering, Mumbai 7 7 0
8 Institute of Rural Management , Anand 8 - -
9 S P Jain Institute, Mumbai 9 8 1
10 Xavier Institute Of Management, Bhubaneswar 10 12 2
11
Sub-parameters their Weightages for CFORE Survey
Parameters Sub-parameters Marks Total Marks Percen-tage
Placement performance Percentage of student placed 55 385 24
Placement performance Median salary 55 385 24
Placement performance Maximum salary 55 385 24
Placement performance Minimum salary 55 385 24
Placement performance Percentage of students placed abroad 55 385 24
Placement performance Max salary (abroad jobs) 55 385 24
Placement performance ROI index (AvgMedMin Salary/Fee) 55 385 24
Intellectual capital and faculty Books 35 250 15
Intellectual capital and faculty Research papers 70 250 15
Intellectual capital and faculty Cases 35 250 15
Intellectual capital and faculty Journal 20 250 15
Intellectual capital and faculty Faculty/student ratio 20 250 15
Intellectual capital and faculty Teaching experience of Faculty 15 250 15
Intellectual capital and faculty Corporate experience of Faculty/Students 25 250 15
Intellectual capital and faculty PhD/students ratio 15 250 15
Intellectual capital and faculty Faculty with Ph.D.(abroad) 15 250 15
12
Sub-parameters their Weightages for CFORE
Survey contd
Parameters Sub-parameters Marks Total Marks Percen-tage
Industry interface Revenue from consultancy 50 230 14
Industry interface Revenue from MDP 100 230 14
Industry interface Seminars 40 230 14
Industry interface Workshops 40 230 14
Infrastructure and facilities Area (In Acres) 25 205 13
Infrastructure and facilities Built-Up Area 40 205 13
Infrastructure and facilities Computers (PII) Pre Batch 35 205 13
Infrastructure and facilities Amphitheatre Class Room 15 205 13
Infrastructure and facilities Library Books 25 205 13
Infrastructure and facilities Electronic Database 25 205 13
Infrastructure and facilities Residential Facilities 15 205 13
Infrastructure and facilities Single Occupancy Room 15 205 13
Infrastructure and facilities MDP Hostel 10 205 13
Perception score Perception score 320 320 20
Extra-curricular activities National-level events organized 40 80 5
Extra-curricular activities Awards won by students 40 80 5
International linkages Student Exchange Programme 25 150 9
International linkages Faculty Exchange Programme 25 150 9
International linkages Students actually exchanged 50 150 9
International linkages Faculty actually exchanged 50 150 9
TOTAL TOTAL TOTAL 1620 100
13
Objective Ranking from Students, Employers
Programme Administrators Perspective

• The cfore ranking - uses surveys of students,
  employers and programme administrators to measure
  their combined perceptions on the quality of MBA
  programmes. we develop objective ranking of
  B-Schools that address the interest of students,
  employers and MBA programme administrators
  separately.
• Student- PP, SAL, and FEE (29 schools lost
  rank- 5 have lost within 5 positions ICFAI
  Hyderabad lost 20 positions. Three schools
  maintained rank)
• Employer -IC, IF, ECA, IL, RS (23
  schools lost their rank out of which 19 lost
  within 5 position )
• Programme Administrators All Parameters (44
  schools lost their rank)
• So we want an alternative ranking method which is
  capable to handle complexities involved in
  ranking the B-Schools and demands for a
  methodology which is mathematically robust.

14
Berlin Principles International Rankings Expert
Group (IREG)-2006- Highlights

• Rankings -effective techniques of assessing
  higher education inputs processes and output.
• Ranking process should recognize the diversity
  among institutions and take the divergent goals
  and missions of the institutions into
  consideration.
• Authenticity of databases.
• The process should take into account the
  linguistic, cultural, economic and historical
  contexts of the educational systems being ranked.
  
• There should be transparency about the
  methodology used for creating the rankings.
• Weights should be assigned to various indicators

15
Choosing The Suitable Technique

• Analytical Hierarchy Process (AHP)
• Bayesian Latent Variable Model
• Data Envelopment Analysis (DEA)

16
Choosing The Suitable Technique contd

• AHP helps capture both subjective and objective
  evaluation measures, providing a useful mechanism
  for checking the consistency of the evaluation
  measures and alternatives suggested by the team
  thus reducing bias in decision-making.
• Though AHP has many advantages ,it has some
  limitations which make the method difficult to
  apply to B-school ranking.
• The B-School ranking problem involves large
  number units (B-Schools) and wide range of
  parameters such situation may make the pair wise
  comparison difficult.
• AHP assumes linear independence of criteria and
  alternatives. If there is dependence among the
  criteria, Analytic Network Process (ANP) is more
  appropriate yet ANP requires far more
  comparisons, which may be formidable in practical
  decision environment (Ozden Birsen, 2005).
• The other drawbacks can be- the highly subjective
  nature of preference weights , Problems with
  inconsistencies in preferences between objectives
  sometimes arise(Qureshi Harrison, 2003).

17
Choosing The Suitable Technique contd

• Not many applications of Bayesian latent variable
  model are found on the body of literature.
• The model works in two steps first it calculates
  the relative importance of parameters using
  information embedded in the data then
  simultaneously determines the degree of
  uncertainty that surrounds the ranks.
• The method considers the variability present in
  the data and according adjustments is made. If an
  institution is performing well in all the
  parameters then clearly it is ranked above all
  other institute.
• Though the method has some merits, but
  application of such method makes it difficult to
  distinguish the performance of public and private
  institutions specially in Indian context where
  public institutions receive lot of Government
  funding and expected do very well in certain
  inputs like infrastructure, faculty strength etc.

18
Choosing The Suitable Technique
contd

• Data Envelopment Analysis on the other hand is
  non-parametric method based on the application of
  a mathematical technique called linear
  programming. It has been successfully employed
  for assessing the relative importance of set of
  firms, which use a variety of identical inputs to
  produce a variety of identical outputs. Unlike
  statistical methods of performance analysis, DEA
  is non-parametric in the sense that it does not
  require an assumption of functional form relating
  inputs to outputs.
• We Choose Data Envelopment Analysis for ranking
  the Indian B-schools.

19
Research Objectives

• The objectives of present study focuses on
  ranking of some B-schools of India based on their
  efficiency scores,
• Find out bench marking institutions and discusses
  improvement areas for inefficient institutions.
• The study is also extended by considering the
  performance of the B-schools across the year.
• A sensitivity analysis is done to know the
  robustness of the model.

20
Data Set

• For our study we have considered the surveys
  conducted by one of the popular Indian magazine
  Outlook.
• The data is collected for two consecutive years
  i.e. 2004 and 2005.
• These surveys are conducted to rank top B-Schools
  in India based on their performance for the
  corresponding years.
• The data is compiled from various sources
  (outlook,2004 Bschool Directory,2005 Business
  School Directory, 2005, www.bschools.com). We
  have top twenty eight Indian B-schools for
  analysis.

21
Data Classification and Reversal for DEA
Applications

• For our DEA analysis the data is classified into
  two categories viz. inputs and outputs. The
  criteria of selection of inputs and outputs are
  quite subjective there is no specific rule for
  determining the procedure for selection of inputs
  and outputs (Ramanathan, 2001). The parameters
  used for the analysis is shown below

22
Input Output Classification
Input Output
X1 IC Y1 II
X2 IF Y2 PP
X3 FEE Y3 IL
Y4 RS
Y5 SS
Y6 FS
Y7 ECA
Y8 SAL
23
Data Reversal

• For applying the DEA we have reversed two of the
  inputs IC and IF.
• The total score for Intellectual capital (IC) and
  infrastructure and facilities (IF) are 250 and
  200 respectively.
• These scores are not directly taken for DEA
  analysis as higher score of IC and IF means they
  have developed more infrastructure, facilities
  and intellectual capital, which is desirable.
• If we directly use the score in the model higher
  value will be reflected as usage of more input
  for producing the desired output, which is
  contradictory.
• So for the DEA analysis the complement of the
  score from the total is used i.e. if an institute
  scored 230 out of 250 in an input variable then
  input value is taken as 20.

24
Analysis and Results

• The general output oriented BCC DEA model is used
  to solve the problem and get the efficiency
  score.
• The result of DEA analysis is shown in Table I.
  The 1st column of the Table-I shows the rank as
  assigned by the Magazine, the 2nd column shows
  the efficiency score as calculated from BCC model
  for the year 2004 and 3rd column for the year
  2005. The 4th column is new rank assigned to the
  B-Schools based on the efficiency score for 2004
  and 5th column for the new rank for the year
  2005. The 6th and 7th column shows the deviation
  in conventional ranking and the DEA ranking for
  2004 and 2005 respectively.

25
Table-I Results of BCC -Output
orientated DEA Model
DMU/ Rank (I) Efficiency 2004 Efficiency 2005 New Rank(II)2004 New Rank(III) 2005 D1 (I-II) D2 (I-III)
1 1 1 1 1 0 0
2 1 1 1 1 1 1
3 1 1 1 1 2 2
4 1 1 1 1 3 3
5 1 1 1 1 4 4
6 0.918367 1 13 1 -7 5
7 0.902937 0.933465 19 21 -12 -14
8 0.918367 0.913751 13 27 -5 -19
9 0.884011 0.977761 22 16 -13 -7
10 0.869822 1 26 1 -16 9
11 0.79845 0.952867 28 19 -17 -8
12 1 1 1 1 11 11
13 0.94808 0.972128 11 18 2 -5
26
Results Contd
14 1 1 1 1 13 13
15 0.897959 0.99376 20 13 -5 2
16 0.973211 1 8 1 8 15
17 0.912843 0.919431 15 25 2 -8
18 0.877607 0.983258 24 15 -6 3
19 0.909846 0.926927 17 23 2 -4
20 0.872909 0.92611 25 24 -5 -4
21 0.959184 0.8083 9 28 12 -7
22 0.906798 0.933034 18 22 4 0
23 0.89258 1 21 1 2 22
24 0.95352 0.988082 10 14 14 10
25 0.910084 0.947667 16 20 9 5
26 0.931626 0.916263 12 26 14 0
27 0.882043 1 23 1 4 26
28 0.815822 0.976355 27 17 1 11
Mean Efficiency (2004) 0.926288, Mean Efficiency (2005) 0.966756 Mean Efficiency (2004) 0.926288, Mean Efficiency (2005) 0.966756 Mean Efficiency (2004) 0.926288, Mean Efficiency (2005) 0.966756 Mean Efficiency (2004) 0.926288, Mean Efficiency (2005) 0.966756 Mean Efficiency (2004) 0.926288, Mean Efficiency (2005) 0.966756 Mean Efficiency (2004) 0.926288, Mean Efficiency (2005) 0.966756
27
Discussion

• The results show that top six Indian B-schools
  are retaining their positions.
• There is a improvement in the mean efficiency
  score of the institute over the year.
• The high value of efficiency score is obtained as
  only very top B-schools are considered for the
  analysis.
• It is also interesting to see the last two
  columns that the position of top five schools is
  not changing over the year.
• DMU1 which is one of the top B-School in India
  (IIM-Ahmedabad) is retaining its position in all
  rankings.
• The highest loser in the year 2004 on technical
  efficiency score is DMU11 which lost seventeen
  position and highest gainer being DMU24 and DMU26
  which gained fourteen positions.
• Similarly the last column of the Table-I shows
  the lose and gain of the B-Schools for the year
  2005.

28
Sensitivity Analysis

• DEA is an extreme point technique because the
  efficiency frontier is formed by the actual
  performance of best-performing DMUs.
• A direct consequence of this aspect is that
  errors in measurement can affect the DEA result
  significantly.
• So according to DEA technique, it is possible
  for a B-School to become efficient if it achieves
  exceptionally better results in terms of one
  output but performs below average in other
  outputs.

29
Sensitivity Analysis

• The sensitivity of DEA efficiency can be verified
  by checking whether the efficiency of a DMU is
  affected appreciably
• If only one input or output is omitted from DEA
  analysis.
• Dropping one efficient DMU at a time from DEA
  analysis.
• For our study the robustness test of the DEA
  results obtained is done in two ways
• Initially the input Intellectual Capital is
  dropped from the analysis and technical
  efficiency of DMUs is calculated, then input
  fee is dropped, similarly the outputs Industry
  Interface and Placement Performance is dropped
  one by one.
• At the second level the efficient units DMU1,
  DMU12is dropped one by one and technical
  efficiency is calculated.

30
Table-II Sensitivity Analysis Report (Output
Oriented DEA, Scale Assumption VRS)
DMU/ Rank (I) Efficiency 2004 Efficiency 2005 Dropping IC 2004 Dropping IC 2005 Dropping FEE 2004 Dropping FEE 2005 Dropping DMU 1, 2004 Dropping DMU 1, 2005 Dropping DMU 12, 2004 Dropping DMU 12, 2005
1 1 1 1 1 1 1 --- --- 1 1
2 1 1 1 1 1 1 1 1 1 1
3 1 1 1 1 1 1 1 1 1 1
4 1 1 1 1 1 1 1 1 1 1
5 1 1 1 1 0.902 1 1 1 1 1
6 0.918 1 0.918 1 0.918 1 0.918 1 0.918 1
7 0.902 0.933 0.902 0.933 0.902 0.933 0.902 0.933 0.902 0.933
8 0.918 0.913 0.918 0.913 0.918 0.901 0.918 0.930 0.918 0.913
9 0.884 0.977 0.884 0.977 0.883 0.977 0.888 0.977 0.884 0.977
10 0.869 1 0.869 1 0.869 1 0.869 1 0.869 1
11 0.798 0.952 0.798 0.952 0.798 0.952 0.814 0.952 0.798 0.952
12 1 1 1 1 0.834 0.879 1 1 --- ---
31
Table-II Sensitivity Analysis Report (Output
Oriented DEA, Scale Assumption VRS)
13 0.948 0.972 0.948 0.972 0.897 0.936 0.948 0.979 0.986 0.972
14 1 1 1 1 0.918 0.922 1 1 1 1
15 0.897 0.993 0.897 0.993 0.897 0.978 0.897 0.995 0.897 0.993
16 0.973 1 0.973 1 0.834 0.957 0.973 1 1 1
17 0.912 0.919 0.912 0.919 0.846 0.871 0.912 0.919 1 0.919
18 0.877 0.983 0.877 0.983 0.828 0.936 0.877 0.987 0.899 0.984
19 0.909 0.926 0.909 0.926 0.857 0.879 0.909 0.938 0.925 0.928
20 0.872 0.926 0.872 0.926 0.822 0.886 0.872 0.930 0.886 0.926
21 0.959 0.808 0.959 0.808 0.959 0.797 0.959 0.836 0.959 0.808
22 0.906 0.933 0.906 0.933 0.869 0.884 0.906 0.946 0.913 0.936
23 0.892 1 0.892 1 0.836 0.948 0.892 1 0.919 1
24 0.953 0.988 0.953 0.988 0.857 0.914 0.953 1 0.974 1
25 0.910 0.947 0.910 0.947 0.834 0.914 0.910 0.954 0.941 0.947
26 0.931 0.916 0.931 0.916 0.846 0.855 0.931 0.916 0.949 0.932
27 0.882 1 0.882 1 0.852 0.980 0.882 1 0.889 1
28 0.815 0.976 0.815 0.976 0.804 0.957 0.815 0.976 0.817 0.976
32
Discussion

• It is observed from the table above that when the
  input IC is dropped from the analysis there is no
  change in the technical score.
• When the input Fee is dropped from the analysis
  then there is change in efficiency scores two
  DMUs viz. DMU5 and DMU14 is becoming inefficient.
• Dropping the efficient DMUs from the analysis is
  not making the efficient units inefficient one.
  The analysis shows the robustness of the model
  used.

33
Time-Series Analysis

• A time series are the values of a function
  sampled at different points in time
• In this section we have observed the DMUs over
  multiple time periods to find the changes in
  efficiency over time. In such a setting, it is
  possible to perform DEA over time by using a
  moving average analogue, where a DMU in each
  different period is treated as if it were a
  "different" DMU. Specifically, a DMU's
  performance in a particular period is contrasted
  with its performance in other periods in addition
  to the performance of the other DMUs (Cooper et
  al.).

34
Results
DMU Period-1 Period-2 Period-3
1 1.000 1.000 1.000
2 1.000 1.000 1.000
3 1.000 1.000 1.000
4 1.000 0.974 1.000
5 1.000 1.000 1.000
6 1.000 0.926 1.000
7 0.906 0.866 0.950
8 0.895 1.000 1.000
9 0.970 0.880 1.000
10 0.959 0.864 0.992
11 0.941 0.784 0.958
12 1.000 1.000 0.933
13 0.964 1.000 0.962
35
Results
14 1.000 1.000 0.972
15 0.939 0.898 0.970
16 1.000 1.000 0.973
17 0.919 0.901 0.888
18 0.954 0.869 0.948
19 0.913 0.902 0.899
20 0.922 0.864 0.905
21 0.786 0.959 0.799
22 0.905 0.901 0.898
23 1.000 0.883 0.976
24 0.967 0.940 0.934
25 0.918 0.897 0.910
26 0.914 0.917 0.877
27 0.993 0.877 0.998
28 0.922 0.814 0.950
36
Discussion

• The above table reflects the stability of
  technical efficiency score over a period of time.
  It is observed that the technical efficiency
  score of inefficient units has decreased in
  period -2, but is maximum in period -3. The
  performance of DMU14 and DMU16 has come down over
  the period and is becoming relatively inefficient
  over the period

37
Conclsion

• As the management education characterizes
  multi-input and multi-output system. Data
  Envelopment Analysis (DEA), with its ability to
  handle multiple inputs and multiple outputs has
  been used in this paper to rank the Indian
  B-Schools based on their technical efficiency
  score.
• The ranking is done using BCC model and the
  results are compared with conventional ranking
  done by popular Indian magazines. The comparison
  shows that ranking using DEA-VRS model differs
  significantly from the conventional ranking.

38
Coclusion

• The sensitivity analysis done shows that there is
  no significant change in the efficiency score of
  DMUs when an input or output is dropped from the
  DEA analysis.
• The dropping of efficient DMUs from the analysis
  also shows the same fact. This shows the
  robustness of the model.
• The time series analysis done over three periods
  of time shows the stability of technical
  efficiency score over a period of time. It is
  observed that the technical efficiency score of
  inefficient units has decreased in period -2.
  The analysis has shown the performance of schools
  has improved over period of time.
• The methodology suggested in the paper can
  provide useful information by identifying
  clusters of DMUs performing better in certain
  contexts. This technique allows the researcher to
  investigate why and how they are able to perform
  better.

39
References

• Banker R.D, Charnes.A , Cooper W.W (1984) Some
  models for estimating technical scale
  efficiencies in Data Envelopment Analysis ,
  Management Science,30 (1984), 1078-1092
• B School Directory 2005 Largest Listing of
  B-Schools by Business India.
•  Business School Directory 2005 by Dalal
  Street.
•  Charnes.A , Cooper W.W Rhodes.E, Measuring
  efficiency of decision making units , European
  journal of Operational Research 2 (1978)
  ,429-444
•  Juran, J.M. and Gryna, F.M. Jr (Eds), (1988),
  Jurans Quality Control Handbook, 4th ed.,
  McGraw-Hill, New York, NY.
• Natarajan, R., 2003, Quality and Accreditation
  in Technical Management Education,
  Productivity, Vol .44 No.2, July-September.
•  Parasuraman, A., Zeithaml, V. A. and Berry, L.L.
  (1985), A Conceptual Model of Service Quality
  and its Implication for Future Research, Journal
  of Marketing, Vol. 49 (Fall), pp. 41-50.
•  Peters, T.J. and Waterman, R.H. (1982), In
  Search of Excellence, Harper and Row, New York,
  NY.
•  Ramanathan.R A Data Envelopment Analysis of
  comparative performance of schools in Netherland
  Opsearch Vol. 38 No.2 2001,Page no. 160-182
•  Sreekumar, G.N.Patel, (2005), Measuring the
  Relative Efficiency of Some Indian MBA
  Programmes- A DEA Approach, Business
  Perspective, Vol.7, No.2, July-Dec 2005,pp-47-59
•  Sreekumar, G.Patel, (2007), Comparative
  Analysis of B-school Rankings and an Alternate
  Ranking Method, International Journal of
  Operations and Quantitative Management, Vol 13,
  No.1, March, 2007, PP-33-46
•  Outlook September 27,2004
•  Outlook September ,2005
•  William W. Cooper, Lawrence M. Seiford and Joe
  Zhu, Data Envelopment Analysis History, Models
  and Interpretations
•  www.indiabschools.com

40

• Thank You