Industrial dynamics and innovation: progress and challenges Franco Malerba CESPRI - Bocconi University Via Sarfatti 25 - 20136 Milan Italy [email protected]

Presidential Address delivered at the 32nd Conference of the European Association for Research in Industrial Economics (EARIE) Porto, 1-4 September 2005

ABSTRACT Within the growing field of industrial dynamics, the analysis of innovation has witnessed major progress in several areas. Contributions at the empirical and modelling levels have greater advanced our understanding of innovation within industrial dynamics. The main point of the paper is that in order to have a deeper and clearer view of the relationship between industrial dynamics and innovation, research has to progress on three fronts: the analysis of demand, knowledge and networks.

I wish to thank Stefano Breschi, Alfonso Gambardella, Francesco Lissoni and Lorenzo Zirulia for useful comments to an earlier draft.

1. Industrial dynamics as a growing research field

Since the late 1970s industrial dynamics has emerged as a major area of inquiry in industrial economics. The analysis of birth, growth and decline of firms and industries and the factors affecting them has generated a very rich empirical and theoretical literature. And most of these contributions have recognised the central role of innovation for firms and industries.

In this Address I will start by recognising the major recent growth of research in the area of industrial dynamics and then concentrate on the major progresses in the analysis of the relationship between industrial dynamics and innovation and on the challenges that lie ahead. In the first part of the paper I will discuss the progress, while in the second I will focus on three big research topics that I think require in-depth research scrutiny: demand, knowledge and networks.

In the discussion I will not focus on the literature of transaction costs or on strategic interaction, but on the contributions regarding learning, the dynamics of technology and market structure and industry evolution. I will place a specific emphasis on a longitudinal perspective, in that it allows to focus on sequences of events, changes and feedbacks in industrial dynamics. This perspective is very important not only for understanding industrial dynamics, but also for the analysis of the broader evolution of markets, as clearly pointed out by the discussion and papers in Geroski-Mata (2001).

2. Industrial dynamics and innovation

Since the late 1970s, industrial dynamics has emerged as a major research area for industrial economists. Pioneering work by Paul Geroski and David Audretsch at the empirical level and by Bojan Jovanovic, Richard Nelson and Sidney Winter at the theoretical level have focussed the attention of researchers on the way industries change over time and on the dynamics processes of entry, selection and growth of firms within industries.

Within the growing interest in industrial dynamics, innovation has been recognized as a key element affecting the dynamics of industries and the rate of entry, survival and growth of firms.

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Looking back at the last 25 years, one has to recognise that on this front progress has been indeed impressive at both the empirical and the theoretical levels.

2.1 The empirical contributions

At the empirical level, since the late 1970s the so-called “SPRU tradition” has greatly contributed to our appreciative understanding of the role of innovation in the evolution of industries, and it has shown that the relationship between innovation and industrial change is multidimensional, involves several actors and differs greatly across industries (Pavitt,1984, Freeman-Soete,1997, Dosi,1988).

Innovation in industries has been found to be the result of the interaction of different actors (firms, universities, public agencies, financial organizations…) which are related both formally as well informally and have actions strongly influenced by their competences, learning processes, the knowledge base of sectors and institutions. In this frame, the notion of sectoral systems of innovation (Malerba,2004) has provided to be a useful tool for examining innovation in a sector.

Industries have been shown to follow life cycles of innovation, firms entry and growth and changes in market structure (Abernathy-Utterback,1978; Utterback,1994). It has also been convincingly found that that these dynamic sequences are different from one industry to another (Klepper,1997, Geroski,2003).

In addition, with the availability of advanced computer technology and new firm level data, econometric analyses have moved from cross sections work during the 1960s and 1970s to longitudinal analyses of industrial dynamics and innovation since the early 1990s. In general, great progress has been obtained in identifying, measuring and understanding stylized facts and statistical regularities and the factors explaining them: intersectoral diversity in firm size distribution; fattailedness of corporate growth rates across industries; heterogeneous firm-specific autocorrelation profiles with each industry; persistence of profitability, labor productivity and TFP differences across firms and across plants within industries at all level of disaggregation. It has been shown that market selection does not seem to work effectively particularly in the short and medium-run, the time span of the available statistics. Concerning then innovation more specifically, some other robust stylized facts and regularities have been identified:

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-High within- industry heterogeneity in innovativeness Heterogeneity of firms innovativeness has been shown to be quite pervasive and persistent over time in spite of competition and selection processes. There is now a very broad set of contributions in the industrial dynamics literature that forcefully point to the role of heterogeneity. In most industries there are few firms which are responsible for a large number of innovation, and there is a core and a fringe of innovators. But there is more than that: heterogeneity concerns also the core and the fringe of innovators. This is not just a question of aggregation. As Griliches and Mairesse (1997) have clearly stated: “The observed variability-heterogeneity does not really decline as we cut our data finer and finer.”

Heterogeneity across firms in innovation means the presence of

idiosyncratic capabilities (absorptive, technological, etc.) and implies that firms not only do different things but, and most importantly, when they do the same thing, they know how to do it in different ways.

This heterogeneity is closely associated to persistence in innovative activity, which is a key phenomenon that affect the patterns of innovative activities in a sector (Malerba-OrsenigoPeretto,1997, Cefis,2003). Some of this heterogeneity is related to entry. It has been found that the entry process is driven by several factors (Geroski,1995, Bartelsman et al., 2005) and that there is a high rate of entry after a technological discontinuity. In particular, new entrants are the vehicles for the introduction of new technologies, as Geroski (1995), Audretsch (1995) and Baldwin (1995) among others have shown. In any case, entry differs very much also with respect to the type of innovation and speed of technological change.

Heterogeneity in innovativeness is then translated into differential profitability, as documented by Geroski-Machin-Van Reenen (1993), while the impact of innovation on corporate growth is still a matter for deep empirical scrutiny, with a less straightforward relationship for firm level data than for plant level data (Baldwin,1995). Actually in some specific industries, such the international pharmaceutical industry, econometric evidence shows that dynamics is driven by the introduction of few major innovations, which create new markets, and by imitation. In this industry innovative firms do not grow more than the other firms. Rather there is a coexistence of quite heterogeneous innovators (Bottazzi et al.,2001)

- Major inter-sectoral differences in the rate of technical change, market structure and organization of innovative activity

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It has been shown that sectors differ greatly not only in terms of rate of innovation, but also of market structure and organization of innovative activities. Work at the patent level has found stylized and robust differences across sectors. In some in sectors innovative activities are concentrated in a few firms, stability of innovators is relevant and new innovators are rare. In other sectors patterns of innovation are distributed across a wide population of firms, with a high turbulence in innovative activity, and new innovators coming from every quarter. These two different models of organization of innovative activities, which could be labelled Schumpeter Mark II and Schumpeter Mark I, have been found in several industries and are quite robust for the same industry across countries (Malerba-Orsenigo,1995, 1997). These findings can be related to the old stylized fact concerning major inter-industry differences in concentration (see Schmalensee,1989) and to the literature that has identified robust inter-industry differences in firms age and size distribution and the characteristics of innovations.

- Universities, venture capital and other non-firm organizations as well as institutions as major contributors to technological change in industries There is now convincing evidence that firms are not the only major actors in the innovation process. Rather, technological change is the result of the contribution of different actors, that may however have different relevance in different industries. For example, in several industries the contributions of universities is quite relevant in the generation and transmission of technological progress. This is because universities generate new knowledge that could be a major input to innovation, train human capital that form the backbone of the R-D laboratories of firms, sometimes patent in certain technologies and often are a source of new firms in specific sectors (such as in biotechnology or electronics, see for example Rosenberg-Nelson,1993, Zucker-Darby-Brewer,1998, MoweryNelson-Sampat-Ziedonis,2004). In some industries venture capital affects innovation, although additional empirical evidence and hard econometric analysis are needed for some conclusive statements (Kortum-Lerner,2000). Finally, the role of different institutions - some of them national, other sectoral - has been recognized to be relevant for innovation and diffusion in industries (see for example Gruber-Verboven,2001 for mobile communications). This is especially true for standards and regulations. Here a rich literature has shown that standards enable innovation in industries by creating an infrastructure that allow sequences of innovations and the achievement of a critical mass in markets for new technologies.

2.2 The theoretical contributions 5

Also at the modelling level one can find different strands of research focussing on different aspects of the relationship between industrial dynamics and innovation.

Technological learning by rational actors (be incumbents or entrants or both) and the competitive process weeding out the heterogeneity in firms population characterise a set of models that aim to explain empirical regularities such as the asymmetric distribution of firm size and different growth rates conditional on age (see for example, Jovanovic,1982). Here there is passive learning and new firms do not know their own potential profitability. Major technological discontinuities create a shake out in industrial dynamics because a radical invention opens up the possibility of an increase in the efficient scale of production and in entry. Thus the transition to the a new technology is associated to the exit of unsuccessful innovators and the survival of firms with larger scale technology (Jovanovic-MacDonald,1994). On the contrary active learning by firms in industrial dynamics is present in Ericson-Pakes (1995) where firms explore the economic environment, invest and, if successful, grow, so that industrial dynamics is driven by the growth of successful firms.

Similar stylised facts are explained by evolutionary models with learning processes and bounded rational actors à la Nelson-Winter (1982). These models however are also able to take into account processes of experimentation and imperfect trial and error. Here selection processes take place on a heterogeneous population of firms (Nelson-Winter,1982; Dosi-Marsili-Orsenigo-Salvatore,1995). These models have a destrategising conjecture, in that differences in structures and processes of change are understood as independent from firms’ micro strategies (Winter-Kaniovski-Dosi,2000).

Another set of models focuses on technological and demand related factors, which make bounds on industrial structures effective via no arbitrage conditions (Sutton,1998). This results in corresponding Nash equilibrium on industry specific entry processes. Here however no attention is paid to the learning processes of firms, and less attention is also paid to industrial dynamics per se.

Another stream of models examines industry life cycle, analyzing together product and process innovations; rate and type of entrants; selection; firm size and growth; market concentration and market niches and shake outs (Klepper,1996 and 2002 and Klepper-Simons,2000 and 2005). In this group of models one can find a strong link between stylized facts, econometric analyses and formal theory, as well as an explanation of different types of industry life cycles.

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Finally, more attention to the specificities and histories of various industries is paid by “history friendly models”, which fall into the evolutionary tradition (Malerba-Nelson-Orsenigo-Winter,1999 and 2001, Malerba-Orsenigo,2002). They pay attention to the evidence and the dynamics of specific industries, intend to develop a dialogue with appreciative/qualitative/historical explanations and aim to model the sequence of events that have shaped a specific industry evolution.

In sum, tremendous progress in the emerging field of industrial dynamics has been obtained in both the empirical studies of innovation in industries and the modelling of industrial dynamics and innovation.

3.

Which research challenges for a deeper understanding of the relationship between

industrial dynamics and innovations?

The studies examined so far focussed on technological change, the dynamics of incumbents as well as new firms and changes in market structure. Technology, firms and market structure are indeed key elements in the relationship between industrial dynamics and innovation. But let me push the research questions further by taking some examples drawn from the evolution of specific industries. And let me show that industrial dynamics and innovation are greatly affected by a set of other factors: demand, the knowledge base of industries and networks.

One could just start by noticing that in several industries demand has been a major factor affecting industrial dynamics and innovation. In semiconductors and computers, public demand such as military procurement has been important for innovation in the early stages of the industries (Malerba,1985). In computers experimental customers have been major actors in the emergent phase of the industry (Bresnahan-Malerba,1999; Bresnahan-Greestein,2001). In information technology users’ involvement has been key for the development and modification of standards. In pharmaceuticals, demand channelled through agencies, physicians and the health system have played a significant role in the diffusion of new drugs. In instrumentation (Von Hippel,1988) or in machine tools lead users have played a major role in innovation and in the dynamics of both the supplier and the user industries.

Similarly, the knowledge at the base of firms’ innovative activities and networks have played a major role in innovation and the dynamics of several industries. For example, in telecommunication 7

equipment and services a convergence of different technologies, demand and industries has taken place, with processes of knowledge integration. This convergence has been associated with the creation of a wide variety of different specialised and integrated actors, ranging from large equipment producers to new service firms. In machine tools the evolution of the industry has been shaped by an application-specific knowledge base and has been characterised by extensive firms specialization. Here user-producer interaction, local networks of innovators and in-house experienced human capital are key factors for innovation. In pharmaceuticals and biotechnology, a wide variety of science and engineering fields are playing an important role in renewing the search space of firms’ R-D . Several are the relevant actors in this industry - large firms, small firms and new biotech firms (NBFs) - and networks are pervasive. In particular, NBFs have entered the sector, competing as well as cooperating with (or being bought up by) established large pharmaceutical firms. In software, a highly differentiated knowledge base in which the context of application is relevant has created several different and distinctive product groups. In addition, the role of large computer suppliers in developing integrated hardware and software systems has been displaced by a lot of specialised software companies which innovate either in package software or in customised software. User-producer interaction and global and local networks for innovation are relevant.

From these empirical cases, it is quite evident that demand, the knowledge base and networks have proven to be relevant for innovation and industrial dynamics in many sectors. And yet, demand, knowledge and networks are not part of

most analysis of industrial economics that concern

industrial dynamics and innovation. Therefore in the following pages I am going to propose them as the next three key research challenges which need to be met if we want to advance our understanding of the relationship between industrial dynamics and innovation. Let me examine them in detail.

4. Demand

The first challenge that I want to explore is the one concerning the role of demand in innovation and industrial dynamics. As a way of introduction, let me first disagree with the usual complaint that demand has not been studied in its relationships with innovation in the last decades. In the literature, we have various empirical and theoretical strands, from the old debate demand pull vs technology push (Schmookler,1966; Meyers-Marquis,1969), to the analysis of demand, market structure and 8

innovation (from Kamien-Schwartz,1975 to Sutton,1991 and 1998). And advertising, bandwagon and networks have been shown to be important factors in influencing the magnitude and orientation of inventive effort and the degree of industry concentration. Demand has also been related to the emergence of disruptive technologies. Here the early development of disruptive technologies serves niche segments that value highly their non standard performance attributes. Further developments in the performance and attributes of

disruptive technologies lead these technologies to a level

sufficient to satisfy mainstream customers (Christensen-Rosenbloom,1995 and Christensen,1997). And also the whole vast literature on diffusion is nothing else than research aimed at understanding the relationship between demand and innovation. Moreover, several contributions on diffusion concern the relationship between new technologies, demand and the changes in the structure of the supplier and the user industries. The same holds for the literature on competing technologies which pays a lot of attention to externalities and increasing returns.

Contrary to all these research developments in the realm of demand and innovation, however, the insertion of demand in the analysis of the relationship between industrial dynamics and innovation is still in its infancy. The challenges for research here may come from trying to answer questions such as the following. In which ways and forms does demand affect innovation and the dynamics of industries? Can demand be distinguished only in terms of

its inertia and receptivity to new

technologies? And (related to the previous question) is demand only a passive recipient of new products, or does it actively contribute to develop and generate new technologies? And which dynamic processes are triggered by demand during the evolution of an industry?

A partial answer to the first question can be found in the introduction of submarkets and in their relationship with the evolution of market structure, as Sutton (1998) and Klepper-Thomson (2003) do in their models. Both of them consider an empirically grounded model of market structure evolution in which submarkets play a major role. In Sutton’s work new growth opportunities appear in the form of new submarkets and contribute to develop a stochastic theory of firm-size distribution (Sutton,1998). In Klepper-Thomson (2003) the creation and the destruction of submarkets are central forces of industrial dynamics and determine the expansion or contraction of firms, and ultimately the relationship between firms size, age, growth and survival. However, in Sutton (1998) and Klepper-Thomson (2003) demand has a passive role. It does not affect the rate of innovation and the direction of technical change, nor it actively contributes to innovation or shake outs.

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Therefore, answers to the questions posed above start from the identification of the various dimensions of demand that affect industrial dynamics and innovation. One dimension is the well known one related to the provision of incentives to firms’ R-D expenditures and innovative efforts. Here the preferences of consumers, market differentiation and segmentation, and the size and growth of demand affect innovative efforts and therefore technical change in various ways.

In this Address I would like to add two other aspects that are relevant for innovation in industries: consumer behaviour and consumer capabilities. Consumer behaviour plays a major role in affecting innovation. It includes the presence of information asymmetries and imperfect information with respect to new products and technologies as well as routines, inertia and habits concerning existing products and technologies. Also consumer capabilities influence technological change in an industry: as an example one could only mention the role of absorptive capabilities and their distribution among consumers and users.

The focus on the behaviour and capabilities of consumers and users opens the way for a very productive analysis of how demand affects innovation and the specific patterns of industrial dynamics. In this respect let me mention some fruitful directions.

One relates to users involvement in innovation. This is a quite common phenomenon in industries. It may range from user-producer interaction (Lundvall,1988) to user initiated innovation (Von Hippel,1986). Users’ involvement in innovation may represent more than simple participation to the innovation process, and may regard learning and knowledge exchanges between the user and the producer.

The other is community of practices, when it refers to the use as well as the generation of new technologies. In some sectors, as in software (open source software), communities of practices are users of software as well as continuous sources of incremental innovations and change. They act as facilitators of innovation, because users and innovators share ideas and resources to develop new software. As Harhoff et al. (2003) show, for innovators it might be beneficial to reveal information inside a community because they may induce improvements by others; be helped to achieve a standard; face low rivalry conditions and expect reciprocity. Franke-Shah (2003) add to these four reasons a fifth one: the fun and enjoyment that arise through engagement in this process.

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Lead users represent another key demand mechanism that affect innovation. Lead users face needs that will be general in the market place but face them months or years before the bulk of that marketplace encounters them. This is the case of instrumentation as well as IT (Von Hippel,1988). Lead users are also positioned to benefit significantly by obtaining a solution to their needs (UrbanVon Hippel,1988). One has to recognize however that the contribution of lead users comes from knowledge related to their experience. Therefore they have a major role in periods of stability of uses and applications, but they may be less relevant when radical change or instability affects demand.

A much more intense and direct role of demand is through co-invention. Here we face innovation by sellers and complementary investments and innovation by buyers (in terms of new products, services, applications and investments in human capital). As Bresnahan-Greenstein (2001) have shown for IT, co-invention involves the technology of the user as well as the one of the supplier. Users’ co-inventions are particularly important in explaining technological change in IT applications (package software, semi-custom IT solutions, turn-key solutions). Co-invention pulls technological change in a variety of directions and ways. This means that in IT there is not “one” standard type of adoption. Rather, co-inventions in IT and its applications represent developments in tightly coupled interconnected technologies. Co-inventions generate new trajectories of improvements in the original technology, new organizational change and new institutions, which in turn generate new co-inventions between users and suppliers. For example, the rise in demand for the world wide web has set in motion entirely new waves of co-inventions, with new application developments, new business models and new institutions, which in turn have fed back on demand, changing it in various ways, and so on (Bresnahan-Greenstein,2001).

From all the examples provided, one sees a clear direction of research centred on the examination of the effects of various types of users on different patterns of innovation and industrial dynamics.

At the modelling level, one would like to be able to model some of these links between demand dynamics, firms dynamics and technology dynamics. These links go both ways. On the one hand, the emergence and development of new technologies create new markets, submarkets and niches. On the other the dynamics of demand in terms of consumer learning may stimulate technological change and the entry of new firms. This is indeed a challenging task. As examples, I can briefly discuss two different models (again grounded in specific cases of industry life cycles) that try to pursue this road. 11

One model addresses the question of the role of experimental customers and of a new type of demand in affecting competition, industrial dynamics and the emergence of new technologies. The methodology is the one of “history friendly models” of industry evolution, discussed in MalerbaNelson-Orsenigo-Winter (2001). In this model various types of customers are present: “standard” ones attracted by established products and guided by product characteristics such as price and performance; experimental customers who crave new technologies in existing products; consumers in new demand segments that look for completely new products. This history-friendly model is inspired by the case of the computer industry, in which experimental users and new demand have played a major role in affecting innovation, competition among technologies and the dynamics of market structure. Here the successful introduction of a radically new technology in an industry, in which a dominant design and a few dominant firms are present, and in which customers are not willing to experiment, may be dependent upon a group of experimental customers who are willing to experiment and buy the new products with the new technology. This allows new firms with the new technology to enter the industry and to stay around long enough to become viable. A similar dynamics is played by potential customers with different preferences, when potential markets are not currently served by incumbent firms. Both types of demand permit new technologies to effectively grow, either through new firms or even within established firms. This can have a profound, long-run effect on industry structure. Within this framework it is also possible to model a convergence among different demand segments and its effects on market structure (MalerbaNelson-Orsenigo-Winter, 2003).

The other model addresses the role of heterogeneity of consumers and demand life cycle in affecting innovation and technical change in an industry (Adner-Levinthal,2001, Adner,2003). In particular, it examines how demand (in terms of performance thresholds, types of preferences, changing utility and differences across market segments) interacts with technological change to guide the evolution of technology and competition during the life cycle of an industry. AdnerLevinthal (2001) model a coevolutionary process in which there is a demand life cycle: early on product innovation increases performance, but then (to the extent to which some performance thresholds are met) process innovation takes on. Later on a new phase starts in which firms, given a certain willingness to pay by demand, focus again on performance increases and product innovation. In a sense, mature consumers may demand for performance, but their appreciation for performance improvements is not reflected any more in their willingness to pay for the improved products. In addition, demand is also modelled in terms of different market segments. In this vein, 12

Adner (2003) shows that technology disruption à la Christensen (discussed above as an empirically interesting case) may be the result of the interplay between the preference overlap of different market segments (i.e. the extent to which performance on product attributes valued in one segment is also valued in another segment) and preference symmetry (i.e. whether or not buyers in one segment discount offers from a second segment to the same extent that buyers in the second segment discount offers from the first segment). This is a very interesting avenue of research because it takes into account demand life cycles and demand segments. In this model however demand is still static in its basic structure: thresholds, preferences and decreasing marginal utility are fixed. In reality, contrary to the model, the value consumers derive from performance improvements can change in response to changes in the environment in which products are used (for example due to changes in complements and changes in standards and regulation) or to firms strategies (such as

marketing or diversification strategies or efforts to change consumers’

evaluation metrics).

The discussion on modelling brings to the forefront of research the need to examine (both empirically and theoretically) the coupled dynamics of demand and technology. In this frame, learning by consumers, feedbacks from users, the emergence of new segments and convergence in demand become key topics in the research agenda.

5. Knowledge

The emphasis on (passive as well as active) learning and the role of absorptive capabilities in models and econometrics of innovation and diffusion, identifies a second challenge: the analysis of the role of knowledge at the base of learning by firms in an industry and its effects on innovation and industrial dynamics. The empirical evidence indicates that knowledge varies greatly across industries in terms of content and sources: in some sectors scientific knowledge is the force driving technological change, in others formal R-D plays a major role in innovation, while in others learning by doing is relevant (Dosi,1988; Rosenberg-Nelson, 1993).

The recognition that knowledge plays a key role in innovation raises a set of questions. In which ways the specific type of knowledge of an industry affects the specific organization of innovation activity and type and intensity of technological competition? Do changes in knowledge affect changes market structure and if so, how and why? If knowledge cannot be fully appropriated by 13

firms, what are the effects of knowledge spillovers on industrial dynamics and how can they be measured effectively?

Let me just indicate two promising ways to proceed in trying to answer these questions. One way is to focus on some specific dimensions of

knowledge such as codification, modularity,

complementarities and spillovers, and link them to innovation, the organization of innovative activity and industrial dynamics. For example, the tacit-codified distinction is an important factor in affecting the division of innovative labour and the size and structure of markets for technologies as Arora-Fosfuri-Gambardella (2001) have clearly shown. However the difficulty for research is that the tacit – codified distinction is not fixed, but is quite sensitive to economic decisions. A lot of tacit knowledge can be codified but at some cost, and the benefits from codification are greater when knowledge has to be transferred and applied broadly. So it is important to separate knowledge that is codifiable and knowledge that is actually codified, and consider their endogenous boundaries, affected by IPR, the disclosure conventions of epistemic communities and the effects of information technologies (Cowan-David-Foray,2000). The extent and intensity of knowledge spillovers within industries and across industries (see for example Jaffe-Trajtenberg,2002) and of mobility of inventors across organizations (Almeida-Kogut,1999 and Breschi-Lissoni,2005) are another key dimension in affecting technology transfer, industrial competition and innovation. An additional dimension of knowledge that plays a major role in affecting market structure and innovation is

modularity: both products and knowledge can be part of broader systems

(Winter,1987 and Foray,2002). Modularity has effects on the horizontal and vertical division of labour among firms, the development of interfaces and the integrating function of firms. Also links and complementarities in knowledge are relevant. They may regard scientific knowledge and technological knowledge (David-Mowery-Steinmueller,1992). For example, complementarities in the stock of knowledge between large pharmaceutical firms and new biotechnology firms have created extensive collaborations networks in biotechnology (Arora-Gambardella,1990). Finally, links and complementarities may regard knowledge inputs affecting the trajectories of incremental technical change in industries (Malerba,1992).

A second way to proceed is to examine the learning and knowledge environment in which firms operate and link it to the patterns of industrial dynamics. One may start form the remark that knowledge may have different degrees of appropriability and accessibility, and that lower appropriability and greater accessibility decrease industrial concentration. Accessibility of knowledge external to the industry may regard scientific or technological opportunities. If external 14

knowledge is easily accessible, transformable into new artefacts and exposed to a lot of agents, then innovative entry takes place. In addition, knowledge may be more or less cumulative (i.e. the degree by which the generation of new knowledge builds upon current knowledge). One can identify three different sources of cumulativeness: learning processes, organizational capabilities and feedbacks from the market (such as "success-breeds-success" processes).

Appropriability, opportunity and cumulativeness are key dimensions of knowledge related to the notion of technological and learning regimes, which differ across sectors (Malerba-Orsenigo,1996). Learning and technological regimes provide a powerful restriction on the patterns of firms’ learning and on the organization of innovative activities in industries. One could advance some general propositions on the relationship between technological regimes and industrial dynamics (Winter, 1984; Breschi-Malerba-Orsenigo,2000). Industries with technological regimes characterised by high levels of opportunities are expected to show patterns of innovation characterised by a remarkable turbulence in terms of technological entry and exit and a high instability in firms hierarchies. Thus high technological opportunities allow for the entry of new innovators. However, if successful, also established firms may end up gaining a substantial leap in their relative competitiveness, thus leading to the elimination from the market of less successful innovators and to an increase in concentration. Conversely, conditions of low opportunities limit innovative entry and restrict the innovative growth of successful established firms. As a consequence, a higher stability of the major innovators may emerge. High degrees of appropriability, by limiting the extent of knowledge spillovers and by allowing successful innovators to maintain their innovative advantages, are expected to result in a relatively higher level of industrial concentration and a lower number of innovators. On the contrary, by discouraging investments in innovative activities and by determining a wider diffusion of the relevant knowledge across firms, low appropriability conditions are more likely to lead to a sectoral structure characterised by the presence of a large population of innovators. Finally, high levels of cumulativeness at the firm level are expected to be associated to persistence in innovative activities. Therefore industries with technological regimes characterised by technological cumulativeness and persistence are expected to have a high degree of stability in the hierarchy of innovative firms and a low rate of innovative entry. In such circumstances, the selection process favours established technological leaders. This difference in the organization of innovative activities across industries may be related to a fundamental distinction between Schumpeter Mark I and Schumpeter Mark II models. Schumpeter Mark I is characterized by "creative destruction" (with technological ease of entry and a major role played by entrepreneurs and new firms in innovative activities). On the contrary, Schumpeter Mark II is characterized by "creative accumulation" (with the prevalence of large established firms and the 15

presence of relevant barriers to the entry for new innovators). In a dynamics fashion, technological regimes and Schumpeterian patterns of innovation change over time. According to an industry life cycle view, Schumpeter Mark I pattern of innovative activities may turn into a Schumpeter Mark II (Klepper,1996), but in the presence of a major technological discontinuity, a Schumpeter Mark II pattern of innovative activities may be replaced by a Schumpeter Mark I.

As a way of conclusion of this part on knowledge, let me briefly discuss the use of indicators of knowledge flows. Patents and patent citations have often been used for this purpose. Patent citations are indeed relational data, and identify a paper trail left by the knowledge flowing from the inventor/applicant of the cited document to the inventor/applicant to the citing one. Criticisms may be advanced for the use of patent citations as an exact measure of inter-personal or interorganizational knowledge flows. In fact often the citation is produced by the patent examiner rather than by the inventor. And when it is the inventor that cites a patent, not necessarily there is an interpersonal knowledge flow: more simply the inventor may retrieve information on the cited patent directly from a database. However I remain rather positive in the use of patent citations in providing some evidence of a paper trail about knowledge links, and in describing some features of knowledge and knowledge networks in an industry, as flows of knowledge can be captured by patent citations even when inventors are unaware of those citations. The analyses by JaffeTrajtenberg (2002) and by Thompson-Fox-Kean (2005) show that patent citations are a noisy signal of the presence of spillovers and that aggregate citations flows can be used as a proxy for knowledge spillover intensity. Thus the use of citations can be used to describe knowledge flows and with that also several types of networks, according to the nature of the nodes and the relations linking those nodes: networks of patents (providing insights on a specific technological field), networks of inventors (here it is assumed that inventors exchange knowledge and key technical information) and networks of organizations (in which firms or universities are related, with the however the problem of the interpreting what exactly a direct citation link between pairs of companies means) (Balconi-Breschi-Lissoni,2004, Podolny-Stuart-Hannan,1996).

In sum, the research challenge regarding knowledge implies that a given knowledge base defines the nature of the problems firms have to solve, affects the division of labour in an industry and influences market structure. And in a dynamic fashion, the very knowledge base of industries also changes as an effect of the behaviour of firms and of technological change.

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6. Networks

Let me move to the last challenge: networks. Here with networks I do not mean network externalities. Rather I refer to the different relationships – cooperative and competitive, market and non market ones – that firms have with other firms and non-firm organisations when they innovative. The relevance of networks for industrial economists is due from the broad recognition that innovative activity is highly affected by the interaction of heterogeneous actors with different knowledge, competences and specialization. Various explanations have been advanced for the importance of networks in innovation, ranging from spillovers of various types, to the presence of variety in technologies, knowledge and capabilities, to the role of complementarities. And the relevance of networks for innovation as well as for production and exchange has been recognised by game theory, transaction cost theory and resource based theory. Models of networks among economic agents abound now, from game theoretic to small world models, including evolutionary game theory, percolation theory and neural networks. They range from static models regarding the effects of different network architectures on performance, to dynamic ones in which the structure influences individual actions and performance and attention is paid to the networks efficiency, stability and feedbacks.

What about the role of networks in industries? Detailed and rich case studies show that networks are indeed present in industries and greatly affect competition. Studies refer to biotechnology (Arora-Gambardella,1990; Powell-Koput-Smith-Doerr,1996; Arora-Gambardella,1998; McKelveyOrsenigo-Pammolli,2004; Orsenigo-Pammolli-Riccaboni,2001; Nesta-Mangematin,2002); ICT (Saxenian,1994; Langlois- Robertson,1995); auto (Dyer,1996); aircraft (Bonaccorsi-Giuri,2001); flight simulation (Rosenkoft-Tushman,1998); steel (Rowley-Behrens-Krockhardt,2000) and semiconductors (Stuart,1998).

Given the empirical relevance of networks in industries, what could one say about the role of networks in innovation and industrial dynamics? Strangely enough, here progress has been more limited. One reason is related to the need to shed light on what is a network for the purpose of the economic analysis of innovation and industry evolution, and how we can define it in such a way that is understandable and useful for economic research.

In this respect, I think that a starting point is the recognition that the emergence of certain types of networks is function of a specific knowledge base, industrial setting, demand and institutions and 17

that their evolution over time is the result of the interplay between firms’ internal capabilities and technological, market and institutional factors. Therefore one could expect that the extent and the structure of relationships and networks differ from industry to industry. If we go along this line, it is then relevant first of all to develop taxonomies of network structures for groups of industries, as the useful one proposed by Kogut (2000).

Kogut relates the type of network to factors such as

technology, resource bottleneck, competing and regulatory rules and strength of property rights, and does it for industries such as microprocessors, information technology, software operating systems, pharmaceuticals and biotechnology, automobiles and financial markets.

From the previous reasoning, some basic questions follow. What is the impact of different network structures on innovation? And why and how specific features and characteristics of networks affect firms profitability, survival and growth? An empirical work that tries to go in this direction is the one by Bonaccorsi-Giuri (2001), which examines networks in vertically related industries. Here the analysis concerns the aircraft industry. It shows that the dynamics of the downstream industry is transmitted to the upstream industry through the structure of networks of vertically related relations and that different types of networks (partitioned vs hierarchical) generate different transmission effects.

And it points that during industry evolution networks assume various structural

configurations and change over time.

A more difficult research task regards the analysis of the coupled dynamics between the dynamics of networks and the dynamics of technology. On the one hand, networks are the outcome of firms choices, industry knowledge base and technological features. On the other hand, networks affect technological change, firms growth and market competition. These changes are often selfreinforcing and may generate path-dependent processes in network formation and evolution.

A

related question is how the dynamics of networks is related to major technological discontinuities. From empirical evidence, it seems that often technological discontinuities are structure loosening in that they favour peripheral agents and reduce the degree of centralization of networks (MadhavanKoka-Prescott,1998). After new networks are created following a technological discontinuity, they may then consolidate with the stabilization of the technology and incumbent firms may increase their centrality.

In any case, we are still at the beginning of the research agenda in answering all these questions. Studies have focussing in various directions, from looking at networks as numbers of connections among firms, to examining the structure of networks, to analysing the effects of the embeddedness 18

of economic activity in networks of various types (Aggraval-Kapur-McHale,1999). From pioneering empirical analyses it seems that strong links among agents favour exploitation and weak links favour exploration (as it has been found in longitudinal analyses for the chemical, semiconductor and steel industries).

And it has been found that networks show stability and

change over the evolution of an industry, as longitudinal data for some industries show. As mentioned above, stable networks are often formed early in industry life cycle, but major industry specific events may re-shape the structure of networks. However, additional robust evidence and deep appreciative theorizing on these and other connected issues are needed. A lot of ground has to be covered by moving from scattered specific evidence to broader and consistent empirical analyses of network evolution in industrial dynamics.

Also at the theoretical level, models of network, knowledge and market structure dynamics have started to spring out. One could only compare the early models of R-D cooperation (such as D’Aspremont-Jacquemin,1988 - in which the drive for networking was R-D spillovers), to the literature of exogenous or endogenous coalition (such as Katz,1986 and Bloch,1995 – which discuss the effects of networks on market structure), to the models of formation of networks of R-D collaborations (such as Goyal-Moraga,2001 and Goyal-Joshi,2003), and witness the progress obtained so far. In the Goyal-Moraga (2001) and Goyal-Joshi (2003) models firms form collaborative links which lead to cost reduction and over time create R-D networks. Along these lines, in a dynamic model considering feedbacks between market competition and firms incentives to engage in collaboration, Zirulia (2004) shows that R-D networks work as strong selection mechanisms, creating self-reinforcing path dependent processes and generating ex-post asymmetries in ex-ante similar firms. R-D networks create differences across firms due to the uneven distribution of links, but differences disappear when networks become denser. The nature of the technological environment affects the speed of the transition and the features of the industry in the long run.

Other models try to link the dynamics of knowledge in a network when agents

have different abilities to innovate and to absorb new technologies. Cowan-Jonard (2003) show that network perform better for innovation and knowledge

diffusion than a world in which

knowledge is diffused to random agents each time. They show that locally dense networks with relatively short paths (the so called “small world regions”) are particularly efficient when knowledge is difficult to absorb. Finally Cowan-Jonard-Ozman (2004) examine the relationship between the type of knowledge of an industry, the type of networks and performance. They show that in industries in which tacit knowledge is relevant and technological opportunities are high, regular structures generate higher knowledge growth, while in industries in which knowledge is 19

codified and technological opportunities are lower, communication without any structure performs better.

Albeit very promising, these lines of research in modelling are in their infancy, and need to be developed further. For example, under which conditions early on in the life cycle of a sector certain types of collaborations (for example to explore knowledge) emerge? And under which conditions in industry maturity other types of collaborations (for example to exploit knowledge) gain in importance? And when and why at certain stages of industry evolution large informal networks rather than formal ones are major sources of knowledge generation? Finally, what is the relationship between different types of industry life cycles and different types of network dynamics?

7.

Conclusions

In this Address I have suggested that within the growing field of industrial dynamics, in the last twenty years the analysis of industrial dynamics and innovation has witnessed a rich and highly diversified set of contributions at the empirical and theoretical levels. So, progress has been substantial.

The main point of the paper however is that a deeper understanding of the relationship between industrial dynamics and innovation pushes research in this area to face new challenges related to a finer grained analysis of the role of demand, knowledge and networks. In this respect, the paper discusses the main contributions and the way to go further ahead along these lines.

A key point that emerges from the discussion in this paper is that longitudinal studies regarding the relationship between innovation and industrial dynamics have to thoroughly examine the coupled dynamics between variables. This represents the most difficult but also the most promising way to go. Longitudinal studies entail variables that change together and the specific feedbacks loops that link them. This paper has suggested that these coupled dynamics involve knowledge, technology, firms, demand and institutions. These dynamics are often path-dependent, take the form of coevolutionary processes and are industry-specific. As it emerges form the work by Klepper (1996) for example, just looking at three elements such as technology, demand and firms, one could claim that in sectors characterized by a system product and consumers with a rather homogeneous demand, the dynamics leads to the emergence of a dominant design and industrial concentration. 20

However in sectors with a heterogeneous demand (or competing technologies with lock-ins) specialized products and a more fragmented market structure may emerge. More in general, and following the lines advanced in this paper, one could say that changes in the specific knowledge base of an industry or in the features of demand affect the specific characteristics of the firms, the organization of R-D, the features of networks and the structure of markets. All these changes may in turn lead to further modifications in technology, knowledge, demand, and so on. For example, the empirical evidence from the computer industry (Bresnahan-Malerba,1999) and chemicals (Arora-Gambardella,1998, Murman,2003) points in this direction. The challenge for research here is to provide a much finer analysis at both the empirical and the theoretical level, and to move from the statement that everything is changing with everything else to answering questions such as the following. What is the specific dynamic process that affects two variables? What are the specific feedback loops that link the variables that change together?

In a way of conclusion, I am convinced that the challenges discussed in this paper have to be faced by using a methodology that identifies first some empirical regularities, stylised facts or puzzles that need to be explained, and then do quantitative analyses and formal modelling, which in turn feed back their results to empirical analyses in the form of tests, insights and questions. And that consistency between empirical evidence, econometric work and modelling has to be present. In a sense, theory should be driven by empirical questions and facts.

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