*G.Jayalakshmi., Ph.D Scholar
INTRODUCTION
The growing field of industrial dynamics, the analysis of innovation has witnessed major progress in several areas. Contributions at the empirical and modeling levels have greater advanced our understanding of innovation, industrial dynamics and evolution of industries. A discussion follows on three key challenges that are required for a better understanding of the relationship between innovation and the evolution of industries: the analyses of demand, knowledge, networks and co evolution.
Innovation and the Evolution of Industries
The analysis of innovation and the evolution of industries have witnessed major progress in several areas. In the last years, several contributions at the empirical and modeling levels have greatly advanced our understanding of innovation, industrial dynamics and the evolution of industries. This paper reviews these contributions.
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.
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 recognized the central role of innovation for firms and industries.
In this Address I will start by recognizing 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. I will place a specific emphasis on a longitudinal perspective, in that it allows to focus on sequences of events, changes and feed backs 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.
2. Industrial dynamics and innovation
Since the late 1970s, industrial dynamics has emerged as a major research area for industrial economists. the theoretical level have focused the attention of various 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. Looking back at the last 25 years, one has to recognize that on this front progress has been indeed impressive at both the empirical and the theoretical levels.
2.1 The empirical contributions
The empirical level, 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. 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 sect oral systems of innovation 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. It has also been convincingly found that that these dynamic sequences are different from one industry to another. 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
2.2 The theoretical contributions
Also at the modeling level one can find different strands of research focusing 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 characterize 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,).
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 new technology is associated to the exit of unsuccessful innovators and the survival of firms with larger scale technology.
On the contrary active learning by firms in industrial dynamics is present in where firms explore the economic environment, invest and, if successful, grow, so that industrial dynamics is driven by the growth of successful firms.
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. Finally, more attention to the specificities and histories of various industries is paid by “history friendly models”, which fall into the evolutionary tradition.
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 modeling 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 focused 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. 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. In computers experimental customers have been major actors in the emergent phase of the industry.
Similarly, the knowledge at the base of firms’ innovative activities and networks has played a major role in innovation and the dynamics of several industries. For example, in telecommunication 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 specialized 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 characterized by extensive firms specialization 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 specialized software companies which innovate either in package software or in customized 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, to the analysis of demand, market structure and innovation 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 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 externalizations 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.
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 behavior and consumer capabilities. Consumer behavior 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 behavior 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 to user initiated innovation.
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.
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.
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 behavior of firms and of technological change
6. Networks
Let me move to the last challenge: networks. Here with networks I do not mean network Externalizations. Rather I refer to the different relationships – cooperative and competitive, market and non market ones – that firms have with other firms and non-firm organizations 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 complementarity. And the relevance of networks for innovation as well as for production and exchange has been recognized 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 feed backs.
CONCLUSION
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.
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 co evolutionary processes and are industry-specific. In the 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.
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