MGMT630 — Midterm Summary (Lectures 1–22)

📘 Lecture 1 — Managing Intangible Resources and Knowledge Workers

📖 Overview: This lecture introduces the fundamental shift from managing tangible resources (land, machinery, labor) to managing intangible resources (knowledge, brand, talent) in modern organizations. It establishes why knowledge workers are the most critical and fastest-growing segment of the workforce, and why their unique characteristics demand new management approaches for organizational success in the 21st century global economy.

🗂️ Topics Covered

The lecture begins by establishing the growing importance of knowledge and knowledge workers, contrasting tangible and intangible resources. It then explores the categories and economic significance of knowledge workers, citing Peter Drucker's predictions about their decisive role in the world economy. The lecture defines what constitutes a knowledge worker and examines their unique characteristics—autonomy, commitment, fair process, and knowledge sharing reluctance. It concludes by discussing knowledge workers as a distinct class requiring different management, and the concept of knowledge capital as the new means of production in the information age.

📝 Lecture Summary

The Growing Importance of Knowledge and Knowledge Workers

Tangible resources like rupees, land/buildings, motors/machinery, and manual/physical labor are important but can be bought or borrowed. In contrast, intangible resources such as brand image, reputation, information, talent, and knowledge cannot be bought or borrowed. These are essential for organizations to survive and thrive in 21st century global markets.

The rise of information and knowledge-based work has been foreseen for decades. Automation in factories and farms freed workers from physical labor. Over the last half-century, computers and pervasive information created demand for workers who produce information, extract meaning from it, and take action. Knowledge intensive organizations—those with a high proportion of knowledge workers—are the fastest-growing and most successful in leading economies like the United States, Singapore, Finland, and Sweden. Their market value (including perception of knowledge) dwarfs their book values (tangible assets only). Even in industrial companies, knowledge is used to differentiate products and fuel service diversification. As Prof. Quinn noted, roughly 90% of workers in semiconductor manufacturing never touch the process but provide knowledge-based services like marketing or customer service.

Firms with the highest degree of knowledge work tend to be fastest-growing and most profitable. Microsoft is one of the most profitable organizations in history. Pharmaceutical firms produce life-saving drugs with high profit margins. Growth industries generally have high proportions of knowledge workers.

Categories of knowledge workers include: Management; Business and financial operations; Software/hardware and electronic engineers; Architecture engineering; Life, physical, and social scientists; Legal personnel; Health care practitioners; Community and social services; Education, training, and library staff; Arts, design, entertainment, sports, media; System Manager/Analyst, Project Manager. This classification yields about 36 million knowledge workers in the United States alone, or 28 percent of the labor force.

Within organizations, knowledge workers are closely aligned with growth prospects: management creates new strategies, R&D creates new products, marketing packages products to appeal to customers. Without knowledge workers, there would be no new products, services, or growth.

Knowledge Workers and the World Economy

Prof. Dr. Peter Drucker first described knowledge workers substantially in his 1959 book Landmarks of Tomorrow. In 1969, he stated: "To make knowledge work productive will be the great management task of 21st century, just as to make manual work productive was the great management task of the 20th century." In 1997, Drucker added: "The productivity of knowledge and knowledge workers will not be the only competitive factor in the world economy. It is, however, likely to become the decisive factor."

🔑 Definition — Knowledge workers: Workers with high degrees of expertise, education, and/or experience, whose primary purpose involves the innovation/creation, sharing/distribution, or application of knowledge.

📌 Example: A bank teller who simply takes deposits and issues receipts is not a knowledge worker. However, a teller who negotiates deposits, handles partial payments for leases/mortgages, places money in current deposits, notices the customer's total deposits are high enough to advise buying treasury bills or investing in higher-dividend funds—this job involves analysis, heuristics, and technology—is knowledge work.

Knowledge workers are critical to the world economy for several reasons: (1) They are a large and growing category—improving their productivity is essential for overall economic health. (2) They are expensive—organizations employ high-cost talent, so under-productivity is doubly harmful. (3) They are key to economic growth—agricultural and manufacturing work has become commoditized and moves to lowest-cost economies. Work that survives in sophisticated economies has high knowledge injected (e.g., biotechnology manufacturing, precision farming with GPS). Jobs remaining in knowledge-based economies are critical to those countries' survival.

Despite their importance, knowledge workers haven't received sufficient attention. Drucker stated that improving knowledge worker performance is the most important economic issue of the age.

What is a Knowledge Worker?

Knowledge workers think for a living. They live by their wits—heavy lifting is intellectual, not physical. They solve problems, understand and meet customer needs, make decisions, collaborate, and communicate. Examples include: physicians, physicists, scientists, scientific writers, airplane pilots, airplane designers, managers, marketers, software/hardware engineers. They don't necessarily work in knowledge-intensive industries—managers of any company are knowledge workers. Even industrial companies have engineers, researchers, marketers, and planners.

Being a knowledge worker is sometimes a matter of degree. Many people use knowledge, but for knowledge workers, knowledge must be central to the job, and they must be educated or expert. Working with data or information alone isn't enough—it would be difficult without a college degree (notable exceptions: Bill Gates, Michael Dell).

Organizational success depends on the innovativeness and productivity of knowledge workers. However, they pose challenges: they are mobile and concerned with future career positioning; they are dispersed across organizational structure and the globe, yet must collaborate across functions, locations, time zones, and even organizations; they must command a body of knowledge requiring constant updating; and their work is inherently emergent—problems and opportunities are novel and rarely standard or routine.

Knowledge Workers as a Class

Some might argue knowledge workers should be managed like other workers using process improvement approaches. However, if managers gave explicit instructions like "Sharpen your pencil before you start that financial plan," knowledge workers would likely leave or not give full commitment. This autonomy difference is substantial enough to justify treating knowledge workers as a separate class deserving different management approaches.

Commitment matters. In the industrial economy, one could work with one's body even without mental or emotional commitment. This isn't the case for knowledge work—great performance requires mental and emotional commitment. The 3M company approach gives researchers 15 percent of their time to work independently on something they think is important. Knowledge workers are willing to do some directed tasks, but a degree of voluntarism helps a lot.

🔑 Definition — Fair process: Workers, particularly knowledge workers, care not only about the fairness of outcomes but also about the fairness of the process used to arrive at outcomes. Fair process builds trust, unlocks ideas, and profoundly influences attitudes and behaviors critical to high performance.

Knowledge workers value their knowledge—it's all they have, the tool of their trade, the means of production. They naturally have difficulty sharing it, fearing their jobs might be threatened. In early knowledge management, people said "Sharing knowledge is an unnatural act." Companies need proper incentives and assurances to encourage sharing.

💡 Why this matters: In the "flat" world, every knowledge worker in western countries wonders if their job will move to India, China, Korea, or Pakistan. A knowledge worker in Pakistan can provide services cheaper than one in North America (quality being equal). Smart organizations put smart approaches in place for knowledge assets.

The global economy has entered a new age—the "Information Age," "Third Wave," or "Electronic Economy." Future development and growth center on automated manufacturing and information-dependent services. OECD economies depend more strongly on production, distribution, and use of knowledge than ever before. Output and employment expand fastest in high-technology industries (computers, electronics, communications, healthcare, edutainment). In the past decade, high-technology share of OECD manufacturing production and exports has more than doubled, reaching 20-25 percent.

Knowledge (Intangible) Capital

Accelerating the conversion of knowledge into financial gains using Information Age alchemy is the real challenge for contemporary organizations.

In industrial-based economies, growth came from accumulating fixed, tangible assets measured as capital investment. In the knowledge economy, intangible assets or knowledge, combined with information technology and network infrastructure, drive growth and value creation. Knowledge assets include information and knowledge stored in patents, copyrights, corporate data warehouses, employees' brains, processes (work rules), and information systems. These leverage employee knowledge to improve core processes.

Just as the means of production in the Industrial Age was industrial capital (plant, equipment, machinery), in today's economy, the means of production is knowledge capital. The information technology industry provides tools to store, disseminate, and manage these vital corporate assets. Network companies provide platforms for moving knowledge, information, and raw data to diverse locations where they are used to complete core processes and reach end-users.

⭐ Key Takeaways

The most critical concept is that intangible resources like knowledge, brand, and talent now outweigh tangible resources (land, machinery, labor) because tangible resources can be bought or borrowed while intangible ones cannot. Knowledge workers are the fastest-growing and most profitable workforce segment, comprising 28% of the U.S. labor force, and their productivity is the decisive competitive factor in the global economy—as Peter Drucker predicted. Unlike industrial workers, knowledge workers require autonomy, mental and emotional commitment, fair process in decision-making, and incentives to share knowledge, as they view their knowledge as a highly valuable personal asset. The means of production has shifted from industrial capital to knowledge capital, meaning organizations must learn to convert knowledge into financial gains through proper management of patents, data warehouses, and employee expertise. A knowledge worker is defined by high expertise, education, or experience with knowledge being central to the job—a bank teller who merely processes transactions is not a knowledge worker, but one who analyzes deposits and advises investments is.

🧠 Quick Revision Questions

1. Why can intangible resources like brand image and knowledge not be bought or borrowed, while tangible resources like land and machinery can?
2. What percentage of the U.S. labor force are classified as knowledge workers, and what are five categories of knowledge workers listed in the lecture?
3. What did Peter Drucker predict in 1969 and 1997 about knowledge workers and their role in the 21st century economy?
4. What are the four characteristics of the "fair process" approach, and why does it matter more for knowledge workers than industrial workers?
5. What is the difference between a bank teller who is not a knowledge worker and one who is, according to the lecture's example?

📘 Lecture 2 — Dynamics and Interconnected Nature of 21st Century Globe

📖 Overview: This lecture explores the rapidly changing business landscape of the 21st century, emphasizing that survival depends on an organization's ability to adapt to dynamic environments. It introduces knowledge management (KM) as the solution for aligning technical capabilities to create competitive advantage, and traces the historical evolution of KM from ancient practices to modern networked knowledge economies.

🗂️ Topics Covered

The lecture covers the transformation from data processing to the knowledge age, the historical evolution of knowledge management from medieval times through modern scholars, the shift from managing knowledge like physical assets to "baking it into" work processes, the role of the Internet and ICT in enabling knowledge sharing, the characteristics of an interdependent globalized world, the networked knowledge economy, changing corporate landscapes, the demise of traditional jobs, and the phenomenon of global knowledge harnessing.

📝 Lecture Summary

In a Nutshell

The 21st century business landscape is no longer linear or predictable. Survival depends entirely on an organization's ability to adjust to the dynamics of the business environment. Changes in information/communication technology (ICT) have created gaps in access and control of information and knowledge. Knowledge management is the solution for realigning the firm's technical capabilities to create the knowledge that drives the firm forward. Key questions include: "Does your company know what you know?" and "How do you make best use of the knowledge you have?" There is less room for "packaged solutions"; KM means thinking outside the boundaries of current practices. We have progressed from the data processing age (1960s-1970s) to the information age (1980s-1990s) to the knowledge age (2000s). Knowledge workers are the backbone of every successful business; they use technology to reason through problems. To manage knowledge, a company must first inventory its people, systems, and decisions. This self-assessment makes a company more cognizant of its strengths and weaknesses.

🔑 Definition — Knowledge workers: Professional workers who use technology to reason through problems and reach successful solutions, forming the backbone of every successful business. 💡 Why this matters: Understanding the shift from data to knowledge ages helps organizations recognize the premium on innovation and creativity in the unpredictable business environment.

Historical Overview

Knowledge has been a source of competitive advantage for centuries, from medieval master-apprentice relationships to passing "family recipes" between generations. Although such transfer was slow, it opened the door to modern KM methods using faster media like the Internet. The recorded history of knowledge dates to Plato and Aristotle, with modern understanding credited to scholars like Daniel Bell (1973), Michael Polanyi (1958, 1974), Alvin Toffler (1980), and Ikujiro Nonaka (1995). Others like Sveiby (1997) and Stewart (2000) promoted knowledge as the core organizational asset. In the early 1970s, researchers at MIT and Stanford analyzed how companies produced, used, and diffused knowledge — the first essential step in KM evolution. With the Internet, KM became feasible, providing more opportunities for knowledge sharing. KM was briefly presented in total quality management (TQM) philosophy; Professor Deming asked managers to develop their theory of knowledge. Business process reengineering (BPR), downsizing, and outsourcing resolved productivity but drained knowledge from organizations.

In round one of KM, companies managed knowledge assets like physical assets, storing them in knowledge repositories and using supply chain management (SCM) to match supply with demand — "doing things right." In round two, companies realized KM had to be "baked into" the job to bring knowledge when needed and export it anywhere (Davenport 1999) — shifting from "doing things right" to "doing the right thing" (working smarter). Technology focus in the 1990s was on cognitive computing to augment human knowledge work. The Internet demonstrated several KM characteristics: it is an incredible information source available worldwide; with the World Wide Web, users share and update information at will; it uses a universal communication standard protocol TCP/IP; and it provides quicker interaction with fellow knowledge workers.

🔑 Definition — Knowledge repository: A digital storage system where intellectual assets are stored, analogous to a warehouse for physical goods. 📐 Concept: "Doing things right" (round one) versus "Doing the right thing" (round two) — Shift from efficiency-driven prediction based on past trends to working smarter by integrating knowledge into work processes. 📌 Example: Historical knowledge transfer — Medieval masters passed knowledge to apprentices, and families passed "secret recipes" across generations, demonstrating early forms of knowledge transfer and sharing.

Setting the Context: An Interdependent World

Globalization, intense competition, demanding customers, regulatory changes, and technology progress are key challenges affecting businesses. Management books like Thriving on Chaos (Tom Peters, 1987), Competing for the Future (Hamel & Prahalad, 1994), and The World is Flat (Thomas Friedman, 2007) prescribe that organizations must be flexible, adaptive, and continually reinvent themselves — or they won't survive. The single most important driving factor is information and communications technology (ICT). ICT is a business enabler that fundamentally transforms the environment, the way we live and work. The biggest change in IT over the last decade is not continual improvement in functionality but interconnectedness — communications and computer networks are pervasive, connecting organizations, governments, and individuals in ways not previously possible.

Networks are becoming more dynamic, with a new layer of value on top of information — knowledge. We are creating global knowledge networks or webs that connect independent, disparate knowledge, leading to new knowledge and opportunities. Knowledge management is a new strategic focus, but is it fundamental or a consultant's fad? The fundamentalist argument: Knowledge is an important contributor to performance, value, and future prosperity, and must be properly managed and exploited. Too frequently, companies do not know what they know, reinventing the wheel or failing to apply best practice because knowledge has not been shared.

🔑 Definition — Interconnectedness: The pervasive networking of organizations, governments, and individuals through communications and computer networks, enabling relationships not previously possible or economic. 💡 Why this matters: Understanding interconnectedness helps organizations recognize that informal personal networks are often the main way things move forward in business and scientific communities.

The Networked Knowledge Economy

The changing world is described as the post-industrial economy, information society, knowledge era, or networked knowledge economy. This new environment has characteristics distinctive from the industrial era. Old certainties no longer exist — we have witnessed change as never before in the 1990s (demise of Soviet Union, fragmentation of Yugoslavia, Asian economic upheavals). China is emerging as a new economic power. Life seems beset with complexity and uncertainty; secure jobs until retirement no longer exist. For organizations, 'business as usual' is rarely sustainable. The changing corporate landscape shows value shifting to service-related and knowledge-intensive industries (health, education, finance, information systems, media, telecommunications). During one year, US household spending on 'old economy' items (food, cars, appliances, clothing) increased less than 1%, while spending on 'new economy' items (telephone, entertainment, cable TV, financial services, home computers) rose 12.5%.

The demise of jobs is associated with dispersion of business activity and growth of self-employment. Most employees can no longer rely on organizations for lifetime jobs. There is rapid growth of self-employment by professionals, especially those with large company experience. Employment should be viewed not as full-time jobs but as work activities parceled out cost-effectively to those with necessary knowledge and skills. The networked economy enables creation of electronic work markets both within and beyond firms.

Globalization is steadily increasing. Many multinational companies design and manufacture at multiple locations based on access to skills, markets, and infrastructure. Manufacturing has migrated from higher-wage countries (Taiwan, Malaysia) to lower-wage countries (China). Global knowledge — in the industrial economy, reasons for going global were economies of scale and reducing transportation costs. Now globalization is a response to regional specialization and expansion of long-distance relationships. Through the Internet, firms can reach distant markets at prices different from local customers. A global enterprise takes advantage of unique skills and resources wherever they are located — software expertise in India, artistic weaving skills in Pakistan, Africa, or Bangladesh. This opportunity to harness knowledge on a scale unimaginable before the Internet makes globalization attractive and exciting.

🔑 Definition — Networked knowledge economy: The post-industrial environment characterized by global knowledge networks, interconnectedness, and value derived from knowledge-intensive activities rather than physical production. 📌 Example: US household spending comparison — 'Old economy' items (food, cars, appliances, clothing) grew less than 1%, while 'new economy' items (telephone, entertainment, cable TV, financial services, home computers) grew 12.5% during the same period. 📌 Example: Global knowledge harnessing — A global enterprise draws on software expertise from India and artistic weaving skills from Pakistani, African, or Bangladeshi villagers, using Internet-enabled networks to coordinate value creation.

⭐ Key Takeaways

Students must remember that the 21st century business environment is fundamentally different from the industrial era, requiring organizations to be flexible, adaptive, and focused on knowledge as a core asset. The evolution of KM has progressed from managing knowledge like physical assets (round one) to "baking it into" work processes (round two), shifting from "doing things right" to "doing the right thing." ICT and the Internet have been the primary drivers of change, enabling interconnectedness and global knowledge networks that allow organizations to harness specialized skills worldwide. The networked knowledge economy is characterized by the demise of traditional lifetime jobs, the rise of self-employment and electronic work markets, and the steady increase of globalization where value is created by combining distributed knowledge. Finally, the fundamental challenge remains that companies often do not know what they know, making KM not a fad but an essential strategic capability for survival and competitive advantage.

🧠 Quick Revision Questions

1. What are the three historical ages of business technology evolution, and what characterizes each?
2. What is the difference between "round one" and "round two" of knowledge management, and what key shift in philosophy distinguishes them?
3. How did the Internet change knowledge management capabilities, and what are its four key characteristics listed in the lecture?
4. What is the "networked knowledge economy," and how does it differ from the industrial era in terms of employment and value creation?
5. Why does the lecture argue that knowledge management is not just a consultant's fad, but a fundamental strategic necessity?

📘 Lecture 3 — Forces Shaping the Future and the Mega Trends of Knowledge Economy

📖 Overview: This lecture explores the fundamental mega trends that are reshaping the global economy toward a knowledge-based structure. It explains how information and knowledge are becoming dominant forces across all industries, and examines the critical role of technology, networking, and virtualization in driving this transformation.

🗂️ Topics Covered

This lecture examines John Naisbitt's concept of mega trends and applies it to the knowledge economy, covering the shift toward information and knowledge-based industries with characteristics like smart products and intangibles. It introduces Masuda's quaternary industries, explores the dual nature of networking (hard and soft), analyzes virtualization at multiple levels from products to communities, and concludes with technology as a fundamental driving force, including Moore's Law and ICT improvements decade by decade.

📝 Lecture Summary

The Mega Trends

The term mega trend was used by John Naisbitt to describe a fundamental underlying trend shaping the future. In his 1982 book Megatrends, he identified ten key shifts reshaping the world, including the shift from industrial society to information society, national economy to world economy, and hierarchies to networking.

💡 Why this matters: Understanding mega trends helps organizations anticipate and adapt to fundamental economic shifts rather than reacting to surface-level changes.

Information and knowledge based industry

Information and knowledge are pervading all sectors of industry as well as creating new industries based around them. There are several distinctive characteristics of this new economy.

1. Every industry is becoming more knowledge intensive. Even in agriculture, knowledge adds value. By combining knowledge about the effect of a fertilizer, soil condition, the state of plant growth (using information from satellite photographs), and forecast weather conditions, farmers can use 40 per cent less fertilizer on their crops, yet achieve the same results. A new generation of combine harvesters automatically measures the weight and moisture content of corn and calculates yields per acre.

2. Smart products. Another manifestation of knowledge intensity comes in so-called 'smart products'. These use information or knowledge to provide better functionality or service that can command premium prices. There is a smart tire that senses the load it has to carry and adjusts its pressure accordingly. Services can be enhanced through better customer knowledge. Marriott Hotels keeps track of individual references to offer superior service when customers check in.

3. Higher information to weight ratios. The value of electronics in cars now exceeds that of the value of the metal chassis, which itself, through better knowledge of structures, is significantly lighter than predecessors. At the macroeconomic level, at the start of the twentieth century the information-to-weight ratio was roughly 1:1. Today the financial value is twenty times higher, while the physical weight of goods is about the same.

4. Value in intangibles. The market value of most companies is several times higher than the value of their physical assets as recorded in their balance sheets.

5. Trade in intangibles. The ultimate information-to-weight ratio is the weightless product or service. Financial markets are almost wholly intangible. Futures options and complex derivatives are perhaps the ultimate intangible knowledge product, created through human ingenuity.

New knowledge industries

A consequence of these trends is the creation of industries that are almost wholly information and knowledge based. Y. Masuda describes a whole set of quaternary industries, as distinct from primary (agricultural), secondary (manufacturing), and tertiary (services) industries.

🔑 Definition — Quaternary Industries: Information and knowledge-based industries that go beyond traditional agricultural, manufacturing, and service sectors, including information industries, knowledge industries, arts industries, and ethics industries.

Quaternary industries as defined by Masuda (1980):

New knowledge-intensive industries are being created constantly. The biotechnology industry is only fifteen years old but has more than 2000 companies and is expected to have annual revenues in excess of $500 billion by the year 2010.

Networking - hard and soft

There are two defining characteristics that are fundamental in practice:

1. Networked organizations are less about organizational structure and more about informal human networking processes.
2. The technology of computer networking both underpins and enhances human networking.

Virtualization

A key effect of information and communications technologies such as the internet is an increase of virtualization in business activities and ways of working. Virtualization overcomes constraints of time and distance.

🔑 Definition — Virtual Corporation (time-based view): A temporary network of independent companies that co-ordinate activities to meet a common objective, such as new product development or to meet a customer need.

🔑 Definition — Virtual Corporation (locus-based view): An organization distributed geographically and whose work is coordinated through electronic communications.

Virtualness can operate at several levels, from individual to inter-organizational, giving rise to many types of virtuality.

Making a virtue of virtuality

Some common types of virtuality include:

1. Virtual products and services. The cost of an electronic transaction is typically a tenth of that of the corresponding traditional transaction. Dell generates over $5 million of business a day on the Internet. Amazon.com sells exclusively this way.

2. Virtual working or telework. Several million people in Europe and thousands in Pakistan now telework for some or part of their working week. With cellular phones and notebook computers, it has been said that "my office is where I hang my modem".

3. Virtual offices. The physical office is replaced by office services. IBM is one of many companies that have adopted 'hot-desking' – employees do not have personal workspaces but are allocated desks whenever they are in the office.

4. Virtual teams. Employees work at locations convenient to them. Examples include engineering teams at Toyota, Ford, or Boeing in locations across Europe and the USA.

5. Virtual organizations. These can range from a stable supply network that works as a single organization to a loose federation of independent firms that come together temporarily.

6. Virtual communities. Instead of a local community, a virtual community is one of shared interests, whatever the location, found on Internet newsgroups, discussion lists, or on an organization's intranet.

Common features distinguishing virtualization from traditional forms:

• Information and communications technology allows operations to be dispersed
• Barriers of time and space are reduced (or even disappear completely)
• Organizational structures are network-like and more dynamic
• The interface with customers and markets is different
• Employees and associates adopt new patterns of work
• The locus of knowledge is diffused – not necessarily in a specific place

Technology – a fundamental driving force

Underpinning each mega trend is the fundamental driving force of technology. Technology amplifies human capabilities. In the industrial revolution, the core technology was steam power that gave humans a 15 times improvement in price-performance over manual methods. In the knowledge era, it is ICT that is boosting our ability to process information.

📐 Formula — Moore's Law: Performance doubles and costs halve roughly every eighteen months. → Plain-English meaning: Computing power increases exponentially while costs decrease at the same dramatic rate.

The Massachusetts Institute of Technology (MIT) landmark study, Management in the 1990s, indicated that over a ten-year period, IT showed a 25 times price-performance improvement, compared to 1.4 times for the six other most improved product groups. This rate of improvement equates to an industrial revolution every seven years.

The Revolution Continues

Moore's Law seems set to continue at least through to 2010, although there are likely to be changes in specific technology used. X-ray lithography should replace optical lithography, leading to circuits only 0.01 microns wide by 2010, compared to 0.25 microns today, and processors that are 1000 times more powerful. Thereafter, new technologies such as holographic memory and molecular computers should maintain the fundamental trend.

ICT Trends: Decade on Decade Improvements

⭐ Key Takeaways

This lecture establishes five critical concepts for understanding the knowledge economy. First, every industry is becoming more knowledge-intensive, demonstrated by examples like precision agriculture using satellite data to reduce fertilizer use by 40% while maintaining yields. Second, value is shifting from physical assets to intangibles, with market values of companies far exceeding their balance sheet assets, and weightless products like financial derivatives representing the ultimate knowledge products. Third, Masuda's quaternary industries framework shows how entirely new knowledge-based sectors (information, knowledge, arts, and ethics industries) have emerged beyond traditional agricultural, manufacturing, and service classifications. Fourth, virtualization is transforming work at multiple levels—from virtual products and telework to virtual teams, offices, organizations, and communities—all enabled by ICT that overcomes constraints of time and space. Fifth, Moore's Law (performance doubles, costs halve every 18 months) demonstrates that technology is the fundamental driving force, with ICT achieving a 25-fold price-performance improvement in ten years compared to only 1.4-fold for other product groups, effectively compressing an industrial revolution into every seven years.

🧠 Quick Revision Questions

1. What are the five distinctive characteristics of the information and knowledge-based economy described in this lecture?
2. According to Masuda, what are the four categories of quaternary industries, and provide one example for each?
3. Explain the difference between a virtual corporation defined by time (temporary network) versus one defined by locus (geographical distribution).
4. What are the six common features that distinguish virtualization from traditional organizational forms?
5. What is Moore's Law, and what evidence does the lecture provide about the price-performance improvement rate of IT compared to other products?

📘 Lecture 4 — Managerial Considerations for Internet Customers and K-Based Marketing

📖 Overview: This lecture contrasts the Industrial Age and Information Age paradigms for managing organizational assets, emphasizing the shift from tangible to intangible knowledge assets. It explores how leading organizations worldwide have adopted knowledge management practices to improve decision-making, customer responsiveness, and innovation, while also highlighting the critical success factors and common pitfalls in implementing KM.

🗂️ Topics Covered

The lecture begins by comparing Industrial Age versus Information Age managerial perspectives, contrasting tangible asset management with knowledge asset management. It then examines Knowledge Management in Practice, including perceived benefits from KM and detailed case studies of organizations like The World Bank and Skandia. A comprehensive table presents Knowledge Management Practices across 25+ global companies. The lecture covers Intellectual Capital concepts, six attributes of knowledge products and services, new competitive imperatives in the e-commerce era, common KM pitfalls, and the key factors triggering interest in knowledge management.

📝 Lecture Summary

Industrial Age vs. Information Age Paradigms

In the industrial era, companies operated on assumptions rooted in tangible-assets-based explanations that tracked the physical transformations of atoms into finished goods. The Sultan of Brunei became wealthy by extracting petroleum atoms transformed into gasoline. These companies had highly standardized operational procedures for simple products, squeezing out design complexity and customizability.

In the modern knowledge-based era, the industrial approach can be suicidal because reverse-knowledge engineering enables competitors to easily produce the same processes/products. PC manufacturing exemplifies commoditization, while Microsoft, Oracle, and SAP capture unique knowledge in code. Bill Gates became wealthier than the Sultan of Brunei by compiling bits into programs—wealth created by selling new and reused computer code.

💡 Why this matters: The assumptions governing knowledge assets differ radically from those governing industrial-era tangible assets, requiring fundamentally different management approaches.

Information Age managers see a set of knowledge assets distributed among people, machines, and processes. They recognize some knowledge assets should remain in employees' brains as intellectual capital, creating leverage and flexibility. The critical problem for management is how to best introduce, utilize, and deploy knowledge throughout core processes.

Industrial Age managers see core processes as piece parts of a machine operating in predetermined ways. Ensuring interchangeable parts is a common goal. Knowledge is embedded within machines with tightly defined job descriptions. Supervision ensures employees behave within well-defined limits—a "trees through the forest" approach based on reductionist assumptions.

🔑 Definition — Knowledge Assets: Intangible resources distributed among people, machines, and processes that are coordinated to produce desired outputs, representing the primary engine of wealth creation in the Information Age.

Knowledge Management in Practice

Organizations worldwide are adopting KM practices at an accelerating pace, combining cultural and procedural changes with enabling technology for bottom-line improvements.

Perceived Benefits from Knowledge Management:

• Improved decision making: 89%
• Improved responsiveness to customers: 84%
• Improved efficiency of people and operations: 82%
• Improved innovation: 73%
• Improved products/services: 73%

The World Bank — In 1996, the president announced the organization would manage and share knowledge with clients worldwide via the Internet. Their conceptual model treats KM as a process of creating, organizing, and applying data with seven goals: assembling a large knowledge base, creating a help desk, establishing an expert's directory, developing country statistics, articulating engagement information, providing dialog space, and facilitating external access.

Skandia — In the early 1980s, managers found traditional accounting did not accurately reflect value in their knowledge-intensive service company. In 1991, Leif Edvisson was named director of intellectual capital management. CEO Bjorn Wolrath viewed intellectual capital (IC) reporting as a tool for internal decisions and describing knowledge assets to shareholders. From 1991–1995, alliances grew from 50,000 to 65,000 and employees from 1,100 to 2,000. In May 1995, Skandia released the first public IC annual report as a supplement to the financial report.

🔑 Definition — Intellectual Capital (IC): A company's employee expertise, unique organizational systems, and intellectual property. When book value is subtracted from market value, the remaining difference represents intellectual, knowledge, and market capital, including patents and other intangibles.

Knowledge Management Practices (Selected Company Examples)

Swedish companies have been pioneers in KM, first to monitor and systematize intelligence activities. Four common features: balance between strategy and operational objectives; systematic supply-on-demand intelligence; information-sharing cultures with systematic community meetings; emphasis on knowledge-sharing acquisition processes.

Intellectual Capital in Practice

At Home Depot, the company uses a nine-box grid system to measure each employee's performance and potential, measuring leadership ability, cultural fit, financial acumen, and project management capabilities. The company posts a bulletin on its intranet with quick references so knowledge is available for employees to pass to customers.

Six Attributes of Knowledge Products and Services

Botkin (1999) suggests these attributes of Smart Business:

1. Learn — The more you use them, the smarter they get and the smarter you get
2. Improve with use — Enhanced rather than depleted when used; grow up instead of being used up
3. Anticipate — Knowing what you want, they recommend what you might want next
4. Interactive — Two-way communication between you and them
5. Remember — Record and recall past actions to develop a profile
6. Customize — Offer unique configuration to your specifications in real time at no additional cost

New Competitive Imperatives in E-Commerce

During the 1960s–1970s, technology focused on automating high-volume static processes. E-commerce (late 1980s–1990s) showed how IT could implement new business ways. New imperatives include:

• Reacting instantly to opportunities → decentralized decision making at front lines, building mutual trust between knowledge workers and management
• Building sensitivity to "brain drain" — Expertise gravitates toward the highest bidder; key HR question: "How does the firm replace expertise when it leaves?"
• Ensuring successful partnering with suppliers, vendors, customers—requires cooperation and coordination of work

Common KM Pitfalls

1. Failing to modify compensation to reward teamwork—traditional "information-hoarding" compensation doesn't work in knowledge-sharing environments
2. Building a huge database for the entire company—generalized systems don't work; knowledge must be stratified by specialized expertise
3. Viewing KM as only technology or HR area—poor coordination between HR and IT defeats KM's purpose
4. Placing too much emphasis on technology—technology is only the enabler; knowledge must be organized for human decision makers
5. Introducing KM via a simple project to minimize losses—should start with strategy and champion focusing on high-profile projects
6. Pursuing KM without being ready—corporations under classical management need major culture, attitude, and communication changes
7. Having poor leadership—KM needs determined champions and top management commitment (e.g., GE's Jack Welch established a KM university and taught classes himself)

Factors Triggering Interest in Knowledge Management

• Pace of change accelerated dramatically—innovation is the one core competency needed by all organizations (Drucker 1969)
• Globalization and geographic dispersion changed organizational scope
• Downsizing and reengineering caused staff attrition and knowledge drain—reengineering assumed one-time fixes that became new problems
• Networking and data communication made knowledge sharing easier and faster—technology alone is insufficient
• Increasing dominance of knowledge as basis for improving efficiency and effectiveness

🔑 Definition — Knowledge Management (KM): Systems developed to gather, organize, refine, and distribute knowledge throughout the business, embedded into every business process—new products, distribution channels, marketing strategies, and industry definitions—with technology as the backbone and human components necessary to utilize it.

⭐ Key Takeaways

The fundamental shift from Industrial Age to Information Age management requires viewing organizational assets as knowledge assets rather than physical parts—Microsoft's Bill Gates became wealthier than the Sultan of Brunei by creating value through bits rather than atoms. Successful KM implementation depends on combining cultural changes, procedural changes, and enabling technology with top management commitment, as demonstrated by Skandia's pioneering intellectual capital reporting and The World Bank's seven-goal knowledge sharing initiative. Knowledge products must learn, improve with use, anticipate needs, interact, remember, and customize, while organizations must avoid seven common pitfalls including over-emphasis on technology and poor leadership. The core competence of any business is knowledge, which can and should be embedded into every business process with technology as the backbone and human components as the necessary utilization force.

🧠 Quick Revision Questions

1. How do Industrial Age and Information Age managers differ in their view of a company's core processes and assets?
2. What were Skandia's key innovations in intellectual capital management between 1991 and 1995?
3. List the six attributes of knowledge products and services according to Botkin (1999).
4. What are three of the seven common pitfalls organizations face when implementing knowledge management?
5. According to the lecture, what distinguishes knowledge products from traditional industrial products in terms of how they behave with use?

📘 Lecture 5 — Framework with New Structure, Strategies and Levers of Strategy

📖 Overview: This lecture explores knowledge as a strategic imperative in modern organizations, examining how companies must adapt their structures and strategies to leverage knowledge effectively. It introduces seven strategic levers for managing knowledge, ranging from customer knowledge to knowledge assets, providing a comprehensive framework for knowledge-based competitive advantage.

🗂️ Topics Covered

The lecture covers knowledge as the new strategic imperative, new organizational structures and strategies for the knowledge economy, the innovation imperative for survival, seven levers of strategy including customer knowledge, knowledge in products/services/people/processes, organizational memory, knowledge in relationships, and knowledge as an asset. Each lever is examined with practical examples and implementation approaches from leading organizations.

📝 Lecture Summary

Knowledge: The Strategic Imperative

Every few years a new management philosophy captures strategic attention. In the 1990s, total quality management (TQM) and business process re-engineering were prominent. More recently, knowledge has taken centre stage as the primary source of competitive advantage.

New Strategies, New Structures

Successful strategies will exploit developments in IC technology, taking advantage of the Internet and electronic commerce to create global markets. Value to customers will be enhanced through information and knowledge. Information products (like databases) and knowledge-based services (like consultancy) will become important revenue generators. Technology will be used to tailor services to individual customer needs and develop closer customer relationships.

In terms of structure, responsive organizations will be more networked. Virtual teams and virtual organizations will create value through unique combinations of skills flexibly combined as needed. The future organization will consist of networks of self-managed teams that reconfigure to adapt to opportunity and change. Teams, not functions or departments, will become the core productive units.

Strategies based on competitive advantage – conventional wisdom in the 1980s – may have done more harm than good. Sustainable wealth comes through creating and growing new markets, not competing in existing ones. Thus competing IT manufacturers increasingly co-operate on standards, while car-makers collaborate on safety.

The Innovation Imperative

One main challenge for any organization is survival. The average life expectancy of most firms is low, around twenty years. One-third of all businesses in 1970 had disappeared thirteen years later. Today the environment is more turbulent and dynamic. Yet companies like Shell (founded 1907), Siemens (1847), Du Pont (1802) and 3M (1902) survive and thrive through adaptation and innovation.

Innovative 3M introduced 500 new products in 1996. A 1997 survey by Arthur D. Little of 700 companies in twenty-three countries showed 84% believed innovation was more crucial than in 1991. They seek innovation for gaining new customers and creating new markets with innovative products, services and processes.

Of all responses to challenges, the most important can be summarized in two words: fast innovation. Continuous improvement gives incremental benefits. What is needed is radical innovation – improvements of not just a few percent but a factor of ten. Prescription spectacles went from days/weeks to 1-2 hours. BP reduced deep-sea oil well drilling from 100 days to five days or less by applying learning gained elsewhere. Research at Rensselaer Polytechnic found a key characteristic of breakthrough organizations is a free flow of ideas, in and out. In every case, networking played a big role: 'the most successful researchers have wide-ranging networks of people'. They have discovered knowledge networking.

💡 Why this matters: Radical innovation through knowledge networking enables organizations to achieve order-of-magnitude improvements rather than incremental gains, directly impacting survival and competitiveness.

Seven Levers of Strategy

Analysis of many cases indicates seven commonly used levers for securing strategic advantage through knowledge:

1. Customer knowledge – developing deep knowledge through customer relationships
2. Knowledge in products and services – embedding knowledge in products and surrounding them with knowledge-intensive services
3. Knowledge in people – developing human competencies and nurturing an innovative culture
4. Knowledge in processes – embedding knowledge into business processes
5. Organizational memory – recording existing experience for future use
6. Knowledge in relationships – improving knowledge flows across boundaries
7. Knowledge assets – measuring intellectual capital and managing its development

The core levers are knowledge in people, processes and products. In most situations, winning strategies are developed by concentrating on just two or three of the seven levers.

Customer Knowledge

Virtually every survey ranks customer knowledge as an organization's most important knowledge. Most companies know less about customers than they claim, placing too much reliance on traditional market research and customer satisfaction surveys that reveal little of customers' real wishes. Customers can provide vital insights into product and service applications, but this requires forging close working relationships.

Developing good customer knowledge needs effective environment scanning and market intelligence systems to gather and collate knowledge. Such systems should cover customers, markets, technology, social, political, economic and regulatory developments.

Knowledge in Products and Services

Almost every product is knowledge intensive. When buying a prescription drug, we buy not merely a tablet but the knowledge it encapsulates – therapeutic benefits and side effects from years of clinical trials. Genetic knowledge creates genetically modified foods like disease-resistant potatoes or square tomatoes.

Companies hold vast amounts of exploitable knowledge including applications knowledge, market knowledge, and how to solve user problems. Much is accumulated during product development and testing but overlooked. Only a fraction is encapsulated into the final product, leaving under-utilized a rich knowledge source that could create additional revenues.

This knowledge can be exploited through additional paid services (consultancy, training) or by making products 'smart' or 'intelligent'. There is an intelligent oil drill that 'knows' the shape of the reservoir. Products can be customized by combining product and customer knowledge – examples include personalized daily news bulletins and Campbell Soups' 'Intelligent Quisine' for people with hypertension or high cholesterol, delivering weekly packages of nutritionally designed meals.

Knowledge in People

'People are our most valuable asset' runs the line in many annual reports. Companies that truly believe this apply the knowledge in people lever through a competence or learning lens. One underlying model is the action-learning cycle:

📐 Formula: Action-Learning Cycle → (Plan → Act → Observe → Reflect) → continuous improvement through experience

• Plan: think, conceptualize, devise a set of actions
• Act: do, gain experience of 'theory in practice'
• Observe: record experiences, share knowledge with others
• Reflect: consider what has been learnt and how to make improvements

Learning programs mesh competence development at three levels: individual (personal development plans), team (learning processes encouraging knowledge sharing), and organizational (competence measurement, corporate universities, HR policies rewarding learning and knowledge sharing). Motivating knowledge workers so they work energetically and are committed to success is another important aspect.

Many organizations fail to effectively use people's knowledge. They allow insufficient time for learning or reflection, regard people as hired hands rather than borrowed brains, and dictate what to do with little discretion. Employees feel undervalued and will 'walk' at the first opportunity, taking their knowledge.

Shell exemplifies nurturing and developing people, with an initiative to 'harness this talent' and make 'better use of this intellectual capital'. Its focus is developing an infrastructure for learning and knowledge leverage, with open learning centers, databases on the intranet, knowledge communities, and skills for quality person-to-person dialogue and reflection.

Teltech Resources combines both product and people levers. It manages a knowledge network of 3000 human experts (academics, industry experts, recent retirees with specialist knowledge). Knowledge analysts provide a human interface between clients, the expert network, and 1600 technical databases. Explicit knowledge is structured according to a thesaurus of knowledge domain classifications. In one case, a medical products developer's heart pump leak problem was solved by an expert in submarine technology whose equipment operates in similar environments.

Knowledge in Processes

Every business process contains embedded knowledge. Ad hoc activities performed by people with specialist knowledge become codified into routine processes, then more readily diffused throughout an organization. However, much tacit knowledge is frequently needed to perform processes effectively and handle exceptions. Explicit process knowledge is typically accompanied by training, procedure manuals and access to experts.

One way to enrich knowledge in processes is to embed backup resource material. Access to human expertise is available through 'click here for help' screen icons, triggering email or computer-generated phone calls to human experts. Other organizations use workflow software to blend computer-held knowledge with human knowledge, applying rules to determine which transactions are automatic and which require human intervention.

Organizational Memory

This lever addresses 'knowing what you know' and avoids repeating past mistakes, drawing lessons from similar situations. Organizational memory exists in people's brains, records, filing cabinets, computer files, physical surroundings, and external sources.

A common approach is to capture important knowledge in explicit form into knowledge databases – document management systems, groupware (Lotus Notes), or intranet web pages. These may contain pointers to knowledge rather than knowledge itself. Examples include:

• Customer histories: interactions with customers (products bought, sales visit reports)
• Best practices: Chevron has databases and resource maps organized by Baldridge quality award categories
• Products and technologies: details of products and history

Explicit knowledge bases typically contain less than 10% of an organization's memory. Therefore, other approaches help access experts' minds, including on-line directories of expertise (Yellow Pages, structured by skill and discipline rather than department). Novartis added Blue Pages with external expert details. Knowledge-sharing events provide another way – Thomas Miller & Co. runs 'knowledge in a nutshell' events where experts give talks recorded on video for distribution. The key is making ongoing experience capture an integral part of everyday work through decision diaries, learning histories and post-project reviews.

Knowledge in Relationships

Many companies have invaluable knowledge developed through individual relationships with customers, suppliers, business partners, and trade associations. When a salesperson leaves, it's not just product or customer knowledge lost, but much of the customer relationship – shared knowledge of behaviors, motivations, personal characteristics, ambitions and feelings. Such depth is not easily replaced.

Organizations can deepen relationship knowledge by increasing interaction with the outside world through regular meetings for knowledge exchange and shared databases. Toshiba collects comparative data on suppliers ranking 200 quantitative and qualitative factors, with an active supplier network where knowledge is shared and suppliers are integrated into future strategies.

Extranets provide another way to develop wider linkages. By increasing contacts with key stakeholders at all levels and functions, organizations become less vulnerable to a single contact's loss. Relationship marketing goes beyond loyalty cards – customer relationship knowledge comes through exploring mutual interests, extensive dialogue, and jointly creating new business opportunities. Activities previously considered confidential (product planning, marketing campaigns, HR competency development) are extended to involve stakeholders. Social events and corporate hospitality also strengthen relationship knowledge.

Knowledge as an Asset

This final lever builds on measuring and managing intellectual capital. While organizations track physical plant and machinery in detail, few devote comparable attention to intellectual capital, yet this is much more valuable. The starting point is understanding its different components.

Intellectual assets are categorized into three groups:

🔑 Definition — Human capital: knowledge, competencies, experience, know-how in individuals' minds

🔑 Definition — Structural capital: "that which is left after employees go home for the night" – processes, information systems, databases

🔑 Definition — Customer capital: customer relationships, brands, trademarks

Dow Chemical exemplifies this lever. In 1994 it had over 29,000 patents worldwide, but maintaining patent validity can cost up to $250,000 over its lifetime. Its Intellectual Asset Management team developed a framework for actively measuring and managing its patent portfolio, finding many patents not effectively exploited and others without ownership. It took measures to exploit patents through internal use, licensing or sale, while allowing others to lapse. Within three years, the team generated $125 million in additional revenues – their original target for 2000.

⭐ Key Takeaways

Knowledge has become the central strategic imperative for organizations, requiring a shift from competitive advantage strategies to creating and growing new markets. The seven strategic levers provide a comprehensive framework, with customer knowledge consistently ranked as most important. Successful knowledge management integrates explicit and tacit knowledge through multiple channels—embedding knowledge in products, processes, people, and relationships while building organizational memory. The action-learning cycle (Plan-Act-Observe-Reflect) is fundamental for developing people knowledge, and intellectual capital must be actively measured and managed as the organization's most valuable asset, as demonstrated by Dow Chemical's $125 million patent portfolio optimization.

🧠 Quick Revision Questions

1. What are the seven levers of strategy for securing strategic advantage through knowledge?
2. Explain the action-learning cycle and its three levels of competence development.
3. Why is customer knowledge considered the most important knowledge, and how can organizations develop it?
4. What are the three categories of intellectual assets, and how did Dow Chemical apply this knowledge lever?
5. How does Teltech Resources combine both product and people knowledge levers in its business model?

📘 Lecture 6 — Historical Shifts in World Economies and the Role of Knowledge and Intelligence

📖 Overview: This lecture traces the evolution of management demands across three major economic eras — Agrarian, Industrial, and Information/Knowledge ages. It explains how the role of workers and managers has transformed and introduces the critical importance of knowledge-based resources and human capital as sources of sustainable competitive advantage in the 21st century.

🗂️ Topics Covered

The lecture begins with a reflection on the distinction between data, information, knowledge, and wisdom. It then provides a historical review of management demands from the Agrarian age through the Industrial Revolution into the Information/Knowledge age. Key topics include the agrarian lifestyle and skill transmission, the invention of the steam engine and factory system, the emergence of managers and labor unions, the shift to a service-driven economy, the knowledge perspective and paradigm shift, the resource-based view of the firm, and a detailed analysis of human capital and knowledge-based resources as strategic assets.

📝 Lecture Summary

Where is Wisdom?

The lecture opens by clarifying that data and information do not convey wisdom, and knowledge is also not wisdom. As knowledge increases, one's awareness of ignorance also grows larger. Education is described as a progressive discovery of our own ignorance. When information and knowledge are impregnated with worthy purposes and principles, you have wisdom.

History of Management:

To understand the current management dilemma, we review the demands on management from the Agrarian age through Industrial Revolution and into the Information/Knowledge age, which has brought with it the quickening and flattening pace of change.

The Agrarian age:

During the agrarian age, people worked first as hunters and gatherers and then as farmers. Most people depended on land, and the rhythm and pace of life were defined by the seasons. Skills were passed down through families, and trade skills were learned under the apprentice/mastery system.

The Industrial age:

The agrarian way of life changed forever when James Watt invented the steam engine in 1763. Steam power helped in steel production and increased our ability to produce goods, bringing the beginning of the Industrial Revolution. The higher tensile strength of steel beams enabled the construction of large factories. Labor came from the land and villages as people flocked from farms to work in cities. The capitalists who owned machines required a new type of worker — the manager, needed to tell poorly educated workers what to do. The steam engine allowed factories to run 24 hours a day, seven days a week, changing the pace of life forever. Early labor exploitation led to the birth of the union movement to fight injustice.

The fundamental principle behind the industrial revolution was that managers needed to be intelligent and trained to direct workers' activity. Managers directed and workers worked. The central idea became that managers know what to do, they tell the workers, and workers do it.

The Information age:

We have moved from the industrial revolution into the information age. Much information now moves down glass fibers at light speed. The pace of change is quickening. Manufactured goods account for far less than they used to, and economies are now more service driven. The endless range of choice has shifted business to a customer focus, customer value, customer loyalty, and creativity to differentiate from competition. In the information age, it is not possible for managers alone to satisfy customers and all stakeholders.

Knowledge Perspective and Paradigm Shift:

It is not only the information and knowledge of select few managers required to sustain and grow in the 21st century global market. To deal with the customer market, you must take care of the internal employee market. Employees know the business process, sales process, marketing process, and operation process. If they are happy and competent, they will provide better service to external customers. The experience and expertise of staff must be continuously enhanced through learning.

Firms must compete in a complex context transformed by globalization, technological development, and increasingly rapid diffusion of new technology. This new landscape requires firms to look to new sources of competitive advantage.

One popular approach is the resource-based view of the firm. According to this view, the explanation for why some firms succeed and others fail can be found in understanding their resources and capabilities. Only those resources that are rare, valuable, and difficult to imitate provide a sustainable competitive advantage.

🔑 Definition — Resource-based view of the firm: A framework explaining that a firm's success depends on its unique resources and capabilities, and only rare, valuable, and difficult-to-imitate resources provide sustainable competitive advantage.

Tangible resources (buildings, machinery, capital) were the most important in the traditional landscape. However, intangible resources are becoming more important. Examples include reputation, brand equity, and most importantly, human capital. Intangible resources are more likely to produce competitive advantage because they are truly rare and more difficult to imitate.

💡 Why this matters: In the modern economy, physical assets can be easily copied. A firm's true competitive edge lies in its people, their knowledge, and the systems that leverage this knowledge — assets competitors cannot easily replicate.

Human Capital as a Strategic Resource

Human capital is a general term referring to all resources individuals directly contribute: physical, knowledge, social, and reputation. During the industrial age, human capital was valued for physical resources like strength, endurance, and dexterity. In the current landscape, human capital is valued for intellect, social skills, and reputation.

Knowledge-based resources refer to skills, abilities, and learning capacity. People develop these through experience and formal training. Social resources (or social capital) include personal relationships that bind organization members and link them to external sources of human capital.

It is not enough to acquire individuals with such attributes. Organizations must develop structures, systems, and strategies to exploit these resources for competitive advantage. For example, a football team that acquires a strong passing quarterback only gains advantage when it shifts its offensive strategy. Professional service firms leverage human capital by forming project teams led by senior partners, allowing younger associates to gain tacit knowledge by doing.

Carly Fiorina, CEO of Hewlett-Packard, emphasized: "The most magical and tangible and ultimately the most important ingredient in the transformed landscape of 21st century and in knowledge based economy is people."

🔑 Definition — Human capital: All resources that individuals directly contribute to an organization, including physical, knowledge, social, and reputation resources. In today's economy, it is valued for intellect, social skills, and reputation rather than physical attributes.

Knowledge-Based Resources

Knowledge-based resources include all intellectual abilities and knowledge possessed by employees, as well as their capacity to learn and acquire more knowledge. These resources are extremely important for sustaining competitive advantage for several reasons:

First, the nature of work has changed so that many jobs require people to think, plan, or make decisions rather than to lift, assemble, or build. This work requires both tacit and explicit knowledge.

Second, work continues to change in unpredictable ways. It is difficult to state what kinds of knowledge a person needs now, and impossible to predict future needs. Change and unpredictability mean that knowledge-based resources such as the ability to learn and adaptability are extremely important. Some organizations have begun rewarding employees financially when they demonstrate an ability to acquire and master new knowledge.

⭐ Key Takeaways

The evolution from Agrarian to Industrial to Information ages has fundamentally changed what organizations value in workers — from physical strength to intellect, learning capacity, and adaptability. Managers can no longer be the sole source of knowledge; organizations must leverage the expertise of all employees. The resource-based view teaches that sustainable competitive advantage comes from rare, valuable, and difficult-to-imitate intangible resources, especially human capital. Knowledge-based resources (skills, abilities, learning capacity) and social capital (relationships) are now the most critical strategic assets. Simply acquiring talented people is insufficient — organizations must develop systems and strategies to leverage these resources effectively.

🧠 Quick Revision Questions

1. According to the lecture, what happens to your ignorance as your knowledge increases?
2. What invention in 1763 marked the beginning of the Industrial Revolution, and what new role emerged as a result?
3. According to the resource-based view of the firm, what three characteristics must a resource possess to provide sustainable competitive advantage?
4. Why is human capital considered more likely to produce competitive advantage than tangible resources in today's economy?
5. What is the difference between how human capital was valued in the Industrial age versus how it is valued in the current economic landscape?

📘 Lecture 7 — Strategic Corporate Assets of a 3rd Millennium Organization, Knowledge Characteristics

📖 Overview: This lecture examines the unique characteristics of knowledge that distinguish it from traditional economic resources, and explores the lifecycle of knowledge within organizations—how it is born, can die, can be owned, exists in both immanent and extant forms, can be stored, and categorized. Understanding these characteristics is crucial for developing effective knowledge management strategies in modern organizations.

🗂️ Topics Covered

This lecture covers the six special characteristics of explicit knowledge according to Harlan Cleveland (expandable, compressible, substitutable, transportable, diffusive, shareable), followed by an exploration of tacit knowledge's unique properties and management challenges. It then examines the philosophical nature of knowledge involving human interaction with reality, belief, judgment, and its social and dynamic nature. The lecture also covers what can happen to knowledge—its birth, death, ownership, immanent vs. extant forms, storage capabilities and challenges, and finally different categories of organizational knowledge including label, process, skill, and people knowledge.

📝 Lecture Summary

Characteristics of Knowledge

Knowledge defies normal economic rules. Harlan Cleveland describes six special characteristics of information or explicit knowledge. It is Expandable—unlike other resources managed because of their scarcity value, the more knowledge is used, the more is generated. It is Compressible—it can be summarized for easier handling and packaged into small physical formats. It is Substitutable—in many situations it can replace physical and other forms of resource, for example telecommunications reduces the need for physical transport. It is Transportable—it can move from place to place, quickly and easily, ready for collecting when the recipient chooses. It is Diffusive—it tends to leak, and as technology improves, it becomes ever more difficult to stop reproduction and transmission. It is Shareable—if it is given to another person, the first person does not lose it.

Tacit knowledge is also expandable, diffusive and shareable, but is not as easily transmitted or diffused. It is intangible and difficult to identify and describe. It is context dependent. These characteristics present interesting management challenges. Making knowledge explicit means that it can be more readily copied, diffused and shared, but this makes it 'leaky' and it could reach undesirable parties. The increasing rate of knowledge generation means much existing knowledge has a short 'half-life' and its value decays quite quickly, needing constant refreshing and revalidating through use.

💡 Why this matters: These characteristics explain why knowledge cannot be managed like physical assets—its expandability and shareability create unique opportunities for value creation, while its diffusiveness creates risks of unwanted dissemination.

Knowledge involves a human interaction with reality (or with information about reality, or information about other knowledge or information), where the human is the subject and acts as the active, creative element, and modifies the latter by way of reconstructing it. Knowledge involves attribution of meaning and significance by the knower as a person. Every reconstruction is a reinterpretation as well.

When I know something, it is relative to me. There can be no knowledge without me. It is always in relation to my existence and my knowing it. With my death dies my world, and with it my knowledge. In knowing something, I individualize, subjectify, and appropriate it and make it my own.

Knowledge is essentially social in nature. We need universal categories for generation, expression, representation, storage, retrieval, and expropriation of knowledge. The categories are universal in the sense that (a) they are capable of holding the same meanings for all humans belonging to the same community and (b) the categories can be socialized in terms of being shared, reconstructed, and applied by other humans belonging to the concerned universe of discourse.

In knowing something, I believe it to be true. Without this belief, it could just be some information, without that stamp of individualized identity marked on it. This belief is a part of a system of beliefs, values, and rationality, and hence constitutes a responsibility and potential commitment.

Knowing takes place in relation to existing knowledge—it is placing things in context, in relation to existing constructions of reality, content, and concepts.

Knowledge involves a judgment, a subsumption of the particular under the universal. It involves a certain amount of synthesis and integration of discreet information under a category, a construction, or an attribution of a causality or justifiability, relative to the knower's frame of reference.

Knowledge has a moment of categorical imperative and can induce a cognitive dissonance between belief and practice, between the past and the present, between the present and the future, between what is and what ought to be, and so on. It can therefore form a springboard for potential action. In other words, knowledge by definition is driven into practice.

Knowledge is always a part of a dynamic system. Knowledge has the tendency to go for more of itself, to bypass itself, and to constantly develop itself. It is only limited by mental and environmental constraints.

Knowledge is gregarious by nature and has a tendency to socialize itself. Socialization is the means by which individual knowledge gets reinforced, challenged, modified, improved, and validated.

Knowledge processes are always a part of an open system. It is like a game where the goalpost keeps shifting itself. The meanings, the dictionaries, and even the rules of the language are always in flux—as volatile as the turns in modern life. Knowledge creation, by definition, is a process of innovation.

What Can Happen To Knowledge

Knowledge Can Be Born

What apparently distinguishes Homo sapiens from the rest of the animal world is our ability to conceive, store, and manipulate ideas linguistically apart from the stimuli that gave rise to them. We can think about and name apples—make recipes for their use, use their visual image for decoration, even name computers after them—without being under the influence of the smell, taste, feel, and appearance of actual apples. We can give birth to ideas as well as manipulate and change them.

Every company desires such intellectual fertility on the part of its employees, particularly its leaders. Questions arise about what circumstances prove most conducive to the birth of new knowledge, which individuals are most fertile in their ability to generate new knowledge, and how these individuals can be discovered and nurtured. These are questions asked by organizations and human enterprises of all kinds. Organizations carve knowledge spanners much as living organisms carve reproductive opportunities and capabilities. In both cases, the motive is the same: survival and maximization of life experience.

Knowledge spanners equip their organizations to confront change successfully. For example, rapidly changing global markets can threaten the viability of even the most established businesses. These companies rely upon new knowledge to maintain and extend their markets. The companies highly value knowledge spanners who come up with the biomedical formula, the algorithm for a faster chip, the alloy for a lighter auto-body, or the economic model for a better deployment of resources that allow their organizations to thrive when others are failing.

Increasingly, the spanning of knowledge involves a partnership between human cognition and machine-based intelligence. When a pharmaceutical company conducts a complex series of drug tests by means of computer analysis, when a physician makes a diagnosis based primarily on output from an expert system, or when an aeronautics corporation designs an aircraft from computer-based flight test data, the question of where requisite knowledge resides is not easily answered. Traditionally based on human inputs, this artificial knowledge is increasingly self-generated by artificial intelligence capabilities. Any plan for knowledge management must make provision for both direct human knowledge and indirect human knowledge, as mediated by machines.

Knowledge Can Die

In terms of sheer quantity, the vast majority of things known by human beings die with them. Few of us record even one-thousandth part of our knowledge accumulated from life experiences. Put in organizational terms, we are individually quite poor at "transition planning." Our stores of knowledge go with us to the grave almost entirely whole, leaving each new generation to reinvent much knowledge that could have been its birthright.

It could be argued that most important knowledge achieved during individual human lives gets preserved in books, journals, patents, documentaries, oral histories, and other means. By this logic, we are the tip of the iceberg and therefore do not mourn the loss of the great unformed and unexamined mass of knowledge beneath the surface. For example, we cling to the works of Mozart and are hardly aware of what it means to lose the capacity (the genius) to produce such works.

The death of knowledge for an organization occurs by means other than the mortality of its members. Firms that downsize without provision to preserve and extend necessary intellectual capital can find themselves brain dead after terminations and layoffs. Knowledge resides primarily within human heads; when "head count" is reduced, inevitably the sum of knowledge within the organization is reduced, sometimes critically so. This happens especially when a firm looks first to its highest paid, longest tenured employees as prime candidates for corporate bloodletting. From a financial management perspective, terminating a few high paid employees may be less traumatic, but from a knowledge management perspective, cutting off the experienced head from the working body may be foolish surgery.

Knowledge can also die due to paradigm shifts. Aspects of knowledge that were important or sacred for one generation may cease to matter for another generation. Interpreting human character and health, for example, was inconceivable for Western medieval men and women apart from the theory of bodily "humors" (behavior-influencing fluids) such as phlegm, choler, and black bile. Their knowledge of these mysterious substances has become obsolete because the paradigms we use to understand mental and physical health have changed.

When paradigm shifts occur, little intellectual effort is spent proving the past wrong. All knowledge resources quickly turn to the larger task of proving the new vision right. When the paradigm shifts, the knowledge of the past is not "killed" or proven wrong. Instead, it is allowed to die from inattention. In this sense, paradigm shifts are largely rhetorical acts arising from the ability of new paradigm thinkers to provide powerful explanations of anomalies in the old paradigm.

Knowledge management takes the death of knowledge seriously and accepts no paradigm shift on blind faith. Knowledge management seeks to understand causes for the failing health or death of knowledge. It memorializes and perpetuates what can and should be salvaged from the demise of a paradigm.

Knowledge Can Be Owned

In spite of high literacy rates in developed countries, most knowledge valuable for increasing wealth is privately held. Knowledge unrelated to or marginally related to wealth is freely available because it serves no one's specific interest in the marketplace. Such free knowledge is the stuff of general education—history, literature, music, art, philosophy, cultural appreciation, languages, and so forth. The works of Shakespeare are available to all of us not because Shakespeare willed it so, but because since Shakespeare's death no one has built an industry based on any kind of special or proprietary knowledge contained within his plays and poetry. The same cannot be said for the knowledge necessary to make paint, preserve food, make or repair computers, or remove air pollution. These functions are based on knowledge that is not made generally available. A company's competitive advantage often lies precisely in its privately held knowledge.

Several implications fan out from the notion of privately owned knowledge. First, the identity of the owner must be clarified. Research and development personnel at computer, drug, cosmetic, and other similar companies routinely sign explicit and binding agreements with their employer that all knowledge accumulated, discovered, or developed during and after their employment remains the sole possession of the employer.

No matter how careful the wording of ownership agreements, truly advantageous knowledge often has a way of "getting out," usually with devastating results in the marketplace. Netscape's "ownership" of Internet browsing technologies, for example, was closely imitated—some have said stolen—by Microsoft, with substantial market losses to Netscape. Knowledge management devises ways to determine what knowledge should be privately held and how it can be protected from competitors and clients.

Modern organizations find unique ways to pierce the shield of privately held knowledge. In many industries, companies acquire proprietary knowledge through friendly or hostile acquisitions, hiring away key employees, and reverse engineering products. Then that knowledge is openly imitated, with the often-cynical strategy that legal challenges will take years in the courts to resolve—years during which the war for market share and profitability will be won and the issue of knowledge ownership will become moot.

By and large, companies have been unsuccessful in attempting to protect knowledge that drives sustained competitive advantage. Even products and processes that are patented or trademarked under the laws of one country are stolen by companies not vulnerable to legal or political sanctions from that country. The blatant theft of U.S. television technology in the 1960s by Asian competitors is a classic example. So devastating was this loss of proprietary knowledge that the U.S. television manufacturing industry ceased to exist by 1980. Similar "borrowing" has occurred more recently in the chip making, disk drives, and telecommunication device industries. U.S. manufacturers have largely given up efforts to stop knowledge piracy through international courts. Instead, they have adopted a "first/best/least" philosophy of hitting the marketplace first and hard with new products, maintaining quality standards, and pricing products at levels that discourage start-up enterprises from copying them.

At best, this appears to be a desperation strategy that conceives and develops new markets only to give them over eventually to the idea pirates. Effective knowledge management assesses what knowledge must be protected for competitive advantage, how that knowledge will be protected, and to what degree legal and political entities can be trusted to enforce laws related to ownership of intellectual properties.

Knowledge is Immanent as Well as Extant

Not all knowledge worth managing in an organization is explicit and visible. Much organizational knowledge is held in creative reserve in the form of human resources and computer expert systems. This immanent and preformed knowledge has the potential for becoming extant and formed at any moment, just as the energy within a battery can be tapped when needed.

A brain surgeon's expertise and capacity for action is an example of immanent knowledge. After years of study and practice, few brain surgeons can list the items within their knowledge bases. Surgeons' core competencies lie in immanent knowledge—deep wells of insight, reflection, memory, and intuition that can be summoned when the need arises. The visible, extant "spark" of correct decisions and actions come to the fore in life-and-death circumstances. Similar knowledge banks are in the minds of virtually all personnel who exercise creative, thinking functions within organizations.

Immanent knowledge remains a challenging but crucially important aspect of knowledge management. Just as brain surgeons must create and maintain their immanent knowledge, organizations may use knowledge management to preserve such vital knowledge. This forces us to confront several key questions: How does one nurture immanent knowledge without force-feeding it in a disruptive way? How does one monitor immanent knowledge to ensure its store of resources is increasingly vital and relevant to the organization's needs? How does an organization prevent unnecessary redundancy in immanent knowledge? A degree of redundancy in immanent knowledge resources probably is desirable if it encourages wholeness of vision and broad perspective in decision making.

Knowledge Can be Stored

It can safely be estimated that more knowledge has been externalized (made observable and preservable) in the last 20 years than in the entire previous history of mankind. On paper, film, tape, and above all by electronic storage means we have "lent our minds out." For example, 12,000 new sites per week continue to appear on the Internet.

But now that we have so energetically externalized knowledge, we face an unexpected and ironic problem: how to internalize knowledge again. Getting knowledge out of our heads and onto disks or paper was a feat of technology; getting facts back into our heads for practical and creative use is a task that involves much more than technology.

The central intellectual work of the 21st century may lie not so much in accumulating externalized banks of knowledge as in developing time-efficient ways to process selected portions of that knowledge through a chip whose essential circuits have not and will not change: the chip between the ears. "Real-time" internalization of knowledge may be the most imposing challenge. A training videotape or movie cannot be internalized by the human mind using a "fast-forward" technique—it must be played in real time for human learning to take place. Traditional lectures and much educational software are similarly bound by real-time constraints. By contrast, still photos and book or magazine pages can be accessed in "mind time," with the roving intellectual eye free to locate and select bits of content without involving the entire surrounding context. CD, videodisk, and "computer search" technologies offer similar accessibility without the necessity to play through a cohesive context.

The most poignant example of this dilemma lies in the efforts of elementary schools to "get wired" to the Internet. When well-intentioned teachers advise students to search for information on topics of interest, both teachers and students quickly confront the chaos of knowledge that currently characterizes the Internet. A second-grader searching for information on "goldfish" using the Info-seek search engine was dumfounded and discouraged to confront more than 100,000 "hits" for his search term—with the option of seeing them 10 at a time. Where does one begin to make sense or use out of a knowledge base that lacks familiar search paths, or heuristics, congenial to human learning and reflection?

Knowledge Can Be Categorized

In addition to the distinctions already suggested between immanent and extant knowledge, the various types of knowledge common within an organization can be enumerated.

Label knowledge is the vast catalog of names that we attach to the flora and fauna that make up the jungle of our particular organization. As a practical organizational necessity, names for things matter for day-to-day operations and efficiency. But label knowledge too often becomes an obsessive-compulsive totem for minds that equate organizational learning with mastery of jargon and labels. In such an environment, newcomers to the organization are pilloried by old-timers until they are able to speak the specialized language of terms, tags, and titles correctly. Entire cultures within branches of the military, academic disciplines, and the professions are built up in large part of such sensitivity to label knowledge. Label knowledge makes up an exclusionary wall by which lawyers separate themselves from the world of common sense.

Process knowledge involves knowing how things work, even if one cannot name all components active within the process (label knowledge). Business environments value process knowledge on the micro-level—engineers who know how a heating system operates—but often fail to recognize the importance of process knowledge at the macro-level. This has occurred in spite of nearly a decade of Business Process Reengineering that explicitly focused management attention on gaining knowledge about processes. Knowledge management should pay attention to both the micro- and macro-levels of process knowledge. If the macro-level process is the building of a pyramid, that knowledge influences the specific work of stonecutters and laborers at the micro-level.

Skill knowledge knows how to do something of value to the organization. This level of knowledge has long been managed by companies through job descriptions, training programs, performance evaluations, and other means. But once skill sets have been determined, companies tend to look upon them as unchanging constellations. These skill sets become the basis of most hiring and define the overall core competencies of the organization. The coming era requires a much more fluid view of skill knowledge. Computer companies have already found that an employee's ability to learn quickly and well is an infinitely more valuable skill in a rapidly changing business environment than a vocationally oriented, specific skill. Knowledge management for the new century requires that skill knowledge be defined and developed so that new patterns can come together quickly to meet emerging market needs.

People knowledge is a diffuse but vitally important category comprising all the insights, intuitions, and relational information we use to work with other people. In the iceberg analogy, this kind of knowledge is truly subsurface within organizations. Usually it is managed ineffectively or not at all precisely because of its lack of visibility. Few companies think about what knowledge their employees should have about one another's motives, communication styles, or professional goals. Interestingly, the same companies expect employees to congeal into efficient, cohesive work teams but devote little thought to the people knowledge that makes such teams possible. Knowledge management brings people knowledge to visibility and to a position of prominence in a framework for understanding and using knowledge within the corporation.

💡 Why this matters: Categorizing knowledge helps organizations identify what types of knowledge they possess and need, enabling more targeted management strategies for each type.

⭐ Key Takeaways

Knowledge has six unique characteristics (expandable, compressible, substitutable, transportable, diffusive, shareable) that defy normal economic rules of scarcity. Knowledge can be born through human creativity and machine-based intelligence, can die through mortality, downsizing, or paradigm shifts, and can be owned as a private asset that provides competitive advantage but is vulnerable to theft and piracy. Knowledge exists in both immanent (latent, unexpressed) and extant (explicit, formed) forms, and while external storage has exploded, the challenge of internalizing knowledge back into human minds remains paramount. Four categories of organizational knowledge—label, process, skill, and people knowledge—each require different management approaches, with people knowledge being the most overlooked yet essential for team effectiveness.

🧠 Quick Revision Questions

1. What are the six special characteristics of explicit knowledge according to Harlan Cleveland, and how do they differ from the characteristics of tacit knowledge?
2. How can knowledge "die" in an organization, and what are the implications of downsizing from a knowledge management perspective?
3. What is the "first/best/least" philosophy adopted by U.S. manufacturers to protect proprietary knowledge, and why is it considered a desperation strategy?
4. What is the difference between immanent and extant knowledge, and why is redundancy in immanent knowledge potentially desirable?
5. What are the four categories of organizational knowledge, and why is people knowledge considered the most challenging to manage?

📘 Lecture 8 — Perspectives of Knowledge Management and the Forces Driving KM

📖 Overview: This lecture explores why Knowledge Management (KM) has become essential in modern business, examining the forces that drive KM adoption and the benefits it provides. It defines KM, distinguishes it from related concepts, and outlines the key drivers—technological, process, personnel, knowledge-related, and financial—that make KM a critical competitive advantage in today's complex, fast-paced business environment.

🗂️ Topics Covered

The lecture begins by explaining “Why Knowledge Management?” and the rise of the prosumer, then details the benefits and justifications for KM implementation, including Botkin's six attributes of knowledge products. It examines common pitfalls of KM adoption and the four key forces driving KM: increasing domain complexity, accelerating market volatility, intensified speed of responsiveness, and diminishing individual experience. The lecture then analyzes five categories of KM drivers—technology, process, personnel-specific, knowledge-related, and financial—before providing multiple definitions of KM, describing the knowledge organization structure, and clarifying what KM is not (e.g., reengineering, intellectual capital, or data management).

📝 Lecture Summary

Why Knowledge Management?

The lecture introduces the concept of the prosumer—a consumer who is educated, demands more, and provides feedback on product design. This shift has initiated radical changes in business. KM provides benefits by making business processes faster and more effective through knowledge sharing, creating exponential benefits as people learn from shared knowledge. The main constraint in KM is initially capturing tacit knowledge, but successful capture and dispersal allows a company to leverage intellectual assets, respond quickly to customers, create new markets, and dominate emergent technologies.

🔑 Definition — Prosumer: An educated consumer who is no longer passive in the market, demands more from products and services, and provides feedback to manufacturers.

📌 Example: Microsoft's Hotmail advanced the wide use of e-mail, allowing users to exchange information through any Web browser, which became the norm for most Internet service providers.

💡 Why this matters: The shift from passive consumers to active prosumers forces businesses to continuously learn and adapt, making KM essential for survival.

KM Justification

KM justifies itself through several key benefits:

• Creates exponential benefits from shared knowledge
• Has a positive impact on business processes
• Enables quick response to customers, new market creation, and product development
• Builds mutual trust between knowledge workers and management
• Builds sensitivity to brain drain (loss of expertise)
• Ensures successful partnering with suppliers, vendors, and customers
• Shortens the learning curve for new employees
• Enhances employee problem-solving capacity

Botkin (1999) suggests six top attributes of knowledge products and services:

1. Learn – The more you use them, the smarter they get
2. Improve with use – Enhanced rather than depleted when used
3. Anticipate – Knowing what you want, they recommend what you might want next
4. Interactive – Two-way communication between you and them
5. Remember – Record and recall past actions to develop a profile
6. Customize – Offer unique configuration to individual specifications in real time

📌 Example: Andersen Consulting (Accenture) developed ANet, an electronic system connecting employees to share knowledge globally. ANet allows employees to use the total knowledge of the entire organization to solve customer problems through electronic bulletin boards, visual and data contacts, and compiled subject files. However, Accenture found that technological changes alone were insufficient—major organizational changes in incentives and culture were needed for successful adoption.

Pitfalls of KM Implementation

Companies that fail to embed viable KM suffer from several oversights:

• Failing to modify compensation systems to reward teamwork rather than individual information-hoarding
• Building a huge, generalized database that fails to address specialized areas of expertise
• Viewing KM as solely a technology or HR area, leading to poorly coordinated efforts
• Placing too much emphasis on technology, forgetting that technology is only the enabler, not the solution itself
• Introducing KM via a simple, low-risk project instead of starting with a strategy and high-profile champion
• Pursuing KM without organizational readiness—classical management principles require major cultural changes
• Having poor leadership—KM requires determined champions and top management commitment

📌 Example: General Electric (GE) under CEO Jack Welch established a knowledge management university where Welch himself taught classes. GE encourages best-practice sharing at all levels, successfully using employee input and knowledge to produce strategic advantage.

The Forces Driving KM

Four major forces drive the need for KM:

1. Increasing Domain Complexity — The complexity of underlying knowledge domains is increasing due to globalization, requiring organizations to meet diverse customer needs across the globe.

2. Accelerating Market Volatility — The pace of change within market domains has increased rapidly. Example: The September 11, 2001 attacks crippled the travel industry overnight, forcing companies to reduce prices below break-even, leading many to bankruptcy.

3. Intensified Speed of Responsiveness — The time required to take action based on subtle changes is decreasing. Rapid technological advances make it imperative that decisions be made quickly. Example: Three-year-old IT curriculums are considered outdated in "Internet years."

4. Diminishing Individual Experience — High employee turnover rates mean decision-makers have less tenure. The dot-com explosion lured IT professionals away, leaving organizations with talent lacking business experience and understanding of organizational culture.

The Drivers

Technology Drivers — The proliferation of technology, data communications, networking, and wireless transmission has revolutionized knowledge storage and exchange. The World Wide Web changed KM from a fad to e-business reality. Technology acts as a core capability leveler, leaving knowledge as a competitive differentiator. While technology moves information at lightning speed, it is people who turn knowledge into timely and creative decisions.

Process Drivers — These drivers aim to improve work processes by eliminating duplicate mistakes and transferring best experiential knowledge across the organization. “Just in time” approaches minimize inventory investment and meet consumer demands more expeditiously. Responsiveness that exceeds competition requires knowledge of control processes.

Personnel-Specific Drivers — These focus on creating cross-functional teams of knowledge workers to serve anywhere in the organization and minimize personnel turnover as a threat to collective knowledge. Another critical driver is minimizing knowledge walkouts—when highly marketable employees with unique knowledge leave, potentially to competitors, causing a competence drain.

Knowledge-Related Drivers — These include revisiting overlooked employee knowledge, making critical knowledge available when needed, and finding mechanisms to expedite knowledge for immediate use. Companies often know what they know but have difficulty locating it.

📌 Example: A customer wanted to return a product purchased from a chain store in a different city. Only one employee at the local store knew the code to transfer funds between stores—and she was on vacation. The customer service employee spent nearly an hour contacting the other store for instructions while the customer waited.

Financial Drivers — Knowledge follows the law of increasing returns: the more knowledge is used, the more value it provides. Unlike physical assets that diminish with use, knowledge assets increase in value as more people use them. The goal of KM is to produce a positive return on investment (ROI) in people, processes, and technology.

What is Knowledge Management?

Knowledge management (KM) is a newly emerging, interdisciplinary business model focusing on knowledge within the framework of an organization. It is rooted in business, economics, psychology, and information management. KM involves people, technology, and processes in overlapping parts.

Each definition of KM contains several integral parts:

• Using accessible knowledge from outside sources
• Embedding and storing knowledge in business processes, products, and services
• Representing knowledge in databases and documents
• Promoting knowledge growth through culture and incentives
• Transferring and sharing knowledge throughout the organization
• Assessing the value of knowledge assets and impact regularly

KM is the process of capturing and making use of a firm's collective expertise anywhere in the business—on paper, in documents, in databases (explicit knowledge), or in people's heads (tacit knowledge). Up to 95% of information is preserved as tacit knowledge. It is the fuel for innovation—the only sustainable competitive advantage in an unpredictable business environment.

Alternative Definitions of Knowledge Management

The lecture provides multiple definitions from various researchers:

• Hibbard (1997): "Knowledge management is the process of gathering a firm's collective expertise wherever it resides—in databases, on paper, or in people's heads—and distributing it to where it can help produce the biggest payoff."
• Sveiby (2000): "Knowledge management is the art of creating value from an organization's intangible assets."
• Malhotra (2000): "KM is a framework within which the organization views all its processes as knowledge processing, where all business processes involve creation, dissemination, renewal, and application of knowledge toward organizational sustenance and survival."
• O'Dell et al. (2000): "KM is a conscious strategy of getting the right knowledge to the right people at the right time; it is also helping people share and put information into action in ways that strive to improve organizational performance."

The Knowledge Organization

The conceptual structure of the knowledge organization has a middle layer addressing the KM life cycle: knowledge creation, knowledge collection/capture, knowledge organization, knowledge refinement, and knowledge dissemination. The ideal knowledge organization is one where people exchange knowledge across functional areas using technology and established processes, freely producing knowledge assets in an environment that supports exchange.

KM is Not About…

The lecture clarifies what KM is not:

1. Not reengineering — Reengineering implies one-shot, radical change; KM implies continuous learning, unlearning, and relearning
2. Not a discipline — It is another way of improving quality, profitability, and growth
3. Not a philosophic calling — KM goes to the core of an organization's intangible assets
4. Not intellectual capital (IC) — IC represents value of trademarks, patents, or brand names; knowledge is the consequence of actions and interactions of people
5. Not based on information — Information becomes knowledge after people use it in ways that create value
6. Not about data — Data (facts without context) or information (patterns of data) is not knowledge
7. Not information value chain — Knowledge value chains view humans as key components assessing information
8. Not limited to gathering from experts or retirees and creating databases
9. Not digital networks — KM is about improving business processes with people and technology in mind
10. Not about "knowledge capture" per se — Knowledge cannot be captured in its entirety

📌 Example: A British supermarket chain used customer data-mining to assess buying behavior and discovered a clear association between diaper and beer purchases by male customers on Friday afternoons. The store began stacking diapers and beer together.

⭐ Key Takeaways

Knowledge Management is an interdisciplinary business model focused on capturing, sharing, and leveraging organizational knowledge—both explicit (documents, databases) and tacit (people's minds)—to create sustainable competitive advantage. The four major forces driving KM are increasing domain complexity, accelerating market volatility, intensified speed of responsiveness, and diminishing individual experience, all of which make traditional decision-making inadequate. Five categories of KM drivers—technology, process, personnel-specific, knowledge-related, and financial—provide compelling justification for KM implementation. Successful KM requires organizational readiness, top management commitment, cultural change, proper incentive systems, and recognition that technology is merely an enabler, not the solution itself. KM is distinguished from related concepts: it is not reengineering, intellectual capital, data management, or simply digital networks—it is a continuous process of learning and adaptation focused on "doing the right thing" rather than "doing things right."

🧠 Quick Revision Questions

1. What are the four forces driving KM, and how does each one create the need for more effective knowledge management?
2. Explain the difference between explicit knowledge and tacit knowledge, and state what percentage of organizational information is preserved as tacit knowledge.
3. List Botkin's six attributes of knowledge products and services, and explain how each attribute adds value.
4. What are the common pitfalls of KM implementation, and how did GE under Jack Welch avoid these pitfalls?
5. How does the law of increasing returns apply to knowledge assets, and why is this different from traditional physical assets?

📘 Lecture 9 — Knowledge Hierarchy .... From Data and Information to Knowledge and Wisdom

📖 Overview: This lecture debunks common myths surrounding Knowledge Management and then builds a foundational understanding of the core concept: knowledge. It provides precise definitions for key terms like data, information, knowledge, intelligence, experience, and common sense, clarifying their distinct roles and hierarchical relationships within an organization. The lecture is crucial for understanding what KM truly is and why it is not merely a technology or data management initiative.

🗂️ Topics Covered

This lecture is divided into two main parts. The first part systematically addresses ten common myths about Knowledge Management, clarifying misconceptions about it being a fad, mere technology, or the same as reengineering. The second part delves into the fundamental understanding of knowledge, defining it in relation to intelligence, experience, and common sense, and then clearly distinguishing between data, information, and knowledge. It concludes by exploring different kinds of knowledge, such as deep vs. shallow, procedural vs. declarative, and tacit vs. explicit.

📝 Lecture Summary

KM Myths

This section presents and refutes several common misconceptions about Knowledge Management. It establishes that effective KM is interwoven into all organizational processes and is not limited to technology or data collection. The myths highlight that KM is a people-centric discipline focused on culture, trust, and ongoing learning.

Myth 1: Knowledge management is a fad. Unlike other trends, true KM becomes embedded in the way people work. Knowing what you know cannot be a fad.

Myth 2: Knowledge management and data warehousing are essentially the same. Data warehousing is a repository of data, not knowledge. Knowledge is how you take information and transform it into action. For example, Sears uses a customer data warehouse to help its sales force identify prospects, showing how data mining supports KM by revealing patterns.

Myth 3: Knowledge management is a new concept. KM has been practiced since the early 1980s by firms like Ford and General Motors. Today's version, like customer profiling, uses advanced technology, but the core idea of sharing and using knowledge is not new.

Myth 4: Knowledge management is mere technology. This is a serious misconception. KM is about people, relationships, and a new way of working. Over 80 percent of technology-centered KM efforts fail because of a lack of attention to people.

Myth 5: Technology can store and distribute human intelligence. While data can be stored, technology cannot predict who needs what information. Tacit knowledge exists in a person’s brain and cannot be stored. Knowledge repositories do not allow for the creation of new knowledge.

Myth 6: Knowledge management is another form of reengineering. Reengineering is an efficiency-driven, one-time radical change. KM is an ongoing renewal of processes to learn about future opportunities, focusing on innovation and creativity.

Myth 7: Company employees have difficulty sharing knowledge. This depends on factors like company culture, trust, and attitude. Knowledge sharing requires a "give to get" attitude. Mature people in a stable environment tend to share more.

Myth 8: Knowledge management works only within an organization. Valuable knowledge also comes from outside sources like suppliers and customers, though this presents challenges with technology compatibility and security.

Myth 9: Technology is a better alternative than face-to-face. The best knowledge resides in human minds, making face-to-face interaction a better alternative for knowledge acquisition and sharing.

Myth 10: It is a "no brainer" to share what you know. Traditional cultures often encourage hoarding knowledge for job security. To promote sharing, businesses need special training and trust-building.

Understanding Knowledge

The most critical word in KM is knowledge. It is a myth that knowledge resides only in documents. Knowledge is neither data nor information, though related to both. Essential criteria for knowledge and learning include memory. A knowledge base is different from a database; it is a set of facts and inference rules for creating new knowledge. Knowledge can be classified as explicit or tacit knowledge.

🔑 Definition — Knowledge: "Understanding gained through experience or study." It is "know-how" or an accumulation of facts, procedural rules, or heuristics.

• Fact: A statement of some element of truth. (e.g., milk is white)
• Procedural rule: A rule that describes a sequence of relations. (e.g., check traffic when entering a freeway)
• Heuristic: A rule of thumb based on years of experience. (e.g., driving 5 km above the speed limit is unlikely to get you a ticket)

🔑 Definition — Intelligence: The capacity to acquire and apply knowledge. It includes the ability to understand language and store/retrieve relevant experience. 📌 Example: A thermos bottle keeps hot stuff hot and cold stuff cold. The joke highlights that this is not "intelligence"; a machine lacks the cognitive ability to "know" how it does this.

🔑 Definition — Experience: Relates to what we have done and what has happened. It leads to expertise. An expert knows what they do not know.

🔑 Definition — Common Sense: The unreflective opinions of ordinary humans (e.g., a child knows not to touch a hot stove). Machines lack common sense, making them "brittle."

💡 Why this matters: Understanding these definitions is the foundation for effective KM. You cannot manage what you do not understand.

Cognition and Knowledge Management

Cognitive psychology provides essential background for understanding knowledge and expertise. It helps identify the cognitive structures and processes of skilled performance. Understanding human limitations (e.g., memory capacity) is crucial for knowledge elicitation (capture) . Experts may have difficulty verbalizing their thought processes, especially for procedural knowledge. Knowledge developers need a strong background in cognitive psychology.

Data, Information, and Knowledge

This section defines the three core concepts in the knowledge hierarchy.

🔑 Definition — Data: Unorganized and unprocessed facts. It is static. (e.g., "Ali is 6 feet tall"). It offers no judgment or basis for action. It is a prerequisite to information.

🔑 Definition — Information: An aggregation of data that makes decision making easier. It is "shaped" data with meaning, purpose, and relevance. (e.g., A profit and loss statement). The focus of information is qualitative.

🔑 Definition — Knowledge: A higher level of abstraction that resides in people's minds. It is "human understanding of a specialized field of interest that has been acquired through study and experience." It is broader, richer, and harder to capture than data or information. (e.g., An investor requires knowledge to evaluate two companies' profit and loss statements).

📌 Example: A store records "number of socks sold" (data). Analyzing sales patterns over time reveals "customers who buy socks often buy shoes" (information). A manager's insight from experience that "stocking more socks before winter drives footwear sales" is knowledge.

💡 Why this matters: The key takeaway is the hierarchy: Data -> Information -> Knowledge. Data becomes information when meaning is added; information becomes knowledge when it is understood and applied for decision-making.

Kinds of Knowledge

This section classifies knowledge in several ways.

• Deep Knowledge: Acquired through years of proper experience.
• Shallow Knowledge: Minimal understanding of the problem area.
• Knowledge as Know-How: Accumulated lessons of practical experience.
• Reasoning and Heuristics:
  • Reasoning by analogy: Relating one concept to another.
  • Formal Reasoning: Using deductive (general to specific) or inductive (specific to general) reasoning.
• Common Sense: Knowledge possessed by almost all humans.
• Classification by type:
  • Procedural knowledge: Understanding how to carry out a procedure.
  • Declarative knowledge: Routine, conscious, shallow knowledge (e.g., facts readily recalled).
  • Semantic knowledge: Highly organized, "chunked" knowledge in long-term memory (e.g., major concepts).
  • Episodic knowledge: Knowledge based on personal experiences or episodes.
• Tacit vs. Explicit Knowledge:
  • Tacit knowledge: Embedded in the human mind through experience (hard to articulate).
  • Explicit knowledge: Codified and digitized in documents, books, reports, spreadsheets, memos, etc.

⭐ Key Takeaways

Knowledge Management is not a fad, a new concept, or mere technology; it is fundamentally about people, relationships, and organizational culture, where trust is critical for knowledge sharing. The lecture establishes a crucial hierarchy: data (unprocessed facts) leads to information (data with meaning), which leads to knowledge (understanding applied for action). Key concepts include the distinction between explicit (codified) and tacit (personal, experience-based) knowledge, and between deep and shallow knowledge. Finally, human intelligence, experience, and common sense are essential attributes that technology cannot replicate, making face-to-face interaction a vital component of KM.

🧠 Quick Revision Questions

1. List three common myths about Knowledge Management and explain why each is incorrect.
2. What is the fundamental difference between data, information, and knowledge? Give a real-world example for each.
3. Define "tacit knowledge" and "explicit knowledge." Why is it more difficult to manage tacit knowledge in an organization?
4. According to the lecture, what is the role of cognitive psychology in Knowledge Management?
5. Explain the difference between procedural and declarative knowledge, and give an example of each.

📘 Lecture 10 — Extracting Gold from Data; Understanding Conversion of Data into Knowledge

📖 Overview: This lecture clarifies the critical distinctions between data, information, and knowledge, and explains how raw data is transformed into valuable knowledge that enables action and decision-making. It also explores different classifications and perspectives of knowledge, including subjective vs. objective views, declarative vs. procedural knowledge, and the various repositories where knowledge resides within organizations.

🗂️ Topics Covered

The lecture begins by differentiating data from knowledge using examples like cricket spectators and computer prices, then justifies that information contains data but not all data is information. It explains why the same data can be useful to some and useless to others, describes the hierarchical and advanced views of knowledge, and emphasizes knowledge's role in creating and utilizing information. The lecture compares subjective vs. objective views of knowledge (as state of mind, practice, objects, access, capability), distinguishes declarative "know what" from procedural "know how" knowledge, differentiates general from specific knowledge, defines expertise, and concludes by contrasting knowledge in people, artifacts, and organizational entities.

📝 Lecture Summary

Q#1 How do the terms “data” and “knowledge” differ? Describe each term with the help of a similar example, elucidating the difference between the two.

Data comprises facts, observations, or perceptions that by themselves represent raw numbers or assertions, and may therefore be devoid of context, meaning, or intent. Examples include: the age and gender of each spectator in a cricket match, or the price of each computer model from every vendor at a particular time.

Knowledge has been distinguished from data in two ways. The more simplistic view places knowledge at the highest level in a hierarchy with information in the middle and data at the bottom. For example, an e-mail address is data; knowing it belongs to a customer is information; knowing this customer needs weekly reminders to pay dues is knowledge. The second way defines knowledge as justified beliefs about relationships among concepts relevant to a particular area.

Using the cricket example: age and gender of spectators is only data, but when combined with buying preferences, stadium planners can forecast products that will sell during a game. Similarly, computer prices plus shipping costs and rebates provide a buyer the knowledge of how much they will likely spend on each model.

💡 Why this matters: Knowledge differs from data because it helps produce information from data or more valuable information from less valuable information, which in turn facilitates an action.

🔑 Definition — Data: Facts, observations, or perceptions that represent raw numbers or assertions, often devoid of context, meaning, or intent.

🔑 Definition — Knowledge (simplistic view): The highest level in a hierarchy (data → information → knowledge) — information that enables action and decisions, or information with direction.

🔑 Definition — Knowledge (advanced view): Justified beliefs about relationships among concepts relevant to a particular area.

📌 Example: An e-mail address is data; knowing it belongs to a customer is information; knowing this customer needs weekly reminders to pay dues is knowledge.

Q#2 “Information” contains “data” but not all “data” is “information.” Justify this statement.

Data is devoid of context, meaning, or intent. Information is a subset of data — it only includes those data that possess context, relevance, and purpose. Information typically involves the manipulation of raw data to obtain a more meaningful indication of trends or patterns.

Examples: The total number of TV viewers who watched the Super Bowl is mere data. However, knowing that maximum viewers occur during the third and fourth quarters is information for companies deciding when to place commercials. The price of a large bag of popcorn at a theatre is data, but averaging popcorn prices across competing theatres to stay competitive is useful information.

Thus, information is derived from data through manipulation. All information is data, but not all data is information.

🔑 Definition — Information: A subset of data that possesses context, relevance, and purpose; data that has been manipulated to reveal meaningful trends or patterns.

📌 Example: The raw number of Super Bowl viewers is data; knowing viewership peaks in the third and fourth quarters is information for commercial placement decisions.

Q#3 Explain why the same set of data can be considered as useful information by some and useless data by others. Further, could this useful information be termed as “knowledge”? Why?

Whether certain facts are information or only data depends on the individual who is using those facts — their context, relevance, and purpose vary by person. The same data may be useful information to one person and meaningless data to another.

Useful information can be termed as knowledge only if it enables action and decisions, or provides information with direction. Knowledge is intrinsically similar to information and data, but is the richest and deepest of the three, and consequently the most valuable.

🔑 Definition — Knowledge (action-oriented): Information that enables action and decisions; information with direction.

📌 Example: Raw sales figures may be meaningless data to a new employee but become useful information and then knowledge to a manager who uses them to decide inventory levels.

Q#4 Describe the ways in which “knowledge” differs from “data” and “information.” Justify your answer with a relevant diagram.

Knowledge can be distinguished from data and information in two ways:

The basic view considers knowledge as being at the highest level in a hierarchy with information at the middle and data at the lowest level. Knowledge refers to information that enables action and decisions — it is intrinsically similar to data and information but is the richest, deepest, and most valuable.

The advanced view considers knowledge as intrinsically different from information. It defines knowledge in an area as justified beliefs about relationships among concepts relevant to that particular area.

To sum up, knowledge helps produce information from data or more valuable information from less valuable information, and this information facilitates action. Based on the newly generated information and relationships with other concepts, knowledge enables the beholder to make decisions.

📐 Diagram Concept (Hierarchical View): Data (lowest) → Information (middle) → Knowledge (highest)

💡 Why this matters: The basic view treats knowledge as "high-value information," while the advanced view treats knowledge as fundamentally different — a system of justified beliefs about relationships.

Q#5 Explain the importance of knowledge in creation and utilization of information.

Knowledge helps produce information from data or more valuable information from less valuable information, resulting in the facilitation of an action or decision. Knowledge helps convert data into information, and the use of information to make decisions requires knowledge as well.

Decisions, along with certain unrelated factors, lead to events, which cause generation of further data. The events, the use of information, and the information system might cause modifications in the knowledge itself.

Knowledge is vital in the ongoing cycle: creation of data → information → decision making → events → further data and information. Knowledge is both the catalyst and the end product of this continuous cycle.

📐 Cycle: Data → (Knowledge applied) → Information → (Knowledge applied) → Decision → Event → New Data → (Knowledge modified) → ...

Q#6 How does the subjective view of knowledge differ from the objective view? Explain how knowledge can be viewed as a state of mind, as a practice, as objects, as access to information and as capability.

The Subjective view of knowledge refers to it as an ongoing accomplishment that continuously affects and is influenced by social practices. It has no existence independent of social practices and human experiences. Two perspectives:

• Knowledge as a state of mind: Knowledge is a state of an individual's mind; organizational knowledge is the beliefs of individuals within the organization.
• Knowledge as a practice: Knowledge is held by a group and cannot be broken down into separate elements possessed by individuals. It resides in practice, not in anyone's head — reflected in organizational activities.

The Objective view holds that reality is independent of human perceptions and can be structured in terms of categories and concepts. Knowledge can be located as an object or capability that can be discovered or improved. Three perspectives:

• Knowledge as Objects: Knowledge can be stored, transferred, manipulated, and exist in various locations.
• Knowledge as Access to Information: Knowledge enables access and utilization of information, emphasizing accessibility of knowledge objects.
• Knowledge as Capability: Knowledge is a strategic capability that can be applied to seek a competitive advantage.

🔑 Definition — Subjective view of knowledge: An ongoing accomplishment that continuously affects and is influenced by social practices, without independent existence.

🔑 Definition — Objective view of knowledge: Reality independent of human perceptions; knowledge can be located as objects or capabilities discovered/improved by human agents.

Q#7 What is the difference between knowledge characterized as “know what” and “know how”? In these situations, how would you classify the knowledge a computer programmer has?

Knowledge can be classified into Declarative Knowledge and Procedural Knowledge.

• Declarative Knowledge (Substantive Knowledge): Focuses on beliefs about relationships among variables. Can be stated as propositions, expected correlations, or formulas. Characterized as "know what." Example: The average fuel consumption of a particular car.
• Procedural Knowledge: Focuses on beliefs relating sequences of steps or actions to achieve a certain outcome. Characterized as "know how." Example: Following steps to fix a car to improve its petrol mileage.

A computer programmer has both types: knowing the syntax of the programming language is declarative knowledge; knowing the logic steps to develop a program is procedural knowledge.

🔑 Definition — Declarative Knowledge: Beliefs about relationships among variables; "know what" — can be stated as propositions, correlations, or formulas.

🔑 Definition — Procedural Knowledge: Beliefs about sequences of steps or actions to achieve an outcome; "know how."

📌 Example: A programmer knows the syntax of Java (declarative/"know what") and the logical steps to write a sorting algorithm (procedural/"know how").

Q#8 What is general knowledge? How does it differ from specific knowledge? Describe the types of specific knowledge with suitable examples.

General Knowledge is possessed by a large number of people and is easily transferred from one person to another. Example: It is general knowledge that the earth revolves around the sun.

Specific Knowledge (Idiosyncratic Knowledge) is possessed by a very limited number of individuals and is difficult to transfer. Example: Scientists who know the exact distance between the earth and the sun based on its orbit.

Two types of specific knowledge:

• Technically-specific knowledge: Deep knowledge about a specific area, including tools and techniques to address problems in that area. Often acquired through formal training and augmented by experience. Example: The exact distance between earth and sun.
• Contextually-specific knowledge: Knowledge of particular circumstances of time and place in which work is performed. Pertains to organization and subunit context; cannot be acquired through formal training but must be obtained from within the specific context. Example: An astrophysicist calculating the exact duration and time an eclipse will occur based on experience.

🔑 Definition — General Knowledge: Knowledge possessed by a large number of people and easily transferred.

🔑 Definition — Specific Knowledge (Idiosyncratic Knowledge): Knowledge possessed by very few individuals and difficult to transfer.

Q#9 What is “expertise”? Distinguish among the three types of expertise.

Expertise is defined as knowledge of higher quality, which addresses the degree of knowledge. It refers to very specific knowledge; those who possess expertise can perform a task much better than those who do not. A person can be an expert at a particular task regardless of how sophisticated that area is.

To understand expertise, skill levels of experts from different domains should not be compared to each other. All experts require more or less the same cognitive skills. The difference lies in the depth of expertise when compared to others from their own domain. Example: A race car driver has more skill than the average car driver.

Note: The lecture text mentions "three types of expertise" in the question but only provides one general description without explicitly delineating three distinct types in the provided content.

🔑 Definition — Expertise: Knowledge of higher quality; very specific knowledge enabling superior task performance.

📌 Example: A race car driver has greater expertise (more skill) than an average car driver, but both are drivers.

Q#10 Contrast the differences between knowledge in people and knowledge in artifacts. Describe the various repositories of knowledge within organizational entities.

Knowledge resides in several different locations or reservoirs:

1. Knowledge in People: A considerable component of knowledge is stored in individuals within organizations. Knowledge also resides within groups due to relationships among members — groups form beliefs about what works. Communities of practice develop as individuals interact frequently on topics of mutual interest.
2. Knowledge in Artifacts: Significant knowledge is stored in practices, organizational routines, or sequential patterns of interaction. Often embedded in procedures, rules, and norms developed through experience. Also stored in technologies, systems, and knowledge repositories (e.g., a log of customer calls to develop an FAQ section).
3. Knowledge in Organizational Entities (three levels):
   • Organizational units/parts: Formal groupings of individuals due to organizational structuring. When incumbents depart, successors inherit knowledge via systems, practices, and relationships.
   • Entire organization (business unit/corporation): Stores norms, values, practices, and culture. Organizational response to environmental events depends on knowledge stored in individuals, units, and overall organizational knowledge from experiences.
   • Interorganizational relationships: Organizations draw upon knowledge embedded in relationships with customers and suppliers. They learn from customer experiences to improve products.

🔑 Definition — Knowledge in People: Knowledge stored in individuals and groups, including communities of practice.

🔑 Definition — Knowledge in Artifacts: Knowledge stored in practices, routines, procedures, rules, norms, technologies, systems, and knowledge repositories.

🔑 Definition — Knowledge in Organizational Entities: Knowledge stored at three levels: organizational units, entire organization, and interorganizational relationships.

Q#11 Determine the various types of knowledge you are used to. You should be able to state at least one of each type.

Knowledge can be classified in several ways:

• Declarative Knowledge – Used in understanding meanings of English words (e.g., "physical," "cognitive") and punctuation marks.
• Procedural Knowledge – Used in the actual reading of the book, e.g., knowing to turn the page when reaching the end.
• Tacit Knowledge – Gained from reading the preface, which helps understand the chapter's contents.
• Explicit Knowledge – Contained in the text of the chapter, helping understand tables and figures.
• General Knowledge – About topics like restaurants, coins, and hurricanes, used to understand concepts in the chapter.
• Specific Knowledge – Used to apply concepts read in the chapter to real-world situations encountered at work.

⭐ Key Takeaways

The most critical understanding from this lecture is that data, information, and knowledge form a hierarchy where data is raw and context-free, information is data with context and purpose, and knowledge is information that enables action and decision-making. Knowledge differs from data and information in that it helps produce information from data and facilitates decisions, and it can be viewed subjectively (as a state of mind or practice) or objectively (as objects, access to information, or capability). Students must remember the key classifications: declarative vs. procedural knowledge, general vs. specific knowledge (with technically-specific and contextually-specific subtypes), and the distinction between knowledge in people (individuals and groups), knowledge in artifacts (routines, rules, repositories), and knowledge in organizational entities (units, entire organizations, interorganizational relationships). Expertise represents the highest quality of knowledge, enabling superior task performance in specific domains.

🧠 Quick Revision Questions

1. What is the difference between the basic (hierarchical) view and the advanced view of knowledge?
2. How does knowledge serve as both a catalyst and an end product in the data-information-decision cycle?
3. Contrast the subjective view of knowledge as a "state of mind" versus knowledge as a "practice."
4. What distinguishes technically-specific knowledge from contextually-specific knowledge, and how is each acquired?
5. Name and describe the three levels at which knowledge is stored in organizational entities.

📘 Lecture 11 — Understanding Data for Analysis and Decision/Policy Making

📖 Overview: This lecture explores the fundamental distinctions between data, information, and knowledge through practical, everyday examples. It demonstrates how these three concepts interact in decision-making processes and examines various theoretical perspectives on knowledge, including objective and subjective views, types of expertise, and different knowledge classifications.

🗂️ Topics Covered

The lecture begins with practical examples illustrating how data, information, and knowledge operate in daily decisions like sending emails, watching TV, and driving to work. It then examines how decisions would be hindered without pre-existing data, information, or knowledge. The text presents two ways of distinguishing knowledge from information and data, provides examples from fast food restaurant contexts, describes various perspectives on knowledge (subjective vs. objective), lists three types of objective knowledge, explains classifications including procedural/declarative, tacit/explicit, and general/specific knowledge, and covers how explicit knowledge transfers to tacit knowledge. The lecture concludes with three types of expertise: associational, motor skill, and theoretical.

📝 Lecture Summary

1. Consider five decisions you might have made today

This section presents five everyday actions and breaks down the data, information, and knowledge involved in each. For sending an email, data is the email addresses of all university individuals, information is the specific friend's email address, and knowledge is how to send an email. For watching a favorite TV program, data is opening the TV guide, information is referring to the guide for air time, and knowledge is turning on the TV and setting the channel. For driving to work, data is details about car functions (brakes, steering), information is directions from home to work, and knowledge is how to drive and maneuver. For answering a ringing telephone, data is hearing the phone ring, information is where the phone is located, and knowledge is how to pick up the receiver and answer. For setting a clock for Daylight Saving Time, data is dates with and without DST, information is the date and time to make the change, and knowledge is which direction and by how much to adjust.

2. How decisions would be influenced by lack of pre-existing data, information, or knowledge

Each action would be hindered or impossible without pre-existing data, information, or knowledge. Sending an email is impossible without the information of the friend's email address, though lack of data on all student emails is not a problem if the specific address is known. Basic knowledge of how to open an email program is necessary. Watching a TV program is possible without a TV guide, but it is a hindrance without knowing the air time. Driving to work absolutely requires knowledge of how to drive and the directions, but not detailed data about car functions. Answering a phone makes hearing the ring vital; information about phone location may be less important since one can move toward the sound. Setting a clock requires information on when and how to make the change and knowledge about direction and extent of adjustment.

3. Inventing a new product and collecting demographic data

This answer focuses on identifying specific kinds of data (e.g., age, gender, education, income) and manipulating this data based on characteristics of the invented product. For example, if the new product targets older men, the average education level and income of men in higher age groups would help identify pricing and the target audience for advertising.

4. Contrasting views of knowledge between manufacturing and services managers

Possible differences include: The manager in the manufacturing organization might identify greater examples of procedural knowledge, while the manager in the services organization might identify greater examples of declarative knowledge. The manufacturing manager might identify greater examples of tacit knowledge, whereas the services manager might identify greater examples of explicit knowledge.

Q1: Two ways for distinguishing knowledge from information and data

The simpler view distinguishes by value: data has little value, information has more value, and knowledge has the greatest value. In this view, knowledge is at the top of an information hierarchy with information value as the vertical axis. The second view states that knowledge is what enables us to produce more valuable information from less valuable information, with importance on the transformation process—it is about relationships between pieces of information.

Q2: Examples of data, information, and knowledge from fast food restaurant perspective

Data: number of burgers ordered, number of burgers served, server and chef salaries, burgers in stock, sales price of an order, cost of a burger. Information: daily sales numbers (rupees, quantity, or percent) for each item, daily reduction in inventory for each item, total number of customers, percentage of customers ordering burgers/shakes/other, reorder quantities, average time spent by a server on each customer. Knowledge: trend of customer numbers indicating future customers and ordering patterns, relationship between projected burger sales and bread inventory, relationship between projected customers and average server time to determine staffing needs.

Q3: Various perspectives on knowledge

Knowledge may be examined either subjectively or objectively. The subjective view ties knowledge to an individual's experience in social interactions and is classified as either a state of mind or as a practice. The objective view claims that knowledge exists in a priori categories or concepts independent of any individual, and may exist as an object, a capability, or simply as access to information.

Q4: Three types of objective knowledge

1. Knowledge as an object: something that can be stored, transferred, and manipulated.
2. Knowledge as access to information: enables access and utilization of information.
3. Knowledge as capability: emphasis on knowledge as a strategic capability, how knowledge may be applied to influence action.

Q5: Three classifications of knowledge

1. Procedural or declarative: knowledge classified as either data-oriented or task-oriented.
2. Tacit or explicit: knowledge held in the heads of individuals/groups or alternately encoded in some other storage medium.
3. General or specific: indicates breadth of ownership—general knowledge held by many, specific knowledge held by few.

Q6: How explicit knowledge can be transferred to tacit knowledge

Explicit knowledge is articulated (and frequently encoded) and may be easily transferred to another individual or group. When an individual acquires knowledge from an explicit form (through reading/hearing/feeling/observing), the knowledge must be remembered to become tacit and is transformed to conform to or modify the individual's existing belief system. Example: An employee looks into a manual to determine how to install new hardware—the manual is explicit procedural information. After performing a few installs, the employee learns the specified procedures and possible improvements, transforming explicit knowledge into tacit knowledge.

Q7: Three types of expertise

1. Associational expertise: comes from years of experience and recognizing patterns in data. Example: a mechanic diagnosing car problems just from listening to sounds.
2. Motor skill expertise: predominantly physical instead of cognitive, resulting from a very large number of practice sessions to develop a particular physical skill such as shooting a basketball.
3. Theoretical or deep expertise: knowledge of a particular topic far beyond the average individual. The ability to go beyond superficial understanding and create novel solutions based on theoretical foundations of the domain.

Test Your Understanding Questions:

1. Select one definition of KM: Any definition is a candidate because of aspects like using accessible knowledge from outside sources, embedding knowledge in business processes, representing knowledge in databases, promoting knowledge growth through culture and incentives, transferring knowledge throughout the organization, and assessing knowledge assets regularly.

2. KM involves people, technology, and processes: The ideal organization exchanges knowledge across functional areas by using technology and established processes. The exchange may be for policy formulation, strategy, training, development, or problem solving in teams. None of the three areas can function independently.

3. KM indicator—thinking ahead: Progress, advancement, and growth are future-oriented and require people to think ahead.

4. Why KM is not about: Reengineering—KM is a mechanical shift, not a one-shot drastic change. A discipline—KM is another way to improve quality, profitability, and growth. Data—Data is facts without context; KM is actionable information to create value. Knowledge capture—Knowledge cannot be captured in its entirety because of implied human maturation over time that upgrades knowledge quality and value, especially tacit knowledge.

⭐ Key Takeaways

The critical distinction between data, information, and knowledge lies in their value hierarchy and the transformation process where knowledge enables creating more valuable information from less valuable information. Knowledge can be viewed subjectively (tied to individual experience) or objectively (existing in a priori categories). Three key knowledge classifications are procedural vs. declarative, tacit vs. explicit, and general vs. specific. Expertise takes three forms: associational (pattern recognition from experience), motor skill (physical practice-based), and theoretical (deep domain knowledge enabling novel solutions). KM operates at the intersection of people, technology, and processes, and cannot function if any one area is independent.

🧠 Quick Revision Questions

1. What are the two ways presented in the text for distinguishing knowledge from information and data?
2. Give one example each of data, information, and knowledge from a fast food restaurant perspective.
3. What are the three types of objective knowledge, and what does each emphasize?
4. How can explicit knowledge be transferred to tacit knowledge? Provide an example.
5. What are the three types of expertise discussed in the text, and what differentiates them?

📘 Lecture 12 — Knowledge Hierarchy... Individual vs Organization

📖 Overview: This lecture explores how humans think and learn, contrasting human cognitive processes with artificial intelligence. It examines the three types of human learning—by experience, by example, and by discovery—and their implications for knowledge management in organizations. Understanding these learning mechanisms is essential for capturing and leveraging expert knowledge effectively.

🗂️ Topics Covered

The lecture covers human thinking and learning processes, comparing them with computer processing; the three types of human learning (experience, example, discovery); implications for knowledge management at organizational level including knowledge sharing and preservation; attributes of intelligent behavior; classifications of knowledge (procedural, declarative, semantic, episodic, tacit, explicit); and reasoning types including deductive, inductive, and case-based reasoning.

📝 Lecture Summary

Human Thinking and Learning

Scientists study the human brain to build computers that may someday duplicate human expert thought processes. According to Marvin Minsky (1991), the human mind is a “society of minds” that is hierarchically structured and interconnected. Both mind and machine accept data and information, manipulate symbols, store items in memory, and retrieve items on command. However, humans receive information via sensing—seeing, smelling, tasting, touching, and hearing—which promotes unique thinking and learning. On a micro level, both the central processing unit of a computer and the human brain receive information as electrical impulses. The key difference is that computers must be programmed to do specific tasks; performing one job does not transcend onto other jobs as it does with humans.

🔑 Definition — Knowledge: Understanding gained through experience
🔑 Definition — Intelligence: The capacity to acquire and apply knowledge

💡 Why this matters: Understanding the differences between human and machine thinking helps knowledge developers design better systems for capturing expertise.

Human Learning

Memory is an essential component of learning because it accommodates learning. Healthy human memory never seems to run out of space. As humans acquire more knowledge, they integrate new facts with what they think is relevant and organize the resulting mix to produce valuable decisions, solutions, or advice.

For humans, learning occurs in one of three ways:

Learning By Experience: The ability to learn by experience is a mark of intelligence. Experts, who know a lot about a particular problem, remember facts in that problem area much more easily and quickly than non-experts. This type of information is important for knowledge developers to understand when evaluating a human expert’s range of knowledge.

🔑 Definition — Experience: The factor that changes unrelated facts into expert knowledge
🔑 Definition — Heuristic: A rule of thumb based on years of experience

Learning By Example: Specially constructed examples are used instead of a broad range of experience. Much classroom instruction is composed of teaching by example—providing examples, cases, or scenarios that develop concepts students are expected to learn. This method allows students to learn without requiring them to accumulate experience, making it more efficient than learning by experience.

Learning By Discovery: This is an undirected approach in which humans explore a problem area without advanced knowledge of the objective. No one understands why humans are so good at this. It is difficult to teach and will be years before we can benefit from this approach commercially.

🔑 Definition — Common sense: Innate ability to sense, judge, or perceive situations that grows stronger over time; inferences made from knowledge about the world

Implications for Knowledge Management at Organization

Knowledge awareness benefits entire organizations. With emphasis on sustainable competitive advantage, added value, and improved productivity, management needs to create, innovate, monitor, and protect its knowledge inventory. A KM environment means focusing on: generating new knowledge; transferring existing knowledge; embedding knowledge in products, services, and processes; developing an environment for facilitating knowledge growth; and accessing valuable knowledge from inside and outside the firm.

Some sources claim that 20 percent of an organization’s knowledgeable personnel can operate 80 percent of the organization’s day-to-day business. Human resources managers can identify the knowledge core of the organization and recommend ways to preserve this critical core. Without such preparation, corporate talent could erode through a brain drain.

Companies should emphasize tapping, sharing, and preserving tacit knowledge and the total knowledge base. A company’s knowledge base includes explicit and tacit knowledge and exists internally as well as within the firm’s external connections. Companies should focus on innovation and processes that convert innovation to new products and services.

Knowledge sharing is unnatural—one person’s knowledge is added value to that person’s career path. Knowledge management solves the problem of un-recycled knowledge. Systems developed to gather, organize, refine, and distribute knowledge should have six attributes: learning capability, improving with use, knowing what you want, two-way communication, recalling past actions to develop a profile, and unique configuration to individual specification in real time.

🔑 Definition — Tacit knowledge: “Know-how” stored in people’s minds, not easy to capture or share; includes intuitions, values, and beliefs stemming from experience
🔑 Definition — Explicit knowledge: Codified and digitized knowledge in the form of records, reports, or documents; reusable for decision making

Key Terms and Classifications

Knowledge classifications:

• Procedural knowledge: Knowledge used over and over again; involves understanding how to do a task (motor in nature)
• Declarative knowledge: Surface information that experts verbalize easily; shallow knowledge
• Semantic knowledge: Highly organized, “chunked” knowledge residing in long-term memory representing concepts, facts, and relationships among facts
• Episodic knowledge: Knowledge based on experiential information chunked as an entity and retrieved from long-term memory on recall

Reasoning types:

• Deductive reasoning: Exact reasoning; takes known principles (exact facts) and applies them to instances to infer an exact conclusion
• Inductive reasoning: Reasoning from a given set of facts or specific examples to general principles or rules
• Case-based reasoning: Reasoning by analogy; human experts reason about a problem by recalling similar cases encountered in the past

🔑 Definition — Chunking: Grouping ideas or details that are stored and recalled together as a unit
🔑 Definition — Compilation: The way a human translates instructions into meaning, language, or response
🔑 Definition — Inferencing: Deriving a conclusion based on statements that only imply that conclusion
🔑 Definition — Premise: Provides evidence from which the conclusion must necessarily follow; evaluates truth or falsehood with some degree of certainty
🔑 Definition — Logic: The scientific study of the process of reasoning and the set of rules and procedures used in the reasoning process
🔑 Definition — Reasoning: The process of applying knowledge to arrive at solutions based on the interactions between rules and data
🔑 Definition — Scenario: The formal description of how a problem situation operates

Summary Points

• Research in artificial intelligence has introduced more structure into human thinking about thinking
• Humans do not receive and process information exactly as machines do
• Human learning: humans learn new facts, integrate them, and organize results to produce solutions, advice, and decisions
• Humans learn through experience, by example, and by discovery
• Knowledge compilation or chunking enables experts to optimize memory capacity and process information quickly
• The more chunking a person does, the more efficient is recall

⭐ Key Takeaways

The three types of human learning—experience, example, and discovery—form the foundation for understanding how expertise develops, with learning by discovery being the least understood. Knowledge is compiled in long-term memory as chunks, which enables experts to process information quickly but can also make it difficult for them to describe their knowledge to others. The distinction between tacit knowledge (in people's minds) and explicit knowledge (codified) is crucial for knowledge management, as is understanding that sharing knowledge is unnatural and must be actively facilitated through culture, not just technology. Organizations must protect their knowledge core (the 20% who operate 80% of business) from brain drain while simultaneously promoting innovation through knowledge sharing. Knowledge classifications (procedural, declarative, semantic, episodic) and reasoning types (deductive, inductive, case-based) provide frameworks for understanding and capturing expert knowledge effectively.

🧠 Quick Revision Questions

1. What are the three ways humans learn, and which one is the least understood?
2. Distinguish between tacit knowledge and explicit knowledge, and give an example of each.
3. What is knowledge chunking, and why does it make it difficult for experts to describe their knowledge?
4. Explain the difference between deductive reasoning and inductive reasoning.
5. According to the lecture, what percentage of knowledgeable personnel operates what percentage of day-to-day business, and why is this important for knowledge management?

📘 Lecture 13 — Dimensions of Human Knowledge, Tree of Knowledge

📖 Overview: This lecture explores the distinction between core and innovative knowledge, focusing on how Japanese companies achieve continuous innovation through knowledge creation. It delves into the critical difference between tacit and explicit knowledge, introduces the two dimensions of knowledge creation (epistemological and ontological), and presents the foundation for the SECI model of knowledge conversion.

🗂️ Topics Covered

The lecture begins by contrasting the mindset of companies that embrace change versus those that defend existing advantages, then examines how Japanese companies externalize knowledge through continuous innovation. It introduces the concept of tacit knowledge with its technical and cognitive dimensions, contrasts this with explicit knowledge, and discusses the implications for organizational learning. Finally, it presents the epistemological and ontological dimensions of knowledge creation and introduces the SECI model of knowledge conversion as a social process.

📝 Lecture Summary

Core Vs Innovative Knowledge

Japanese companies bring about continuous innovation by looking outside and into the future, anticipating changes in market, technology, competition, or product. They are willing to abandon what has long been successful, treating change as an everyday event and a positive force. In contrast, other companies become preoccupied with defending their advantages and treat change with fear, seeking predictability and stability. During times of uncertainty, Japanese companies accumulate knowledge from external sources—suppliers, customers, distributors, government agencies, and even competitors—and share it widely within the organization, stored as part of the company’s knowledge base. This conversion process from outside to inside and back outside again in the form of new products, services, or systems is the key to understanding Japanese success. Continuous innovation leads to competitive advantage through knowledge creation.

💡 Why this matters: The willingness to abandon successful practices and seek external knowledge is a fundamental driver of long-term innovation.

🔑 Definition — Continuous Innovation: The ongoing process of turning external knowledge into new products and services through internal knowledge creation and sharing.

Tacit Knowledge and Its Two Dimensions

Japanese companies recognize that knowledge expressed in words and numbers represents only the tip of the iceberg. They view knowledge as primarily tacit—something not easily visible and expressible. Tacit knowledge is highly personal and hard to formalize, making it difficult to communicate or share with others. Subjective insights, intuitions, and hunches fall into this category. Tacit knowledge is deeply rooted in an individual’s action and experience, as well as in the ideals, values, or emotions they embrace.

Tacit knowledge has two dimensions:

1. The technical dimension: Encompasses informal and hard-to-describe skills or crafts captured in the term "know-how." A master craftsman develops expertise "at his fingertips" after years of experience but often cannot articulate the scientific or technical principles behind what he knows.
2. The cognitive dimension: Consists of schemata, mental models, beliefs, and perceptions so ingrained that we take them for granted. This dimension reflects our image of reality (what is) and our vision for the future (what ought to be).

🔑 Definition — Tacit Knowledge: Knowledge that is highly personal, context-specific, and hard to formalize, communicate, or share with others; includes subjective insights, intuitions, hunches, ideals, values, and emotions.

🔑 Definition — Explicit Knowledge: Knowledge that can be expressed in words and numbers, easily processed by a computer, transmitted electronically, or stored in databases.

Implications of Tacit Knowledge

The distinction between explicit knowledge and tacit knowledge is key to understanding differences between Western and Japanese approaches to knowledge. For tacit knowledge to be communicated and shared within the organization, it must be converted into words or numbers that anyone can understand. It is precisely during this conversion—from tacit to explicit, and back again into tacit—that organizational knowledge is created.

First implication: The organization is viewed not as a machine for processing information but as a living organism. Sharing an understanding of what the company stands for, where it is going, and how to make that vision a reality becomes more crucial than processing objective information. Highly subjective insights, intuitions, and hunches are integral parts of knowledge, along with ideals, values, emotions, images, and symbols.

Second implication: Innovation is not just about putting together diverse bits of data and information. It is a highly individual process of personal and organizational self-renewal. The personal commitment of employees and their identity with the company and its mission become indispensable. Creating new knowledge means literally re-creating the company and everyone in it. Knowledge must be built on its own, frequently requiring intensive and laborious interaction among members of the organization—not simply a matter of learning from others.

🔑 Definition — Self-renewal: The ongoing process of recreating the company and everyone in it through personal and organizational knowledge creation.

Two Dimensions of Knowledge Creation

Our basic framework contains two dimensions:

The ontological dimension: In a strict sense, knowledge is created only by individuals. An organization cannot create knowledge without individuals. The organization supports creative individuals or provides contexts for them to create knowledge. Organizational knowledge creation must be understood as a process that "organizationally" amplifies the knowledge created by individuals and crystallizes it as part of the knowledge network of the organization.

The epistemological dimension: Based on Michael Polanyi's (1966) distinction between tacit knowledge and explicit knowledge. Tacit knowledge is personal, context-specific, and hard to formalize and communicate. Explicit or "codified" knowledge refers to knowledge transmittable in formal, systematic language. Polanyi contends that human beings acquire knowledge by actively creating and organizing their own experiences. As Polanyi states, "We can know more than we can tell."

In traditional epistemology, knowledge derives from the separation of the subject and the object of perception. In contrast, Polanyi contends that human beings create knowledge by involving themselves with objects through self-involvement and commitment, or what he called "indwelling." To know something is to create its image or pattern by tacitly integrating particulars. Indwelling breaks the traditional dichotomies between mind and body, reason and emotion, subject and object, and knower and known.

Knowledge of experience tends to be tacit, physical, and subjective. Knowledge of rationality tends to be explicit, metaphysical, and objective. Tacit knowledge is created "here and now" in a specific, practical context and entails an "analog" quality. Explicit knowledge is about past events or objects "there and then" and is created by "digital" activity.

🔑 Definition — Indwelling: The process of knowing something by self-involvement and commitment, breaking dichotomies between mind and body, reason and emotion, and subject and object.

📐 Formula: Polanyi's Principle → "We can know more than we can tell."

Knowledge Conversion/Creation: Interaction Between Tacit and Explicit Knowledge

Westerners tend to emphasize explicit knowledge; the Japanese tend to stress tacit knowledge. However, tacit knowledge and explicit knowledge are not totally separate but mutually complementary entities. They interact with and interchange into each other in the creative activities of human beings. Our dynamic SECI model of knowledge creation is anchored to a critical assumption that human knowledge is created and expanded through social interaction between tacit knowledge and explicit knowledge. We call this interaction "knowledge conversion." This conversion is a social process between individuals and not confined within an individual. Through this social conversion process, tacit and explicit knowledge expand in terms of both quality and quantity.

🔑 Definition — Knowledge Conversion: The social process through which tacit and explicit knowledge interact and interchange, creating and expanding human knowledge; the foundation of the SECI model.

⭐ Key Takeaways

The lecture establishes that knowledge has two fundamental dimensions: tacit (personal, context-specific, hard to formalize) and explicit (codified, transmittable in systematic language). Tacit knowledge itself has two dimensions—technical (know-how, crafts, skills) and cognitive (mental models, schemata, beliefs, perceptions)—which shape how individuals perceive reality and envision the future. The key to organizational knowledge creation is the conversion of tacit knowledge into explicit knowledge and back again, a social process that expands knowledge in quality and quantity. Japanese companies achieve continuous innovation by treating the organization as a living organism, emphasizing personal commitment, and recognizing that knowledge creation requires intensive interaction rather than mere learning from others. The ontological dimension reminds us that while only individuals create knowledge, organizations amplify and crystallize it through expanding communities of interaction.

🧠 Quick Revision Questions

1. What are the two dimensions of tacit knowledge, and how do they differ from each other?
2. According to Polanyi, what does the term "indwelling" mean, and why does it break traditional dichotomies?
3. How does Japanese companies' approach to knowledge differ from Western companies' emphasis on explicit knowledge?
4. What are the two dimensions of the knowledge creation framework (epistemological and ontological), and why are both necessary?
5. What is "knowledge conversion" in the SECI model, and why is it considered a social rather than individual process?

📘 Lecture 14 — Dimensions of and Multiple Views of KM in Organizations

📖 Overview: This lecture explores the multidimensional nature of knowledge management (KM) in organizations, highlighting its economic significance and the confusion surrounding its implementation. It examines the current state of KM, its importance for value creation, and provides practical principles for managing customer knowledge, deploying knowledge in information technology, and monitoring knowledge assets.

🗂️ Topics Covered

This lecture covers the overview of knowledge management as a preeminent economic resource, the relationship between knowledge management and value creation with tangible benefits from companies like Chevron and Dow Chemical, the current state of KM research revealing numerous conflicting definitions and implementation strategies, why KM is crucial for competitive advantage in the Information Age, and practical principles for managing customer knowledge, deploying knowledge in IT, and monitoring and measuring knowledge assets.

📝 Lecture Summary

Knowledge Management Overview

Knowledge has become the preeminent economic resource, more important than raw material or money. In today's Information Age economy, knowledge is regarded as the preeminent contributor to value creation across industrial and service landscapes. However, the proliferation of tools enabling companies to manage and leverage information has led to a proliferation of KM approaches, measurement tools, initiatives, definitions, and procedures, creating confusion that inhibits companies.

According to leading practitioner in the field, the potential impact of knowledge management on the national and global economy is immense. International Data Corporation (IDC) believes the market impact of KM will be analogous to that of the Internet. Primary points include: KM will be a catalyst for many IT product and service markets; KM will allow companies to establish exclusive market niches; and KM will be an integral enhancement for many existing offerings.

Principal difficulties associated with designing and implementing KM practices include: culture change can be painful and exceptionally slow; investment in necessary tools can be tenuous and incremental; KM is a high-level solution sell; and a wall of confusion about knowledge measurement inhibits growth. Most practitioners have focused on qualitative issues; few have employed reliable measurement tools or applied rigorous quantitative analysis.

Companies like Chrysler, Ford, General Motors, Amoco, Dow, Monsanto, Columbia/HCA Healthcare Corp., and Fruit of the Loom have embraced KM. KM offers opportunities for companies to: capture and analyze corporate information and apply it strategically through data warehousing and data mining; create processes for worldwide access to information enabling faster decisions through intranets, groupware, and group decision support systems; and leverage accumulated knowledge of past experiences across the company.

Knowledge Management And Value Creation

Companies making the investment in knowledge management can realize huge bottom line benefits, while those neglecting to do so can suffer tremendous costs in terms of lost revenues, customers, and markets.

🔑 Definition — Tangible benefits: Real, measurable financial gains realized through KM implementation.

📌 Example: Chevron realized a $170 million annual savings by pooling and sharing knowledge that had been scattered and localized in various offices around the world. One team saved $150 million by sharing ways to reduce the use of electric power and fuel. Another team saved $20 million by comparing data on gas compressors.

📌 Example: Dow Chemical increased its annual licensing revenues by $100 million by strategically managing its patents and licenses.

KM should be seen as a remedy for earlier attempts at "reengineering" rather than its latest version. KM's focus on identifying and maximizing knowledge value creation stands in sharp contrast to the "slash-and-burn" techniques associated with many reengineering strategies. Many reengineering efforts have led to downsizing that actually cut huge swaths out of the knowledge base of these companies.

The State Of Knowledge Management

Research into knowledge management reveals interesting anecdotal evidence and varied literature on current methodologies, techniques, tools, and case studies. The current state is characterized by: numerous and conflicting definitions of KM; wide diversity of implementation strategies with many companies in disparate industries engaged in KM initiatives; no comprehensive understanding of the best techniques for designing and launching KM initiatives; very few detailed case studies of corporate experiences with KM and knowledge gaps; restricted access to information on how companies have resolved specific problems; ad hoc and non-comprehensive discussion of techniques for measuring the value of KM; unclear links between knowledge asset utilization and financial results; and general confusion about the difference between information retrieval and knowledge management.

Why is Knowledge Management Important?

Knowledge management is crucial because it points the way to comprehensive and clearly understandable management initiatives and procedures. When companies fail to utilize tangible assets, they suffer economic consequences, and this failure is clearly observable. Although knowledge assets are harder to quantify, they are just as critical for the long-term survival and growth of the company.

Success in today's competitive marketplace depends on the quality of knowledge and knowledge processes organizations apply to key business activities. For example, maximizing the efficiency of the supply chain depends on applying knowledge of diverse areas such as raw materials sources, planning, manufacturing, and distribution. Likewise, product development requires knowledge of consumer requirements, recent scientific developments and new technologies, and marketing.

Deployment of knowledge assets to create competitive advantage becomes even more crucial as: the marketplace becomes increasingly competitive and the rate of innovation continues to rise; corporations reorganize business units to create customer value; competitive pressures reduce the workforce that holds corporate knowledge; employees have less unstructured time to acquire knowledge; and technologies increase complexity by allowing small operating companies to link with suppliers into transnational sourcing operations.

Restructuring often results in changes in strategic direction and loss of knowledge in specific functional areas. Effective KM initiatives can help eliminate the need for drastic restructurings as they help companies evolve with the changing economic environment. They can also capture knowledge assets that would otherwise be lost due to necessary restructurings, retirement, and departing employees. This can result in increased revenues, increased customer satisfaction and loyalty, enhanced competitive standing, and the ability to respond easily to changing market conditions. In this sense, KM is as critical for companies in the Information Age as the assembly line and production management were in the Industrial Age.

💡 Why this matters: This section establishes that knowledge has replaced physical assets as the primary driver of economic value. Unlike tangible assets, knowledge assets are invisible and harder to measure, making their management a distinct strategic challenge requiring new approaches.

Practical Principles for Managing Knowledge

Theorists and practitioners are struggling to find a common set of principles to apply in successfully managing knowledge. Principles have been categorized according to how to create, collaborate, disseminate, reuse, embed, store, monitor, and measure knowledge. Customer knowledge, deploying knowledge in information technology, and monitoring and measuring knowledge assets are the places where KM principles can be practically applied.

Customer Knowledge

The first set of principles aims to lower transaction costs, increase the volume of transactions, and improve customer satisfaction by embedding customer knowledge and fail-safeing the transaction process.

1. Identify the knowledge that customers really value and make sure it is deployed in products, services, and self-service opportunities. Following this principle leads the manager to ask how much knowledge a customer employs in completing a transaction with the company. For example, an "e-tailer" such as eToys created a transaction process where the customer visits their website and uses the company Web interface to obtain a desired toy, seek suggestions, find out what others have purchased, or review the company's toy inventory. By visiting the company website, the customer becomes part of the transaction process by activating the knowledge embedded in company sales, order, provisioning, and production software.

The customer-activated knowledge costs the company next to nothing, as long as the site is well designed. Costs are incurred only if the site interface is so bad that customers make errors requiring human intervention. A site with a robust technology platform allows a very large number of customers to complete transactions at the same time.

To prevent customer induced errors, company interfaces must facilitate customers' self-service without generating errors. One method is to use the notion of "e-Poke-Yoke". The concept of mistake-proofing or Poke-Yoke originated in Japanese manufacturing practice. "Mistake-proofing is a powerful and comprehensive method for eliminating mistakes and defects, ensuring quality products and services."

Superior interfaces that embed customer knowledge within the transaction process on a personalized basis can lead to faster and more satisfying transactions. Several points to consider in designing a superior customer interface include: the time an average customer is willing to spend activating transaction knowledge; the amount of knowledge a customer will employ before losing interest; and how much value is added each time they execute knowledge.

The goal is to find the optimal upper and lower limits and develop an interface that: reduces the time a customer needs to complete the transaction process; reuses a customer's knowledge by embedding it in the transaction process; and ensures that value is added for the customer each time they execute knowledge.

A further step in providing customer value occurs when customers can compare their transaction behavior to that of others through social comparison. This is facilitated by the use of collaborative filters, which compare user input with that of other users. For example, movie ratings and CD purchases can be tabulated to generate composite scores and recommend purchases of popular items.

Personalized knowledge may be obtained from customers by providing financial incentives, such as lower prices or discounts, or using Web-based client-server technologies to track browser behavior. Benefits include: customer perception of more control over the transaction process; closer bonding with customers; lower company transaction costs; and greater volume of transactions per time period.

2. Make sure the customer product description and company description are as close as possible. Customers expect that products and services will match their descriptions. Knowledge-based descriptions can be used to ensure they are delivered as specified. This is especially true for business-to-business transactions, where outsourcing decisions are predicated on the belief that the outsourced service is delivered as specified.

Customers use lists of ingredients, fat content, calories per serving, certifications, and so forth as guides. Brand names often serve as a surrogate for products and services that meet customer expectations. Over time, customers have become more discerning and look for more than brand names. Companies can use this principle to guide their advertising, requirements for outsourcers, and production processes to ensure the knowledge required to produce a high-quality product/service has actually been applied.

Deploying Knowledge in Information Technology

Getting technology to do the work of humans has been the Holy Grail of the Information Age. The essence of the problem is deciding what human knowledge to deploy in information technology (IT). In general, the more complex the knowledge, the harder it is to deploy in IT.

1. Move simple, procedural knowledge that is employed frequently to IT. The focus of early automation efforts followed this principle as companies developed file-processing systems to do much of the tedious work in accounting, billing, and basic manufacturing. Since this knowledge is employed frequently and follows very specific, well-defined rules, moving it to IT allowed companies to dramatically lower the cost per use of the knowledge.

The latest attempt to follow this principle can be found in enterprise resource planning (ERP) software from companies such as SAP, PeopleSoft, Baan, JD Edwards, and Oracle. These systems have succeeded largely where they have stuck to this principle. They have failed where they have attempted to tackle more complex knowledge or knowledge used infrequently. For example, attempts to use an ERP system at Hewlett-Packard Labs failed largely because the system attempted to embed engineering knowledge. Studies on Nova Corporation and CBPO found that attempts to automate simple knowledge used infrequently resulted in costs that far exceeded those of leaving the knowledge in human operators' heads and hands.

2. Capture and embed knowledge in IT that is volatile and might be lost when employees leave the company. When employees leave a company, they often take with them knowledge critical to continued smooth operations. In one case, a business development executive of an Internet start-up company described his strategy as "a knowledge redundancy strategy": two key technical employees for every key technical job. This is a rational approach because such complex knowledge is not in ready supply in the employment marketplace and is nearly impossible to embed in IT. However, management realized that its long-term sustainability depends on capturing and embedding critical technical knowledge in less volatile forms such as IT.

The field of artificial intelligence supports this general principle and has spawned expert systems and neural networks. Many of the earliest commercial attempts to embed complex knowledge in IT systems were based on what would be lost when "experts" in well-defined areas retired or left the company. Neural networks use an inductive approach, learning from the patterns that evolve from the behaviors of quasi-animate objects such as electronic ant colonies.

Groupware systems have attempted to capture critical complex knowledge assets so they can be indexed and reused by others. Large consulting firms such as Arthur Andersen and Ernst & Young use groupware systems like Lotus Notes for this purpose. Ernst & Young has a system named Ernie that allows clients to "ask Ernie" when they confront problems involving relatively complex consulting knowledge.

As information technology advances allow for greater embedding of complex human knowledge, they will provide a way to capture and reuse critical employee knowledge. However, until someone discovers the algorithm for creativity, it is unlikely that all employee knowledge will be amenable to embedding in IT.

Monitoring and Measuring Knowledge

The basic goal for monitoring knowledge is to determine how well it is producing value in corporate processes. This requires following the use of knowledge throughout an organization's core processes and its interactions with the marketplace. The rate at which learning can be transformed into corporate core process knowledge will determine how quickly value is created through new products and services.

1. Accelerate the learning-knowledge-value cycle through monitoring of the transformation process. This principle requires corporate management to go beyond the traditional view of "build it and they will come." Management must accelerate the pace at which they embed critical marketplace learnings within their core processes and determine what value the introduction of this new knowledge produces. If embedding does not produce good return on the new knowledge, then management has done a poor job of synthesizing learnings from the marketplace or the marketplace has changed.

Conducting a knowledge-gap assessment aids management in determining the gaps in knowledge necessary for current operations. The assessment can identify knowledge assets that will be required to produce future value. Combining the concepts of sense, monitor and respond with a knowledge-gap assessment will help management identify the most promising knowledge for embedding in core processes.

2. Identify existing and future knowledge gaps. Monitoring the learning-knowledge-value cycle will reveal gaps in current performance. Planning for future products and services will reveal gaps in knowledge required to produce these future products and services. The knowledge-gap assessment is a powerful method for identifying the gaps:

• Begin with a definition or mapping of core processes in terms of the knowledge required to conduct normal operations.
• Make a list of the knowledge potential not currently in use within the core processes.
• Make a list of the knowledge no longer necessary to successfully generate the outputs.
• List the kinds of knowledge the company will need in the future to meet its long-and short-term goals.
• Compare the current knowledge assets deployed in the processes and identify the gaps.

Enhancing, maintaining, and acquiring knowledge assets to fill knowledge gaps involves: listing methods to maintain the current level of knowledge assets deployed; listing methods to remove knowledge no longer needed; listing methods to narrow or remove the gaps; and listing current strategies for knowledge maintenance and acquisition through hiring, training, outsourcing, information systems, and work rules.

3. Identify the best practices for embedding knowledge in IT, people, and processes. Best practices in knowledge management have been benchmarked by the American Productivity and Quality Center and at Arthur Andersen and are available in various forms from both organizations.

4. Measure the value-added by knowledge to create an internal marketplace. This principle can be followed best by creating a simple knowledge accounting system to monitor knowledge utilization. The system should allow managers to establish a price and cost per unit of knowledge, tied directly to companies' normal financial performance measures such as ROI, cash flow, and earnings per share. This provides management with feedback about how well they are managing the learning-knowledge-value cycle.

Providing price and cost per unit of knowledge will lead to new performance ratios such as: knowledge in use compared to knowledge in inventory; total knowledge compared to amount reused; and knowledge in people compared to knowledge in IT. Such measurement systems will lead to better protections for investors in companies with large market capitalization based on intangible assets contained in intellectual capital.

⭐ Key Takeaways

Knowledge management is the preeminent economic resource in the Information Age and is fundamentally different from Industrial Age reengineering—it focuses on identifying and maximizing knowledge value rather than "slash-and-burn" cost-cutting. The lecture establishes that while KM offers immense benefits (Chevron saved $170 million annually; Dow Chemical increased licensing revenues by $100 million), the field suffers from confusion and a lack of standardized measurement tools. Three critical domains for practical application are identified: customer knowledge (embedding knowledge in self-service interfaces using Poke-Yoke principles to prevent errors and using collaborative filters), deploying knowledge in IT (moving simple procedural knowledge to IT while capturing volatile knowledge that would be lost when employees leave), and monitoring and measuring knowledge (accelerating the learning-knowledge-value cycle, conducting knowledge-gap assessments, and creating knowledge accounting systems with price and cost per unit of knowledge). Success in today's marketplace depends on the quality of knowledge applied to key business activities, and the ability to transform learning into core process knowledge determines how quickly value is created.

🧠 Quick Revision Questions

1. What are the four principal difficulties associated with designing and implementing knowledge management practices according to the lecture?
2. How did Chevron realize a $170 million annual savings through knowledge management, and what were the two specific team savings mentioned?
3. What is the concept of "Poke-Yoke" (or "e-Poke-Yoke") and how does it apply to embedding customer knowledge in transaction interfaces?
4. What are the two basic principles for deploying knowledge in information technology, and why did the ERP implementation at Hewlett-Packard Labs fail?
5. What are the five steps in conducting a knowledge-gap assessment, and what four types of new performance ratios does the lecture suggest for measuring knowledge value?

📘 Lecture 15 — How KM Impacts Organizations?

📖 Overview: This lecture explores the multifaceted ways knowledge management affects organizations across four critical levels: people, processes, products, and overall organizational performance. Understanding these impacts is essential for justifying KM investments and designing effective KM strategies that deliver tangible business value.

🗂️ Topics Covered

The lecture covers how KM impacts people through enhanced learning and job satisfaction, improves organizational processes along dimensions of effectiveness, efficiency, and innovation, and contributes to both value-added and knowledge-based products. It also examines direct and indirect impacts on organizational performance through economies of scale and scope, sustainable competitive advantage, and specific industry examples such as Shell's oil exploration community of practice. Finally, it explores how internalization, externalization, socialization, and communities of practice facilitate employee learning.

📝 Lecture Summary

How KM Impacts Organizations

Knowledge management can impact organizations in various ways and at several levels by way of people, processes, products, and overall organizational performance. At all of these levels, knowledge management affects organizations in two ways: first, it can help create knowledge, which can then contribute to improved performance; second, it can directly cause improvements along these four levels.

The impact on people involves facilitating learning throughout the organization, allowing constant growth and change in response to market and technology, making employees more flexible, and enhancing their job satisfaction. The impact on processes enables improvements in organizational processes such as marketing, manufacturing, accounting, engineering, and public relations along three dimensions: effectiveness, efficiency, and degree of innovation. The impact on products can be seen in value-added products and knowledge-based products. The impact on organizational performance can occur either directly or indirectly.

💡 Why this matters: Understanding these four levels helps managers target their KM initiatives precisely, ensuring resources are directed toward areas that will yield the greatest returns.

State the importance of KM with specific reference to its impact on employee adaptability and job satisfaction

On employee adaptability, knowledge management encourages employees to continually learn from each other, making them likely to possess the information and knowledge needed to adapt whenever organizational circumstances require. When employees are aware of ongoing and potential future changes, they are less likely to be caught by surprise. Awareness of new ideas and involvement in free-flowing discussions not only prepare them to respond to changes but also make them more likely to accept change. Thus, KM tends to facilitate greater adaptability among employees.

On job satisfaction, two benefits accrue directly to individual employees: they are able to learn better than employees in firms lacking KM, and they are better prepared for change. These impacts cause employees to feel better because of knowledge acquisition and skill enhancement, and also enhance their market value relative to other organizations' employees. KM also provides employees with solutions to problems they face in case those same problems have been encountered earlier and effectively addressed. This helps keep employees motivated, for a successful employee would be highly motivated, while an employee facing problems in performing their job is likely to be demotivated. As a result of their increased knowledge, improved market value, and greater on-the-job performance, KM facilitates employees' job satisfaction.

🔑 Definition — Employee Adaptability: The ability of employees to respond effectively to organizational changes through awareness, learning, and acceptance of new circumstances.

Explain why poor KM reduces the effectiveness of organizational processes

Effectiveness is performing the most suitable processes and making the best possible decisions. Poor KM can result in mistakes by the organization because they risk repeating past mistakes or not foreseeing otherwise obvious problems. Organizations lacking in KM find it difficult to maintain process effectiveness when faced with turnover of experienced and new employees. In contrast, a good knowledge management system can enable organizations to become more effective by helping them to select and perform the most appropriate processes. Effective KM enables the organization's members to collect information needed to monitor external events, resulting in fewer surprises for leaders and consequently reducing the need to modify plans and settle for less effective approaches. Further, knowledge management enables organizations to quickly adapt their processes according to current circumstances, thereby maintaining process effectiveness in changing times.

🔑 Definition — Process Effectiveness: Performing the most suitable processes and making the best possible decisions to achieve organizational goals.

What three dimensions are relevant for examining the impact of KM on business processes?

Knowledge management is an important factor for the effectiveness of organizational processes such as marketing, manufacturing, accounting, engineering, and public relations. The impact of KM can be seen along three major dimensions: effectiveness, efficiency, and degree of innovation of the processes.

🔑 Definition — Process Efficiency: Performing the processes quickly and in a low-cost fashion. 🔑 Definition — Process Innovation: Performing the processes in a creative and novel fashion that improves effectiveness and efficiency.

State reasons how KM helps improve process effectiveness, efficiency, and innovation

Process Effectiveness: Poor KM can result in mistakes because organizations risk repeating past mistakes or not foreseeing obvious problems. Organizations lacking KM find it difficult to maintain process effectiveness when faced with employee turnover. A good knowledge management system enables organizations to become more effective by helping them select and perform the most appropriate processes. Effective KM enables members to collect information needed to monitor external events, resulting in fewer surprises and reducing the need to modify plans. KM also enables organizations to quickly adapt processes according to current circumstances.

Process Efficiency: KM can enable organizations to be more productive and efficient. The ability to effectively create and manage network-level knowledge sharing processes results in productivity advantages enjoyed by the organization.

Process Innovation: Organizations increasingly rely on knowledge shared across individuals to produce innovative solutions to problems as well as to develop more innovative organizational processes. Knowledge management has been found to enable riskier brainstorming and thereby enhance process innovation.

Describe how KM can contribute to an organization's products

Knowledge management can impact the organization's products in two respects: value-added products and knowledge-based products.

Impact on Value-Added Products: With the aid of KM processes, organizations can offer new products or improved products that provide significant additional value compared to earlier products. Value-added products also benefit from KM due to its effect on organizational process innovation.

Impact on Knowledge-Based Products: Knowledge-based products, such as those in consulting or software development industries, can also benefit from knowledge management. Using KM, consulting firms can quickly access and combine the best available knowledge and bid on proposals that would otherwise be too costly or too time-consuming to put together. In such industries, KM is necessary for mere survival. However, knowledge-based products can also sometimes play an important role in traditional manufacturing firms. For example, to design an automated machine to spin yarn, the organization must observe an expert hand-spinner to learn how the process takes place in order to give the machine proper functionality.

How can we assess the direct impact of KM on organizational performance?

Knowledge management affects the overall performance of the organization either directly or indirectly.

Direct impact on organizational performance occurs when knowledge is used to create innovative products that generate revenue and profit, or when the KM strategy is aligned with business strategy. Such a direct impact concerns revenues and/or costs and can be explicitly linked to the organization's vision or strategy. Consequently, measuring direct impact is relatively straightforward — it can be observed in terms of improvements in return on investment. Unlike indirect impacts, direct impacts can be associated with transactions and, therefore, are easily measured.

Describe the ways in which the indirect impacts of KM in an organization may be observed

Indirect impact on organizational performance comes about through activities that are not directly linked to the organization's vision, strategy, revenues, or costs. Such effects occur through the use of KM to demonstrate intellectual leadership within the industry, which, in turn, might enhance customer loyalty. Alternatively, it could occur through the use of knowledge to gain an advantageous negotiating position with respect to competitors or partner organizations. Indirect impact cannot be associated with transactions and, therefore, cannot be easily measured.

Further, a company's output is said to exhibit economy of scale if the average cost of production per unit decreases with increase in output. A company's output is said to exhibit economy of scope when the total cost of that same company producing two or more different products is less than the sum of the costs that would be incurred if each product was produced separately by a different company. Knowledge management can contribute to economies of scale and scope by improving the organization's ability to create and leverage knowledge related to products, customers, and managerial resources across businesses. Product designs, components, manufacturing processes, and expertise can be shared across businesses, thereby reducing development and manufacturing costs, accelerating new product development, and supporting quick response to new market opportunities. Economies of scope also result from the deployment of general marketing skills and sales forces across businesses.

Another indirect impact of KM is to provide a sustainable competitive advantage. Knowledge can enable the organization to develop and exploit other tangible and intangible resources better than competitors can. Knowledge, especially context-specific tacit knowledge, tends to be unique and therefore difficult to imitate, and it cannot easily be purchased in a ready-to-use form. To obtain similar knowledge, the company's competitors have to engage in similar experiences, but this takes time. Therefore, competitors are limited in the extent to which they can accelerate their learning through greater investment.

🔑 Definition — Economy of Scale: Situation where the average cost of production per unit decreases with an increase in output. 🔑 Definition — Economy of Scope: Situation where the total cost of producing two or more different products by the same company is less than the sum of costs if each product was produced separately by different companies.

Knowledge management is an invaluable tool to the oil and gas industry

Oil exploration often involves extrapolating from sketchy data and comparing exploration sites to known ones. This allows geoscientists to decide if enough reserves exist on a site to make developing it worthwhile.

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

📌 Example — Shell's Community of Practice Savings: One site contained layers of oil-bearing sand less than an inch thick. To decide if thin sand beds could extend over a large enough area for efficient pumping, a Shell exploration team asked one of Shell's communities of practice, including geoscientists from several disciplines, for help. By comparing this site to others, the community helped in the team's analysis of where to drill more accurately, resulting in fewer expensive exploratory wells. The Shell team estimated that discussions enabled them to drill and test three fewer wells a year, saving US$20M in drilling and an additional US$20M in testing costs for each well — an annual saving of US$120M. The leader estimated the community could claim 25 percent of savings and was 80 percent sure of this estimate. So the community may be argued to have saved 25 percent of 80 percent of US$120M, i.e., US$24M annually. Since it costs between US$300K and US$400K annually to run the community, this represented an annual return of 40 times the investment.

Describe the impact of internalization, externalization, socialization, and communities of practice on employee learning

Knowledge management can help enhance employees' learning and exposure to the latest knowledge in their fields through externalization, internalization, socialization, and communities of practice.

Internalization is the conversion of explicit knowledge into tacit knowledge. It works in conjunction with externalization to help individuals learn. If an employee in an accounting firm reads a book on well-established accounting practices, he can use this externalized knowledge to acquire tacit knowledge and improve his daily work.

Externalization is the process of converting tacit knowledge into explicit forms. It works in conjunction with internalization to help individuals learn. Externalization could be demonstrated by means of a report made at the end of a project, indicating the procedures followed and the lessons learned.

Socialization also helps individuals acquire knowledge by means of joint activities, such as meetings, informal conversations, etc. By participating in these meetings or activities, individuals obtain both explicit and implicit knowledge.

Communities of Practice are an extension of socialization. They are an organic and self-organized group of individuals who are dispersed geographically or organizationally but communicate regularly to discuss issues of mutual interest. They result in increased learning among all the participants.

🔑 Definition — Internalization: The conversion of explicit knowledge into tacit knowledge. 🔑 Definition — Externalization: The process of converting tacit knowledge into explicit forms. 🔑 Definition — Socialization: The acquisition of knowledge by individuals through joint activities such as meetings and informal conversations.

⭐ Key Takeaways

Knowledge management impacts organizations at four critical levels — people, processes, products, and overall organizational performance — either by creating knowledge that improves performance or by directly causing improvements. For people, KM enhances employee adaptability and job satisfaction through continuous learning, problem-solving support, and increased market value. For processes, KM improves effectiveness (selecting and performing the best processes), efficiency (quick, low-cost performance), and innovation (creative, novel approaches). For products, KM enables both value-added enhancements and knowledge-based product development, particularly critical in consulting and software industries. Organizational performance benefits from KM through both direct impacts (measurable improvements in ROI through innovative products and aligned strategy) and indirect impacts (economies of scale and scope, sustainable competitive advantage through unique tacit knowledge, and intellectual leadership). The Shell oil exploration example demonstrates that communities of practice can deliver an ROI of 40 times the investment, while the four knowledge conversion modes (internalization, externalization, socialization, and communities of practice) form the foundation for employee learning.

🧠 Quick Revision Questions

1. What four levels of organizational impact does KM affect, and what are the two main ways KM impacts organizations at these levels?

2. How does KM specifically contribute to employee job satisfaction, and why does poor KM reduce process effectiveness?

3. What are the three dimensions for examining KM's impact on business processes, and how does KM help improve each dimension?

4. What is the difference between direct and indirect impacts of KM on organizational performance, and provide one example of each?

5. In the Shell oil exploration example, how was the annual savings of the community of practice calculated, and what was the resulting return on investment?

📘 Lecture 16 — Three Schools of Thought and Approaches to KM. Economic School and KVA

📖 Overview: This lecture introduces the growing recognition of knowledge as a critical organizational resource and presents Earl’s (2001) taxonomy of knowledge management schools. It focuses in depth on the Economic School, which treats knowledge as an asset to be exploited for revenue, and explains key concepts including intellectual capital accounting and the Knowledge Value Added (KVA) methodology for measuring return on knowledge.

🗂️ Topics Covered

The lecture begins by defining knowledge management and its purpose in organizations. It then introduces Earl’s three schools of KM: economic, organizational, and strategic. The primary focus is the Economic School, covering intellectual capital accounting, the three components of intellectual capital (human, relational, structural), market-to-book value ratios, and the Knowledge Value Added (KVA) methodology for calculating return on knowledge (ROK). A detailed numerical example illustrates KVA application.

📝 Lecture Summary

Approaches to KM: Introduction

Knowledge is increasingly recognized as a critical resource for organizations, yet traditionally it has not been managed with the same systematic effort as human, material, or financial resources. Firms that leave knowledge to its own devices may face severe jeopardy. Knowledge management (KM) can be defined as a method to simplify and improve the process of sharing, distributing, creating, capturing, and understanding knowledge in a company. It involves describing, organizing, sharing, and developing knowledge, managing knowledge-intensive activities, and identifying and leveraging collective knowledge to help the company compete. The purpose of KM is to help companies create, share, and use knowledge more effectively, leading to fewer errors, less work, more independence for knowledge workers, fewer questions, better decisions, less reinventing of wheels, improved customer relations, improved service, and improved profitability.

Earl (2001) developed a taxonomy for KM that he labeled schools of knowledge management, each representing an ideal type. These schools are not mutually exclusive. Three relevant schools are:

• Economic School: Focus on income; aim is to exploit knowledge assets.
• Organizational School: Focus on networks; aim is knowledge pooling.
• Strategic School: Focus on competitive advantage; aim is to identify, exploit, and explore knowledge capabilities.

💡 Why this matters: Understanding these schools helps organizations choose their KM orientation based on their strategic objectives.

The Economic School

According to Earl (2001), the Economic School is explicitly concerned with both protecting and exploiting a firm’s knowledge or intellectual assets to produce revenue streams (or rent). It manages knowledge as an asset, where knowledge or intellectual assets include patents, trademarks, copyrights, and know-how. Intellectual property is another way to describe the object being managed. This school focuses more on exploitation of knowledge and less on exploration. A critical success factor is developing a specialist team or function to aggressively manage knowledge property through intellectual capital accounting, intellectual capital management, and creation of an effective and efficient knowledge marketplace.

🔑 Definition — Intellectual Capital Accounting: The process of measuring and reporting intellectual capital, recognizing that intellectual capital provides a crucial source of value for contemporary business enterprises that requires careful management.

Intellectual Capital Accounting

Intellectual capital is the focus of significant discussion across management disciplines, reflecting recognition that it provides crucial value for contemporary business enterprises. For publicly quoted businesses, success in managing intellectual capital is increasingly mirrored in their market values, which are often many times their book values. Bridging this gap constitutes one motivation for accounting for intellectual capital. Another motivation is the need to manage intellectual capital successfully, requiring new approaches to performance measurement.

Stewart (1997) suggested tools for measuring intellectual capital. Value is defined by the buyer, not the seller. Therefore, market value = price per share × total number of shares outstanding. One measure of intellectual capital is the difference between market value and book equity, assuming everything left in market value after accounting for fixed assets must be intangible assets.

📐 Formula: Intellectual Capital = Market Value – Book Equity → This formula calculates the intangible asset value of a firm by subtracting its recorded book value from its market capitalization.

📌 Example: If Microsoft is worth 100 billion dollars and its book value is 10 billion dollars, then its intellectual capital is 90 billion dollars (100 – 10 = 90).

Three components of intellectual capital are identified:

1. Human Capital: The know-how, capabilities, skills, and expertise of human members of an organization.
2. Relational Capital: Any connection that people outside the organization have with it, together with customer loyalty, market share, the level of backorders, and so forth.
3. Structural Capital: Systems and networks, cultures and values, together with elements of intellectual property such as patents, copyrights, trademarks, and so forth.

Beginning intellectual capital accounting requires accepting that it is possible to include objective measures of value within financial statements, as with tangible assets. Intangible assets like goodwill are already problematic in accounting. In the UK, only purchased goodwill can be reported in accounts. Intellectual capital, as "new goodwill," multiplies these difficulties, taking many more forms than goodwill. According to Roslender and Fincham (2001), we can think in terms of degrees of intangibility: brands, patents, and know-how count as intangible assets; customer data, distribution channels, and employee qualification profiles are more intangible; employee commitment, organizational culture, and corporate values are even more intangible yet ensure impressive market-to-book value ratios.

📐 Formula: Market-to-Book Value Ratio = Market Value / Book Value → This ratio indicates the value of intellectual capital in an organization. Three decades ago it was close to 1; today it averages about 4.

📌 Example: In 1997, Microsoft had a book value of 11 billion dollars and a market value of 200 billion dollars, giving a market-to-book ratio of approximately 18.2 (200/11). Afuah and Tucci (2003) argue this ratio is caused by intellectual capital.

Knowledge Value Added (KVA)

The knowledge-value-added (KVA) methodology as described by Housel and Bell (2001) addresses the need to leverage and measure knowledge resident in employees, information technology, and core processes. KVA analysis produces a return-on-knowledge (ROK) ratio to estimate the value added by given knowledge assets, regardless of location. The essence of KVA is that knowledge utilized in core processes is translated into numerical form, allowing allocation of revenue in proportion to the value added by the knowledge as well as the cost to use that knowledge.

Tracking the conversion of knowledge into value while measuring bottom-line impacts enables managers to increase productivity of these critical assets.

🔑 Definition — Knowledge Value Added (KVA): A methodology for allocating revenue and cost to a company’s core processes based on the amount of change each produces, using the knowledge required to make these changes as a way to describe the conversion process.

🔑 Definition — Knowledge (in KVA): The know-how required to produce process outputs, proportionate to the time it takes to learn it. Learning time is a quick and convenient way to measure the amount of knowledge contained in any given process.

📌 Example: In a widget company, there is one owner who makes and sells widgets for $1. The owner’s sales production knowledge can be used as a surrogate for the dollar of revenue generated. We determine how long it would take a new owner to learn all necessary sales and production knowledge, then use these learning times to allocate the dollar of revenue between sales and production processes.

Assume it takes 100 hours for the new owner to learn the processes: 70 hours learning how to make the widget (production) and 30 hours learning how to sell it (sales). This indicates 70% of knowledge and value added is in production and 30% in sales. Therefore:

• $0.70 of revenue allocated to production knowledge
• $0.30 of revenue allocated to sales knowledge

Next, determine the cost to use sales and production knowledge. Assume total cost to sell and produce a widget is $0.50: $0.25 for sales and $0.25 for production. Cost is directly tied to how long the new owner spends performing each process.

📐 Formula: Return on Knowledge (ROK) = Revenue Allocated to Process / Cost to Use Knowledge in That Process → This ratio measures the productivity of knowledge assets in each process.

📌 Example (continued):

• Production ROK = $0.70 / $0.25 = 280%
• Sales ROK = $0.30 / $0.25 = 120%

Conclusion: The production process is a more productive use of the knowledge asset (280%) than the sales process (120%).

💡 Why this matters: KVA enables managers to quantify which knowledge assets are generating the most value relative to their cost, guiding investment in knowledge processes and human capital development.

⭐ Key Takeaways

The most critical things a student MUST remember from this lecture are: (1) Earl’s three schools of KM—economic (exploit knowledge assets for income), organizational (pool knowledge through networks), and strategic (leverage knowledge for competitive advantage)—each have distinct focuses and aims. (2) The Economic School treats knowledge as an asset including patents, trademarks, copyrights, and know-how, and uses intellectual capital accounting to measure its value. (3) Intellectual capital has three components: human capital (skills and expertise), relational capital (external connections and loyalty), and structural capital (systems, culture, intellectual property). (4) Market-to-book value ratio is a simple indicator of intellectual capital value, calculated as market value divided by book value, and has risen from ~1 to ~4 on average. (5) The KVA methodology allocates revenue and cost to core processes based on learning time as a proxy for knowledge, producing a Return on Knowledge (ROK) ratio that compares value added to the cost of using knowledge.

🧠 Quick Revision Questions

1. What are the three schools of knowledge management according to Earl (2001), and what is the focus of each?
2. How is intellectual capital calculated using market value and book equity? Provide a formula and example.
3. What are the three components of intellectual capital, and what does each include?
4. In the KVA methodology, how is knowledge defined, and how is it measured?
5. Using the widget company example, calculate the Return on Knowledge (ROK) for both the production and sales processes, and explain which process is more productive.

📘 Lecture 17 — Economic School: Management and Knowledge Metrics

📖 Overview: This lecture explains how organizations must measure and manage knowledge assets to directly impact their bottom-line profitability. It introduces knowledge metrics as a tool for translating creative knowledge into value, and critically reviews four traditional valuation approaches (cost, income, market, and real options) that can be adapted for measuring intangible knowledge assets.

🗂️ Topics Covered

The lecture first discusses the need for knowledge metrics to tie core process performance to profitability, distinguishing between creative (non-modifiable) and modifiable knowledge. It then introduces and explains four fundamental valuation methodologies: the cost approach (asset-based valuation), the income approach (present value of future earnings), the market approach (comparable company multiples), and the real options approach (valuing investment opportunities under uncertainty). Each method's applicability to knowledge assets is examined.

📝 Lecture Summary

Management and Knowledge Metrics: Transforming Knowledge into Value

To remain competitive, an organization’s core processes must produce bottom-line profitability that attracts investors, maintains market capitalization, and enhances corporate value while delivering customer value. Managers need measures that quantify the performance of core process knowledge assets and tie them directly to the bottom line. Current management design options rely on heuristics ("rules-of-thumb") that provide semi-empirical support but cannot produce modifiable insights about whether actual or proposed process changes impact the firm's bottom line.

The use of creative knowledge represents a special case for knowledge measurement because it is by definition not modifiable. Trying to manage and measure this type of knowledge is problematic. For example, the value of creative knowledge in R&D can only be determined after its outputs have been translated into core processes that produce final products. Knowledge metrics become useful here because managers can track the speed with which creative knowledge results in changes in core processes and the amount of new "modifiable" knowledge embedded in those processes. This provides a means to identify, quantify, and help manage the transformation of knowledge into value.

💡 Why this matters: Knowledge metrics enable managers to measure what was previously unmeasurable — the transformation of creative, non-modifiable knowledge into tangible value through core processes.

Additional Valuation Methodologies Vis-À-Vis Knowledge Metrics

It is critical for the successful and widespread use of knowledge metrics that they be interoperable with traditional accounting and finance valuation approaches whenever possible. Just as the theory of relativity drove physics into the next millennium, so knowledge metrics will be the driver of accounting and finance valuation methodologies.

Cost, Income, Market, And Real Options Approaches

The cost, income, and market approaches are the three fundamental approaches used by the business valuation profession to value ownership interests in privately held companies. The real options approach was developed to value stock options but also can be applied to valuing intangible assets. A review of these approaches frames a discussion of valuing assets, including intangibles such as knowledge.

The Cost Approach

The cost approach is based on the concept that a company is worth the market value of all its assets minus the market value of all its liabilities. Not only each balance sheet asset/liability but also each off-balance-sheet asset/liability (tangible and intangible) is identified, valued, and included on the balance sheet. Bringing the historical cost of each asset and liability to its current market value is time-consuming and difficult, potentially requiring additional experts to value specific asset categories (e.g., real estate, machinery).

Variations of the cost approach are generally used to value holding and investment companies and asset-intensive companies (natural resources, utilities). Asset-based methods are reliable in early-stage companies where book values proxy for fair market value. A particular form — the excess earnings approach — is regularly used to value professional practices and service companies.

🔑 Definition — Cost Approach: A valuation method where a company is worth the market value of all its assets minus the market value of all its liabilities, including off-balance-sheet items. 📐 Formula: Company Value = Market Value of All Assets – Market Value of All Liabilities → This calculates the net asset value, requiring all assets and liabilities to be stated at current market value. 📌 Example: For an early-stage technology startup with few assets, the book value of equipment ($500,000) minus liabilities ($100,000) gives a cost approach value of $400,000, serving as a reasonable proxy until future earnings can be projected.

The Income Approach

The income approach is based on the concept that a company is worth the present value of its future earning power. Future economic income is projected from the valuation date using historical trends and management's professional judgment about future growth. If recent cash flows are stable and future growth is incremental and sustainable, a single projection is made into perpetuity. If cash flows have peaks and valleys or future growth involves high/uneven rates, projections are made for each year of five years (one business cycle), then a single projection is made from year five into perpetuity. Projected cash flows are converted back to present value using a total rate of return comparable to market rates for investments of similar risk. The resulting estimate is adjusted for whether a controlling or minority ownership interest is being valued and for marketability. The income approach is generally used to value operating companies and specific projects proposed by management.

🔑 Definition — Income Approach: A valuation method where a company is worth the present value of its projected future earnings, discounted by a rate of return comparable to similar-risk investments. 📐 Formula: Present Value = Σ (Future Cash Flows / (1 + Discount Rate)^n) → This converts projected earnings into today's value, accounting for risk and time. 📌 Example: A stable company projects $1 million in annual cash flows forever. Using a 10% discount rate, the present value is $10 million ($1M / 0.10). If cash flows are uneven, each year's flow is discounted separately for the first five years, then a terminal value is computed.

The Market Approach

The market approach is based on the concept that the value of a privately held company can be reasonably estimated by examining, adjusting, and using the market multiples (such as price/earnings ratios) of "guideline" publicly held companies that bear enough similarity to the "subject" privately held company. First, the fundamental financial variables of both the subject and guideline companies are adjusted for comparability. Financial ratios are calculated and compared. One or several guideline company market multiples are selected and adjusted to reflect the relative growth prospects and risks (strengths and weaknesses) of the subject company. Finally, these adjusted multiples are weighted by degree of importance and applied to the fundamental financial variable of the subject company. The resulting estimate is adjusted for controlling/minority interest and marketability. Variations of the market approach are used in conjunction with the cost and/or income approaches for valuing all kinds of companies.

🔑 Definition — Market Approach: A valuation method where a privately held company's value is estimated by applying adjusted market multiples from similar publicly held "guideline" companies to the subject company's financial variables. 📐 Formula: Subject Company Value = Adjusted P/E Multiple × Subject Company Earnings → This applies a comparable company's valuation ratio to generate the subject's estimated value. 📌 Example: A publicly traded competitor (guideline company) has a P/E ratio of 15. The subject private company has earnings of $2 million. After adjusting the P/E to 12 (reflecting higher risk/lower marketability), the market approach value is $24 million (12 × $2M).

The Real Options Approach

The real options approach has grown out of options theory. The value of an option increases as the variability in the value of the underlying asset (cash flow per unit) increases. There are six key parameters that affect the value of a real option: the market value of the asset, the exercise price of the option, the time remaining until the option matures, the volatility of the underlying asset, the risk-free rate of the asset, and the amount of dividends paid by the underlying risky asset. This measure not only values a project's immediate return but allows inclusion of the potential value generated in multiple investment outcomes. The real options approach is a basic capital budgeting technique that focuses on measuring the value of an individual project, in conditions of uncertainty, before the project begins. It is not used to value ownership interests in privately held companies but to value internal and external investment opportunities for an individual company, public or private. As such, it is a strategic business valuation tool. It is widely used by the Internet venture capital community for determining the potential future value of companies with no economic history and has been applied to the valuation of patents and licenses.

🔑 Definition — Real Options Approach: A capital budgeting technique for valuing individual projects under uncertainty by treating investment opportunities as options, incorporating potential value from multiple outcomes. 📐 Formula: Option Value = f(Market Value of Asset, Exercise Price, Time to Maturity, Volatility, Risk-Free Rate, Dividends) → This captures the value of flexibility and future choices in uncertain investments. 📌 Example: A pharmaceutical company has a patent (license) for a new drug. Using the real options approach, the investment in clinical trials is valued as an option: if trials succeed (high future cash flows), the company exercises the option to launch; if they fail, the company lets the option expire, limiting losses. Volatility increases option value because of greater upside potential.

⭐ Key Takeaways

Knowledge metrics are essential for quantifying how creative, non-modifiable knowledge transforms into value through changes in core processes, and these metrics must interoperate with traditional accounting and finance approaches. The cost approach values a company by its net asset value, suitable for asset-intensive or early-stage companies but difficult for knowledge assets that are off-balance-sheet. The income approach values future earning power discounted to present value, applicable to operating companies but requiring stable or predictable cash flows. The market approach uses comparable company multiples to estimate value, relying on the availability of similar publicly traded firms. The real options approach values flexibility under uncertainty by considering six key parameters, making it uniquely suited for valuing patents, licenses, and internet ventures with no economic history. For the exam, remember the distinguishing features of each method and specifically that real options is the only approach designed for uncertain, pre-project investment opportunities where variability increases value.

🧠 Quick Revision Questions

1. Why is creative knowledge difficult to measure directly, and how do knowledge metrics help overcome this challenge?
2. What are the four fundamental valuation approaches discussed in this lecture, and which one is specifically used for valuing projects under uncertainty before they begin?
3. In the cost approach, what must be included beyond balance sheet items, and what form of this approach is used for professional practices?
4. What are the six key parameters that affect the value of a real option, and what happens to option value when asset volatility increases?
5. Under what conditions does the income approach use a single perpetuity projection versus separate projections for five years?

📘 Lecture 18 — Measures of Intellectual Capital

📖 Overview: This lecture introduces the knowledge-value-added (KVA) methodology as a robust approach to measuring the value of knowledge assets within an organization. It explains how to calculate a return-on-knowledge (ROK) ratio to estimate the value added by knowledge assets, regardless of where they are located in the organization.

🗂️ Topics Covered

The lecture covers the measurement of return on knowledge, the knowledge-value-added (KVA) methodology with a practical example of a widget company, and the KVA theory including its fundamental assumptions. It also details three approaches to KVA: the learning time method, the process description method, and the binary query method, along with a seven-step knowledge audit process.

📝 Lecture Summary

Measuring Return on Knowledge

The lecture provides a broad review of approaches to valuing knowledge assets, noting that reliable approaches require a common language to discuss the underlying value of an organization’s knowledge assets. The knowledge-value-added methodology is presented as one of the most robust approaches because it conforms to this requirement. Understanding how this methodology works requires a detailed review to work through practical issues involved in measuring knowledge at a granular level. This granular level will provide new raw data for Information Age financial and accounting professionals, who can then provide analysis and insight for investors, managers, and customers.

Knowledge-Value-Added Methodology

The knowledge-value-added (KVA) methodology addresses a need recognized by executives by showing how to leverage and measure the knowledge resident in employees, information technology, and core processes. KVA analysis produces a return-on-knowledge (ROK) ratio to estimate the value added by given knowledge assets regardless of where they are located. The essence of KVA is that knowledge utilized in corporate core processes is translated into numerical form. This translation allows allocation of revenue in proportion to the value added by the knowledge as well as the cost to use that knowledge. Tracking the conversion of knowledge into value while measuring its bottom-line impacts enables managers to increase the productivity of these critical assets.

KVA Example

The lecture begins with an example of an "average" person who needs to learn how to produce all the outputs of a given company. This person's knowledge of the company would be the embodiment of the company's value-adding processes, including selling, marketing, producing, accounting for, financing, servicing, and maintaining. These core processes add value while converting inputs into outputs that generate the company's revenue.

KVA provides a methodology for allocating revenue and cost to a company's core processes based on the amount of change each produces. Knowledge is defined as the know-how required to produce process outputs. This knowledge is proportionate to the learning time it takes to learn it. Learning time is a quick and convenient way to measure the amount of knowledge contained in any given process.

In the widget company example, one owner makes and sells widgets for $1. If it takes 100 hours for a new owner to learn the processes, with 70 hours learning how to make the widget and 30 hours learning how to sell it, this indicates that 70 percent of the knowledge and value added was in the production process and 30 percent in the sales process. Therefore, $0.70 of the revenue would be allocated to production knowledge and $0.30 to sales knowledge.

To measure return on knowledge (ROK), the cost to use the knowledge must be determined. Assuming the total cost to sell and produce a widget was $0.50, with $0.25 for sales and $0.25 for production, and the new owner spends equal time on both, the cost to use the knowledge of each process is the same. The ROK for the production process is 0.70/0.25 = 280 percent, and for the sales process is 0.30/0.25 = 120 percent, indicating that the production process is a more productive use of the knowledge asset.

💡 Why this matters: This example demonstrates how learning time can be used as a surrogate measure for knowledge, allowing for a quantifiable allocation of revenue and cost to different processes.

The KVA methodology can be applied at any level in a company. For a quick-and-dirty KVA of a corporation like SBC, executives from core processes would estimate how long it takes the average person to learn how to produce the outputs of the core areas, with a boundary condition of a total of 100 months for the average person to learn everything necessary to generate the annual revenue. These estimates would be weighted by the number of employees in each core process to estimate how frequently the knowledge is employed in a typical year.

To make a back-of-the-envelope estimate of the knowledge embedded in information technology, the percentage of the process that is automated is asked. The percentage of knowledge for each process, including its supporting IT, is calculated by dividing process knowledge by the total amount of knowledge. Revenue is then allocated proportionately. The revenue attributable specifically to the knowledge embedded in IT and the cost to use it would provide the ROK for IT within and among processes. This can reveal that "all IT is not created equal," as some highly automated processes may provide much lower ROKs than others with less automation but more "bang for the buck."

KVA Theory

KVA is rooted in the Information Age and allows managers and investors to analyze the performance of corporate knowledge assets in core processes in terms of the returns they generate, whether the knowledge is embedded in information technology or employees' heads. This is accomplished by postulating a common unit of knowledge that can be observed in core processes and counted in terms of its price and cost. The results of a KVA analysis are ratios that compare the price and cost for these common units of knowledge.

The fundamental assumptions of KVA are:

1. If input X equals output Y, no value has been added.
2. Value is proportionate to change.
3. Change can be measured by the amount of knowledge required to make the change.

The principle of replication states that if we have the knowledge necessary to produce a change, then we have the amount of change introduced by the knowledge. If we have not captured the knowledge required to make the necessary changes, we will not be able to produce the output as determined by the process.

The knowledge audit of KVA methodology can be delineated in seven steps:

The knowledge within a process can be represented as learning time, process instructions, or bits. Any approach that satisfies the basic KVA assumptions will work. Based on the fundamental assumption of KVA, the correlation between any two or more estimates should be at a high level to ensure an accurate estimate, and this simple matched correlation measures the reliability of an estimate.

The KVA approach is being embedded in the "Process Edge" TM process modeling tool suite, which will allow analysts to gather and represent KVA data within a process work-flow model and monitor the ongoing return on knowledge (ROK) and return on process (ROP).

⭐ Key Takeaways

The KVA methodology is a robust approach for valuing knowledge assets by using learning time as a proxy for knowledge, which allows for the allocation of revenue and cost to core processes. The key output is the return-on-knowledge (ROK) ratio, which compares the revenue allocated to a process based on its knowledge content against the cost of using that knowledge. The fundamental assumption of KVA is that value is proportionate to change, and change can be measured by the amount of knowledge required to make it. Three main approaches to estimating knowledge are learning time, process instructions, and binary query methods, all of which can be used in a seven-step knowledge audit process.

🧠 Quick Revision Questions

1. What does the KVA methodology stand for, and what is its primary purpose?
2. How is "knowledge" defined within the KVA framework, and what is used as a convenient measure of it?
3. In the widget company example, how is revenue allocated between the sales and production processes?
4. What is the formula for calculating Return on Knowledge (ROK) in the context of the KVA methodology?
5. What are the three different approaches to estimating the value of knowledge embedded in core processes, as summarized in the KVA table?

📘 Lecture 19 — Knowledge Market Model and KVA Case Study

📖 Overview: This lecture explores two key approaches to knowledge management: the knowledge market framework, which views knowledge exchanges as marketplace transactions within organizations, and the Knowledge Value Added (KVA) methodology, applied through a detailed case study of Exodus Communications Inc. The lecture demonstrates how KVA can be used to measure the value created by knowledge assets in core business processes, particularly valuable for companies not amenable to traditional financial analysis.

🗂️ Topics Covered

The lecture begins with the Knowledge Market Framework, defining buyers (local and global), sellers, and brokers in organizational knowledge markets. It then presents a comprehensive KVA case study of Exodus Communications Inc., an Internet infrastructure company, showing how KVA analysis works at both the aggregate level (core areas of Management, S&GA, and Operations) and the operational level (the sales provisioning process with its six sub-processes). The lecture concludes with practical applications and benefits of KVA analysis for management.

📝 Lecture Summary

Knowledge Market Framework

Within the economic school, knowledge transfers occur in knowledge markets. This is a transactional perspective where knowledge exchanges happen in a marketplace. In defining any market, one must be clear about who the buyers and sellers are, and what pricing system exists to determine what the consumer pays. Knowledge markets exist within every organization, including not only codified knowledge in processes, structure, technology, or strategy, but also all dynamic exchanges of knowledge between buyers and suppliers.

According to Grover and Davenport (2001), organizations have two categories of buyers of knowledge: local buyers and global buyers. Local buyers are people searching for knowledge assets to address an issue they need to resolve — they require more than information, needing expertise, experience, insight, and judgment. They could pay for knowledge in hard currency via a consultant from outside, or buy the knowledge from internal suppliers. The global knowledge buyer is the firm itself, which has a vested interest in realizing knowledge assets into valuable products and services. The global knowledge buyer, represented by organizational stakeholders, has a strong interest in transferring local knowledge to global knowledge to reduce dependency on knowledge sellers.

Knowledge sellers are people who have knowledge (usually tacit) to sell. The quality of this knowledge might be high or low depending on the credibility of the source. Davenport and Prusak's approach to knowledge management is concerned with knowledge markets. A knowledge market can be defined as a system in which participants exchange a scarce unit for present or future value. Buyers, sellers, and brokers play roles on knowledge markets.

🔑 Definition — Knowledge Market: A system in which participants exchange a scarce unit for present or future value, with buyers, sellers, and brokers.

💡 Why this matters: Understanding knowledge markets helps organizations recognize that tacit knowledge is a valuable asset that can be exchanged internally, reducing reliance on external consultants and retaining critical expertise when employees leave.

KVA: Case Study of Exodus Communications Inc.

The following example shows how KVA (Knowledge Value Added) can be applied to a company in the Internet infrastructure marketplace. The same general approach can be extended to any company — the KVA methodology is generic and robust enough to be applicable to companies and core processes in any industry.

Exodus Communications Inc. Financial Summary: Exodus is a leading provider of web hosting services, offering data center, Internet access, and managed services. Key facts:

• Price: $34, 52-week Range: $15-$89
• Shares outstanding: 412.4 million
• EPS 99A: $(0.36), EPS 2000E: $(0.6)
• P/E: NM (not meaningful), Market capitalization: NM

The company is a typical Internet infrastructure company that cannot be meaningfully evaluated by traditional financial ratios — the P/E ratio is not derivable because the company has no positive net income. The price-to-book value of Exodus is 44.59 while the industry average is 16.69 and the S&P 500 is 9.66. The price-to-tangible value is 67.99 while the industry average is 20.51 and the S&P 500 is 12.77. This shows that Exodus stock is being valued more richly relative to the value of its assets than the S&P 500, illustrating that most of the company's value comes from underlying knowledge assets embedded in the company structure and culture, not reflected on traditional accounting statements.

Company Description: Founded in 1994, Exodus offers system and network management solutions and technology professional services. As of December 31, 1999, the company had over 2,200 customers under contract and managed over 27,000 customer servers worldwide. Customers include Yahoo!, USA TODAY.com, weather.com, priceline.com, British Airways, and Nordstrom. The company operates Internet data centers in nine U.S. metropolitan areas plus London and Tokyo. Exodus offers three types of services: (1) Internet server hosting, (2) Network solutions, and (3) System management and monitoring services.

Current Issues: Exodus has three areas of concern:

1. Decreasing profit margin: Bandwidth and co-location services are becoming commodities with smaller margins due to increased competition.
2. Expansion opportunity: Core customers are mostly "blue chip" Fortune 500 companies; opportunities exist for smaller customers requiring standardized solutions, but the current labor-intensive process of service selection and network architecture and design (NAD) makes expansion difficult.
3. Emerging competition: Will lower the average revenue per user.

The goal of the KVA exercise is to identify areas for focus on increasing revenue from existing knowledge assets, rather than just cutting costs.

Aggregate-Level KVA

A rough-cut estimate KVA on Exodus is targeted at the aggregate level of analysis. Top executives can benchmark the company's use of knowledge assets against other industries, and management can look at the level of performance in core processes before deciding how to improve.

Assumptions and Methodology: The KVA team interviews process subject matter experts (SMEs), makes observations, and talks with process employees and managers to obtain average learning-time estimates and the number of roughly equivalent process instructions required to complete each sub-process. Numbers such as number of employees and expenses were annualized figures from 1999 financial statements.

Step 1: Determine the core areas — Executives categorize the company's functions at the aggregate level into three areas: a. Management: Includes finance and strategic management b. S&GA (Sales and General Administration): Includes all supporting functions such as HR, public relations, and marketing c. Operations: Includes sales support and design, service selection and NAD, procurement, integration, troubleshooting, and final testing

Step 2: Gather data on knowledge embedded using the learning time approach: a. Ranking: Executives rank the three areas from hardest to easiest to learn (or most to least complex). This creates a framework for first-cut analysis of knowledge created in each area and is assumed to correlate with the 100-month learning time estimate. b. Learning time estimation: Executives estimate how long it would take the average person to learn to produce the outputs of each core area using the 100-month approach — there is a total of only 100 months for an average person to learn everything in the above areas necessary to generate the annual revenue at Exodus.

Step 3: Weight the amount of knowledge executed in the process: a. Determine the number of employees within each core area b. Ask for the percentage of the process that is automated c. Calculate the percentage of knowledge contained in each process, including its supporting technology (amount of knowledge = relative learning time × number of employees + automation) d. Determine the annual budget for each core process e. Calculate the ROK ratio (Return on Knowledge) to estimate the value added by given knowledge assets in each process

Table 7.2 High-Level Aggregate KVA Analysis:

Key calculations from the table:

• Column 6 (Knowledge in automation): Learning time × Employees × Automation%

  • S&GA: 20 × 855 × 80% = 13,680
  • Operations: 45 × 600 × 60% = 16,200
  • Management: 35 × 255 × 80% = 7,140

• Column 7 (Total knowledge): (Learning time × Employees) + Knowledge in automation

  • S&GA: (20 × 855) + 13,680 = 30,780
  • Operations: (45 × 600) + 16,200 = 43,200
  • Management: (35 × 255) + 7,140 = 16,065

• Column 8 (Knowledge allocation %): Total knowledge for area ÷ Total knowledge for all areas × 100%

  • Total knowledge = 30,780 + 43,200 + 16,065 = 90,045
  • S&GA: (30,780/90,045) × 100% = 34.18%
  • Operations: (43,200/90,045) × 100% = 47.98%
  • Management: (16,065/90,045) × 100% = 17.84%

• Column 9 (Revenue allocation): Total revenue ($242M) × Knowledge allocation%

  • S&GA: $242 × 34.18% = $82.7M
  • Operations: $242 × 47.98% = $116.1M
  • Management: $242 × 17.84% = $43.2M

• Column 11 (ROK): Allocated revenue ÷ Cost to use knowledge

  • S&GA: $82.7/$118.8 = 70%
  • Operations: $116.1/$197.2 = 59%
  • Management: $43.2/$51.0 = 85%

🔑 Definition — ROK (Return on Knowledge): The ratio of revenue allocated to each core area compared to its corresponding expenses, used to compare efficiency in performance of core areas.

📌 Example: The ROK for Operations is 59%, meaning for every dollar spent, only $0.59 in revenue is generated from knowledge assets in that area. This is lower than S&GA (70%) and Management (85%), indicating Operations needs improvement.

💡 Why this matters: The theory predicts that rankings (column 2) and learning times (column 3) should be 100% correlated. However, due to measurement error, the level of correlation should reach a minimum of 85% for rough-cut aggregated estimation and 95% for more detailed core process analyses.

Management Implication

Among the three core function areas, the performance of Operations (59% ROK) is relatively low compared to S&GA (70%) and Management (85%). To make the company profitable, KVA analysis can identify areas where the company can be more effective in exploiting its knowledge resources.

Currently, the sales provisioning process is one of the core processes in Exodus' Operations. It presents a major opportunity for further business expansion with the explosive growth in demand for data storage. However, it is also the area where customer turnaround is the slowest due to lack of automation. Five out of the six sub-processes of sales provisioning fall within the Operations area. The lower ROK in the Operations area confirms management's guess that the sales provisioning process needs improvement.

KVA: Sales Provisioning Process

Assumptions and Methodology:

1. Data center: Exodus has 22 Internet data centers worldwide; the analysis is based on the El Segundo center in Los Angeles, assuming all centers are staffed and operated similarly
2. Learning time and process instruction approach: Two independent measures of knowledge — learning time estimates and number of process instructions required to produce outputs. Both measures should be defined in terms of roughly equal complexity. The higher the correlation between the two, the better the estimates

Seven Steps of KVA on Sales Provisioning Process:

Step 1: Identify core and sub-processes — six processes: sales support and design, service selection and NAD, procurement, integration, troubleshooting, and final testing

Step 2: Establish a common definition of learning time — SMEs estimate how long it would take to teach an "average" person to learn to produce outputs. Total learning time for the whole sequence is approximately 2,000 weeks.

Step 3: Calculate total time to learn each sub-process:

• Learning time approach: Distribution of 2,000 weeks — Sales: 240, Service selection and NAD: 400, Procurement: 60, Integration: 500, Troubleshooting: 500, Final testing: 300
• Process instruction approach: For example, sales support required 240 learning weeks or 280 process instructions

Table 7.3 KVA on the Sales Provisioning Process:

Table 7.4 Learning Time and Process Instructions Correlation (0.8903 or 89% correlation, indicating high accuracy)

Step 4: Designate a sampling time period — annualized period was used, so number of employees was the weighting factor

Step 5: Multiply learning time for each sub-process by the number of times the sub-process executes during the sample period (employees × learning time + automation = total knowledge)

Step 6: Calculate cost to execute each sub-process (Table 7.5) — based on assumption that all 22 data centers share the same cost structure as El Segundo

Table 7.5 Cost Calculation:

Step 7: Compute ROKs — revenue allocated for each sub-process divided by cost for each sub-process

📌 Example: Final testing has ROK of 200% (highest in the process), meaning for every dollar spent, $2.00 in revenue is generated. This significantly outperforms the industry average of 125%. In contrast, Procurement has only 77% ROK versus the industry average of 150%, indicating serious underperformance.

Benefits of KVA analysis (twelve applications):

• Tool to control operations: Provides contemporaneous feedback about how well the company is self-organizing and adapting to the dynamic market environment
• New set of raw data: Uses validated data for ROP and ROK calculations that are harder to manipulate when value and cost are matched for given core areas
• Increase employee understanding: Helps employees understand the value they contribute, even those not familiar with finance and accounting
• Enhance employee productivity: Creates a framework encouraging managers and employees to think and behave like owners
• Efficient resource allocation: Increases shareholder value through improved allocation of knowledge assets and capital resources
• Tool to measure manager performance: Makes managers responsible for operations they control, focusing on knowledge-created value rather than external market factors
• Benchmark with industry or competitors: Provides a value-based method for comparing knowledge asset performance
• Starting point to improve financial and business policy: Helps companies identify and invest in processes, technologies, and people providing the greatest return

⭐ Key Takeaways

The Knowledge Market Framework conceptualizes organizations as internal marketplaces where tacit knowledge is exchanged between buyers (local and global) and sellers, with pricing mechanisms determining value. The KVA methodology provides a systematic way to measure the value created by knowledge assets by allocating revenue to core processes based on learning time and automation estimates, then calculating Return on Knowledge (ROK) ratios. For Exodus Communications, the aggregate-level analysis revealed Operations had the lowest ROK (59%), confirming management's intuition about the sales provisioning process being the biggest problem area. The detailed KVA of the sales provisioning process showed wide variation in ROK across sub-processes, from 200% (final testing) to 77% (procurement), enabling targeted improvement decisions. The use of two independent knowledge measures (learning time and process instructions) with correlation testing (minimum 85-95%) provides confidence in the accuracy of knowledge asset valuations.

🧠 Quick Revision Questions

1. What are the two categories of knowledge buyers according to Grover and Davenport, and how do they differ in their goals?
2. In the aggregate-level KVA analysis for Exodus, which core area had the lowest ROK and what management implication did this suggest?
3. How is the total amount of knowledge in a process calculated in KVA analysis, and why is automation included in this calculation?
4. In the sales provisioning process KVA, what was the correlation between learning time and process instruction estimates, and why is this correlation important?
5. List at least four practical ways that KVA analysis can benefit managers in making resource allocation and performance improvement decisions.

📘 Lecture 20 — SVEIBY’S INTELLIGENT ASSET MONITOR

📖 Overview: This lecture introduces Sveiby's knowledge-based theory of the firm and his Intelligent Asset Monitor framework for managing intellectual capital. It explains the three families of intangible assets—external structure, internal structure, and individual competence—and maps the nine possible knowledge transfers between and within these families, providing a strategic tool for intellectual capital management.

🗂️ Topics Covered

The lecture covers the classification of intangible assets into three families (external structure, internal structure, and individual competence), the definition of competence as part of intellectual capital, the distinctive features of knowledge transfers compared to tangible goods transfers, and a detailed examination of all nine knowledge transfer types with corresponding strategic questions and management activities.

📝 Lecture Summary

Intellectual Capital Management

One of the key authors in the area of intellectual capital is Sveiby (2001), who developed a knowledge-based theory of the firm to guide strategy formulation. He distinguished between three families of intangible assets. The external structure family consists of relationships with customers and suppliers and the reputation (image) of the firm. Some of these relationships can be converted into legal property such as trademarks and brand names. The value of such assets is primarily influenced by how well the company solves its customers' problems, and there is always an element of uncertainty here. The internal structure family consists of patents, concepts, models, and computer and administrative systems. These are created by the employees and are thus generally owned by the organization. The structure is partly independent of individuals, and some of it remains even if a large number of employees leave. The individual competence family consists of the competence of the professional staff, the experts, the research and development people, the factory workers, sales and marketing—in short, all those that have a direct contact with customers and whose work is within the business idea.

🔑 Definition — Competence: the sum of knowledge, skills, and abilities at the individual level. With this definition, knowledge is part of competence, and competence is part of intellectual capital.

These three families of intangible resources have slightly different definitions when compared to the capital elements. The external structure seems similar to relational capital, the internal structure seems similar to structural capital, while individual competence seems similar to human capital.

To appreciate why a knowledge-based theory of the firm can be useful for strategy formulation, Sveiby (2001) considers some of the features that differentiate knowledge transfers from tangible goods transfers. In contrast to tangible goods, which tend to depreciate in value when they are used, knowledge grows when used and depreciates when not used. Competence in a language or a sport requires huge investments in training to build up; managerial competence takes a long time on-the-job to learn. If one stops speaking the language it gradually dissipates.

Knowledge Transfer Within and Between Families of Intangible Assets (Sveiby, 2001)

Given three families of intangible assets, it is possible to identify nine knowledge transfers. These knowledge transfers can occur within a family and between families, as illustrated in a nine-cell matrix (not fully reproduced here). Each of the nine knowledge transfers is explained as follows:

1. Knowledge transfers between individuals concern how to best enable the communication between employees within the organization. The strategic question is: How can we improve the transfer of competence between people in the organization? Activities for intellectual capital management focus on trust building, enabling team activities, induction programs, job rotation, and master/apprentice schemes.

2. Knowledge transfers from individuals to external structure concern how the organization's employees transfer their knowledge to the outer world. The strategic question is: How can the organization's employees improve the competence of customers, suppliers, and other stakeholders? Activities focus on enabling employees to help customers learn about products, getting rid of red tape, enabling job rotation with customers, holding product seminars, and providing customer education.

3. Knowledge transfers from external structure to individuals occur when employees learn from customers, suppliers, and community feedback through ideas, new experiences, and new technical knowledge. The strategic question is: How can the organization's customers, suppliers, and other stakeholders improve the competence of the employees? Activities focus on creating and maintaining good personal relationships between the organization's own people and people outside the organization.

4. Knowledge transfers from competence to internal structure concern the transformation of human capital into more permanent structural capital through documented work routines, intranets, and data repositories. The strategic question is: How can we improve the conversion from individually held competence to systems, tools, and templates? Activities focus on tools, templates, processes, and systems so they can be shared more easily and efficiently.

5. Knowledge transfers from internal structure to individual competence is the counterpart of the above. Once competence is captured in a system, it needs to be made available to other individuals in such a way that they improve their capacity to act. The strategic question is: How can we improve individuals' competence by using systems, tools, and templates? Activities focus on improving the human-computer interface of systems, action-based learning processes, simulations, and interactive e-learning environments.

6. Knowledge transfers within the external structure concern what customers and others tell each other about the service of an organization. The strategic question is: How can we enable the conversations among customers, suppliers, and other stakeholders so they improve their competence? Activities focus on partnering and alliances, improving the image of the organization and the brand equity of its products and services, improving the quality of the offering, and conducting product seminars and alumni programs.

7. Knowledge transfers from external to internal structure concern what knowledge the organization can gain from the external world and how the learning can be converted into action. The strategic question is: How can competence from customers, suppliers, and other stakeholders improve the organization's systems, tools, processes, and products? Activities focus on empowering call centers to interpret customer complaints, creating alliances to generate ideas for new products, and research and development alliances.

8. Knowledge transfers from internal to external structure is the counterpart of the above. The strategic question is: How can the organization's systems, tools, processes, and products improve the competence of customers, suppliers, and other stakeholders? Activities focus on making the organization's systems, tools, and processes effective in servicing customers, extranets, product tracking, help desks, and e-business.

9. Knowledge transfers within the internal structure involves the internal structure as the backbone of the organization. The strategic question is: How can the organization's systems, tools, processes, and products be effectively integrated? Activities focus on streamlining databases, building integrated information technology systems, and improving the office layout.

💡 Why this matters: These nine knowledge transfers provide a comprehensive diagnostic tool for any organization to identify gaps in their intellectual capital management and to design targeted strategies for improving knowledge flow and value creation.

⭐ Key Takeaways

Sveiby's framework classifies intangible assets into three families—external structure, individual competence, and internal structure—which correspond to relational, human, and structural capital respectively. Knowledge is unique because it grows when used and depreciates when not used, unlike tangible goods. The nine knowledge transfers represent all possible flows between and within these three families, each with a specific strategic question and set of management activities. Master/apprentice schemes and job rotation facilitate individual-to-individual knowledge transfer, while documented work routines, intranets, and data repositories convert individual competence into permanent structural capital. Effective intellectual capital management requires organizations to actively manage all nine transfers to maximize knowledge creation, sharing, and utilization.

🧠 Quick Revision Questions

1. What are the three families of intangible assets in Sveiby's framework, and which traditional capital elements do they correspond to?
2. How does knowledge differ from tangible goods in terms of value depreciation and growth?
3. What is the strategic question for knowledge transfer from individuals to external structure, and name two management activities for this transfer?
4. In transfer type 4 (competence to internal structure), what is the primary activity focus, and why is this conversion important?
5. What distinguishes knowledge transfers within the internal structure from other transfers, and what activities support it?

📘 Lecture 21 — STRATEGIC PERSPECTIVE OF KNOWLEDGE AND STRATEGIC SCHOOL APPROACH IN KM

📖 Overview: This lecture examines knowledge as a strategic resource from the resource-based theory of the firm, arguing that intangible knowledge assets can provide sustainable competitive advantage. It explains the link between knowledge and strategy through frameworks like SWOT analysis and knowledge gap analysis, and introduces the strategic school perspective on knowledge management.

🗂️ Topics Covered

The lecture covers the strategic view of knowledge through the resource-based theory of the firm, knowledge as a strategic resource including its sustainability and increasing returns property, distinctions between data/information/knowledge/wisdom, the knowledge-strategy link via SWOT analysis and knowledge mapping, and the strategic school perspective that treats knowledge management as a dimension of competitive strategy.

📝 Lecture Summary

Strategic View of knowledge

Business strategy has traditionally focused on products and services for competitive advantage. Recent work in strategic management and economic theory focuses on the internal side—the firm's resources and capabilities. This is the resource-based theory of the firm, which states that performance differences across firms can be attributed to variance in firms' resources and capabilities. Resources that are valuable, unique, and difficult to imitate provide the basis for competitive advantages and produce positive returns.

The essence of the resource-based theory lies in emphasis on internal resources rather than external opportunities and threats dictated by industry conditions. Firms are considered highly heterogeneous, with different bundles of resources due to different initial endowments and managerial decisions affecting resource accumulation and utilization.

To generate sustainable competitive advantage, a resource must provide economic value and be presently scarce, difficult to imitate, non-substitutable, and not readily obtainable in factor markets. This theory rests on two key points: resources are determinants of firm performance, and resources must be rare, valuable, difficult to imitate and non-substitutable.

Research on competitive implications of resources like knowledge, learning, culture, teamwork, and human capital was boosted by resource-based theory. Intangible resources are more likely than tangible resources to produce competitive advantage because they are difficult to change except over the long term. Firm-specific knowledge allows firms to add value to incoming factors of production.

🔑 Definition — Resource-based theory of the firm: A strategic management perspective holding that performance differences across firms are attributed to variance in firms' resources and capabilities, and that resources must be valuable, rare, difficult to imitate, and non-substitutable to generate sustainable competitive advantage.

Knowledge As A Strategic Resource

The knowledge-based view of the firm argues that products and services produced by tangible resources depend on how they are combined and applied, which is a function of the firm's know-how. Knowledge is embedded in individual employees and entities like organization culture, identity, routines, policies, systems, and documents. Knowledge assets may produce long-term sustainable competitive advantage because knowledge-based resources are socially complex to understand and difficult to imitate.

Companies with superior knowledge can coordinate and combine traditional resources in new ways, providing more value for customers than competitors. By having superior intellectual resources, an organization can exploit traditional resources better than competitors, even if those traditional resources are not unique. Therefore, knowledge can be considered the most important strategic resource, and the ability to acquire, integrate, store, share and apply it the most important capability for building and sustaining competitive advantage.

Long-term sustainable competitive advantage comes from the firm's ability to effectively apply existing knowledge to create new knowledge and take action. Knowledge existing at any given time alone is not sufficient; organizations must actively learn and create.

Knowledge-based competitive advantage is sustainable because the more a firm already knows, the more it can learn. This creates increasing returns—unlike physical goods consumed with use, knowledge provides increasing returns as it is used, creating a self-reinforcing cycle. Sustainability also comes from knowing more about some things than competitors, combined with time constraints competitors face in acquiring similar knowledge.

Organizations should use learning experiences to build on knowledge positions that provide current or future advantage. Knowledge mapping—systematically mapping, categorizing, and benchmarking knowledge—helps make knowledge accessible and allows organizations to focus learning efforts on strategic areas. While knowledge advantage may be sustainable, building it internally is long-term, explaining the attraction of strategic alliances for quicker knowledge access.

💡 Why this matters: Knowledge has a strategic role only if unique firm knowledge can be successfully applied to value-creating tasks and capitalize on existing business opportunities. Since competitors benchmark against industry leaders, knowledge must remain difficult to imitate.

The lecture distinguishes between data, information, knowledge and wisdom:

• Data: letters and numbers without meaning; independent, isolated measurements, characters, numerical characters and symbols.
• Information: data included in a context that makes sense; data endowed with relevance and purpose. Example: "40 degrees" has different meanings in medical (fever), geographical (latitude), or technical (angle) contexts.
• Knowledge: information combined with experience, context, interpretation and reflection. Knowledge is a renewable resource that accumulates through use and combination with employees' experience. Knowledge cannot exist outside human heads.
• Wisdom: knowledge combined with learning insights and judgmental abilities. Wisdom is more personal and obscure, cannot be created like data or shared like knowledge, requiring introspection, retrospection, interpretation and contemplation.

From a resource-based perspective: data are raw numbers and facts, information is processed data, and knowledge is information combined with human thoughts. Information converts to knowledge once processed in the mind, and knowledge becomes information once articulated to others.

🔑 Definition — Knowledge-based view of the firm: A strategic perspective arguing that knowledge assets embedded in individuals and organizational entities may produce long-term sustainable competitive advantage because they are socially complex and difficult to imitate.

📐 Formula: Knowledge advantage sustainability = Existing knowledge depth + Learning ability + Time constraints on competitors → Increasing returns (knowledge becomes more valuable with use, unlike physical goods)

The Knowledge-Strategy Link

The traditional SWOT framework, updated for knowledge-intensive environments, provides a basis for describing a knowledge strategy. Firms need to perform a knowledge-based SWOT analysis, mapping their knowledge resources and capabilities against strategic opportunities and threats to understand points of advantage and weakness.

Knowledge strategy is balancing knowledge-based resources and capabilities with knowledge required for providing products/services in ways superior to competitors. Essential elements include identifying which knowledge-based resources are valuable, unique, and inimitable, and how they support product and market positions (Zack, 1999).

To explicate the link between strategy and knowledge, an organization must:

1. Articulate its strategic intent
2. Identify knowledge required to execute its intended strategy
3. Compare that to actual knowledge, revealing strategic knowledge gaps

Every firm competes through a competitive strategy—either from explicit decisions or accumulation of incremental decisions. Every strategic position is linked to intellectual resources and capabilities. Strategic choices regarding technologies, products, services, markets, and processes influence knowledge, skills, and core competencies required.

What a firm knows limits how it can compete. The firm's existing knowledge creates both opportunities and constraints for selecting viable competitive positions. Success requires dynamically aligning knowledge-based requirements and capabilities (Zack, 1999).

Assessing knowledge position requires cataloging resources through a knowledge map. Knowledge taxonomies distinguish between:

• Tacit vs. explicit knowledge
• General vs. context-specific knowledge
• Individual vs. collective knowledge

By type, knowledge includes: declarative (knowledge about), procedural (know-how), causal (know-why), conditional (know when), and relational (know-with) (Zack, 1999).

Every firm's strategic knowledge can be categorized by ability to support competitive position:

• Core knowledge: minimum required to compete
• Advanced knowledge: enables competitive parity or modest advantage
• Innovative knowledge: allows significant differentiation from competitors

Knowledge is not static—what is innovative today becomes core knowledge tomorrow. The ability to learn, accumulate knowledge, and reapply it is itself a strategic competence (Zack, 1999).

The Strategic Knowledge Framework (Figure) allows taking a snapshot of where the firm is today vs. its desired knowledge profile. It can be applied by competency area, business unit, division, product line, function, or market position.

After mapping competitive knowledge position, organizations perform a gap analysis. The gap between what a firm must do to compete and what it actually does is a strategic gap. Underlying this is a potential knowledge gap—a gap between what the firm must know to execute strategy and what it does know.

The Knowledge Gap is derived from and aligned with the Business Gap (Figure). The greater the number, variety, or size of knowledge gaps, and the more volatile the knowledge base, the more aggressive the knowledge strategy required. A firm not capable of executing its strategy must either align strategy with capabilities or acquire the capabilities (Zack, 1999).

🔑 Definition — Knowledge strategy: Balancing knowledge-based resources and capabilities with the knowledge required for providing products or services in ways superior to competitors (Zack, 1999).

📌 Example: Knowledge gap analysis—A firm competing as an innovator must have innovative knowledge in certain areas. If its actual knowledge only reaches core or advanced levels, a knowledge gap exists. The firm must develop or acquire innovative knowledge to close this gap and execute its intended strategy.

The Strategic School Perspective

The strategic school sees knowledge management as a dimension of competitive strategy. Indeed, it may be seen as the essence of a firm's strategy. Approaches to knowledge management are dependent on management perspective.

⭐ Key Takeaways

The most critical understanding from this lecture is that knowledge, particularly intangible and firm-specific knowledge, can be the most important source of sustainable competitive advantage according to the resource-based theory of the firm. Unlike physical resources that provide decreasing returns, knowledge provides increasing returns—becoming more valuable the more it is used. Students must remember the distinction between data, information, knowledge, and wisdom, as well as the three categories of strategic knowledge (core, advanced, innovative). The knowledge-strategy link requires firms to perform gap analysis comparing what they know with what they must know to execute their strategy. Finally, the strategic school perspective treats knowledge management not as a support function but as a core dimension of competitive strategy itself.

🧠 Quick Revision Questions

1. According to the resource-based theory of the firm, what four characteristics must a resource have to generate sustainable competitive advantage?
2. Why does knowledge produce "increasing returns" rather than decreasing returns like physical goods?
3. What are the five types of knowledge identified by type in the lecture (declarative, procedural, etc.), and what does each refer to?
4. How does the Strategic Knowledge Framework distinguish between core, advanced, and innovative knowledge?
5. What is the relationship between a firm's strategic gap and its knowledge gap according to Zack (1999)?

📘 Lecture 22 — Strategic School: Stock, Flow, and Growth Strategy in KM

📖 Overview: This lecture examines how knowledge management is viewed as a dimension of competitive strategy. It explores the strategic school's three perspectives (information, technology, culture), contrasts codification and personalization strategies for managing knowledge, and introduces the stock, flow, and growth strategies based on business type. Understanding these frameworks helps organizations align their KM approach with their competitive goals.

🗂️ Topics Covered

The lecture covers the strategic school and its three perspectives (information-based, technology-based, culture-based); the codification strategy (people-to-documents) and personalization strategy (person-to-person) for knowledge management, including their differences in competitive strategy, economic model, IT use, and HR approach; three questions for choosing the right strategy (standardized vs. customized products, mature vs. innovative products, explicit vs. tacit knowledge); the stock, flow, and growth strategies linked to expert-driven, experience-driven, and efficiency-driven businesses; and a table of characteristics of each KM strategy.

📝 Lecture Summary

The Strategic School

The strategic school sees knowledge management as a dimension of competitive strategy, possibly even the essence of a firm’s strategy. Approaches to KM depend on management perspective. Three distinctions are made:

• Information-based perspective is concerned with access to information: "I have a problem, and I am looking for someone in the organization who has knowledge that can solve my problem."
• Technology-based perspective is concerned with applications of information technology: "How can we use this technology to systematize, store and distribute information to knowledge workers?"
• Culture-based perspective is concerned with knowledge sharing: "We can draw on each other’s expertise."

All three perspectives are needed for a successful KM project. However, the main focus varies depending on corporate situation. If reinventing the wheel is the big problem, the information-based perspective should dominate. If the technology cannot provide basic services, the technology-based perspective should dominate. If knowledge workers are isolated and reluctant to share, the culture-based perspective should dominate.

Codification and Personalization Strategy

Some companies automate KM, while others rely on people to share knowledge through traditional means. In some companies, the strategy centers on the computer. Knowledge is carefully codified and stored in databases, where it can be accessed and used easily. These companies use a people-to-documents approach: knowledge is extracted from the person who developed it, made independent of that person, and reused for various purposes. Knowledge objects (e.g., interview guides, work schedules, benchmark data, market segmentation analysis) are stored in an electronic repository. This allows many people to search and retrieve codified knowledge without contacting the original developer. Hansen et al. (1999) call this the codification strategy.

In other companies, knowledge is closely tied to the person who developed it and is shared mainly through direct person-to-person contacts. The chief purpose of computers is to help people communicate knowledge, not store it. These companies focus on dialogue between individuals. Knowledge is transferred in barnstorming sessions, one-on-one conversations, over the telephone, by email, and via video conferences. Networks are fostered by transferring people between offices, supporting a culture of prompt response, creating directories of experts, and using knowledge managers. These firms may have electronic document systems, but the purpose is to find out who has done work on a topic and then approach those people directly. Hansen et al. (1999) call this the personalization strategy.

🔑 Definition — Codification Strategy: A KM strategy where knowledge is carefully codified and stored in databases for easy access and reuse by anyone in the company, using a people-to-documents approach.

🔑 Definition — Personalization Strategy: A KM strategy where knowledge is closely tied to the person who developed it and is shared mainly through direct person-to-person contacts and dialogue.

Codification and personalization can be contrasted using several criteria:

• Competitive strategy: Codification provides high quality, reliable, fast information-systems implementation by reusing codified knowledge. Personalization provides creative, analytically rigorous advice on high-level strategic problems by channeling individual expertise.
• Economic model: Codification uses reuse economics—investing once in a knowledge asset and reusing it many times. Personalization uses expert economics—charging high fees for highly customized solutions to unique problems.
• KM strategy: Codification uses people-to-documents (developing an electronic document system that codifies, stores, disseminates, and allows reuse of knowledge). Personalization uses person-to-person (developing networks for linking people so tacit knowledge can be shared).
• IT investment: Codification invests heavily in IT to connect people with reusable codified knowledge. Personalization invests moderately in IT to facilitate conversations and exchange of tacit knowledge.
• Human resources: Codification trains people in groups and through computer-based distance learning. Personalization trains people through one-on-one mentoring.

💡 Why this matters: The choice between codification and personalization is not arbitrary—it must align with the company's competitive strategy and how it creates value for customers.

Choosing the Right Strategy

Competitive strategy must drive KM strategy. Executives must articulate why customers buy their products or services and how knowledge adds value. Assuming the competitive strategy is clear, managers consider three questions (Hansen et al., 1999):

1. Do you offer standardized or customized products? Standardized products fit the codification strategy; customized products fit the personalization strategy.
2. Do you have mature or innovative products? Mature products fit the codification strategy; innovative products fit the personalization strategy.
3. Do your people rely on explicit or tacit knowledge to solve problems? Explicit knowledge (knowledge that can be codified, such as simple software code and market data) fits the people-to-documents approach. Tacit knowledge (difficult to articulate, acquired through personal experience—including scientific expertise, operational know-how, business judgment, and technological expertise) fits the person-to-person approach.

Incentives are critical. In the codification model, managers need to reward people for writing down what they know and getting documents into the electronic repository—the level and quality of contributions should be part of annual performance reviews. In the personalization model, managers need to reward people for sharing knowledge directly with other people.

Stock, Flow, and Growth Strategy

Approaches to KM are dependent on knowledge focus in the organization. Three business types are distinguished:

• Expert-driven business: Solves large, complex, risky, new, and unusual problems. Competitive advantage is achieved through continuous improvisation and innovation. Knowledge workers apply general high-level knowledge to understand, solve, and learn. Characterized by new problems and new methods for solution.
• Experience-driven business: Solves large and complicated problems that are new but can be solved with existing methods in a specific context. Competitive advantage is achieved through effective adaptation of existing problem-solving methodologies. Characterized by new problems and existing methods for solution.
• Efficiency-driven business: Solves known problems. Quality is found in fast and inexpensive application. Competitive advantage is achieved in making small adjustments at a low price. Characterized by known problems and known methods for solution.

Few knowledge-intensive firms are active in only one business. For example, medical doctors are mainly in experience-driven business but sometimes in expert-driven business. Lawyers are often in expert-driven business but most often in experience-driven business. Some engineers are in efficiency-driven business but most often in experience-based business.

These differences lead to three KM strategies:

• Stock strategy: Focused on collecting and storing all knowledge in information bases. Information is stored in databases and made available to knowledge workers. Workers use databases to keep updated on relevant problems, methods, news, and opinions. Information accumulates over time. This can also be called person-to-knowledge strategy.
• Flow strategy: Focused on collecting and storing knowledge as long as it is used in knowledge work processes. If certain knowledge work disappears, that information becomes obsolete and can be deleted. This is a yellow-pages strategy where information on knowledge areas covered by individuals is registered, and the person source can be identified. When starting a new project, a worker searches databases to find colleagues with experience in those problems. This can also be called person-to-per strategy.
• Growth strategy: Focused on developing new knowledge. New knowledge is developed in innovative work processes when solving new problems with new methods. Several persons are involved in the innovation through a learning process. When starting a new project, a worker uses intra-organizational and inter-organizational networks to find information on work processes and learning environments that colleagues have used successfully.

There is a strong link between these three KM strategies and the three business types. Typically, efficiency-driven businesses apply the stock strategy, experience-driven businesses apply the flow strategy, and expert-driven businesses apply the growth strategy.

The table of characteristics is presented below:

🔑 Definition — Stock strategy: A KM strategy focused on collecting and storing all knowledge in information bases (databases) for accumulation and availability to all knowledge workers.

🔑 Definition — Flow strategy: A KM strategy focused on collecting and storing knowledge as long as it is used, with a yellow-pages approach to connect knowledge workers to colleagues with experience.

🔑 Definition — Growth strategy: A KM strategy focused on developing new knowledge through innovative work processes and learning environments.

💡 Why this matters: The choice of stock, flow, or growth strategy must align with the business type (efficiency-driven, experience-driven, or expert-driven) and the nature of problems and solutions the organization typically handles.

⭐ Key Takeaways

The most critical concepts from this lecture are: (1) The strategic school views KM as central to competitive strategy, with three perspectives (information, technology, culture) that must all be present but may vary in emphasis based on organizational problems. (2) Codification strategy uses people-to-documents and reuse economics for standardized/mature products relying on explicit knowledge, while personalization strategy uses person-to-person and expert economics for customized/innovative products relying on tacit knowledge. (3) Three questions help choose the right strategy: standardized vs. customized products, mature vs. innovative products, and explicit vs. tacit knowledge. (4) Stock, flow, and growth strategies correspond to efficiency-driven, experience-driven, and expert-driven businesses respectively, with different knowledge bases, important persons, management tasks, and learning approaches. (5) Incentive systems must align with the chosen strategy—rewarding document contributions for codification/stock strategies and direct knowledge sharing for personalization/flow strategies.

🧠 Quick Revision Questions

1. What are the three perspectives of the strategic school, and under what circumstances should each dominate a KM project?
2. What is the difference between codification and personalization strategies in terms of economic model (reuse economics vs. expert economics)?
3. What three questions should managers consider when choosing between codification and personalization strategies?
4. What are the three knowledge management strategies (stock, flow, growth), and which business type (efficiency-driven, experience-driven, expert-driven) does each typically serve?
5. What incentive systems are appropriate for codification/stock strategies versus personalization/flow strategies?