The document explores knowledge management (KM) initiatives in the Indian commercial banking sector, emphasizing the need for organizations to identify their knowledge orientation and develop effective KM models to enhance productivity and competitiveness. It highlights the critical role of KM in managing intellectual assets, fostering a learning organization, and adapting to regulatory changes in a rapidly evolving financial landscape. Examples from major banks like ICICI and Bank of Maharashtra illustrate the implementation of KM strategies to improve service delivery and innovation.

1. Knowledge management initiatives in India: A
study of Indian commercial banking sector
Himanshu Dutt
Doctoral Research Scholar
Centre for Management Studies,
Jamia Millia Islamia (Central University)
New Delhi, INDIA
Dated: February 15, 2013

2. “If only HP knew what HP knows, we would be three-times more
productive.”
― Lew Platt, former CEO of HP
“We know more than we can tell.”
― Michael Polanyi, Chemist & Philosopher
“In contrast to the traditional factors of production that were governed by
diminishing returns, every additional unit of knowledge used effectively
results in a marginal increase in performance.”
– Yogesh Malhotra in Knowledge Assets in the Global Economy: Assessment
of National Intellectual Capital

3. scenario
• globalization and increased market competitiveness have required organisations
to become knowledge-intensive.
• organisations have started rating strategic value of knowledge considerably high.
• but there could be difference in knowledge requirements like for a consulting
business and for an organization selling a physical product.
• so they need finding out what sort of knowledge they should look out for, how it
should be acquired, and put to use – in nutshell a KM model.
• this require organizations to identify its knowledge orientation – mix of elements
contributing to its body of knowledge in some proportion.
• without which it is not possible or suggested to set-up knowledge management.
• trouble is old knowledge is constantly being challenged by the new body.

4. research background
• KM requires identifying the knowledge orientation first.
• which is composed of organisational knowledge elements
• each knowledge element has its own strategic value.
• the contribution of each element (to amount of knowledge created) hence must be measured
• to determine which element has helped organisation produce how much value.
• element that didn’t add significant value can be dropped while critical ones can be
strengthened, to setup/balance the KM model.
• important knowledge elements thus can be segregated from the others – like those producing
superior competitive advantage vs. those who require improvising performance either by
eliminating or reducing them, or by shaping them continuously to meet minimum performance
benchmark.

5. ‘knowledge’ contexts
• societal guidance and governance (Hayek, 1945)
• most powerful engine of production (Marshall, 1965)
• knowledge economy (Drucker, 1969)
• intrinsically the common property of a group or nothing at all (Kuhn, 1970)
• product of intentional and sometimes unconscious human activity but not some
abstract entity (Habermas, 1972)
• economic activity that fuels economic growth, nationally and globally (Romer in late
1990s)
• an organized combination of ideas, rules, procedures and information (Bhatt, 2000)
• reasoned judgement or experimental result (resulting in IPR) (Gao, Li and Clark, 2008)

6. Authors Context of Knowledge
Senge (1990) Effective use of knowledge depends upon organizational learning.
Henry & Walker (1991) Ranked knowledge as scientific, technical & social.
Quinn (1992) Intellectual capital (or knowledge) = commitment x competence
Hamel & Prahalad (1994) Knowledge is the source of distinctive capabilities and competitive advantage.
Nonaka & Takeuchi (1995) Tacit & explicit knowledge types.
Davenport, Long & Beers (1998) Experience, context, interpretation and reflection are the components of knowledge.
Ulrich (1998) Knowledge means customer or commercial value created.
Madhvan & Grover (1998) Embedded & embodied knowledge types.
Zack (1999) Knowledge is the information that could be acted upon.
Lim, Ahmed & Zairi (1999) Knowledge is meaningfully organized accumulated information.
It is an object (could be stored) and process (for problem solving or locating opportunities)
McAdam & Reid (2000) Knowledge management (construction, its interchange & use) is a social process.
Gupta & Govindrajan (2000) Intellectual capital = function of {stock of knowledge accumulated by individuals and units}
multiplied with {extent to which this knowledge is mobilized}
Long & Fahey (2000) Knowledge has 3 types – human (individual know-how), social (relationships between
individuals or within groups) & structured (organization systems, processes).
Sorensen & Snis (2001) Cognitive and Community based knowledge models for innovation.
Salisbury & Plass (2001) Four knowledge dimensions: factual, conceptual, procedural & meta-cognitive.
Peter Murray (2002) Knowledge exists in 3 locations: codified information sets, inside the head of individuals and
within team groupings.
Birkinshaw and Sheehan (2002) Knowledge life cycle as creation, mobilization, diffusion and commoditization of knowledge.
Bij, Song and Weggeman (2003) Knowledge is justified true (human) belief, validated by experience, shaped as rules for
actions that benefit organizational performance.
Various other authors
(2003-2008)
Intangible source of economic growth and corporate value; input for product development;
organizational outcome, strategic action to problem or opportunities, innovation etc.

7. ‘knowledge’ as value
• knowledge is principal differentiator (Drucker, 1996).
• a function of improved performance (Ulrich, 1998; Zack, 1999).
• is linked to economic performance and industrial value (McCampbell,
Clare and Gitters, 1999).
• a problem-solving activity (Cross, Parker, Prusak & Borgatti, 2000).
• reduces the operating cost, increases the returns to scale and adds value
to the organization (Ofek & Sarvary, 2001).
• identified as a source of competitiveness (Hamel & Prahalad, 2005).
• an essential practice for learning organizations (Senge, 1990; 2007).
• societal progress and success (Wiig, 2007).

8. ‘knowledge’ as challenge
• Tacit – implicit dimension of knowledge (Polanyi, 1969); SECI model (Nonaka & Takeuchi, 1995)
• highly fragmented, difficult to locate and quite inconsistent (Davenport, Long & Beers, 1998)
• meaning made by the mind that easily becomes everything and nothing (Despres and Chauvel,
1999)
• deals with human understanding and mental models, and how these are used in work to derive
the economic value (Wiig, 1999)
• constructed through a systematic set of process defined logically, and stored as a codified
object (Murray, 2002)
• has a shorter life cycle due to increased continuous reduction in lead time for its creation, use,
and share (Birkinshaw & Sheehan, 2002)
• on one hand, it is monetary mass resulting from technological and educational based
productivity and on the other as equilibrium between social-cultural elements (Carrillo, 2009)

9. knowledge management
• meaningful knowledge comes from cognitive structure of people in organizations
who re-organize information to derive meaning, to meet business goals (Sussman
and Seigal, 2003).
• this organizing of credible information as knowledge by the people using
technologies, information-sharing culture, processes and management is often
labeled as knowledge management (Hult, 2003) .
• knowledge management is, therefore, a social system that develops and uses
tools, processes, strategies, and cultures for creating, sharing and using knowledge
(Long and Seemann, 2000).
• Value is extracted from the stocks of knowledge (intellectual capital) - which
depreciates if remain unused (Curado, 2008).

10. knowledge management …2
• knowledge management is what organisations know about customers, products, processes,
mistakes and successes; that gets accumulated over a period of time.
• it combines different levels of experience, culture and competence therefore to realize the
best value of its knowledge assets.
• its goal is to create a learning organisation that is capable of measuring, storing and
capitalizing on the expertise of employees to create an organisation that is more than sum of
its part.
• due to commodification of intellectual capital, there has been fundamental shift in
mobilisation, organisation and transformation of resources for value creation. Knowledge is
the driver of this value creation (Mahesh and Suresh, 2009).
• a formal process of identifying the knowledge needs, and using it to organization’s benefit
and further devising ways to make it available to the concerned members (Singh, 2008).

11. km evolution
• First Generation (1990-1995) focused on defining knowledge management,
investigating its benefits for organisations and designing specific KM-based
projects based on IT based sharing tools (Laszlo and Laszlo, 2002).
• Second Generation (1996 onwards) started with organisations recognizing
knowledge management as a function with titles like knowledge managers,
knowledge specialists and chief knowledge officers, mainly focused on developing
taxonomies, and brought organisational change to re-develop management
practices (Metaxiotis, Ergazakis and Psarras, 2005).
• Third Generation that is now emerging has focus shifted from knowledge sharing
(supply side) to knowledge creation (demand-side) (Vorakulpipat and Rezgui,
2008).

12. criticism
• KM as discipline lacks a common universally accepted framework. Defined as process
(capture, distribute and use knowledge effectively); as method (exploration and
transformation of knowledge into asset); as approach (to enhance organization’s learning
and performance); as system (composed of human and technological tools/components);
and as mechanism (capture, codification, storage, tracking, retrieval, diffusion) (Pitt and
MacVaugh, 2008), it still remains one of the most blurred concepts in management
literature (Meyer and Sugiyama, 2007)
• the reason mostly is inability to address the entire KM process that includes strategy,
organisational culture and learning, distinction between tacit-explicit, and knowledge
tasks.
• multi-discipline composition of knowledge adds to complexity of knowledge management.
• it is scattered, unstructured and lacks uniformity as a model and therefore shows great
disparity (Faucher et al., 2008).

13. India’s km initiatives
• information-based developments pushing Indian economy to maintain
competitiveness (Hussain, 2011).
• throwing up both – opportunities and risks resulting from the interaction between
complex social, technological and economic systems – e.g. digital ecosystem for
competitive agriculture (Sarkar and Chatterjee , 2011).
• national policies centered on enhancing productivity and growth rate are demanding
knowledge intensive activities (Banik and Bhaumik, 2011; Kumar, 2011)
• shift from industrial to information society, national to global economy and from
hierarchies to networking (Kumar, Sinvhal and Nangia, 2011)
• key strategy for national competitiveness and social development in India calling for
re-orientation of educational and training systems and institutions (Mathews and
Rajagopalan, 2011)
• measures like Knowledge Economy Indicator (KEI) for various states of India based on
IT infrastructure (Mahajan, Chandra and Sarkar, 2011).
• km has contributed to the Indian society in terms of expanding consumer choices,
reducing costs, increasing productivity, employment opportunities and income (Batra,
2011).

14. but why banking?
• mandatory automation (computerization) as part of financial sector reforms resulting in
various information systems. RBI (2003) Trends and Progress in Indian Banking Sector.
• banking generates a pile of data from their multiple products and services (accounts,
loans, insurance, financial advisory) and different interaction points (ATM, internet,
mobile), that often causes ‘information overload’.
• with international norms like BASEL accord & core-banking; the demand for
technological infrastructure to meet competitiveness is rising upwardly (Mohan, George
and Nedelea, 2006).
• risk management is another area that statutorily requires banks to document its
processes (service point interactions) and systems (technology, software etc.) for
compliance management (Baruah, 2008).
• above all, the fact is tangible assets can help up to a certain extent and businesses are
continuously looking towards its intellectual assets to derive competitiveness.

15. ICICI Bank example
• ICICI bank, one of largest private bank in India, has addressed: “need for becoming
learning organization is critical for being competitive in products and services and
meeting customer expectations” in their 8th
Annual Report (2001-02, p. 65)
(Chandana, 2008).
• This concern reflected bank’s focus on becoming competitive and customer centric
that started with its KM initiatives launched through KM-based portal ‘WiseGuy’ in
June, 2000.
• “Today its 30,000 employees in more than 600 branches most of whom are
customer-facing staff are aware of competitive business challenges in the external
environment and have become open to new thoughts and ways of working”
(Prabhu, 2006).

16. Bank of Maharashtra example
• Bank of Maharashtra encourages archives, engaged in gathering the experience of
peers for learning, recording significant contribution made by the bank, and the
milestones that the banks display, which the newcomers are encouraged to go
through.
• “One of the significant contributions that bank has made not only from the point of
view of business loans (which is one of its core businesses) but also reaching out to
certain important segments. For example, BoM has sponsored nearly 75,000 Self
Help Groups (SHG) in the year 2009 mostly with its rural branches through its KM
initiative” - Allen C. A. Pereira, Chairman & Managing Director, Bank of Maharashtra
– Pereira Allen C. A. (2009) Knowledge management in banks
http://www.youtube.com/watch?v=M5qNF3xq6uM accessed on July 22, 2011.

17. why banking? ...2
• “from affordable, convenient and safe banking that helps customer better manage
their finances in form of potential savings on loans or credit offer; to the banking
that lets consumer transact across different financial products through the bank’s
portal – banks are doing more than just improving the speed of processes and
offerings. They are working with the knowledge to create service innovations, new
products, and customer-centricity. In this complex and challenging operating
environment their knowledge orientation and ability to leverage it can only
differentiate them to help them continue to grow”.
– IBM Institute for Business Value (2006), “Dare to be different: why banking
innovation matters now”, pg.16.

18. why banking? ...3
• “banking has been deemed as riskiest business that has effects on economy while
knowledge has been recognized as engine for growth. Through information, the banks
mitigate risks, turning information into required knowledge using collection,
compilation, analysis of its massive data. Analyzing volumes of data and information
for new products, services and strategies for growth; knowledge management can help
banks increasing their capacities”.
• “knowledge management in banks can help them become competitive on products
and pricing to attract new customers and retain existing, manage their financial
resources and networks well for greater business value, and adapt fast to the changes
in their regulatory environment for minimizing banking risks”.
Source: Goyal, O.P. (2007), Knowledge management: analysis and design for
Indian commercial banking system, Kalpaz Publications: India.

19. Source: RBI http://www.rbi.org.in/scripts/PublicationsView.aspx?Id=3285
Commercial Banks Co-operative Banks
Public
Sector
Banks
Private
Sector
Banks
Foreign
Banks in
India
Regional
Rural
Banks
Nationalized
Banks
SBI & its
Subsidiaries
banking structure in India
92 represent the number of commercial banks excludes regional rural banks as on March 2008.

20. our km model
This research defines knowledge management as a model that is a mix of:
– ‘process’ (of creating, sharing, and managing knowledge also known as
knowledge lifecycle) and;
– ‘system’ (whose components are technology, culture, strategy, leadership
and processes)

21. knowledge management model
= knowledge process (lifecycle)+ knowledge system
Environment
Strategy
Technology
Creation
ManagingSharing
KNOWLEDGE
LIFECYCLE
Leadership
Culture
P
r
o
c
e
s
s
KM as process: It involves any systematic activity related to the capture and sharing of knowledge by
the organisation (i.e. the stages of knowledge lifecycle) (Nonaka & Takeuchi, 1996; Singh, 2008).
KM as system: It is a strategy, a cultural practice, a technology driven process and a leadership agent
(for organisation and behavioural changes) to leverage and extract value from intellectual assets.
These are the enablers (components) of the knowledge management system (Anantamula and
Kanungo, 2010).

22. a typical knowledge process
or KM lifecycle model
Creation
ManagingSharing
Knowledge
Lifecycle
Obtaining Knowledge
Using Knowledge
Learning from
Knowledge
Contributing to Knowledge
Assessing Knowledge
Building Knowledge
Divesting Knowledge
Measure

23. focus of this research
• Identify composition of knowledge elements (27 manifest elements) to
define knowledge orientation of Indian commercial banks.
• based on that, develop a KM model that is a mix of KM process (process
to obtain, use, learn, contribute, assess, divest, and build) and a KM
system (whose components are leadership, culture, technology, processes,
and strategy) for Indian commercial banks.
• and, measure its (perceived) effectiveness (expected benefit, factor critical
to implementation, challenge, and metric).

24. research steps
• identify (27) knowledge orientation variables
first-phase research
• measure contribution of each element
• examine (29) knowledge process (knowledge lifecycle stages) variables
• examine (18) knowledge system variables second-phase research
• measure contribution of each element under process and system
• Measure (17) perceived effectiveness of KM practice
• rank elements based on criticality / importance for each phase
• build a KM model for banks

25. research objectives
• identify knowledge variables and analyse their respective contribution in defining
Knowledge Orientation of the banks.
• examine Knowledge Management Process (or knowledge lifecycle) i.e.
contribution of obtain, use, learn, share, assess, build and divest in developing
knowledge in the banks.
• examine Knowledge Management System i.e. contribution of technology,
leadership, process, strategy and culture in implementing knowledge
management in the banks.
• assess Knowledge Management Practice to find out its contribution in improving
(or adding) business value for banks.

26. working hypotheses
• Null Hypothesis:
H0:data from banks fits the KM model – stands accepted.
• Working Hypotheses:
–each knowledge element has equal contribution in developing knowledge
orientation in banks.
–each variable of KM Process has equal contribution in developing knowledge in
banks.
–each variable of KM System has equal contribution in implementing knowledge
management in banks.
–each variable of KM Practice has equal contribution in improving business
value of banks.

27. Note:
• present research restricts itself to find out – if the data
obtained from the bank staff fits well to all the manifest
variables within the accepted standards of hypothesis testing
or not.
• so if p-value is more than or equal to 0.05 it would mean
accept the model.

28. – questionnaire type: 5 point rating scale (Likert type)
– questionnaire reference: KM Field Book by Wendi and Bukowitz (1999); Maier and
Moseley (2003) KMAT, Knowledge Management Practices Survey, 2001 of Statistics
Canada; OECD KM Survey 2003, KPMG’s European Knowledge Management Survey
2002-03, American Productivity & Quality Center (APQC) and Arthur Anderson (AA)
KMAT(1995).
– population: 26 banks implementing KM fully or partially as against a total of 92
banks (as on March, 2008): 14-private, 9-foreign, 3-public
– sample size fit to proceed for data analysis: 280 (in the first-phase) and 164 (in the
second-phase)
– respondents: KM professionals from Indian commercial banks at senior-middle
level positions in different functions.
– data collection period: third quarter of year 2009 for the first phase &
subsequently in the second quarter of year 2010 for the second phase.
research design

29. research design … 2
– questionnaire validation for reliability: done using sigma 2-tailed correlations
and negative correlations were removed before proceeding to analysis.
– data analysis: done using Confirmatory Factor Analysis (CFA) from Structural
Equation Modeling (SEM) as given by Jöreskog and Sörbom (1989). Lisrel
software was deployed to validate data.
– SEM is a statistical tool to analyse data if latent variables or indirect effects
have occurred. CFA is a method to validate the data, that investigate if the
data collected from the banking staff fits well to the KM model developed
here.
– Researcher has known which manifest variables are measurement for which
latent variables hence, we used CFA.
– criteria of analysis: respondent quantity requirement of min. 5 times to
manifest variable if data is distributed normally; else, respondent quantity
must be at least 20 times of survey questions.

30. research design … 3
– measures of fit: were chi-square value, p-value, and Root Mean Square Error of
Approximation (RMSEA).
• small chi-square corresponds to good fit i.e. 0 is perfect fit.
• p-value: 1.0 is perfect fit however 0.05 means good fit. Decision towards the
hypothesis has been tested based on p-value.
• RMSEA value: 0.05 indicates good fit; however 0 is perfect fit. Values up to
0.08 also indicate ‘reasonable’ fit.

31. note:
• Chi-square is a statistical test commonly used to compare observed data with data
we expect to obtain according to a hypothesis - "goodness to fit" is represented
through difference between the observed and expected.
• Chi-square test tests the null hypothesis, which states that there is no significant
difference between the expected and observed result.
• p-value is the probability that the null hypothesis is actually correct.
• If the p value for the calculated Chi-square is p > 0.05, accept your hypothesis else
reject.
• RMSEA is a measure of discrepancy per degree of freedom, which is calculated
using this equation:
Source: R.A. Fisher and F. Yates, Statistical Tables for Biological Agricultural and Medical
Research, 6th ed., Table IV, Oliver & Boyd, Ltd., Edinburgh

32. analysis
Total Commercial Banks excluding regional-rural banks
(as on March, 2008) 92
A. Public Banks Implementing 3
B. Indian Private Banks Implementing 14
C. Foreign Private Banks Implementing 9
Total KM Implementing Commercial Banks (as on March, 2008) 26
Number of Banks neither have KM in place nor considering 32
Note: 34 (out of 92) banks did returned the questionnaire– if implementing KM or
not.
• 32 banks have ‘no KM programme in place’ and neither ‘considering one’ in
upcoming 6 months.
• only 7 banks (out of these 32) feel that they are ‘currently missing out on
the business opportunity by failing to successfully exploit available
knowledge’

33. analysis …2
• only about one-third of banks are implementing knowledge management.
• mostly the foreign private banks (9) followed by Indian private banks (14) and then
the Indian public banks (3).
• 2 public sector banks have reported to be ‘currently setting up a knowledge
management programme while another bank has plans to introduce it in next 6
months’.
• km is not a ‘very familiar’ concept or a practice in banks. Even if they may be
practicing it, the fact is they may not be familiar with KM taxonomies (like CoPs,
knowledge repositories).
• analysis is based on estimation that foreign private banks were less in numbers
than Indian private and public banks.

34. respondent’s analysis
Respondents' s Dept./Group/Function
in the bank in %
Information Tech 21
Admin/Operations 11
Mktg./Sales 2
Knowledge Mgr./Off 47
Human Resource 3
Legal Affairs/Compliance 2
Accounting & Finance 1
Client Service/Delivery 5
MD/CEO/Top Mgmt./No specific area 7
Research & Development 2

35. respondents’ analysis …2
Respondent's Role with the Bank in %
Group or Dept. Head/ Mgr at Sr-Middle
level 35
Non-mgr. role/ Admin role on Sr-Middle
level 7
Knowledge Mgr/ officer/ coordinator etc. 37
Knowledge Mgmt. role but not mgr. role 17
CEO/MD/Chairman/Top Mgmt. 3
Business Unit Head/ Branch Head 1

36. objective: 1
Identify knowledge orientation

37. KNOWLEDGE ORIENTATION
(RESEARCH VARIABLE)
EXPERIENCE, CONTEXT,
INTERPRETATION, REFLECTION
EXPLICIT, TACIT
COMMITMENT, COMPETENCE
CULTURE, STRUCTURE, PROCESS,
LEADERSHIP, TECHNOLOGY
BEST PRACTICE, FAILED PRACTICE
HUMAN (INDIVIDUAL),
SOCIAL (GROUP)
SKILL, CREATIVITY, LEARNING
FACTUAL, CONCEPTUAL,
PROCEDURAL, META-COGNITIVE
SCIENTIFIC, TECHNICAL, SOCIAL
KNOWLEDGE
FORM
KNOWLEDGE
COMPONENTS
KNOWLEDGE
TYPES
KNOWLEDGE
CAPITAL
KNOWLEDGE
DETERMINANTS
KNOWLEDGE
EFFECT
KNOWLEDGE
VALUE
KNOWLEDGE
IMPROVISERS
KNOWLEDGE
INFLUENCERS
LATENT VARIABLES MANIFEST VARIABLES
model for
knowledge orientation

38. e.g. knowledge components

39. methodology (for knowledge orientation) for
obtaining response
Knowledge Elements Meaning Rating Option
Knowledge
Forms
Kindly rate each knowledge
form based on the scale of 1
(least) to 5 (highest) in terms of
their (perceived) contribution in
building knowledge in your
(department / team) bank?
Scientific (adjective)
1. Involving science; 2. Connected
with science; 3. Having to do with
science, 4. Derived from, or consistent
with the scientific method.
1__2__3__4__5
1 = Least
5 = Highest
Technical (adjective) 1. Connected with the practical use of
machinery, methods, etc. in science
and industry; 2. Of or pertaining to the
useful or mechanic arts, or to any
academic, legal, science, engineering,
business, or the like terminology with
specific and precise meaning.
1__2__3__4__5
1 = Least
5 = Highest
Social (adjective)
1. Connected with your position in
society; 2. Being extroverted or
outgoing.
1__2__3__4__5
1 = Least
5 = Highest

40. analysis: first-phase research
model validation of knowledge form
Interpretation:
Chi-square value 0 implies perfect fit; p-value 1 also implies perfect fit; RMSEA value
0 also is a perfect fit. Every one unit increase (+) in Social Knowledge will add 0.41
units to Knowledge Forms whereas every one unit increase in Technical
knowledge will decrease (-) 0.06 units from Knowledge Form.

41. model validation of knowledge
components
model validation of knowledge types &
knowledge capital

42. model validation of knowledge determinants
model validation of knowledge effect
& knowledge value

43. model validation of knowledge improvisers
model validation of knowledge influencer

44. Knowledge Orientation Scorecard for Banks
S.
No. Latent Variables Manifest Variables
Increase
( + )
Decrease
( - ) Neutral
1 Knowledge Forms
Social Knowledge 0.41
Scientific Knowledge 0.21
Technical Knowledge 0.06
2 Knowledge Components
Experience 0.14
Context 0.03
Interpretation 0.07
Reflection 0.86
3 Knowledge Types
Tacit 0.35
Explicit 0.36
4 Knowledge Capital
Competence 0.22
Commitment 0.33
5
Knowledge
Determinants
Culture 0.33
Structure 0.44
Processes 0.04
Leadership 0.50
Technology 0.47
6 Knowledge Effect
Best Practices 0.10
Failed Practices 0.89
7 Knowledge Value
Human (Individual) 0.08
Social (Group) 1.00
8 Knowledge Improvisers
Skills 1.00
Creativity Neutral
Learning 0.03
9 Knowledge Influencers
Factual 1.00
Conceptual 0.04
Procedural 0.05
Meta-Cognitive 0.02
Scorecard Interpretation: Every
one unit increase (+) in Social
Knowledge will add 0.41 units to
Knowledge Forms whereas every
one unit increase in Technical
Knowledge will decrease ( -) 0.06
units from Knowledge Forms.

45. analysis: banks’ knowledge orientation
• It can be said from the above that social knowledge, human reflection, explicit
knowledge, commitment, leadership, learning from failed practices, individual
knowledge, skill-set and factual knowledge (8 variables) defines knowledge
orientation best in Indian commercial banks.
• Accordingly the KM practice model in banks should be designed.
• KO scorecard gives banks the scope to analyze their knowledge orientation vis-à-
vis to the industry, externally, and internally on what knowledge variables to use,
shape, drop or re-develop.
• This will ultimately help the banks in determining the strategic value of their
knowledge for improving business value (or the competitive advantage).

46. conclusion: KM orientation
• knowledge orientation of Indian commercial banks is largely made up of
social knowledge, by making it readily available for access.
• knowledge is highly social, less technical, but scientific, driven more by
(staff) reflection and less by experience.
• to socialize knowledge, the role of leadership of the top management is
most important for banks followed by technology and structure.
• banks use mostly the codified knowledge (explicit) and demands
individual commitment and skill-set – the two most important attributes
to develop knowledge.
• banks regard failed practices more than successful practices for
organisational learning and developing knowledge.

47. analysis: second-phase research
KM Process - KM System - KM Practice

48. methodology (for obtaining response) for
knowledge process
Q. 1 1 2 3 4 5
1.1 Obtaining Knowledge: Strongly
Disagree
Disagree Slightly
Disagree
Slightly
Agree
Strongly
Agree
A Employees provide complete explanations when
they make information requests.
B Employees routinely document and share
information about their expertise.
C Information is easy to identify because everyone
knows where to look for it.
D Employees can search for information across a
wide variety of applications and databases.
E Employees can quickly contact subject matter
experts who play a role in identifying important
information and tools for people to work.

49. obtain10.75
obtain20.73
obtain30.72
obtain41.23
obtain51.27
use10.80
use20.55
use30.52
learn10.52
learn21.01
learn30.24
learn40.48
contri10.98
contri20.88
contri30.84
contri40.84
asses10.84
asses20.74
asses30.43
asses41.20
asses51.56
build10.40
build20.80
build30.74
divest10.85
divest20.71
divest30.51
divest41.58
divest51.56
obtain 1.00
use 1.00
learn 1.00
contri 1.00
asses 1.00
build 1.00
divest 1.00
Chi-Square=303.32, df=266, P-value=0.05744, RMSEA=0.030
0.45
0.81
0.66
0.51
0.16
0.76
0.85
0.47
0.43
0.53
0.75
0.37
0.49
0.78
0.69
0.85
1.00
0.40
0.70
0.93
0.59
0.89
0.45
0.73
0.46
1.01
1.07
0.17
0.33
0.41
model validation of knowledge process/ lifecycle

50. conclusion: knowledge process
• to obtain knowledge, banks routinely document and share information.
• involve customers in developing new products or services, and learn by
gathering feedback from them.
• to contribute to knowledge, knowledge seeking and sharing behaviour is
linked with performance appraisal system but most important is
technology (electronic tools) that has been integrated into work activities.
• experts have been appointed to lead the KM efforts to help in building
knowledge in banks.
• the decision to acquire knowledge is based on how much a bank could
leverage it.

51. methodology (for obtaining response) for
knowledge system
Q. 9 1 2 3 4 5
9.1 Obtaining Knowledge: Strongly
Disagree
Disagree Slightly
Disagree
Slightly
Agree
Strongly
Agree
A
Employees provide complete explanations
when they make information requests.
B
Employees routinely document and share
information about their expertise.
C
Information is easy to identify because
everyone knows where to look for it.
D
Employees can search for information across a
wide variety of applications and databases.
E
Employees can quickly contact subject matter
experts who play a role in identifying important
information and tools for people to work.

52. model validation for knowledge system
PROCESS10.82
PROCESS20.57
PROCESS31.05
LEADER10.66
LEADER20.63
LEADER30.34
LEADER40.80
CULTURE10.65
CULTURE20.65
CULTURE30.88
CULTURE40.52
TECHNO10.96
TECHNO20.52
TECHNO30.56
STRATEG10.59
STRATEG20.46
STRATEG30.22
STRATEG40.74
process 1.00
leader 0.85
culture 1.00
techno 1.00
strateg 1.00
Chi-Square=117.49, df=94, P-value=0.05091, RMSEA=0.040
0.72
0.71
0.64
1.05
0.86
1.00
1.01
0.82
0.37
0.79
0.75
0.67
0.98
0.81
0.71
0.62
0.74
0.72

53. conclusion: knowledge system
• KM process identifies the knowledge gaps and accordingly
these are closed.
• leaders encourage managers to include KM in their business
plan.
• technology infrastructure encourages experienced workers to
transfer their knowledge faster to the new or less
experienced.
• knowledge strategy eases the collaboration between
branches and partners that are separated due to physical
limitation.

54. methodology for obtaining response for
(effectiveness of) KM Practice
Q. 11
1 2 3 4 5
11.1 Perceived KM Benefits are: Strongly
Disagree
Disagree Slightly
Disagree
Slightly
Agree
Strongly
Agree
A Employees better connected to collaborate
faster.
B People are more aware, involved and
focused.
C Clear financial benefits and returns.
D Reduced exposure to various risks.

55. benefit10.41
benefit20.42
benefit30.37
benefit40.46
metric10.43
metric20.37
metric30.56
metric40.30
imple10.22
imple20.25
imple30.50
imple40.58
imple50.94
imple61.13
challe10.19
challe20.35
challe30.47
benefit 1.00
metric 0.83
implemen 0.36
challeng 0.18
Chi-Square=110.44, df=89, P-value=0.06150, RMSEA=0.040
0.55
0.58
0.93
0.70
0.96
1.00
0.83
1.09
0.92
0.91
0.80
0.60
0.74
1.00
1.55
1.00
1.13
model validation for (effectiveness of) KM practice

56. conclusion: KM practice
• banks measure effectiveness of their KM practice through increase
in the ability to capture, use and share knowledge both – from up
to down (hierarchy) and across the functions or groups.
• most critical factor to KM implementation in banks is that real
experts are too busy to help.
• and, challenge in implementing KM is getting employee
involvement for updating and maintaining the knowledge database.

57. Index Acceptance value Value
Chi-Square (P-value) > 0.01 0.0509
Root Mean Square Error (RMSEA) < 0.08 0.0404
Goodness of fit statistics for knowledge process
Goodness of fit statistics for knowledge system
Index Acceptance value Value
Chi-Square (P-value) > 0.01 0.057
Root Mean Square Error (RMSEA) < 0.08 0.030
Index Acceptance Value Value
Chi-Square (P-value) > 0.01 0.0615
Root Mean Square Error (RMSEA) < 0.08 0.0397
Goodness of fit statistics for KM practice

58. Knowledge
Orientation
Knowledge
Management
Process
Knowledge
Management
System
Knowledge
Management
Practice
FIRST PHASE RESEARCH
Stage –I Stage -II Stage -III Stage -IV
1. Socialize the banks’
knowledge.
2. Socialization is based
on human refection of
activities that is
responsive and
sensitive (meta -
cognition)
3. Uses mostly explicit
knowledge codified for
ready to use
4. Relies on individual
commitment and skills
5. Learns from failed
practices
6. Driven by leadership of
management
1. Routinely document
and share the
information
2. Involves customers to
develop products
3. Learns from feedback
4. Electronic tool s are
seamlessly integrated
into work activities of
people.
5. Identify ability to
leverage the
knowledge
6. Have KM experts to
lead the efforts
1. Processes identify the
knowledge gaps
2. Leadership pushes KM
in business plans of the
managers
3. Technology
infrastructure for faster
knowledge transfer
from experts to less
experienced
4. Value system as part of
culture to promote
knowledge sharing
5. Strategy focuses on
easing collaboration of
employees and
partners
1. Expects clear
financial benefits
2. Measures success
based on the
ability of
employees to
capture, use and
share knowledge
at all the levels.
3. Experts are
critical to KM
implementation
but they are too
busy to help
4. Challenge is to
get employees
involve
SECOND PHASE RESEARCH
KM model for Indian commercial banks

59. findings
• it is important to find out strategic value of knowledge for better, faster & ideal
decisions which require an organisation to identify its knowledge elements that
shapes its knowledge orientation.
• knowledge orientation describes the competitive priorities or business value
expected from KM practice. Based on knowledge orientation, KM model can be
made depending upon what elements to improve, develop or drop.
• there is no one single, uniform KM model exists that fits all organisations.
Therefore, it becomes more vital for organisations to identify its knowledge
orientation first, to aptly design and implement KM.

60. findings …2
• KM in its third generation as business practice is moving from knowledge sharing to
knowledge construction (content generation) and is widely being recognized with
socio-cultural activity rather than an IT application as perceived earlier.
• KM model in general can be visualized to be made up of a KM Process (creating,
managing and sharing knowledge) and a KM System (culture, leadership,
technology, processes and strategy) whose elements interact at each knowledge
lifecycle stage to produce business value.
• in Indian commercial banks, knowledge orientation is largely shaped by social
knowledge, based more on human reflection and, is highly facts based (factual). It
uses mostly the explicit knowledge codified ready for use. It relies on individual
commitment and skills rather group’s. Banks learn from their failed practices and its
main driver is the leadership of top management.

61. findings …3
• KM process in banks is mainly designed to routinely document the information
for sharing, involving customers to develop products, learning from feedbacks
from stakeholders, linking knowledge sharing to performance appraisals,
identifying its own ability to leverage the knowledge identified aptly, and
having KM experts to drive lead the efforts.
• KM system in banks give importance to identifying the knowledge-based gaps.
Leaders push KM in business plans of their managers. Technology helps in
faster/timely knowledge transfers from experts to less experienced. Value
system promotes the culture of knowledge sharing, while KM strategy focuses
on easing collaboration between employees and partners.

62. findings …4
• Indian banks expect clear financial benefits from their KM practice.
They measure success based on the ability of their employees to
capture, use and share knowledge at all levels. Banks believe
appointing KM experts are critical to successful KM implementation but
these experts are too busy to help.
• The biggest challenge is to get employees involved in KM operations,
particularly in knowledge sharing. It is because people by nature tend
to hoard knowledge for power or to maintain their importance at the
workplace.

63. limitations
• sample size must be minimum 5 times of research questions. This condition however
could only be fulfilled for the First Phase research. In the Second Phase, the response
dropped from 280 to only 164. But all the 91 manifest variables were duly validated.
Now going by this fact that only 26 banks participated in this study which is merely
28% of Indian commercial banks; the sample size obtained was 11 and 6 people per
bank for first and second phase research respectively which makes the sample qualify
for the study.
• KM is still in infant stages of development in Indian Banking sector, and mostly evident
in Private Foreign Banks followed by Private Indian Banks. Almost very little or no KM
based development was evident for Public Banks going by their number and size.
Therefore, the model prescribed suits more appropriately to foreign and Indian private
banks.

64. future scope for researchers
This research can further be helpful to Indian banks to identify:
• The progress in use and attitude towards KM in the banks.
• Act as benchmark for the current state of KM in the banks.
• Probe the extent to which banks are aware of KM and pursuing initiatives to
implement it effectively.
In general, this research will help the banks and other organisations in locating:
• What are an organisation’s knowledge needs?
• What knowledge elements (tacit and explicit knowledge) does it have and
where are they?
• How is that knowledge identified, created, stored, shared and used?
• What obstacles are there to knowledge flows or to what extent does its
processes, technology, culture, leadership and strategies support or hamper
the effective movement of knowledge?
• What gaps and duplications exist in the organisation’s knowledge?

65. my published work in this direction
• Dutt, Himanshu; Qamar, Furqan and Jha, Vidhu Shekhar (2012) “An approach towards
analyzing knowledge elements and designing knowledge management model”, published in
conference proceedings of National Conference on Emerging Challenges for Sustainable
Business, June, IIT Roorkee. ISBN: 978-93-81583-46-3.
• Dutt Himanshu, Qamar, Furqan & Jha, Vidhu Shekhar (2011), “A research to identify
knowledge orientation in Indian commercial banks”, Int. J. Knowledge Management Studies,
Vol. 4, No. 4, pp. 389-418. ISSN: 1743-8276 (Inderscience Publications, U.K).
• Dutt, Himanshu; Qamar, Furqan & Jha, Vidhu Shekhar (2011), “Measuring strategic value of
knowledge using knowledge lifecycle model: a case of Indian banking”, Global Journal of e-
Business & Knowledge Management, Vol. 7, No. 1, pp. 19-33. ISSN: 0974-0198 (Glogift).
• Dutt, Himanshu; Jha, Vidhu Shekhar and Qamar, Furqan (2010), “Critical analysis of
knowledge constituents and impact on organisational knowledge orientation – an
exploratory study”, IIMS Journal of Management Science, Vol. 1, No. 2, July-December, pp.
93-105. ISSN: 0976-030X.

66. Thanks!