BRAVE OLD WORD: ACCOUNTING FOR “HIGH TECH” …static.gest.unipd.it/~birolo/didattica11/Materiale... · Brave old word: Accounting for “high tech” knowledge in “low-tech”

Paper to be presented at the DRUID Summer Conference 2004 on

INDUSTRIAL DYNAMICS, INNOVATION AND DEVELOPMENT

Elsinore, Denmark, June 14-16, 2004

BRAVE OLD WORD:

ACCOUNTING FOR “HIGH TECH” KNOWLEDGE IN “LOW-TECH” INDUSTRIES

Sandro Mendonça

Dinâmia and ISCTE University

[email protected] Nick von Tunzelmann

SPRU, University of Sussex [email protected]

May 28, 2004

JEL classification: C81; L20; O31 Keywords: innovation; patents; technological diversification; traditional industries.

S. Mendonça and N. von Tunzelmann – Brave Old World – Version 1.0 – 2004

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Brave old word: Accounting for “high tech” knowledge in “low-tech” industries

Sandro Mendonça

Dinâmia and ISCTE University [email protected]

Nick von Tunzelmann

SPRU, University of Sussex [email protected]

Version 1.0

THIS IS A (VERY) PRELIMINARY VERSION. UNRIVISED. PLEASE DO NOT CITE.

ALL COMMENTS WELLCOME.

JEL classification: C81; L20; O31 Keywords: innovation; patents; technological diversification; traditional industries.

1. Introduction

Innovation and the investment in new technologies are not necessarily strange to large

established companies specialised in traditional businesses. “Low-tech” industries

remain, however, a rather unprivileged research topic in the economics of technical

change notwithstanding the demand of analysis and practical insights by managers and

policy-makers. This paper appreciates “old economy” corporations as evolving bundles

of heterogeneous technologies and assesses the dynamics of technological

diversification in traditional industries towards new disruptive technologies such as

information and communication technologies (ICT) and biotechnology.

The phenomenon of technological diversification emerged as a major feature of

corporate capitalism in the late twentieth century. It has been shown in a seminal

contribution by Teece et al. (1994) that firms exhibit a significant degree of coherence

in their productive activities. However, corporate coherence at the technology portfolio


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level is much less pronounced that at the product level. Indeed, while product

diversification came to be favoured by corporate boards during the 1960s and 1970s it

later on fell out of fashion being surpassed by technological diversification. Product and

technology diversification became two negatively related phenomena in the 1980s and

1990s. Granstrand and Sjölander (1990, p.36) first defined the “multi-technology

corporation” (MTC) as a “corporation that operates in at least three different

technologies”. Scholars that today speak of the MTC have provided evidence from large

data sets and company case studies to show that dispersion of corporate capabilities

across wide and heterogeneous bodies of knowledge constitutes a key characteristic of

the large innovating firm (Cantwell et al., 1994). Thus, manufacturing corporations

came to manage a more diverse of technologies than lines of business (Brusoni et al.,

2001). The activity of technological exploration of a multiplicity of different cognitive

fields tends to be done incrementally and in a path-dependent way, as a consequence of

bounded rationality and industry-specific trajectories of knowledge accumulation

(Antonelli, 2000; Patel and Pavitt, 1997; Brechi, Lissoni and Malerba, 2004). At least

for some large electronic and chemical firms, the development of new technological

capabilities in scientific fields (ICT and biotechnology, respectively) in the

neighbourhood of the ones is correlated with good performance when disciplined with a

greater business focus (Gambardella and Torrisi, 1998; Suzuki and Kodama, 2004).

The question that occupies this paper is the degree to which less-high-tech industries

embarked in this general trend, i.e., we want to assess and attempt to explain their

degree of technological incoherence. More precisely, this paper explicitly considers the

extent, direction and rate of technological diversification of large firms operating in a

variety of sectors and seeks to offer an empirical-based account of the position occupied

by the more “low-tech” sectors within this complex and evolving phenomenon. The

research into long established companies belonging to “old economy” sectors will be

critical to test the nature of the overall process of technological diversification that

characterises the development of corporate global players. A key motivation for this

work is to partially fill the gap in the technological diversification literature concerning

the ways in which traditional industries have reflected the general tendency towards the

accumulation of multi-technology capabilities. We find that the dynamics of

diversification has not been a simple process of slow localised learning into

neighbouring technological fields. We find that the trajectory of evolution of large


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corporations has been attracted by the new generic technologies of ICT and

biotechnology, depending of their principal product category, with new materials

technology behaving as a backdrop technology.

This paper proceeds in section 2 by presenting the data and the approach adopted to

make sense of the empirical information. Section 3 constitutes the empirical part of this

paper. In this section we cluster industries into different groups in terms of

technological profiles in the beginning of the 1980s, i.e., the configuration of different

technologies and their relative importance in the industries’ patent portfolios. Our

research then proceeds to track the movement towards new generic technologies (ICT,

biotechnology and new materials) by these clusters of similar industries. We find that

traditional industries accompanied the movement towards of technological

diversification exhibited by high-tech sectors. However, traditional industries do not

behave all a like. For example, industries such as Food and Drink & Tobacco also have

to combine a wide number of technologies spanning their classic competencies in order

to develop their products. The direction of technological diversification is quite

heterogeneous: the Metals industry being attracted by ICT and the Food, Drink &

Tobacco by drugs and biotechnology even if these industries do not specialise in the

semiconductors or the pharmaceuticals markets. It is generally acknowledged that

twentieth century corporate life cannot be understood without considering economies of

scale and scope in large multi-product firms (Chandler, 1990). In section 4 of this paper

we conclude that economists and managers will increasingly need to pay attention to the

causes and dynamics of technological incoherence in order to predict and favour the

development of twenty-first century innovating business organisations. This is no less

true in the case of “lower-tech” industries. This section also raises final comments

emphasising policy implications and questions that remain under-researched.

2. Data and methodology

2.1 The data

This paper uses the SPRU database, a privileged empirical material for the purposes at

stake. The data-set contains all patents obtained by the 463 world’s largest companies at

the US Patent and Trademark Office (USPTO) from 1980 to 1996. More specifically,


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the population is made up of the largest US, European and Japanese companies as

ranked by sales revenues according to the Disclosure Global WorldScope database.

Each company was consolidated so that on the whole the database integrates about 4500

subsidiaries and divisions appearing with different assignee names that were owned by

the parent company as of 1992. Companies are classified according to principal product

group and patents come already assigned to 15 industries. Thus, we cannot perform

intra-industry analysis or analyse the profiles of individual companies. Each individual

patent is assigned to one of 34 individual technological fields based on information

provided by USPTO (see Table 1). The database contains a total of 274 904 patents

accumulated for 15 industries for three sub-periods: 1980-85, 1986-90, and 1991-96. As

we shall see this temporal window of 17 years constituted was a period of transition.

Table 1. The SPRU patent classes 1. Inorganic Chemicals 2. Organic Chemicals 3. Agricultural Chemicals 4. Chemical Processes 5. Hydrocarbons, mineral oils, fuels and igniting devices 6. Bleaching Dyeing and Disinfecting 7. Drugs and Bioengineering 8. Plastic and rubber products 9. Materials (including glass and ceramics) 10. Food and Tobacco (processes and products) 11. Metallurgical and Metal Treatment processes 12. Apparatus for chemicals, food, glass, etc. 13. General Non-electrical Industrial Equipment 14. General Electrical Industrial Apparatus 15. Non-electrical specialised industrial equipment 16. Metallurgical and metal working equipment 17. Assembling and material handling apparatus

18. Induced Nuclear Reactions: systems and elements 19. Power Plants 20. Road vehicles and engines 21. Other transport equipment (excluding aircraft) 22. Aircraft 23. Mining and wells machinery and processes 24. Telecommunications 25. Semiconductors 26. Electrical devices and systems 27. Calculators, computers, and other office equipment 28. Image and sound equipment 29. Photography and photocopy 30. Instruments and controls 31. Miscellaneous metal products 32. Textile, clothing, leather, wood products 33. Dentistry and Surgery 34. Other - (Ammunitions and weapons, etc.)

2.2 Patent as a proxy of technical competence

Along with a large number of authors we will take technology to mean a collection of

engineering knowledge more or less based in scientific disciplinary principles and

learning by doing. As it is difficult to measure corporate knowledge structures directly

we will use patents as an indicator of firm’s expertise in certain technological fields.

Since this intellectual property right is awarded following standards of newness,

originality and industrial applicability it can be assumed that patents indicate that

corporate in-house capabilities are at, or close to, the technological frontier at that field.

This approach will lead us to scrutinise the paths of diversification of knowledge

resources pursued by large established companies operating in given industries.


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This methodological avenue imposed by the nature of our database cannot be asserted

with qualification. The weaknesses of patents as indicators of technological activity are

well known and will not be discussed in detail here although much can be learnt from

many contributions on the subject (Grilliches, 1990; Pavitt, 1988). Patents are an

institutional record of invention and, unfortunately, cannot be assumed to be in direct

and continuous correspondence to innovative efforts. There are, for instance, different

inter-firm propensities to patent across technologies and across industries and these can

change with time (Sherer, 1981). Recent research has, however, reinforce our belief that

patent statistics are a reliable resource for the objectives of the present study. Namely,

Cohen, Nelson, Walsh (2000) in a survey questionnaire administered to 1478 R&D labs

have found that patents have become a more central protection mechanism for large US

manufacturing companies in the 1990s.

2.3 Data analysis

The main objective of the data analysis will be to understand how and why industries

and patent classes correlate among themselves. Given that we have 34 patent classes it

will be easier to compare the XX industries by using a few composite indexes. With this

purposes in mind we will use a multivariate approach that captures latent technological

dimensions for the different sectors. Multidimensional scalling (MDS) is a multivariate

method that deals with a reasonably large number of observations (patents) made on

each object (industry) simultaneously. MDS addresses the problem of variable

interdependence by positioning objects in a space. With MDS, our main goal will be to

group industries according to the structure and evolution of their technological

portfolios. One important point is that the approach will allow the analysis of the

relationships among different patent classes within each sector under analysis. In this

way we will be able to identify one or more technological dimensions that differentiate

industries and compare industries’ position with respect to these dimensions. The output

of MDS analysis is the location of the industries on the dimension(s) and will be termed

a technological map for the purposes of this paper. Distances between objects represent

technological dissimilarities. In synthesis, the MDS is an exploratory technique that

structuring data in such a way that new information about objects is created.


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In order surpass some of the limitation of the database and to gain extra leverage in data

analysis in our analysis we proceed to do two further reorganisations of the data. In

what concerns technologies, we re-categorise the 34 patent classes into 9 technology

families (see table 2). This aggregation procedure follows technological similarity

criteria allows us to build separate data-sets providing complementary information. The

new technological families are constructed to represent families of associated

technologies. The U.S. patent classification system clusters patents according to

function, effect and end product. However, there are grounds to believe, following

Narin and Olivastro (1988), that it is possible to construct reasonable (re-)classifications

according to criteria of technological similarity. For instance, under the ICT label we

regroup technological areas that have been strongly influenced by the advent of the

microchip in 1972 and incorporate a strong digital element. The main advantage of this

re-classification exercise is to provide new empirical patterns and understand more

profoundly the results emerging form the raw patent data.

Table 2. Technology families

Chemicals Fine Chem Drugs & Biotech Materials Mechanical Transport ICT Instruments & Electrical

Other

InOrChem OrgCh Drugs and bioeng Materials NonElMach VehiEngi Telecoms Instruments Medical

AgrCh ChePro SpecMach OthTran Semicond Photog&C MiscMetProdHydroc MetalWEq Aircraft Computers ElectrDevi Metallu Pro Bleach AssHandApp Image&Sou ElEqup Nuclear Plastic Mining PowerP

ChemApp Food&T TextWoodetc Other

As for industries we assort the 15 industries originally identified in the database into the

four OECD technology-intensive sectors: High-technology, Medium-High-technology,

Medium-Low-technology and Low-technology industries, henceforth H-tech, MH-tech,

ML-tech and L-tech, respectively (see appendix 1). The OECD uses the business

expenditures on research and development (known as BERD) over production to

classify industries into the four-tier typology as in table 3. A high R&D intensity for an

industry, the ratio of research and development expenditures to production or value

added, is often believed to indicate commitment to knowledge creation in new

technologies. On the other hand lower-tech are held to identify traditional activities. The

R&D intensive criterion, however, has many pitfalls as a technological indicator (Smith,

2004). Here we will take this conventional classification of sectors because it is


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generally known among academics, popular among government policy making and,

finally, because it is a goal of the current research to assess whether there a clear link

between “lower tech industries” (or “old economy sectors”) and old (or traditional)

technologies.

Table 3. OECD technology-intensive sector classification

High-tech sector (#4)

Medium-High-tech sector (#4)

Medium-Low-tech sector (#4)

Low-tech sector (#3)

Aerospace Electrical/Electronics Mining & Petroleum Paper Pharmaceuticals Motor Vehicles and parts Rubber & Plastics Food, Computers Chemicals Materials Drink & Tobacco Photography and photocopy

Machinery Metals

In a nutshell, our goal is to do appreciative economic analysis with the help of concepts,

indicator and classification schemes presented above.

3. The nurture of diversified technical knowledge

3.1 Contemporary corporations as technological melting pots

Technological diversification refers to the range of technical knowledge of collection of

firms in an industry in terms of patent classes in a specific time period. From period to

period the degree of breath or spread of technological capabilities of a group of firm

may increase or decrease, disperse or concentrate in given technical fields as defined by

patent classes. In a recent restatement of the issue, Cantwell and others (2004) observe

that MTCs is a persistent empirical regularity of innovating organisations since the first

industrial revolution, moreover they even precede the rise of multi-national

corporations.

The corporate technology mix constitutes, therefore, the basic research focus of this

paper. Recent research on the organisation of technical change at the corporate level has

consistently is a persistent phenomenon in a wide variety of industries. However, what

this trend meant to traditional industries remains largely unexplored in the literature. In

this paper we will be especially concerned with evidence of technological incoherence

stemming from lower-tech sectors. Are lower-tech sectors a technologically passive set

of industries or are they increasingly changing their technological portfolios to


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encompass the new technologies of the third industrial revolution? When the breath of

technical knowledge of a company specialised in a traditional productive activity spans

over the core technologies that defined the industry, then the technological

diversification trend can be characterised as a major source of change in the

contemporary corporate world.

3.2 How different are technologically diversified industries?

Technological competencies are path-dependent as they accumulate long technological

trajectories that draw on scientific knowledge and learning arising from manufacturing

experience. The implication is that the structure of technological portfolios,

operationalised here as the distribution of patent by technological field, that constitutes

an industry’s knowledge base is rather stable and substantially different accross

different sectors (Patel and Pavitt, 1997).

This stylised fact is evident from figure 1. The MDS analysis combined industries

technological attributes into two dimensions for the three periods1. Figure 1. Technological map of the 15 industries

1 ASCALL algorithm based on individual differences (weighted) Euclidean distance model. Control parameters are rather satisfactory: Stress = 25.9%, RSQ =69.6% Best practice suggests that to be a slightly poor fit (Johnson and Wichern, 2002, p. 701). Overall importance of each dimension: (horizontal) 0.4837, (vertical) 0.2126.

Source: Elaborations from the SPRU database

-

0

1

2

aerospacechemicals

computer

drink &

electrical

foo

machinery

materials

metal

petroleum

Motor vehicles pape

pharmaceuticals

photography

rubbe


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The interpretation of the resulting dimensions is not always easy and is a task that takes

place outside the MDS technique. Additional information must be introduced to

facilitate the extraction of meaning from the dimensions and understand why objects are

located in their relative positions. The MDS output positions industries according to the

similarity of their technological portfolios, i.e., breath and depth of their patenting

performance: the first quadrant concentrates industries in which fine chemistry and bio-

chemistry plays an important role; the second quadrant shows industries more

dependent of petro-chemical and bulk chemistry; in the third quadrant one can see

industries in which the core technological capabilities were in mechanical technology;

finally, in the fourth quadrant are locate industries active in electronics and digital

technologies.

Across time the industries relative locations in the technological map are relatively

stable. Technological profiles tend to be industry-specific. The only clear change

happens with the Paper industry, which leaves the third quadrant and moves to the

second quadrant. Thus, it still makes sense to talk about different industries.

Figure 2 shows the similar analysis, a summary of the three periods made on the basis

of the aggregation of all technologies for the four OECD technology-intensive sectors.2

The position of objects on the horizontal axis indicates a “new economy”-“old

economy” dimension. What the vertical axis represents is less obvious. The vertical

dimension shows, nonetheless, that High-tech and Low-tech industries are somehow

similar. In fact, one who adheres to the conventional view would expect that High-tech

and Low-tech sectors should oppose each other, but that does not happen. Instead, the

Low-tech sector appears to oppose the Medium-Low-tech sector. Why?

2 ASCALL algorithm based on individual differences (weighted) Euclidean distance model. Control parameters are rather satisfactory: Stress = 9.9%, RSQ = 0.97287. Best practice suggests that to be a good fit (Johnson and Wichern, 2002, p. 701). Overall importance of each dimension: (horizontal) 0.4983, (vertical) 0.4745.


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Figure 2. Technological map of the four OECD technology-intensive sectors


3.3. How distinctive are traditional industries in terms of technological diversification?

That the distances between sectors exists and is persistent across time, does not mean

however that sector are not affected by changes in their patenting structures. Figure 3

and figure 4 show a one-dimensional MDS computation for the four OECD sectors in

which the used variables are the new technologies of ICT, Drugs & Biotech and new

Materials. The MDS outputs show reliable adjustment parameters (1981-85: Stress =

18.2%, RSQ = 87,6%; 1991-96: Stress = 33,2%, RSQ = 83,8%) and give a clear notion

of change taking place behind the scenes.

The Low-tech sector distanced itself from the Medium-Low-tech sector. Moreover, the

Low-tech sector surpassed the Medium-High-tech sector in terms of patenting in new

technologies having get very close to the High-tech sector in the last period of our data-

set. Low-tech sectors are increasingly taking aboard and adding to emerging

technologies, those developing fields in science and engineering such as ICT,

biotechnologies, new materials, etc. The observed trend is indicative that “new

economy” technologies are increasingly impacting on the knowledge bases of the large

established companies of the “old economy”.

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

-1

0

1

2

H-tech

MH-tech

ML-tech

L-tech


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Figure 3. One-dimensional technological map of the four OECD technology-intensive sectors, 1981-85 (composite variable: new technologies)


Figure 4. One-dimensional technological map of the four OECD technology-intensive sectors, 1991-96 (composite variable: new technologies)


-1.0 -0.5 0.0 0.5 1.0

-1

0

1

2

HTec

MHTec

MLTec

LTec

-1.0 -0.5 0.0 0.5 1.

-1

0

1

2

HTec

MHTec

MLTec

LTec


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The once unproblematic connection between emerging technological fields and high-

tech or “new economy” sectors is appears to be increasingly under strain. What we learn

is that a realistic perspective on innovation has to include a more complex relationship

between “technologies” and “industries”.

One way to assess the nature of the transformation taking place among the so-called

Low-tech industries is to employ a measure of technological diversification. For that we

will use the Hirshmann-Herfindhal index (HHI), a measure that is obtained by

calculating the sum of the squares of the shares of all the technologies for each sector. A

lower HHI indicates that companies or industries are spreading their patents across a

broader set of fields or, in other words, it reveals that the agents command cutting-edge

knowledge in more technologies. A higher HHI shows a concentration of the

technological portfolio in fewer science and engineering fields. By calculating the HHI

of the OECD sectors on the basis of the nine technology families we find that, indeed,

the Low-tech sector has been consistently opening up its knowledge base during the 16

years of the period under analysis. For this sector the HHI decreased systematically for

the whole period while the Medium-Low-tech sector started by exhibiting a pattern of

concentration and to reverse the trend in the early nineties. Overall, the lower-tech

sectors countered the generalised tendency for technological coherence lead by the

High-tech sectors as they concentrated in the newly developing technologies.

Table 4. Evolution of technological concentration IHH (81-85) (86-90) (86-90) (91-96)

H-tech + + MH-tech + + ML-tech + - L-tech - -

Al sectors + + Source: Elaborations from the SPRU database

3.4. What is causing technological incoherence among “lower-tech” industries?

The High-tech sector leads the process of patent growth during the eighties and early

nineties as can be observed in figures 5, 6 and 8.3 Within this set of industries it is the

rise in ICT patents more than any other technology that represents the most dramatic


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change: H-tech sectors are increasingly active in ICT. The MH-tech industries show a

more distributed patenting growth process, but, nevertheless, also ICT-driven.

In terms of the lower-tech sectors the performance of the ML-tech sectors is the most

dismaying. For instance, comparing 1981-85 to 1991-96 the L-tech sectors experienced

a growth in patents twice as high. The patenting performance of the L-tech sector was

indeed quite robust, and up the pace of the MH-tech sector. While the ML-tech sector

accompanied the higher-tech sectors in developing ICT capabilities, the behaviour of

the companies belonging to the L-tech sector was quite different. The L-tech sector was

consistent in developing capabilities in Fine Chemicals throughout the period, while in

the eighties new Materials constituted the key priority and in the transition to the

nineties knowledge in Drugs & Biotech rose fast in importance followed by ICT. Table 5. Variation rates in patenting from the sub-period 1981-85 to 1985-90

Var. rates (81-85) (85-90) H-tech MH-tech ML-tech L-tech Grand Total Chemicals 19% 6% -12% 27% 1% Fine Chemicals -8% 7% -1% 29% 3% Drugs & Biotech 26% 38% 21% 17% 30% Materials 92% 46% 15% 104% 47% Mechanical 15% 27% 9% 28% 22% Transport 3% 22% 6% 60% 20% ICT 82% 63% 51% -12% 69% Inst. & Elect. 36% 33% 10% 67% 32% Other 46% 45% 22% 23% 39% 40% 32% 5% 31% 30%


Table 6. Variation rates in patenting from the sub-period 1985-90 to 1990-96

Var. rates (85-90) (91-96) H-tech MH-tech ML-tech L-tech Grand Total Chemicals 49% 22% 7% 19% 20% Fine Chemicals 48% 41% 25% 49% 39% Drugs & Biotech 50% 40% 32% 129% 47% Materials 46% 49% 10% 18% 37% Mechanical 44% 14% -7% 20% 15% Transport 39% 19% 8% -13% 20% ICT 99% 54% 50% 40% 70% Inst. & Elect. 58% 22% 3% -10% 32% Other 55% 16% -5% 18% 21% 68% 32% 10% 28% 39%


3 Figures in bold emphasise technological fields in which growth was above average forma giver sector.


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In conclusion, ICT, Drugs & Biotech were the most dynamic fields in the years leading

to the twenty-first century, and the L-tech industries was not disconnected from this

shift in technical knowledge. The overall patenting performance by the L-tech sector

has been clearly above the ML-tech sectors and very close to the MH-tech sector. Large

firms in L-tech industries are becoming “smarter” in Fine Chemicals, Drugs & Biotech

and new Materials, fields in which they contribute to the technological frontier.

Table 7. Variation rates in patenting from the sub-period 1981-85 to 1991-96

Var. rates (81-85) (91-96) H-tech MH-tech ML-tech L-tech Grand Total Chemicals 77% 29% -5% 52% 21% Fine Chemicals 36% 51% 24% 92% 43% Drugs & Biotech 89% 94% 59% 169% 92% Materials 181% 118% 26% 141% 101% Mechanical 66% 45% 2% 53% 40% Transport 43% 45% 15% 40% 44% ICT 262% 150% 126% 23% 187% Inst. & Elect. 116% 62% 14% 51% 75% Other 127% 68% 16% 46% 69% 135% 75% 15% 68% 81%


4. Discussion of the results

4.1 The dynamics of technological incoherence

Technological evolution within large established manufacturing firms is expected to be

path-dependent and based on a relatively slow accumulation of capabilities in the

vicinity of previously known technological (Patel and Pavitt 1997). The persistence of

stable and industry-specific technological profiles is to be explained by a pattern of

localised search that strongly constrains the directions of knowledge diversification. The

corollary is the predominance of creative accumulation over creative destruction, in the

sense new capabilities do not replace older ones but rather link with existing capabilities

in a complementary and enhancing fashion (Granstrand, 1998; Pavitt, 1998).

Recent research is complicating this picture of smooth and incremental change.

Empirical studies drawing from the Reading University patent database (Fai and von


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Tunzelmann, 2001a) confirm that industry-specific competencies have remained quite

distinct throughout the last century. However, there are increasing signs of convergence

in the direction of growing technologies across industries. In this paper we provide

complementary evidence. First, changes in corporate technological profiles are related

to the advent of new opportunities created by the new emerging technologies. Second,

Lower-tech industries should not be taken has being excluded of this movement or

being dragged by major technological developments taking place outside their typical

domains of expertise, i.e., those fields of knowledge most related to their manufacturing

activity. Low and Medium-Low-tech sectors are increasingly diversifying into the “new

economy” technologies and have been active players in their development.

Lower-tech industries were among the pioneers of the multi-divisional mode of

organising the production and distribution of a continuous flow of branded, package

goods (Chandler, 1990). Indeed, these sectors have a tradition of dynamism. For

instance, companies from food, beverages and tobacco sector constituted 18% and 15%

of total entries in the 200 largest manufacturing firms in 1930 and 1948, respectively

(Louçã and Mendonça, 2002, pg. 834). Innovation in Low-tech industries should,

therefore, not be seen as a contradiction in terms. Possibilities for learning and

renovation exist, especially in the wake the new opportunities emerging with ICT and

biotechnology.

Interpreted in the context of the diffusion of new technological paradigms these findings

suggest that the multi-technology phenomenon might be a piece of a broader (dynamic)

puzzle. The tendency among large companies from all industries to patent in ICT, it was

found that patenting in ICT, biotechnology and new materials technology in non-

specialist sectors clearly intensified after 1980 as compared to other technologies

(Mendonça, 2003). In spite of the stability in the structure of patent portfolios,

something dramatic changed in the large corporation’s knowledge base in the last two

decades of the last century. An implication is that technology diversification can be

related to the concept of industrial revolutions and that the large multi-technology

company is one organisational expression of that phenomenon.


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4.2. The organisation of knowledge and organisational knowledge

Among the contemporary business organisations the main economic activities are

changing from physical processes to information processing. Knowing what should be

known and knowing how to increase and managing knowledge are of the essence for the

survival and success of companies involved in the world markets. The MTC

phenomenon constitutes as aspect in which “Brains dominate hands” as a source of

sustained competitive advantage.

Technological diversification gives a notion of the extent to which companies have

become pressured to develop absorptive capacities and technological sense-making.

Today, technical information cannot be characterised as a scarce resource anymore. On

the contrary, the real challenge is to select and interpret all the data available. This

implies that companies must interpret the emergence new possibilities of invention in

real time, they must create surveillance mechanisms in order to be aware of the

technologies they should absorb, and they must continually network in order to keep in

touch in the customers and partners needs and ambitions. And this means that

sophisticated players (e.g. systems integrators) must develop social capabilities along

with their technological competencies. Part of these social capabilities is the ability to

interact with and coordinate an evolving collection of network partners. Large multi-

technology corporations increasingly emerge as orchestrators of specialised actors that

perform different intellectual task in the vertically disintegrated innovation process

leading to ever more complex products.

As a consolidating economic institution of capitalism it is not yet clear how MTCs

affects the dynamics of industries and the competitiveness of the territories in which

they operate. It is therefore important to realise what are the key challenges posed by

technological diversification to the social technology of governance mechanisms

(Nelson and Sampat, 2000).

5. Conclusions

4.1 Summary of the results

Corporate knowledge systems are evolving and this trend is widespread “new economy”

and “old economy” sectors. Large companies active in Low-tech sectors are


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increasingly blending new technological capabilities with old ones. The rising

technological incoherence exhibited by Lower-tech industries is related to the increasing

importance of the emerging science and engineering fields of ICT, biotechnology and

smart materials. In terms of patenting in these developing bodies of knowledge, the

Low-tech sector has been show to approximate the High-tech sector and even to

challenge the dynamism of the Medium-High-tech sector. The High-tech/Low-tech

classification scheme, therefore, is increasingly under strain; the life cycle of its

applicability is coming to an end. The interdependence between both the “old” and

“new economy” sectors and new technologies has developed rapidly at the end of the

past century.

4.2 Implications for policy and future research

The substance of corporate technological knowledge matters both for economists and

for managers. There has recently been a revival of interest in how institutions influence

economic performance. We would argue that, the MTC, as new for knowledge

organisation should connect with this debate.

Industrial economists concerned with predicting the way in which large incumbent

corporations interact with competitors, suppliers, universities and knowledge brokers

should monitor the uneven the rate and direction of change of the various components

of their technological portfolios. In particular, economists and business researchers

involved in studying sectoral systems of innovation (Malerba, 2004) and evaluating the

properties of markets for technology (Arora and Fusfuri, 2003) are among those whose

work is more related to the stylised facts emphasised in this paper. Our results show that

technological diversification is a powerful force governing the on-going reorganisation

of innovative capabilities in high-tech as well as lower-tech industries. What large firms

know and the capabilities they will use to create new products and processes will impact

on market circumstances and exert a major influence on the broader networks that

enable innovation in the industry.

If knowledge means power in technological-based relationships the implications of this

to competition policy remains poorly understood. What this developing form of


18

industrial organisation means constitutes a challenge for competition policy analysis in

the context of dynamic competition (Audretsch et al., 2001).

Noting the relevance of technological diversification for both new and old economy

sectors is also relevant to technology and regional policy as it broadens the framework

economists use to identify factors that support innovation. National policies should not

mistake ‘high technologies’ with ‘high-technology industries’. One implication for

intermediate economies specialised in traditional sectors is that policy-makers should

not isolate lower-tech industries from mainstream science and technology policy. All

lower-tech industries contain some segments or activities which are or can be high-tech

based. Developing the ‘high-tech component’ of lower-tech industries should be more

sustainable than overloading in ‘high-tech industries’ and may thus be a stronger

commitment to using high-tech (and medium-tech) in intermediate and developing

countries.

Business strategy in Lower-tech sectors cannot afford to nurture an innovation

management component. Lower-tech industries must sustain their inventing as well

absorptive capacities at the same time as they development the networking capabilities

needed to manage internal and external divisions of labour. Prudent strategising should

be about retaining market strengths in areas of traditional (static) comparative

advantage, by dynamically deploying high technologies

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Appendix 1 OECD Sectors Average R&D Intensity

High-technology Industries

BERD/Production > 5%

Aircraft and spacecraft Aerospace Pharmaceuticals Pharmaceuticals Office, accounting and computing machinery

Computers

Radio, television and communications equipment

Medical, precision and optical instruments

Photography and Photocopy

Medium-high-technology industries

3% < BERD/Production < 5%

Electrical machinery and apparatus

Electrical/Electronics

Motor vehicles and trailers Motor Vehicles and parts Chemicals Chemicals Railroad and transport equpt. n.e.c.

Machinery and equpt n.e.c. Machinery Medium-low-technology industries

0.9% < BERD/Production < 3%

Coke, refined petroleum products and nuclear fuel

Mining & Petroleum

Rubber and plastic products

Rubber & Plastics

Other non-metallic mineral products

Materials

Building and repairing of ships and boats

Basic metals Fabricated metals products Metals Low-technology industries

0% < BERD/Production < 0.9%

Manufacturing n.e.c. and recycling

Wood, pulp, paper, paper products, printing and publishing

Paper

Food products, beverages and tobacco

Food, Drink & Tobacco

Textiles, textile products, leather and footwear

Documents

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