Decomposing technological change at the twilight of the twentieth century: evidence and lessons from the world's largest innovating firms.

We examine the evolution of each industrial group of firms with respect to the changes in the technologies produced (patents obtained) by the entire group of 463 firms. Thus, this study takes technological development in this population of

463 firms as an approximation of the relevant technological landscape. We acknowledge that the entire technological landscape extends well beyond the horizon provided by these organisations alone to include contributions by small high-tech firms, innovation consortia, public and private research institutions, universities, etc. Similarly, the variable propensity to patent across industries means that our reliance on patent data as a proxy provides, at best, a limited picture of the technological landscape. These constitute limitations of the present analysis, nevertheless, studies have shown that the correlation between inventions and patents is stronger for large firms than small firms (Acs and Audretsch, 1988) and that whilst patent protection is a limited motivation for the introduction of a commercial invention, corporations from all industries nevertheless utilise the patent system extensively for patentable inventions (Mansfield 1986). Moreover, Cohen et al. (2000) found that patents maybe relied upon more heavily by large firms in the late twentieth century than they were in the 1980s which corresponds with our period of analysis. As such we utilise patent data of the 463 firms here as a proxy for the technological developments at the industry level albeit with caution and acknowledging its shortcomings.

5. RESULTS AND DISCUSSION

Today it is well recognised that something revolutionary happened in the world economy in the last two decades of the twentieth century. Several authors describe this moment as the third industrial revolution, the information revolution (Freeman and Louca, 2001), setting the stage for an ICT paradigm (Freeman, 2007), an era of informational capitalism (Castells, 2000). In this new age it is argued that the factors for competitiveness have become more dynamic and ever more dependent on knowledge and intangibles. What matters, are those capabilities that explore new knowledge and which gather, recombine and exploit, old knowledge.

Statistical evidence of the economic significance of this revolution is usually sought in the changing industrial composition of the economy. For instance, evidence of this stylised fact can be found in the rise of ICT-based firms in the top two hundred US firms of the Fortune magazine in the 1970s and 1980s (Louca and Mendonca, 2002). However, the process of adjustment taking place within industries (or firms) themselves is a form of structural change which occurs 'under the radar' and for which evidence in the extant literature is less abundant. The following analysis tries to fill this gap.

5.1 Industry analysis

Table 2 reports the result of the SDA described earlier and orders the industries according to growth in patent share over the period 1981/85 to 1991/96. It confirms that the fastest growing industries are Computers, Photography & Photocopying and Electrical/Electronics and the slowest are mining and Petroleum, Chemicals and Materials. Strikingly every industry outside the top three, suffers falling patent growth if their existing technological profiles are held constant in the face of a changing technological environment (ST effect is negative).

Among the top three industries, the TS effect indicates that computers and photography & photocopy both would have experienced internal growth in patent shares in the absence of any change in their technological environment suggesting that their core technological areas (as indicated in Table 1) provided them with many opportunities for growth. All three of the industries with the greatest technological growth benefited from a favourable change in the technological environment (the ST effect) i.e. the environment altered to provide these industries with more opportunities for growth. We can also see from the combined GA and SA effects that computers, photography & photocopy grew in areas of technological opportunity (positive GA), whilst electrical/electronics benefited for a different reason, moving out of the technological areas offering fewer opportunities (positive SA); notably out of the more mature field of electrical devices.

At the opposite end, whilst the core technologies in the chemical industry continue to offer some opportunities for growth (TS=0.54) it suffers because the general technological environment does not favour it (negative ST). Similarly the environment does not favour Materials nor Mining & Petroleum, but they suffer also because their own internal growth is negative. All three have negative GA effects suggesting they failed to move into, or worse, moved out of the more influential technological fields of this period although Materials and Mining & Petroleum do also move out of stagnating technological fields to some degree (positive SA).

5.2 Technological field analysis

For this part of the analysis we apply the SD analysis to the technological fields in our database. It now traces how the technological fields themselves performed across industries in the face of a changing industrial structure rather than vice versa as above. In particular, we are interested in how the non-core technological fields in each industry, performed as a proportion of industrial shares of patents. However, being non-core technologies, changes in their industrial distribution can be quite small, therefore we have aggregated the 34 technologies of the SPRU dataset (based directly on the USPTO original patent classes) into 9 broader technological groupings (constructed on the basis of technological proximity) to give movements greater visibility in our findings (such aggregation procedures are often crude but can yield very interesting results, e.g. Robertson and Patel, 2007). Table 3 illustrates our aggregation of the technological fields, into broader technological groups according to technological similarity. For instance, under the ICT label we cluster technological areas that have been strongly underpinned by the advent of the microchip and that incorporate a strong digital element (for more details on this re-grouping see Mendonca, 2003).

Tables 4 and 5 apply the SD analysis to the aggregated technology groups in our database. Previous work has observed that the last two decades of the twentieth century were marked by an explosively uneven change in technological opportunity across the spectrum of patent classes (Mendonca, 2006). The last column in both Tables 4 and 5 shows how these broad technological groups grew in the total portfolio of all industries from the 1980s into the early 1990s and confirms these findings. Although with some variability over time, we observe that ICTs, New Materials and Pharmaceuticals & Biotech grew in importance over the entire period. (1) The same cannot be said about the other technologies. Moreover, given our focus on only patents registered in non-core technical fields, this strongly signals that these technologies were offering the most vibrant technological prospects for all industries, not just the sectors having the new technologies as their core technologies.

The SDA confirms that industries other than the industries in which the new technologies originated and emerged, also aggressively pursued the cluster of revolutionary new technologies. To illustrate, in the 1980s whilst the TS effect dominates, the ST effect in the technologies associated with the third technological revolution (ICT, Materials and Pharmaceutical & Biotech) also has a positive influence. In other words, even in 1981/5 to 1986/90 we see that these technologies are also being picked up by other industries outside of those with which they would be most closely associated. These technologies extend their reach beyond the boundaries of their industrial origin. Furthermore, with the exception of Pharmaceuticals & Biotech, this influence grows stronger in the later period (Table 4) where the ST effects are greater in magnitude than the earlier period. This phenomenon shows the pervasiveness of the technologies of the third technological revolution across industrial boundaries from their industries of origin to the industries of use (Scherer, 1982).

6. CONCLUSIONS

This paper focused on how new technologies are modifying the profile of technological competencies of industries as represented by large US, European and Japanese manufacturing firms. Inter-sectoral structural change is commonly acknowledged as an important phenomenon in face of technological shifts, as industries rise and fall in terms of relative dynamism. But we still do not understand many things about the internal aspects, namely the intra-sectoral dimensions, of technological evolution. By using a structural decomposition approach, we have made an attempt to reveal some of the dynamics of endogenous knowledge diversification when technologies are of uneven attractiveness and when some technologies (ICT, Pharmaceuticals and Biotech, new Materials) offer more opportunities for creative accumulation than others by being combined with pre-existing competences.