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Knowledge Management Model for Indian Commercial Banking Sector
 

Knowledge Management Model for Indian Commercial Banking Sector

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This presentation tries to outline a KM model for Indian Banks, basis on their KM orientation, KM process and KM system.

This presentation tries to outline a KM model for Indian Banks, basis on their KM orientation, KM process and KM system.

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    Knowledge Management Model for Indian Commercial Banking Sector Knowledge Management Model for Indian Commercial Banking Sector Presentation Transcript

    • 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
    • “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
    • 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.
    • 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.
    • ‘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)
    • 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.
    • ‘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).
    • ‘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)
    • 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).
    • 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).
    • 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).
    • 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).
    • 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).
    • 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.
    • 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).
    • 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.
    • 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.
    • 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.
    • 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.
    • 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)
    • 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).
    • 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
    • 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).
    • 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
    • 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.
    • 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.
    • 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.
    • – 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
    • 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.
    • 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.
    • 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
    • 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’
    • 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.
    • 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
    • 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
    • objective: 1 Identify knowledge orientation
    • 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
    • e.g. knowledge components
    • 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
    • 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.
    • model validation of knowledge components model validation of knowledge types & knowledge capital
    • model validation of knowledge determinants model validation of knowledge effect & knowledge value
    • model validation of knowledge improvisers model validation of knowledge influencer
    • 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.
    • 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).
    • 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.
    • analysis: second-phase research KM Process - KM System - KM Practice
    • 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.
    • 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
    • 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.
    • 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.
    • 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
    • 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.
    • 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.          
    • 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
    • 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.
    • 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
    • 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
    • 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.
    • 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.
    • 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.
    • 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.
    • 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.
    • 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?
    • 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.
    • Thanks!