Facilitating New

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Facilitating new knowledge creation and

obtaining KM maturity

Priscilla A. Arling and Mark W.S. Chun

Abstract

Purpose The purpose of this paper is to describe a framework designed to assess the capacity of a

knowledge management (KM) system to facilitate new knowledge creation.

Design/methodology/approach A longitudinal case study methodology, in a single company, Pratt

Whitney Rocketdyne (PWR), was used to test the framework.

Findings New knowledge creation is best supported through mature KM systems that include all four

modes of knowledge creation: combination, externalization, socialization, and internalization. KM

systems andenvironments as a whole reachmaturityby progressing through stages, whichis presented

as a KM maturity model.

Research limitations/implications By combining Nonakas knowledge creation theory with

Wittrocks generative learning activities, the paper illuminates both the why and how of new knowledge

creation, in a waythat canbe applied to KM technological initiatives. Oneof the limitations of this study is

the generalizability of the findings, which may be limited by the single case study method used.

Practical implications The frameworkprovides a rubricagainst whichboth old andnew KM initiatives

can be assessed to determine whether they are capable of generating new knowledge. The maturity

model provides a template against which organizations can map their progress towards a mature KM

environment.

Originality/value Much of the literature on KM systems has focused on capturing knowledge and

disseminating it. Few studies have provided practical, theoretically based advice on how to create new

knowledge and what aspects of information systems can facilitate that creation. The framework and

maturity model can serve as guides in that process.

Keywords Knowledge management, Case studies, Modelling

Paper type Case study

1. Introduction

Despite the importance of knowledge as an asset, few organizations truly understand how to

manage knowledge to achieve their goals (Yu, 2005). To actualize knowledge management,

firms frequently turn to technology-based information systems such as knowledge

repositories and expert databases (Durcikova and Gray, 2009). These information

systems, developed to support and enhance organizational knowledge processes, are

referred to as knowledge management systems (KMSs) (Alavi and Leidner, 2001). Much of

the literature on KMSs has focused on the process of capturing and disseminatingknowledge. However to gain a competitive advantage from knowledge, firms must

accomplish more than the redistribution of existing knowledge, they must generate new

knowledge (Alavi and Leidner, 2001). The process of creating new knowledge has been

referred to as double-loop learning (Argyris, 1977) or generative learning (Senge, 1990).

Generative learning is challenging to achieve in organizations because it requires more than

the application of existing knowledge to new situations. Generative learning focuses on the

reframing and re-visioning what is currently known, in order to create what is currently

unknown (Senge, 1990). Senge (1990) distinguishes generative learning by comparing it to

DOI 10.1108/13673271111119673 VOL. 15 NO. 2 2011, pp. 231-250, Q Emerald Group Publishing Limited, ISSN 1367-3270 j JOURNAL OF KNOWLEDGE MANAGEMENT j PAGE 231

Priscilla A. Arling is an

Assistant Professor of

Management Information

Systems at the College of

Business, Butler University,

Indianapolis, Indiana, USA.

Mark W.S. Chun is an

Associate Professor of

Information Systems at

Graziadio School of

Business and

Management, Pepperdine

University, Los Angeles,

California, USA.

Received: 18 May 2010Accepted: 18 October 2010


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adaptive learning. Adaptive learning focuses on solving problems using an existing

framework and making small, incremental changes. Generative learning questions the

existing framework of problem solving to create new options and new knowledge. This type

of learning requires an understanding of systems and relationships that link key issues and

events (Slater and Narver, 1995).

The KM literature is replete with examples of innovative approaches to capturing and

sharing knowledge. People-finder systems, knowledge databases, search capabilities and

blogs, are a few examples of how KMSs have made existing knowledge more widely

available. However, what is less frequently discussed is how KMSs are facilitating generative

learning and the extent to which new knowledge is being created. While prior work has

provided high level frameworks for knowledge management, few studies have offered

prescriptive advice on what features of KMSs facilitate knowledge creation. In order to

improve future KMS implementations, the authors wanted to know What are the features of

KMSs that foster the creation of new knowledge?

This article describes a framework that can be used to assess the capacity of a KMS to foster

generative learning. To test the framework the authors conducted a longitudinal case study

in a single company, Pratt Whitney Rocketdyne (PWR). The article describes how PWR

moved from a KM environment that focused primarily on capturing and storing data to an

environment that facilitated new knowledge creation. The article also presents a model,

called the KM Maturity model, which illustrates how firms develop knowledge management

competencies that lead to on-going new knowledge generation. Both the framework and the

model can serve as tools for organizations seeking to generate new knowledge forcompetitive advantage.

To the authors knowledge, this article is among the few to present a set of specific

characteristics that can be incorporated into knowledge management systems in order to

facilitate new knowledge creation. Together, the framework presented and case study

highlight knowledge creation activities that are often found in in-person knowledge

initiatives, but can be easily be missed in technology-based initiatives. By combining

Nonakas knowledge creation theory with Wittrocks generative learning activities, the

framework highlights the why and how of new knowledge creation, in a way that can be

applied to KM technological initiatives.

2. Background

2.1 What is new knowledge?

In order to assess the capacity of an organizational system to generate new knowledge, the

first step is to define knowledge and then to how determine if it is new. Knowledge is

defined as a justified belief that increases an entitys capacity for effective action (Alavi and

Leidner, 2001; Nonaka, 1994). The belief is justified because it is grounded in information as

well as the values and prior understandings of the holder (Nonaka, 1994), which means that

knowledge is relational and context-specific. The belief is related to prior beliefs and in order

to be meaningful, the context in which it was developed must be understood (Nonaka et al.,

2001). The belief must also be linked in some way to effective action, so that the creation of

knowledge also implies the creation of something of value (von Krogh, 1998). Whether or not

a belief has value, and therefore whether or not it is considered knowledge, is based on thecontext in which it is created or used, including the beliefs of others (Nonaka et al., 2001).

Nonakas theory is based on Polanyis (1966) notion that there are two types of knowledge,

explicit and tacit. Explicit knowledge can be articulated, codified and transmitted in some

type of symbolic form or natural language (Alavi and Leidner, 2001). Tacit knowledge on the

other hand has a personal quality, and is rooted in action, commitment and involvement in a

specific context (Nonaka, 1994). Tacit knowledge is difficult to articulate, and is often

characterized as personal skills, mental models and know-how that are deeply ingrained

in an individual (Polanyi, 1966).

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Nonaka (Nonaka, 1994; Nonaka and von Krogh, 2009) posits that new knowledge is created

through the conversion of tacit and explicit knowledge. There are four modes of conversion:

socialization, combination, externalization or internalization. Socialization is the process of

converting one individuals tacit knowledge to another individuals tacit knowledge through

interpersonal interaction. Combination is the process of creating new explicit knowledge by

reconfiguring, re-categorizing and re-conceptualizing existing explicit knowledge.

Externalization is the process of converting tacit knowledge to explicit knowledge, while

internalization is the process of converting explicit knowledge to tacit knowledge. An

example of externalization is the articulation of best practices or lessons learned, while

internalization is exemplified by the learning that occurs from reading (Alavi and Leidner,

2001).

While Nonakas four categories are useful at a high level, they provide little guidance as to

specific actions that can be taken to facilitate knowledge creation. In order to actualize these

Nonakas modes in a firm, managers and system developers need to understand what

activities facilitate the converting, relating and combining of knowledge into new knowledge.

They need to understand the activities that correspond to generative learning.

2.2 Generative learning and feedback in the creation of new knowledge

The process of combining, converting and relating ideas to create new knowledge is called

generative learning (Senge, 1990; Wittrock, 1990). Wittrock (1990) states that for generative

learning to occur, the learner must understand not only how existing knowledge components

relate to each other, but also how those components fit in with the learners internal

knowledge and memory. He lists two general types of activities that aid in generative

learning:

Organizing activities. These are activities that generate relationships that organize

information. Examples are composing titles and headings, writing summaries,

constructing main ideas, drawing graphs, preparing tables, stating objectives and asking

questions.

Integrating activities. These are activities that generate integrated relationships between

what the learner sees, hears or read and his internal knowledge or memory. Examples are

participating in demonstrations, composing metaphors, drawing analogies, providing

examples, drawing pictures, developing interpretations, paraphrasing, drawing inferences.

Wittrocks generative learning activities suggest concrete ways in which Nonakas four

modes of knowledge creation can be enacted. Organizing activities relate to creating

explicit knowledge either through combination or externalization. Tables combine,

categorize and relate existing explicit knowledge and in doing so, create new explicitknowledge. Summaries, titles and headers reconfigure existing explicit knowledge, making

it more concise, and can also re-categorize knowledge. Graphs transform quantitative

knowledge to visual knowledge. When the existing knowledge is tacit, stating objectives and

asking questions can help organize and externalize the knowledge. Asking questions helps

organize knowledge by prompting reflection on the implications and consequences of the

knowledge (Grabinger and Dunlap, 2002).

Integrating activities relate to the creation of new tacit knowledge. New tacit knowledge can

be created through demonstrations via the modes of internalization or socialization.

While Nonakas four categories are useful at a high level, theyprovide little guidance as to specific actions that can be takento facilitate knowledge creation.

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Hands-on application of knowledge is often termed learning by doing and is one way in

which explicit knowledge is internalized and converted to new tacit knowledge for an

individual. The category of demonstrations also includes the sharing of experiences and

perspectives. By just being around others, common perspectives develop and socialization,

or tacit to tacit knowledge conversion can arise (Nonaka, 1994). Demonstrations can also

provide opportunities for the externalization of knowledge as they provide the opportunity to

convert tacit knowledge to explicit knowledge that is shown to others. Metaphors and

analogies aid in creating new knowledge by helping individuals to articulate their own

perspectives. They help capture the complexities of issues, revealing otherwise hidden tacit

knowledge (Nonaka, 1994). For similar reasons pictures, examples, interpretations,paraphrases and inferences aid in creating relationships and thereby help make tacit

knowledge explicit. These activities help others understand complex knowledge in order to

internalize it, integrate it with existing tacit knowledge and make it their own.

The generating, organizing and integrating of relationships are key activities related to new

knowledge creation. However in order for those relationships to be considered new

knowledge, one more step must be taken. The potential new knowledge must be justified

and deemed meaningful in the current context. Potential new knowledge is often justified by

getting feedback from others (Cross and Sproull, 2004) and interacting with others (Nonaka

and von Krogh, 2009). Feedback allows individuals to compare their tentative knowledge to

others knowledge, and to validate the viability and value of the tentative knowledge (Wasko

and Faraj, 2005). Feedback received during learning can either facilitate or hinder new

knowledge creation (Argyris, 1977). New knowledge can be considered to be threatening or

inconsistent in relation to existing knowledge, and can be deemed to be non-viable or of little

value. Alternatively organizations can advance the proposition that alternative knowledge

can facilitate goal attainment. In such situations tentative new knowledge is more likely to

have the opportunity to be deemed valuable and to be justified. Feedback assists in all four

modes of knowledge creation, since it can be used to justify both tacit and explicit new

knowledge.

Together the concepts of Nonakas four modes, generative learning and feedback offer

important insights into how new knowledge creation can be facilitated. Yet few firms

understand how the processes, procedures and systems put in place as part of

organizational KM may or may not contribute to new knowledge creation (Cross et al., 2001).

In the next section a framework is presented which can be used to assess the degree to

which KM initiatives have the capacity to facilitate the creation of new knowledge in a firm.

A generative learning assessment framework

The proposed framework, shown in Table I, begins by leveraging Wittrocks (1990) work on

generative learning activities. The authors suggest that KM initiatives that can accommodate

these activities will facilitate new knowledge creation. Each item in Table I lists a possible KM

facility, that is, a system function that can foster new knowledge creation. A KM initiative

can be evaluated as to the extent to which it accommodates the activities listed and thereby

facilitates new knowledge creation. The organizing processes suggested by Wittrock make

up the first six facilities in the framework. The integrating processes comprise the next six

facilities. Column 3 in Table I notes the primary purpose of each facility, either organizing or

integrating. Column 4 notes Nonakas mode of knowledge creation associated with each

facility and indicates the type of new knowledge created, either tacit or explicit. Finally,

several authors have noted that feedback is key to justifying tentative new knowledge

(Argyris, 1977; Cross and Sproull, 2004). Therefore item number 13 is included in theframework, the facility to provide and receive feedback.

3. Research method

The framework was tested through a single longitudinal case study. Case studies are

appropriate when the unit of analysis is a system of action rather than individuals or groups,

and the viewpoint of multiple respondents is desired (Yin, 2002). The authors focused on

understanding the KM initiatives of one company, Pratt Whitney Rocketdyne, and how those



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initiatives facilitated new knowledge creation. The longitudinal analysis aided in providing a

rich understanding and evaluation of continuity and change in the KM initiatives. In

particular, it enabled the authors to examine the phenomenon in a natural setting and to

explore new theoretical ideas where there has been relatively little prior research and theory

formulation (Miles and Huberman, 1994; Yin, 2002). PWR was selected for study because

they were in the process of implementing multiple KM initiatives.

3.1 Data collection

Data collection involved multiple sources of historical data, which were triangulated to

establish construct validity and reliability. The data collection was performed in two phases

during a 21-month time period. In the first phase, one of the authors collected both public

and confidential corporate archival data related to the KM initiatives. The primary sources ofdata were archived corporate internal analyses, organization charts, strategic planning

documents from the KM department, minutes of meetings, external consultant reports,

internal correspondence, memos, and e-mails. Secondary sources included industry

reports, public disclosures, media publications, and internet articles. While collecting

archival data, the authors together documented the general direction of the process that

PWR followed to design and implement KM initiatives, the primary actors involved, as well as

the features and use of the KM systems.

In the second phase of data collection, one of the authors and 15 members of PWRs KM

team together spent two months conducting formal interviews with individuals who

sponsored, supported, or participated in the project. Included were 40 top executives from

the firms eight product groups and six program teams. These interviews provided detailed

data on how the KM systems were perceived and experienced, and how initiatives evolved.To ensure accuracy and to promote triangulation, case data were reviewed and verified by

key actors involved in the project. Participant observation activities were conducted, which

culminated in field notes and journal reflections. Covered were activities such as informal

hallway conversations with employees, status report meetings, and planning meetings. A

database was generated to organize and store the data.

The data extracted from these multiple sources were coded to reflect the constructs

identified in the theory being studied. After the data had been coded and grouped, it was put

into a temporal process model which was used to identify gaps to compare trends in the

Table I New knowledge creation framework for evaluating KM systems facility to assist in new knowledge creation

Facility no. Facility description Facility purpose Knowledge creation mode(s)

1 Facility to create and provide headers and titles Organizing Combination: expl icit-to-explici t2 Facility to create summaries or state main ideas Organizing Combination: expl icit-to-explici t3 Facility to create tables Organizing Combination: explicit-to-explicit4 Facility to create graphs Organizing Combination: explicit-to-explicit5 Facility to state objectives Organizing Combination: explicit-to-explicit or

Externalization: tacit-to-explicit6 Facility to ask questions Organizing Combination: explicit-to-explicit or

Externalization: tacit-to-explicit7 Facility to demonstrate knowledge Integrating Socialization: tacit-to-tacit or

Internalization: explicit-to-tacit8 Facility to capture metaphors or analogies Integrating Externalization: tacit-to-explicit or

Internalization: explicit-to-tacit9 Facility to provide examples of the application of

knowledge

Integrating External ization: tacit-to-explicit or

Internalization: explicit-to-tacit10 Facility to provide pictures Integrating Externalization: tacit-to-explicit or

Internalization: explicit-to-tacit11 Facility to provide interpretation or paraphrases Integrating Externalization: tacit-to-explicit or

Internalization: explicit-to-tacit12 Facility to make inferences Integrating Externalization: tacit-to-explicit or

Internalization: explicit-to-tacit13 Facility to solicit and obtain feedback Justifying new knowledge n/a



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observed data with those predicted by theory (Yin, 2002). The technique of pattern matching

was used to move back and forth between the empirical data and possible theoretical

conceptualizations (Eisenhardt, 1989; Yin, 2002). Specifically the authors began by looking

for examples of new knowledge creation through socialization, combination, externalization

and internalization. The authors then looked for examples of one of the 13 facilities in the

framework. In instances where the researchers identified gaps between the empirical data

and possible theoretical conceptualizations, the authors revisited the data by going back to

the interviewees to obtain additional data or to clarify data that already had been collected.

4. Case study

PWR focuses on the development and manufacturing of rocket propulsion and space

exploration engines for the defense industry. In total, there are over 4,000 engineers at PWR

that comprise the key group of employees who are responsible for creating and developing

leading defense-industry knowledge. Engineers were hired into process groups and were

then assigned to one of the six program-groups. The duration of typical program

assignments was anywhere between six months to five years, depending on the nature and

portion of the program for which they were assigned. Throughout their careers at PWR,

engineers were encouraged to switch process and program groups in order to diversify their

skills. Limited project budgets encouraged a competitive environment at PWR. This caused

engineers to generally not want to share their expertise with other engineers, so that they

may be deemed more valuable to the firm. The motivation to help others through knowledge

sharing was constrained and generative learning was stifled.

PWR was under constant pressure from their customers to develop products faster and

more cheaply. However, individualized KM in the groups continued to plague effective KM in

the organization as a whole, because engineers rarely shared their knowledge with others

outside their group. Methodologies for managing knowledge were typically documented

within process groups and program groups and existing knowledge was stored at the desks

of the engineers. Knowledge that was created within process and program groups often

remained in the minds of the seasoned engineers, or was documented on notepads and

stored in personal filing cabinets and computer hard drives. This made the creation of new

knowledge challenging across the organization since existing knowledge was rarely

exploited or refined by others. Existing knowledge was seldom experimented with in new

contexts and knowledge generation was not promoted.

As a first step in improving knowledge management at PWR, Kiho Sohn was hired into thenewly created chief knowledge officer (CKO) position and was tasked with rejuvenating the

firms KM efforts. Within a month of investigating the current state of the firms KM

environment, Kiho and the KM team found there were two key issues that plagued the firms

ability to leverage existing knowledge and create new knowledge. First, engineers did not

leverage existing knowledge because they were not aware that other knowledge sources

existed within the firm. The team recommended that the IS infrastructure be improved to

support and maintain knowledge so that new knowledge could be generated. Second, the

KM team acknowledged that the culture of the firm typically did not support leveraging of

existing knowledge, as engineers hoarded knowledge to make themselves more valuable to

the firm. As a result, engineers were prevented from learning of their colleagues work and

using it to generate new knowledge.

4.1 Early KM initiatives

Over a period of six years multiple KM initiatives were implemented by the PWR KM team.

This section describes the initiatives, which were studied by the authors after

implementation. Each initiative was analyzed using the framework, looking for evidence of

Wittrocks generative learning activities and Nonakas modes of knowledge creation. An

overview of the analysis results is shown in Table II.

Department and program-group databases. One of the earliest attempts to manage

knowledge at PWR came in the form of department and program-group databases. These



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Table II Facilities leveraged for new knowledge creation by PWR KM system initiatives

Initiative

Mode of new knowledge

creation Facilities Leveraged

Department and program-group

databases

Organizing through combination No. 1. Facility to create and provide headers and titles

No. 2. Facility to create summaries, state main ideas

No. 5. Facility to state objectives

Organizing through

externalization

No. 9. Facility to provide examples of application of knowledge

No. 11. Facility to provide interpretation or paraphrasesLibrary services Organizing through

combination/externalization

No. 1. Facility to create and provide headers and titles


No. 3. Facility to create tables

No. 4. Facility to create graphs


No. 6. Facility to ask questions

Integrating through

socialization/externalization/

internalization

No. 7. Facility to demonstrate knowledge


No. 10. Facility to provide pictures

ROSC Organizing through

externalization





No. 5. Facility to state objectivesIntegrating through

externalization

No. 8. Facility to capture metaphors or analogies


No. 11. Facility to provide interpretation or paraphrases

No. 12. Facility to make inferences

Mentoring program Organizing through

externalization




Integrating through


internalization





Justifying new knowledge No. 13. Facility to solicit and obtain feedback

Lunch-time KM technical

seminars

Organizing through








Technical forums Integrating through


internalization








AskMe and Goldfire system Organizing through



No. 2. Facility to create summaries, state main ideasNo. 5. Facility to state objectives


Integrating through

externalization/internalization









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databases consisted of internal documents that were previously siloed in folders and file

cabinets, each accessible to only a small proportion of the PWR staff. Analysis showed that

knowledge creation occurred primarily through Nonakas modes of combination and

externalization, and Wittrocks mode of organizing. Indicative of combination, there was

evidence of existing knowledge that was reconfigured, re-categorized and

re-conceptualized. Documents were also put into a form that could facilitate new

combinations by others.

Multiple examples of Wittrocks organizing activities were found (see Table II). Internal

documents of prior studies were indexed by key words, titles, and authors names. The

documents were then put into the library computers systems so that librarians could performkey word searches. The initiative provided the opportunity to re-categorize documents that

had previously been in knowledge silos. Since the librarians understood the bigger picture

of the vast types of knowledge across PWR, they were able to find new and multiple

categories to which the existing knowledge might apply. In doing so they re-conceptualized

what was once thought as knowledge applicable only to a single project, and made it useful

to multiple departments and projects.

In addition to organizing existing knowledge, the initiative created knowledge in the form of

summaries and abstracts. In these summaries and abstracts, two modes of knowledge

creation were evident, combination and externalization. New knowledge was created

through combination when the abstracts stated the main ideas and objectives of the work.

There was also evidence of Wittrocks integrating activities and Nonakas externalization. To

create the summaries and abstracts, knowledge in the documents was interpreted andparaphrased, and this was explicitly documented.

PWR library services. The PWR library services was another early KM initiative. The librarys

primary role in knowledge creation was to aid in the leveraging of existing knowledge. PWRs

library services consists of 4,000 square feet of books, white papers from completed

projects, as well as other documents containing knowledge of the firm (for example, 100,000

reports resided on microfiche and 25,000 reference books). In this initiative, new knowledge

was created by Wittrocks organizing activities, through Nonakas combination and

externalization. Librarians created header, titles, summaries, document main ideas and

recorded the objectives of the stored work. Comments from one of PWRs librarians, Susie (a

pseudonym), illustrate Wittrocks integrating activities and Nonakas internalization mode.

Susie describes how library services helped one scientist create new knowledge by

applying existing knowledge in a new context:

We recently had a request from an engineer working on the J2X rocket engine. He was in the

process of testing a component of the new generation of rocket engines. . . . [He] wanted to learn

from the experiences and knowledge of other the engineers who had worked on the earlier

generations of the engine [J2 engine, was used in the Saturn V] in hopes of avoiding any costly

mistakes. The engineer was able to learn about the knowledge learned from the testing efforts of

the prior generation rockets and apply the knowledge that he learned from reviewing the

documentation that the firm had retained and saved the firm over $150,000 because he was able

to forego re-running tests on the rocket engines that had already done in prior years.

The library services initiative also showed evidence of Wittrocks integrating activities, that

supported knowledge creation through Nonakas modes of socialization, internalization and

externalization. This was accomplished by providing venues where scientists could

demonstrate their knowledge to others, display pictures of work, and provide examples

knowledge applications. The library helped to facilitate over 20 knowledge sharing seminarseries and seven KM share fair conferences. Evidence was also seen of Wittrocks

organizing via Nonakas modes of combination and externalization, as scientists prepared to

exhibit their work in the seminars and conferences. Exhibitors were encouraged to create

tables of data as well as graphs to aid in depicting their work. Displays and presentations

provided headers and titles, stated the main ideas and objectives of the work, and

participants were encouraged to ask questions.

Rocketdyne Operations Support Center. Rocketdyne Operations Support Center (ROSC)

provided engineers with an opportunity to learn from prior projects in order to reduce the



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time it took from development to market. The center was an electronic collection of

predictions, hypotheses and findings developed by scientists regarding rocket componentsand functions. Thomas (a pseudonym), a lead member of the KM Team, explained the value

of ROSC and the typical procedure that engineers would take to leverage existing

knowledge:

When engineers complete their projects, they are asked to document the details of their efforts

and note the predictions that were established during their development efforts. Examples of this

would include the reuse levels of pumps or components that went into engine design. Engineers

who work on similar projects look at the assessments made by theprior tests andare expectedto

use this knowledge to establish a new and improved set of predictions. This new set of

predictions helps them to see how well the engines will fare [computer simulation] before actually

going to physical test.

Analysis of the ROSC documents showed that the center facilitated Wittrocks organizingand integrating activities through the externalization mode. This was accomplished by

providing a platform for scientists to document the application of knowledge and their

personal experiences during hands-on testing of rocket components. The tacit to explicit

knowledge conversion took several forms. In the ROSC documentation engineers stated

their objectives for each test, making explicit knowledge that was formerly only in their

heads. Predictions and insights gained from the analysis of test data were also made

explicit, often expressed in tables and graphs. Summaries of the tests and analyses added

context and details that likely enhanced others understanding of the knowledge.

Documentation of tests included interpretation of the findings as well as analogies to other

equipment or other types of uses. Finally, the completed documents included headers and

titles. There was also evidence that this tacit-turned-explicit knowledge was later used in

re-conversion of explicit-to-tacit knowledge. Thomas explained:

[The new engineers] actually use knowledge generated in prior tests and incorporated their own

predictions in the testing and analysis to determine if the rocket engines are sick [not working]

before actually engaging in any physical tests [. . .] These procedures not only save the company

money because it reduces testing and analysis costs, but it also improves the knowledge that

already exists in the firm [. . .] the decision and knowledge generated by prior projects helps the

current projects become more efficient and knowledgeable [. . .] after the project is completed,

these same engineers are then asked to document theknowledge generated and the predictions

used in their tests and analysis so that other engineers can use it for future projects.

In other words, new engineers interpreted the explicit knowledge in the context of their new

engines and made inferences regarding possible results. This new tacit knowledge resulted

in new predictions and savings in terms of both time and money in the testing of rockets.

Of the three initiatives implemented at this point, the case study analysis suggested thatlibrary services had been the most effective at facilitating new knowledge creation.

Librarians and users noted that the services helped them explore new ideas for projects and

provided a structure that helped them combine others ideas with their own. The

demonstrations and examples of knowledge application were deemed particularly valuable

in terms of helping users learn and create. Interviewees cited cases where attending

demonstrations aided in solving new problems in their projects. The other initiatives,

specifically the databases and the ROSC, were useful, but were not deemed as effective at

facilitating new learning.

The generating, organizing and integrating of relationshipsare key activities related to new knowledge creation.



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Despite the improvements made in all of the early initiatives, there was a feeling that existing

knowledge was not being leveraged to its full potential in terms of creating new knowledge.

Kiho, the firms CKO, recalled:

Earlier in our KM efforts, we were really good at capturing existing knowledge. However, one of

the challenges that we continued to face was that we werent doing a good job at facilitating,

capturing and documenting newly created knowledge. Because of this, we had to expand our

KM initiatives.

4.2 Expanded KM initiatives

As the KM team matured and gained experience, the initiatives exhibited a greater number

and variety of knowledge creating facilities. The KM team began to host inter-company

activities aimed at providing the firms engineers with opportunities to establish a

professional network with subject matter experts. These experts had the knowledge,

experience, and skills needed for successful project development efforts. Another goal was

to provide an opportunity to tacitly learn from other engineers. The aim of offering these

activities was to bridge gaps among the engineers knowledge sources and to share the

knowledge of others in hopes of providing alternative ways to approach product

development. These activities included the mentoring program, lunchtime KM seminars

and share fairs.

Mentoring program. In the mentoring program senior engineers were paired with younger or

newly hired engineers in order to expose them to the expertise and knowledge that had been

developed at PWR. This one-on-one mentorship was intended to supplement the knowledge

and skills that the younger engineers possessed. It enabled new engineers to learn how the

senior engineers developed products and understand the methodologies used to create

knowledge. The program also exposed new engineers to additional knowledge sources

which they might otherwise not had been aware of. The mentorship program also enabled

scientists to share new technologies and techniques with their mentors (i.e., reverse

mentoring). This allowed the more seasoned engineers to learn and apply more recent and

innovative approaches to product development.

Interviews revealed that the program offered repeated opportunities to create new

knowledge. Both senior and junior engineers spoke about the ability to ask questions, state

objectives, and summarize ideas, all of which were evidence of Wittrocks organizing and

Nonakas modes of combination or externalization. Numerous examples of integrating

through externalization and internalization were also evident. Metaphors, analogies, andinterpretations were leveraged to both explain concepts and help learners integrate new

concepts with their own tacit experiences. The personal, one-on-one aspect of the

mentorship meetings facilitated the understanding of more complex knowledge. It also

provided an opportunity to solicit and obtain feedback, in order to justify new knowledge.

Lunchtime KM technical seminars and forums. Once a month the KM team hosted a lunch

brown bag seminar, where a senior engineer was invited to share how he or she solved a

problem during program development. During these seminars, engineers shared their

methodology with other engineers who had addressed similar problems or were interested

in learning more about process and program group development. The idea behind this

event was to create an environment where younger engineers could network with other

engineers and to provide opportunities to learn from more senior engineers. Often the result

was that engineers would be immediately able to apply the learning to their current projects.The KM team also sponsored an annual internal technical forum, aimed at exposing and

highlighting knowledge creation across different process and program groups. The content

and focus of the fair was changed annually. A KM team member remarked:

The purpose of the internal Technical Forum is to make our engineers aware of the

accomplishments, resources, and knowledge of others process and program groups within

the firm. More importantly, this event gives our engineers the opportunity to learn and apply new

tools, methodologies, and processes to their current projects [. . .] Every year, 40 teams are

invited to showcase their KM capabilities and to teach their colleagues how to replicate their



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success in their department teams [.. .] The event is set uplikea country fair, where the teams set

up booths to show off their accomplishments and the tools / knowledge that were available for

others to share. This format allows other engineers to learn from their accomplishments [. . .] Last

year, we had over 350 employees participate in this event [. . .] One of the main benefits of this

event was the ability to highlight the key projects within the firm and to facilitate an environment of

knowledge sharing, transfer, and application among our engineers.

Interviews of event attendees suggested that the forums and seminars were deemed

excellent opportunities for knowledge creation. Attendees leveraged the opportunities to

ask questions, in order to clarify ideas and resolve misunderstandings. The fairs allowed

attendees to wander around, take time to digest the current knowledge being displayed,and to think about how it might apply to their own work. Attendees remarked that although it

could be difficult to find time to attend, these events were often the most useful of the KM

initiatives in terms of sparking new ideas and adding value to their current work. Participants

noted that the fairs allowed them to simultaneously interact with multiple experts. This made

it easier to combine and integrate knowledge from multiple sources. There was evidence of

the seminars and forums facilitating new knowledge generation across a wide variety and

number of projects, validated by the subsequent success of those projects.

Analysis showed that the seminars and forums offered every type of Wittrocks organizing

and integrating activities, via all four of Nonakas modes of knowledge creation. Combination

and externalization was evidenced when presenters created new knowledge through

creating headers and titles, stating main ideas, and constructing tables and graphs for the

presentations. Audience members asked questions, helping to externalize their new tacit

knowledge and making it explicit. The seminars and forums also provided integrating

opportunities, where scientists demonstrated their knowledge, offered examples of the

application of that knowledge, posted pictures and offered interpretations. The activities

also evidenced Nonakas socialization and internalization modes. Scientists heard

metaphors and analogies, as presenters tried to help their audience make sense of the

knowledge. Engineers who attended the seminars interpreted and made inferences within

the context of their own projects. Presenters and audience members also had the

opportunity to solicit and obtain feedback. The presenters asked for feedback on the

projects being presented, and the audience solicited feedback on their new interpretations

of the knowledge. In sum, the KM team and participants felt that the seminars and technical

forums were the best approach to date for creating new knowledge.

4.3 Advanced technology-based KM initiativesWith the knowledge gained from earlier initiatives, the KM team began working on more

sophisticated technology-enabled efforts that they hoped would further facilitate new

knowledge creation. PWR implemented two technology applications, AskMe and Goldfire.

The focus of these systems was to enable the firms engineers to search for existing

knowledge within the firm and to engage in dialogue with the firms experts.

AskMe and Goldfire systems. AskMe is an application whose primary intent was to increase

networking among employees. The application allowed engineers to locate and contact

knowledge experts, locate knowledge communities, publish shared documents, and share

frequently asked questions. The application also allowed users to publish lessons learned

and to create blog entries to stimulate discussion. Additionally, scientists were able to search

through project and product communities and to scan lessons learned to find knowledge

sources. A KM team lead, Frank (a pseudonym), remarked:

Among all the initiatives, the in-person group seminars andforums were the most effective at generating newknowledge.



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AskMe was implemented because we wanted to enable our engineers to post questions for input

or comment from the engineering community. For example, an engineer developer can post a

question, How can engine thrust performance be measured? The application allows the

engineering community to share their opinions and expertise via emails or blog posts. These

contributions or collection of knowledge nuggets are documented within the system and made

availableto other engineerswho canresearch the same topic in future development projects [. . .]

AskMe was implemented to encourage and improve social networking among the firms

engineers; it allows for the introduction of new colleagues whom the engineers did not know were

experts on specific topics [. . .] It also encourages a collaborative learning environment for

engineers to learn from each other and keeps a live document of the new knowledge.

The Expert Yellow Page is a function within the AskMe application. It contains a directory that

allows engineers to identify themselves as experts on certain topics. It also provides a profile

that specifies the engineers contact information and a list of their expertise and skills, so that

other engineers can contact them and leverage their knowledge and expertise. By providing

contact information, the application facilitates one-on-one discussion and knowledge

exchange.

Goldfire is an advanced KM search engine that utilizes natural semantic language to

enable engineers to conduct sophisticated searches across the companys numerous

knowledge sources. The search engine marks and indexes key words in documents

across the company. The application then establishes links between words and between

documents. Goldfire also allows engineers to conduct knowledge searches across sources

outside of the firm via the Internet. The system helped engineers to conduct patent

searches and innovation trend analyses, and assisted in finding information regardingscientific effects. The implementation of Goldfire increased the ability of the engineers to

locate knowledge within and outside of the firm by facilitating sophisticated and focused

knowledge searches.

Together, AskMe and Goldfire showed evidence of Wittrocks organizing and integrating

opportunities. The home page of AskMe, shown in Figure 1, demonstrates how headers and

titles were used to organize communities of knowledge, simultaneously combining main

ideas from separate communities. The content on the screen shows how users tended to

summarize key points quickly and state main ideas early in their discussions. Figures 1 and 2

show questions being asked and answered in the system. In these ways new knowledge

was created through Nonakas mode of combination and externalization. Figure 2 shows

evidence of both tacit-to-explicit knowledge creation and a request for justification of new

knowledge:

Somebody mentioned that BE levels for these gadgets would be 65-135% (as opposed to

30-50%). However, I have never seen anything like his mentioned in our manual or regulations

[...]

The scientist had previously talked to someone who had tacit knowledge regarding BE

levels. The engineer states his tacit knowledge about the BE levels, thereby making the

tacit knowledge explicit. Next, because he has not previously seen written, formal

confirmation of this knowledge, he asks for feedback from others to verify that this new

explicit knowledge is justified.

In the system scientists had the opportunity to reply, comment, rate quality and nominate

knowledge posted as a best practice. In this way, the knowledge was reinterpreted and

further justified. Figure 2 also shows how scientists posted pictures of themselves that

accompanied their responses. Another feature of AskMe was Blogs, where scientistsorganized and integrated knowledge. The Blog in Figure 3 shows evidence of the use of

headers and titles as well as the stating of main ideas and objectives. As with the

conversation shown in Figure 2, scientists asked questions and commented on Blogs as

well. They provided paraphrasing and interpretation of existing knowledge. Other entries in

the system showed evidence of providing analogies, metaphors and examples.

Scientists also had a unique opportunity to state objectives or main ideas when using

Goldfires semantic search engine. Key ideas such as combustion and fuel or

sentences that state objectives could be entered as search criteria. Goldfire would often



13/20

Figure 1 AskMe homepage

Figure 2 AskMe system conversation example



14/20

group previously unrelated documents and present that information to the scientist.

Knowledge creation occurred when the scientist interpreted the documents and made

inferences as to the applicability and validity of the systems proposed connection between

the documents.

In interviews and other documentation, the AskMe and Goldfire systems were deemed

among the most useful initiatives for generating new learning, second only to the technical

seminars and forums. Interviewees remarked that the ability to find and meet others, ask

questions and engage in conversations was key to understanding and interpreting othersknowledge. This in turn often led to new knowledge. The ability to express existing

knowledge and potential new knowledge in a variety of forms was also a critical feature that

aided knowledge creation. While tables and graphs were at times the best way to express

ideas, at other times paraphrasing, metaphors or pictures were needed to enhance

understanding. There was also evidence that the system helped users validate the value of

potential new knowledge, by exposing it to others. Evidence of the new knowledge used in

projects was also provided.

5. Discussion

The new knowledge creation framework shown in Table I proved to be a useful tool for better

understanding PWRs KM initiatives. The framework provided the impetus to analyze the

initiatives post-implementation and to determine how the initiatives contributed toknowledge creation at a detailed level. Applying the framework also sparked ideas of how

new initiatives could be designed so that they could support new knowledge creation. Two

key findings emerged from the analysis. First, the authors suggest that new knowledge

creation is best supported through mature KM systems that include all four modes of

knowledge creation: combination, externalization, socialization and internalization. Second,

the authors suggest that KM systems and environments as a whole reach maturity by

progressing through the stages of the KM maturity model. In the next sections the findings

are discussed in detail.

Figure 3 AskMe system blog example



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5.1 The need for mature KM systems

Among all the initiatives, the in-person group seminars and forums were the most effective at

generating new knowledge. Seminars and forums showed the most evidence of helping

PWR staff create new knowledge that was employed in their work. These forums provided

employees with the opportunity to engage in a dynamic and real-time exchange of

knowledge that allowed them to simultaneously obtain the knowledge that they were

seeking, get feedback, integrate the knowledge, and ask questions. All four modes of

knowledge creation were exercised within one instance of engagement among the

employees. Reviewing Table II, the authors believe KM initiatives were most effective when

they supported all modes of new knowledge creation (combination, externalization,

socialization and internalization) and all facilities (e.g. creating headers, stating objectives).

KM systems that leverage all modes of new knowledge creation are considered in this

research to be mature KM systems.

In contrast, analysis of the more technology-centric initiatives (i.e. databases, ROSC,

AskMe, and Goldfire) showed less evidence of new knowledge creation. Even though the

users of AskMe and Goldfire shared examples where new knowledge was created, these

examples were not quite as numerous, diverse or as certain as the evidence provided from

seminars and forums. In an interpretation of the case data, knowledge creation in these

contexts may have been limited due to the delay in responses, that is, the lack of real-time

discussions or interactions. In addition, the technology-based initiatives tended to employ

more limited modes of knowledge conversion and fewer facilities. Many of the same

knowledge creation facilities were found in each of these initiatives, regardless of thesophistication of the technology implemented. The technology-centric initiatives used

facilities no. 1 through no. 5, which centered on summarizing activities, such as developing

headers, titles, main ideas, objectives and creating tables and graphs. The initiatives also

used facilities no. 8, no. 9, no. 11 and no. 12, which involved the use of metaphors and

analogies, interpretations, paraphrases and inferences. Although the technology-centric

initiatives facilitated some modes of knowledge creation, they were not fully mature KM

systems.

Socialization and feedback. One particularly surprising finding from the analysis of the

AskMe system was that the system did not support socialization. In conversations with

the KM team and system users, AskMe was often perceived and described as a social

system, where scientists found each other, interacted with each other and created

knowledge through interaction. Indeed, a primary intent of the technology was to

simulate or recreate, in a digital form, the socialness of the in-person seminars, forums

and mentoring initiatives. The application of the framework however suggested that true

socialization, which is the direct conversion of tact-to-tacit knowledge, was not possible

through the text-based technology. Scientists used the AskMe Yellow Pages to find

experts on a topic, but they often did not directly contact these scientists to engage in

conversation. Instead, they used the experts name to research the existing explicit

documents written by the subject matter expert. New tacit knowledge was created in

two steps, externalization first, and then internalization. Experts converted their tacit to

explicit knowledge, and stored that knowledge. Other scientists found that stored

knowledge and converted it from explicit to tacit knowledge. The system did not differ

extensively from earlier technology-centric initiatives in the way that new knowledge

could be generated.

Upon further review of the framework and the data, the authors realized that theperception that AskMe involved socialization was due to facility no. 13, the ability to solicit

and obtain feedback. This facility in AskMe was able to mimic, in a technology-centric

text-based initiative, the socialization previously only available through the in-person

seminars, forums and mentoring. The reason that earlier technology initiatives had not

proved as useful as AskMe, in terms of knowledge creation, was due to the lack of

feedback. Prior initiatives had lacked the means by which to justify and deem new

knowledge meaningful. The give and take of a dialogue is often critical to new knowledge

creation (Tsoukas, 2009) and the feedback facility in AskMe was able to support a



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dialogical process. Without the facility to justify new ideas and thoughts, those ideas and

thoughts can dissipate. Ideas can fail to be recognized as new knowledge. This finding

explained why the early technology initiatives had created some new knowledge, but had

left the KM team feeling that existing knowledge was not fully being leveraged. Simply by

adding a feedback mechanism, AskMe became a mature KM system, able to create

knowledge through the modes of combination, externalization, and internalization, and by

mimicking socialization.

Facilitating interaction between modes. So why are all four modes of creation necessary to

fully leverage existing knowledge in the generation of new knowledge? Nonaka (1994)

notes that while each of the four modes can create new knowledge independently,organizational knowledge creation relies on the dynamic interaction between the modes.

He states that it is in the interaction between modes, and in particular, the interaction

between externalization and internalization, where new organizational knowledge is

created. Through its feedback mechanisms the AskMe system facilitated a dialogue

between externalization and internalization, so that new knowledge could be justified. The

reason why new knowledge generation was limited in earlier technology-centric initiatives

was due to the lack of dialogue between multiple modes and an inability to justify any

potential new knowledge generated. For instance, the department and program-group

databases combined and externalized knowledge, but did not facilitate socialization or

internalization. The presence of a dialogue between modes also explains why the less

technology-centric initiatives, such as lunchtime seminars and forums, were deemed so

successful at generating new knowledge.

Each mode can trigger the enactment of another mode, thereby supporting further

knowledge generation. These triggers were evident in the seminars and forums as well as

the AskMe system. For instance, through socialization in these initiatives, individuals

become aware of subject matter experts. When these experts made their tacit knowledge

explicit, it was more likely to be deemed meaningful. Hence the final step in creating true

new knowledge was taken, in that the knowledge was justified. This in turn would prompt

another individual to internalize that knowledge, and in doing so, create new tacit

knowledge. When explicit knowledge was justified, it was also more likely to be combined in

the generation of further, new explicit knowledge. In addition the loop of knowledge creation

in some initiatives spurred the use of other KM systems to generate knowledge. The new

knowledge acquired in seminars, forums and AskMe frequently encouraged individuals to

find related knowledge in the program databases and ROSC, and to use the library services

to find other knowledge by an expert.

As the authors considered the development of the more mature KM initiatives, they realized

that the later initiatives such as AskMe and Goldfire were very much the product of earlier KM

efforts. PWR had not imagined these more mature KM projects out of thin air, but had rather

determined the KM needs and focus for each initiative in stages. Each initiative had built on

the lessons learned in prior efforts. The initiatives showed a progression from an

individualistic view of knowledge to an integrated, generative view of knowledge. When the

initiatives were put in sequential order and analyzed, they showed a firm growing in stages

within a KM maturity model.

In reviewing existing KM maturity models, the authors saw a need for a model based on

empirical evidence that was publicly available and could be applied in a variety of

organizations. Many existing KM models are either proprietary, specific to an industry (such

as software development or construction), or have not been published in a peer-reviewed,established journal (for an broad-based, overview of maturity models see Hain and Back

(2009)). The next section describes a KM Maturity model that can be used by various firms to

assess progress in their KM efforts.

5.2 A KM maturity model

Ross (2003) has suggested that organizational competencies in information technology

architectures develop in four stages, progressing from the application silo stage, to

standardized technology, to rationalized data and finally to a modular architecture stage.



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That work is applied and extended here to suggest that organizations develop KM

competencies in stages as well, comprising a KM maturity model, shown in Figure 4. In many

organizations, most knowledge is siloed, residing primarily in the individual. At this first stage

of maturity, the siloed knowledge stage, there are few organized knowledge management

initiatives. New knowledge creation occurs mostly through single modes. This was the initial

KM environment at PWR; only the knowledge needs of individuals were being met.

As organizations become more aware of the value of their existing knowledge, initiatives to

encode and store knowledge are started. In the second stage, the standardized knowledge

stage, initiatives focus on making knowledge available to specific project or product teams.

There is little integration and the mixing of modes of knowledge creation remains low. The

first initiatives of the PWR KM team were illustrative of this stage. The team centered their

attention on combining existing knowledge, standardizing, encoding and storing it in

databases. The focus expanded to serving the knowledge needs of project and product

teams, but within those contexts the knowledge was still siloed.

In the third stage, organizations realize that the power behind existing knowledge is not just

in re-using that knowledge, but also in converting it into new knowledge. As environments

and problems change, the new knowledge serves as a foundation for further new

knowledge. KM initiatives begin to focus on organizing and disseminating knowledge

across the organization so that new knowledge can be created through the integration of

multiple modes. At PWR, Library Services was one of the early initiatives that focused on

disseminating knowledge throughout the company. Through these services individuals were

exposed to multiple modes of knowledge creation at one time.

Finally, initiatives in an organization begin to reach the fourth stage, the generative

knowledge stage. In this stage, all four modes of knowledge creation are leveraged to

ensure that new knowledge is created, justified and deemed meaningful to the organization.

Initiatives seek to provide knowledge that is customized to the current task at hand. PWR

entered this stage with the advent of mentoring, lunchtime seminars, forums and AskMe. The

power of integrating all four modes of knowledge creation was realized. By implementing

mature individual KM initiatives PWR has moved further along in achieving a fully mature and

integrated KM environment. These later initiatives facilitated the just-in-time discovery of

existing knowledge and conversion to new knowledge by simultaneously leveraging the

multiple modes of combination, externalization, socialization and internalization.

Figure 4

Individual

Employee

Needs

Project/

Product

Specific

Needs

Cross Project

and Intra-

company

Needs

Customized

Needs

Few

Organized

Initiatives for

Knowledge

Encoding

and Storing

Knowledge

Disseminating

Knowledge

Generating

New

Knowledge

Low Low

MediumHigh

KM MATURITY

Siloed

Knowledge

Standardized

Knowledge

Integrated

Knowledge

Generative

Knowledge

Needs

Met

Initiative

Focus

Level of

Mode

Integration

hgiHwoL



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5.3 Insights for practitioners

While this article reviewed the KM initiatives of one particular company, PWR, the case offers

insights for other practitioners embarking on their KM journey. Lessons learned at PWR and

from this case study include:

Pay attention to the details. The new knowledge creation framework helped to focus attention

on the specific facilities in systems that aid in creating new knowledge. When designing new

systems, think about these details and embed them in the design.

Look holistically at KM initiatives in the organization. The portfolio of KM systems should worktogether to provide the modes needed to generate new knowledge. As was done in this

case, take time to review and reflect on how initiatives relate to each other. Use checklists

and frameworks such as the one presented here to assess prior efforts, identify gaps and

build lessons learned into new initiatives.

Benchmark KM efforts against one another and against KM efforts by competitors. The

comparisons of systems performed here highlighted benefits and drawbacks of each

system. Similarly, an awareness of how competitors are addressing KM issues, as well as

knowledge of the KM maturity of those firms, can help set expectations and guide future

efforts.

Share key learning through regional and national KM conferences. This study highlighted

that new knowledge is best generated when all four modes of knowledge creation are

enacted together, as in seminars and forums. Leverage external conferences to take

learning from other industries and adapt them to the context of the firm. Build a cadre of

like-minded executives with whom ideas, problems and solutions can be freely exchanged.

6. Contributions, limitations and conclusions

Creating a mature, knowledge generating KM environment does not happen overnight or

without planning. While prior work has provided high-level frameworks describing KM

processes, there is a need to understand what specific aspects of KM initiatives aid new

knowledge creation. A major contribution of this work is the integration of the work of Nonaka

and Wittrock, in a framework that helps explain both the why and the how of new knowledge

creation. Together the framework and the KM maturity model can serve as tools for firms

seeking to generate new knowledge for competitive advantage.The value of the tacit/explicit knowledge distinction is, in part, its ability to help distinguish

between knowledge assets that are immediately visible and knowledge assets that require

interpretation to be understood (Nonaka and von Krogh, 2009). The framework here adds

additional value to the tacit/explicit distinction by facilitating a better understanding of KMS

attributes that support both immediately visible and perhaps somewhat hidden, interpreted

knowledge. The framework provides a quick and easy rubric against which both old and

new KM initiatives can be assessed. Finally, the maturity model provides a template against

which organizations can map their progress towards attaining an integrated and knowledge

generating KM environment.

One of the limitations of this study is the generalizability of the findings, which may be limited

by the single case study method used. The study used a theoretically-based approach to

propose why the assessment framework and maturity model would be appropriate for otherKM environments. However, knowledge processes are composed of requirements that are

complex and distributed across different actors whose knowledge base is uncertain

(Markus and Majchrzak, 2002). While the application of the framework at PWR led to useful

insights, the findings may not be directly translatable to other organizations. More studies

are necessary to assess the validity and reliability of the framework and maturity model in the

context of multiple organizations.

With a growing understanding of their KM environment, and what it takes to facilitate new

knowledge generation, PWR continues to develop initiatives to help them progress further



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along the KM maturity scale. In particular, they are in dialogue with other aerospace and

defense firms to benchmark their KM initiatives. There are two primary benefits for their

efforts. First, they want to be able to share their best-known methods and learn from the best

known initiatives of other firms. Second, they want to help facilitate an open dialogue with

other firms in their industries. Together the firms can collaborate, brainstorm, and discuss the

common KM initiatives that may work for the entire aerospace and defense industry, as well

as any firms KM environment.

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About the authors

Priscilla A. Arling is an Assistant Professor of Management Information Systems in theCollege of Business, Butler University. She holds a PhD in Information and DecisionSciences from the Carlson School of Management, University of Minnesota, and an MBAfrom the University of Missouri-Kansas City. Her research interests include social networkanalysis, communication, health care management, knowledge management and systemstheory. Priscilla A. Arling is the corresponding author and can be contacted at:[email protected]

Mark W.S. Chun is the Director of the Center for Applied Research, the Julian VirtueProfessor (2008-2010), and an Associate Professor of Information Systems at PepperdineUniversitys Graziadio School of Business and Management. He earned a PhD in InformationSystems from the University of Colorado at Boulder, an MBA from the University of California,Irvine, with an emphasis on management strategy, and a Bachelor of Business

Administration with an emphasis on management information systems from the Universityof Hawaii at Manoa.

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