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Chapter 3 The Determinants of Innovation

3.7 Firm Type

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It may well be the case that no single firm size is uniquely conducive to technological progress (Scherer, 1980, p. 418). In this case innovative output may be maximized by a diversity of sizes, each with its own special advantages and disadvantages. A particular focus of this paper is to investigate the effect of enterprise size since on innovative output in the New Zealand biotech sector hence we test the following hypothesis.

Hypothesis 5

Innovation output and innovation rate increase with enterprise size and the number of ideas workers.

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source and direction. For example “innovating firms principally in electronics and chemicals, are relatively big, and they develop innovations over a wide range of specific product groups within their principal sector, but relatively few outside.

Firms principally in mechanical and instrument engineering are relatively small and specialised”. Pavitt classified these characteristics and variations using a three part taxonomy of firms as supplier dominated, production intensive or science based. Similarly it may be argued that particular types of firms may be more innovative than others because “firms have different capabilities and perceptions that lead them to pursue different sets of approaches to innovation” (Cohen &

Klepper, 1992, p. 2).

Another strand of the economics literature focuses on Dedicated Biotechnology Firms (DBFs) that can be viewed as a special type of science-based firm. DBFs are totally dedicated to biotech knowledge production with much interest being centred on the relative importance of DBFs and incumbents (for whom biotech activity may be a small percentage of total activity) and the linkages between them (McKelvey, 2001). On the other hand diversified firms may be better able to produce and market unanticipated inventions:

research, and especially basic research is a venture into the world of uncertainty, yielding inventions and discoveries in unexpected areas. The company with interests in a diversity of fields generally will be able to produce and market a higher proportion of these unanticipated inventions than will an enterprise whose product line is narrow (Nelson, 1959).

Firms that conduct R&D are expected to have a higher innovative output than those that do not, although it is important to note that innovation can also result from other activities such as imitation, and technology adoption (Tether, 2001, p.


The reason why there is so much interest in modern biotechnology is because of the idea that it may be a generic technology of the kind that has so deeply transformed industrial economies in the past; by implication firms that are involved in modern biotechnology are expected to have a higher innovative output than those using traditional processes. The empirical section of this thesis (chapter 6) addresses the importance of specialisation in biotech as well as whether firms

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conduct R&D or not and whether they are involved in modern or traditional biotechnology.

Hypothesis 6

Innovation output and innovation rate vary with firm or organisational type.9



When new knowledge is transmitted easily from creators to competitors and particularly when it is embodied in new products or processes that can be copied at relatively low cost, appropriable rewards may be insufficient to justify innovative effort. This idea that the free market may not provide sufficient incentive for inventive activity has been around for hundreds of years and gave rise to the Statute of Monopolies passed by the English Parliament in 1623 (Cohen & Levin, 1989, p. 1091). Arrow (1962b, p. 619) provides a detailed examination of the issue concluding that:

a free enterprise economy will under invest in invention and research (as compared with an ideal) because it is risky, because the product can be appropriated only to a limited extent, and because of increasing returns in use. This under investment will be greater for more basic research.

Further, to the extent that a firm succeeds in engrossing the economic value of its inventive activity, there will be an under utilization of that information compared with an ideal allocation.

In theory patents provide a solution to the problem of imperfect appropriability.

Society grants exclusive rights to inventors thus ensuring that society benefits from technological change by providing incentives to invest in R&D. However, even when patents protect the inventor from imitation, the patent holder’s monopoly profit is less than the full social benefit (unless the patent holder can price discriminate). Thus although patents may encourage additional research, they may induce less than the optimal level (Carlton & Perloff, 2000, p. 511).

9 Firm type refers to several different dimensions:- industry sector or technological field (e.g.

modern or traditional biotechnology), involved in R&D or not, specialised versus more general, high or low absorptive capacity (Scherer, 1980, p. 432) etc.

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In practice a strong body of evidence suggests that patents are perceived to be ineffective in most industries and are not the most common method by which firms appropriate R&D returns.

In many industries, however, firms find other means of appropriation to be quite satisfactory. In some instances, imitation is costly despite the absence of strong patent protection. In others, investment in complementary assets such as marketing, sales efforts and customer service can facilitate appropriation when neither strong patent protection nor technical barriers to imitation are present (Cohen & Levin, 1989, p.


Hypothesis 7

R&D spending (and hence innovation)10 increases as the degree of appropriability of R&D outputs increases (and more R&D spending leads to more innovation).


Spillovers, Clustering and Alliances

Technological change is driven in part by knowledge spillovers, where new knowledge produced by individuals, firms and research organisations becomes available for use by others. Knowledge spillovers are a form of externality in that the knowledge producer does not obtain any direct benefit for outward flows of knowledge that are not paid for. They provide theoretical support for many of the empirical findings on the benefits of clusters and alliances. The basic proposition that the quantity and quality of interaction between organisations is an important determinant of innovation finds support in several strands of the literature; most notably in the literatures on externalities, knowledge spillovers, clusters and Systems of Innovation (SI).

The idea that externalities can be a source of increasing returns and productivity growth has a long history dating back to Edgeworth11 and before (Griliches, 1992, p. S34). More recently Griliches (1958) highlighted the issue of knowledge spillovers in his study of the social rate of return to research into hybrid corn;

10 at the level of the enterprise or individual

11 Francis Edgeworth (1845-1926) held the Chair of Political Economy at Oxford University from 1891-1926.

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while Scherer (1982b) constructed a detailed matrix showing how new technology flowed through the U.S. economy from the firms carrying out R&D in 1974 to businesses, government agencies and consumers, see Error! Reference source not found.

A further resurgence of interest in spillovers was set off by Romer’s (1990) model of endogenous technical change12. This led Scherer (1999, p. 35) to comment that inspired by Romer “economists began looking under every lamp-post for empirical evidence of knowledge spillover effects”. Good evidence of substantial spillovers has been found, both between firms and between industries, leading to productivity gains and even to changes in the structure of production. There is also evidence that spillovers have an important effect on innovative activity itself.

These effects vary according to R&D intensity in both firms and industries (Baptista, 1998, p. 37).

The literature on spillovers overlaps with the recent upsurge in research into clustering, although the idea that businesses can receive major benefits from being located in what are now called clusters is not at all new. The great British economist Alfred Marshall wrote in 1920 that:

When an industry has chosen a locality for itself it is likely to stay there long: so great are the advantages which people following the same skilled

12 driven in part by knowledge spillovers Source: Scherer (1982b)

Figure 3.2 Technology Flows in the U.S. Economy 1974

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trade get from neighbourhood to one another … And presently subsidiary trade grows up in the neighbourhood (Marshall, 1920, p. 271-2).

Since then the arguments underpinning the location of production and innovation activities have been developed in several different streams of literature (urban and regional economics, geography, history, evolutionary economics and new growth theory); see Baptista (1998, p. 14).

Stern et al. (2000, p. 8) provide a useful summary of some of the main benefits of clusters. They note that innovation rate (in a cluster) is increased by the presence of related industries (and increases with the degree and strength of interconnection since the presence of related industries generates positive externalities both from knowledge spillovers and cluster-level scale economies, especially when clusters are concentrated geographically13. Andersen (1992, p. 90) summarising some of the findings of a research programme conducted using SI approaches concludes that “production and linkage patterns are important determinants of learning and innovation”. Later he pointed out the need to identify the consequences of different sets of linkage pattern:

What are the properties of the connecting flows… Does interaction change the way organizations search for new knowledge, does it lead them to abandon some lines in favour of others? Does it lead to measurable changes in R&D programmes? Does it lead to explicit forms of co-ordination in the generation of new knowledge (Andersen, Metcalfe,

& Tether, 1999, p. 36).

Arora and Gambardella (1990) reporting on the results of an early empirical investigation into the use of external linkages by large biotech firms found that large firms “were no longer the sole focus of innovative activity. The locus of innovation should be thought of as a ‘network’ of inter-organizational relations”.

Since then biotechnology alliances have been the subject of intense investigation (Deeds & Hill, 1996; Mytelka, 1999; Orsenigo, Pammolli, & Riccaboni, 2001;

Prevezer & Toker, 1996; van Geenhuizen, 1999).

13 Stern describes three other determinants of innovation rates in a cluster; the number of trained scientists and engineers, and whether R&D personnel are specialised in disciplines congruent with emerging opportunities; the extent of local rivalry and openness to international competition; and the numbers of sophisticated, quality sensitive local customers. Henderson (1986) and Porter (1998) provide other lists of the benefits of clusters.

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Andersen (1992, p. 85) suggests that “intranational relationships are normally better than international relationships as a means of transferring semi-formal and informal information” and that “the creation of new channels of the innovative type is easiest between members of a national production system”, based partly on his investigations into Scandinavian socio-economic evolution. However international linkages are of particular significance for a small geographically isolated country such as New Zealand. For example, recent research on networks in biotechnology found that “while the importance of physical distance has been decreasing, the importance of communication links to the centre have been increasing … fewer and fewer inventors exist in isolation from research centres as connections drive more and more research activity” (Johnson & Mareva, 2002, p.

28). Furthermore, recent work has shown that international (not domestic) technology diffusion is the major source of technical change leading to productivity growth in OECD countries (Keller, 2001).

The extent of the literature on spillovers, clustering and alliances is reflected in the effort put into testing these effects. Analysis reported in chapter 6 tests the importance of interactions between organisations and specifically investigates the relative importance of international and domestic linkages, with a variety of indicators being used to try and describe quality of interaction.

Hypotheses 8 and 8a

Innovation output and innovation rate increase with the quantity and quality of interaction between organisations making up the innovation system.

International linkages have a stronger positive effect than domestic linkages.