production of knowledge_d.foray_chapter3 -ebrubasak

The Economics of Knowledge

Chapter 3: “Production of Knowledge”

by Dominique Foray

Ebru BAŞAK [email protected] 646612

2

•  Research: A “Distance” Activity of Production and Consumption

•  Different Types of Research? •  Why Is R&D Important? •  The Diffusion of Science-‐Based Research •  Research Collaboration •  Increasing Returns in the Production of

Knowledge

Production of Knowledge-‐1

Production of Knowledge-‐2

3

•  Learning-‐by-‐Doing •  The Main Economic Issue •  Learning as Experimentation during Production •  Users at the Heart of Knowledge Production •  Maximizing Learning Potential •  Transition toward the Knowledge Economy •  Coordination Model of Knowledge Production •  Collaboration in Knowledge Production

Production of Knowledge

4

Crea%ng Genera%ng

Producing

Knowledge is produced in different ways that can be defined in terms of a dual

dichotomy.

5

Off-‐line

•  through formal R&D work off-‐line (ie isolated and sheltered from regular production of goods and services)

On-‐line •  through learning, on-‐line, where individuals learn-‐by-‐doing, (not isolated, individuals can assess what they learn and share practices)

Production of Knowledge

Four Forms of Knowledge Production

6

R&D

Formal integration

Off-‐line process of knowledge creation

Learning-‐by-‐doing

Informal Integration

On-‐line process of knowledge creation

Table 3.1

Search Model

Coordination Model

It is useful to create a second dichotomy between two types of knowledge genera;ng ac;vi;es

7

search model

•  knowledge generation may involve search processes within domains that are relatively unexplored or underexploited. This is the search model of knowledge generation.

coordination model

•  the processes of increasing complexity in industrial architectures involve somewhat different needs for the systems of knowledge generation. There is a need to produce “integrative knowledge,” such as norms, standards, and common platforms. These processes comprise a coordination model of knowledge generation.

Key Concepts

8

Research: Concept that when knowledge is produced through search processes, in some kind of organized and formal way Research and Development (R&D): is used for intellectual creation undertaken systematically for the purpose of increasing the stock of knowledge

Research: A “Distance” Activity of Production and Consumption

9

Research Centers, universities and R&D Labs are the main institutions that have the explicit aim of creating knowledge The main characteristic of these activities is their situation “at a certain distance” from places of production and consumption. This distance, which can at once be spatial, temporal, and institutional, is needed to nurture the talent of:

“philosophers or men of speculation, whose trade it is not to do anything, but to observe everything; and who, upon that account, are often capable of combining the powers of the most distant and dissimilar objects. In the progress of society, philosophy or speculation becomes like every other employment, the principal or sole trade and occupation of a particular class of citizens.” (Adam Smith, Wealth of Nations)

Research: A “Distance” Activity of Production and Consumption

10

This notion of distance is essential because It enables us to distinguish researchers from other producers of knowledge. The distance can be large or small—research is far from industry or close to it—and even if it is a source of problems, it has to exist to allow for the division of labor and the development of research related occupations (Mowery 1990; Nelson and Wright 1992). In 20th century, R&D labs to companies à upsurge of specific occupations and skills. Even if the share of research in the stock of intangible capital necessarily remained small, R&D activity has been a mainstay of national innovation systems since the beginning of the 20th century.

Different Types of Research

11

Basic (Fundamental) Research

Applied Research

Production of Infratechnology

12

Basic (Fundamental) Research

Basic or fundamental research aims at producing basic knowledge that allows for a fundamental understanding of the laws of nature or society. This first category is like surveying: it generates maps, that is, informational outputs, that raise the return to further investment in exploration and exploitation

13

Applied Research

Applied research and development aims at producing knowledge that facilitates the resolution of practical problems. This second category deals with the practical implementation of basic knowledge that gives rise to applied product and process technologies.

14

Production of Infratechnology

There is finally a particular class of activity which is functionally different from the first two but is difficult to identify and measure. This category concerns the production of infratechnology, meaning sets of methods, scientific and engineering databases, models, and measurement and quality standards that support and coordinate the investigation of fundamental physical properties of matter and the practical implementation of basic knowledge (Tassey 1992).

Different Types of Research

15

These categories are defined in terms of the extent of their exploratory nature and their distance from commercial application. This categorization not seem to correspond to the reality of certain sectors in which basic research seems closely related to the market (e.g., the pharmaceutical and biotechnology sectors).

Different Types of Research?

16

The dis%nc%on between these two types of basic research is important because it prepares people’s minds for analyzing situa;ons in which basic research is close to the market.

Why Is R&D Important?

17

In the process of knowledge production the notion of “distance” activity makes R&D an important functionality: 1.  Economic aspect: meaning that R&D cannot be subjected to the same kind of cost-‐effective and just-‐in-‐time managerial approach as the regular activity of goods and service production. 2. Cognitive aspect means that the distance between the laboratory and the real world makes it possible to undertake experiments while using the lab to control some aspects of the reality.

Economic Aspect

18

The main motivation of explicit R&D activities is the production of knowledge. An entrepreneur or a policy maker who is launching a R&D program is perfectly aware that this activity is fraught with many uncertainties. Given this uncertainty, research activities cannot be managed and outputs evaluated in the same way as in the regular production of goods and services. This creates a sort of “isolated or protected world” for R&D which is less dependent on cost effectiveness and timely delivery of outputs than are other economic activities. Even a failure in R&D can be viewed as a useful informational output. Particularly when it happens at the basic research stage, the failure contributes to a better “map” of cognitive opportunities.

Economic Aspect

19

“Sheltered” when managerial decisions taken under some kind of economic constraint Following economic and management analysis of the Japanese-‐type firm (Aoki 1988), efforts were made to bring the R&D function closer to product development. The aim was to subject processes of knowledge production to the immediate needs of the market. The decline of corporate R&D laboratories, the relocation of research structures and budgets within operating divisions, and the creation of internal markets for research were all trends toward a stronger dependence of research on market needs, emphasis on shorter-‐term objectives, and introduction of more cost effective research techniques and practices. .

Economic Aspect

20

But there is a basic confusion between the idea that research is “endogenous” because it is constrained, influenced, and oriented by the economy and society (Rosenberg 1982), and the incorrect idea that all distance must be reduced. By eliminating all distance it seems that one loses the capacity, peculiar to research, to trigger radical changes by conceiving major innovations that will create tomorrow’s markets.

Cognitive Aspect

21

Explicit R&D activity is also important because it makes it possible (in most cases) to conceive and carry out well-‐defined and controlled experimental probes of possible ways to improve technological performance and to get relatively sharp and quick feedback on the results (Nelson 1999). Well-‐defined and controlled experimental probes require isolation of the technology from its surroundings. Experimentation often uses simplified versions (models) of the object and environment to be tested. Using a model in experimentation is a way of controlling some aspects of reality that would affect the experiment, in order to simplify analysis of the results. The ability to perform exploratory activities that would not otherwise be possible in real life is a key factor supporting rapid knowledge advances.

The Diffusion of Science-Based Research

22

Scientifically Based Research: research that is guided and informed by a science which has reached the predictive stage. Only science in the predictive stage provides results, which are usable immediately to advance technological knowledge (Kline and Rosenberg 1986) Some industrial sectors have used for a long time scientific approaches to create knowledge (electricity, chemicals) (Rosenberg 1992). Yet most major technological breakthroughs were not directly based on science. It has been the slow expansion of the model of science illuminating technology that has spawned innovation in sectors where scientific research rarely or never resulted in innovation.


23

A scientific approach contributes to innovation in three different ways

Provides a more systema;c and effec;ve base for discovery and innova;on.

Allows for beIer control (quality, impact, regula;on) of the new products and processes introduced.

May be at the origin of en;rely new products or processes.


24

These 3 scientific approaches seem to conquer new ground all the time, even those sectors that appear a priori to resist them. Drug discovery is a good example of a domain that has recently been characterized by a shift from a random approach through large-‐scale screening toward a more science-‐guided approach relying on knowledge of the biological basis of a disease to frame a research strategy.


25

“Double-‐blinding method” in the health and pharmaceu%cal sector, This method, reduces the risk that wishful thinking or other poten;al biases may influence the outcome. Randomized controlled trial or randomized field trial is the kind of scien%fic method offering a large poten%al to generate scien%fic knowledge and robust evidences on a broad range of topics in various fields of social and educa%onal research (Fitz-‐Gibbon 2001)


26

The cons;tu;on of scien;fic knowledge bases directly useful to innova;on, in most sectors. The idea is not to rehabilitate the old linear, so-‐called “science push” innova%on model, but to grasp the structure of knowledge systems characterizing areas with the biggest advances in knowledge and know-‐how.

Research Development

Production Marketing


27

The connection of scientific research to innovation has two distinct forms: First, scientific knowledge production upstream from industrial sectors allows more effective innovative research that escapes from empiricism. •  For example, knowledge of the properties of transition of certain

materials renews innovation in the adhesive sector. Second, the appearance within the firm itself of scientific investigation tools. •  For example, in the automotive sector fast and inexpensive

simulations enable massive and repid experimentation required for developing complex safety devices. (Thomke 2001, 75).


28

These different developments all point to the idea that any research problem warrants an effort at collecting scientific data, and that appropriate forms of experimentation are necessary and most often possible. As shown by K. Smith (2000), one of the features of the knowledge economy is that many industries are now firmly based on complex scientific knowledge.

Research Collaboration

29

Rationales of collabration: ü Sharing research costs ü Avoiding duplicative projects ü Creating larger pools of knowledge ü Which in turn generate greater variances from which

more promising avenues of research can be selected ü The economic gains to be generated from division of

labor in research activities. Those rationales still apply for the collaboration developed in the domain of basic research.

Research Collaboration

30

Example, Genom project contortia Taking advantage from different teams’ specializations and to combine them Consortia are set up to put together a large enough collection of samples in order to produce knowledge of a better quality, or knowledge which could not be obtained otherwise.

Increasing Returns in the Production of Knowledge

31

Various Forms of Complementarity Many forms of complementarity between elements of knowledge are at the base of knowledge production. These complementarities have been studied extensively in the fields of technological knowledge (Maunoury 1972; Gille 1978) and scientific knowledge (David, Mowery, and Steinmueller 1992; Rosenberg 1992). Everywhere, transfers, transpositions, and new combinations allow knowledge to advance.


32

Increasing Returns: This no;on of complementarity in knowledge produc;on is a way of saying that I do not share the argument that exploita%on of a knowledge field is governed by the law of decreasing returns that applies in the world of exhaus%ble resources. In terms of that law, the more one invents the less there remains to invent, so that it is necessary to devote more resources to obtain a result at best equivalent to past achievements.


33

34


There are limits to the effect of complementarities and indivisibilities; knowledge production can enter into a zone of decreasing returns.

Everything depends on the ar;cula;on and balance between pure basic research and applied research and between public-‐sector and private sector research. 34

Basic Research (Creation of generic

knowledge)

Shift in the productive function

Trigger

Decreasing returns Push

Constant increase in research

activity, without moving too

deeply into the domain of decreasing returns

Learning-by-doing

35

•  Learning-‐by-‐doing as a “joint” activity related to both production and use

•  Learning-‐by-‐doing is a form that is related to manufacturing (and/or utilization)

•  It leads many kinds of productivity improvements

•  Productive experiences (the accumulation of doing)

•  Improvement of productive performance

Learning-by-doing

36

•  “The unit cost of manufactured good production tends to decline significantly as more are produced”

•  This phenomenon was first observed in the aircraft industry

•  This systematic relationship is shown by Arrow’s theory of endogenous technical change

•  Productive performance is increased by productive experience and improvements

•  Learning-‐by-‐doing should not be confused with incremental innovation

Learning-by-doing

37

•  Learning-‐by-‐doing generates only technological or organizational increments

•  Most incremental innovations are not produced only through learning-‐by doing mechanism

•  Idea of learning as a joint activity has been effectively developed by Arrow who said: “

«The motivation for engaging in the activity is the physical output, but there is an additional gain which may be relatively small in information yet which reduces the cost of further production.»

Learning as experimentation doing production

38

•  Horndhall effect: based on repetition and incremental development of expertise

•  By repeating a task, one becomes more effective in executing that task.

•  Another level of learning is “explicitly cognitive” it consist of online experiments.

•  This learning is based on an experimental concept, where data is collected so that the best strategy or better design for the future.

An example for online experiments

39

Adam Smith mentions a little boy who repeatedly opens and closes the valve between a boiler and a cylinder, and who thus discovers a device enabling the valve to open and close automatically: “One of the greatest improvements that has been made upon this machine, since it was first invented, was in this manner the discovery of a boy who wanted to save his own labour.”

Repeated Action Experimental Learning

More effective way of doing

Learning as experimentation doing production

40

The importance of experimental learning depends on the nature of the activity: •  There are high-‐risk activities in which the agents have

to limit their experiments •  since they could carry out their “normal performance”

that has to be achieved. Airline pilots or surgeons cannot learn in this way

•  By contrast, a teacher can carry out educational experiments

•  A craftsman can look for new solutions to a particular problem during the production process.

Users at Knowledge Production

41

Nathan Rosenberg emphasizes learning-‐by-‐doing related to the use of a product or process: •  using generates problems; problem-‐solving capacities are opened and learning occurs.

•  Faced with new and unexpected local situations, users have to solve problems, thus in a position to teach and inform those who design systems.

Learning-by-using

42

Learning-‐by-‐using process has two aspects Final users learn how to use the product This learning process can be extremely important when use of the product involves complex tasks, including maintenance, operating procedures, and optimal control. Final users learn about the performance characteristics of the product which are highly uncertain before the product has been used for a long period. This improved understanding of the relationship between specific design and performance. The feedback loops in the development stage leading to some kind of optimal design after many repetition are crucial. In this case, when learning-‐by-‐using results in design modification, Rosenberg uses the notion of embodied knowledge.

Learning-by-using

43

In innovation activities q the user will be motivated to find a solution that will fit exactly

with his or her specific needs and circumstances. There is a subjective case at modify process.

q Users in a very broad sense acquire a certain kind of knowledge that is particular to a specific site and/or usage. This is the case for the user of a machine tool or a medical instrument and for the “user” of a valley or beach

q the impact of “sticky” knowledge when knowledge is costly to transfer (e.g., knowledge about some particular circumstances of the user), the position of problem solving activity can shift from supplier to user.

Maximizing learning potential

44

First, in a “doing” context, maximizing the learning benefit requires the addition of instrumentation in order to take advantage of observational opportunities on the production line. Second, organizational design matters. Extreme technical specialization is of course damaging to cognitive learning.

Ø Practice-‐based learning environment. (Taylorist and Fordist divisions of labor)

Ø The design of fault-‐tolerant organizations, thanks to fault-‐tolerant organizational designs, errors and failures are not result in totally blocking the system. (more robust, less dependent)

Third, it is important to create special incentive structures and organizational forms,

Ø to support learners and encourage them to reveal new knowledge (acquired by doing)

Ø to create documents and thus generate knowledge objects, and to memorize and share that knowledge.

The Coordination Model of Knowledge Production

45

•  Complex coordination problems produce “integrative knowledge” •  Norms, standards, infratechnologies, common product development

platforms •  integrative knowledge: –  compatibility, interoperability, interconnectivity between subsystems –  exploitation external network •  Collaboration in Knowledge production, cannot be explained only by

economics of R&D •  The traditional solution relied on vertical integration •  changing outsourcing and supply management •  requiring strong coordination mechnanisms

Innovation in Knowledge Economy

46

There are three models for innovation in knowledge economy: •  The first is related to scientific nature of research methods

•  Secondly, users’ engagement which is based on knowledge production is vital to understand role of it

•  Thirdly, increasing complexity and modularity of industry creates integrative knowledge

47 Table 3.3

Three Critical Models of Innovation

Model 1 Model 2 Model 3

Innovative Opportunities

Scientific developments

User needs and capabilities

Problems raised by integration in complex technological systems

Critical Relations, Crucial Organizations

Universities-‐industries, startups

User-‐producers User communities

Architect and module designers, strategic and standardization consortia

production of knowledge_d.foray_chapter3 -ebrubasak

Technology