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8/2/2019 Facilitating New
Facilitating new knowledge creation and
obtaining KM maturity
Priscilla A. Arling and Mark W.S. Chun
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
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
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
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
University, Los Angeles,
Received: 18 May 2010Accepted: 18 October 2010
8/2/2019 Facilitating New
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.
2.1 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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8/2/2019 Facilitating New
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,
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