A true learning person also has a “to-learn” list, and the items on that list carry at least as much weight in how one organizes his or her time as the to-do list.

More specifically, The Big Question is about:

• If you have a to-learn list and are willing to share, and willing to share how you work with that list, that would likely be helpful information.
• As Knowledge Workers, work and learning are the same, so how does a to-learn list really differ from a to-do list? How are they different than undirected learning through work, blogging, conferences, etc.?
• Are to-learn lists really important to have? Are they as important as what Jim Collins tells us?
• Should they be captured? If so how?
• How does a to-learn list impact something like a Learning Management System in a Workplace or Educational setting?
• What skills, practices, behaviors do modern knowledge workers need around to-learn lists?

So it’s really a bunch of Big Questions this month. I’m not going to answer any of them.

First of all, some context - I undertook some research on the text that the idea of the To-Learn List originated from. Learning Journeys contains 37 two- to three-page essays by

some of the best and most well known of human resources and organizational leaders and pioneers. From Stephen Cover to Dave Ulrich, from Marshall Goldsmith to Robert Eichenger, these individuals have done much to shape current thought in the areas of organizational development and leadership.

Amazon.com reviewer

As such, the text is orientated towards the needs of HR and organizational development people, rather than learning & development professionals. No harm: it’s always useful to get a different perspective (or to put it another way - a friend of mine reads a certain national newspaper because he “…likes to know what the enemy is thinking!”). One of the elements of the text that became apparent to me (from reading a part of the text using Amazon.com’s Look Inside functionality) was that the learning perspectives discussed would probably work better for people who are not professionally involved with training others: in this capacity, it indicated some potentially useful approaches on how to engage others in continuous learning, and pointed towards some avenues that might assist in achieving this objective.

As discussed in a previous blog entry on the E-Learning Curve, one of the central components of the impact of learning (and specifically the development of knowledge workers’ expertise in organizations) is the context within which the learning takes place. A central element of this context is the type or format of the learning taking place. In the literature, it is apparent that a dichotomy exists between the paradigms of formal, goal-directed training programs, and informal “learning at the watercooler” (Grebow, 2002); what Michael Eraut (2000) describes as incidental learning that takes place almost as a side effect of work: “it is difficult to make a clear distinction between formal and informal learning as there is often a crossover between the two” (McGivney, 1999, p.1). Another complexity in the discussion is where is non-formal learning located in relation to the diametric opposites?

I support Alan Rogers’ (2004) view that a “new paradigm” for learning exists, in which “most programmes will be partly formal and partly informal” going from formal to informal and from informal to formal in both directions along a continuum (see Figure 1) . “Both forms of education are important elements in the total learning experience” (looking again at non-formal and informal education - towards a new paradigm, 2004).

Figure 1 the Learning Continuum

Similarly, Hodkinson and Hodkinson argue that focusing on the extent to which learning is planned and intentional may be a way of by-passing the distinction between formal, non-formal and informal altogether.” (Colley, Hodkinson & Malcolm, 2002).

Sp, to return to the topic, my To-Learn List has one entry: to learn.

I would categorize myself as a “learnivore” - I continually acquire new knowledge and information through my Web-, book-, podcast-, and presentation reading, blogging (reading and writing), academic study and research, and work-based learning-related tasks. These activities are drivers for the information I to take on board in my attempts to enhance my skills, abilities, and expertise.

_____________

References:

Colley, Hodkinson, Malcolm (2002) non-formal learning: mapping the conceptual terrain. a consultation report [Internet] Available from: http://www.infed.org/archives/e-texts/colley_informal_learning.htm [Accessed 28th January 2008]
Coombs, P. (1968) The World Educational Crisis, New York, Oxford University Press.

Eraut, M. (2000) Non-formal learning, implicit learning and tacit knowledge, in F. Coffield (Ed) The Necessity of Informal Learning: Policy Press. Bristol

Goldsmith, M. Kaye, B. Shelton, K (eds.) (2000) LEARNING JOURNEYS Top Management Experts Share Hard-Earned Lessons on Becoming Great Mentors and Leaders. Davies-Black Publishing

Grebow, D. (2002) At the Water Cooler of Learning [Internet] Available from: http://agelesslearner.com/articles/watercooler_dgrebow_tc600.html [Accessed 30th January 2008]

McGivney, V. (1999) Informal learning in the community: a trigger for change and development NIACE. Leicester.

Rogers, A. (2004) Looking again at non-formal and informal education - towards a new paradigm [Internet] Available from: http://www.infed.org/biblio/non_formal_paradigm.htm [Accessed 30th January 2008]

–

In the learning domain, this rule can be applied to the process of training prioritization. In organizations the purpose of learning and development is to improve worker performance. There are many ways and opportunities for employees to improve their performance:

• reducing procedural mistakes
• identifying new customers
• influencing the behavior of others
• reducing error in products

Effective training should focus on providing what’s necessary to enable people to improve performance. However, the 80/20 rule suggests that not all performance improvement opportunities merit the same focus. Rather it suggests that 20% of opportunities, if addressed 80% of the total potential value of a training solution (assuming the ‘perfect’ solution will address 100% of opportunities completely. Focusing training efforts on that top 20% is a far more efficient model than attempting to cover the remaining 80%. For rapid e-learning with a relatively short duration, having this focus is critical.

In this context, Kineo recommend a value-driven model for identifying and prioritising opportunities for rapid e-learning to influence performance that will contribute the most to organizational performance.

According to Stephen Walsh from Kineo, this process has a number of key activities:

• Ensure that you have identified prioritized needs that rapid e-learning is best placed to address
• Use the 80/20 rule to guide your analysis of the prioritization of training
• Use the three-step approach (see Figure 1) to conduct rapid training needs analysis

1. Identify the full range of performance improvement opportunities
2. Establish criteria and rank accordingly with experts – concentrating your efforts on top 20%
3. Translate to objectives and seek approval

• Use rapid tools (check-lists) and rapid methods (phone, workshops, virtual classroom, surveys) to gather your data.

Figure 1. Value-driven model for rapid training needs prioritisation (courtesy Kineo)

For a more detailed look at this approach, go to Kineo’s website, or download the Rapid Guide on How to Rapidly Identify Training Needs.

–

Over the weekend, I encountered a very useful paper describing a practical application of the 80/20 rule (though not, as we’ll see, the Pareto Principle).

Now read on…

My experience of the e-learning industry is that the best and most effective practitioners are always looking for even more efficient content development methodologies, media production technologies, and approaches to content distribution to enable them to design, develop and deliver content to learners. In this regard, British e-learning development house Kineo always seem to have something interesting to say about the potential uses - and value - of Rapid E-learning, and one particular piece published in this month’s Newsletter struck me as being particularly useful. In the article How to Rapidly Identify Training Needs Stephen Walsh discusses a strategy to pinpoint and prioritize training needs - “rapidly of course.”

The value of any learning intervention is the change it achieves. If you know where the business challenge is, and you can pinpoint how learning can change behaviour, correct costly errors, and improve performance at the right time, you’re setting yourself up for laser-accurate learning.

In his seminal article Rapid E-learning: Disintermediate or die! Ted Cocheu asserts that:

Learning is rapidly changing from today’s hierarchically-driven, curriculum-based training process where instructional designers and trainers intervene to transform subject matter knowledge into courseware, into a “flattened” world where experts transfer their knowledge more directly to those who need it. In this brave new flattened learning world, training professionals will focus more time on capturing and transferring expert knowledge than developing formal courses.

To support these changes in the design, development, and delivery of content, a number of factors need to be considered:

• Speed is king.
• Knowledge is exploding.
• Budgets are shrinking
• Classrooms are not scalable.
• Workplace learning is informal.
• People forget.
• Key knowledge is proprietary.

Rapid E-learning “changes the development model, leverages new tools, and dramatically changes the economics of content development” (p.2). By focusing on specific learning needs, e-learning developers can generate a much higher return on investment in training when compared to content designed using a more traditional ISD-based approach.

The methodology suggested by Kineo is designed to assist learning professionals identify and prioritize learning needs that are appropriate for rapid e-learning in organizations using a “value-based analysis.”

This will get you to the outcome that a more detailed training needs would, but in a fraction of the time, by focusing specifically on the greatest points of pain and potential to add value. Identifying those key points means taking an 80/20 approach to needs analysis.

The Kineo interpretation of the Pareto Principle is based upon Dr. Joseph M. Juran’s pioneering work in Quality Management in the 1930s and ’40s. He “reduced to writing” his generally applicable observation (which I suggest that we should really call Juran’s Axiom) of the relative importance of the “vital few and trivial many.” Juran observed that in any activity or set of tasks, a few (20 percent) of the functions are vital, and many (80 percent) are trivial. However, as he stated in his 1975 article The Non-Pareto Principle; Mea Culpa he “…mistakenly applied the wrong name to the principle.” In Pareto’s case it meant one-fifth of the people owned four-fifth’s of the wealth. In Juran’s research for the US Government, he identified 20% of defects in a production process causing 80% of the quality issues. Project Managers know that 20 percent of the work (the first 10 percent and the last 10 percent) consume 80 percent of time and resources. Extending from this, Juran asserted that the 80/20 Rule could be generalized, from the science of management to the physical world, and indeed to learning and development processes.

In this context, 20 percent of learning interventions, if properly identified, should address 80 percent of the learning needs of an organization, and 80 percent total potential value of a training solution (assuming the ‘perfect’ solution will address 100% of learning.

Focusing training efforts on that top 20% is a far more efficient model than attempting to cover the remaining 80%. For rapid e-learning with a relatively short duration, having this focus is critical.

(How to Rapidly Identify Training Needs p.2)

As such, implementing a value-driven model based on the 80/20 Rule for identifying and prioritizing learning interventions can be a highly effective means to enhance worker performance in an organization.

More..

References:

Cocheau, T. (2005) Rapid eLearning: Disintermediate or Die! [Internet] Available from: http://ww.elearningforum.com/downloads/rapid_elearning.doc [Accessed 15th May 2006]

Juran, J.M. (1975) The Non-Pareto Principle; Mea Culpa [Internet] Available from: http://www.projectsmart.co.uk/docs/the-non-pareto-principle.pdf [Accessed 30 August 2008]

Walsh, S. (2008) How to Rapidly Identify Training Needs [Internet] Available from: http://www.kineo.com/documents/Kineo_Rapid_Guide_Identify_Needs.pdf [Accessed 30 August 2008]

–

Although I’m so tired I’ll have another cigarette
And curse Sir Walter Raleigh
He was such a stupid get.*

(So Tired, Lennon & McCartney)

Slightly off-topic today, but let’s see if we can find an e-learning angle…

As I alluded to by quoting from So Tired on the Beatles’ White Album, a month ago I got tired of cursing Walter Raleigh - explorer, importer of tobacco to Europe, and erstwhile courtier of Elizabeth I of England, and decided to stop smoking.

Four weeks later, I’m delighted and rather chuffed with myself to say that I’m still off cigarettes and on the wagon (as it were). Rather than outline a whole bunch of clichés about how good I feel, all I’m going to do is say how good I feel, which I’ve now done, so I’ll move on.

Apart from the physiological aspects of smoking cessation (the cravings most recently-stopped ex-smokers are familiar with), I have found that I am having to “re-educate” myself to be a non-smoker again, as I transition away from well-established behaviors, activity patterns, and habits I acquired as a nicotine addict. Interestingly, I haven’t (yet) transformed from poacher to gamekeeper in terms of my attitudes to those still smoking (and I hope I never turn into a rabid anti-smoking fascist) - it’s now more in sorrow and pity than in anger that I behold people huddled outside office buildings, staring into the middle distance under bus shelters, or with their car windows rolled down , exhaling smoke into the rain.

And here’s the e-learning angle…

If you’re a learning professional you’re no doubt familiar with Bloom’s Taxonomy of Educational Objectives as a means of evaluating a learner’s current knowledge- or skill assets. As is traditional, I will now mention the domains Benjamin Bloom identifies:

• Cognitive: mental skills (Knowledge)
• Affective: growth in feelings or emotional areas (Attitude)
• Psychomotor: manual or physical skills (Skills)

Now I will deviate from established custom and practice by actually discussing the affective and psychomotor domains - in my view it is in these aspects that my primary learning activities in relation to smoking are taking place.

Skills in the affective domain describe the way people react emotionally and characterizes their ability to empathize. Affective objectives typically target the awareness and growth in attitudes, emotion, and feelings.

Table <!–[if supportFields]> SEQ Table \* ARABIC <![endif]–>1<!–[if supportFields]><![endif]–> Bloom’s Taxonomy: Affective Domain

Bloom’s Taxonomy: Affective Domain
Receiving	The lowest level; the learner passively pays attention. Without this level no learning can occur.
Responding	The learner actively participates in the learning process, not only attends to a stimulus, the student also reacts in some way.
Valuing	The learner attaches a value to an object, phenomenon, or piece of information.
Organizing	The learner can put together different values, information, and ideas and accommodate them within his/her own schema; comparing, relating and elaborating on what has been learned.
Characterizing	The learner has held a particular value or belief that now exerts influence on his/her behaviour so that it becomes a characteristic.

Skills in the psychomotor domain describe the ability to physically manipulate a tool or instrument like a hand or a hammer. Psychomotor objectives usually focus on change and/or development in behavior and/or skills.

Table <!–[if supportFields]> SEQ Table \* ARABIC <![endif]–>2<!–[if supportFields]><![endif]–> Bloom’s Taxonomy: Psychomotor Domain

…though of could when you stop smoking, you’re learning how not to do things…

Normal service resumes next time.

____________

*get Noun (slang). An idiot, a contemptible person.

–

Systems of creation, production and distribution

The commonly-held notion that a knowledge economy is a services economy is misleading. As information and knowledge add value to basic products manufacturing and services are becoming increasingly integrated into complex chains of creation, production and distribution. At the core of the economy are goods producing industries, linked into value chains which see inputs coming from knowledge-based business services and goods related construction and energy industries, and outputs going to goods related distribution service industries[1].

Convergence or divergence

One feature of the emerging knowledge economy is increasing evidence that the nations of the world are polarizing, rather than converging, in economic terms. Standard growth theories suggest that economies subject to market forces should converge in terms of per capita GDP levels, either absolutely or relatively. But the reality is quite different.

Countries appear to be moving towards two peaks or nodes, one at high incomes and one at relatively low incomes. This polarisation of countries into different strata of economic activity and of living standards is becoming both pronounced and persistent – what is often referred to as “twin-peaks dynamics[2].” What the future will show as the knowledge economy unfolds remains to be seen, but there is little in the recent historical record to assure policy makers that market forces will deliver a continuing process of convergence to US levels. In such a world the consequences of policy failure or inaction can be dramatic.

Divergence and concentration

These same dynamics may cause changes in the industrial structure of knowledge economics. Many contend that increasing inequality can be observed at the international, national, regional, household and personal levels – that the rich are getting rich, while the poor are getting poorer. Some economists suggest that increasing returns from network economies and learning economies characteristic of knowledge economies will lead to industrial concentration – a world of winner takes all[3]. Others contend that the expansion of the knowledge driven economy will create a proliferation of material, firms and activities at all points and at all levels, suggesting that no one can expect to enjoy continued control of markets.

There may be temporary monopolies but they cannot last. And it is misconceived to think that the key lies in being at the point of delivery of the product: the low cost and ease of access to the delivery mechanism mean that the rents are driven down at the delivery level and instead migrate back up the value chain to those with genuinely scarce factors and competitive advantages[4].

Whichever proves true, the knowledge economy will see the development of new business models.

Footnotes:

[1] Sheehan, P. Tegart, G. (Eds.) (1998) Working for the Future: Technology and Employment in the Global Knowledge Economy. Victoria University Press.

[2] Sheehan, P. and Tegart, G. (Eds.) (1998) Working for the Future: Technology and Employment in the Global Knowledge Economy, Victoria University Press, p100. See also Quah, D. (1996) ‘Convergence Empirics Across Economies with (Some) Capital Mobility,’ Journal of Economic Growth, 1(1) pp. 95-125 [Internet] Available form: http://www.jstor.org/pss/2235377 [Accessed 20 August 2008]

[3] Arthur, W.B. (1996) ‘Increasing Returns and the New World of Business’, Harvard Business Review, July-August 1996, pp. 100-109

[4] Kay, J. In: DTI (1999) Economics of the Knowledge Driven Economy, Conference Proceedings, Department of Trade and Industry, London.

–

Learning organizations and innovation systems

In a knowledge economy, organizations search for linkages to promote inter-organizational learning, and for outside partners and networks to provide complementary assets. These relationships help organizations

1. spread the costs and risks associated with innovation
2. gain access to new research results, acquire key technological components
3. share assets in manufacturing, marketing and distribution.

As they develop new products and processes, organizations determine which activities they will undertake individually, in collaboration with other organizations, in collaboration with universities or research institutions, and with the support of government[1]. We can say that, as such, innovation is the result of numerous interactions between actors and institutions, which together form an “innovation system.”

These innovation systems consist of the information flows and relationships which exist among industry, government and academic and other institutions in the development of science and technology. The interactions within these systems influences the innovative performance of organizations - and ultimately of the economy. The ‘knowledge distribution power’ of the system, or its capability to ensure timely access by innovators to relevant stocks of knowledge, is can be seen as a major determinant of economic growth.

Strategy and location

One of the consequences of globalization combined with advances in communications technologies has been a strengthening of world competition, and the emergence of a new form of ‘global competition’. Most organizations in a dominant market position are, by necessity, multinational or transnational organizations. To compete successfully with their rivals, organizations must compete head-to-head in all markets (including their home market), and they must rapidly attain a global scale in production and/or rapidly roll out products and services into multiple markets in order to do so. In this environment, competitiveness depends increasingly on the coordination of, and alignment of a broad range of specialized industrial, financial, technological, commercial, administrative and cultural skills which can be located in many locations around the world[2].

Production is being rationalized globally, with organizations combining the factors, features and skills of various locations in the process of competing in global markets. There are three major dimensions of change involved:

• increasing national (locational) specialization
• increased international ‘fracturing’ of value chains or chains of production – witnessed in increased intra-industry and intra-firm trade
• greater line-item by line-item trade imbalances

An increasingly apparent consequence of this development in industrial ertia is substantial structural dislocation in local, regional and even national economies, and a consequent need for substantial structural adjustment.

Clustering in the Knowledge Economy

Networks and geographical clusters of firms are a particularly important feature of the knowledge economy. Organizations find it increasingly necessary to work with other firms and institutions in technology-based alliances, because of the rising cost, increasing complexity and widening scope of technology. Many organizations are becoming multi-technology corporations locating around centers of excellence in different countries. Despite improved capability for global communication, firms increasingly co-locate because it is the only effective way to share understanding[3]. Consequently, skills and life-style are becoming increasingly important locational factors.

As we enter the age of human capital, where organizations merely lease knowledge-assets, organizations’ location decisions are increasingly based upon quality-of-life factors that are important to attracting and retaining this economic asset. In high-tech services, strict business-cost measures are becoming less important to growing and sustaining technology clusters … Locations that are attractive to knowledge assets will play a vital role in determining the economic success of regions[4].

Economics of knowledge

In the knowledge economy there are new ground rules. Knowledge has fundamentally different characteristics from ordinary commodities and these differences have crucial implications for the way a knowledge economy must be organised[5]. The whole nature of economic activity, and our understanding of it, is changing.

Unlike physical goods information is non-rival – not destroyed in consumption. Its value in consumption can be enjoyed again and again. Hence, social return on investment in its generation can be multiplied through its diffusion. Ideas and information exhibit very different characteristics from the goods and services of the industrial economy. For example, much more than is the case with a frozen dinner or a haircut, the social value of ideas and information increases to the degree they can be shared with and used by others. More important, the costs associated with their production are distributed very differently over time. While up front costs associated with the production of traditional goods such as a car or house may not necessarily be high, each item is still costly to produce. The more of these one produces, the more likely one will eventually encounter scarcities that drive up production costs and reduce the size of social returns. In the case of innovation, ideas and information, however, the opposite would seem largely to be the case. While up front development costs can be very high, the reproduction and transmission costs are low. The more such items are (re)produced, the greater the social return on investment[6].

Traditional economics is founded on a system which seeks to optimise the efficient allocation of scarce resources, but because of the unique characteristics of information and knowledge the very meaning of scarcity is changing. Indeed, the scarcity defying expansiveness of knowledge is the root of one of its most important defining features. Once knowledge is discovered and made public, there is essentially zero marginal cost to adding more users[7].

More…

References:

Sheehan, P. Tegart, G. (Eds.) (1998) Working for the Future: Technology and Employment in the Global Knowledge Economy. Victoria University Press.

Footnotes:

[1] OECD. (1996) The Knowledge-Based Economy, OECD Paris, p. 16. [Internet] Available from: http://www.oecd.org/dataoecd/51/8/1913021.pdf [Accessed 20 August 2008]

[2] Hatzichronoglou, T.(1996) Globalisation and Competitiveness: Relevant Indicators, STI

Working Paper 1996/5, OECD, Paris, p. 7.

[3] Cantwell, J. In: DTI (1999) Economics of the Knowledge Driven Economy, Conference Proceedings, Department of Trade and Industry, London.

[4] DeVol, R.C. (1999) America’s High-Tech Economy: Growth, Development and Risks for Metropolitan Areas, Milken Institute, Santa Monica

[5] DTI (1999) Economics of the Knowledge Driven Economy, Conference Proceedings, Department of Trade and Industry, London, p.5

[6] Industry Canada (1997) Towards a Society Built on Knowledge [Internet} Available from: http://strategis.ic.gc.ca/SSG/ih01644e.html [Accessed 20p August 2008]

[7] DTI (1999) Economics of the Knowledge Driven Economy, Conference Proceedings, Department of Trade and Industry, London, p. 6.

–

In the 21st century, comparative advantage will become much less a function of natural resource endowments and capital-labour ratios and much more a function of technology and skills. Mother nature and history will play a much smaller role, while human ingenuity will play a much bigger role.

(New Tools, New Rules: Playing to win in the new economic game. p.101)

As I discussed in much finer detail (and in the context of e-learning rather than the economy) in this post, in my view we as a society are on the cusp of a knowledge revolution akin to the explosion of information made possible after the general availability of printed texts following the invention of movable type and the printing press in 1440.

As this century unfolds, the skills used by people will increasingly be those that are complementary with information and communication technology; not those that are substitutes.

Now read on…

What makes the emergence of the knowledge economy important is that it is, in some significant respects, different from the industrial economy we have known for most of the last two hundred years. Some of the key differentiators include:

Information revolution

The IT revolution has intensified the move towards knowledge convergence, and increased the share the knowledge stock of advanced economies. All knowledge that can be distilled as information can be transmitted globally at relatively little cost. Knowledge per se has attained more of the properties of a commodity. [1]

Flexible organization

Flexible organizations reduce waste and increase the productivity of both labor and capital by integrating worker cognition and action at all levels of their operations.In doing so they eliminate many layers of middle management, which are dysfunctional in terms of information flow[2]. Flexible organizations also avoid excessive specialization and compartmentalization by defining multi-task job responsibilities (which calls for multi-skilled workers) and by using teamwork and job rotation.

Flexible organizations merge agility and high product quality with the speed and low unit costs of mass production. They do this by more fully utilising the human capabilities of their workers.

Knowledge, skills and learning

Information and communication technologies have reduced the cost and enhanced the capacity of organizations to converge knowledge, and process and communicate information. In doing so they have substantially altered the ‘balance’ between explicit and tacit knowledge in the overall quantum of knowledge. As access to information becomes easier and less expensive, the skills and competencies relating to the selection and efficient use of information become more crucial, and tacit knowledge in the form of the skills needed to handle explicit knowledge has become more important than ever.

Information and communication technology investments are complementary with investment in human resources and skills[3]. Whereas machines replaced labor in the industrial era, information technology will be the locus of explicit knowledge in the knowledge economy, and work in the knowledge economy will increasingly demand uniquely human (and tacit) skills – such as conceptual and inter-personal management and communication skills.

Innovation and knowledge networks

The knowledge economy increasingly relies on the creation, distribution and use of knowledge assets. The success of enterprises will become more reliant upon their effectiveness in creation, harvesting, absorption and utilization of knowledge.

A knowledge economy is driven by the acceleration of the rate of change and the rate of learning of the contributors to the economy, where the opportunity and capability to get access to and join knowledge-intensive and learning-intensive relations determines the socio-economic position of individuals and firms[4]. Companies must become learning organizations, continuously adapting management, organization and skills to accommodate new technologies and grasp new opportunities. They will be increasingly joined in networks, where interactive learning involving creators, producers and users in experimentation and exchange of information drives innovation.

More…

References:

Sheehan, P. Tegart, G. (Eds.) (1998) Working for the Future: Technology and Employment in the Global Knowledge Economy. Victoria University Press.

Thurow, L. (1991) New Tools, New Rules: Playing to win in the new economic game. Prism.

Footnotes:

[1] This post is primarily drawn from Sheehan, P. Tegart, G. (Eds.) (1998) Working for the Future: Technology and Employment in the Global Knowledge Economy. Victoria University Press.

[2] Oman C. (1996) The Policy Challenges of Globalisation and Regionalisation, Policy Brief No. 11, OECD Development Centre, OECD, Paris, p. 19. [Internet] Available from: http://www.oecd.org/dataoecd/51/8/1913021.pdf [Accessed 20 August 2008]

[3] Soete, L. (1997) Macroeconomic and Structural Policy in the Knowledge-based
Economy. In: Industrial Competitiveness in the Knowledge-based Economy: The New Role
of Governments, OECD, Paris, p. 136.

[4] David, P. Foray, D. (1995) ‘Accessing and Expanding the Science and Technology Knowledge Base,’ STI Review, No 16, OECD, Paris.

–

…firms are increasingly required to adopt global strategies to deal with the new realities. Global competition in all major markets between competitors from all major countries, the increasing multinational origin of the inputs to production of both goods and services, the growing intra-industry and indeed intra-product nature of world trade and the interdependent role of the various elements of globalisation are all contributing to a transformation of the global economy.

(1998, p.8)

The emergence of the knowledge economy can be characterized in terms of the “increasing role of knowledge” (p.9) as a factor of production and its impact on skills, learning, organization and innovation. These, then, are the key circumstances and characteristics in the development of Globalized Knowledge Economy:

• There is an enormous increase in the codification of knowledge, which together with networks and the digitalization of information, is leading to its increasing commodification.
• Increasing codification of knowledge is leading to a shift in the balance of the stock of knowledge – leading to a relative shortage of tacit knowledge.
• Codification is promoting a shift in the organization and structure of production.
• Information and communication technologies increasingly favour the diffusion of information over re-invention, reducing the investment required for a given quantum of knowledge.
• The increasing rate of accumulation of knowledge stocks is positive for economic growth (raising the speed limit to growth). Knowledge is not necessarily exhausted in consumption.
• Codification is producing a convergence, bridging different areas of competence, reducing knowledge dispersion, and increasing the speed of turnover of the stock of knowledge.
• The innovation system and its ‘knowledge distribution power’ are critically
  important.
• The increased rate of codification and collection of information are leading to a shift in focus towards tacit (‘handling’) skills.
• Learning is increasingly central for both people and organizations.
• Learning involves both education and learning-by-doing, learning-by-using and learning-by-interacting.
• Learning organizations are increasingly networked organizations.
• Initiative, creativity, problem solving and openness to change are increasingly important skills.
• The transition to a knowledge-based system may make market failure systemic.
• A knowledge-based economy is so fundamentally different from the resource-based system of the last century that conventional economic understanding must be re-examined.

More…

_________________

References:

Sheehan, P. Tegart, G. (Eds.) (1998) Working for the Future: Technology and Employment in the Global Knowledge Economy. Victoria University Press.

–

[We] are living through a period of profound change and transformation of the shape of society and its underlying economic base … The nature of production, trade, employment and work in the coming decades will be very different from what it is today. The determinants of economic success will be different to those that were relevant in the past or those required at the present time.

Shaping our Future (p. 3)

In yesterday’s post I asserted that the knowledge worker emerged from two defining forces: the rise in complexity of economic activities, and the increasing globalization of economic affairs. At the conclusion of the piece, I suggested that the the knowledge economy has it’s roots in the Cold War: to extend the agricultural analogy, a case could be made for saying that this economy was ’seeded’ by the growth of the United States’ sphere of influence in the period 1947-1964 (the first-generation “hi-tech” economies including Japan and Taiwan, as well as in Federal Republic of Germany following the “Miracle on the Rhine” were all central to the US Government’s policy of Containment). The rapid industrialization (or re-industrialization in the case of West Germany) and growth in these regions were the perfect environment for the emergence of the nascent knowledge-working class.  

From the 1980’s, through the 1990’s and on to the early 21st century, the rise in knowledge complexity is being driven by the combined forces of the information technology revolution and the increasing pace of technological change. Globalization is being driven by national and international deregulation, and by the IT-related communications revolution. As noted by Houghton & Sheehan in A Primer on the Knowledge Economy (2000) in the last twenty years in particular, an exponential growth has occurred in the application of computing and communications technologies in all areas of business and community life. This “explosion” has been driven by substantial falls in the cost of computing and communications per unit of performance, and by the rapid development of applications relevant to the needs of users (p.5).

As is well-established by now, the Internet enabled these technologies come together, “and it is the Internet phenomenon that exemplifies the IT revolution” (p.5). In the first decade after its invention,
the Internet was an academic research network. By 1989 there were 159,000 Internet hosts worldwide. Just ten years later, there were more than 43 million (see Figure 1).

According to the Internet Systems Consortium by July 2008, that figure had increased to five hundred million Internet domains (see Figure 2).

Figure 2 Internet Domain count, July 2008

We can say that information technology as realized through the Internet has enabled the construction of a stable platform which provides the facility to manipulate, store, and transmit large quantities of data at low cost; and the pervasiveness of Internet technologies has made information technology an important channel for every component of the economy. With the low cost of managing information,  the application of knowledge to all aspects of the economy has grown, and the knowledge complexity of economic activities greatly increased.

However, as noted Working for the Future: Technology and Employment in the Global Knowledge Economy

…it is important to note that the term ‘Knowledge Economy’ refers to the overall economic structure that is emerging, not to any one, or combination of these phenomena.

More…

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References:

Forfás (the policy advisory and coordination board for industrial development, science
and technology in Ireland) (1996) Shaping our Future. [Internet] Available from: http://www.forfas.ie/publications/archive/sof/chap1.htm  [Accessed 16 August 2008]

Houghton, J. Sheehan, P. (Eds.) (2000) A Primer on the Knowledge Economy. Centre for Strategic Economic Studies. Victoria University

McDowell, A. (2006) Globalisation and the Knowledge Economy – the Case of Ireland. Forfás  [Internet] Available from: http://www.oecd.org/dataoecd/59/5/37563948.pdf [Accessed 16 August 2008]

Sheehan, P. Tegart, G. (Eds.) (1998) Working for the Future: Technology and Employment in the Global Knowledge Economy. Victoria University Press.

In his 1998 text Rise of the Knowledge Worker, James Cortada describes three approaches to understanding the emergence of knowledge workers:

1. Information and knowledge have their own history with patterns of behavior and use that are of practical concern to business, government, and private use
2. The (short) history of knowledge workers - those primarily concerned with gathering and using information or knowledge, typically as an economic activity
3. Knowledge workers in the context of knowledge management, also a relatively new topic of investigation

The author asserts that

these three sets of historical experiences teach us much about the nature of knowledge

(p.4)

and, I would suggest, about knowledge workers themselves.

It can be said that knowledge workers as a class or economic influencer emerged when

a body of related information … to be collected, applied an built on for subsequent action [emerged]

(p.14)

As the volume of information about a subject or economic activity expands, the potential for a “specialized workforce” (p.14) to work with, interpret, and manipulate that information will likely be realized. Similarly, knowledge work is often created by the introduction of some new “knowledge-handling” (p.15) technology; characteristics of this kind of work include

• An “intimate” understanding of the mechanics of the technology (how to send a fax, program a computer etc)
• An understanding of how to apply that technology to create new information or knowledge
• Expansion of the socio-cultural milieu and enhanced insight into the body of knowledge being utilized
• An understanding of how to enhance and improve the information or knowledge corpus

This process typically leads to increased complexity of of both the information and knowledge, as well as the types of work that it is applied to, which inevitably generates an increase the number of knowledge workers where complexity of economic activities expands at the same times as the type of work or the organization undertaking knowledge work expands.

I would suggest that a strong case could be made for asserting that the modern era of the knowledge worker grew directly from the development of the industrial-military complex that was created in the United States to enable the Allies successfully prosecute World War Two.

Global spheres of influence of the US and USSR at the height of the Cold War (courtesy Wikipedia)
[Click to enlarge]

Beginning with the Marshall Plan, the political and economic conditions of the succeeding Cold War provided the impetus to enable a global implementation of telecommunications and digital technology, as well as the exponential growth of transnational corporations. This created the conditions whereby huge territories in Western Europe and the Asia-Pacific region underwent technological development at an accelerated pace, as one of the strategies deployed to promote Capitalism and defeat International Communism.

More…

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References:

Cortada, J. (1998) Rise of the Knowledge Worker (Resources for the Knowledge-Based Economy). Butterworth-Heinemann

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