Designing Knowledge Organizations E-Book

Joseph Morabito

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 718

Veröffentlichungsjahr: 2017

Beschreibung

A pedagogical approach to the principles and architecture of knowledge management in organizations

This textbook is based on a graduate course taught at Stevens Institute of Technology. It focuses on the design and management of today's complex K organizations. A K organization is any company that generates and applies knowledge. The text takes existing ideas from organizational design and knowledge management to enhance and elevate each through harmonization with concepts from other disciplines. The authors—noted experts in the field—concentrate on both micro- and macro design and their interrelationships at individual, group, work, and organizational levels.

A key feature of the textbook is an incisive discussion of the cultural, practice, and social aspects of knowledge management. The text explores the processes, tools, and infrastructures by which an organization can continuously improve, maintain, and exploit all elements of its knowledge base that are most relevant to achieve its strategic goals. The book seamlessly intertwines the disciplines of organizational design and knowledge management and offers extensive discussions, illustrative examples, student exercises, and visualizations. The following major topics are addressed:

• Knowledge management, intellectual capital, and knowledge systems
• Organizational design, behavior, and architecture
• Organizational strategy, change, and development
• Leadership and innovation
• Organizational culture and learning
• Social networking, communications, and collaboration
• Strategic human resources; e.g., hiring K workers and performance reviews
• Knowledge science, thinking, and creativity
• Philosophy of knowledge and information
• Information, knowledge, social, strategy, and contract continuums
• Information management and intelligent systems; e.g., business intelligence, big data, and cognitive systems

Designing Knowledge Organizations takes an interdisciplinary and original approach to assess and synthesize the disciplines of knowledge management and organizational design, drawing upon conceptual underpinnings and practical experiences in these and related areas.

Leseprobe

Table of Contents

Cover

Title Page

Acknowledgments

Introduction to Knowledge Systems

I.1 Machine Versus Art Metaphor

I.2 Design and the Ordering of Ideas

I.3 Organization of the Book

I.4 How to Read This Book

I.5 A Journey Through KS

References

1 Understanding Knowledge

Chapter Preview

1.1 The New Pangaea

1.2 Characterizing the Knowledge Economy

1.3 A Glimpse into the Knowledge Society

1.4 Industrial Revolutions

1.5 The Social Challenge of the Knowledge Economy

1.6 A Macro Perspective of Knowledge Management

1.7 Architecture of the Organization

1.8 Data, Information, and Knowledge

1.9 Distinctions in the Information Continuum

1.10 Revisiting the Information Continuum

1.11 Knowledge As Such

1.12 A Brief Comparative Perspective and the Knowledge Triangle

1.13 Conceptions of Knowledge in Practice

1.14 The Relationship among Different Perspectives

1.15 Intangible Assets and Organizational Response

1.16 Valuation, Intangibles, and Intellectual Capital

1.17 Closing Remarks

1.18 Class Exercises

References

2 Designing Knowledge Systems

Chapter Preview

2.1 Perspectives of Knowledge

2.2 Knowledge Worlds

2.3 Inquiry Systems and the Search for True Knowledge

2.4 The Basics of Design in the Knowledge Era

2.5 New Directions in Knowledge Design

2.6 Closing Remarks

2.7 Class Exercises

References

3 Organizations and Systems

Chapter Preview

3.1 Organizations

3.2 Organizational Design

3.3 Systems Theory

3.4 HT and Design

3.5 Organization Molecules

3.6 Symmetrical Structures, Discourse, and Conversation

3.7 Closing Remarks

3.8 Class Exercises

References

4 Knowledge Work and Technology

Chapter Preview

4.1 What Is Knowledge Work?

4.2 Classifying Knowledge Workers

4.3 Tacit Aspect of Knowledge Work

4.4 Characterizing Thick Knowledge Work

4.5 Architectural Perspective of Knowledge Work

4.6 Process and Thin Work

4.7 Practice and Thick Work

4.8 Knowledge Work and Supporting Technology

4.9 KM Tools and Technologies

4.10 Robot Economy

4.11 Closing Remarks

4.12 Class Exercises

References

5 Organizations and Knowledge

Chapter Preview

5.1 Organizations and KM

5.2 Knowledge Revisited

5.3 Organizational Knowledge Cycles and Models

5.4 Application of Concepts: Case Study on the PC

5.5 Knowledge Formation

5.6 Knowledge Exchange and Transfer

5.7 Knowledge Base

5.8 Organizational Design Representations

5.9 Architecture of the Learning Organization

5.10 Closing Remarks

5.11 Class Exercises

References

6 Social Aspects of Knowledge Management

Chapter Preview

6.1 Social Networks

6.2 Knowledge Network Design in Organizations

6.3 Culture in the Knowledge Organization

6.4 Industry Example of Culture: Toyota

6.5 Trust

6.6 Illustrative Example: Interorganizational K Exchange and Creation—Effects of Ties and Culture

6.7 Collaboration

6.8 Collaborating with Creatives

6.9 Office Design

6.10 Promoting Conversations and Dialogue

6.11 Closing Remarks

6.12 Class Exercises

References

7 Strategy and Leadership for Knowledge Management

Chapter Preview

7.1 What Is Strategy?

7.2 Strategy Continuum and the Knowledge Organization

7.3 Setting the Stage: Intangibles and a Knowledge Strategy

7.4 Leadership and KM

7.5 Getting Started

7.6 Strategies for the Knowledge Organization—Tacit Bundle

7.7 Culture Change

7.8 Chief Knowledge Office

7.9 People Value Stream

7.10 Closing Remarks

7.11 Class Exercises

References

8 Knowledge Horizons

8.1 Knowledge Arises

8.2 Digital Economy

8.3 End of Course Questions for Discussion and Research

References

Appendix

Index

End User License Agreement

List of Tables

Chapter 02

Table 2.1 Selected thin and thick archetype organizational characteristics.

Table 2.2 Knowledge binding design characteristics.

Table 2.3 K‐Binding example—project characteristics for plan‐driven (early K‐binding) and agile (late K‐binding) software development methods.

Table 2.4 Contract comparison between transactional and relational contracts.

Chapter 03

Table 3.1 Dimensions of structure and three organizational forms.

Table 3.2 Design characteristics associated with the Perrow tasks.

Table 3.3 Generic forms and organizational characteristics.

Table 3.4 Systems teleology.

Table 3.5 A comparison between industrial, information, and knowledge organizations.

Chapter 04

Table 4.1 Degrees of internalization—extension of Wiig (1993).

Table 4.2 Characteristics of thin and thick work.

Table 4.3 Characterization of K‐intensive processes and technologies—extension of Davenport.

Chapter 05

Table 5.1 Sample dialectic perspectives.

Table 5.2 Example: dialectic strategy for Lexus LS‐400.

Table 5.3 Characteristics of SECI.

Chapter 06

Table 6.1 Sample characteristics of weak and strong ties.

Table 6.2 Sample occupational assumptions.

Table 6.3 Comparing tacit‐ and explicit‐oriented cultures.

Table 6.4 Blended culture.

Table 6.5 Strategy making, dialogue, and K creation.

Chapter 07

Table 7.1 Summary of Singapore research on K worker HR strategies.

Appendix

Table A Key topics from Nonaka and colleagues’/coauthors’ research publications together with text references from

Designing Knowledge Organizations

(DKO).

List of Illustrations

Chapter 01

Figure 1.1 A macro perspective of knowledge management.

Figure 1.2 Architecture of the organization.

Figure 1.3 Information continuum.

Figure 1.4 Illustration of information.

Figure 1.5 Information continuum in an organizational context.

Figure 1.6 Distinguishing aspects of knowledging—Adapted from Earl (1998).

Figure 1.7 Details and barriers in D‐I‐K and the information continuum.

Figure 1.8 Data‐oriented KM disciplines and the information continuum.

Figure 1.9 The inverted tree of ancient Indian wisdom.

Figure 1.10 The knowledge triangle of the three great traditions.

Figure 1.11 Justified true belief.

Figure 1.12 Observations on knowledge.

Figure 1.13 Extension of Polanyi’s dynamic cognitive experience—Adapted from Gill (2000).

Figure 1.14 Data, information, knowledge interaction—Adapted from Novak (1998).

Figure 1.15 The knowledge spiral.

Figure 1.16 Various perspectives on tacit–explicit interaction.

Figure 1.17 Intangible asset model— Sveiby (1997).

Figure 1.18 Know‐how capital model— Konrad Group.

Figure 1.19 Two intellectual capital models—IC index and holistic value.

Figure 1.20 Student exercise: two views of the same financial services company.

Figure 1.21 (a) Student elaboration of intellectual capital. (b) Student details of selected variables.

Figure 1.22 Information continuum and the explosion of ideas in knowledge as such.

Chapter 02

Figure 2.1 Knowledge worlds and modes of K interaction.

Figure 2.2 The K‐worlds of science and art.

Figure 2.3 The K‐worlds of the knowledging organization.

Figure 2.4 Asymmetrical K‐spiral synthesis.

Figure 2.5 SECI knowledge creation model—Adapted from Nonaka and Konno (1998).

Figure 2.6 Transferring deep knowledge—Adapted from Leonard and Swap (2004).

Figure 2.7 SECI and the cultural dialectic.

Figure 2.8 Generic process map for SECI (5 phase)—Adapted from Nonaka and Toyama (2004).

Figure 2.9 Knowledge stickiness versus people distance.

Figure 2.10 Ba and organizational design.

Figure 2.11 Middle–up–down management.

Figure 2.12 Information processing versus K‐creating organizations: combining Mintzberg (1983) and Nonaka and Takeuchi (1995).

Figure 2.13 Hypertext organizational structure components.

Figure 2.14 SECI and the hypertext synthesis—Adapted from Nonaka and Toyama (2004).

Figure 2.15 Thin and thick city neighborhoods.

Figure 2.16 Thin and thick design spaces.

Figure 2.17 Knowledge spaces.

Figure 2.18 Asymmetrical and symmetrical structures.

Figure 2.19 Thin and thick layers.

Figure 2.20 Thin and thick layers in film and research organizations.

Figure 2.21 The organizational creative process of knowledging.

Figure 2.22 Discourse structures and organizational layers.

Figure 2.23 Knowledge binding.

Figure 2.24 A new contract continuum.

Chapter 03

Figure 3.1 Centralized, decentralized, and federated representation—Adapted from Rockart

et al

. (1996).

Figure 3.2 Characterization of knowledge tasks—Adapted from Perrow (1967).

Figure 3.3 Examples of routinization using the Perrow framework.

Figure 3.4 Perrow model adapted to the major functions of the pharmaceutical industry.

Figure 3.5 Vertical and horizontal information linkages—Adapted from Daft (1998).

Figure 3.6 Updated information processing model—Adapted from Goodhue

et al

. (1992).

Figure 3.7 Product structure with embedded functions.

Figure 3.8 Hybrid organization structure at a large international bank.

Figure 3.9 Mintzberg’s five generic organizational components—Adapted from Mintzberg (1983).

Figure 3.10 An organization as an open system.

Figure 3.11 A molecule of water from a hierarchical systems perspective.

Figure 3.12 Frequency range in subsystem levels.

Figure 3.13 Generic organization molecule.

Figure 3.14 Process and information molecules.

Figure 3.15 Process layering: architecture‐in‐the‐large and architecture‐in‐the‐small.

Chapter 04

Figure 4.1 Sample range of paired K tasks.

Figure 4.2 Comparing a Broadway actor and an assembly line worker.

Figure 4.3 Primary task correspondence for Broadway actor, physician, professor, and assembly line worker using the Perrow model.

Figure 4.4 (a) Sample solution 1: actor versus assembly worker. (b) Sample solution 2: actor versus assembly worker. (c) Sample solution 3: physician versus professor.

Figure 4.5 The four power players—Adapted from Sveiby (1997).

Figure 4.6 Sample K roles in context.

Figure 4.7 Historical progression of work.

Figure 4.8 Three dimensions of tacit knowledge.

Figure 4.9 (a) Social practice continuum: theoretical–practical knowledge interaction. (b) Three dimensions of practice.

Figure 4.10 Workings of practice—core K‐molecule.

Figure 4.11 K‐molecule interactions.

Figure 4.12 Classification for K‐intensive processes and technologies—Adapted from Davenport (2005).

Figure 4.13 Student assignment—analysis of K job.

Figure 4.14 (a) Student assignment—KMO responsibilities and requirements. (b) Student assignment—KMO job classifications. (c) Student assignment—KMO key tasks. (d) Student assignment—KMO task classifications. (e) Student assignment—KMO Bloom cognitive analysis (partial). (f) Student assignment—KMO task artifact analysis. (g) Student assignment—KMO workstation arrangement.

Figure 4.15 Information continuum and the evolution of DSS.

Figure 4.16 Smart environments.

Chapter 05

Figure 5.1 KM divides and waves.

Figure 5.2 Sampling of second divide KM waves.

Figure 5.3 Knowledge and the emergence of wisdom.

Figure 5.4 Plato’s justified true belief revisited.

Figure 5.5 Social knowledge spaces.

Figure 5.6 Tacit–explicit properties.

Figure 5.7 Public–industry–organizational K—Adapted from Leonard (1995).

Figure 5.8 Individual–social K—Adapted from Spender (1993).

Figure 5.9 Information factory Adapted from Meyer and Zack—with sample application for the enterprise data office.

Figure 5.10 KM cycle with key activities—Adapted from Wiig (1993)—with sample application for a CIO and a data center manager.

Figure 5.11 Authors’ integrated K cycle with key activities.

Figure 5.12 States of knowing and knowledge and organizational strategies—Adapted from Earl (1998).

Figure 5.13 KM ecology—Adapted from Snowden (1998b).

Figure 5.14 SECI model of KM—Adapted from Nonaka and Takeuchi (1995).

Figure 5.15 Application of SECI to workflow for new product development—Adapted from Nonaka and Takeuchi (1995).

Figure 5.16 Analysis of workflows including knowledge points—Adapted from Gilbert

et al

. (2010).

Figure 5.17 Proposed KM systems for the Peace Corps.

Figure 5.18 Information continuum—communications media linkages in the Peace Corps.

Figure 5.19 Knowledge point analysis in the Peace Corps.

Figure 5.20 (a) Knowledge Continuum and Knowledge Formation. (b) Knowledge Continuum and Knowledge Formation—details.

Figure 5.21 Organizational best practices and K‐oriented use cases—student input from organizations.

Figure 5.22 Knowledge capture, acquisition, and system codification.

Figure 5.23 Codified information model of an adverse event—Based on Wong

et al

. (2007) and Bohmer

et al.

(2009).

Figure 5.24 Five CoP in a product‐organized automobile manufacturer—Adapted from Wenger and Snyder (2000).

Figure 5.25 Contents and governing representation of an organizational K‐base.

Figure 5.26 Organization‐level knowledge planning context.

Figure 5.27 Archetype organizational design process.

Figure 5.28 Metaperspective on K design.

Figure 5.29 (a) Archetype thick dominance organization: practice‐oriented craft K systems. (b) Archetype thin dominance organization: process‐oriented automating–informating systems. (c) Distribution of knowing in a large K organization.

Figure 5.30 DNA representation for Toyota—Adapted from Liker (2004).

Figure 5.31 Organizational metaschema.

Figure 5.32 Structural schema with selected K‐spaces.

Figure 5.33 Organizational learning—Adapted from Senge

et al

. (1994).

Figure 5.34 Key components of organizational learning.

Chapter 06

Figure 6.1 Primary and derivative organizational networks.

Figure 6.2 Sample organizational network compositions.

Figure 6.3 K worker interactions and task support.

Figure 6.4 Common organizational communications networks for small groups.

Figure 6.5 Social interaction between clusters.

Figure 6.6 Framework for K‐network effectiveness—Adapted from Pugh and Prusak (2013).

Figure 6.7 Model of organizational culture—Adapted from Schein (1999).

Figure 6.8 Generic occupational subcultures—Adapted from Schein (1999).

Figure 6.9 Toyota cultural manifestations—Adapted from Liker and Hoseus (2008).

Figure 6.10 Toyota DNA—Adapted from Liker and Hoseus (2008).

Figure 6.11 Toyota Way House: culture with embedded DNA—Adapted from Liker and Hoseus (2008).

Figure 6.12 People value stream—Adapted from Liker and Hoseus (2008).

Figure 6.13 Servant leadership—Adapted from Liker and Hoseus (2008).

Figure 6.14 Inquiry versus inquisition: open and closed questions—Adapted from Lynch (2011).

Figure 6.15 Design considerations with creatives.

Figure 6.16 Sample office design for a consumer product organization.

Figure 6.17 Open‐private range of office configurations.

Figure 6.18 Discourse factors.

Figure 6.19 Multidimensional social interaction.

Figure 6.20 Organizational and social network analysis.

Chapter 07

Figure 7.1 Strategy as a synthesis of position and internal resources.

Figure 7.2 Strategy schools and thin and thick organizational states—Adapted from Mintzberg and Lampel (1999).

Figure 7.3 Strategy formation as a single process—Adapted from Mintzberg and Lampel (1999).

Figure 7.4 Balanced scorecard and strategy map—Adapted from Kaplan and Norton (2000, 2004).

Figure 7.5 Strategy continuum—sampling of K strategies, outcomes, and strategy schools.

Figure 7.6 Integrated K‐space and culture change strategy.

Figure 7.7 Decision‐making and K strategy.

Figure 7.8 Strategies for knowing and knowledge—extension of Earl.

Figure 7.9 Primary and secondary mechanisms for culture formation—Adapted from Schein (1999).

Figure 7.10 Chief K office structure.

Figure 7.11 Contents of an organizational K‐base.

Figure 7.12 Sample K portal structure with access paths.

Figure 7.13 Integral hiring considerations and characteristics.

Chapter 08

Figure 8.1 Three triangles of knowledge—tacit‐practice‐culture.

Guide

Cover

Table of Contents

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Designing Knowledge Organizations

A Pathway to Innovation Leadership

Joseph Morabito, Ira Sack, and Anilkumar Bhate

This edition first published 2018© 2018 John Wiley & Sons, Inc.

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The right of Joseph Morabito, Ira Sack, and Anilkumar Bhate to be identified as the authors of this work has been asserted in accordance with law.

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The publisher and the authors make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties; including without limitation any implied warranties of fitness for a particular purpose. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for every situation. In view of on‐going research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of experimental reagents, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each chemical, piece of equipment, reagent, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. The fact that an organization or website is referred to in this work as a citation and/or potential source of further information does not mean that the author or the publisher endorses the information the organization or website may provide or recommendations it may make. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this works was written and when it is read. No warranty may be created or extended by any promotional statements for this work. Neither the publisher nor the author shall be liable for any damages arising here from.

Library of Congress Cataloging‐in‐Publication Data

Names: Morabito, Joseph, 1946– author. | Sack, Ira, 1942– author. | Bhate, Anilkumar, 1947– author.Title: Designing knowledge organizations : a pathway to innovation leadership / Joseph Morabito, Ira Sack, Anilkumar Bhate.Description: Hoboken, NJ : John Wiley & Sons, 2018. | Includes bibliographical references and index.Identifiers: LCCN 2017009402 (print) | LCCN 2017011361 (ebook) | ISBN 9781118905845 (cloth : alk. paper) | ISBN 9781119078777 (pdf) | ISBN 9781119078784 (epub)Subjects: LCSH: Knowledge management. | Organizational effectiveness.Classification: LCC HD30.2 .M655 2018 (print) | LCC HD30.2 (ebook) | DDC 658.4/038–dc23LC record available at https://lccn.loc.gov/2017009402

Cover image: © John Lund/GettyimagesCover design: Wiley

Acknowledgments

We want to thank our students for their participation in our graduate course on knowledge and organizations. Through their questions, participation in class exercises, and experiences in their respective organizations, we have been able to test ideas, create unique problems, and shape thought‐provoking diagrams and text.

We deeply appreciate several faculty colleagues for their feedback on selected chapters that correspond to their respective areas of expertise.

We also express our sincere gratitude to our research assistants. In particular, we thank Rishikesh Bhosale, Priyam Mishra, Prannoy Pal, Shamanth Umesh and Anisha Vappala.

Introduction to Knowledge Systems

In his last book, Management Challenges for the 21st Century, management guru Peter Drucker asserts that the most important issue for 21st century organizations is the improvement of knowledge worker productivity. Drucker (1999) goes on to claim that knowledge management (KM) is not merely another management idea or tool but the harbinger of a new economy that will fundamentally change society and organizations. We only need to look around us to see how knowledge and technology are transforming the world.

More than a 100 years ago, Frederick Winslow Taylor, an alumnus of Stevens Institute of Technology, transformed the world with Scientific Management: the scientific analysis of work and the separation of work from worker—all elements of mechanical design. In fact, Scientific Management is largely responsible for the flourishing of industrial economies. However, the 21st century requires that we solve a new problem: the design and management of organizational knowledge and knowledge work. What is needed, in effect, is an approach that does for knowledge work what Scientific Management did for industrial work.

I.1 Machine Versus Art Metaphor

In his lectures at Harvard University in 1932, John Dewey, widely acknowledged as the foremost American philosopher for cultural criticism and aesthetics, compared a machine with a work of art. The machine is an outcome of imagination—there are many imagined possibilities, but one is selected to produce a final result. The machine operates in the physical realm, arranged by human contrivance. In contrast, “the work of art … is not only the outcome of imagination, but operates imaginatively rather than in the realm of physical existences. What it does is to concentrate and enlarge an immediate experience.” The machine–art metaphor accurately contrasts data and information with knowledge as well as industrial and knowledge societies. As with a machine, data and information are of the world; as with art, knowledge is of the mind. Whereas the machine is a tangible thing that gets diminished with use, knowledge is imaginative and enlarged with application. Hence, in contrast to the machinery of the industrial society, the knowledge society is characterized by intangible assets and resources. Finally, as with the machine, the industrial society is of the imagination; as with art, the knowledge society is in the imagination.

Continuing with the machine–art metaphor, Dewey (1934) states: “The formed matter of esthetic experience (art) directly expresses … the meanings that are imaginatively evoked; it does not, like the material brought into new relations in a machine, merely provide means by which purposes … may be executed. And yet the meanings imaginatively summoned, assembled, and integrated are embodied in material existence…” Similarly, unlike an industrial or service organization, the design of the knowledge organization is tied to the human imagination and the character of knowledge itself—this makes it substantially different than designing a manufacturing or information processing organization. Designing the knowledge organization requires that we formulate the elements to summon the imagination and create knowledge, assemble and integrate the new knowledge with other knowledge threads, and embody the outcome in a physical world. Such elements are the “stuff” of design.

The machine–art metaphor encapsulates our research into knowledge and the organization. The knowledge economy is handicapped by its industrial history—our education, management, and legal systems carry the legacy of the industrial era into the modern era. For example, a performance review is an artifact of industrial work where performance is compared to a predetermined specification or set of goals. The performance review still exists today—complete with forced distribution quotas and rankings. How is this useful in an organization which makes its living by thinking?

(We borrow from the title of Davenport’s (2005) book Thinking for a Living—a good metaphor for knowledge work, which we elevate to include the knowledge organization as well as knowledge work.)

The knowledge organization concentrates, enlarges, and embodies its imagination into physical products and not‐so‐physical services. Knowledging, as with art making, is an imaginative endeavor that operates imaginatively, concentrating and expanding itself with use. In the knowledge organization, everything we design and manage should promote imagination and thinking, concentration and enlargement, integration and application.

I.2 Design and the Ordering of Ideas

How, we may ask, do we design a knowledge organization? Is it really different than designing traditional organizations? As we will see in subsequent chapters, one anecdotal solution is to “do the opposite.” If the factory system requires control, the knowledge system (KS) demands autonomy; if the factory system requires the formal implementation of strategy and knowledge (i.e., top‐down, early knowledge binding), the KS demands that we first gather or create knowledge and then implement through lateral relations (i.e., bottom‐up, late knowledge binding); and so on. Of course, as interesting as “doing the opposite” may be, a formal approach to design is required.

In this regard, it is instructive to continue with the art metaphor. In 1908, Henri Matisse described the actual process of painting: “If, on a clean canvas, I put at intervals patches of blue, green and red, with every touch that I put on, each of those previously laid on loses in importance … It is necessary that the different tones I use be balanced in such a way that they do not destroy one another. To secure that, I have to put my ideas in order; the relationship between tones must be instituted in such a way that they are built up instead of being knocked down. A new combination of colors will succeed to the first one and will give the wholeness of my conception.”

How do we select and order our ideas so that each reinforces rather than undermines the other? In our previous book, Organization Modeling, we introduced several artifacts for the formal selection and ordering of organizational design elements (Morabito et al. 1999). Our approach to organization design included the idea of “organization molecules” for the domains of organizations—process, information, culture, and other domain‐specific molecules. Organization molecules give structure and order to the domains and may be considered organizational building blocks. In Chapter 4, we introduce a “knowledge molecule” for the domain of organizational knowledge work.

While we finished Organization Modeling with a short discussion of knowledge (K), in this book it is appropriate that we concentrate fully on K. Despite the recommendation of our students, we never developed a “knowledge molecule.” This is because designing KM carries with it the weight of knowledge itself—it is inherently different from, say, designing a business process. As with Matisse painting a picture, the colors and tones must be selected and balanced to bring out a larger whole, even when neither the goals nor the colors are fully known in advance. In fact, as we will subsequently see, the colors of knowledge are everywhere in our society. Furthermore, the colors and tones are in the hands and mind of an artist—they are not mechanical pieces readied for assembly according to a blueprint. Hence, we think of K design more as an artistic rather than as an engineering endeavor, operating dynamically in the imagination, much as Matisse painting a picture.

I.3 Organization of the Book

Despres and Chauvel (2000) have observed that KM is “intuitively important but intellectually elusive”; hence, from a management perspective KM is “everything and nothing.” This is a critically important observation because, indeed, KM is everywhere and thus cries out for the structure—that is, the ordering of ideas—necessary to bring coherence to the discipline. The world is surrounded by knowledge—the impact of KM is everywhere (see Figure I.1a).

Figure I.1 (a) Impact of Knowledge Management. (b) Extending the Discipline of Knowledge Management.

Whereas most approaches to KM concentrate on the organization, we have extended the discussion to the very small and the very large (see Figure I.1b). Again, KM is everywhere and demands an integrated line of thinking.

This book is based on our graduate course delivered at Stevens Institute of Technology, “Design of the Knowledge Organization.” In this course, we discuss the discipline of KM from a variety of perspectives. Accordingly, the organization of this book is as follows:

A targeted literature review

. This book draws a linkage between KM and organizational design. Since the book is based on a graduate course, our aim is not to provide a complete summarization of the literature but to present a foundation for our approach to design and KM.

Enriching principles

. We do not undo existing ideas on organizational design or KM but enhance and elevate each through assimilation with concepts from other disciplines. Our discussion of philosophy, for example, is not merely a historical review, but an argument for shifting our view of K from a technical perspective to one based on moral behavior and social action, ethics and justice, and conscientiousness and altruism. From philosophy, we develop a model of a

K practice

as a mediated interaction between theoretical K and social K that is foundational to K work in and out of organizations.

Knowledge systems

. KS exist everywhere in the world. Every human being is a KS, as is everything he or she creates and builds. In this book,

everything we discuss is a KS of some sort

. Naturally, we are selective, but by intertwining several disciplines, we are better positioned to accommodate new K waves or KS, such as social networking and big data. Accordingly, our approach is to elucidate the construction of knowledge competencies throughout the organization, from the small to the large—this is illustrated in

Figure I.1

a and b. We believe that an

integral design

approach represents the natural evolution of KM into both a more rigorous and a practical phase.

This book consolidates several years of research, teaching, and experience with graduate students from a variety of industries. In fact, we can argue that the

seeds

of this book lie with our students.

The

pedagogy

we employ is to follow a course‐like structure with graphics and accompanying text. This is not the common course‐book, but one organized around a classroom setting with extensive discussion, board‐work, examples, our questions and student responses, and visual representations. Rather than using standard cases, we use student exercises that represent real‐world applications of KM.

Chapters 1

–

7

begin with

food for thought

questions that are suitable for student reflection and classroom discussion. Also, each chapter ends with

class exercises

that take the form of questions and projects that require a certain level of analysis. Several of both the food for thought and class exercise questions are suitable for research. We conclude our last chapter (

Chapter 8

) with a

final exam

that includes both new and existing food for thought questions that have been selected to allow the student to pursue his or her future knowledge work.

We started this book with an outline of 12 or more chapters. As we progressed in our writing, we made an important observation: we could not untangle the ideas we were discussing. Several concepts had an affinity for each other, though they were part of different topics. In effect, they were

integral in forming a larger whole

. For example, we discovered that knowledge work and technology were better understood in light of each other; social networks, office design, and culture belonged together; and so on. Moreover, these topics peek into each other, so that a discussion of one (in a given chapter) may appear with a discussion of another (in some other chapter).

Knowledge is an integral construct and a discussion of K is better understood as such. This is in contrast to modular thinking, which characterizes mechanical design. Indeed, knowledge design is more like painting a picture than designing a machine—writing this book illustrated this point to the authors.

Our solution has been to combine topics into a handful of themes, each of which is somewhat dense and can be expanded into a separate book. We believe our organization will promote its use by students, faculty, researchers, and practicing managers. This is illustrated in Figure I.2.

Figure I.2 Themes of Designing Knowledge Organizations

I.4 How to Read This Book

The chapters in this book are organized around “themes.” Themes have an architectural flavor and remain comparatively constant; only their content changes. In the new and dynamic discipline of KM, we believe it advantageous to establish stable themes. Each of these “territories” of KM contains a variety of ever‐changing topics. Furthermore, since knowledge is like a spider web where everything is connected to everything else (in addition to being recursive), several topics require discussion in more than one context (i.e., territory). An obvious example is knowledge creation, a topic with a stand‐alone discussion in KM literature but also one that manifests itself in every context—business process and strategy, for example. Accordingly, we recommend that you read each theme or chapter in several sittings and cross‐reference other chapters when necessary:

You may read this book as you would any other book—in sequence, starting at the beginning.

You may reference a specific topic or territory and use the embedded backward and forward references to establish a fuller context.

You may combine the discipline of reading a traditional book with a wiki‐like reference experience.

I.5 A Journey Through KS

Culture, strategy, work, conversations—virtually every human activity or endeavor, even the layout of your office or school, is a K system. This book is a journey through such systems from the perspective of the organization. Again, we do not cover every possible KS, but we invite the reader to join our journey as we explore several of the endless possibilities. Also, we ask that you consider for yourself the KS with which you have experience—even your personal culture—and how it fits into the new world of knowledge organizations.

References

Davenport, T.H. (2005),

Thinking for a Living: How to Get Better Performance and Results from Knowledge Workers

. Harvard Business School Press.

Despres, C., and Chauvel, D. (2000), “A Thematic Analysis of the Thinking in Knowledge Management”. In

Knowledge Horizons: The Present and the Promise of Knowledge Management

. Eds. Despres, C., and Chauvel, D. Butterworth Heinemann.

Dewey, J. (1934),

Art as Experience

. A Perigee Book, The Berkley Publishing Group.

Drucker, P.F. (1999),

Management Challenges for the 21st Century

. HarperBusiness.

Morabito, J., Sack, I., and Bhate, A. (1999),

Organization Modeling: Innovative Architectures for the 21st Century

. Prentice Hall.

1Understanding Knowledge

Chapter Preview

Introduction

In this chapter, we discuss the new world of people and organizations. There is a revolution—based on new knowledge and technical artifacts—that is turning the world upside‐down. Knowledge and technology are substantially changing the way people think and behave. The young are breaking with old ways and bringing new expectations to their work. They will interact with organizations differently than did their parents.

Organizations are changing too. As with technology and people, the organization finds itself an integral part of a global mind, what we refer to as the new Pangaea. In this chapter, we discuss the economic and social stresses—for people and organizations—the new Pangaea brings. We call attention to the role of culture in the development of knowledge. We ask, can we really separate knowledge from moral behavior or social responsibility? We conclude with a discussion of competence and organization‐level models of intellectual capital (IC). No one really knows where all this will take us, but hopefully we are on a pathway to a better place!

Food for Thought

Can knowledge arise solely from the faculty of the mind?

Do you believe there are cultural or social tendencies in our thinking patterns that may lead to different perspectives on the meaning of knowledge?

Imagine a fully automated world. What would the social order look like, and what would you do about it?

What are some of the positive and negative impacts of globalization and technology on your organization or school?

How do you know what knowledge is most important in your organization or school?

Topic Layout

The figure below illustrates the topic layout of Chapter 1. The left section informs the organization on the characteristics of the knowledge economy and its social implications. We discuss the cultural perspectives of knowledge through the prism of Indian, Chinese, and Greek/Western traditions. We also introduce the K‐triangle as a metaphor to represent a cultural synthesis. The right side of the figure shows the artifacts associated with data, information, and knowledge (D‐I‐K). We discuss the information continuum as an artifact that characterizes levels of richness between data, information, and knowledge, as well as their interactions. The center of the figure represents the conceptions of knowledge in practice. We consider popular models, including Plato, Polanyi, and Nonaka, and Takeuchi. We discuss tacit and explicit knowledge. We further characterize how organizations may represent their K assets—intangibles and IC. We conclude with a student exercise on the development of an IC model for their organization.

Concept roadmap to

Chapter 1

.

1.1 The New Pangaea

Once upon a time, say 300 million years ago, there was a single global supercontinent known as Pangaea that was home to all land life. This single land mass promoted a certain mixing and combining of life constrained, of course, by geological formations and weather conditions. Information flow was relatively easy.

In time, earth forces broke apart Pangaea, thereby creating the continents—and this movement continues today. With this separation of land, life too became isolated, and “cultures” soon developed. As an example, Charles Darwin observed the many species of finches in the Galapagos Islands—species that arose from isolation caused by the creation of islands in close proximity to one another. Information flow, initially inhibited by geographic isolation, was further inhibited by cultural isolation.

Periods of isolation characterize the history of life and humankind on earth. The many races and languages as well as beliefs and experiences demonstrate what happens when humans are separated from each other. Communication and information flow were also difficult, usually requiring travel by foot or occasionally by water. Conflict followed separation and culturation—and this too continues today. However, something different is happening now. It is the figurative formation of a new type of Pangaea. In this new Pangaea, there is once again a mixing and combining, not in a biological sense, but of human culture, information, and knowledge. Thomas Friedman (2005) calls this movement a “flat world”; others simply call it “globalization.”

The ancient landmass of Pangaea serves as a useful starting point for our discussion of knowledge and knowledge management (KM). The history of knowledge is the history of human beings. First, isolation and, now, interaction are the creative forces that give rise to the fabric of human knowledge.

1.2 Characterizing the Knowledge Economy

How do we characterize KM? Despres and Chauvel (2000) have described two “divides” associated with KM. The first asserts that KM is a progression from managing by objectives to managing by (organizational) development, to total quality management, to business process reengineering, and finally to KM. In this perspective, KM is an evolutionary process creating yet another collection of organizational technologies, matrices, and techniques. The subtheme is that managing knowledge is not very different from managing data or information. In effect, the organization’s management practices continue those of the industrial age with certain structural addendums, such as teams.

The second divide, advocated by Peter Drucker (1999) and supported by the authors, is that KM is a harbinger of a new age in both management and economics. The subtheme here is that KM is a global phenomenon, what we characterize as the new Pangaea, with dimensions that extend well beyond the organization into virtually every human endeavor. Its impact will be largely cultural and social, with profound effects on the design and management of organizations. And this takes us back to Drucker’s assertion that the single most important issue for 21st century organizations is the improvement of knowledge worker productivity.

In this new way of thinking, Despres and Chauvel (2000) illustrate both the importance and dilemma that KM presents:

KM is “intuitively important but intellectually elusive.”

“With rare exceptions, the productivity of a modern corporation or nation lies more in its intellectual and systems capabilities than in its hard assets.”

“To define knowledge in a non‐abstract and non‐sweeping way seems to be very difficult. Knowledge easily becomes everything and nothing.”

There is a certain theme to these quotes: KM is of overwhelming importance to society yet difficult to grasp. This presents an organizational problem—if knowledge is everything, then in practice, it becomes nothing. And this is our central design challenge: if, indeed, knowledge is everywhere, its design must be based on new and creative concepts so as to avoid becoming nothing.

1.3 A Glimpse into the Knowledge Society

Lester Thurow (2009) has identified several attributes of our knowledge society. Of course, the list is endless and constantly changing, but Thurow (2009) makes two interesting observations. In the first, he asks from where will new ideas come. He answers, “corporate democracy.” This approach is contrary to the way organizations typically create knowledge. Social networking, for example, extends knowledge transfer beyond the organization’s boundaries into the global arena. As importantly, what of the inner workings of the organization itself? We believe that the organization must shift its corporate governance (and design) from one where, say, the organization chart exists to implement the knowledge of those at the top (or domain experts) to one where it exists to gather the knowledge of the organization’s members. This gets to the very process of knowledge origination and construction, what we have termed knowledge binding: early K‐binding (top‐down approach) and late K‐binding (bottom‐up approach). It is late K‐binding that comes closest to the corporate democracy described by Thurow (2009). We will discuss K‐binding in Chapters 4 and 5.

The second observation is “winner‐takes‐all markets.” The world’s best opera singers, for example, may be seen directly on television or on the Internet—it is no longer necessary to patronize local opera houses to see lesser opera singers. But this raises an important KM question: where will the new opera singers come from? How will great opera singers become great in the first place? Indeed, the best talent may capture most of the markets, but our society, schools, and organizations still need a farm system in which to develop that talent.

What happens when we lose a farm system? Eliza Gray (2013) noted at the Institute for Advanced Study, “Demands for quick results are everywhere, from corporations focused on quarterly reports to universities increasingly obsessed with private enterprise partnerships that can spawn start‐ups and burnish their image with students and donors.” Further noting, “Pursuing questions for which the value of the answers isn’t obvious may be a luxury that America can no longer afford—or at least appreciate the importance of….” “There will be less of these game‐changing discoveries—at least in this country (i.e., USA),” commented Marc Kastner, Dean of MIT’s School of Science.

1.4 Industrial Revolutions

Thurow (2009) goes on to describe three industrial revolutions—“industrial” being used broadly for the organization of work. The first industrial revolution, around 1800, was focused on transportation. It was the invention of the steam engine that propelled Great Britain to world power.

The second industrial revolution, around 1900, was distinguished by two breakthroughs. The first was the German development of systematic industrial research and development. German leadership in the chemical engineering and pharmaceutical industries followed this innovation. The second was electrification, which, in addition to the initial function of providing light, created opportunities for other industries to develop (much like the Internet does today).

The third industrial revolution, around 2000 (i.e., today), is characterized by six transformative technologies: biotechnology, machine tools and robotics, material sciences, microelectronics, telecommunications, and computers. These technologies, individually and in the aggregate, are driving innovation to new frontiers and transforming society.

As we can see, most of the innovations through the centuries have been technological. The one exception is the development of R&D, which may be described as an organizational innovation. We can add other prior organizational innovations, such as the division of labor, the factory system, and, of course, scientific management. According to Peter Drucker, the next great organizational innovation will be the elucidation and refinement of knowledge work and with that the improvement in the productivity of knowledge workers.

1.5 The Social Challenge of the Knowledge Economy

In discussing the transformation of America’s economy, Robert Reich (Labor Secretary under President Clinton) asserts on Moyers & Company (PBS, 2013) and in his film Inequality for All (2013) that starting in the 1970s, two driving forces—globalization and technology—displaced middle‐class workers and transformed the economy. Many blue‐collar jobs were automated or sent overseas, permanently reducing wages, wealth, and dreams.

Reich went on to discuss “tipping points” which are political and social responses to perceived inequities and poverty. In 1901, for example, the rise of the progressive movement led to a progressive tax system and the breakup of trusts, among other reforms. And now we move to the 21st century: knowledge may be seen as a tipping point giving rise to a political and social revolution.

We can extend this revolution into the idea of knowledge work (K work) and the modern economy. We can confidently assert that (primarily) those with knowledge and knowledge‐oriented jobs will flourish in the 21st century. A global renewal will come through the promotion of knowledge and knowledging (K&K), in all its forms, at all levels of organizations and society. The answer to the challenge of KM comes in many forms: conceptual and practical; individual and organizational; and political and social.

1.5.1 The Challenge of Robots

In discussing the robot economy, Von Drehle (2013) states, “If your job involves a set of logical rules to task after task—from grilling a hamburger to completing a tax return—you are ripe for replacement by a robot.” What will millions of truck drivers do when robot trucks replace them? This shift in the nature of work extends to office as well as industrial workers, creating modern tipping points. Blue‐collar jobs have been replaced by white‐collar jobs; white‐collar jobs now give way to gold‐collar jobs. But there is a major economic issue: office work largely (but not completely) replaced industrial work, easing in part the stress in the transformation of work. However, gold‐collar work cannot replace white‐collar work in terms of employment numbers. K workers create knowledge intensity and, in a macroeconomic sense, are transforming our society, but far fewer such workers are needed. For example, social networking companies have capitalizations well above the very largest industrial and many service organizations but employ a fraction of the people.

Not long ago, our ancestors left the farm for the factory and then the factory for the office. Where will they now go? We’re not sure, but the answer may be home or at least a network of smaller organizations. Our society and the entire world are in the throes of reconfiguring themselves to accommodate the requirements of K development. We are rapidly moving to a society centered on K workers as individuals or as participants in collaborative groups. And this brings us to the unanswered social question of the 21st century: what happens to those who are not part of the K economy?

1.6 A Macro Perspective of Knowledge Management

Illustrated in Figure 1.1 is our representation of KM. First, let us consider our use of the terms knowledge and knowledging. The former is the noun and the latter the verb, somewhat analogous to Earl’s (1998) state of knowledge and state of knowing, respectively. That is to say, knowledge represents a mental state, whereas knowledging is the process of knowledge creation or application. They are inextricably intertwined, and though we sometimes separate the two for analytical purposes, human experience embodies both simultaneously.

Figure 1.1 A macro perspective of knowledge management.

We consider K&K to be a human endeavor, and therefore they exist in all human activities and disciplines. This is illustrated on the right side of Figure 1.1 where we identify nine representative disciplines, each of which practices KM in its own distinctive way. That is to say, K&K is synonymous with human existence and is practiced (KM) according to the traditions and practices of a given discipline. On the left side of the diagram, we show the organization in practice, meaning knowledge developed in the field, outside of the laboratory, sometimes known as experiential or management knowledge of context. This is experiential or management knowledge of context referred to by James March (2006) (see Introduction). Note too that we use the term KM to mean K&K within a given context or tradition. This extends KM beyond a handful of techniques and management practices—and takes us into the second KM divide of a new age in organizations and societies.

In keeping with March’s description of scholarship as the combination of academic and experiential knowledge, we consider K&K to be an abstraction of human discipline and practice (the layer above the level of disciplines in Figure 1.1). The term “knowledge management,” as popularly used today, refers to the practice of K&K in organizations (top, left layer). Note too that we refer to this as traditional KM and include the individual, group, and organizational sublevels of organizations. Occasionally, an interorganization sublevel may be added. However, in our view, KM in the modern organization extends outside organizational life (not just outside the organization) into both the psychological and social realms, embracing all cultural traditions; in turn, this brings us full circle to the disciplines associated with human existence. This is shown in the upper right of Figure 1.1.

1.7 Architecture of the Organization

Illustrated in Figure 1.2 are two related arguments—an architectural perspective of organizations and the drivers of design. An organization may be represented with three architectural views: structural, process, and cognitive. The structural architecture refers to the layout of things—everything from the organization chart to the network architecture. This is the traditional industrial era view of organizations where organizational elements are designed from a structural perspective. For example, in this view, it is not uncommon to see the organization of work force fitted into the organization chart.

Figure 1.2 Architecture of the organization.

The process view is relatively new in organizational design and may be seen in the emphasis on systems development and business process design and reengineering. This perspective embraces the first divide to which Despres and Chauvel (2000) make reference; that is, traditional organizational scientists believe (unconsciously) KM to be an extension of the process or activity architecture.

The third perspective refers to the essential or cognitive architecture, what we refer to as the “thinking of the organization” (somewhat analogous to the term “thinking for a living” as used by Davenport 2005). This perspective is necessary if we are to embrace the second divide of Despres and Chauvel (2000) (and Drucker 1988, 1999) where K&K and KM are revolutionary ideas. This perspective requires that we illuminate knowledge as such.

Finally, we show two driving forces. The left‐to‐right driver illustrates the architectural importance of structure and is characteristic of the industrial economy. In contrast, the right‐to‐left driver is characteristic of the knowledge economy. In this latter perspective, advantage comes from the development of the cognitive architecture of the organization, with the process and structural elements designed to conform to the requirements of the cognitive, not the other way around.

1.8 Data, Information, and Knowledge

We now come to our discussion of D‐I‐K. We do not intend to restate the endless discussions of D‐I‐K but to elucidate on each from a unique architectural perspective.

In our view, D‐I‐K lay on an information continuum that is illustrated in Figure 1.3. In its most general form, the information continuum moves from the less rich to the more rich. Increasing richness requires increasing cognitive and perhaps social activity. Each successive representation in Figure 1.3 is an instantiation of the continuum discussed.

Figure 1.3 Information continuum.

The following is a brief description of the elements shown in the lowest representation. This is supplied to us from a young colleague who was describing the continuum to his students:

Data can be thought of as variables, observations, numbers, as the building blocks of the information continuum. There is no richness in raw data.

A database is an organized structure of a given dataset. This can be a simple text file, with one entry per line, or it could be a relational database with tables, constraints, etc. A database is used to bring order to a dataset, store it, and help us interact with it.

Routine knowledge includes standards and policies known to everyone. It is procedural knowledge with very little richness.

Explicit knowledge is objective knowledge that can be easily expressed and communicated. This includes well‐known or obvious facts.

Communal knowledge is a rich type of knowledge, shared only by the members of a population. This could be a team, a company, or an organization.

Finally, Individual knowledge is the richest of the six. By using his own personal experience and expertise, a person can mine this type of knowledge from the data.

As seen in Figure 1.3, it is reasonable to observe that data is information with the least richness, whereas knowledge is information with the most richness. Though not entirely wrong, this is an engineering perspective and does not fully account for the human and dynamic nature of knowledge (but one cannot put everything into a single representation—see Figure 1.4 for another perspective, discussed later).

Figure 1.4 Illustration of information.

Data is a state, a reference to a business or an environmental event, such as a sales event. We record these events by collecting corresponding data in a database. Information, in turn, has many definitions or characterizations (e.g., “message”—Davenport and Prusak 1998); the one we prefer is Peter Drucker’s definition that information is data endowed with relevance and purpose. (However, we need to elaborate a bit here: the data to which relevance and purpose are applied must be of sufficient quality in the first place; quality will be determined, in part, by context.) But where do relevance and purpose come from? From human beings and their organizations. This is shown in Figure 1.4as a collision between data and knowledge. The right side of Figure 1.4 is, of course, knowledge itself and is largely the subject of this book. Here we use Plato’s definition of knowledge as justified true belief. It is elaborated further in “Knowledge As Such.”

Finally, human beings often change their minds about what is relevant: Drucker has often stated, what we all intuitively know, that today’s high knowledge is tomorrow’s common knowledge or garbage. This change of mind suggests that information, like knowledge, is a function of context and time, indicated by the notation f(context, time) in Figure 1.5.

Figure 1.5 Information continuum in an organizational context.

Similarly, relevance and purpose is shown as f(knowledge, data, context). Typical contexts include business process and strategy. Furthermore, observe that differentiation and routinization are opposite processes. This again is expressed by our colleague in his explanation of the continuum:

Going back to the information continuum, we know that, in order to get from raw data to individual knowledge, we need to specialize, differentiate, look for patterns and trends, and generally interpret the data. On the other hand, the reverse process, which we refer to as routinization, is ALSO very important. Imagine how useful it would be to take this individual tacit knowledge and put it into writing. How useful it would be to distill the wisdom in the mind of a specialist, document it, and subsequently make it available to “everyone” in the company.

1.9 Distinctions in the Information Continuum

In our earlier book, we discussed three macro organizational configurations: automating, informating, and knowledging. Figure 1.6 is an adaptation from Michael Earl’s (1998) distinctions in D‐I‐K and may, in part, be used as the basis for our organizational configurations. As we can see in the left column, data is distinguished by business events and transactional systems. The key information task is to adequately represent the data, as in, for example, a data model. There is no special human activity, other than observation of the transaction; on the other hand, the organization intent is to automate and modularize the transaction.

Figure 1.6 Distinguishing aspects of knowledging—Adapted from Earl (1998).

The middle column describes informating. This corresponds to traditional management science and includes business intelligence (BI) and analytics. The whole point is the reduction of uncertainty (defined as insufficient information) and the support of managerial decision making.

This central column distinguishes the observable work of knowledge workers in the field—this is what K workers and managers actually do. Therefore, we may ask, what does the right column represent? Knowledging! But how does this work? The K worker, living, as it were, in the middle column, interacts with the environment in a decision‐making context. The internal effect of doing work (middle column) on the human being is what the right column in Figure 1.6 represents. This internal effect—at both the individual and organization levels—is the essence of knowledging. Note too we illustrate the boundary between information and knowledge with a heavy line representing a fundamental shift between the external and the internal, the visible and the not