Journal of Knowledge Management Practice,

Journal of Knowledge Management Practice, Vol. 12, No. 3, September 2011

Knowledge Management Maturity Model: An Engineering Approach

K.K.Kuriakose, Baldev Raj, S.A.V.Satya Murty, P.Swaminathan, Indira Gandhi Centre for Atomic Research, Kalpakkam, India

ABSTRACT:

Knowledge Management Maturity Model is a structured approach for implementing knowledge management. It can also be considered as engineering of knowledge management Many practitioners and researchers have developed knowledge management maturity models, which have many strengths and inadequacies. This paper attempts to develop a new model combining the strengths and eliminating the inadequacies of the existing models, with flexibility, adaptability and practical usability as the core objectives. The concept of Key Maturity Indicator is introduced which makes the model more flexible.

Keywords: Knowledge management, Maturity model, Key maturity indicator

1. Introduction

Knowledge Management (KM) is an interdisciplinary field covering various areas like Information and Communication Technology, Information Science, Systems Science and Engineering, Knowledge Engineering, Collaborative Engineering, Human Resource Management, Organizational Development, Change Management, Performance Management etc. Knowledge Management is a conscious strategy of getting the right knowledge to the right people at the right time and helping people share and put information into action in ways that will improve organizational performance(APQC,2000). The most fundamental processes in Knowledge Management are knowledge creation, knowledge sharing and knowledge utilization. From a systems perspective knowledge management can be considered as a system with the subsystems of People, Process, Technology and Knowledge. Consistent with the terminologies used in the literature, the sub systems are referred as Key Areas(KA).

Data, information and knowledge form a continuum. According to Davenport and Prusak (1998), when experience and insight are added to information, it becomes knowledge. Knowledge is classified into explicit knowledge and tacit knowledge. According to Nonaka “explicit knowledge is the knowledge that is easily expressed, captured stored and reused. In contrast, tacit knowledge is highly personal. It is hard to formalize and therefore difficult to communicate to others” (Nonaka, 1991). However the term ‘explicit knowledge’ is used where the knowledge is already available explicitly in the form of documents , audio and video recordings etc in electronic or non-electronic form and the term ‘tacit knowledge’ where the knowledge still resides in the minds of people in the form of experience, feelings, opinions, intuition etc. It is possible to convert certain percentage of the tacit knowledge into explicit by suitable knowledge elicitation methods.

A maturity model provides a guiding road map. This paper reviews the literature on Knowledge Management Maturity(KMM) models and proposes a new model which combines the strengths of the existing models and eliminates their inadequacies. The inadequacy only indicates that the feature is not explicitly mentioned in the referred literature. The paper is organized as follows. The first section reviews maturity models in general and KMM models in particular. The second section describes the new KMM model. The third section details the unique features of the new model . The fourth section dwells on the conclusion and the future work

2. Knowledge Management Maturity Model - An Engineering Approach

Maturity models describe the development of an entity over time. The entity can be anything of interest. It can be a human being, an organization, a technology, a product, a process etc. Maturity model gives a path to improvement. Maturity Model can also be used as a basis for comparison (Klimko, 2001). Maturity models are driven by the necessity to have a clear cut road map for any organization that is embarking on knowledge management implementation. It provides the clear vision with a description of the path ahead. Knowledge Management Maturity Model(KMMM) can be considered as an application of structured approach to knowledge management implementation. In other words development of a KMMM is nothing but engineering of KM. IEEE Standard 610.12 define ‘software engineering’ as the application of a systematic, disciplined, quantifiable approach to the development, operation and maintenance of software- that is the application of engineering to software(IEEE, 1990). In consistent with this definition we can define Knowledge Management Maturity Model as the “application of systematic, disciplined, quantifiable approach- that is an engineering approach to development, implementation and successive progression to attain maturity in knowledge management”. Maturity model can also provide a common understanding of the terminologies involved in knowledge management implementation to various stakeholders. Maturity models have the following properties(Klimko,2001,Weerdmeester et al., 2003).

The development of a single entity is simplified and described with a limited number of maturity levels(usually four to six).
Levels are characterized by certain requirements which the entity has to achieve on that level.
Levels are sequentially ordered, from an initial level to a final level of perfection.
During development, the entity progresses forward from one level to the next. No levels can be skipped.

Maturity models are basically application of life cycle approach. The entity develops through the levels , until the highest level, which is the level of perfection.

A well known maturity model is Maslow’s hierarchy of human needs(Maslow 1943). Maslow postulates that there are five levels in human needs. The human needs start with physiological needs and progresses to safety needs, needs of love and belonging, esteem needs and finally to self actualization needs.

Another very popular maturity model is Capability Maturity Model (CMM) and its latest version Capability Maturity Model Integration (CMMI) developed by Software Engineering Institute of Carnegie Mellon University for process improvement. CMMI supports both a staged representation and a continuous representation. In the staged representation the model has five levels. The lowest level is called “Initial”, which is characterized by ad hoc and chaotic processes and progresses through “Managed”, “Defined”, “Quantitatively Managed” to the final level of “Optimizing”, which is characterized by continual improvement of process performance through continual and innovative process and technological improvements(Chrissis et.al, 2007).

In this paper literature survey of fifteen KMM models has been carried out, identified their strengths and inadequacies, and a new model which combines the strengths and eliminates the inadequacies of the existing models is proposed. These maturity models were identified based on literature survey through academic journals, web sites of various organizations and references used in some of the models. The fifteen models were selected based on the adequacy of the information provided in the published literature. The characteristics of the fifteen KMM models reviewed, along with their strengths and inadequacies are summarized in the table 1. In the table column 2 lists the model names followed by the authors. The models are named with the name of the first author, wherever available. Column 3 lists the key areas identified in the model. The models which did not identify any key areas is represented as ‘Generic’. Column 4 lists the number of levels of the models followed by the names of the levels. Column 5 lists the characteristics of the maturity levels in progression from the lowest level to the highest level. Column 6 and 7 list the strengths and inadequacies identified by the authors. The inadequacy does not necessarily mean that the feature is not present in the model, but only indicates that the feature is not explicitly mentioned in the referred literature.

Table.1. Characteristics of Knowledge Management Maturity Models

Sl. No.

Model Name

and Author

Key Areas

No of Levels

and Names

Characteristics of Levels

Strengths

Inadequacies

KMMM (Kochikar)

Kochikar (2000)

People, Process, Technology.

Default, Reactive, Aware, Convinced, Sharing.

Fragmented knowledge,

Need based knowledge sharing;

Organization-wide knowledge sharing systems with visible link between KM processes and results;

Self-sustaining KM movement; Institutionalization of knowledge sharing culture.

Detailed description of behavioral characteristics and identification of parameters at each level.

An objective assessment methodology

No validation.

Knowledge is not a KA.

No classification of parameters at level 5

No Extended organizational maturity.

KMMM (Hubert )

Hubert and Lemons (2010)

Generic

Initiate,

Develop,

Standardize, Optimize,

Innovate.

Informal and inconsistent KM processes;

Establishment of a KM strategy that is tightly linked to the business strategy;

Refining the KM processes into standard replicable methodologies; Expansion of KM strategy through out the organization; Continuous improvement, Institutionalization and breakthrough innovation.

KM strategy that is linked to business strategy and driven by return on investment.

Individual, departmental and organizational performance assessment aligned with the KM strategy.

No Key Areas.

No Assessment methodology.

No validation.

No Extended organizational maturity.

KMCA

(Kulkarni)

Kulkarni and Freeze

( 2004)

Knowledge

Difficult,

Possible, Encouraged, Enabled, Managed, Continuously Improved.

Discouragement for knowledge sharing;

Selective knowledge sharing ;

Recognition and reward for knowledge sharing;

KM enabling of normal workflow; Monitoring and measuring of knowledge sharing;

Systematic measurement and improvement of knowledge sharing.

Detailed assessment methodology.

Validation of the model

Only ‘knowledge’

Key Area.

No Extended organizational maturity.

KMMM

(Klimko)

Klimko

( 2001)

Generic

Initial,

Knowledge Discoverer, Knowledge Creator,

Knowledge Manager, Knowledge Renewer

Lack of specific attention for KM activities;

Recognition of the importance of existing knowledge;

Identification and creation of new knowledge required for future activities;

Institutionalization of KM function with dedicated KM unit; Documented and measurable KM processes;

Knowledge sharing with other organizations and exploiting common ways of knowledge creation.

Advanced and Innovative knowledge.

Documented and measurable KM processes.

Extended organizational maturity.

No Key Areas

No validation.

No Assessment methodology

Knowledge Journey

KPMG (2000)

People,

Process,

Content,

Technology.

Knowledge Chaotic,

Knowledge Aware,

Knowledge Focused,

Knowledge Managed, Knowledge Centric

Lack of visible relationship between KM and achievement of organizational goals; Implementation of KM pilot projects;

Organization-wide usage of KM tools and realization of business benefits of KM;

Implementation of integrated framework for KM tools and procedures;

Adoption of KM procedures and tools as integral part of organizational and individual processes.

Identification of characteristics in terms Key areas like people, process, technology and content.

Partially normative model since freedom is given to select the requirements to reach a maturity level.

No validation.

No assessment methodology

KMMM (Natarajan)

Natarajan (2005)

Business Process Readiness, Technology, Infrastructure,

Human Behaviour, Leadership.

K-stages

Lack of specific KM;

Establishment of information sharing mechanism;

Establishment of systematic KM processes;

Institutionalization of KM as an integral part of business activity;

Validation of the model

Specific to software industry sector.

Maturity stage requirements are not in terms of Key Areas

KPQM (Paulzen)

Paulzen and Perc(2002)

Organization, People, Technology.

Initial,

Aware,

Established,

Quantitatively Managed, Optimizing.

Unplanned knowledge processes; Implementation of the first structure to ensure a higher process quality;

Systematic structure and definition of knowledge processes; Enhancement of the process management through tracking the performance measures; Establishment of structures for continuous improvement.

Integration of KM processes in to business process.

Excessive concentration on process, with very minimum concentration on people, technology and knowledge.

5iKM3

(Mohanty)

Mohanty and Chand (2005)

People,

Process, Technology.

Initial,

Intent

Initiative,

Intelligent,

Innovative.

Lack of formal processes for effective usage of organizational knowledge; realization of the potential in harnessing organizational knowledge for business benefits; knowledge enabled business processes and realization of its business impacts; matured collaboration and collective organizational intelligence; utilization of organizational knowledge for consistent and continuous process optimization and business advantage

Integration of KM processes to business process and business benefits

An assessment model that includes a proposed solution.

No parameters for Key Areas.

No validation

K3M

Wisdom Source (2004)

Generic.

Standardized Infrastructure For Knowledge Sharing,

Top-Down Quality Assured Information Flow,

Top-Down Retention Measurement, Organizational Learning, Organizational Knowledge Base,

Process-Driven Knowledge Sharing, Continual Process Improvement,

Organizational Self-Actualization.

Capturing and delivering of knowledge in repeatable steps; Identification of executive block of knowledge that is critical to lead the organization as a cohesive unit;

Measurement of team understanding of executive knowledge;

Culture of knowledge sharing and organizational learning;

Culture of continuous improvement and innovation;

Result focused process frame work;

Culture of knowledge creation; Continuous process development .

Good concentration on process improvement.

No Key Area like people, technology and knowledge.

No validation

No assessment methodology.

Number of stages are too high

KMMM

(Gottschalk )

Gottschalk (2002)

Technology,

Knowledge.

End User Tools, Who Knows

What,

What They Know ,

What They Think

Usage of standardized productivity tools by knowledge workers; Creation of knowledge maps; Usage of data mining technology to retrieve relevant knowledge; Availability of Artificial Intelligence(AI) techniques for solving knowledge problems.

Good concentration on technology including AI.

Lack of concentration on people and process.

No validation.

No assessment methodology

KMMM

(Ehms)

Ehms and Langen (2002)

Strategy & Knowledge Goals, Environment & Partnerships, People & Competencies,

Collaboration & Culture,

Leadership & Support,

Knowledge Structures & Knowledge Forms,

Technology & Infrastructure, Processes, Roles & Organization.

Initial,

Repeated,

Defined, Managed,

Optimizing

Lack of conscious control of knowledge process;

Recognition of the importance of KM activities in business;

Stable and practiced KM activities that are integrated into day to day work processes with necessary technical systems and KM roles; Measurement of KM efficiency; Organizational ability to adapt to any new KM requirements.

Integration of KM processes in to business process.

KM Measurements

An objective assessment methodology

Key Areas(8) and topics(64) covered are too many.

Details of the topics are not given

No validation

No illustration of the assessment model.

Strategic KMMM

(Kruger)

Kruger and Snyman (2007)

Generic

Initial, Repeated,

Defined,

Managed, Optimizing

Lack of awareness on the importance of knowledge as a strategic resource and ineffective management of Information and Communication Technology)ICT;

Recognition of the importance of KM function and evolution of ICT systems into data and information systems;

Formulation organization-wide KM policy and implementation of ICT systems to support management decisions and knowledge work;

Encouragement of KM activities and effective ICT and knowledge infrastructure;

Strategies for institutionalization of KM practices;

Extension of ICT and KM to the extended value chain of the organization.

KM policy and strategy.

Extended organizational maturity

Lack of concentration on Key Areas other than technology.

No validation.

No assessment methodology

KM3

(Gallagher)

Gallagher and Hazlett

Knowledge Infrastructure, Knowledge Culture, Knowledge Technology.

K-Aware,

K-Managed,

K-Enabled,

K-Optimized

From lack of awareness of knowledge management in the first stage to a complete and focused knowledge strategy that is tightly coupled to business strategy and ultimately results in improved business performance in the final stage.

Knowledge strategy that is tightly coupled to the business strategy and business performance

An objective assessment methodology.

The embryonic form of the model, without details or illustration.

G-KMMM (Pee)

Pee and Kankanhalli (2009)

People,

Process,

Technology.

Initial;

Aware; Defined; Managed; Optimizing.

Lack of intention to formally manage knowledge;

The intention to formally manage the knowledge;

Basic infrastructure to support KM activities;

Well established KM initiatives;

Automatic integration of KM into organizational process and continuous improvement;

Integration KM process into organizational process.

Validation of the model

Knowledge Key Area is not considered.

KMMM (Boyles )

Boyles et al (2009)

Human Resource,

Training,

Documentation, Technology,

Tacit Knowledge, KM Culture.

Each Key Area progresses from ‘not utilized’ , ‘to a little extent’, ‘to some extent’, ‘to a great extent’ and ‘ to a very great extent’.

Detailed assessment criteria were listed.

The model is not validated

3. The New Knowledge Management Maturity Model

From the literature survey it is clear that each model has its own strengths and inadequacies. Some of the models were developed by practitioners, for the use in their own organization or for use as consultancy service[KMMM (Kochikar), KMMM(Ehms) etc. ]. Hence the details of assessment methodology, validation etc. are not available publicly. Some of the models are developed by academicians, where the details of assessment methodology, validation etc. are not done in detail [KMMM(Klimko), KPQM(Paulzen) etc.]. Also flexibility and adaptability of the model to various organizational environments is less. Hence practically adapting the model to another organization has difficulties

Hence a new model is required which will satisfy the following requirements:

The model should be practically adaptable to any organization

It should be flexible enough for continual improvement to suit to various changes in organizational environments

It should combine the strengths of the existing models and eliminate their inadequacies

The new model is proposed with the following premises:

Since the main objective of KM is to improve organizational performance, higher level of KM maturity implies higher level of organizational performance and thus a higher Return On Investment (ROI)
The Key Areas in KM are People, Process, Technology, Knowledge and ROI

Each Key Area is identified with certain number of parameters called Key Parameters(KP). Each parameter is identified with certain values called Key Values(KV). Key Parameters identified for different Key Areas and the Key Values are listed below.

3.1. KeyParameters

Key Parameters (People)

The parameters for People KA are Awareness, Participation, Reward and Recognition Scheme:

Awareness:This parameter indicates the level of understanding and acceptance of employees the practical meaning of KM as applicable to them.

Participation: This parameter indicates the level of participation of employees in formal KM activities.

Reward and Recognition Scheme: This parameter indicates the effectiveness of reward and recognition schemes to motivate employees for voluntary participation in formal KM activities.

KM roles, Communities of Practice, Mentoring and Succession Planning:

KM roles: This parameter indicates the effectiveness of KM roles which can be full time or part time.

Communities of Practice: This parameter indicates the effectiveness of knowledge sharing communities.

Mentoring and Succession Planning: This parameter indicates the effectiveness of mentoring and succession planning.

Key Parameters (Process)

The parameters for Process KA are KM Policy, KM Strategy, KM Processes, Process Integration:

KM Policy: This parameter indicates the effectiveness of KM Policy which is a statement of intent of what one wants to achieve with KM.

KM Strategy: This parameter indicates the effectiveness of KM Strategy which is a statement of how one wants to achieve KM.

KM Processes: The KM processes considered are knowledge identification, knowledge creation, knowledge acquisition, knowledge preservation, knowledge quality, knowledge sharing, knowledge utilization, KM ROI measurement. The parameter indicates the over all effectiveness of KM processes.

Process Integration: Process integration refers to the integration of KM processes with normal work processes. The parameter indicates the level of integration and its effectiveness

Key Parameters (Technology)

The parameters for Technology KA are Network, Data and Information management, Explicit Knowledge Management, Tacit Knowledge Management, , Artificial Intelligence(AI) and Knowledge Engineering(KE) techniques, Technology Integration:

Network : Network refers to organization-wide connectivity of computer systems and other related resources. The parameter indicates the effectiveness of the network.

Data and Information Management: The parameter indicates the effectiveness of organization-wide data and information system.

Explicit Knowledge Management: The parameter indicates the effectiveness of technology for content management.

Tacit Knowledge Management: The parameter indicates the effectiveness of technology for collaboration

AI and KE Techniques: The parameter indicates the effectiveness of AI and KE for knowledge elicitation, knowledge representation, knowledge retrieval, inference etc.

Technology Integration: Technology integration refers to the integration of various systems of the organization like Data Management Systems, Information Management Systems, Content Management Systems, Collaboration Systems, AI and KE Systems etc. The parameter indicates the level of integration and its effectiveness

Key Parameters (Knowledge)

The parameters for Knowledge KA are Knowledge Classification, Knowledge Capability Areas, Knowledge Organization:

Knowledge Classification: Knowledge classification refers to the classification of knowledge into core, advanced and innovative(Zack, 1999, Gottschalk. 2002). The parameter indicates the combined effectiveness of core , advanced and innovative knowledge.

Knowledge Capability Areas: The parameter includes the knowledge capability areas identified by Kulkarni and Freeze( Kulkarni and Freeze, 2004) viz data, knowledge documents, lessons learned, expertise and knowledge in the form of Frequently Asked Questions (FAQ). Also it includes unapproved and un solicited knowledge in the form of blogs, wikis etc. The combined effectiveness is indicated by the parameter.

Knowledge Organization: Knowledge organization refers to the organization of the knowledge based on knowledge map, meta knowledge, taxonomy etc. and its combined effectiveness is indicated by the parameter.

Key Parameters (ROI)

This paper uses Employee Satisfaction as the only parameter for ROI. The parameter indicates the level of satisfaction on KM activities.

3.2. Key Values

The ‘Key Values’ identified for the Key Parameters are ‘Nil’, ‘Low’, ‘Medium’, ‘High’ and 0 – 100. The value ‘Nil’ indicates that the parameter is either not applicable, or not assessed or does not exist. The values ‘Low’, ‘Medium’ and ‘High’ indicates that the parameter is assessed qualitatively. The value 0-100 indicates that the parameter is assessed quantitatively and it is expressed in percentage.

The new KMM model has six maturity levels( level 0 to level 5). The maturity levels are named as ‘Default’, ‘Initial’, ‘Qualitative Development’, ‘Quantitative Development’, ‘Maturity’ and ‘Extended-Organizational Maturity’. The new KMM model identifies different maturity levels by a specific combination of Key Maturity Indicators(KMI). Each KMI is identified by a specific combination of KA, KP and KV. For an organization to be in a specific maturity level all the KMIs pertaining to that level and all preceding levels need to be satisfied. No levels can be skipped. If an organization satisfies all the KMIs pertaining to one level say level 1 and at least one KMI pertaining to the next level for each KA, then organization can be considered to be in a level 1+. Similarly if the organization satisfies at least 50% of the KMIs pertaining to level 2 for each KA, then that organization can be considered to be in a level of 1++. Also if the organization satisfies all KMIs of level 1 and satisfies at least one KMI or at least 50 % of the KMIs of level 2 in one or more specific KAs alone, the organization is considered to be in level 1 in the over all maturity and 1+ or 1++ in the specific KAs alone. The Maturity Levels and Key Maturity Indicators are listed below.

3.3 Maturity Levels

The maturity level, their general characteristics and characteristics in terms of Key Areas are described below.

Level 0: Default

Level 0 is the basic level. By default all organizations will be at a minimum of level 0. It is characterized by the absence of any formal KM activity. The organization recognizes and rewards only individual expertise and capabilities. Organization is in a level of ‘unconscious incompetence’ in KM

People: Awareness of KM may not exist . People work in isolation and compete with each other. The thinking is “we do not know anything about KM”

Process: The only KM processes are mandatory reports, formal training and informal socialization.

Technology : Generally individual productivity tools are being used.

Knowledge : Only routine knowledge required for survival is created and shared through training and informal socialization.

ROI: This Key Area is not applicable at this level, since formal KM is not existing.

Level – 1 : Initial:

It is characterized by the intention of the management to start formal knowledge management activity. Though organization does not have the clarity on how to proceed, it initiates KM activities. Organization generally works as silos and the knowledge sharing takes place only within the silos. Though islands of excellence exists, pool of excellence is lacking. Organization is in a level of ‘conscious incompetence’ in KM

People : A low level of awareness of formal Knowledge Management and the need for Knowledge Management exists among the employees.

Participation in KM activities is low. Only part-time KM roles exist. Mentoring and succession planning is prevalent in an adhoc way. Thee

thinking is “ we need KM , but it is too difficult and time consuming”

Process: A documented KM policy and KM strategy exists. Organization-wide procedure for documenting and selective sharing of routine and procedural knowledge exists. Procedure for formal knowledge sharing sessions exist.

Technology: Organization wide network exists. Isolated/networked systems for data, information and explicit knowledge like publication,

progress report, project reports etc., exist. Also technology infrastructure for tacit knowledge sharing exists in a primitive level.

Knowledge: The quantity of routine and procedural knowledge shared have improved.

ROI: Since formal KM activities are only initiated ROI may be negligible.

Level 2 : Qualitative Development

This stage is characterized by qualitative assessment of KM activities and its impact on the performance of individuals, department and organization. Based on the qualitative assessment the performance of KM activities and its impact on the performance is good.

People: Organization wide awareness and participation of KM activities is monitored qualitatively and is good. Dedicated full time KM roles were created in addition to part time roles with clear mandate and review mechanism. A committee of senior management reviews the progress and takes appropriate corrective actions. Reward and recognition schemes are introduced. Knowledge sharing communities are encouraged. Mentoring and succession planning is practiced with appropriate knowledge transfer. The thinking is “we are doing KM”

Process: The effectiveness of KM policy and KM strategy is improved in a qualitative way. Formal processes for knowledge identification, creation, acquisition, approval, quality rating, preservation, sharing, utilization and impact assessment on performance of individuals, department and organization exists. All the formal processes are documented, the effectiveness is measured qualitatively and corrective mechanisms are incorporated. The effectiveness of the formal processes is good

Technology:A user friendly knowledge portal with necessary content management and collaboration technologies, and necessary security features is operational. Integration of organizational data and information system with knowledge portal is being explored. Knowledge engineering techniques are being explored for knowledge acquisition, knowledge representation, knowledge retrieval and inference. The portal is so configured in such a way that employees can do the information/knowledge oriented work from the portal itself. All the necessary links to other internal and external websites and utilities are provided. The effectiveness of the portal monitored qualitatively and is good.

Knowledge:In addition to routine knowledge, advanced knowledge required for performance improvement and future activities is created/ acquired and shared. Tacit knowledge is elicited and shared across the organization, in addition to sharing in communities. Knowledge is organized with Knowledge Map, Meta –Knowledge structure and taxonomy. Knowledge in the form of Lessons Learned, Frequently Asked Questions (FAQ), Experise, Data etc. are documented, preserved and shared. Unapproved and unsolicited knowledge also is being shared. The quality of knowledge and its organization is measured qualitatively and is good.

ROI: Since formal KM activities are improved qualitatively the ROI should be good.

Level 3 – Quantitative Development

This stage is characterized by quantitative assessment of KM activities and its impact on the organizational performance. The organization is able to quantitatively link the KM activities and the organizational effectiveness in terms of various performance indicators. The effectiveness of the measured parameters reaches more than 50% of the targeted value. Organization reaches the level of ‘conscious competence’ in KM

People: Awareness and acceptance of KM activities is improved significantly. More than 50% of the employees are active participants in KM activities. Knowledge sharing communities exist irrespective of departmental boundaries and more than 50% of the employees are members in one or more knowledge sharing communities. People have started recognizing that knowledge management is a part of the normal work. Dedicated KM roles, reward & recognition scheme and mentoring and succession planning continues with quantitatively measurable ROI. The thinking is “we are doing KM very well”

Process: The effectiveness of KM policy and KM strategy is improved and is more than 50% of the targeted value. Organization wide KM processes get integrated with normal work processes with quantitative measurements and corrective mechanisms. More than 50% of the normal work processes have integrated KM processes

Technology: Integration of organizational data and information system with knowledge portal is successful in locating the relevant knowledge. Isolated applications using Knowledge Engineering techniques used for knowledge acquisition, knowledge representation, knowledge retrieval and inference like natural language processing, speech recognition, ontology, knowledge discovery through data mining / text mining, case based reasoning, rule based reasoning etc are successful and is being integrated with the knowledge portal. The portal is configured in such a way that more than 50% of the employees can do the information/knowledge oriented work from the portal itself. The effectiveness of the knowledge portal is measured quantitatively and is more than 50%.

Knowledge: In addition to routine and advanced knowledge, innovative knowledge required for innovations and leadership positions is created/ acquired and shared. The quality of knowledge in Knowledge Capability Areas and its organization is measured quantitatively in addition to qualitative measurements. The over all quality of the knowledge shared is more than 50% of the targeted value

ROI: More than 50% of the targeted value.

Level 4 – Maturity:

Knowledge management has become an integral part of every activity and got embedded into the organizational culture. The level is characterized by continual improvement and institutionalization of the knowledge management practices. The effectiveness of the measured parameters reaches more than 90%. Organization reaches the level of “unconscious competence” in KM.

People: Everyone recognizes knowledge management as an integral part of their work. They are able to see the visible link which is backed by qualitative and quantitative measurements between KM activities and performance and growth of individuals, department and organization. People have become insensitive to organizational hierarchies and affiliations as far as Knowledge Management activities are concerned. Collaborative activities and knowledge sharing communities are widespread throughout the organization. The effectiveness of KM roles has reached a level where dedicated senior level KM roles like Chief Knowledge officer may get replaced with part-time roles, though lower level roles for technology enhancement/maintenance may continue. The effectiveness of reward & recognition scheme and mentoring and succession planning have reached a level where exclusive schemes may get vanished and may become a part of the normal work culture. Continual improvement in effectiveness of various parameters, performance, growth and ROI is monitored and is moiré than 90%. The thinking is “we have achieved in making KM a way of our life”

Process: The effectiveness of KM policy, KM strategy, KM processes and process integration is continually improved and is moiré than 90%.of the targeted value. Process integration have reached a level, where KM processes have become an integral part of every organizational activity including organizational performance measurements

Technology: The data and information system of the organization get seamlessly integrated with the knowledge management portal. Knowledge Engineering techniques used for knowledge acquisition, knowledge representation, knowledge retrieval and inference like natural language processing, speech recognition, ontology, knowledge discovery through data mining / text mining, case based reasoning, rule based reasoning etc are matured and get seamlessly integrated with the knowledge management portal. The portal is configured in such a way that all the employees can do all the information/knowledge oriented work from the portal itself. All the employees have made the KM portal as their preferred home page. The security, reliability, availability, user friendliness and effectiveness of the of the KM portal is continually improved and is more than 90%

Knowledge: The quality and quantity of knowledge shared is continually improved and is more than 90% of the targeted value. The knowledge necessary to carry out the current and future activities of the organization is guaranteed as an integrated package of explicit and tacit knowledge.

ROI: More than 90% of the targeted value

Level 5 – Extended - Organizational Maturity:

Level 5 is characterized by achieving maturity with respect to partnering organizations like, suppliers, customers and other alliance organizations and seamless integration with these organizations. Essentially, organizational boundaries with respect to knowledge management breaks down and the partnering organizations together as a single entity reach the KMIs of level 4 maturity. However to achieve level 5 maturity, the extended organization may have to assess the current level and gradually progress from that level, however low it is.

3.3. Key Maturity Indicators

The Key Maturity Indicators for various levels of maturity levels are summarized in Table 2 and they are pictorially represented in Figure 1.

Table 2. Maturity Levels And Key Maturity Indicators

Level

People

Process

Technology

Knowledge

ROI

Default

Only routine _

Initial

Awareness- Low
Participation -Low
KM roles –Low
Mentoring and Succession Planning-Low
Communities of Practice-Nil
Reward and Recognition Scheme- Nil

KM Policy-Low
KM Strategy-Low
KM Processes-Low
Process Integration-Nil

Network-Medium
Data and Information management-Medium
Explicit Knowledge Management-Low
Tacit Knowledge Management-Low
KE techniques-Nil
Technology Integration- Nil

Knowledge Classification-Low
Knowledge Capability Areas-Nil
Knowledge Organization-Nil

Employee Satisfaction-Nil

Qualitative

Development

Awareness- Medium
Participation -Medium
KM roles -Medium
Mentoring and Succession Planning-Medium
Communities of Practice-Low
Reward and Recognition Scheme- Low

KM Policy-Medium
KM Strategy-Medium
KM Processes-Medium
Process Integration-Low

Network-High
Data and Information management-High
Explicit Knowledge Management-Medium
Tacit Knowledge Management-Medium
KE Techniques-Low
Technology Integration-Low

Knowledge Classification-Medium
Knowledge Capability Areas-Medium

Knowledge Organization-Medium

Employee Satisfaction-Medium

Quantitative

Development

Awareness->50
Participation ->50
KM roles ->50
Mentoring and Succession Planning->50
Communities of Practice->50
Reward and Recognition Scheme- >50

KM Policy->50
KM Strategy->50
KM Processes->50
Process Integration->50

Network->50
Data and Information management->50
Explicit Knowledge Management->50
Tacit Knowledge Management->50
KE techniques->50
Technology Integration->50

Knowledge Classification->50
Knowledge Capability Areas->50
Knowledge Organization->50

Employee Satisfaction->50%

Maturity

Awareness->90
Participation ->90
KM roles ->90
Mentoring and Succession Planning->90
Communities of Practice->90
Reward and Recognition Scheme- >90

KM Policy->90
KM Strategy->90
KM Processes->90
Process Integration->90

Network->90
Data and Information management->90
Explicit Knowledge Management->90
Tacit Knowledge Management->90
KE techniques->90
Technology Integration->90

Knowledge Classification->90
Knowledge Capability Areas->90

Knowledge Organization->90

Employee Satisfaction->90%

Extended- organizational maturity

Same as level 4 with extended value chain of the organization.

Same as level 4 with extended value chain of the organization

Figure 1: Maturity Levels And Key Maturity Indicators

3.4. Assessment Methodology

The methodology includes assessment of current level of maturity and recommendation to improve the maturity. An objective assessment methodology consisting of verification of records, in depth interview, questionnaire and focus group discussion is proposed. Any organization that is to be assessed has to start with level 1. If an organization is assessed to be in level 1, it can further be assessed for level 1+, 1++, 2 etc. The current maturity level of the organization and the possible solution to improve the maturity has to be arrived at in consultation with various stake holders of the organization.

3.5. Validation

The KMM model needs to be validated based on one or more organizational study. The validation process is currently being carried out, based on the study of a Government R&D organization with ten major departments and more than two thousands employees. The validation process will also demonstrate the assessment methodology.

4. Unique Features Of The Model

The approach based on the unique concept of KMI makes the model more flexible and adaptable. Each organization which intends to adapt the model, can select the KMIs applicable to them. They can also introduce additional KMIs, which are applicable to them. Since KMI is a combination of KA, KP and KV, the flexibility in selecting the KMI indicates, the flexibility in selecting KA, KP and KV as well. This concept also makes the model amenable for continual improvement of the model itself, by introducing additional KMIs, removing the unwanted KMIs or modifying the KMIs to adapt to the changes in the organizational environments.

The model attempts to combine the strengths and eliminate the inadequacies of the existing models. The features of the model which are strengths in other models are summarized in Table 3. The absence of the same features in other models can be considered as inadequacies in those models. For example the feature ‘parameters of maturity level’ is considered as one of the strengths of KMMM(Kochikar). The absence of the same feature can be considered as one of the inadequacies of other models. The absence does not necessarily mean the real absence of the feature in the model, but it only indicates that the feature is not explicitly mentioned in the referred literature.

Table 3: Features Of The New Model As Strengths Of Other Models

Sl.No	Features of the new KMMM	Strengths of other models
1	Parameters of maturity level	KMMM(Kochikar)
2	KM strategy	KMMM(Hubert), KM3, Strategic KMMM(Kruger)
3	KM policy	Strategic KMMM(Kruger)
4	KM ROI	KMMM(Hubert)
5	Four key areas(People, Process, Technology and Knowledge)	Knowledge journey
6	Knowledge classification(core, advanced and innovative)	KMMM(Klimko), KMMM(Gottschalk)
7	Documented and measurable KM process	KMMM(Klimko), KMMM(Ehms )
8	Extended organizational maturity	KMMM(Klimko),StrategicKMMM(Kruger)
9	Process integration	KPQM(Paulzen),G-KMMM(Pee), 5iKM3(Mohanty),KMMM(Ehms)
10	Concentration on technology including AI and KE	KMMM(Gottschalk)
11	Objective assessment methodology	KMMM(Kochikar),5iKM3(Mohanty), KMMM(Ehms), KM3
12	Validation (being done)	KMCA(Kulkarni), KMMM(Natarajan), G-KMMM(Pee)

5. Conclusion And Future Work

Formal knowledge management is in the fore front of the business strategy of many organizations. Deriving business benefits from KM depends on many factors. A guiding KMM model is essential for any organization, embarking on formal KM, to bench mark its activities. This paper described a new model combining the strengths of the existing models and eliminating their inadequacies. The model is highly flexible with its unique KMI concept and can be adapted to any organizational environment. The model uses a balanced approach with adequate concentration on various Key Areas viz People, Process, Technology, Knowledge and ROI. The final maturity level considers the target organization along with other partnering organizations as a single entity. Hence the model extends the traditional boundary of the organization, and a step forward in the direction of ‘National’ and ‘Global’ knowledge management.

Through an organizational study, the model will be validated and the assessment procedure including the probable solutions to improve the maturity will be demonstrated. The model can be improved by including additional Key Areas and Key Parameters.

6. References:

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http://www.apqc.org/knowledge-base/download/33020/a%3A1%3A%7Bi%3A1%3Bs%3A1%3A%222%22%3B%7D/inline.pdf?destination=node/33020

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Kochikar,V.P.(2000) “The Knowledge Management Maturity Model: A Staged Framework for Leveraging Knowledge”, KM World 2000, Santa Clara, CA..

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(Accessed 10, November, 2009)

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( Accessed 12, May, 2009)

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About the Authors:

K.K. Kuriakose graduated with honours in Electrical Engineering from the Regional Engineering College (now known as the National Institute of Technology), Calicut, India in 1977. After undergoing training in Nuclear Science and Engineering from Bhabha Atomic Research Centre (BARC) Training School, he joined the Indira Gandhi Centre for Atomic Research (IGCAR), India in 1979. He had also obtained Master of Engineering (first class) in Electrical Communication Engineering from the Indian Institute of Science, Bangalore, India in 1986, and Master of Business Administration from Indira Gandhi National Open University, India in 2000. Currently he is the Head of the Knowledge Management Section and a doctoral-level research scholar in the area of knowledge management with Homi Bhabha National Institute, Mumbai, India. He has twenty five publications in national and international conferences/ journals/ reports in the area of Information Management, Knowledge Management and Simulation. His research interests include information management systems, knowledge management, organizational learning and software engineering. He is the corresponding author and can be contacted at kuriakose@igcar.gov.in, kkkuriakose2003@yahoo.com.

Dr. Baldev Raj, b 1947; BE, Ph.D, D.Sc.; Member, International Nuclear Energy Academy, German National Academy of Sciences, Fellow, Third World Academy of Sciences and Fellow of all Engineering and Science Academies in India. He is a Distinguished Scientist & Director, Indira Gandhi Centre for Atomic Research, Kalpakkam, Tamil Nadu. His specializations include materials characterization, testing and evaluation using non-destructive evaluation methodologies, materials development and performance assessment and technology management. He has more than 750 publications in leading refereed journals and books. He has co-authored 12 books and co-edited 32 books and special journal volumes. He has 5 Indian Standards and 18 patents to his credit. He is Editor-in-Chief of two series of books: one related to NDE Science & Technology and another related to Metallurgy & Material Science. He is on the editorial boards of national and international journals. He is member of many national and international committees and commissions. He has been invited to deliver plenary and panel speeches in the most eminent international forums and more than fifty occasions in thirty countries. He has won many national and international awards and honours. He has passion for teaching, communications and mentoring. His other interests include science and technology of cultural heritage and theosophy.

S.A.V. Satya Murty did his BTech at Jawaharlal Nehru Technological University, India in 1977, for which he was a university gold medalist. Later, he joined a one-year orientation course in Nuclear Science and Engineering at BARC. He was awarded the Homi Bhabha prize for getting 1^st place. He joined the Indira Gandhi Centre for Atomic Research (IGCAR) in 1978. He played a key role in the establishment of a mainframe computer system for IGCAR. He was also instrumental in establishing internet and e-mail facilities at IGCAR. He was responsible for the upgrading of the IGCAR Campus Network. He took keen interest in network security and commissioned many security servers, a high-performance computing facility, a intra-DAE VSAT network and a grid computing facility at IGCAR. He has more than 70 journal publications/conference proceedings and edited one international conference proceedings. At present, he is the Associate Director, Electronics and Instrumentation Group at IGCAR, and a. doctoral-level research scholar with Homi Bhabha National Institute.

Dr. P. Swaminathan graduated with honours in Electronics and Communication Engineering from Regional Engineering College, Tiruchirapalli, India in 1971. He also holds a Ph.D from Satyabama Unniversity, Chennai, India. He is a gold medallist of the University of Madras, India. Also he is a senior professor with Homi Bhabha National Institute , Mumbai, India. After undergoing a one-year course in Nuclear Science and Engineering from BARC Training School, he joined Indira Gandhi Centre for Atomic Research (IGCAR) in 1972. He further underwent a one-year course in mainframe systems from International Honeywell Bull Training Institute, Paris, France. He is the main architect for the design, development, installation and commissioning of the fault-tolerant safety-critical real-time computer system for the fast breeder test reactor. As Outstanding Scientist and Director of the Electronics & Instrumentation Group at IGCAR, he is engaged in the development of safety instrumentation, a full-scope training simulator and a knowledge management system for a fast breeder reactor programme. He has over 40 publications in international journals/ seminars. Recently he has been honored with Indian Nuclear Society Award for his R&D Contributions.