MGMT630 — Final Term Summary (Lectures 23–45)

📘 Lecture 23 — The Organizational School of Thought in KM

📖 Overview: This lecture explores the organizational school of thought in knowledge management, which emphasizes using organizational structures and networks to share and pool knowledge. It covers key approaches like managing common knowledge and the SECI process, as well as the critical components of knowledge architecture for communication, including people and technical cores. The lecture also examines how knowledge is created in organizations and the factors that influence knowledge transfer.

🗂️ Topics Covered

The lecture begins by defining the organizational school of thought and presenting its key approaches: managing common knowledge and the SECI process. It then details the knowledge architecture for communication in organizations, breaking it down into the people core (including identifying knowledge centers, activating content satellites, and assigning experts) and the technical core (hardware, software, and specialized human resources). The lecture concludes with a discussion on knowledge creation in organizations through teamwork and the factors that can encourage or retard knowledge transfer.

📝 Lecture Summary

The Organizational School

According to Earl (2001), the organizational school describes the use of organizational structures, or networks, to share or pool knowledge. Often described as knowledge communities, the archetypal arrangement is a group of people with a common interest, problem, or experience. These communities are designed and maintained for a business purpose and can be intra- or inter-organizational. Two key approaches are presented: managing common knowledge and the socialization externalization combination internalization (SECI) process.

Managing Common Knowledge

Dixon (2000) defines common knowledge as the knowledge that employees learn from doing the organization’s tasks. Common knowledge is managed through knowledge transfer mechanisms. Knowledge transfer in an organization can be defined as the process by which one unit (e.g., a group, department, or division) is affected by experiences. Another definition suggests that knowledge transfer at the individual level is how knowledge acquired in one situation applies.

Knowledge Architecture for communication in organizations

• Knowledge architecture can be regarded as a prerequisite to knowledge sharing.
• The infrastructure can be viewed as a combination of people, content, and technology.
• These components are inseparable and interdependent.

The People Core

• By people, here we mean knowledge workers, managers, customers, and suppliers.
• As the first step in knowledge architecture, our goal is to evaluate the existing information/documents used by people, the applications they need, the people they usually contact for solutions, the associates they collaborate with, the official emails they send/receive, and the database(s) they usually access.
• All the above-stated resources help to create an employee profile, which can later be used as the basis for designing a knowledge management system.
• The idea behind assessing the people core is to assign job content to the right person and to ensure that the flow of information, once obstructed by departments, now flows to the right people at the right time.
• To expedite knowledge sharing, a knowledge network has to be designed to assign people authority and responsibility for specific kinds of knowledge content, which means:
  • Identifying knowledge centers:
    • After determining the knowledge that people need, the next step is to find out where the required knowledge resides and how to capture it successfully.
    • Here, the term knowledge center means areas in the organization where knowledge is available for capturing.
    • These centers support identifying expert(s) or expert teams in each center who can collaborate in the necessary knowledge capture process.
  • Activating knowledge content satellites:
    • This step breaks down each knowledge center into more manageable levels, satellites, or areas.
  • Assigning experts for each knowledge center:
    • After the final framework has been decided, one manager should be assigned for each knowledge satellite to ensure integrity of information content, access, and update.
    • Ownership is a crucial factor in knowledge capture, transfer, and implementation.
    • In a typical organization, departments usually tend to be territorial.
    • Often, conflict can occur over budget or control of sensitive processes (including the kind of knowledge a department owns).
    • These reasons justify the process of assigning department ownership to knowledge content and knowledge processes.
    • Adjacent/interdependent departments should be cooperative and ready to share knowledge.

The Technical Core

• The objective of the technical core is to enhance communication as well as ensure effective knowledge sharing.
• Technology provides many opportunities for managing tacit knowledge in the area of communication.
• Communication networks create links between necessary databases.
• The term technical core refers to the totality of the required hardware, software, and specialized human resources.
• Expected attributes of technology under the technical core: Accuracy, speed, reliability, security, and integrity.
• Since an organization can be thought of as a knowledge network, the goal of the knowledge economy is to push employees towards greater efficiency/productivity by making the best possible use of the knowledge they possess.
• A knowledge core usually becomes a network of technologies designed to work on top of the organization's existing network.

Knowledge Creation in Organizations

• Knowledge update can mean creating new knowledge based on ongoing experience in a specific domain and then using the new knowledge in combination with existing knowledge to come up with updated knowledge for knowledge sharing.
• Knowledge in organizations can be created through teamwork.
• A team can commit to perform a job over a specific period of time.
• A job can be regarded as a series of specific tasks carried out in a specific order.
• When the job is completed, the team compares the experience it had initially (while starting the job) to the outcome (successful/disappointing).
• This comparison translates experience into knowledge.
• While performing the same job in the future, the team can take corrective steps and/or modify the actions based on the new knowledge they have acquired.
• Over time, experience usually leads to expertise where one team (or individual) can be known for handling a complex problem very well.
• This knowledge can be transferred to others in a reusable format.

Factors Influencing Knowledge Transfer

• Personality is one factor in knowledge sharing.
• For example, extrovert people usually possess self-confidence, feel secure, and tend to share experiences more readily than the introvert, self-centered, and security-conscious people.
• People with positive attitudes, who usually trust others and who work in environments conducive to knowledge sharing, tend to be better at sharing knowledge.
• Vocational reinforcers are the key to knowledge sharing.
• People whose vocational needs are sufficiently met by job reinforcers are usually found to be more likely to favour knowledge sharing than people who are deprived of one or more reinforcers.

⭐ Key Takeaways

The organizational school emphasizes using structured networks and communities for knowledge sharing, with two primary approaches being managing common knowledge and the SECI process. A successful knowledge architecture is built on an inseparable and interdependent combination of people, content, and technology, starting with a thorough assessment of the "people core" to create employee profiles and identify knowledge centers. The "technical core" provides the hardware, software, and expertise necessary for effective communication and knowledge sharing. Knowledge is created through teamwork, where experience is compared against outcomes to generate new insights, and this knowledge can then be transferred. Finally, effective knowledge transfer is influenced by personal factors like personality and attitude, as well as the presence of vocational reinforcers that motivate sharing.

🧠 Quick Revision Questions

1. According to Earl, what is the fundamental principle of the organizational school of knowledge management?
2. What are the three inseparable and interdependent components of a knowledge architecture for communication?
3. What is the purpose of identifying "knowledge centers" within the people core of a knowledge architecture?
4. How does a team create new knowledge from performing a job, according to the lecture?
5. What two personal factors are mentioned as influencing an individual's propensity to share knowledge?

📘 Lecture 24 — Importance of Tacit and Explicit Knowledge

📖 Overview: This lecture explores the fundamental concepts of knowledge creation, capture, and transfer within organizations. It examines the distinction between tacit and explicit knowledge, the process of knowledge creation through teams, and the practical challenges and methodologies involved in capturing expert knowledge for knowledge-based systems.

🗂️ Topics Covered

The lecture covers knowledge creation as the process of combining new experiences with initial knowledge to improve performance, knowledge transfer through teams including the steps and impediments, tacit knowledge capture and its unique characteristics, the three-step process for knowledge capture, indicators for identifying expertise, guidelines for working with experts, and the benefits and limitations of using multiple experts.

📝 Lecture Summary

Test Your Understanding

1. What is knowledge creation?

Knowledge creation is using the new knowledge acquired from ongoing experiences in a particular problem area, in combination with the initial knowledge, to come up with extended knowledge that should improve the quality and effectiveness of performing the same job the next time around.

💡 Why this matters: Knowledge creation transforms routine work into a continuous learning cycle, making organizations more adaptive and efficient over time.

2. A job is more than a task. Do you agree? Give an example.

True. A job is a series of specific tasks carried out in a specific order, format, or sequence.

📌 Example: The job of procurement coordination requires several tasks such as:

• Planning short-term purchases
• Creating profiles for suppliers
• Selecting the best supplier
• Tracking orders progress...etc.

3. How is knowledge created and transferred via teams?

Essentially, a team commits to performing a job with an initial knowledge → The team performs the job → Realizes outcome → Compares outcome to action "before and after" → New experience/knowledge is gained → Knowledge captured and represented in a form usable by others → This new knowledge is reusable by same team on next job.

4. Explain the main impediments in knowledge sharing.

The main impediments are: Personality, Attitude, Vocational reinforcers, and Work norms.

5. Explain the main steps in knowledge transfer.

A team gets together with initial knowledge → It performs a specific job → The job outcome is realized and is compared to action → A new experience or knowledge is gained → The new experience is captured and codified in a form usable by others → The new knowledge is reusable by the same team on the next job.

6. In your own words, define tacit knowledge capture. What makes it unique?

Knowledge capture is the process of extracting the knowledge of how an expert arrives at a solution for a particular problem. This includes the actual steps and reasoning involved in arriving at the solution as well as the subjective logic that an expert uses in addressing the problem.

Knowledge capture is unique, in that the procedure does not follow an algorithmic flow or a particular syntax to solve a problem.

7. Are there any particular steps involved in knowledge capture? Explain briefly.

Knowledge capture involves three steps:

Step 1: Using an appropriate tool to elicit the information from the expert. Extensive interview with the expert usually accomplishes this step.

Step 2: Interpreting the verbal information and inferring the expert's underlying knowledge and reasoning process. In this step, the knowledge developer decides where the information gathered fits into the development process of the knowledge-based system. Throughout the interviewing process, the knowledge developer gathers information on the expert's rationale for arriving at a decision. It is important that the knowledge developer thoroughly questions the expert on all angles of the problem domain.

Step 3: Taking the results from step two and using it to build the rules that represent the expert's thought process or solutions. This step may require several checks to ensure the resulting system meets the needs of the user and has captured "the expert" as closely as possible. Flowcharts, flow diagrams, decision trees, decision tables, and other graphic representation can be used to depict the rules for the expert's solution.

8. How would one identify expertise?

The collection of the several indicators of expertise would help the knowledge developer identify who would be an appropriate expert for a problem domain. These include: a. Genuine respect from peers with regard to the expert's decisions as good decisions b. People consult the expert when a problem arises c. Admitting to not knowing everything about a problem which demonstrates his or her confidence and provides a realistic view of limitations, avoiding irrelevant information and focusing on the facts d. Working with a clear focus e. Being able to explain the information to different audience levels f. Depth of detail and exceptional quality in explanations g. Demonstrating no arrogance regarding personal credentials h. Years of experience i. Strong ties with people in power

9. Working with experts requires certain skills and experience. What suggestions or advice would you give to an inexperienced knowledge developer concerning: a. working with or approaching an expert b. preparing for the first session

A and b are interrelated in the overall effort and interactions with the expert(s). Any knowledge developer, no matter how well experienced, must educate himself or herself in the expert's area and be fully prepared for the knowledge acquisition phase. Perceptions are extremely important in knowledge capture.

10. Working with multiple experts has definite benefits and limitations. Cite an example in which the use of multiple experts is a must. Explain your choice.

An example in which the use of multiple experts is a must could be for development of a knowledge-based system to predict the next direction of a given stock on the New York Stock Exchange. The reasons you would need multiple experts are the complexity of the problem domain, listening to a variety of views on stock exchange theory and behavior before attempting an approach or a solution. With this example, there is no single individual who is an expert in all aspects of the company stocks or the stock exchange or even the economy.

⭐ Key Takeaways

Knowledge creation involves combining ongoing experiences with initial knowledge to improve job performance, following a cyclical team-based process of action, outcome comparison, and knowledge capture. Tacit knowledge capture is unique because it does not follow algorithmic flows but instead requires extracting subjective reasoning through a structured three-step process of elicitation, interpretation, and rule-building. Expertise can be identified through multiple indicators including peer respect, clarity of focus, ability to explain at different levels, and years of experience. When working with experts, knowledge developers must thoroughly educate themselves in the domain and prepare extensively for knowledge acquisition sessions. Multiple experts are essential for complex problem domains where no single individual possesses comprehensive expertise.

🧠 Quick Revision Questions

1. What are the main impediments to knowledge sharing within teams?
2. Describe the three specific steps involved in the knowledge capture process.
3. Why is tacit knowledge capture considered unique compared to explicit knowledge capture?
4. List at least five indicators that help identify an appropriate expert for a problem domain.
5. Provide an example where using multiple experts is necessary and explain why.

📘 Lecture 25 — SECI Process and BA for K. Creation

📖 Overview: This lecture explores Nonaka's influential model of organizational knowledge creation, focusing on the dynamic conversion between tacit and explicit knowledge. It explains the SECI process (Socialization, Externalization, Combination, Internalization), the concept of ba as the shared context for knowledge creation, and the role of knowledge assets. Understanding this model is crucial for managers seeking to foster innovation and leverage knowledge as a strategic resource.

🗂️ Topics Covered

The lecture covers Nonaka's Model of Knowledge Creation & Transformation, detailing the four modes of knowledge conversion: Socialization, Externalization, Combination, and Internalization (the SECI process). It then explains ba as the shared context for knowledge creation, distinguishing it from mere physical space. Finally, it introduces the concept of knowledge assets, including their four types: experiential, conceptual, systemic, and routine, and discusses the role of management in fostering knowledge creation.

📝 Lecture Summary

Nonaka's Model of Knowledge Creation & Transformation

In 1995, Nonaka introduced tacit knowledge (personal, context-specific, hard to formalize) and explicit knowledge (codified, transmittable in formal language) as the two main types of human knowledge. The key to knowledge creation lies in the conversion between these types through four modes.

• Tacit to tacit communication (Socialization): Takes place between people in meetings or team discussions.
• Tacit to explicit communication (Externalization): Articulation among people through dialog (e.g., brainstorming).
• Explicit to explicit communication (Combination): This transformation phase can be best supported by technology. Explicit knowledge can be easily captured and then distributed/transmitted to a worldwide audience.
• Explicit to tacit communication (Internalization): This implies taking explicit knowledge (e.g., a report) and deducing new ideas or taking constructive action. One significant goal of knowledge management is to create technology to help users derive tacit knowledge from explicit knowledge.

Socialization-Externalization-Combination-Internalization Process

Organizations create and define problems, develop and apply knowledge to solve them, and then further develop new knowledge through action. Developing new knowledge is often more important than keeping track of existing knowledge. The organization is not merely an information processing machine but an entity that creates knowledge through action and interaction.

Nonaka et al. (2000) argue that the most important aspect of a firm's capability is the dynamic capability to continuously create new knowledge out of existing firm-specific capabilities, rather than the stock of knowledge at any one point. Knowledge creation is a continuous, self-transcending process through which one acquires a new context, a new view of the world, and new knowledge.

To understand how organizations create knowledge dynamically, Nonaka et al. (2000) proposed a model with three elements:

1. The SECI process: the process of knowledge creation through conversion between tacit and explicit knowledge.
2. Ba: the shared context for knowledge creation.
3. Knowledge assets: the resources required to enable knowledge creation (inputs, outputs, and moderators).

The three elements interact to form the knowledge spiral. Through the conversion process, tacit and explicit knowledge expand in both quality and quantity. The four steps are:

• Socialization: The conversion of tacit knowledge to tacit knowledge. New tacit knowledge is acquired through shared experiences, such as spending time together or apprenticeships. It is the sharing of tacit knowledge between individuals, usually through joint activities rather than written or verbal instructions.
• Externalization: The conversion of tacit knowledge to explicit knowledge. Tacit knowledge is articulated into explicit knowledge, which can be expressed in words and numbers. Successful conversion depends on a common knowledge space and the use of means such as metaphors, analogy, and mental models.
• Combination: The conversion of explicit knowledge to explicit knowledge. Explicit knowledge is collected from inside and outside the organization, then combined, edited, and processed to form new explicit knowledge. This involves synthesizing knowledge from different sources into one context (e.g., a financial report).
• Internalization: The conversion of explicit knowledge to tacit knowledge. Individuals convert explicit knowledge (e.g., from documents or manuals) into tacit knowledge, which becomes part of existing mental models and know-how. Learning by doing, on-the-job training, and observation are key examples.

The movement through the four modes forms a spiral, not a circle, as knowledge expands and develops continuously.

🔑 Definition — SECI Process: [The four-step process of knowledge creation through the continuous, dynamic interaction between tacit and explicit knowledge, involving Socialization, Externalization, Combination, and Internalization.]

Ba: The Platform for Knowledge Creation

The second element of the model is ba, the location or context where knowledge creation takes place. Knowledge needs a physical, mental, or virtual context to be created. Ba is the real cultural, social, and historic context which is of importance to each knowledge worker, enabling them to understand and appreciate information. It is the place where information is understood so that it becomes knowledge.

The key concept in understanding ba is interaction. Knowledge is created through interactions among individuals or between individuals and their environments. Participants of ba cannot be mere onlookers; they must be committed through action and interaction. Ba lets participants share time and space, yet it transcends time and space, as it can be mental or virtual.

Knowledge Assets

The third element is knowledge assets, which are firm-specific resources used to create value for the firm. They are resources required to support the knowledge-creating process. Important knowledge assets include:

• Trust: Stimulates knowledge workers to share knowledge.
• Roles: Define how the knowledge creation process is to take place.
• Routines: Ensure consistency in how different knowledge workers handle knowledge creation.

Knowledge assets must be built and used internally. Nonaka et al. (2000) categorized them into four types:

1. Experiential Knowledge Assets: Shared tacit knowledge built through shared hands-on experience (e.g., skills, know-how).
2. Conceptual Knowledge Assets: Explicit knowledge articulated through images, symbols, and language (e.g., brand, concepts).
3. Systemic Knowledge Assets: Systematized and packaged explicit knowledge (e.g., manuals, product specifications, databases).
4. Routine Knowledge Assets: Tacit knowledge routinized and embedded in actions and practices (e.g., organizational culture, daily routines).

💡 Why this matters: These four asset types form the basis of the knowledge-creating process. To manage knowledge creation effectively, a company must map its stocks of knowledge assets, understanding that they are dynamic and new assets can be created from existing ones.

Management and the Knowledge-Creating Process

The three elements—SECI, ba, and assets—represent requirements that all must be managed for successful knowledge creation. Executive management is responsible for articulating corporate knowledge ambitions. Middle management is responsible for creating and sustaining ba. Both levels are responsible for the availability of knowledge assets. The knowledge-creating process cannot be managed through traditional control of information flow, but managers can lead by providing conditions for knowledge creation.

Fostering love, care, trust, and commitment amongst organizational members is fundamental to knowledge creation, as it creates a safe atmosphere for sharing tacit knowledge. It is also crucial for leaders to cultivate commitment based on a corporate knowledge vision.

🔑 Definition — Knowledge Assets: [Firm-specific resources that are indispensable to create value for the firm; they serve as inputs, outputs, and moderating factors of the knowledge-creating process.]

⭐ Key Takeaways

The SECI process is the core dynamic of knowledge creation, describing a spiral of continuous conversion between tacit and explicit knowledge through Socialization, Externalization, Combination, and Internalization. This process does not occur in a vacuum; it requires a shared context, or ba, which provides the physical, mental, or virtual space for interaction and can transcend traditional boundaries. Knowledge assets—including experiential, conceptual, systemic, and routine—are the essential firm-specific resources that fuel this process, requiring internal development and management. Successful knowledge creation is not a matter of controlling information but of fostering trust, commitment, and a supportive environment led by management at all levels. Ultimately, the dynamic capability to create new knowledge, not the stock of existing knowledge, is the most critical competitive advantage for a firm.

🧠 Quick Revision Questions

1. What are the four modes of knowledge conversion in the SECI process, and what type of knowledge is converted to what in each mode?
2. Explain the concept of "ba" and why it is more than just a physical location in the context of knowledge creation.
3. List and define the four types of knowledge assets proposed by Nonaka et al. (2000).
4. How does the SECI process form a "spiral" rather than a "circle"?
5. What is the role of "love, care, trust, and commitment" in the knowledge-creating process, and why are they so important for tacit knowledge sharing?

📘 Lecture 26 — Knowledge Spiral and Sustained Organizational Advantages Through SECI Process

📖 Overview: This lecture examines the dynamic process of organizational knowledge creation through the interaction between tacit and explicit knowledge. It introduces the SECI model (Socialization, Externalization, Combination, Internalization) as a framework for understanding how knowledge is converted and expanded within organizations. The lecture is critical for understanding how companies can systematically create, share, and leverage knowledge for sustained competitive advantage.

🗂️ Topics Covered

The lecture begins with the concept of knowledge conversion as social interaction between tacit and explicit knowledge, then systematically examines each of the four modes of knowledge conversion: socialization (tacit to tacit), externalization (tacit to explicit), combination (explicit to explicit), and internalization (explicit to tacit). It concludes with an analysis of how these modes interact through the knowledge spiral at different organizational levels.

📝 Lecture Summary

Knowledge Conversion: Interaction between Tacit and Explicit Knowledge

The lecture establishes a critical foundation: tacit knowledge and explicit knowledge are not separate but mutually complementary entities that interact through social processes. This interaction is called "knowledge conversion" — a social process between individuals, not confined within an individual. Through this social conversion process, both tacit and explicit knowledge expand in quality and quantity.

The lecture notes that the ACT model from cognitive psychology partially aligns with this view, as it hypothesizes that declarative knowledge (explicit) must transform into procedural knowledge (tacit). However, the ACT model has a limitation: it views this transformation as mainly unidirectional from explicit to tacit. The SECI model argues that transformation is interactive and spiral.

💡 Why this matters: Understanding that knowledge conversion is a social, bi-directional process rather than a simple one-way transfer is essential for designing effective knowledge management systems.

🔑 Definition — Knowledge Conversion: The social interaction between tacit and explicit knowledge through which human knowledge is created and expanded.

Four Modes of Knowledge Conversion

The assumption that knowledge is created through the interaction between tacit and explicit knowledge allows for four different modes of knowledge conversion:

1. Socialization — from tacit knowledge to tacit knowledge
2. Externalization — from tacit knowledge to explicit knowledge
3. Combination — from explicit knowledge to explicit knowledge
4. Internalization — from explicit knowledge to tacit knowledge

Three of these modes (socialization, combination, and internalization) have been discussed in organizational theory, but externalization has been somewhat neglected.

Socialization: From Tacit to Tacit

Socialization is a process of sharing experiences and thereby creating tacit knowledge such as shared mental models and technical skills. An individual can acquire tacit knowledge directly from others without using language. The key to acquiring tacit knowledge is experience — without shared experience, it is extremely difficult to project oneself into another's thinking process.

📌 Example 1 — Ronda's "Brainstorming Camps" (tama dashi kai): Informal meetings held outside the workplace at resort inns where participants discuss difficult problems while sharing meals and bathing together. The meetings are open to any interested employees. There is one taboo: criticism without constructive suggestions. These camps serve as forums for creative dialogue, sharing experience, and enhancing mutual trust.

📌 Example 2 — Matsushita's Bread-Making Machine: Ikuko Tanaka and engineers apprenticed themselves to the Osaka International Hotel's head baker to capture the tacit knowledge of dough-kneading skill. Through observation, imitation, and practice, Tanaka noticed the baker was "twisting" the dough — the secret for making tasty bread that X-ray comparisons could not reveal.

📌 Example 3 — NEC's Customer Interaction: NEC developed its first personal computer through sharing experiences and continuing dialogues with customers at the BIT-INN display service center. A wide variety of customers, from high school students to professional computer enthusiasts, contributed to the development of the best-selling PC-8000.

🔑 Definition — Socialization: A process of sharing experiences and thereby creating tacit knowledge such as shared mental models and technical skills.

Externalization: From Tacit to Explicit

Externalization is a process of articulating tacit knowledge into explicit concepts. It is a quintessential knowledge-creation process where tacit knowledge takes the shapes of metaphors, analogies, concepts, hypotheses, or models. Externalization is triggered by dialogue or collective reflection.

Among the four modes, externalization holds the key to knowledge creation because it creates new, explicit concepts from tacit knowledge. The answer lies in a sequential use of metaphor, analogy, and model.

🔑 Definition — Metaphor: A way of perceiving or intuitively understanding one thing by imaging another thing symbolically. Metaphors reconcile discrepancies in meaning and create networks of new concepts by relating different things, even abstract to concrete ones.

🔑 Definition — Analogy: Reduces the unknown by highlighting the "commonness" of two different things through rational thinking focused on structural/functional similarities.

🔑 Definition — Model: A logical representation where all concepts and propositions must be expressed in systematic language and coherent logic.

📌 Example — Honda City "Tall Boy": Project leader Hiroo Watanabe used the metaphor of "Automobile Evolution" viewing the car as an organism. The concept "man-maximum, machine-minimum" emerged through an analogy between this concept and a sphere (maximum volume within minimum surface area), resulting in the short but tall car design.

📌 Example — Canon Mini-Copier: A task force exploring how to produce a disposable drum cylinder at low cost discovered the analogy of an aluminum beer can. By applying the beer can manufacturing process to the drum cylinder, they developed a low-cost manufacturing process for the disposable drum.

🔑 Definition — Externalization: A process of articulating tacit knowledge into explicit concepts, taking the shapes of metaphors, analogies, concepts, hypotheses, or models.

Theory of Organizational Knowledge Creation in Product Development

The lecture presents a table summarizing how metaphor and analogy influenced concept creation:

Metaphor is driven by intuition and holistic imagery and does not aim to find differences. Analogy is carried out by rational thinking focusing on structural/functional similarities and their differences. Thus, analogy bridges the gap between an image and a logical model.

Combination: From Explicit to Explicit

Combination is a process of systemizing concepts into a knowledge system. This mode involves combining different bodies of explicit knowledge through such media as documents, meetings, telephone conversations, or computerized communication networks. Reconfiguration of existing information through sorting, adding, combining, and categorizing leads to new knowledge.

In the business context, combination is most often seen when middle managers break down and operationalize corporate visions, business concepts, or product concepts. Middle management plays a critical role in creating new concepts through networking of codified information and knowledge.

📌 Example — Kraft General Foods: The company developed "micro-merchandizing" using POS data to create new sales systems. By analyzing data with unique classification of stores and shoppers into six categories, the system pinpoints who shops where and how, controlling four elements of category management: consumer and category dynamics, space management, merchandising management, and pricing management.

📌 Example — Asahi Breweries: The grand concept "live Asahi for live people" was combined with the mid-range concept of "richness and sharpness" to develop Asahi Super Dry beer. This mid-range concept made the grand concept more explicitly recognizable and altered the company's product development system, requiring cooperation between production and sales departments.

🔑 Definition — Combination: A process of systemizing concepts into a knowledge system by combining different bodies of explicit knowledge.

💡 Why this matters: Combination shows how explicit knowledge from different sources can be reconfigured to create new knowledge, highlighting the importance of documentation, databases, and formal communication networks.

Internalization: From Explicit to Tacit

Internalization is a process of embodying explicit knowledge into tacit knowledge. It is closely related to "learning by doing." When experiences through socialization, externalization, and combination are internalized into individuals' tacit knowledge bases in the form of shared mental models or technical know-how, they become valuable assets.

For explicit knowledge to become tacit, it helps if the knowledge is verbalized or diagrammed into documents, manuals, or oral stories. Documentation helps individuals "re-experience" the experiences of others indirectly.

📌 Example — GE Answer Center: Over 200 telephone operators respond to up to 14,000 calls daily, using a database of 1.5 million potential problems and solutions. Product development people visit the center to chat with operators, thereby "re-experiencing" their experiences. Four full-time programmers update the database with new solutions by the following day.

📌 Example — Matsushita MIT'93: The company launched a policy to reduce yearly working time to 1,800 hours. The MIT'93 promotion office advised each department to experiment for one month by working 150 hours. Through this bodily experience, employees internalized what working 1,800 hours a year would be like.

Internalization can also occur through reading success stories — if the story makes members feel the realism and essence, the experience may change into a tacit mental model. When shared by most members, tacit knowledge becomes part of organizational culture.

🔑 Definition — Internalization: A process of embodying explicit knowledge into tacit knowledge, closely related to "learning by doing."

Contents of Knowledge and the Knowledge Spiral

Each mode of knowledge conversion yields different content:

These contents interact in the knowledge spiral:

• Sympathized knowledge about consumers' wants → becomes explicit conceptual knowledge about a new product concept through socialization and externalization
• Conceptual knowledge → becomes a guideline for creating systemic knowledge through combination
• Systemic knowledge → turns into operational knowledge for mass production through internalization
• Operational knowledge → often triggers a new cycle of knowledge creation

The knowledge spiral occurs as the interaction between tacit and explicit knowledge becomes larger in scale, moving up through expanding communities of interaction that cross sectional, departmental, divisional, and organizational boundaries. Organizational knowledge creation starts at the individual level and moves upward.

Each mode is triggered differently:

1. Socialization starts with building a "field" of interaction
2. Externalization is triggered by meaningful "dialogue or collective reflection"
3. Combination is triggered by "networking" newly created and existing knowledge
4. Internalization is triggered by "learning by doing"

🔑 Definition — Knowledge Spiral: A process where the interaction between tacit and explicit knowledge becomes larger in scale as it moves up ontological levels, starting at the individual level and moving through expanding communities of interaction.

💡 Why this matters: The knowledge spiral explains how individual knowledge becomes organizational knowledge, showing that knowledge creation is not linear but a continuous, expanding cycle.

⭐ Key Takeaways

The SECI model demonstrates that organizational knowledge creation is a dynamic, spiral process driven by the social interaction between tacit and explicit knowledge across four modes: socialization (sharing experiences), externalization (articulating tacit knowledge through metaphor and analogy), combination (systematizing explicit knowledge), and internalization (embodying explicit knowledge through learning by doing). Externalization is the most critical mode because it transforms valuable tacit knowledge into explicit concepts that can be shared across the organization. The knowledge spiral operates at multiple ontological levels, moving from individual to group to organizational knowledge, with each mode producing different content (sympathized, conceptual, systemic, and operational knowledge). For sustained organizational advantage, companies must create mechanisms that support all four modes — such as brainstorming camps for socialization, metaphor/analogy use for externalization, databases and networks for combination, and hands-on experimentation for internalization.

🧠 Quick Revision Questions

1. What are the four modes of knowledge conversion in the SECI model, and what triggers each mode?
2. Why does the lecture argue that externalization holds the key to knowledge creation, and what role do metaphor and analogy play in this process?
3. How did Matsushita's bread-making machine development team use socialization to capture the master baker's tacit knowledge?
4. Explain how the knowledge spiral operates across different ontological levels, using an example from the lecture.
5. What type of knowledge content does each SECI mode produce, and how do these contents interact in the spiral of knowledge creation?

📘 Lecture 27 — Enablers of SECI Process

📖 Overview: This lecture presents the five-phase model of the organizational knowledge-creation process, building upon the SECI framework and the five enabling conditions discussed previously. It explains how organizations can systematically convert individual tacit knowledge into organizational explicit knowledge through a structured, spiraling process that integrates time as a dimension.

🗂️ Topics Covered

The lecture covers the five phases of organizational knowledge creation: (1) sharing tacit knowledge, which corresponds to socialization; (2) creating concepts, corresponding to externalization; (3) justifying concepts; (4) building an archetype, corresponding to combination; and (5) cross-leveling knowledge across organizational levels. Each phase is explained with examples from Matsushita's Home Bakery team and Honda's City development team, emphasizing the role of enabling conditions like autonomy, redundancy, requisite variety, and creative chaos.

📝 Lecture Summary

Enabling Conditions for Organizational Knowledge Creation

The organization's role in the knowledge-creation process is to provide the proper context or "field" for facilitating group activities. Without this supportive environment, individual tacit knowledge cannot be amplified into organizational knowledge.

Five-Phase Model of the Organizational Knowledge-Creation Process

This integrated model incorporates the time dimension into the SECI framework. It consists of five phases: (1) sharing tacit knowledge; (2) creating concepts; (3) justifying concepts; (4) building an archetype; and (5) cross-leveling knowledge. The process starts with sharing tacit knowledge (socialization), converts it to explicit concepts (externalization), justifies those concepts, builds tangible archetypes (combination), and extends knowledge across the organization and beyond.

The First Phase: Sharing Tacit Knowledge

Tacit knowledge held by individuals is the basis of organizational knowledge creation, but it cannot be easily communicated because it is acquired primarily through experience. Sharing tacit knowledge among multiple individuals with different backgrounds, perspectives, and motivations is the critical first step. Individuals' emotions, feelings, and mental models must be shared to build mutual trust.

To effect that sharing, a "field" is needed where individuals can interact through face-to-face dialogues, share experiences, and synchronize their bodily and mental rhythms. The typical field is a self-organizing team, where members from various functional departments work together to achieve a common goal. At Matsushita, the Home Bakery team members apprenticed themselves to the head baker to capture kneading skill through bodily experience. At Honda, the City team shared mental models and technical skills while discussing the ideal car, often away from the office. This phase corresponds to socialization.

💥 Why this matters: Without this initial sharing of tacit knowledge, all subsequent phases of knowledge creation are impossible. This phase relies heavily on trust and direct human interaction.

A self-organizing team facilitates knowledge creation through requisite variety of team members, who experience redundancy of information and share interpretations of organizational intention. Management injects creative chaos by setting challenging goals and endowing team members with a high degree of autonomy. An autonomous team sets its own task boundaries and, as a "boundary-spanning unit," interacts with the external environment.

The Second Phase: Creating Concepts

The most intensive interaction between tacit and explicit knowledge occurs here. Once a shared mental model is formed, the self-organizing team articulates it through continuous dialogue and collective reflection. The shared tacit mental model is verbalized into words and phrases, finally crystallized into explicit concepts. This phase corresponds to externalization.

This conversion is facilitated by multiple reasoning methods: deduction, induction, and particularly abduction, which employs figurative language such as metaphors and analogies. The Honda City team used phrases like "Automobile Evolution," "man-maximum, machine-minimum," and "Tall Boy." Dialectics raises the quality of dialogue by using contradictions and paradoxes to synthesize new knowledge.

Concepts are created cooperatively through dialogue. Autonomy helps team members diverge their thinking freely, while intention converges thinking in one direction. Requisite variety provides different angles for looking at a problem. Fluctuation and chaos help members change their way of thinking fundamentally. Redundancy of information enables better understanding of figurative language and crystallization of shared mental models.

The Third Phase: Justifying Concepts

Knowledge is defined as justified true belief, so new concepts must be justified. Justification involves determining if newly created concepts are truly worthwhile for the organization and society—a screening process. While individuals justify unconsciously throughout the process, the organization must conduct justification explicitly to check if organizational intention is intact and if concepts meet society's needs. The most appropriate time for this screening is right after concepts are created.

For business organizations, normal justification criteria include cost, profit margin, and the degree to which a product contributes to the firm's growth. Criteria can be both quantitative and qualitative. In the Honda City case, the "Tall Boy" concept had to be justified against top management's vision (a product fundamentally different from anything before) and against middle management's concept ("man-maximum, machine-minimum"). More abstract criteria may include value premises like adventure, romanticism, and aesthetics. Thus justification criteria need not be strictly objective and factual; they can also be judgmental and value-laden.

🔑 Definition — Justification: The process of determining if newly created concepts are truly worthwhile for the organization and society, serving as a screening process.

In a knowledge-creating company, top management primarily formulates justification criteria as organizational intention (strategy or vision). Middle management can also formulate criteria as mid-range concepts. Other organizational units may have autonomy in deciding their own sub-criteria. For example, 200 young employees at Matsushita determined that employees should become "voluntary individuals." Justification criteria should be consistent with value systems or needs of society at large, which should be reflected in organizational intention. Redundancy of information facilitates the justification process.

The Fourth Phase: Building an Archetype

The justified concept is converted into something tangible or concrete—an archetype. This can be a prototype in new-product development or a model operating mechanism for service or organizational innovation. It is built by combining newly created explicit knowledge with existing explicit knowledge. Because justified concepts (explicit) are converted into archetypes (also explicit), this phase is akin to combination.

Organizational members engage in building a prototype by pulling together people with differing expertise (R&D, production, marketing, quality control), developing specifications that meet everyone's approval, and manufacturing the first full-scale form. For building a model of a new organizational structure, people from affected sections and experts from different fields (HR, legal, strategic planning) are assembled to draw up organizational charts, job descriptions, reporting systems, or operating procedures. Attention to detail is key.

Due to complexity, dynamic cooperation of various departments is indispensable. Requisite variety and redundancy of information facilitate this process. Organizational intention serves as a tool for converging different kinds of know-how and promoting interpersonal and interdepartmental cooperation. Autonomy and fluctuation are generally not relevant at this stage.

The Fifth Phase: Cross-Leveling of Knowledge

Organizational knowledge creation is a never-ending, continuously upgrading process. The new concept moves to a new cycle of knowledge creation at a different ontological level. This interactive and spiral process is called cross-leveling of knowledge and takes place both intra-organizationally and inter-organizationally.

Intra-organizationally: Knowledge realized as an archetype can trigger new cycles, expanding horizontally and vertically. Horizontal cross-fertilization occurred at Matsushita when Home Bakery induced creation of other "Easy & Rich" products (automatic coffee maker in the same division, large-screen TVs in another). Vertical cross-fertilization occurred when Home Bakery inspired the corporate-level umbrella concept "Human Electronics," which led to MIT'93 (Mind and Management Innovation Toward '93), reducing annual working hours to 1,800 and freeing time for front-line employees.

Inter-organizationally: Knowledge can mobilize knowledge of affiliated companies, customers, suppliers, competitors, and others. An innovative budgetary control system in one company could change an affiliated company's financial control system. Customer reaction to a new product may initiate a new development cycle. At Apple Computer, engineers build prototypes and bring them directly to customers for feedback, potentially initiating new development rounds.

For effective cross-leveling, each organizational unit must have autonomy to take knowledge developed elsewhere and apply it freely across different levels. Internal fluctuation (e.g., frequent personnel rotation) facilitates knowledge transfer, as do redundancy of information and requisite variety. In intra-organizational cross-leveling, organizational intention acts as a control mechanism on whether knowledge should be cross-fertilized.

⭐ Key Takeaways

The five-phase model provides a complete, temporal framework for understanding how organizations create knowledge systematically, starting with sharing tacit knowledge and ending with cross-leveling across organizational boundaries. Each phase corresponds to a SECI mode: sharing tacit knowledge (socialization), creating concepts (externalization), building an archetype (combination), while justification and cross-leveling add screening and diffusion dimensions. The enabling conditions—intention, autonomy, fluctuation/creative chaos, redundancy, and requisite variety—play different roles in each phase, with autonomy and fluctuation most critical in early phases and intention most important in later phases for convergence. The process is spiral and never-ending, as cross-leveling triggers new cycles of knowledge creation at higher ontological levels. A knowledge-creating company operates as an open system, constantly exchanging knowledge with the external environment including customers, suppliers, competitors, and universities.

🧠 Quick Revision Questions

1. What are the five phases of the organizational knowledge-creation process, and which SECI modes do the first, second, and fourth phases correspond to?
2. Why is a self-organizing team considered the typical "field" for sharing tacit knowledge, and what enabling conditions does management use to support this phase?
3. What role does abduction (figurative language like metaphors and analogies) play in the second phase of creating concepts?
4. What are the key justification criteria for business organizations, and why can they be both quantitative and qualitative?
5. Explain the difference between horizontal and vertical cross-fertilization of knowledge, providing examples from Matsushita.

📘 Lecture 28 — Process Approach to KM and Info-Com Technology (ICT) in KM Systems

📖 Overview: This lecture explores the core processes of Knowledge Management—discovery, capture, sharing, and application—and explains how Information and Communication Technology (ICT) supports each of these processes. Understanding this framework is critical for designing effective KM systems that enhance organizational performance.

🗂️ Topics Covered

The lecture covers the four main KM processes: Knowledge Discovery (combination and socialization), Knowledge Capture (externalization and internalization), Knowledge Sharing (exchange and socialization), and Knowledge Application (routines and direction). It then examines the role of ICT in each KM process, including knowledge creation, storage/retrieval, transfer, and application. The lecture concludes with a discussion of Knowledge Management Systems (KMS), requirements for KM, and the benefits of IT in KM.

📝 Lecture Summary

KM Processes

KM processes are the broad processes that aid in discovering, capturing, sharing, and applying knowledge. These include combination, socialization, externalization, internalization, exchange, directions, and routines. For example, internalization processes benefit from simulations or experiments, which enable individuals to learn through experience, as well as from face-to-face meetings, on-the-job training, and demos.

1. Knowledge Discovery

Knowledge discovery may be defined as the development of new tacit or explicit knowledge from data and information or from the synthesis of prior knowledge. Two important ways of managing knowledge discovery are combination and socialization.

The discovery of new explicit knowledge relies most directly on combination, wherein multiple bodies of explicit knowledge, data, or information are synthesized to create new, more complex sets of explicit knowledge. Existing explicit knowledge, data, and information are reconfigured, recategorized, and recontextualized to produce new explicit knowledge. For example, data mining techniques may be used to uncover new relationships amongst explicit data that may lead to create predictive or categorization models that create new knowledge.

The discovery of new tacit knowledge, on the other hand, relies most directly on socialization, which involves the integration of multiple streams for the creation of new knowledge. It is the synthesis of tacit knowledge across individuals, usually through joint activities rather than written or verbal instructions. For example, a simple discussion among an organization’s employees during a coffee break can help in group-wise knowledge sharing.

2. Knowledge Capture

Knowledge capture can be defined as the process of retrieving either explicit or tacit knowledge that resides within people, artifacts, or organizational entities. The knowledge capture process benefits most directly from two KM sub-processes, externalization and internalization. Externalization and Internalization help capture the tacit knowledge and explicit knowledge, respectively.

3. Knowledge Sharing

Knowledge sharing refers to the process through which explicit or tacit knowledge is communicated to other individuals. Knowledge sharing involves effective transfer, so that the recipient of knowledge can understand it well enough to act on it. What is shared is knowledge rather than recommendations based on the knowledge. Knowledge sharing may take place across individuals as well as across groups, departments, or organizations. Depending on whether explicit or tacit knowledge is being shared, exchange or socialization processes are used.

4. Knowledge Application

Knowledge application refers to the use of knowledge to make decisions and perform tasks, thereby contributing to organizational performance. Knowledge application depends on the available knowledge, which in turn depends on the processes of knowledge discovery, capture, and storage. Applying knowledge does not necessarily mean that the party that uses it also understands it. All that is needed is that somehow the knowledge be used to guide decisions and actions. Knowledge application benefits from two processes that do not involve the actual transfer or exchange of knowledge between the concerned individuals: routines and direction.

Direction refers to the process through which the individual possessing the knowledge directs the action of another individual without transferring to him the knowledge underlying the direction. This preserves the advantages of specialization and avoids the difficulties inherent in the transfer of tacit knowledge. An example of Direction would be when a computer programmer calls his software project manager to ask how to solve a particular problem with a piece of code, and then proceeds to solve the problem based on the instructions given by the project manager. He does this without acquiring the knowledge himself, so that if a similar problem reoccurs in the future, he would be unable to identify it as such and would therefore be unable to solve it himself without calling an expert.

Routines involve the utilization of knowledge embedded in procedures, rules, and norms that guide future behavior. Routines economize on communication more than directions as they are embedded in procedures or technologies. However, since they require constant repetition, they take time to develop. For example, a computerized inventory management system utilizes considerable knowledge about the relationship between demand and supply, but neither the knowledge nor the directions are communicated through individuals.

Comparison of Internalization and Externalization

Internalization is the conversion of explicit knowledge into tacit knowledge. The explicit knowledge may be in the form of action and practice, so that the individual acquiring the knowledge can re-experience what others have gone through. Alternatively, individuals could acquire tacit knowledge in virtual situations, either vicariously by reading manuals or others' stories, or experientially through simulations or experiments. An example of internalization would be a doctor, fresh out of medical school, reading a book on new surgery techniques, and learning from it. This learning helps the doctor, and the hospital he works for, capture the knowledge contained in the book.

Externalization involves converting tacit knowledge into explicit forms such as words, concepts, visuals, or figurative language. It helps translate individuals’ tacit knowledge into explicit forms that can be more easily understood by the rest of their group. It is a complex process because tacit knowledge is often difficult to articulate. An example of externalization is a doctor transcribing and documenting his thoughts and observations while examining a patient so as to save it in the patient’s medical file for future reference. This captures the tacit knowledge acquired by the doctor and makes it available for future use by the hospital.

Thus, internalization and externalization both add value to the knowledge capture process. However, externalization helps capture tacit knowledge while internalization helps capture explicit knowledge.

Knowledge Sharing vs. Knowledge Application

Knowledge sharing is the process through which explicit or tacit knowledge is communicated to other individuals. Knowledge sharing involves the recipient acquiring the shared knowledge as well as being able to take action based on it, as opposed to recommendations based on the knowledge being shared, which only results in the utilization of knowledge without the recipient internalizing the shared knowledge. Depending on whether explicit or tacit knowledge is being shared, exchange or socialization processes are used. Socialization facilitates the sharing of tacit knowledge. Exchange, on the other hand, focuses on the sharing of explicit knowledge.

Knowledge application depends on the available knowledge, which in turn depends on the processes of knowledge discovery, capture, and storage. In knowledge application, the party that makes use of the knowledge does not necessarily need to understand it, but should be able to use the knowledge to guide decisions and actions. Knowledge application benefits from two processes that do not involve the actual transfer or exchange of knowledge between the concerned individuals – routines and direction.

🔑 Definition — Exchange: A KM sub-process that focuses on the sharing of explicit knowledge among individuals, groups, and organizations.

🔑 Definition — Socialization: A KM sub-process that facilitates the sharing of tacit knowledge in cases where new tacit knowledge is being created, as well as when new tacit knowledge is not being created.

ICT in Knowledge Management

Information and communication technology can play an important role in successful knowledge management initiatives. Modern information technology (e.g., the Internet, intranets, extranets, browsers, data warehouses, data filters, software agents, expert systems) can collect, systematize, structure, store, combine, distribute, and present information of value to knowledge workers. The low cost of computers and networks has created a potential infrastructure for knowledge sharing and opened up important knowledge management opportunities.

In practice, what companies manage under the banner of knowledge management is a mix of knowledge, information, and unrefined data. In the case of data and information, there are often attempts to add more value and create knowledge. This transformation might involve the addition of insight, experience, context, interpretation, or the myriad of other activities in which human brains specialize.

One view is that knowledge is a social process. It asserts that knowledge resides in people’s heads and that it is tacit. Technology, within this perspective, can only support the context of knowledge work. While technology can be used with knowledge management initiatives, Ward and Peppard (2002) argue that it should never be the first step. Knowledge management is to them primarily a human and process issue. Once these two aspects have been addressed, then the created processes are usually very amenable to being supported and enhanced by the use of technology.

Knowledge Management Processes and ICT

Alavi and Leidner (2001) developed a systematic framework with four sets of socially enacted knowledge processes: (1) creation, (2) storage and retrieval, (3) transfer, and (4) application.

Knowledge Creation

Organizational knowledge creation involves developing new content or replacing existing content within the organization’s tacit and explicit knowledge. The model developed by Nonaka et al. (2001) involving SECI (socialization, externalization, internalization, combination), ba, and knowledge assets views organizational knowledge creation as involving a continual interplay between the tacit and explicit dimensions. Four types of ba corresponding to the four modes of knowledge creation are identified: (1) originating ba (socialization), (2) interacting ba (externalization), (3) cyber ba (combination), and (4) exercising ba (internalization).

For knowledge creation, there is currently idea-generation software emerging. This software is designed to help stimulate a single user or a group to produce new ideas, options, and choices. Idea Fisher, for example, has an associative lexicon that cross-references words and phrases based on analogies and metaphors.

Knowledge Storage and Retrieval

Organizational memory includes knowledge residing in various component forms, including written documentation, structured information stored in electronic databases, codified human knowledge stored in expert systems, documented organizational procedures and processes, and tacit knowledge acquired by individuals and networks of individuals. Advanced computer storage technology and sophisticated retrieval techniques, such as query languages, multimedia databases, and database management systems, can be effective tools in enhancing organizational memory.

Semantic memory refers to general, explicit and articulated knowledge, whereas episodic memory refers to context-specific and situated knowledge. Document management technology allows knowledge of an organization’s past to be effectively stored and made accessible. The most common objective of knowledge management projects involves some sort of knowledge repository. Common repository technologies include Lotus Notes, Web-based intranets, and Microsoft’s Exchange.

Knowledge retrieval can find support in content management and information extraction technology, which represent a group of techniques for managing and extracting information from documents. Tasks include: abstracting and summarizing, visualization, comparison and search, indexing and classification, translation, question formulation and query answering, and extraction of information.

Knowledge Transfer

Knowledge transfer occurs at various levels in an organization. Knowledge transfer channels can be informal or formal, personal or impersonal. IT can support all four forms of knowledge transfer. An innovative use of technology for transfer is use of intelligent agent software to develop interest profiles of organizational members. IT can increase knowledge transfer by extending the individual’s reach beyond formal communication lines. Computer networks and electronic bulletin boards and discussion groups create a forum that facilitates contact between the person seeking knowledge and those who may have access to the knowledge. Corporate directories may enable individuals to rapidly locate the individual who has the knowledge.

Knowledge Application

An important aspect of the knowledge-based view of the firm is that the source of competitive advantage resides in the application of the knowledge rather than in the knowledge itself. Information technology can support knowledge application by embedding knowledge into organizational routines. Technology-enforced knowledge application raises a concern that knowledge will continue to be applied after its real usefulness has declined. IT can enhance knowledge integration and application by facilitating the capture, updating, and accessibility of organizational directives. IT can also enhance the speed of knowledge integration and application by codifying and automating organizational routines. Workflow automation systems and rule-based expert systems are examples.

Knowledge Management Systems

Knowledge management systems (KMS) refer to a class of information systems applied to managing organizational knowledge. These systems are IT applications to support and enhance the organizational processes of knowledge creation, storage and retrieval, transfer, and application (Alavi & Leidner, 2001).

Requirements from Knowledge Management

The critical role of IT and information systems lies in the ability to support communication, collaboration, and those searching for knowledge, and the ability to enable collaborative learning. Key implications include:

1. Interaction between information and knowledge: Information becomes knowledge when combined with experience, interpretation, and reflection.
2. Interaction between tacit and explicit knowledge: A shared knowledge space is required for individuals to exchange knowledge.
3. Knowledge management strategy: Efficiency-driven businesses may apply the stock strategy (databases), effectiveness-driven businesses the flow strategy (information networks), and expert-driven businesses the growth strategy (networks of experts).
4. Combination in SECI process: Creative use of computerized communication networks and large-scale databases can facilitate this mode.
5. Explicit transfer of common knowledge: Five mechanisms exist: serial transfer, explicit transfer, tacit transfer, strategic transfer, and expert transfer.
6. Link knowledge to its uses: Knowledge management initiatives should not become ends in themselves.
7. Treat knowledge as an intellectual asset in the economic school.
8. Treat knowledge as a mutual resource in the organizational school.
9. Treat knowledge as a strategy in the strategy school.
10. Value configuration determines knowledge needs in primary activities.
11. Incentive alignment: Three dimensions of information systems design: software engineering, technology acceptance, and incentive alignment.

Benefits from Knowledge Management Systems

IT is applied in KM for several important reasons: it enables improved individual performance, improved organizational performance, and improved interorganizational performance. A U.S. survey ranked reasons for IT in KM: improving profitability (67%), securing talent (54%), improving customer service (52%), securing market share (44%), shortening time to market (39%). The survey also ranked software spending: infrastructure (61%), intelligent systems for search (39%), data warehouse (21%), document handling (17%), company portals (16%), groupware (13%).

General Electric's CEO on Knowledge Sharing

General Electric’s CEO Jack Welch took personal responsibility for three business processes: allocation of resources, development of people, and knowledge sharing. This underscores the critical importance of knowledge sharing in enhancing organizational innovativeness and performance.

⭐ Key Takeaways

The lecture establishes that KM consists of four core processes—discovery, capture, sharing, and application—each with specific sub-processes (combination/socialization for discovery, externalization/internalization for capture, exchange/socialization for sharing, and routines/direction for application). ICT plays a crucial role in supporting all four KM processes, with specific technologies enhancing knowledge creation (data mining, idea-generation software), storage/retrieval (repositories, databases, document management), transfer (electronic bulletin boards, discussion forums, corporate directories), and application (expert systems, workflow automation). However, technology should never be the first step in KM; it is primarily a human and process issue. Finally, the SECI model (socialization, externalization, combination, internalization) and the concept of ba are essential frameworks for understanding organizational knowledge creation, and the alignment of incentives is a critical dimension of KMS design.

🧠 Quick Revision Questions

1. What are the four main KM processes, and what are the two sub-processes associated with each?
2. Explain the difference between internalization and externalization in the knowledge capture process, and provide an example of each.
3. How does knowledge application differ from knowledge sharing, and what are the two processes that support knowledge application without transferring knowledge?
4. What are the four types of ba in Nonaka's model, and how does each correspond to a mode of knowledge creation in the SECI process?
5. What are the three reasons why IT is applied in knowledge management, and what was the top-ranked reason for IT in KM according to the U.S. survey?

📘 Lecture 29 — Organizational Issues in Managing Knowledge Worker

📖 Overview: This lecture explores the nature of knowledge workers, their professional attributes, and the organizational challenges in managing them effectively. It covers the critical distinction between management and leadership in learning organizations, addresses work management tasks, technology support, and the crucial issue of knowledge worker loyalty. Understanding these organizational dynamics is essential for leveraging human intellectual capital for competitive advantage.

🗂️ Topics Covered

The lecture defines knowledge workers as individuals who transform business and personal experience into knowledge, typically working in marketing, engineering, programming, and intellectual property. It examines personality and professional attributes of knowledge workers, their business roles in learning organizations, the distinction between traditional managers and smart managers/leaders, work management tasks, technology support for knowledge work, and the factors influencing knowledge worker loyalty including positive contributors like emotional bonds and compensation, and negative contributors like frustration and alternative employment opportunities.

📝 Lecture Summary

Knowledge Workers

A knowledge worker is a person who transforms business and personal experience into knowledge. These individuals work in marketing, intellectual property, engineering, programming, and other occupations involving more thought than physical labor. Knowledge workers add value by contributing to corporate knowledge assets, documenting problem-solving activities, reporting best practices, and disseminating information through newsletters and online publications. Customer support representatives are considered knowledge workers because they work with information from customers through direct contact, phone, email, or observing customer activity. Managers at all levels can be considered knowledge workers if they are involved in creating new revenues from existing knowledge by reformatting and repackaging information or introducing existing products into new markets. Most KM initiatives revolve around knowledge workers interacting with customers directly, indirectly through computer systems, or with other knowledge workers and managers.

🔑 Definition — Knowledge Worker: A person who transforms business and personal experience into knowledge through capturing, assessing, applying, sharing, and disseminating it within the organization to solve specific problems or create value. 🔑 A knowledge worker is usually innovative, creative, and fully aware of organizational culture. 🔑 A knowledge worker can be thought of as a product of values, experiences, processes, education, and training.

Personality/Professional Attributes

Knowledge workers possess several distinctive attributes: they understand and adopt organizational culture; align personal and professional growth with corporate vision; possess an attitude of collaboration and sharing; have innovative capacity and a creative mind; have clear understanding of the business they are involved in; are always willing to learn and adopt new methodologies; possess self-control and can learn independently; and are willing to accommodate uncertainties.

Core competencies of knowledge workers include: thinking skills for envisioning how products or companies can be better; ability to work in innovative teams through collaboration, cooperation, and coordination; commitment to continuous learning involving unlearning and relearning in tune with fast-changing conditions; innovation and creativity; risk taking for potential success; a culture of responsibility towards knowledge; and decisive action taking.

💡 Why this matters: These attributes distinguish knowledge workers from traditional employees and determine how organizations must manage them differently.

Knowledge Worker’s Business Roles in Learning Organization

A learning organization is an organization of people with total commitment to improve their capacity to create and produce. It can respond to uncertainty, challenges, and change in general. The rate of learning of an organization can turn out to be the most critical source of competitive advantage.

Management and Leadership

In KM, we distinguish between managers and leaders. Traditional managers focus on the present, are action-oriented, and spend most time supervising, delegating, controlling, and ensuring compliance with set procedures. They were once workers promoted to managers and are aware of each aspect of the business. Smart managers focus on organizational learning to ensure operational excellence. Because of continuing change, smart managers cannot be expected to have mastered the work of subordinates. They take on the role of leaders where change is the primary goal. The challenge is to get the organization moving towards achieving goals in line with the rate of change.

The leader's role in a learning organization is more of a facilitator than a supervisor, acting more like a teacher than an order giver. In teaching, focus is on transfer of knowledge from instructor to learner. The instructor is the expert and delivers quality content with potential. Learning should promote a way of thinking, not just convey facts. The smart manager provides opportunities for knowledge workers to brainstorm ideas, exchange knowledge, and come up with new and better ways of doing business.

🔑 Traditional Managers: Focus on stability, meeting deadlines, action-oriented, delegate, supervise, control, ensure compliance. 🔑 Smart Managers/Leaders: Focus on organizational learning and change, act as facilitators and teachers, provide opportunities for brainstorming and knowledge exchange.

Work Management Tasks

Work management tasks include: retrieving, creating, sharing, and using knowledge in everyday activities; managing knowledge workers and nurturing their knowledge-oriented activities; ensuring readiness to work; maintaining work motivation among knowledge workers; allocating effort and switching control among tasks; managing collaboration and concurrent activities among knowledge workers; sharing information and integrating work among knowledge workers; and recruiting knowledge-seeking and bright individuals.

Factors limiting knowledge worker productivity include: time constraint where there is always more work to do; working smarter and harder but accomplishing little due to limited time, staff support, or financial constraints; knowledge workers doing work the organization did not hire them to do; work schedule issues; and motivation problems where knowledge workers avoid task uncertainty or job complexity.

Technology and Knowledge Worker

Primary activities of knowledge work include: assessment, decision making, monitoring, and scheduling. A knowledge worker can act as a manager, supervisor, or clerk who is actively engaged in thinking, information processing, analyzing, creating, or recommending procedures based on experience and cumulative knowledge.

IT plays a key role in the learning organization in: knowledge capture, information distribution, and information interpretation. Technology supporting knowledge workers includes: email, LAN (Local Area Network), and intelligent workstations. Intelligent workstations automate repetitive and tedious tasks and should perform administrative support functions, personal computing functions, and managing intelligent databases.

💡 Why this matters: The ultimate goal of technology is to serve organizational memory and create a working environment that provides conditions for effective knowledge work.

Knowledge Worker Loyalty

Knowledge worker loyalty is one of the corporation's major intangible assets that can be enhanced through knowledge worker management. Although loyalty is difficult to quantify exactly, knowledge worker behavior consistent with loyalty can be quantified by considering factors that positively and negatively affect behaviors associated with loyalty, such as a worker continuing in a relationship with the corporation even when competing companies offer greater compensation.

The issue of knowledge worker loyalty typically arises when management considers investing additional resources in a particular knowledge worker or group. In the computerized knowledge economy where someone with in-demand skills can work from virtually anywhere with a computer and Internet connection, loyalty is especially important.

Positive contributors to knowledge worker loyalty include: difficulty locating alternative employment (greater difficulty = greater loyalty effect); the emotional bond between the knowledge worker and the company (the greatest contributor); the knowledge worker's investment of time in the company (more time invested = more likely the relationship continues); and compensation (greater compensation = more likely to stay).

Negative contributors to loyalty behavior include: numerous employment alternatives (more alternatives = less likely to stay); and a high level of frustration with the company, including frustration with management or personal problems with other knowledge workers.

Modeling loyalty behavior shows how knowledge worker behavior can be influenced depending on which elements are stressed. A generous compensation package and a friendly, supportive work environment contribute to a continued relationship, while little or no compensation increase and ignoring complaints sends a clear message that workers should look elsewhere.

💡 Why this matters: Understanding loyalty factors enables organizations to strategically invest in retention of valuable knowledge workers and prevent loss of intellectual capital.

⭐ Key Takeaways

The most critical points from this lecture are: (1) Knowledge workers transform business and personal experience into knowledge and possess unique attributes including innovation, collaboration, continuous learning, and self-control that require distinct management approaches. (2) Smart managers in learning organizations act as facilitators and teachers rather than supervisors, focusing on organizational learning and enabling knowledge workers to brainstorm and innovate. (3) Knowledge worker loyalty is influenced most strongly by emotional bonds with colleagues, followed by difficulty of finding alternative employment, time invested, and compensation, while frustration and numerous alternatives negatively impact loyalty. (4) Technology through IT systems, intelligent workstations, and communication tools supports knowledge capture, distribution, and interpretation, enabling knowledge workers to perform assessment, decision-making, monitoring, and scheduling. (5) Work management tasks must address productivity constraints including time limitations, misalignment of work with worker expertise, scheduling issues, and motivation challenges.

🧠 Quick Revision Questions

1. What are the five core competencies of knowledge workers and why are they called "core" competencies?
2. How does the role of a traditional manager differ from that of a smart manager/leader in a learning organization?
3. What are the four positive contributors to knowledge worker loyalty and which is the greatest contributor?
4. What are the four primary activities of knowledge work and how does IT support them?
5. What five factors limit knowledge worker productivity and how can smart managers address each constraint?

📘 Lecture 30 — Overview of KM Solutions and Processes

📖 Overview: This lecture provides a comprehensive overview of Knowledge Management solutions and processes, covering the justification for KM systems, the challenges in their development, and the key steps in the KM system life cycle. It explains the critical roles of strategic planning, team formation, knowledge capture, and rapid prototyping, distinguishing them from conventional systems development. Understanding these foundational processes is essential for successfully implementing and managing KM initiatives within an organization.

🗂️ Topics Covered

The lecture begins with justifying a KM system by evaluating knowledge loss, need, and expert availability. It then contrasts KM systems development with conventional approaches, highlighting differences in life cycle, testing, and orientation. The role of strategic planning in forming a KM team is explored, followed by methods for capturing explicit and tacit knowledge. The lecture details the iterative process of rapid prototyping and the crucial roles of expert selection and the knowledge developer. Finally, it covers the steps of designing the KM blueprint, testing (verification and validation), implementation, quality assurance, and post-system evaluation, concluding with managerial implications and a set of review questions.

📝 Lecture Summary

KM System Justification

Justifying a KM system involves answering several key questions to determine its viability and necessity. The primary concerns are whether existing knowledge might be lost (e.g., through retirement) and if the system is needed across multiple locations. It's also crucial to confirm that experts are available and willing to contribute and that the problem at hand requires years of experience and cognitive reasoning to solve.

🔑 Definition — KM System Justification: The process of evaluating an organization's readiness and need for a KM system by assessing potential knowledge loss, expert availability, problem complexity, and the ability to find a champion.

Other critical questions include whether the expert can articulate the problem-solving process, how critical the knowledge is, whether the tasks are non-algorithmic, and if a project champion can be found within the organization. A positive answer to these questions indicates a strong justification for proceeding with a KM system.

Challenges in KM Systems Development

Several challenges must be addressed when developing a KM system, starting with Changing Organizational Culture, which involves shifting people's attitudes and behaviors. Knowledge Evaluation assesses the worth of information, while Knowledge Processing involves identifying techniques for acquisition, storage, and distribution.

Knowledge Implementation requires an organization's commitment to change, learn, and innovate, extracting meaning for specific missions and storing lessons learned. The lecture also highlights key differences between conventional systems and KM systems:

• The systems analyst gathers data from users who know the problem but not the solution.
• The knowledge developer gathers knowledge from knowledgeable people who know both the problem and the solution.
• Conventional systems development is primarily sequential and process-driven.
• Knowledge Management System Life Cycle (KMSLC) is incremental, interactive, and result-oriented, with testing from the beginning and support for rapid prototyping.

Role of Strategic Planning in KM Solutions

Based on an evaluation of the existing infrastructure, an organization should develop a strategic plan to advance its objectives with the proposed KM system. This plan must consider three key areas:

• Vision: The overall goal for the KM system.
• Resources: The required personnel, technology, and budget.
• Culture: The organization's readiness to accept and use the KM system.

Forming a KM Team

Forming a KM team involves identifying key stakeholders across units, branches, or divisions. The team should be balanced strategically, technically, and organizationally in terms of size and competency. Factors that impact team success include the quality of members, team size, project complexity, team motivation and leadership.

Capturing Knowledge Capturing Knowledge involves extracting, analyzing, and interpreting the knowledge an expert uses to solve a problem. Explicit knowledge is captured from documentation, while tacit knowledge is captured from experts and organizational databases. Interviewing and data mining are key methods. The knowledge developer acquires heuristic knowledge from experts to build the knowledge base. The process includes determining feasibility, selecting the expert, tapping their knowledge, and verifying the knowledge base.

The Role of Rapid Prototyping

Rapid Prototyping is a spontaneous and iterative process of building a knowledge base. The knowledge developer creates a prototype based on limited initial knowledge, then the expert reacts, and the developer modifies the system on the spot. This cycle of show-react-modify-test continues until the expert is satisfied.

Expert Selection

The selected expert must have excellent communication skills to convey information understandably. Key questions include verifying the expert's expertise, ensuring their commitment to the project, planning for backup, and defining the boundaries of their knowledge.

The Role of the Knowledge Developer

The knowledge developer is the system's architect, responsible for identifying the problem domain, capturing knowledge, and writing heuristics. Necessary attributes include strong communication skills, knowledge of capture tools, team working ability, tolerance for ambiguity, and conceptual thinking.

Designing the KM Blueprint

This phase begins designing the IT infrastructure. The KM Blueprint addresses issues like system interoperability, finalizing system scope, and deciding on necessary components. It involves developing key layers of the KM architecture, including the user interface, security, application, and repository layers.

Testing the KM System Testing involves two steps:

• Verification Procedure: Ensures the system is built correctly (the programs do their intended task).
• Validation Procedure: Ensures the right system is built (it meets user expectations and is usable).

Implementing the KM System After capturing, encoding, verifying, and validating the knowledge, the system is implemented on a server. Implementation involves converting the new system into actual operation, which requires a major conversion step, post-implementation review, and system maintenance.

Quality Assurance This involves developing controls to ensure a quality system, looking for errors such as reasoning errors, ambiguity, incompleteness, and false representation.

Post system Evaluation Key evaluation questions assess how the system has improved decision-making, caused organizational changes, affected user attitudes, changed operational costs, affected user relationships, and whether benefits justify costs.

Implications for KM

Managerial factors include commitment to user training, informing top management of cost/benefit analysis, training knowledge developers, recognizing experts, and strategic planning. For system maintenance, management must assign responsibility, define required skills, plan training, provide incentives, allocate funding, and establish a working relationship with the IT staff.

💡 Why this matters: The post-implementation evaluation is critical as it directly ties the KM system's performance to tangible business outcomes, justifying the initial investment and guiding future improvements. 💡 Why this matters: Addressing these managerial implications proactively prevents many common causes of KM system failure, such as poor user adoption and lack of ongoing support.

⭐ Key Takeaways

The KMSLC is distinct from the conventional SDLC as it is incremental, result-oriented, and promotes rapid prototyping with continuous verification and validation. The success of a KM system heavily depends on the quality of the KM team, the selection of a communicative expert, and the skills of the knowledge developer who acts as the system's architect. Knowledge capture is an iterative process of extraction and refinement, best facilitated by rapid prototyping to build a knowledge base that satisfies the expert. Testing is a two-part process of verification (building the system right) and validation (building the right system), which must occur throughout development. Ultimately, a successful KM implementation requires strong top management support, user participation, and a strategic plan that addresses organizational culture and resource allocation.

🧠 Quick Revision Questions

1. What are the key differences between a conventional systems development life cycle (SDLC) and the Knowledge Management System Life Cycle (KMSLC)?
2. Describe the process of rapid prototyping in the context of KM systems development.
3. What are the two critical steps involved in testing a KM system, and what does each step ensure?
4. According to the lecture, what are the primary roles and necessary attributes of a knowledge developer?
5. List three critical managerial factors that must be considered for the successful implementation and maintenance of a KM system.

📘 Lecture 31 — KM Systems, Solutions, and Infrastructure

📖 Overview: This lecture examines the comprehensive framework of Knowledge Management, detailing how KM is facilitated through processes, systems, mechanisms, technologies, and infrastructure. It explains the crucial roles of organizational culture, structure, and information technology in building a successful KM foundation, and provides a classification of KM systems based on the processes they support.

🗂️ Topics Covered

This lecture covers four levels of KM facilitation: KM Processes, KM Systems, KM Mechanisms and Technologies, and KM Infrastructure. It then explores each of these components in detail, including examples and their interrelationships. The lecture also provides a classification of KM systems into Knowledge Discovery, Capture, Sharing, and Application systems. Finally, it discusses the importance of organizational culture, organizational structure, and information technology infrastructure as foundational elements for effective KM.

📝 Lecture Summary

1. Describe the ways to facilitate KM, along with suitable examples.

KM is facilitated in a number of ways by means of KM solutions. These may be divided into four broad levels: (1) KM Processes; (2) KM Systems; (3) KM Mechanisms and Technologies; and (4) KM Infrastructure.

a. KM Processes -- are the broad processes that aid in discovering, capturing, sharing, and applying knowledge. These include combination, socialization, externalization, internalization, exchange, directions, and routines. For example, internalization processes benefit from simulations or experiments, which enable individuals to learn through experience, as well as from face-to-face meetings, on-the-job training, and demos.

b. KM Systems -- are the integration of technologies and mechanisms, developed to support the above four KM processes. KM systems include expert-seeker systems, which help locate individuals possessing knowledge in a particular area, and rely on a combination of information technologies and mechanisms for classifying knowledge areas.

c. KM Mechanisms and Technologies -- are used in KM systems, each of which utilize a combination of multiple mechanisms and multiple technologies, which again in turn could, under differing circumstances, support multiple KM systems. Examples of KM mechanisms include on-the-job training and apprenticeship, while examples of KM technologies include databases and the Internet.

d. KM Infrastructure -- reflects the long-term foundation for KM. KM mechanisms and technologies rely on the KM infrastructure for their success. Examples of KM infrastructure include the data contained in an organization’s databases and the quality of the organization’s employees (in terms of their tacit knowledge).

🔑 Definition — KM Solutions: A set of four broad levels (Processes, Systems, Mechanisms & Technologies, and Infrastructure) that facilitate knowledge management.

2. Explain the importance of KM mechanisms and KM technologies to KM systems. Give examples of each.

Both KM mechanisms and KM technologies support KM systems. Their differences are explained below:

KM mechanisms are organizational or structural means used to promote KM. They enable KM systems and are supported by KM infrastructure. KM mechanisms may or may not utilize technology. They involve some kind of organizational arrangement or social or structural means of facilitating KM. Examples of KM Mechanisms include learning by doing, on-the-job training, learning by observation, and face-to-face meetings. More long-term KM mechanisms include the hiring of a chief knowledge officer, interdepartmental projects, traditional hierarchical relationships, organizational policies, standards, initiation, and training process for new employees, and employee rotation across departments.

KM technologies support KM systems and also benefit from the KM infrastructure, especially the information technology infrastructure. KM technologies are a vital component of KM systems. Technologies that support KM include artificial intelligence (AI) technologies including case-based reasoning systems, electronic discussion groups, computer-based simulations, databases, decision support systems, enterprise resource planning systems, expert systems, management information systems, expertise locator systems, video-conferencing, and information repositories including best practices databases and lessons learned systems.

Examples of the use of KM technologies include World Bank’s use of a combination of video interviews and hyperlinks to documents and reports to systematically record the knowledge of employees that are close to retirement. Similarly, at BP, desktop video-conferencing has improved communication and enabled many problems at offshore oil fields to be solved without extensive traveling.

💡 Why this matters: This distinction clarifies that KM is not just about technology; organizational and social mechanisms are equally vital for success.

3. Briefly explain the four kinds of classifications for KM systems based on the process supported.

Depending on the KM process most directly supported, KM systems can be classified into four kinds:

1. Knowledge Discovery Systems support the process of developing new tacit or explicit knowledge from data and information or from the synthesis of prior knowledge. These systems support two KM sub-processes associated with knowledge discovery: combination, enabling the discovery of new explicit knowledge, and socialization, enabling the discovery of new tacit knowledge. Mechanisms and technologies can support knowledge discovery systems by facilitating combination and/or socialization. Mechanisms that facilitate combination include collaborative problem solving, joint decision making, and collaborative creation of documents. Technologies facilitating combination include knowledge discovery systems, databases, and Web-based access to data. Repositories of information, best practices, and lessons learned also facilitate combination. Technologies can also facilitate socialization, but to a smaller extent than they can facilitate combination.

2. Knowledge Capture Systems support the process of retrieving either explicit or tacit knowledge that resides within people, artifacts, or organizational entities. These systems can aid in the capture of knowledge that resides within or outside organizational boundaries, including within consultants, competitors, customers, suppliers, and prior employers of the organization’s new employees. Knowledge capture systems rely on mechanisms and technologies that support externalization and internalization. KM mechanisms can enable knowledge capture by facilitating externalization or internalization.

3. Knowledge Sharing Systems support the process through which explicit or implicit knowledge is communicated to other individuals. They do so by supporting exchange and socialization. Discussion groups or chat groups facilitate knowledge sharing by enabling an individual to explain her knowledge to the rest of the group. Some of the mechanisms that facilitate exchange are memos, manuals, progress reports, letters, and presentations. Technologies facilitating exchange include groupware and other team collaboration mechanisms, Web-based access to data, and databases, and repositories of information, including best practice databases, lessons learned systems, and expertise-locator systems.

4. Knowledge Application Systems support the process through which some individuals utilize knowledge possessed by other individuals without actually acquiring, or learning, that knowledge. Mechanisms and technologies support knowledge application systems by facilitating routines and direction.

🔑 Definition — Knowledge Discovery Systems: KM systems that support developing new tacit or explicit knowledge from data/information or synthesis of prior knowledge. 🔑 Definition — Knowledge Capture Systems: KM systems that support retrieving explicit or tacit knowledge residing within people, artifacts, or organizational entities. 🔑 Definition — Knowledge Sharing Systems: KM systems that support communicating explicit or implicit knowledge to other individuals. 🔑 Definition — Knowledge Application Systems: KM systems that support utilizing knowledge possessed by others without acquiring or learning it.

4. State the roles of (a) organizational culture and (b) organizational structure for the development of a good KM infrastructure.

KM infrastructure is the foundation on which KM resides. Organizational culture and organizational structure are two of its main components.

Organizational Culture reflects the norms and beliefs that guide the behavior of the organization’s members. It is an important enabler of KM in organizations. A supporting organization culture helps motivate employees to understand the importance and benefits from KM and to find time for it. Getting people to participate in knowledge sharing is considered the hardest part of KM, and a vital part of implementing KM is in making it a part of the organization’s culture. A KM enabling culture is one that understands the value of KM practices, has support for KM at all managerial levels, provides incentives that reward knowledge sharing, and encourages organizational interaction for the creation and sharing of knowledge. In contrast, cultures that stress individual performance and hoarding of information within units encourage limited employee interaction, and lack of an involved top management creates inhibited knowledge sharing and retention.

Organizational Structure is another vital aspect on which KM depends. Several aspects of organization structure are relevant. First, the hierarchical structure of the organization affects the people with whom each individual frequently interacts, and to or from whom he is consequently likely to transfer knowledge. Traditional reporting relationships influence the flow of data and information, the nature of groups who make decisions together, and consequently affect the sharing and creation of knowledge. By decentralizing or flattening their organization structures, companies aim to increase knowledge sharing with a larger group of individuals. Organization structures can facilitate KM through communities of practice, which is an organic and self-organized group of individuals who are dispersed geographically or organizationally but communicate regularly to discuss issues of mutual interest. They provide access to a larger group of individuals than possible within traditional departmental boundaries. Consequently, there are more numerous potential helpers, and this increases the probability that at least one of them will provide useful knowledge. Further, they also provide access to external knowledge sources.

🔑 Definition — Communities of Practice: An organic and self-organized group of individuals, dispersed geographically or organizationally, who communicate regularly to discuss issues of mutual interest.

5. In what way does information technology infrastructure contribute to KM within an organization?

An organization’s information technology infrastructure greatly contributes to KM. While organizations could develop specialized IT infrastructure to pursue KM, usually the existing IT infrastructure, developed to support the organization’s information systems needs, also facilitates KM.

Information technology infrastructure includes data processing, storage, and communication technologies and systems. It comprises the entire spectrum of an organization’s information systems, including transaction processing systems and management information systems. It includes databases and data warehouses, as well as enterprise resource planning systems.

IT infrastructure provides capabilities in four important aspects: reach, depth, richness, and aggregation.

Reach pertains to access and connection, and the efficiency of such access. Depth, in contrast, focuses on the detail and amount of information that can be effectively communicated over a medium. The richness of a medium is based on its ability to provide multiple cues, quick feedback, personalize messages, and use natural language to convey subtleties. Finally, aggregation involves the collection of large volumes of information from multiple sources for processing.

💡 Why this matters: The four capabilities (reach, depth, richness, aggregation) provide a framework for evaluating how an organization's existing IT infrastructure can be leveraged for KM purposes.

Knowledge Exercises

1. How would you develop a KM system? What are the possible mechanisms and technologies you could utilize? In developing KM systems to support KM processes, I would utilize a variety of KM mechanisms and technologies.

KM mechanisms involve some kind of organizational arrangement or social or structural means of facilitating KM. The possible KM mechanisms that could be utilized are learning by doing, on-the-job training, learning by observation, and face-to-face meetings. More long-term KM mechanisms include the hiring of a chief knowledge officer, co-operative projects across departments, traditional hierarchical relationships, organizational policies, standards, initiation process for new employees, and employee rotation across departments.

KM technologies benefit from the KM infrastructure, especially the information technology infrastructure. Examples of KM technologies are the use of a combination of video interviews and hyperlinks to documents and reports to systematically record the knowledge of employees close to retirement, desktop video-conferencing for communication and enabling problem solving at offshore locations without the need for extensive traveling.

2. How would you utilize knowledge discovery systems and knowledge capture systems in an organization that is spread across the globe? Does geographic distance hamper the utilization of these systems? In an organization spread across the globe, the use of knowledge discovery systems and knowledge capture systems do tend to get hampered to some extent due to geographic distances, but due to the increasing use of technology, these problems are getting smaller and smaller.

Knowledge discovery systems support the process of developing new tacit or explicit knowledge from data and information or from the synthesis of prior knowledge. Mechanisms and technologies can support knowledge discovery systems by facilitating combination and/or socialization.

• Mechanisms that facilitate combination include collaborative problem solving, joint decision making, and collaborative creation of documents. In a global organization, sharing documents among senior management results in the creation of new explicit knowledge, resulting in a better understanding of products and a corporate vision. Mechanisms that facilitate socialization include apprenticeships, employee rotation across areas, conferences, brainstorming retreats, cooperative projects across departments, and initiation process for new employees. In a global organization, this could become expensive, however, as it would involve the physical transfer of employees from one location to another.

• Technologies facilitating combination include knowledge discovery systems, databases, and Web-based access to data. Repositories of information, best practices, and lessons learned would also facilitate combination in global organizations. Technologies can also facilitate socialization, but to a smaller extent than they can facilitate combination. Some of the technologies for facilitating socialization in a global organization include video-conferencing and electronic support for communities of practice.

Knowledge capture systems support the process of retrieving either explicit or tacit knowledge that resides within people, artifacts, or organizational entities. Knowledge capture systems rely on mechanisms and technologies that support externalization and internalization.

• Mechanisms can enable knowledge capture by facilitating externalization, i.e., the conversion of tacit knowledge into explicit form, or internalization, i.e., the conversion of explicit knowledge into tacit form. The development of models or prototypes, and the articulation of best practices or lessons learned are some examples of mechanisms that might enable externalization in a global organization. Learning by doing, on-the-job training, learning by observation, and face-to-face meetings are some of the mechanisms that might facilitate internalization in a global organization.

• Technologies can also support knowledge capture systems by facilitating externalization and internalization. Externalization through knowledge engineering is necessary for the implementation of intelligent technologies such as expert systems, case-based reasoning systems, and knowledge acquisition systems. Technologies that facilitate internalization include computer-based training and communication technologies.

⭐ Key Takeaways

The most critical elements for exam success are: (1) KM is facilitated through four interrelated levels: Processes, Systems, Mechanisms & Technologies, and Infrastructure, each building upon the previous. (2) KM Systems are classified into four types based on the KM process they support: Discovery (combination/socialization), Capture (externalization/internalization), Sharing (exchange/socialization), and Application (routines/direction). (3) Organizational culture is a critical enabler; a KM-enabling culture must value KM, provide support at all levels, reward sharing, and encourage interaction, while a culture stressing individual performance inhibits KM. (4) Organizational structure, particularly through flattening and communities of practice, directly impacts knowledge flow and sharing beyond traditional departmental boundaries. (5) The existing IT infrastructure provides four key capabilities—reach, depth, richness, and aggregation—that can be leveraged for KM without necessarily needing specialized systems.

🧠 Quick Revision Questions

1. What are the four broad levels of KM solutions, and how do they relate to each other?
2. Explain the difference between a KM mechanism and a KM technology, providing two examples of each.
3. List and briefly define the four classifications of KM systems based on the KM process they support.
4. Describe the characteristics of a KM-enabling organizational culture versus one that inhibits KM.
5. What are the four capabilities provided by IT infrastructure that contribute to KM, and what does each one pertain to?

📘 Lecture 32 — Knowledge Architecture, Internet and E-World

📖 Overview: This lecture explores the fundamental components of knowledge architecture and how they enable effective knowledge sharing in organizations. It examines the crucial intersection of people, content, and technology while detailing how modern digital tools like intranets, extranets, groupware, and e-business systems facilitate knowledge transfer in the electronic world.

🗂️ Topics Covered

The lecture covers knowledge architecture including the people core (knowledge workers, managers, customers, suppliers), the technical core (hardware, software, specialized human resources), knowledge transfer in the e-world through intranets, extranets, and groupware, e-business concepts, value chain analysis, supply chain management (SCM), and customer relationship management (CRM) with its operational and analytical technologies.

📝 Lecture Summary

Knowledge Architecture

Knowledge architecture can be regarded as a prerequisite to knowledge sharing. The infrastructure can be viewed as a combination of people, content, and technology. These components are inseparable and interdependent.

The People Core

By people, here we mean knowledge workers, managers, customers, and suppliers. As the first step in knowledge architecture, our goal is to evaluate the existing information/documents which are used by people, the applications needed by them, the people they usually contact for solutions, the associates they collaborate with, the official emails they send/receive, and the database(s) they usually access. All the above stated resources help to create an employee profile, which can later be used as the basis for designing a knowledge management system. The idea behind assessing the people core is to do a proper job in case of assigning job content to the right person and to make sure that the flow of information that once was obstructed by departments now flows to right people at right time.

In order to expedite knowledge sharing, a knowledge network has to be designed in such a way as to assign people authority and responsibility for specific kinds of knowledge content. This involves three key steps:

First, identifying knowledge centers: After determining the knowledge that people need, the next step is to find out where the required knowledge resides, and the way to capture it successfully. Here, the term knowledge center means areas in the organization where knowledge is available for capturing. These centers support to identify expert(s) or expert teams in each center who can collaborate in the necessary knowledge capture process.

Second, activating knowledge content satellites: This step breaks down each knowledge center into some more manageable levels, satellites, or areas.

Third, assigning experts for each knowledge center: After the final framework has been decided, one manager should be assigned for each knowledge satellite that will ensure integrity of information content, access, and update. Ownership is a crucial factor in case of knowledge capture, knowledge transfer, and knowledge implementation. In a typical organization, departments usually tend to be territorial. Often, fight can occur over the budget or over the control of sensitive processes (this includes the kind of knowledge a department owns). These reasons justify the process of assigning department ownership to knowledge content and knowledge process. Adjacent/interdependent departments should be cooperative and ready to share knowledge.

💡 Why this matters: The people core recognizes that knowledge management is fundamentally about human behavior and organizational structures, not just technology. Without proper ownership and identification of knowledge centers, knowledge remains siloed and inaccessible.

The Technical Core

The objective of the technical core is to enhance communication as well as ensure effective knowledge sharing. Technology provides a lot of opportunities for managing tacit knowledge in the area of communication. Communication networks create links between necessary databases. Here the term technical core is meant to refer to the totality of the required hardware, software, and the specialized human resources.

Expected attributes of technology under the technical core: Accuracy, speed, reliability, security, and integrity. Since an organization can be thought of as a knowledge network, the goal of knowledge economy is to push employees towards greater efficiency/productivity by making best possible use of the knowledge they possess. A knowledge core usually becomes a network of technologies designed to work on top of the organization's existing network.

Knowledge Transfer in E-World

E-World encompasses all digital environments where knowledge transfer occurs. Key technologies include:

Intranet: Serves the internal needs of an organization. Links knowledge workers and managers around the clock and automates intraorganizational traffic. An organization needs intranet if: a large pool of information is to be shared among large number of employees, or knowledge transfer needs to be done in hurry.

Extranet: Links limited and controlled trading partners and allows them to interact for different kinds of knowledge sharing. Intranets, extranets, and e-commerce do share common features. Internet protocols are used to connect business users; on the intranet administrators prescribe access and policy for a specific group of users; on a Business-to-Business (B2B) extranet, system designers at each participating company collaborate to make sure there is a common interface with the company they are dealing with. Extranets can be considered as the backbone of e-business. The benefits are faster time to market, increased partner interaction, customer loyalty, and improved processes. Security varies with type of user, the sensitivity type of the transferred knowledge and the type of communication lines used. Access control deals with what the users can and what they cannot access. The issue of the level of authentication for each user should be considered. Extranet helps the organization in ensuring accountability in the way it does business and exchanges knowledge with its partners. It promotes collaboration with partners and improves the potential for increased revenue.

💡 Why this matters: Understanding the difference between intranet, extranet, and internet is crucial for designing appropriate knowledge sharing systems that balance accessibility with security.

Groupware: A software helping people to collaborate (especially for geographically distributed organizations). Supports to communicate ideas, cooperate in problem solving, coordinate work flow and negotiate solutions. Groupware is categorized according to: users working in the same place or in different locations, and users working together at the same time or different times.

Key considerations include: group concepts and how group members behave in a group setting. Reasons for using groupware include: works well with groups having common interests and where it is not possible for the individuals to meet face to face; some problems are better solved by group than by individuals; groups bring multitude of opinions/expertise to a work setting; facilitates telecommuting (time saver); often faster and more effective than face to face meetings.

Critical prerequisites for system success: compatibility of software and perceived benefit to every group member.

For a face-to-face session, the protocol is standard and the communication is highly structured. If the communication structure is known, then a groupware can take advantage of it to speed up the communication and improve the performance of the exchange. This communication environment is called a technologically mediated communication structure. An alternative communication structure is known as a socially mediated communication structure where the individuals send a request (e-mail) through technology without any control over how soon or whether the recipient will respond.

A session represents a situation where a group of individuals agrees to get together to conduct a meeting in person, over the telephone, or by videoconferencing etc. Groupware systems require that the sessions be conducted within the framework of protocols designed to ensure integrity, privacy and successful completion of each session. Session control determines who can enter and exit the session, when they can enter and how. Rules used in session control include: making sure that users do not impose a session on others; identifying conversational group members before allowing them into a session; controlling unnecessary interruptions or simultaneous transmissions; allowing group members to enter and exit at any time; determining the maximum number of participants and the length of the session(s); ensuring accountability, anonymity and privacy during the session(s).

Key applications of groupware include: E-mail/Knowledge transfer, Newsgroups/Work-Flow Systems, Chat Rooms, Video Communication, Group Calendaring/Scheduling, and Knowledge Sharing.

E-Business

E-Business brings the worldwide access of the internet to the core business process of exchanging information between businesses, between people within a businesses, and between a business and its clients. The focus is on knowledge transfer/sharing. It connects critical business systems to critical constituencies (customers, suppliers, vendors etc) via the internet, intranets, and extranets.

E-Business helps to attain the following goals: developing new products/services; gaining recent market knowledge; building customer loyalty; enriching human capital by direct and instant knowledge transfer; making use of existing technologies for research and development; gaining competitive edge and market leadership.

Value Chain

The value chain is a way of organizing the primary and secondary activities of a business in a way that each activity provides productivity to the total business operation. Competitive advantage is gained when the organization links the activities in its value chain more cheaply/effectively than its competitors do.

The knowledge-based value chain provides a way of looking at the knowledge activities of the organization and how various knowledge exchange adds value to adjacent activities and to the organization in general. Everywhere value is added is where knowledge is created, shared or transferred. By the process of examining the elements of the value chain, executives can find the ways to incorporate IT and telecommunications to improve the overall productivity of the firm. In case of E-Business, we integrate the KM life cycle from knowledge creation to knowledge distribution via: Business to Consumer, Business to Business, and Business within Business.

Supply Chain Management (SCM)

Supply Chain Management (SCM) incorporates the idea of having the right product in the right place, at the right time, in the right condition and at the right price. This is an integral part of Business to Business framework. SCM employs tools that allow the organization to exchange and update information in order to reduce cycle times, to have quicker delivery of orders, to minimize excess inventory and to improve customer service.

Customer Relationship Management (CRM)

Customer Relationship Management (CRM) helps the organization to improve the quality of its relationship management with customers. It is a business strategy used to learn more about customer needs and customer behavior patterns in order to develop better and stronger relationship with them. It can improve/change an organization's business processes for supporting new customer focus and apply emerging technologies to automate these new processes. The technologies can allow multiple channels of communication with customers (and supply chain partners) and can use customer information stored in corporate databases and knowledge-bases to construct predictive models for customer purchase behavior.

Benefits of CRM include: increased customer satisfaction; enhancing efficiency of call centers; cross selling products efficiently; simplifying sales processes; simplifying marketing processes; helping sales staff to close deals faster; finding new customers.

Critical elements of CRM software include:

• Operational technology: Uses portals that facilitate communication between customers, employees, and supply chain partners. Basic features included in portal products: personalization services, secure services, publishing services, access services, subscription services.
• Analytical technology: Uses data-mining technologies to predict customer purchase patterns.

The architectural imperative for CRM is to do: allowing the capture of a very large volume of data and transforming it into analysis formats to support enterprise-wide analytical requirements; deploying knowledge; calculating metrics by the deployed business rules.

⭐ Key Takeaways

Knowledge architecture fundamentally relies on three interdependent components: people, content, and technology. The people core requires identifying knowledge centers, activating knowledge content satellites, and assigning ownership through experts to overcome territorial departmental behaviors. The technical core must provide accuracy, speed, reliability, security, and integrity. Knowledge transfer in the e-world utilizes intranets for internal sharing, extranets for controlled partner collaboration, and groupware for geographically distributed teamwork. E-business integrates the entire KM lifecycle through value chain activities, SCM, and CRM, with CRM's operational and analytical technologies enabling customer behavior prediction and relationship improvement. Successful knowledge management is inextricably linked to collaboration—it would be superfluous to think of knowledge sharing without collaborative success.

🧠 Quick Revision Questions

1. What are the three components of knowledge architecture, and why are they considered inseparable and interdependent?
2. Explain the three steps involved in designing a knowledge network within the people core of knowledge architecture.
3. What are the major differences between intranet, extranet, and internet in terms of their purpose and users?
4. What are the critical prerequisites for groupware system success, and how do technologically mediated and socially mediated communication structures differ?
5. What are the two critical elements of CRM software, and how do they contribute to improving customer relationships?

📘 Lecture 33 — CORPORATE INTRANET, EXTRANET, AND PORTAL

📖 Overview: This lecture explores knowledge portals as web-based applications that provide a single point of access to online information, acting as virtual workplaces for knowledge sharing. It covers portal evolution, technologies, functionality, and business applications, explaining how organizations can leverage portals to manage both structured and unstructured data for competitive advantage.

🗂️ Topics Covered

The lecture covers the definition and evolution of portals from search engines to knowledge portals, business challenges that drive portal adoption, portal technologies including collaboration tools, content management and intelligent agents, and a comprehensive test section with 13 questions addressing portal implementation, benefits, and practical applications across different business contexts.

📝 Lecture Summary

KM Tools and Knowledge Portals

Portals are Web-based applications which provide a single point of access to online information. These can be regarded as virtual workplaces which promote knowledge sharing among end-users (e.g., customers, employees), provides access to data stored in databases and data warehouses, and helps organize unstructured data.

Evolution of Portals:

• Initially portals were merely search engines
• In the next phase they transformed to navigation sites
• Portals evolved to include advanced search capabilities and taxonomies
• Also called Information portals because they deal with information
• Organizations are becoming increasingly aware of opportunities from using dormant information
• Portals can integrate applications by combining, analyzing, and standardizing relevant information
• Knowledge portals provide information about all business activities and supply metadata to support decision making
• Knowledge portals focus on how information will be used by knowledge workers, not just content
• Knowledge portals have two kinds of interface: knowledge consumer interface and knowledge producer interface
• Enterprise Knowledge Portals (EKP) can distinguish knowledge from information and produce knowledge from raw data

🔑 Definition — Knowledge Portal: A web-based application that provides information about all business activities and is capable of supplying metadata to support decision making, focusing on how information will be used by knowledge workers.

Business Challenge

Most businesses face an inherent pressure to optimize operational processes to reduce cost and enhance quality. Customer-oriented systems allow organizations to understand customer behavior patterns and offer the right product at the right time. Often, organizations need to commercialize their products at the lowest possible price.

💡 Why this matters: Understanding business challenges helps identify why portals are necessary for modern organizations to stay competitive.

Portals and Business Transformation

Problems arise from two fundamental aspects of present computing technology:

1. The explosion in quantity of business information captured in electronic documents leads organizations to lose grip on information as they upgrade processes
2. The fast speed of information content growth requires strict internal discipline to expose and integrate enterprise knowledge sources

Types of pressures faced by most organizations:

• Shorter time to market
• More demanding investors/customers
• Knowledge worker turnover

Market Potential

Knowledge portals are emerging as key tools for supporting the knowledge workplace. The infrastructure components of the Enterprise Information Portal (EIP) market include:

• Business intelligence
• Content management
• Data management
• Data warehouses/data marts

Knowledge Portal Technologies Functionality

The key functionalities include:

• Gathering
• Categorization
• Collaboration
• Distribution
• Personalization
• Publishing
• Searching/Navigation

Collaboration

The aim of collaboration tools is to create a collaborative KM system supporting sharing and reusing information. Collaboration implies the ability for more than one person to work together in a coordinated fashion over time and space using electronic devices.

Types of collaboration:

• Asynchronous collaboration: Human-to-human interactions via computer systems having no time/space constraints
• Synchronous collaboration: Human-to-human interactions via computer systems that occur instantly

Push Technology: Places information where it is easily visible. Pull Technology: Requires specific actions to retrieve information.

🔑 Definition — Asynchronous Collaboration: Human-to-human interactions via computer systems with no time or space constraints. 🔑 Definition — Synchronous Collaboration: Human-to-human interactions via computer systems that occur instantly.

📌 Example: Email and discussion forums are examples of asynchronous collaboration, while instant messaging and web conferencing are examples of synchronous collaboration.

Content Management

Content management requires directory/indexing capabilities to automatically manage ever-growing enterprise data warehouses. It addresses the problem of searching for knowledge in all information sources, including structured and unstructured internal information like office documents, collaborative data, MIS, experts, and external information.

Metadata is required to define the types of information. Content management handles how documents are analyzed, categorized, and stored. Categorizing organizes similar documents into smaller groups. Since document collections are not static, portals must provide taxonomy maintenance — as new documents are added, they must be placed in the proper taxonomy using classification technology.

🔑 Definition — Content Management: A system requiring directory/indexing capabilities to automatically manage enterprise data, handling how documents are analyzed, categorized, and stored.

Intelligent Agents

Agents are software able to execute a wide range of functional tasks (e.g., comparing, learning, searching). Intelligent agents are tools applied in the context of EKPs, though still in their infancy with most applications being experimental.

As relationships between organizations and customers become more complex, intelligent agent technology can help address needs for understanding customer relationships. Customers set priorities while purchasing; intelligent agents can master individual customer demand priorities by learning from experience and analyzing these priorities qualitatively and quantitatively.

Customer services benefiting from intelligent agents:

• Customer assistance (customized) with online services
• Customer profiling and integrating profiles into marketing activities
• Forecasting customer requirements
• Executing financial transactions on behalf of customers
• Negotiating prices/payment schedules

🔑 Definition — Intelligent Agents: Software tools that can execute functional tasks like comparing, learning, and searching, applied in EKP contexts to understand customer priorities and needs.

Test Your Understanding

Question 1: Need for portals arises from pressures including shorter time to market, knowledge worker turnover, and more demanding customers/investors. Data warehouses give access to collected data; portals provide the interface to reach and manipulate data in data warehouses plus collaboration services.

Question 2: Enterprise portals provide the same interface for employees, managers, customers, and suppliers globally. It's better to deploy portals over the Internet rather than Intranet for unrestricted worldwide access.

Question 3: Enterprise Information Portals use push/pull technologies, integrate disparate applications, and support bi-directional information exchange. Enterprise Knowledge Portals are goal-directed toward knowledge production, acquisition, transmission, and management, focused on business processes, and include all EIP functionalities.

Question 4: Companies need strategies and processes to utilize intellectual resources at strategic and operational levels. Next-generation information platforms (enterprise portals) and real-time tools (instant messaging, web conferencing, streaming media) are required.

Question 5: Static portals provide unified interface to enterprise applications. Dynamic portals have collaboration and interactivity features.

Question 6: Content management in EKP uses directory/indexing capabilities with metadata to manage structured and unstructured data. It can go outside the enterprise using crawlers to find pertinent Internet data, index it, and deliver to analysts.

Question 7: For globally distributed organizations, bandwidth is a fundamental constraining factor for many portal applications.

Question 8: Examples include Amazon.com (B2C), Plasticexchange.com (B2B for plastics), www.e-government.govt.nz (C2G), and www.firstgov.gov (comprehensive government portal).

Question 9: Portal accessibility technologies include Internet, Intranet, Extranet, and mobility portals.

Question 11: For an audit firm, recommended system is an Enterprise Knowledge Portal (EKP) with functionalities: Gathering (capture accounting standards), Categorization (organize Word/Excel files), Distribution (support electronic documents), Collaboration (asynchronous tools like email/discussion forums), and Search/Navigate.

Question 12: A hardware retailer can use a knowledge portal with synchronous collaboration tools for real-time customer support. Benefits include improved customer retention, reduced costs (fewer phone calls/site visits), and new market segments. Additional support includes FAQ sections updated from customer input, helping manufacturers improve product quality.

Question 13: Knowledge portals enable different business units to interact and collaborate for best results, enabling web-based workplace for drag-and-drop file sharing, multithreaded discussions, real-time messaging, and polling. Productivity increases as users across business units stay informed about enterprise activities.

⭐ Key Takeaways

The most critical concepts from this lecture are that knowledge portals serve as virtual workplaces providing single-point access to online information, evolving from simple search engines to sophisticated platforms with knowledge consumer and producer interfaces. Enterprise Knowledge Portals (EKP) can distinguish knowledge from information and produce knowledge from raw data, integrating functionalities including gathering, categorization, collaboration, distribution, personalization, publishing, and searching/navigation. Collaboration can be asynchronous (no time/space constraints) or synchronous (instant interactions), supported by push and pull technologies. Content management requires directory/indexing capabilities with metadata to handle structured and unstructured information, while intelligent agents can learn and analyze customer priorities. The test questions illustrate practical applications across B2B, B2C, B2G, C2C, and C2G contexts, emphasizing how portals address business pressures like shorter time to market, knowledge worker turnover, and demanding customers.

🧠 Quick Revision Questions

1. What are the two interfaces of knowledge portals and what is the purpose of each?
2. Distinguish between asynchronous and synchronous collaboration, providing an example of each.
3. What is the difference between push technology and pull technology in the context of knowledge portals?
4. List five customer services that can benefit from intelligent agent technology.
5. What are the three types of pressures faced by most organizations that drive portal adoption?

📘 Lecture 34 — KM Systems and Technical Layers

📖 Overview: This lecture explores the architecture and technical layers of Knowledge Management systems, focusing on how technology enhances communication and knowledge sharing in the e-world. It details each layer of the KM system architecture, from user interface to repositories, and explains their roles in enabling effective knowledge transfer, security, and collaboration.

🗂️ Topics Covered

The lecture covers the technical core of KM systems, user interface layer, authorized access layer, collaborative intelligence and filtering layer, knowledge-enabling application layer, transport layer, middleware layer, and repositories layer. It also distinguishes between key concepts like transport and application layers, usability and portability, and collaborative intelligence versus intelligent agents.

📝 Lecture Summary

Knowledge Transfer in E-World — The Technical Core

The technical core aims to enhance communication and ensure effective knowledge sharing. Technology provides opportunities for managing tacit knowledge in communication areas. Communication networks create links between necessary databases. The term technical core refers to the totality of required hardware, software, and specialized human resources. Expected attributes include accuracy, speed, reliability, security, and integrity. Since an organization can be thought of as a knowledge network, the goal of knowledge economy is to push employees toward greater efficiency and productivity by making best possible use of the knowledge they possess. A knowledge core usually becomes a network of technologies designed to work on top of the organization's existing network.

💡 Why this matters: The technical core establishes the foundation upon which all KM system layers operate, determining system performance and capability.

User Interface Layer

A web browser typically represents the interface between the user and the KM system. This is the top layer in the KM system architecture. The way text, graphics, and tables are displayed on the screen simplifies the technology for the user. The user interface layer should provide a way for the proper flow of tacit and explicit knowledge. Necessary knowledge transfer between people and technology involves capturing tacit knowledge from experts, storing it in a knowledge base, and making it available for solving complex problems.

Features to consider in user interface design:

• Consistency
• Relevancy
• Visual clarity
• Usability
• Ease of navigation

Authorized Access Layer

This layer maintains security and ensures authorized access to knowledge captured and stored in organizational repositories. Knowledge is usually captured using internet, intranet, or extranet. An organization's intranet represents the internal network of communication systems. Extranet is a type of intranet with extensions allowing specified people (customers, suppliers, etc.) to access some organizational information. Issues related to the access layer include access privileges and backups. The access layer focuses on security, use of protocols (like passwords), and software tools like firewalls.

Firewalls can protect against:

• E-mails that can cause problems
• Unauthorized access from the outside world
• Undesirable material (movies, images, music, etc.)
• Unauthorized sensitive information leaving the organization

Firewalls cannot protect against:

• Attacks not going through the firewall
• Viruses on floppy disks
• Weak security policies

Collaborative Intelligence and Filtering Layer

This layer provides customized views based on stored knowledge. Authorized users can find information (through a search mechanism) tailored to their needs. Intelligent agents (active objects which can perceive, reason, and act in a situation to help problem solving) are found to be extremely useful. In client/server computing, there is frequent and direct interaction between the client and the server. In mobile agent computing, the interaction happens between the agent and the server. A mobile agent roams around the internet across multiple servers looking for correct information, with benefits in fault tolerance, reduced overall network load, and heterogeneous operation.

Key components of this layer:

• The registration directory that develops tailored information based on user profile
• Membership in specific services (e.g., sales promotion, news service)
• The search facility (e.g., a search engine)

Prerequisites for this layer include: security, portability, flexibility, scalability, ease of use, and integration.

Knowledge-Enabling Application Layer (Value-Added Layer)

This creates a competitive edge. Most applications help users do their jobs in better ways. They include knowledge bases, discussion databases, decision support, and other tools.

Transport Layer

This is the most technical layer. It ensures the organization becomes a network of relationships where electronic transfer of knowledge is routine. This layer associates with LAN (Local Area Network), WAN (Wide Area Network), intranets, extranets, and internet. In this layer we consider multimedia, URLs, connectivity speeds/bandwidths, search tools, and managing network traffic.

Middleware Layer

This layer makes it possible to connect between old and new data formats. It contains a range of programs to perform this connection function.

Repositories Layer

This is the bottom layer of the KM architecture, representing the physical layer where repositories are installed. These may include legacy applications, intelligent data warehouses, operational databases, etc. After establishing the repositories, they are linked to form an integrated repository.

Distinguish Between Key Concepts

a. Transport Layer vs. Application Layer The Transport Layer standardizes exchanges between operating systems of computers in the system. It includes LANs, WANs, intranets, extranets, and the Internet. The Application Layer provides applications that help users do their jobs better, including knowledge bases, discussion databases, sales force automation tools, yellow pages, decision support, and imaging tools.

b. Usability vs. Portability Usability ensures user-friendly software. Portability is a measure of how well software will run on different computers.

c. Profiling vs. Repository Profiling in KM systems is generating a graphic or textual representation of people in terms of criteria such as skills, personality traits, etc. Repository is a storage subsystem, such as a database for data, information, or knowledge storage.

d. Collaborative Intelligence vs. Intelligent Agent Collaborative Intelligence provides customized or personalized views based on stored knowledge, designed to reduce search time by combining the knowledge sought and the user's profile. Intelligent agents are active objects that can perceive, reason (learn from past mistakes), and act in situations to assist in problem solving, retrieving the right information, behaving as personal assistants that cooperate with human users.

⭐ Key Takeaways

Students must understand the layered architecture of KM systems, from the user interface at the top to the repositories at the bottom. The User Interface Layer simplifies technology for users and enables flow of tacit and explicit knowledge. The Authorized Access Layer ensures security through firewalls, passwords, and protocols. The Collaborative Intelligence and Filtering Layer provides customized views using intelligent agents and mobile agents for efficient information retrieval. The Transport Layer (including LAN, WAN, intranets) and Middleware Layer handle connectivity and data format conversion. Understanding distinctions between layers and concepts like usability vs. portability and collaborative intelligence vs. intelligent agents is critical for exam preparation.

🧠 Quick Revision Questions

1. What are the five expected attributes of technology under the technical core?
2. What features should be considered in user interface design for KM systems?
3. What three things can firewalls protect against, and what three things can they not protect against?
4. What is the difference between client/server computing and mobile agent computing?
5. What is the bottom layer of KM architecture, and what types of repositories might it include?

📘 Lecture 35 — KM System and Life Cycle Approach: Conventional vs KM

📖 Overview: This lecture explores the fundamental differences and similarities between conventional systems development life cycles and Knowledge Management System Life Cycles (KMSLC). It emphasizes why a life cycle approach is essential for building KM systems, compares the roles of systems analysts versus knowledge developers, and introduces key concepts like rapid prototyping, verification, validation, and user-expert attributes. Understanding these distinctions is critical for designing effective KM systems that are result-oriented and interactive.

🗂️ Topics Covered

The lecture begins by detailing the key differences between conventional and KM systems, followed by their key similarities. It then discusses structuring problems and tasks within a rapid prototyping cycle, compares user and expert attributes, outlines various KMSLC approaches, explains the importance of evaluating the existing infrastructure, and concludes with a test-your-understanding section that clarifies concepts like verification vs. validation, knowledge developer vs. systems analyst, pupil vs. tutor user, and projection vs. avoidance.

📝 Lecture Summary

Key Differences between Conventional vs. KM Systems

The lecture highlights several critical distinctions between conventional information system development and KM system development. In conventional systems, the systems analyst gathers data from users who know the problem but not the solution, and the process is primarily sequential, process-driven, and documentation-oriented. In contrast, a knowledge developer gathers knowledge from knowledgeable people who know both the problem and the solution. The KMSLC is incremental and interactive, result-oriented, and allows for verification and validation from the beginning of the cycle. Testing in conventional systems occurs after the system is built, while KMSLC supports rapid prototyping that can incorporate changes on the spot. Systems management is much more extensive for conventional systems than for KMSLC.

🔑 Definition — Systems Analyst: A specialist in building information systems who acts as the architect, including designing, testing, and installing the system, and primarily interacts with novice users who know the problem but not the solution.

🔑 Definition — Knowledge Developer: A specialist in building knowledge-based systems who acts as the key architect, interacting with knowledgeable experts who know both the problem and the solution.

📐 Key Distinction: Conventional SDLC → Sequential, process-driven, testing at end. KMSLC → Incremental, interactive, result-oriented, verification from start, supports rapid prototyping.

Key Similarities

Despite their differences, both cycles share fundamental similarities. Both life cycles start with a problem and end with a solution. The early phase of conventional systems involves information gathering, while in KMSLC, the early phase requires knowledge capture. Verification and validation of a KM system are often very similar to conventional systems testing. Lastly, both the systems analyst and the knowledge developer must choose the appropriate tools for designing their intended systems.

Structuring the Problem / Rapid Prototyping

The lecture presents a visual cycle for rapid prototyping in KMSLC. The process involves: Structuring the Problem → Reformulating the Problem → Structuring the Task → Making Modifications → Building a Task. This cycle repeats, emphasizing that KMSLC is inherently iterative, unlike the linear conventional approach.

Users and Experts: A Comparison

The lecture provides a comparative table of attributes for users vs. experts. Users have high dependence on the system, low ambiguity tolerance, and their contribution is information, whereas experts have low dependence on the system, high ambiguity tolerance, and their contribution is expertise/knowledge. Users’ knowledge is readily available, while experts’ knowledge is not readily available. Cooperation is required for users but is not required for experts.

📌 Example — User vs. Expert: In a troubleshooting KM system, a junior technician (user) has high dependence on the system to find a solution, tolerates ambiguity poorly, and provides information about the problem. A senior engineer (expert) has low dependence on the system, can handle ambiguous problems, and provides deep expertise rather than raw data.

KMSLC Approaches

Due to a lack of standardization, multiple approaches have been proposed for the KMSLC. While the conventional systems development approach can still be used for developing KM systems, it is usually being replaced by iterative design and prototyping, reflecting the dynamic nature of knowledge capture and sharing.

Evaluating the Existing Infrastructure

KM systems are developed to improve productivity and potential of employees and the company. The existing knowledge infrastructure is evaluated to ensure that the new system does not simply abandon the present ways of doing things. This evaluation provides the perception that the new system builds upon, rather than replaces, existing practices.

⭐ Key Takeaways

The single most important distinction is that conventional SDLC is sequential and documentation-oriented, while KMSLC is incremental, interactive, and result-oriented, supporting prototyping and early verification. Students must remember the contrasting roles of a systems analyst (data from novice users) versus a knowledge developer (knowledge from experts). The rapid prototyping cycle (Structuring Problem → Reformulating → Structuring Task → Making Modifications → Building Task) is central to understanding KM system development. Additionally, the attributes of users (high system dependence, low ambiguity tolerance) and experts (low system dependence, high ambiguity tolerance) explain why different approaches are needed. Finally, both cycles share the fundamental structure of starting with a problem and ending with a tested solution.

🧠 Quick Revision Questions

1. What are the three key differences between conventional systems development and the Knowledge Management System Life Cycle (KMSLC)?
2. How does the role of a systems analyst differ from the role of a knowledge developer in terms of their main interface and the type of user/expert they interact with?
3. Describe the rapid prototyping cycle for KMSLC as presented in the lecture (list the steps in order).
4. Compare the attributes of a "user" and an "expert" regarding their dependence on the system and their tolerance for ambiguity.
5. What is the primary purpose of evaluating the existing knowledge infrastructure before developing a new KM system?

📘 Lecture 36 — KM System Justification and Feasibility Issues, Implementation Issues and Resistance

📖 Overview: This lecture addresses the critical phases of justifying, scoping, and implementing a Knowledge Management system, focusing on the practical challenges of feasibility analysis, knowledge capture, and user adoption. It provides a structured framework for evaluating whether a KM initiative is viable and outlines the key steps from team formation through post-implementation evaluation, emphasizing the human and organizational factors that determine success.

🗂️ Topics Covered

The lecture covers KM system justification through key questions about knowledge loss risk and expert availability; scoping and feasibility analysis using traditional project evaluation methods; user support requirements; strategic planning's role; forming a balanced KM team; knowledge capture methods including interviewing and rapid prototyping; expert selection criteria; the knowledge developer's role; designing the KM blueprint architecture; system testing through verification and validation; implementation and conversion; quality assurance for error types; user training classification; managing resistance to change; post-system evaluation questions; organizational implications for maintenance; and detailed factors for successful implementation including motivation, training logistics, and top management commitment.

📝 Lecture Summary

KM System Justification

KM system justification involves answering a series of critical questions to determine whether a system is truly needed and viable. Key questions include: whether existing knowledge will be lost through retirement, transfer, or departure; whether the system is needed in multiple locations; whether experts are available and willing to support building the system; whether the problem requires years of experience and cognitive reasoning to solve; whether the expert can articulate how the problem is solved; how critical the knowledge is; whether the tasks are non-algorithmic in nature; and whether a champion can be found within the organization.

Scoping means limiting the breadth and depth of the project within financial, human resource, and operational constraints. Feasibility study involves addressing whether the project can be completed within the expected timeframe, whether it is affordable, whether it is appropriate, and how frequently the system would be consulted and at what associated cost.

The traditional approach to feasibility study includes: forming a knowledge management team, preparing a master plan, performing cost/benefit analysis of the proposed system, and quantifying system criteria and costs.

💡 Why this matters: Without proper justification and feasibility analysis, organizations may invest in KM systems that address the wrong problems or cannot be sustained.

User Support

User support considerations include: whether the proposed user is aware of the new KM system development and how it is perceived; how much involvement can be expected from the user during the building process; what type of user training will be needed when the system is operational; and what kind of operational support should be provided.

Role of Strategic Planning

As a consequence of evaluating the existing infrastructure, the organization should develop a strategic plan that aims at advancing the organization's objectives with the proposed KM system in mind. Areas to be considered include: vision, resources, and culture.

Forming a KM Team

Forming a KM team involves identifying key units, branches, divisions, etc., as the key stakeholders in the prospective KM system, and strategically, technically, and organizationally balancing the team size and competency.

Factors impacting team success include: quality and capability of team members (personality, experience, and communication skill), size of the team, complexity of the project, team motivation and leadership, and promising only what can be actually delivered.

Capturing Knowledge

Capturing knowledge involves extracting, analyzing, and interpreting the knowledge that a human expert uses to solve a specific problem. Explicit knowledge is typically captured in repositories from documentation and files. Tacit knowledge is captured from experts and from the organization's stored database(s).

🔑 Definition — Knowledge Developer: In KM systems development, the knowledge developer acquires the necessary heuristic knowledge from the experts for building the appropriate knowledge base.

Interviewing is one of the most popular methods used to capture knowledge. Data mining is also useful for using intelligent agents that analyze the data warehouse and discover new findings. Knowledge capture and knowledge transfer are often carried out through teams. Knowledge capture includes determining feasibility, choosing the appropriate expert, tapping the expert's knowledge, retapping knowledge to plug gaps, and verifying/validating the knowledge base.

The Role of Rapid Prototyping

In most cases, knowledge developers use an iterative approach for capturing knowledge. For example, the knowledge developer may start with a prototype based on limited knowledge captured during the first few sessions.

The process can become rapid prototyping when: the knowledge developer explains a preliminary procedure based on rudimentary knowledge extracted from the expert; the expert reacts with remarks; while the expert watches, the knowledge developer enters additional knowledge into the computer-based prototype; and the developer runs the modified prototype and continues adding knowledge iteratively until the expert is satisfied.

🔑 Definition — Rapid Prototyping: The spontaneous, iterative process of building a knowledge base is referred to as rapid prototyping.

Expert Selection

The expert must have excellent communication skill to be able to communicate information understandably and in sufficient detail. Common questions regarding expert selection include: how to know the so-called expert is actually an expert; whether they will stay with the project until completion; what backup is available if the expert loses interest or quits; and how the knowledge developer knows what lies within and outside the expert's area of expertise.

The Role of the Knowledge Developer

The knowledge developer can be considered the architect of the system. They identify the problem domain, capture knowledge, write and test heuristics that represent knowledge, and coordinate the entire project.

Necessary attributes of a knowledge developer include: communication skills, knowledge of knowledge capture tools/technology, ability to work in a team with professionals/experts, tolerance for ambiguity, ability to think conceptually, and ability to interact frequently with the champion, knowledge workers, and knowers in the organization.

Designing the KM Blueprint

This phase marks the beginning of designing the IT infrastructure/Knowledge Management infrastructure. The KM Blueprint (KM system design) addresses several issues, including: aiming for system interoperability/scalability with existing IT infrastructure, finalizing the scope of the proposed system, deciding about necessary system components, and developing the key layers of the KM architecture.

The key layers of KM architecture are: user interface, authentication/security layer, collaborative agents and filtering, application layer, transport internet layer, physical layer, and repositories.

Testing the KM System

Testing involves two steps: Verification Procedure ensures that the system is right—the programs do the task they are designed to do. Validation Procedure ensures that the system is the right system—it meets user expectations and will be usable on demand.

Implementing the KM System

After capturing appropriate knowledge, encoding it in the knowledge base, and verifying/validating, the next task is to implement the proposed system on a server. Implementation means converting the new KM system into actual operation. Conversion is a major step in implementation, followed by post-implementation review and system maintenance.

Quality Assurance

Quality assurance indicates the development of controls to ensure a quality KM system. The types of errors to look for include: reasoning errors, ambiguity, incompleteness, and false representation.

Training Users

The level and duration of training depends on the user's knowledge level and the system's attributes. Users can range from novices (casual users with very limited knowledge) to experts (users with prior IT experience and knowledge of latest technology).

Users can also be classified as: tutors (who acquire working knowledge to keep the system current), pupils (unskilled workers trying to gain understanding of captured knowledge), or customers (interested in how to use the KM system). Training should be geared to the specific user based on capabilities, experience, and system complexity, and can be supported by user manuals, explanatory facilities, and job aids.

Managing Change

Implementation means change, and organizational members usually resist change. Resistors may include: experts, regular employees (users), troublemakers, and narrow-minded people.

Resistance can manifest as: projection (hostility towards peers), avoidance (withdrawal from the scene), or aggression.

Post System Evaluation

Key questions in the post-implementation stage include: How has the new system improved accuracy/timeliness of decision-making? Has the system caused organizational changes, and how constructive are they? Has the system affected end-user attitudes? How has the system changed business operation costs? In what ways has the system affected relationships between end users? Do the benefits justify the cost of investment?

Implications for KM

Managerial factors to consider include: the organization must commit to user training/education prior to building the system; top management should be informed with cost/benefit analysis; knowledge developers and potential knowledge engineers should be properly trained; domain experts must be recognized and rewarded; and the organization needs long-range strategic planning.

Questions regarding systems maintenance: Who will be in charge? What skills are needed? What is the best way to train the maintenance specialist? What incentives ensure quality maintenance? What types of support/funding will be required? What relationship should be established between KM system maintenance and the IT staff?

Q.1 Successful KM system implementation depends on several factors:

• Level of motivation of the user: Good documentation cannot compensate for low motivation or poor attitude. Promoting motivation must be planned in advance.
• Computer literacy and technical background: A computer-literate user is easier to work with. First-time users often require education and training.
• Communication skills of the trainer: Selling people on change is more an art than a science. Communication skills can determine user acceptance or rejection.
• Time availability and funding for training: A training program run on a shoestring is usually a loser. Training should be part of the implementation phase.
• Place of training: Off-site training offers dedicated, uninterrupted learning; on-site training saves travel costs but may suffer interruptions.
• Ease and duration of training: Depends on trainer caliber, trainee attitude, and "chemistry." A reasonable training period with measurable goals is essential.
• Ease of access and explanatory facilities: Systems should be easy to access and work with. Explanatory facilities promote ease of use and solution integrity.
• Ease of maintenance and system update: Good documentation and module-oriented design make the difference between easy maintenance and a nightmare.
• Payoff to the organization: Measurable payoff early in the development life cycle promotes successful implementation.
• Role of the champion: Solid top management support and a champion pushing for adoption can make the difference between success and lukewarm installation.

Q.2 How important are organizational factors in system implementation? The primary organizational factor is top management commitment, evident through adequate funding, ensuring hardware and personnel availability, and allowing the champion to function. The second organizational factor is user participation in the building process, which increases commitment and fosters a sense of ownership. Other factors include organizational politics (jockeying for leverage) and organizational climate. User readiness can also influence implementation success.

⭐ Key Takeaways

The most critical concepts from this lecture are the comprehensive justification framework that evaluates knowledge loss risk, expert availability, and non-algorithmic task suitability before committing to a KM project; the distinction between verification (building the system right) and validation (building the right system) as two essential testing phases; the rapid prototyping approach as an iterative, spontaneous method for capturing tacit knowledge with active expert participation; the recognition that user resistance manifests through projection, avoidance, and aggression, requiring proactive change management; and the ten critical success factors for implementation, with top management commitment, user motivation, and a strong champion being the most decisive elements for KM system success.

🧠 Quick Revision Questions

1. What are the six key questions that must be answered during KM system justification?
2. What is the difference between verification and validation in KM system testing?
3. Describe the rapid prototyping process and how it differs from traditional knowledge capture methods.
4. List and explain the three forms of personal resistance to change that may occur during KM implementation.
5. Why is the role of the champion considered critical to successful KM system implementation, and what organizational factors support the champion's effectiveness?

📘 Lecture 37 — Capturing Tacit Knowledge and Expert’s Evaluation

📖 Overview: This lecture focuses on the process of capturing tacit knowledge from experts, which is crucial for building knowledge management (KM) systems. It covers how to identify and evaluate experts, methods for working with single or multiple experts, and the use of interviews as a primary tool for eliciting tacit knowledge. Understanding this process is vital for converting human expertise into usable system rules.

🗂️ Topics Covered

The lecture begins by defining knowledge capture and its important steps, then moves to expert evaluation including indicators of expertise, qualifications, and levels of expertise. It compares the advantages and disadvantages of working with single versus multiple experts. The lecture then discusses developing relationships with experts, including creating impressions, understanding expert styles, preparation, and different approaches for multiple experts. Finally, it covers interviewing as a tacit knowledge capture tool, including types of interviews, guidelines, reliability issues, and rapid prototyping.

📝 Lecture Summary

Capturing the Tacit Knowledge

Knowledge Capture is defined as the process using which the expert's thoughts and experiences can be captured. In this case, the knowledge developer collaborates with the expert in order to convert the expertise into the necessary program code(s). The important steps include: using appropriate tools for eliciting information; interpreting the elicited information and consequently inferring the experts' underlying knowledge/reasoning process; and finally, using the interpretation to construct the necessary rules which can represent the expert's reasoning process.

Expert Evaluation

Indicators of expertise include: the expert commands genuine respect; the expert is found to be consulted by people in the organization when a problem arises; the expert possesses self-confidence and has a realistic view of limitations; the expert avoids irrelevant information and uses facts and figures; the expert can explain properly and customize their presentation according to the audience level; the expert exhibits depth of detailed knowledge with exceptional quality of explanation; and the expert is not arrogant regarding their personal information.

Expert's qualifications require that the expert should know when to follow hunches and when to make exceptions, be able to see the big picture, possess good communication skills, tolerate stress, think creatively, exhibit self-confidence, maintain credibility, operate within a schema-driven/structured orientation, use chunked knowledge, generate enthusiasm and motivation, share expertise willingly, and emulate an ideal teacher's habits.

Experts levels of expertise include highly expert persons, moderately expert problem solvers, and new experts.

Capturing single vs. multiple experts' tacit knowledge: Advantages of working with a single expert include being ideal for building a simple KM system with few rules, ideal when the problem lies within a restricted domain, facilitating logistics aspects of coordination, easier resolution of personal conflicts, and greater confidentiality. Disadvantages include that the expert's knowledge is often not easy to capture, provides a single line of reasoning, experts are more likely to change meeting schedules, and knowledge is often dispersed. Advantages of working with multiple experts include benefiting complex problem domains, stimulating interaction, allowing consideration of alternative ways of representing knowledge, and formal meetings generating thoughtful contributions. Disadvantages include frequent disagreements, complicated scheduling, harder confidentiality, process loss from overlapping mental processes, and often requiring more than one knowledge developer.

Developing Relationship with Experts

Creating the right impression: The knowledge developer must learn to use psychology, common sense, and technical as well as marketing skills to attract the expert's respect and attention.

Understanding the expert's style of expression: Experts usually use one of these styles: Procedure type (logical, verbal, procedural); Storyteller type (focused on content at the expense of the solution); Godfather type (compulsive to take over); Salesperson type (spends time dancing around the topic, explaining why their solution is best).

Preparation for the session: Before the first appointment, the knowledge developer must acquire knowledge about the problem and the expert. Initial sessions are most critical. The developer must build trust, be familiar with project terminology, review existing documents, and make a quick rapport with the expert.

Deciding the location for the session: Protocol calls for the expert to decide the location. The expert is usually more comfortable having necessary tools and information available. The meeting place should be quiet and free of interruptions.

Approaching multiple experts: The individual approach has sessions with one expert at a time. The approach using primary and secondary experts involves sessions with the senior expert early for clarification, then asking other experts for detailed probing. The small groups approach has experts gather together to discuss the problem domain, providing a pool of information. Responses are monitored and functionality is tested against others. This requires experience in assessing tapped knowledge and cognition skills, and the developer must deal with power issues affecting opinions.

Interviewing as a Tacit Knowledge Capture Tool

Advantages of using interviewing as a tacit knowledge capture tool include: flexibility, excellent for evaluating validity of information, very effective for eliciting information about complex matters, and people often enjoy being interviewed.

Types of interviews: Unstructured types are difficult to conduct and used when exploring an issue. Structured types are goal-oriented and used when specific information is needed, with question types including multiple-choice, dichotomous, and ranking scale questions. Semi-structured types involve predefined questions but allow the expert some freedom in expressing answers.

Guidelines for successful interviewing include setting the stage and establishing rapport, phrasing questions, listening closely and avoiding arguments, and evaluating the session outcomes.

Reliability of information can be reduced by uncontrolled sources of error: expert's perceptual slant, failure to exactly remember what happened, fear of unknown, communication problems, and role bias. Errors in the knowledge developer's part involve interviewer effect where something about the developer colors the expert's response, including gender, age, and race effects.

Problems encountered during interviewing include response bias, inconsistency, communication problems, hostile attitude, standardizing questions, and setting interview length.

Process of ending the interview: The end should be carefully planned. One procedure calls for halting questioning a few minutes before the scheduled ending time and summarizing key points. This allows the expert to comment and schedule future sessions. Verbal and nonverbal cues can be used for ending (refer to Table 5.2, page 148 of textbook).

Issues to be prepared for include: eliciting knowledge from experts who cannot say what they mean, setting up the problem domain, dealing with uncertain reasoning processes, handling difficult relationships with experts, and dealing with situations where the expert dislikes the developer.

Rapid Prototyping in interviews: Rapid prototyping is an approach to building KM systems where knowledge is added with each knowledge capture session. This iterative approach allows the expert to verify rules as they are built. It opens up communication through demonstration, reduces risk of failure through instant feedback and modification, allows the developer to learn with each change, and is highly interactive. However, the prototype can create user expectations that become obstacles to further development.

⭐ Key Takeaways

Students must remember that knowledge capture is about converting expert thoughts into system rules through a structured process of elicitation, interpretation, and rule construction. Expert evaluation requires assessing both indicators of expertise and specific qualifications, with experts falling into different levels of proficiency. Working with single experts offers simplicity and confidentiality but limited perspectives, while multiple experts provide richer knowledge but introduce coordination and conflict challenges. Building effective relationships with experts requires understanding their communication style, proper preparation, and choosing appropriate approaches for multiple experts. Finally, interviewing is a flexible but complex tool that requires managing reliability issues, avoiding interviewer effects, and handling common problems, with rapid prototyping offering an iterative way to build systems while verifying rules.

🧠 Quick Revision Questions

1. What are the three important steps in the knowledge capture process?
2. List three indicators of expertise and three qualifications that an expert should possess.
3. What are the advantages and disadvantages of working with a single expert compared to multiple experts?
4. Identify and describe the four types of expert expression styles mentioned in the lecture.
5. Define rapid prototyping and explain three benefits and one drawback of using this approach in interviews.

📘 Lecture 38 — Knowledge Elicitation: Data Mining and Knowledge Codification Methods

📖 Overview: This lecture covers methods for eliciting knowledge from experts and codifying it into usable forms for organizational use. It distinguishes between data mining purposes (description and prediction), explores various knowledge elicitation techniques (observation, brainstorming, Delphi, concept mapping, etc.), and details knowledge codification tools like decision tables, decision trees, frames, and production rules. Understanding these methods is critical for building effective knowledge management systems.

🗂️ Topics Covered

The lecture begins with the two basic purposes of data mining (descriptive and predictive), then systematically covers knowledge elicitation methods including on-site observation, brainstorming (traditional and electronic), nominal group technique, Delphi method, concept mapping, blackboarding, and knowledge capture systems. It transitions to knowledge codification, explaining modes of knowledge conversion, codification tools such as knowledge maps, decision tables, decision trees, frames, and production rules, case-based reasoning, knowledge-based agents, and finally the knowledge developer's required skill set.

📝 Lecture Summary

Data Mining and Web Mining Purposes

There are two basic purposes for data mining: descriptive and predictive. Descriptive data mining tries to understand patterns in people or things—for example, purchasing habits during Super Bowl week or financial dealings of people suspected of money laundering. This explains behavior of individuals or phenomena. Predictive data mining attempts to predict behavior by building a model from history, such as modeling the stock market to forecast future performance.

Knowledge Elicitation Methods: On-Site Observation (Action Protocol)

This process involves observing, recording, and interpreting the expert's problem-solving while it occurs. The knowledge developer listens more than talks, avoids giving advice or passing judgment, and never argues with the expert during task performance. Compared to interviewing, on-site observation brings the developer closer to actual steps and procedures. Disadvantages include some experts disliking observation, distraction from others in the setting, and concerns about accuracy and completeness of captured knowledge.

Brainstorming

Brainstorming is an unstructured approach for generating creative solutions involving multiple experts in a session. Questions can be raised for clarification, but no evaluations occur on the spot. Similarities that emerge are grouped logically and evaluated by asking: what benefits are gained if an idea is followed, what problems can be solved, and what new problems may arise. The general procedure includes introducing the session, presenting the problem, prompting idea generation, and looking for convergence. If experts cannot agree, the developer may call for a vote or consensus.

Electronic Brainstorming

Electronic brainstorming is a computer-aided approach for multiple experts. It begins with a pre-session plan identifying objectives and structuring the agenda, presented to experts for approval. During the session, each expert sits at a PC and engages in a predefined approach to resolve the issue, generating ideas. This allows experts to present opinions without waiting for turns. Comments are displayed electronically on a large screen without identifying the source, protecting introvert experts and preventing tagging of comments to individuals. Benefits include improved communication, effective discussion of sensitive issues, and closure with concise recommendations, leading to convergence of ideas and joint ownership of the solution.

Nominal Group Technique (NGT)

Nominal Group Technique (NGT) provides an interface between consensus and brainstorming. The panel of experts becomes a nominal group whose meetings are structured to pool individual judgment effectively. Idea writing is a structured group approach for developing ideas and exploring their meaning, usually resulting in a written report. NGT is itself an idea-writing technique.

Delphi Method

The Delphi method is a survey of experts where a series of questionnaires pool experts' responses for solving a specific problem. Each expert's contributions are shared with others by using the results from each questionnaire to construct the next one.

Concept Mapping

Concept mapping is a network of concepts consisting of nodes and links. A node represents a concept, and a link represents the relationship between concepts. It is designed to transform new concepts/propositions into existing cognitive structures related to knowledge capture. It is a structured conceptualization that allows groups to function without losing individuality. Concept mapping can be used to design complex structures, generate ideas, communicate ideas, or diagnose misunderstanding. The six-step procedure is: preparation, idea generation, statement structuring, representation, interpretation, and utilization. Similar to concept mapping, a semantic net is a collection of nodes linked together to form a net, where each idea is represented by a node connected by arcs showing relationships between nodes.

Blackboarding

In blackboarding, experts work together to solve a problem using a blackboard as their workspace. Each expert gets equal opportunity to contribute. It is assumed all participants are experts, though they may have acquired expertise in different situations. The process continues until a solution is reached. Characteristics include diverse problem-solving approaches, common language for interaction, efficient and flexible information representation, iterative problem-solving, and organized participation. Components include: the Knowledge Source (KS) — each an independent expert observing the blackboard and contributing partial solutions; the Blackboard — a global memory structure storing partial solutions; and a Control Mechanism — coordinating the pattern and flow of solution. The inference engine and knowledge base are part of this system.

Knowledge Capture Systems: Systems that Preserve and Formalize Knowledge

Stories may be elicited through anthropological observation using a naïve but interested interviewer, whose naïveté facilitates natural volunteering of stories. Using a group with common context (e.g., community of practice) forms storytelling circles. Useful methods include fish tales, alternative histories, shifting characters or context, indirect stories for security, and using metaphors to start storytelling.

Concept maps represent knowledge through concepts (patterns or regularities in objects/events) shown as text inside geometric shapes. Different concepts are connected by lines representing propositions, labeled with verb phrases or prepositions indicating relationship nature. More general concepts appear at the top with specialization toward the bottom. Cross-links represent inter-domain relations.

Context-based reasoning (CxBR) models tactical situations and operations during special tactical situations. Its three tenets are: (1) tactical situations call for specific sets of actions and procedures; (2) situations are dynamic and may require transition to new contextual sets of actions; (3) what is likely to happen in a situation is limited by the situation itself.

Knowledge Codification

Knowledge codification means converting tacit knowledge to explicit knowledge in usable form for organizational members. Tacit knowledge (human expertise) is identified and leveraged through forms producing highest business return. Explicit knowledge is organized, categorized, indexed, and accessed—often using decision trees and decision tables. Codification must build the knowledge base, which supports training and decision making in: diagnosis, training/instruction, interpretation, prediction, and planning/scheduling. Before codification, note that recorded knowledge is often difficult to access, diffusion of new knowledge is slow, knowledge is hoarded rather than shared, and knowledge may not be in proper form, available at the correct time, present in the proper location, or may be incomplete.

Modes of Knowledge Conversion

• Tacit to tacit: socialization — developer looks for experience in knowledge capture.
• Tacit to explicit: externalizing — explaining/clarifying tacit knowledge via analogies, models, or metaphors.
• Explicit to tacit: internalizing — fitting explicit knowledge to tacit knowledge.
• Explicit to explicit: combining — categorizing, reorganizing, or sorting different bodies of explicit knowledge to lead to new knowledge.

Codifying Knowledge

Before codification, an organization must focus on: what organizational goals the codified knowledge will serve, what knowledge exists to address these goals, how useful existing knowledge is for codification, and how to codify knowledge. Codifying tacit knowledge entirely is often difficult because it is developed and internalized over long periods.

Codification Tools/Procedures: Knowledge Maps

Knowledge maps originated from the belief that people act on things they understand and accept, indicating self-determined change is sustainable. A knowledge map is a visual representation of knowledge that can represent explicit/tacit, formal/informal, documented/undocumented, and internal/external knowledge. It is not a repository but a directory pointing to people, documents, and repositories. It may identify strengths to exploit and gaps to fill. Knowledge mapping is useful for visualizing complex systems like ecosystems, the internet, or customer-supplier chains. It is a multi-step process where keys are extracted from databases or literature, placed in tabular form as lists of facts, and connected in networks. A popular example is a skills planner matching employees to jobs, built by: developing knowledge requirement structure, defining knowledge required for specific jobs, rating employee performance by knowledge competency, and linking the map to training programs.

Decision Table

A decision table consists of conditions, rules, and actions. Example: A phone card company gives discounts—5% for orders over $35, 4% for orders between $20 and $35, no discount under $20—all conditional on payment within two weeks. The decision table organizes these conditions (paid within two weeks? order>$35? $20≤order≤$35? order<$20?) into rules (1-4) with corresponding actions (5% discount, 4% discount, no discount).

🔑 Definition — Decision Table: A technique for knowledge codification that uses a matrix of conditions, rules, and actions to represent decision logic.

📌 Example: For the phone card company, Rule 1: Paid within 2 weeks=Y, Order>$35=Y → 5% discount. Rule 2: Paid within 2 weeks=Y, $20≤Order≤$35=Y → 4% discount. Rule 3: Paid within 2 weeks=Y, Order<$20=Y → No discount. Rule 4: Paid within 2 weeks=N → No discount (regardless of amount).

Decision Tree

A decision tree is a hierarchically arranged semantic network. The telephone company discounting policy can be represented as a decision tree showing the sequential decision points (paid within 2 weeks? then order amount ranges) leading to discount outcomes.

Frames

A frame is a codification scheme for organizing knowledge through previous experience, dealing with combined declarative and operational knowledge. Key elements: Slot — a specific object being described or an attribute of an entity; Facet — the value of an object/slot.

Production Rules

Production rules are conditional statements specifying an action to take if a certain condition is true. They codify knowledge as premise-action pairs using syntax: IF (premise) THEN (action). Example: IF income is 'standard' AND payment history is 'good' THEN 'approve home loan'. In knowledge-based systems, rules are based on heuristics. Rules can incorporate uncertainty levels; a certainty factor (synonymous with confidence level) is a subjective quantification of expert judgment. The premise is a Boolean expression that must be true for the rule to apply. The action clause follows THEN and may contain statements separated by AND's or commas.

In knowledge-based systems, planning involves: breaking the system into manageable modules, considering partial solutions linked through rules, deciding on programming languages and software packages, testing and validating, developing the user interface, promoting clarity and flexibility, and reducing unnecessary risk.

The role of inferencing implies deriving a conclusion from statements that only imply that conclusion. An inference engine is a program managing inferencing strategies. Reasoning is applying knowledge to reach a conclusion, depending on premise and general knowledge.

Case-Based Reasoning

Case-based reasoning (CBR) reasons from relevant past cases, similar to humans using past experiences to arrive at conclusions. It records and documents cases, then searches appropriate cases to determine usefulness for solving new cases. The aim is to bring up the most similar historical case matching the present case. Knowledge is expanded by adding new cases and reclassifying the case library. A case library may require considerable database storage and efficient retrieval.

Knowledge-Based Agents

An intelligent agent is program code capable of performing autonomous action in a timely fashion. Agents can exhibit goal-directed behavior by taking initiative and can interact with other agents or humans using agent communication language. In knowledge-based systems, an agent can learn from user behavior and deduce future behavior to assist the user.

Knowledge Developer's Skill Set

Knowledge requirements include: computing technology and operating systems, knowledge repositories and data mining, domain-specific knowledge, and cognitive psychology. Skills requirements include: interpersonal communication, ability to articulate project rationale, rapid prototyping skills, personality attributes, and understanding of job roles.

⭐ Key Takeaways

A student must remember the distinction between descriptive and predictive data mining, and know that knowledge elicitation methods range from on-site observation to blackboarding—each with specific procedures and purposes. The modes of knowledge conversion (socialization, externalization, internalization, combination) are foundational to understanding how tacit knowledge becomes explicit. For codification, the key tools are knowledge maps (directories, not repositories), decision tables (condition-action matrices), decision trees (hierarchical semantic networks), frames (slot-facet structures), and production rules (IF-THEN statements). Case-based reasoning mirrors human experience-based problem solving, while knowledge-based agents provide autonomous, goal-directed assistance. Finally, the knowledge developer must possess both technical knowledge (computing, data mining, domain expertise, cognitive psychology) and interpersonal skills (communication, articulation, rapid prototyping).

🧠 Quick Revision Questions

1. What are the two basic purposes of data mining, and how do they differ?
2. List and briefly describe five knowledge elicitation methods covered in this lecture.
3. What are the four modes of knowledge conversion, and what does each mode accomplish?
4. How does a decision table differ from a decision tree in representing knowledge codification?
5. What are the three components of a blackboard system, and what role does each play?

📘 Lecture 39 — Knowledge Sharing and Transfer Systems

📖 Overview: This lecture explores systems that organize and distribute knowledge, focusing on the crucial requirements for successful implementation. It categorizes and explains different types of knowledge-sharing systems, from incident databases to expertise locators, highlighting how each serves distinct organizational needs.

🗂️ Topics Covered

The lecture covers five crucial requirements for successful implementation of knowledge-sharing systems: collection and systematic organization of information, minimization of up-front knowledge engineering, exploiting user feedback for maintenance and evolution, integration into existing environment, and active presentation of relevant information. It then discusses five different types of knowledge-sharing systems: incident report databases, alert systems, best practices databases, lessons learned systems, and expertise locator systems.

📝 Lecture Summary

Knowledge Sharing Systems: Systems that Organize and Distribute Knowledge

1. Describe the crucial requirements for the successful implementation of knowledge-sharing systems.

a. Collection and systematic organization of information from various sources.

Most organizational business processes require information and data including CAD drawings, e-mails, electronic documents such as specifications, and even paper documents. This requisite information may be dispersed throughout the organization. This first step requires the collection of this information throughout the organization.

b. Minimization of up-front knowledge engineering.

Knowledge-sharing systems must take advantage of explicit organizational information and data, such that these systems can be built quickly, generate returns on investment, and be able to adapt to new requirements. This information and data is mostly found in databases and documents.

c. Exploiting user feedback for maintenance and evolution.

Knowledge-sharing systems should concentrate on capturing the knowledge of the organization’s members. This includes options for maintenance and user feedback so the knowledge can be kept fresh and relevant. Furthermore, knowledge-sharing systems should be designed to support user’s needs and their business process workflows.

d. Integration into existing environment.

Knowledge-sharing systems must be integrated into an organization’s information flow, by integrating with the IT tools currently used to perform the business tasks. Humans, by nature, will tend to avoid efforts to formalize knowledge (ever met a computer programmer that enjoys commenting her code?). In fact, as a rule of thumb, if the effort required in formalizing knowledge is too high, it should be left informal, to be described by humans, and not attempt to be made explicit. For instance, consider the possibility of capturing the “how–to” knowledge, of how to ride a bicycle. Clearly an understanding of the laws of physics can help explain why a person stays on the bicycle while it’s moving, but few of us recall these laws while we ride. Other than the proverbial “keep your feet on the pedal,” which doesn’t explicate much about the riding process, most of us learned to ride a bicycle through hours of practice, and many falls, while we were kids. It would be impractical to try to codify this knowledge and make it explicit. On the other hand, it might be useful to know who’s a good bicycle rider, in particular if one is looking to put together a cycling team. 💡 Why this matters: This requirement underscores a critical design principle: forcing knowledge formalization when it is too difficult compromises system adoption and effectiveness. It's often better to leave certain knowledge informal and instead build systems that connect people who hold that knowledge.

e. Active presentation of relevant information.

Finally, the goal of an active knowledge-sharing system is to present its users with the required information when and wherever it’s needed. These systems are envisioned to become intelligent assistants, automatically eliciting and providing knowledge that may be useful in solving the current task, whenever and wherever it’s needed.

2. Discuss which the different types of knowledge-sharing systems are.

a. Incident report databases

These are used to disseminate information related to incidents or malfunctions, for example, of field equipment (like sensing equipment outages) or software (like bug reports). Incident reports typically describe the incident together with explanations of the incident, although they may not suggest any recommendations.

b. Alert systems

These were originally intended to disseminate information about a negative experience that has occurred or is expected to occur. However, recent applications also include increasing exposure to positive experiences.

c. Best practices databases

These describe successful efforts, typically from the reengineering of business processes that could be applicable to organizational processes. Best practices differ from lessons learned in that they capture only successful events, which may not be derived from experience.

🔑 Definition — Lessons Learned: knowledge gained from experience (successful or unsuccessful) that can be applied to future situations.

d. Lessons learned systems (LLS)

The goal of LLS is “to capture and provide lessons that can benefit employees who encounter situations that closely resemble a previous experience in a similar situation.” LLS could be pure repositories of lessons or sometimes intermixed with other sources of information (e.g., reports).

e. Expertise Locator Systems (ELS)

These serve the purpose to identify experts in the organization. Experts may need to be identified to help solve technical problems or staff project teams, to match employee competencies with positions within the company, or to perform gap analysis that point to intellectual capital inadequacies within the organization. The intent of these systems is to catalog knowledge competencies, including information not typically captured by human resources systems, in a way that could later be queried across the organization.

⭐ Key Takeaways

Knowledge-sharing systems must balance explicit knowledge capture with practical implementation constraints, minimizing upfront engineering effort while maximizing user feedback and integration into existing workflows. The five types of systems serve different purposes: incident report databases for malfunction documentation, alert systems for disseminating experiences (both positive and negative), best practices databases for capturing only successful efforts, lessons learned systems for experience-based knowledge that can benefit others in similar situations, and expertise locator systems for identifying internal experts and cataloging knowledge competencies. A critical design rule is that if formalizing knowledge requires too much effort, it should remain informal and instead be connected through human networks. Successful systems actively present relevant information to users when and where it is needed, acting as intelligent assistants. The distinction between best practices (only successful events) and lessons learned (all experiences, positive or negative) is a key exam concept.

🧠 Quick Revision Questions

1. What are five crucial requirements for successful implementation of knowledge-sharing systems?
2. How do best practices databases differ from lessons learned systems?
3. What is the primary purpose of an Expertise Locator System (ELS)?
4. When should knowledge be left informal rather than made explicit in a knowledge-sharing system?
5. What distinguishes alert systems from incident report databases in terms of what they disseminate?

📘 Lecture 40 — Corporate Memory; Types of Knowledge Repositories

📖 Overview: This lecture explores corporate memory and its relationship to knowledge management, detailing the lessons learned process and various knowledge repository types. It focuses on practical implementations of knowledge-sharing systems, including expertise locator systems at major organizations, the role of taxonomies, and communities of practice in sharing tacit knowledge.

🗂️ Topics Covered

The lecture covers corporate memory definition and its relationship to KM, the five-step lessons learned process (collect, verify, store, disseminate, apply), the role of taxonomies in knowledge-sharing systems, comparing ELS characteristics at HP, NSA, and Microsoft, communities of practice for tacit knowledge sharing, document management systems, barriers to knowledge-sharing system use, and five specific types of knowledge-sharing systems.

📝 Lecture Summary

Corporate Memory and KM Relationship

Corporate memory is the collection of all explicit and tacit knowledge that may or may not be explicitly documented, but is specifically referenced. Corporate memory is crucial to the operation and competitive advantage of an organization. A focus of KM is the development of mechanisms and technologies that prevent corporate memory loss through knowledge sharing mechanisms, technologies, and applied systems. Such loss may result from the lack of appropriate technologies for the organization and exchange of explicit information and a lack of support for communication.

💡 Why this matters: Without deliberate KM systems, organizations lose valuable knowledge when employees leave or retire, directly affecting competitive advantage and operational efficiency.

The Lessons Learned Process (LLS)

1) Collect the Lessons

This task involves collecting the lessons (or content) that will be incorporated into the LLS. There are six possible lesson content collection methods:

a) Passive - the most common form of collection. Contributors submit lessons through a paper or Web-based form.

b) Reactive - where contributors are interviewed by a third party for lessons. The third party will submit the lesson on behalf of the contributor.

c) After-action collection - where lessons are collected during a mission debriefing, as for example, in military organizations.

d) Proactive collection - where lessons are automatically collected by an expert system, which may suggest that a lesson exists based on analysis of a specific content. For example, an expert system could monitor individual’s e-mail and prompt him/her when it understands that a lesson is described.

e) Active collection - where a computer-based system may scan documents to identify lessons in the presence of specific keywords or phrases.

f) Interactive collection – where a computer-based system collaborates with the lesson’s author to generate clear and relevant lessons.

2) Verify the Lessons

Typically a team of domain experts performs the task required by this component, which requires the verification of lessons for correctness, redundancy, consistency, and relevance. The verification task is critically important, but sometimes introduces a significant bottleneck in the inclusion of lessons into the LLS, since it’s a time-consuming process. Some systems, like for example Xerox’s Eureka LLS, provide a two-staging process.

3) Store the Lesson

This task relates to the representation of the lessons in a computer-based system. Typical steps in this task include the indexing of lessons, formatting, and incorporating into the repository. In terms of the technology required to support this task, LLS could be based on structured relational or object-oriented databases as well as case libraries (case-based reasoning) or semi-structured document management systems. LLS can also incorporate relevant multimedia such as audio and video, which may help illustrate important lessons.

4) Disseminate the Lesson

This task relates to how the information is shared to promote its reuse. Six different dissemination methods have been identified:

a) Passive dissemination -- where users look for lessons using a search engine.

b) Active casting – where lessons are transmitted to users that have specified relevant profiles to that particular lesson.

c) Broadcasting – where lessons are disseminated throughout an organization.

d) Active dissemination – where users are alerted to relevant lessons in the context of their work (for example by a software help-wizard that alerts a user of related automated assistance).

e) Proactive dissemination – where a system anticipates events used to predict when the user will require the assistance provided by the lesson.

f) Reactive dissemination – when a user launches the LLS in response to a knowledge need, for example when he launches a Help system in the context of specific software.

5) Apply the Lesson

This task relates to whether the user has the ability to decide how to reuse the lesson. There are three categories of reuse:

a) Browsable – where the system displays a list of lessons that match the search criteria.

b) Executable – where users might have the option to execute the lesson’s recommendation (like when the Word processor suggests a specific spelling for a word).

c) Outcome reuse – when the system prompts users to enter the outcome of reusing a lesson, in order to assess if the lesson can be replicated.

Role of Taxonomies in Knowledge-Sharing Systems

Taxonomies, also called classification or categorization schemes, are considered to be knowledge organization systems that serve to group objects together based on a particular characteristic. Knowledge taxonomies are used to organize knowledge (or competencies) relevant to the organization. In the case of ELS, the knowledge taxonomy is used to describe the organization’s critical knowledge areas used to index people's knowledge.

🔑 Definition — Taxonomy: A classification or categorization scheme that groups objects together based on a particular characteristic; used to organize knowledge relevant to the organization.

Differentiating Characteristics of ELS at HP, NSA, and Microsoft

Communities of Practice for Sharing Tacit Knowledge

A community of practice, also known as a knowledge network, is an organic and self-organized group of individuals who are dispersed geographically or organizationally but communicate regularly to discuss issues of mutual interest. Communities of practice are supported through technology that enables interaction and conversations amongst its members.

🔑 Definition — Community of Practice: An organic and self-organized group of individuals dispersed geographically or organizationally who communicate regularly to discuss issues of mutual interest, supported by technology enabling interaction and conversation.

Document Management Systems and Knowledge Sharing

Document management systems are composed of two pieces: a repository of documents and technology support for classifying, organizing, storing, and retrieving documents. The repository itself may be centralized or distributed and access points into the repositories are normally distributed. Most document management systems provide a knowledge portal that is a common, yet customizable, platform independent interface to distributed repositories. The common interface and support for finding information through document classification and organization and support for retrieval make finding information and getting information easier for individuals within the organization.

Barriers to Use of Knowledge Sharing Systems

A business culture may exist in which an organizational unit considers information from outside the unit as worthless. Typically this culture discourages knowledge consumers from participating in the knowledge market and organizational rewards are tied to creating knowledge (even when unnecessary) and not to sharing or knowledge re-use. Additionally, those organizations that separate knowledge from the knowledge owners/producers tends to discourage knowledge owners from volunteering knowledge to be shared. On the human side, knowledge sharing was anathema to traditional hierarchical organizations where knowledge was equated to power hence employees need to be properly motivated to engage in knowledge sharing initiatives. Without proper management support and motivation to share, knowledge sharing is not likely to occur.

💡 Why this matters: Understanding these barriers is critical because technology alone cannot make knowledge sharing successful—organizational culture and motivation are equally important.

Five Specific Types of Knowledge Sharing Systems

1. Incident report databases record and disseminate information related to either incidents or malfunctions.

2. Alert systems disseminate information regarding either negative or positive experiences.

3. Best practices databases describe successful (winning) efforts, typically regarding business process reengineering.

4. Lessons learned systems capture and disseminate lessons that benefit users that encounter similar situations.

5. Expertise locator systems catalogue knowledge competencies.

⭐ Key Takeaways

Corporate memory encompasses all explicit and tacit knowledge in an organization and KM systems are designed to prevent its loss. The lessons learned process has five critical stages—collection (with six methods from passive to interactive), verification (often a bottleneck), storage (with indexing and multimedia options), dissemination (with six methods including active casting and broadcasting), and application (browsable, executable, or outcome reuse). Taxonomies are essential for organizing knowledge and indexing expertise in systems like ELS. Three different ELS implementations—CONNEX at HP, KSMS at NSA, and SPuD at Microsoft—show differing approaches to purpose, self-assessment, participation, and data maintenance based on organizational culture. The five specific types of knowledge-sharing systems each serve distinct purposes: incident report databases, alert systems, best practices databases, lessons learned systems, and expertise locator systems.

🧠 Quick Revision Questions

1. What are the six methods for collecting lessons in a Lessons Learned System, and which is considered the most common?

2. Why is the verification step in the LLS process considered a potential bottleneck, and how did Xerox's Eureka system address this?

3. What are the three categories of lesson reuse in the Apply the Lesson step, and give an example of the "executable" category.

4. How did the purpose, self-assessment approach, and data maintenance differ between CONNEX (HP), KSMS (NSA), and SPuD (Microsoft)?

5. What are the five specific types of knowledge-sharing systems, and what is the primary purpose of each?

📘 Lecture 41 — Knowledge Sharing Structure and Services, Culture and Knowledge Communities

📖 Overview: This lecture explores the framework for analyzing and comparing knowledge management tools through the Knowledge Structure and Services (KSS) Matrix and Checklist. It explains how different levels of knowledge formalization and types of knowledge services can be used to classify, evaluate, and select appropriate electronic tools for managing organizational knowledge, which is critical for turning information into actionable knowledge in the Information Age.

🗂️ Topics Covered

The lecture covers electronic tools for knowledge management, the two key dimensions of the KSS framework (knowledge structure and knowledge services), the hierarchy of knowledge forms from implicit to formal knowledge, the three service layers (infrastructure, core, and packaged services), the KSS Matrix and Checklist for tool evaluation, and a comparative analysis of five leading knowledge management tools including Documentum 4i, OpenText LiveLink, Autonomy Knowledge Server, Lotus R5, and PeopleSoft CRM.

📝 Lecture Summary

Electronic Tools for Knowledge Management

Identifying, nurturing, and harvesting knowledge is a principal concern in the Information Age. Effective use of knowledge-facilitating tools and techniques is critical, and a number of computational tools have been developed. However, analyzing or comparing specific tools remains difficult because knowledge management is a young discipline evolving rapidly as more people encounter new problems. New technologies support previously impossible applications, and the multidisciplinary character of knowledge management combines business, management, computer science, and cybernetics. The field is frequently defined so broadly that anything can be incorporated.

KSS: Knowledge Structure and Services

Two dimensions are central to analyzing and comparing knowledge management tools: knowledge structure and knowledge service. These two dimensions can be used to form a matrix in which specific knowledge management tools are positioned.

Knowledge Structure

There is a wide range of levels of formalization or structure in the ways knowledge is represented in knowledge management systems. The knowledge forms are not discrete or exhaustive, and other levels could be added. Examples include:

• Creative knowledge is intrinsically nonformalizable and may not be representable in any formalization.
• Audio and video contain multiple "streams" of knowledge such as music, voices, faces, and objects. Humans recognize these features but creating machine recognition is extremely complex.
• A raw text document is the formal equivalent of an audio track and is comparable to natural language that is also difficult for machines to understand.

From top to bottom we increase the formalization and precision of knowledge, while from bottom to top we accommodate more informality and ambiguity. Knowledge forms toward the bottom increasingly demand knowledge engineering and incremental analysis.

• In contrast, an HTML document with markup tags can display the text's structure. Irregularities in the structure can aid in interpreting the content. For example, "wrappers" convert structural marks into semantic descriptions.
• Structured documents using formats like XML or its ancestor, SGML, explicate the semantics implicit in HTML markups. For example, instead of deducing that tag <H1>USA</H1> indicates "USA" is a country name, an XML document could contain <country name>USA</country name> that makes the text an explicit country name.
• XML documents are linear representations of "tuples" of data, the essence of information stored in databases. For example, a sequence of tags can contain a <population> tag inside a <country> tag to indicate a relationship, facilitating efficient storage and retrieval.
• Categorized information is at roughly the same level as structured information in databases. Taxonomies such as those used in biology are examples, used extensively by directory sites like Yahoo! to provide taxonomies of concepts.
• Formal knowledge is used in the mathematical sense. Logical statements such as theorems and equations are used rigorously to make sure all semantics are explicit and rules are followed, making it easy for machines to interpret.

The level of structure in the knowledge directly affects the amount of automated processing that can be performed because more structured knowledge employs powerful semantics. Managing highly unstructured knowledge requires more structured descriptions of the content, just as video indexing employs close-captioned text and HTML pages are indexed by metatags. Most knowledge found on the Web falls near the top of the scale, and most knowledge management tools concentrate on this range. Semantics and interpretation of less structured forms of knowledge depend on contextual knowledge. Raw text files representing a speech eliminate many possible ambiguities in speech recognition, using contextual knowledge about the subject, person, and voice to "reduce" less structured forms to more structured forms.

🔑 Definition — Taxonomy: A system for categorizing and classifying concepts, ideas, or subjects, similar to biological classification systems, used extensively by directory sites.

Knowledge Services

Another useful dimension is the range of services knowledge management tools provide. By services, we mean tasks or activities in handling knowledge that can be at least partially automated. Knowledge services are divided into three main types: infrastructure services, core services, and packaged services that build on one another such that packaged services make use of core services, which employ infrastructure services.

Infrastructure Services

Infrastructure services are usually needed to implement any knowledge management solution. Five basic types include:

• Communication services enable electronic communication between users through e-mail, file transfer, chats, and similar vehicles.
• Collaboration services allow groups of people to communicate through online meetings, shared whiteboards, and discussion groups, as well as directory services. Built upon communication services, these tools are also known as groupware, and the best-known example is Lotus Notes.
• Translation services transform knowledge from one file format to another or from one language to another.
• Workflow management services define workflows and support online execution and control of workflows, allowing users to execute and enter results of subtasks and view status of other subtasks.
• Intranets and extranets include other infrastructure services. Intranets are Web-based applications restricted to specific organizations while extranets connect several organizations by providing access from one organization to another's content and services.
• Intelligent agents are software components capable of accomplishing tasks on behalf of a user, going beyond "information on demand" to make selected decisions based on predetermined environmental scanning methods. They can also summarize relevant data by aggregating and performing synthesizing functions before presenting to executive decision makers.

🔑 Definition — Groupware: Software tools that enable groups of people to communicate and collaborate through online meetings, shared whiteboards, and discussion groups, with Lotus Notes being the best-known example.

Core Services

Core services define knowledge management solutions because they explicitly and directly access knowledge repositories. They are built around core processes of creating, organizing, and using a knowledge repository. Different core processes involve people or systems with different roles, including knowledge producer (creates knowledge), knowledge holder (learns from other sources), knowledge organizer (works like a librarian to allow orderly addition for retrieval), and knowledge user (consumes knowledge to execute tasks).

Key features of the five core services include:

• Knowledge generation services produce knowledge in forms that can be stored in the repository. Used by knowledge producers, these tools distill, refine, or create new knowledge, typically involving automated learning including data mining techniques and pattern recognition.
• Knowledge capture services facilitate addition to repositories, allowing users to enter new documents and may employ meta-information for indexing purposes. A simple example is the "document properties" mechanism of Microsoft Word containing author, revision number, subject, and date.
• Knowledge organization (indexing) services help knowledge managers arrange items in a repository to facilitate retrieval and use, adding to or modifying knowledge about repository indexes, taxonomies, and directories.
• Access management services determine who can access elements of the repository based on a directory of users, restricting access by permission levels.
• Retrieval services include searching and navigating functions as well as translation, visualization, and integration, creating value by making knowledge available for specific uses and providing personalization and configuration services.

Packaged Services

Packaged services aggregate lower-level services to solve specific types of problems. Much knowledge management literature concentrates on these because they are clearly connected to end-user needs. Three classes are:

• Customer Relationship Management (CRM) services provide information about a company's clients in an integrated way, allowing internal channels to share and add to the same central knowledge base. Siebel and PeopleSoft are leading providers.
• Business Intelligence services manage knowledge about competitors and partners, usually aggregating and providing unified interfaces to information from news agencies, public and private databases, economics and social information, and the World Wide Web, while filtering and classifying information into categories.
• Enterprise Information Portals (EIP) are specialized gateways providing access to internal and external sources of knowledge, offering one-stop access with typical examples including search engines and My Yahoo!.

The Knowledge Structure and Services (KSS) Matrix and the KSS Checklist

Different tools provide distinct arrays of services and manage specific types of knowledge. Two diagrams display relationships between knowledge management tools based on types of knowledge they handle and services they offer: the KSS Matrix and the KSS Checklist.

The KSS Matrix ensures that the types of knowledge handled are intimately connected with the core services provided. The horizontal axis recognizes the five core knowledge services while the vertical axis displays the eight basic levels of knowledge structure dimension. One KSS Matrix is used for each tool analyzed. Cells are filled with squares—a large square denotes a major offering with comprehensive features, while a small square marks a service offered in restricted scope or functionality.

The KSS Checklist recognizes services beyond core services. It lists the five infrastructure services and the three packaged services, with squares indicating whether a service is provided. As with the matrix, square size represents scope of service offered.

The KSS Matrix and Checklist provide quick assessments of each tool, can be used to compare tools quickly, and more importantly, to evaluate knowledge management tools. Filling in the diagrams forces users to explore and analyze tools in detail. The KSS framework can also select tools for specific uses, with specifications represented as "target" diagrams for ideal offerings.

Using the KSS Matrix and Checklist to Compare Current Knowledge Management Tools

Five leading knowledge management tools are analyzed: Documentum 4i, OpenText LiveLink, Autonomy Knowledge Server, Lotus R5, and PeopleSoft Customer Relationship Management.

Documentum 4i is an integrated software suite centered on document management, with core strengths in dealing with documents. It supports audio/video and taxonomy, with some coverage of categorized information. It does not support knowledge generation services or formal knowledge, structured information, or implicit knowledge. It is intended to be an Enterprise Information Portal tool and supports workflow management and collaboration.

OpenText LiveLink is an integrated software suite focused on document management, providing a core set of services to handle document management and structured information from databases. It is an Enterprise Information Portal tool that supports translation and collaboration, including discussion groups and group scheduling.

Autonomy Knowledge Server is a software suite for content management providing sophisticated services for classifying material based on document content. It is unique in covering formal knowledge, and its use of learning algorithms facilitates some knowledge generation services. It has some translation and collaboration services but does not incorporate workflow management.

Lotus Notes R5 is virtually synonymous with groupware, handling only unstructured types of knowledge. It supports implicit knowledge through detailed descriptions of people's information and skills. Lotus R5 does not attempt to be a packaged service as defined here because it focuses exclusively on collaboration.

PeopleSoft Customer Relationship Management is typical vertical solution software specializing in CRM. It supports structured information but only handles information about customers, making it a good choice for CRM but a poor choice for general knowledge management problems.

Conclusion

Modern-day alchemy is about turning information into knowledge. The combination of knowledge management tools with databases and knowledge in the minds of employees is fostering knowledge groups, knowledge enterprises, and knowledge industries. The KSS framework provides a convenient way to characterize knowledge management tools by defining the types of knowledge they can handle and services they provide. The KSS Matrix and Checklist help visualize coverage of specific tools, providing a convenient way to quickly compare different tool offerings. They can evaluate specific needs and match them to services provided by available tools. Services are key elements in understanding knowledge management tools, but complete evaluations should include other aspects such as hardware, software, and budget constraints. Electronic tools provide necessary "horsepower" and number-crunching ability to deal with real-world complexity, assessing difficult problems and giving us simple answers, but we must exercise caution in demanding answers that are simple rather than simplistic.

⭐ Key Takeaways

The KSS framework provides two critical dimensions for analyzing knowledge management tools—knowledge structure (ranging from implicit knowledge to formal knowledge) and knowledge services (infrastructure, core, and packaged services). Knowledge structure directly determines automated processing capability; more structured knowledge with explicit semantics is easier for machines to interpret, while unstructured knowledge requires more contextual knowledge and knowledge engineering. Infrastructure services (communication, collaboration, translation, workflow management, intranets/extranets) form the foundation, core services (generation, capture, organization, access management, retrieval) directly access knowledge repositories, and packaged services (CRM, business intelligence, EIP) solve specific end-user problems. The KSS Matrix and Checklist enable systematic evaluation and comparison of tools by examining which knowledge types each tool handles and which services it offers. Understanding that different tools have different strengths—Documentum for document management, LiveLink for enterprise portals, Autonomy for content classification, Lotus for collaboration, and PeopleSoft for CRM—helps organizations select appropriate tools for specific knowledge management needs.

🧠 Quick Revision Questions

1. What are the two key dimensions in the KSS framework for analyzing knowledge management tools, and how are they used to position specific tools?
2. List the eight levels of knowledge structure from least to most formalized, and explain why more structured knowledge enables greater automated processing.
3. What are the three main types of knowledge services, and how do they build upon one another in the service hierarchy?
4. How does the KSS Matrix differ from the KSS Checklist, and what does the size of the square in each cell represent?
5. For each of the five analyzed tools (Documentum 4i, OpenText LiveLink, Autonomy Knowledge Server, Lotus R5, PeopleSoft CRM), identify their primary strength and one key limitation according to the KSS framework.

📘 Lecture 42 — Knowledge Application Systems and CRM

📖 Overview: This lecture focuses on designing knowledge application systems to support business needs, using case-based reasoning as a primary methodology. It covers the system architecture, development processes, and real-world examples from industry leaders like Daimler Chrysler and General Electric, while also introducing the concept of knowledge communities as social learning systems.

🗂️ Topics Covered

The lecture begins by outlining a case-based knowledge application system design for software support teams, describing the Case Method Cycle and case development sub-processes. It then covers system architecture design with database, web-based, search, and reasoning technologies. Three industry examples are presented from Daimler Chrysler, Shuttleworth Business Systems, and General Electric. The lecture concludes with an examination of knowledge communities, their defining elements, and modes of belonging as described by Etienne Wenger.

📝 Lecture Summary

1. Design a knowledge application system to support your business needs. Describe the type of system and the foundation technologies that you would use to develop such system.

A knowledge-application system designed for business needs would be a case-based knowledge application for software support teams (up to 4 tiers) to address platform issues. Many organizations currently use tracking tools to control and track issues, storing ticket history with reporting features, but these tools lack mechanisms to aid in solution finding. The objective is to move beyond mere issue tracking to active solution support.

To design this case-based application, one applies the Case Method Cycle comprising six processes:

• System development process: Develop an application to store cases and retrieve them based on similar historic cases, showing resolution processes. The system must integrate with current tracking and business request tools.
• Case library development: Collect case library information from current case history repositories and develop maintenance mechanisms.
• System operation process: Define installation, implementation, and support following standard development processes.
• Database mining: Analyze collected case information to infer relationships between cases and define resolution processes.
• Management process: Ensure organizational support for the project.
• Knowledge transfer: Ensure users maintain and add cases to the case library.

Case development follows three sub-processes:

• Case collection: Determine cases for each supported application, aided by support personnel experience and historical information.
• Attribute-value extraction: Organize the support case library by identifying attributes or characteristics of each case. Determine relationships between cases for similar case searching. Consider interdependencies between different applications and map hierarchy into the database.
• Feedback: Ensure personnel maintaining the system receive necessary feedback to guarantee information quality, minimizing ticket resolution time.

💡 Why this matters: The quality of cases and resolution processes directly impacts how quickly support tickets are closed, making feedback loops essential.

The foundation technologies include databases, Web-based technologies, search engines, and a case-based reasoning engine.

2. Design the system architecture for the system described above.

The system architecture integrates the case-based reasoning engine with a database containing the case library, web-based interfaces for user access, and search engines for retrieval. The architecture connects to existing tracking tools and business request systems to allow proper retrieval of updated information. This integrated design ensures that historical case data flows between the tracking system and the knowledge application system seamlessly.

3. Identify three recent examples in the literature of knowledge application systems.

Example 1: Daimler Chrysler Freightliner The vehicle manufacturing division chose a case-based approach for a diagnosing system. Diagnostic problems like trucks running hot or school buses vibrating were difficult to connect to particular solutions because vehicles are customized with different histories. Carlo Nardini, director of technical support, stated: "We determined that case-based reasoning gave us the chance to deal with things that were not so concrete." A model-based reasoning system might have worked for discrete problems like electrical wiring, but the multitude of indeterminate variables made case-based reasoning the most flexible option. Problems without finite descriptions or definitions demand this technology. Using a web-based interface, Freightliner offers its system to dealers, franchisees, and third-party operators like FedEx Corp., accepting legacy information from call centers and engineering groups.

Example 2: Shuttleworth Business Systems Facing the loss of two key support staff, joint Managing Director Andy King implemented a knowledge application system solution. His vision was to give customers the ability to answer their own questions by accessing a technical knowledge base. Shuttleworth implemented a help desk system first, gaining in-depth understanding of support team operations and workload. This allowed identification of problem causes originating in other departments like sales, development, or training, enabling elimination at the source.

Example 3: General Electric ICARUS System GE developed ICARUS, a Case-Based Reasoning System for Locomotive Diagnostics. The application analyzed fault logs from locomotives to predict and preempt serious faults. Previously, a rule-based system had been tried but was too hard to maintain, taking four person-years to reach a stage where it was deemed unworkable. The CBR system used a simple approach, costing approximately $250,000 and 14 person-months to build. The system solves 75% of known faults across 600 locomotives, with estimated savings of $5 million per year.

Knowledge Communities

Etienne Wenger (1998) defines communities of practice as social units of learning within larger systems forming constellations of interrelated communities of practice. The subtitle of his book — Learning, meaning and identity — shows his interest in community dimensions comprising identity, belonging, and boundaries. Wenger (2000: 229) explicitly states that communities of practice are the social containers of the competences that make up a social learning system.

Three elements define competence:

• The sense of joint enterprise: To be competent is to understand the enterprise well enough to contribute to it.
• Mutuality: To be competent is to be able to engage with the community and be trusted as a partner in interactions.
• A shared repertoire of communal resources: Language, routines, sensibilities, tools, stories, etc. To be competent is to have access to this repertoire and use it appropriately.

In a community of practice, knowing involves two components: the competence the community has established over time, and the subjective experience of the world as a member. Wenger distinguishes three modes of belonging according to different forms of participation:

• Engagement: Doing things together — the way we engage shapes our experience of who we are.
• Imagination: Constructing an image of ourselves and our communities — these images are essential for our sense of self and interpretation of participation in the social world.
• Alignment: Making sure local activities are sufficiently aligned with other processes to be effective beyond our own engagement. Alignment coordinates perspectives, interpretations, and actions.

🔑 Definition — Communities of Practice: Social units of learning that serve as containers of competences within a social learning system, defined by joint enterprise, mutuality, and a shared repertoire of resources.

🔑 Definition — Case-Based Reasoning (CBR): A problem-solving methodology that uses past cases to find solutions to new problems, particularly useful when problems have no finite descriptions or definitions.

📌 Example — Daimler Chrysler CBR System: A customized truck with a rough-ride problem involving a similar but non-matching transmission and engine type is connected to a previous instance using case-based reasoning, allowing technicians to find solutions without exact symptom-to-solution matches.

⭐ Key Takeaways

The Case Method Cycle is the essential framework for building knowledge application systems, consisting of system development, case library development, system operation, database mining, management, and knowledge transfer processes. Case-based reasoning is most valuable when problems lack finite descriptions or definitions, as demonstrated by Daimler Chrysler's success with customized vehicles and GE's locomotive diagnostics system saving $5 million annually. Foundation technologies must include databases, web-based interfaces, search engines, and a reasoning engine, all integrated with existing tracking tools. Knowledge communities are social learning systems defined by joint enterprise, mutuality, and shared resources, with participation occurring through engagement, imagination, and alignment. The success of knowledge application systems depends critically on feedback mechanisms to maintain case quality and on knowledge transfer processes to ensure ongoing user contributions.

🧠 Quick Revision Questions

1. What are the six processes of the Case Method Cycle for designing a case-based knowledge application system?
2. Why did Daimler Chrysler choose case-based reasoning over model-based reasoning for their Freightliner diagnostic system?
3. What three elements define competence in a community of practice according to Wenger?
4. What were the cost and savings associated with General Electric's ICARUS CBR system for locomotive diagnostics?
5. What are the three modes of belonging in communities of practice, and how does each contribute to participation?

Here is the summary of Lecture 43, following the exact format provided.

📘 Lecture 43 — Role of Communities in Learning and Product Development

📖 Overview: This lecture explores the role of knowledge communities, particularly Communities of Practice (CoPs), in organizational learning and product development. It contrasts these communities with other group structures like teams and networks, explains how they are sustained, and details the critical role of virtual moderation in fostering knowledge exchange. The lecture underscores the shift from a management-driven approach to a supportive, ecology-based organizational environment for community success.

🗂️ Topics Covered

This lecture begins by defining and contrasting knowledge communities with teams and networks, highlighting their self-selecting membership and focus on applied knowledge. It then details the stages of community development and the critical role of virtual moderators in stimulating discussion and guiding knowledge development. The lecture concludes by outlining strategies for sustaining communities, including providing supportive facilities and a healthy organizational culture, and illustrates these concepts with the case study of the Knowledge Ecology Fair.

📝 Lecture Summary

Knowledge Communities

Knowledge communities are described as social structures that connect "islands of knowledge" into self-organizing, knowledge-sharing networks. They transcend disciplines, bringing different perspectives to exchange and apply knowledge. The lecture uses a table to distinguish communities from other groups. The main differences are that knowledge community membership is self-selecting rather than recruited, and the focus is on applied knowledge, with goals that are evolving and purposeful.

🔑 Definition — Knowledge Community: A cohesive, self-selecting cluster of people within a diffuse knowledge network that focuses on exchanging, developing, and applying knowledge, often around a customer or problem focus. 📐 Key Differences: The table shows that compared to a work group (focused on tasks) or a team (focused on output), a knowledge community is characterized by self-selection, fluid boundaries, and an evolving, purposeful focus on applied knowledge. 📌 Example: The lecture contrasts a team, which is "recruited for team fit" and has explicit goals, with an in-house knowledge community, which is best managed "hands-off" by providing a supportive climate rather than direct orders. Etienne Wenger lists eight stages of community development: Latent, Coalescent, Active, Legitimized, Strategic, Transformational, in Diaspora, and Member able.

Virtual moderation for knowledge development

The role of a conference moderator is to stimulate virtual discussion and guide the community's knowledge development. A good moderator has enthusiasm and likes networking. They activate knowledge development through key activities like setting up conferences, defining the scope and ground rules, keeping conversations developing, and cross-linking different conversational threads.

🔑 Definition — Conference Moderator: A person who stimulates virtual discussion and guides a knowledge community forward in its thinking, often performing this role as an important added daily activity. 📌 Example: A good moderator's tasks include "summarizing" contributions, "managing inappropriate contributions" (often privately), and "engaging people in conversation," such as by acknowledging good contributions or seeking input from members with specific expertise. 💡 Why this matters: The moderator’s role is not just administrative; it is central to sparking new ideas and momentum through cross-fertilization of different conversational threads.

Sustaining communities

Organizations encourage communities as an integral part of corporate knowledge programs to gain benefits like thought leadership, new product ideas, and increased knowledge capital. A potential threat is that formalization could stifle them. The watchword is endorsement, not enforcement, requiring a supportive organizational environment.

🔑 Definition — Knowledge Ecology: An approach to knowledge management that emphasizes a supportive, natural, and self-organizing environment for communities, as opposed to a structured, top-down management emphasis. 📌 Example: To minimize the risk of stifling communities, organizations should provide facilities for meeting (web space, physical places), offer facilitation to improve processes, provide connection information to help new members join, and encourage note-taking to share "knowledge nuggets." 💡 Why this matters: The whole ethos of a successful community is based on knowledge ecology rather than a rigid knowledge management emphasis. The Knowledge Ecology Fair (1998) is a key example, featuring virtual keynote presentations, workshops, discussion groups, and an "Open Space Circle" for participant-generated discussions. This event led to new initiatives like KEN (Knowledge Ecology Network).

⭐ Key Takeaways

The most critical distinction is that knowledge communities are self-selecting, social structures focused on applied knowledge, and are best managed indirectly through endorsement rather than direct control. Their success depends on a high flow of communication and passionate, effective leaders who act as virtual moderators to stimulate and guide discussions. Organizations must actively foster a supportive "knowledge ecology" by providing facilities and a culture that encourages participation, while being careful not to over-formalize and stifle the community. As illustrated by the Knowledge Ecology Fair, these communities can be powerful drivers of innovation and thought leadership within an organization.

🧠 Quick Revision Questions

1. What are the key differences between a knowledge team and a knowledge community in terms of membership, focus, and goals?
2. List four specific tasks a conference moderator performs to activate knowledge development within a virtual community.
3. According to Etienne Wenger, what is the difference between a "Latent" community and a "Strategic" community?
4. What is the recommended management style for an in-house knowledge community, and what is the single "watchword" for managing them?
5. How does the concept of "Knowledge Ecology" differ from traditional "Knowledge Management" in the context of sustaining communities?

📘 Lecture 44 — Learning Organization

📖 Overview: This lecture explores the concept of learning organizations (LO) and organizational learning (OL), challenging traditional views of knowledge as a commodity to be transferred. It examines how the field of OL has been socially constructed and institutionalized, and critically analyzes the underlying assumptions and power dynamics within OL and knowledge management discourses.

🗂️ Topics Covered

The lecture begins by critiquing the conventional view of learning as knowledge delivery from a source to a passive recipient. It then introduces a social and situated view of learning where knowledge is created through participation and negotiation. The birth and institutionalization of the learning organization as a field is discussed, including the social process of manufacturing knowledge and creating new organizational identities. The distinction between descriptive (OL) and prescriptive (LO) research is examined. Finally, the lecture analyzes OL as a disciplinary discourse, critiquing its assumptions, and concludes with a discussion of knowledge management's reification of knowledge.

📝 Lecture Summary

Conventional wisdom

Our society is dominated by a view of learning as "knowledge delivery" where information flows from a knowledgeable source (teacher or textbook) to a learner lacking that information. Learning is seen as the acquisition of accumulated data, facts, and practical wisdom stored in books. This view equates learning to eating or banking: knowledge is food for the mind that the learner ingests. Teaching becomes the transfer of "gold" to pupils' heads. Learning is believed to occur mainly during early development through schooling, instruction, and training, with occasional supplementary training for job changes or updating knowledge.

However, this familiar conception is a highly reductive account of how people learn for at least two reasons. First, it suggests learning is separate from other activities, restricted to specific occasions like taking a class. In reality, learning is deeply rooted in everyday activities and experiences; most know-how distinguishing an expert from a novice is acquired day-to-day by acting and reflecting—thinking about what we do and why, and talking about it with others (Schön, 1983).

Second, viewing learning as a totally individual activity (like ingesting food) is misleading. People and groups create knowledge by negotiating the meaning of words, actions, situations, and material artefacts. They participate in a world that is socially and culturally structured and constantly reconstituted by all who belong to it. Knowledge is not what resides in a person's head, books, or data banks. "To know is to be capable of participating with the requisite competence in the complex web of relationships among people, material artefacts and activities" (Gherardi, 2001b). Learning is always a practical accomplishment—"knowledge is something people do together"—done in every mundane activity.

The institutionalization of the field: the birth of the learning organization

The field of OL has developed as "problem driven"—producing instrumental knowledge. However, this knowledge sets conditions for research to shift to "mystery-driven" learning. A paradigmatic episode illustrates this: at a conference, a colleague from a developing country tested whether successful firms were learning organizations but found low correlation between economic success and being a LO, leaving her puzzled.

This episode demonstrates the social process of "manufacturing knowledge": a heuristic concept (OL) acquires legitimacy in the scientific community, spreads through consultants and practitioners, produces distinguishing "characteristics," and coins the label "learning organization." This label travels through time and space, appropriated by organizations that incorporate it into their identities. A realist assumption replaces a heuristic device: learning becomes a "real" phenomenon "out there" that can be measured, compared, and validated. The touchstone for learning is the concept of change—organizational change is the outcome of a rational procedure of knowledge production and application.

This process of mobilizing credibility creates cultural artefacts (books, conferences, university courses) and new identities: learning organizations (LO) are born. Companies like Shell, Mercedes Benz, and Isvor Fiat baptize themselves LOs, devoting resources to creating recognizable corporate identities. The existence of LO has become naturalized through institutionalization. Mary Douglas (1986) argues that institutions are founded on an analogy with nature; naturalization of social classifications protects the institution when it is still fragile convention.

We may distinguish between LO and OL based on the dichotomy between prescriptive and descriptive research (Tsang, 1997). However, the issue is not about academics failing to generate useful implications versus practitioners lacking rigorous methodologies. The contrast is between a realist ontology (assuming learning as an empirical phenomenon) and a constructionist one. If OL is a "live metaphor" (Tzoukas, 1991)—a means to represent the organization as if it were a system that learns—then the problem is not what constitutes "effective learning" but determining the amount of further knowledge yielded by the metaphor proposed.

🔑 Definition — Organizational Learning (OL) as heuristic metaphor: A means to represent the organization as if it were a system that learns, used to see new things and see existing knowledge differently.

🔑 Definition — Learning Organization (LO): A realistic, prescriptive concept describing an ideal type of organization in which learning is maximized, often subject to empirical verification.

💡 Why this matters: The interest of knowledge shifts from "how does an organization learn or should learn?" to "if we depict an organization as a system which learns, are we able to see something new and to see something that we already know differently?" The former question concerns explanation of OL; the latter relates to understanding (Verstehen) of it (Weber, 1922).

Organizational learning as a disciplinary discourse

The literature on LO has been suspected of colluding with the "ruling courts" governing organizations (Coopey, 1995) and employing a discourse of democracy and liberation ideologically (Snell and Chak, 1998). Both OL (heuristic) and LO (realistic) converge on the same social practice legitimizing managerial techniques based on claims of scientific knowledge. Both contribute to institutionalization of the field as a disciplinary discourse and its superseding through "institutional reflexivity."

From a Foucauldian perspective, discourses are systems of thought contingent upon material practices and informed by those practices through particular power techniques. Much of nineteenth-century social science was shaped by the "disciplinary gaze" of surveillance. In organization studies, the personnel function under "human relations" had a tutelary role, and "organizational learning" is now following suit.

The lecture illustrates a set of premises implicit in OL and LO theorization that sustain a disciplinary discourse which disciplines concrete behaviours:

1. OL is always ameliorative and disinterested. Learning is regarded as always positive—"the more, the better." OL assumes an ameliorative vision where learning is incremental and knowledge cumulative. The alleged universality, neutrality, and transparency of knowledge neglect the role of power in structuring organizational knowledge. Only those in power learn the right things.

2. OL is intentional. If learning resembles appropriation of something external, the ways it is appropriated can be specified and normatively sustained. OL may be embodied in SOPs (standard operating procedures), periodically overhauled and updated (Kieser, Beck and Taino, 2001), as "the one best way of learning."

3. OL is an extorted result. The LO requires work groups to "learn" and transfer knowledge to organizational structures, with learning leading to improved performance. The use of power in transferring knowledge is silenced, and OL is conceived as grounded on free transfer, transparency, voluntariness, and chain of authority—not micro-conflictuality, micro-negotiation, and systematic distortion/extortion of knowledge.

4. OL presumes change but not its understanding. Learning proposes change in behavior (actual or potential) of individuals or groups, or cognitive change. It does not require individuals to understand the logic behind changes in SOPs (Child and Markoczy, 1993). If change is manifest, a learning process has taken place, but change does not require any learning.

Rorty (1989) writes that learning is part of a final vocabulary—a value in itself that cannot be further questioned. It is associated with improvement, error correction, and fast reaction to environmental changes. The positive connotation induces a priori assumption of what needs to be empirically demonstrated. Learning, as the founding myth of the OL scientific community, obscures the myopia of learning from experience (Levinthal and March, 1993).

🔑 Definition — Disciplinary discourse: A system of thought that is contingent upon material practices and informs those practices through particular power techniques, sustaining normative behavior and providing resources for normalization.

The reification of knowledge in the knowledge management literature

A quantitative survey (Scarbrough, Swan and Preston, 1998) shows that since 1997 the term "knowledge management" (KM) has supplanted "organizational learning." Interest has switched from questions about appropriation of knowledge by individuals and organizations to techniques and technologies of knowledge management. Academic disciplines now predominant are not psychological but economic, with a new alliance between economics of knowledge and information technology.

The concepts of "knowledge work" and "knowledge worker" were first introduced by Peter Drucker (1939), set in contrast to manual work and service work. Epochal changes between the eighteenth and twentieth centuries reveal: first, knowledge was applied to artefacts, processes, and products through technologies, patents, and tacit knowledge; second, knowledge was applied to human labour through scientific analysis of work; third, knowledge was applied to knowledge itself, constituting knowledge work. The endeavour to manage knowledge as if it were a resource reflects this trend, reifying what is fundamentally a social, situated, and practical accomplishment.

🔑 Definition — Knowledge Management (KM): The techniques and technologies for managing knowledge within organizations, which since 1997 has supplanted the term "organizational learning" and is dominated by economics of knowledge and information technology.

🔑 Definition — Reification of knowledge: Treating knowledge as a tangible, manageable resource or commodity that can be captured, stored, and transferred, rather than as a social, situated, and practical accomplishment.

⭐ Key Takeaways

The lecture fundamentally challenges the conventional view of learning as knowledge delivery and acquisition, instead presenting learning as a practical, social accomplishment done together through participation and negotiation. The learning organization is not a naturally occurring phenomenon but a socially constructed concept that was institutionalized through processes of legitimation, identity creation, and naturalization of social classifications. OL and LO theorization sustain a disciplinary discourse with problematic assumptions: that learning is always positive and disinterested, intentional, extorted through power, and assumes change without requiring understanding of its logic. Students must recognize that the shift from OL to knowledge management represents a move toward treating knowledge as a reified, manageable resource driven by economics and information technology, which obscures the social, situated, and practical nature of knowing.

🧠 Quick Revision Questions

1. According to the lecture, what are the two main reasons the conventional view of learning as "knowledge delivery" is considered a highly reductive account?
2. How did the concept of "learning organization" become institutionalized and naturalized as a "real" phenomenon?
3. What is the key difference between viewing OL as a realist phenomenon versus as a "live metaphor" (heuristic device)?
4. What are the four premises implicit in OL and LO theorization that sustain a disciplinary discourse?
5. How and why has the term "knowledge management" supplanted "organizational learning" since 1997, according to the lecture?

📘 Lecture 45 — The Future of Knowledge Management

📖 Overview: This lecture examines what makes Knowledge Management initiatives successful, focusing on the strategic and operational requirements for success. It explores the fundamental tension between tacit and explicit knowledge approaches, detailing their respective advantages, disadvantages, and practical applications in real organizations such as Toyota, Motorola, and Philips.

🗂️ Topics Covered

The lecture begins with golden nuggets derived from KM research about success factors. It then presents the two fundamentally different views of knowledge management practice: the tacit knowledge approach and the explicit knowledge approach. For each approach, the lecture explains underlying beliefs, provides organizational examples, and analyzes advantages and disadvantages. The lecture concludes by highlighting the challenges organizations face when implementing explicit knowledge management.

📝 Lecture Summary

What makes a KM initiative successful?

The lecture opens with critical questions about KM success and presents research-derived "golden nuggets." KM success factors are dominated by management ones—culture, process, and organization—with technology being the least important. KM requires the integration and balancing of leadership, organization, learning, and technology in an enterprise-wide setting. An atmosphere of trust is necessary for sharing knowledge, and national culture affects KM implementation especially at lower levels. Knowledge assets are strategic and must be accounted for and valued accordingly.

💡 Why this matters: These findings challenge the common assumption that technology is the primary driver of KM success.

Tacit knowledge versus explicit knowledge approaches

Many KM recommendations appear contradictory but actually derive from two fundamentally different views of knowledge itself. These are characterized as the tacit knowledge approach and the explicit knowledge approach.

The tacit knowledge approach

The salient characteristic of the tacit knowledge approach is the basic belief that knowledge is essentially personal in nature and therefore difficult to extract from the heads of individuals. This approach holds that knowledge dissemination can best be accomplished by moving people as 'knowledge carriers' from one part of an organization to another. Learning occurs when individuals come together under circumstances that encourage sharing ideas and developing new insights.

🔑 Definition — Tacit Knowledge Approach: The belief that knowledge is personal and difficult to extract, requiring people movement and interaction for knowledge transfer.

Managers are urged to identify the knowledge possessed by various individuals and arrange interactions between knowledgeable individuals. A common initiative is creating 'know-who' forms of knowledge. For example, Philips created a 'yellow pages' on its intranet listing experts with different kinds of knowledge; typing keywords retrieves contact information for knowledgeable people worldwide.

📌 Example — Toyota's Tacit Knowledge Transfer: When Toyota opened a new factory in Valenciennes, France, it selected 200-300 new employees to train for several months in an existing factory. These workers were then sent back to the new factory, accompanied by 100-200 experienced Toyota workers who worked alongside all new employees to implant the production process.

📌 Example — Toyota's Quality Circles: At the end of each work week, groups of Toyota production workers spend 1-2 hours analyzing performance to identify problems. Each group proposes 'countermeasures' to correct problems and discusses results. Through personal interactions, employees share ideas, devise tests, and assess results. This practice progressively identifies, eliminates, and prevents errors, making Toyota's production system one of the highest-quality in the world.

The explicit knowledge approach

The explicit knowledge approach holds that knowledge can be explained by individuals, though some effort may be required. This approach assumes that useful knowledge can be articulated and made explicit. Formal organizational processes can help individuals articulate knowledge to create knowledge assets. Explicit knowledge assets can be disseminated through documents, drawings, standard operating procedures, and manuals of best practice. Information systems play a central role in facilitating dissemination over company intranets or the Internet.

🔑 Definition — Explicit Knowledge Approach: The belief that knowledge can be articulated and codified into explicit knowledge assets that can be disseminated through documents and information technologies.

New knowledge can be created through a structured, managed, scientific learning process. Recommendations focus on generating, articulating, categorizing, and systematically leveraging explicit knowledge assets.

📌 Example — Motorola's Pager Design: Motorola introduced new pager generations every 12-15 months. Each design team received a manual of design methods from the previous team. Each team had three deliverables: (1) improved pager design, (2) design of more efficient assembly lines, and (3) an improved design manual incorporating new methods. This manual was passed to the next team, systematically capturing and leveraging knowledge.

📌 Example — Toyota's Explicit Documentation: Toyota creates detailed task description documents for each assembly line task, specifying how each task is performed, how long it takes, the sequence of steps, and self-checking procedures. When improvements are suggested in Quality Circles, production engineers evaluate them and formally incorporate them into revised task documents.

📌 Example — Chrysler's Book of Knowledge: Several platform teams of 300-600 engineers create next-generation vehicle platforms. Each platform team is required to place its selected design solution for each aspect in a 'Book of Knowledge' on Chrysler's intranet, making good design solutions available to all teams.

📌 Example — GE Fanuc Automation: Engineers create detailed design methodologies for each type of component. These methodologies are encoded in software, so design of new component variations is automated. Performance parameters are entered, and the computer system automatically generates a design solution.

Advantages and disadvantages of tacit versus explicit knowledge approaches

Advantages of the tacit knowledge approach:

• Relatively easy and inexpensive to begin—just identify what individuals claim to know
• May lead to improvements in employee satisfaction and motivation when knowledge is officially recognized
• Avoids motivational difficulties of getting people to make knowledge explicit
• Leaving knowledge in tacit form protects proprietary knowledge from leaking to competitors

Disadvantages of the tacit knowledge approach:

• Individuals may not actually have the knowledge they claim
• Knowledge profiles need frequent updating
• Knowledge transfer is constrained by moving people, which is costly and limits speed and reach
• Organization may lose key knowledge if key people leave, become incapacitated, or are recruited by competitors

Advantages of the explicit knowledge approach:

• Articulated knowledge can be moved instantaneously anytime anywhere by information technologies
• Codified knowledge can be proactively disseminated to people who can use it
• Explicit knowledge can be discussed, debated, and improved, stimulating incremental organizational learning
• Makes knowledge base more visible and analyzable, helping discover knowledge deficiencies
• Systematic dissemination minimizes risk of losing vital knowledge if key individuals leave

Disadvantages of the explicit knowledge approach:

• Considerable time and effort may be required to help people articulate knowledge
• Individuals may resist due to fear that job security or influence depends on tacit knowledge
• Employment relationships may need redefining to motivate knowledge articulation
• Expert committees must evaluate explicit knowledge assets, using valuable people's time
• Application must be assured by adoption of best practices, requiring organizational discipline
• Explicit knowledge assets may leak to competitors, requiring security measures

💡 Why this matters: The advantages of the explicit approach are a "mirror image" of the tacit approach's disadvantages, and vice versa—the tacit approach is easier to start but offers limited benefits, while the explicit approach is harder to start but offers greater long-term potential.

⭐ Key Takeaways

KM success is dominated by management factors (culture, process, organization) rather than technology. There are two fundamental approaches to KM: the tacit approach, which treats knowledge as personal and requires moving people to transfer it, and the explicit approach, which treats knowledge as articulable and codifiable into assets that can be disseminated via information systems. Toyota uniquely uses both approaches simultaneously—moving people for tacit transfer while also documenting tasks explicitly. The tacit approach is easier to start but risks losing knowledge when people leave, while the explicit approach offers greater long-term benefits but requires overcoming resistance, evaluating knowledge, and ensuring application through best practices.

🧠 Quick Revision Questions

1. What are the two fundamentally different views of knowledge in KM practice, and what does each believe about the nature of knowledge?
2. How does Toyota use the tacit knowledge approach to transfer its production system to new factories?
3. What three deliverables did each Motorola pager design team produce, and how did this create organizational learning?
4. What are the main advantages and disadvantages of each knowledge management approach?
5. Why might individuals resist making their knowledge explicit, and what organizational changes are needed to overcome this resistance?