Attempting a definition
The OECD (Organisation for Economic Cooperation and Development) defines a knowledge-based economy as the “production, utilisation and distribution of knowledge in all activities”.
We can interpret it as intellectual capital or simply, knowledge capital or assets as the key source of value. Intellectual assets, rightly used, produced and distributed, will then be the key source of wealth creation.
From this simple definition, there is a myriad of things that we can do to build knowledge assets in the economy and use them to create value or even wealth.
Two key questions arise:
1. How do we build intellectual assets/capital?

2. How do we determine value, and translate it into wealth?
Knowledge originates from data and information. Data is numbers, words or sounds. Its value is determined by how it is processed, stored, or otherwise manipulated. When arranged into meaningful patterns, it creates information. Data and information are stock. The utilisation, distribution and reproduction of data and information are the dynamic processes that add further value. Knowledge is created by learning and applying one’s skill in using information, and building for a purpose.
Learning is the process that adds further value to knowledge. The more the knowledge flows, the more value is created. Knowledge, with further learning, coupled with experience and spiritual development, may create wisdom.

So, knowledge assets are built through continuous learning. To learn, we have to go back to the fundamentals, such as conducting research and having some social interaction to “test” research findings so as to add more value. The easiest way is by reading and the fastest way to understand what you learn is by discussing and writing what you have read. This is when you begin to formulate your own ideas.
An individual who continues to learn can make all the difference (be a change agent) to a society or organisation. He or she can add or create value qualitatively and quantitatively. If everyone in organisation did this, knowledge accumulation would be exponential, not only due to collective knowledge and learning, but also because of optimal complementarity. For example, if I have knowledge on K-economy, and you have knowledge on K-society, and we exchange ideas with the aim of helping the country to achieve both, that is a form of optimal complementarity.
When we refer to the learning-thinking-writing-thinking processes, we are referring to mental processes. We are touching on cognitive science, explaining how the human mind works. Focussing on the cognitive abilities of an individual gives rise to a new pedagogy known as constructivism, which emphasises a learner’s metacognitive skills. Smartness is the result of meaningful knowledge construction, which happens only in a conducive learning environment.
All this answers the first question, so that now, we can generalise and raise the discussion to a higher level, that is, knowledge. Further, rich and conducive learning environments embody qualitative characteristics which provide the chance for breakthroughs. They are the means to increasing returns in an increasingly complex global environment. We shall deal with this later, in a more advanced discussion.
Traditionally, when “labour” is put to perform a task, there is output. If we are not satisfied with the output, we give “labour” capital to buy tools so he can increase output. If still we are dissatisfied, we train or educate him to work more efficiently. If this fails, we give him technology and knowledge. He now becomes “a knowledge worker” who can now do more things in less time, hence reaping benefits (not just output) and lowering costs — which is the traditional “efficiency and effectiveness” argument in economics.
The diagram shows how we can shift the P-work to the K-work through the traditional factors of production, but with the addition of skills and knowledge.
The bell curve shows the skill spectrum, from low to high skill. The distribution of the population is somewhere between the low and the high skill, with most of it bunched at the low skill. It is reasonable to think in terms of value-adding skills while in transition to the K-economy. Continuous learning enables one to step up one category. For example, most of the skill population in agriculture do not have the ability to jump one step from P-agriculture to K-agriculture. It may be easier to move from P-agriculture to P-factory. The biggest challenge is, then, how to shift P-agriculture to at least the lower ends of K-agriculture.
The diagram depicts the most obvious sources of value-add (as well as value creation) that is skill and knowledge. Combining some of the factors which constitute value (as discussed in the second article of this series) and today’s analysis, we look at a formulation of the term “value”, and hence, wealth creation:

With the above questions answered, we can start building a K-economy according to our own mould, and find niche areas that we can focus development on.

In the Malaysian context, lessons of the past tell us not to depend on one fixed internationally-prescribed growth model and no one growth model can sustain successful development.
Key to this aspect is the over-dependence of information-gathering based on past data for current or future course of action. While historical data has some value, the complete value of an analysis is its ability to postulate future conditions (foresights) based on certain assumptions (some assumptions are obvious).
Evolving knowledge and learning are, thus, key to successful sustenance of value, adding value or creating value in the entire economy.
An analysis of the past shows that:

1. The manufacturing and services sectors have been pushed to increase productivity and value-add. However, the recent trend indicates that the manufacturing sector loses its competitiveness prior to an economic downturn, as evidenced by declining growth rates. The “star” performer, the electronics and electrical sector (Graph 1), loses competitiveness in terms of wages per worker and value-add when seen in the global context. The wages per worker in Malaysia is US$4,000 compared with its global counterpart of US$20,000 on average. The value-add for the industry for Malaysia is US$15,000 compared with its global counterpart of US$100,000 on average. The services sector’s contribution to growth domestic product (GDP) has been very small and performance on the current account balance has been negative in history if taken in aggregation, although very few sub-sectors are performing well. That is to say, the external performance has always been “eroding foreign exchange” (Graphs 2 and 3). In value terms or in terms of contribution to GDP, it is far below those of newly-developing economies;
   
2. The aim of the Second Industrial Masterplan is to move towards high technology with capital-intensive manufacturing — hence, materialising to the extent that more capital invested adds more value, but not to the extent of creating more value or an exponential increase in it, because the right kind of skills are just not available. Human capital is still the “lagging” factor and there is a mismatch of skills. Our concentration of human capital is not in the highly-skilled, nor the very low-skilled category. It is suggested that the kinds of goods produced and exported need re-examining because we are now competing with countries having either very cheap labour or highly-skilled human resources. The key question remains as to how to move resources to where they need to be — capital-intensive and high-tech manufacturing;
   
3. Gaps in skills persist and the skew is still towards the production of traditional arts and social sciences;
   
4. Extensive information on each services sub-sector is not easily available; and
   
5. All sectors of the economy are distinctive stand-alones and hardly interlinked... hence, lacking collaboration;
   this contributes to multi-level intermediaries and wasted resources.