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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).

    PAGE 232 j JOURNAL OF KNOWLEDGE MANAGEMENT j VOL. 15 NO. 2 2011

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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