History-Oriented Empirical Research Methods

As regards the “methodological variety and openness” in evolutionary economic geography, I entirely agree with Boschma and Frenken (2006a) that the methodological

openness may be considered a strength of evolutionary economic geography, but all concrete research methods should be based on more realistic assumptions (like bounded rationality and disequilibrium) and real history, otherwise the over-openness of research methods will doubtlessly bring about potential dangers (see, Boschma and Martin, 2007). I elevate history to the methodological foundation of evolutionary economic geography in Chapter 2.1.3, on which concrete research methods should be based, and clearly state that to better understand evolution in economic geography should place history in historical time and historical contexts. For historical contexts, the concepts of path creation and path dependence should be used together in historical study. Here, I will focus on how to bring real history to a case study on cluster evolution in practice.

Probably the history-friendly research method is a good choice.

There are actually some history-oriented research methods that have already been employed in industrial evolution studies. These history-oriented models, which are based on a detailed rigorous illustration of a specific industry, add “richer, history-based, phenomenological details to the formal representation” (Bottazzi et al., 2001, p:

614). A more recently used approach are the ‘history friendly models’ which Malerba et al. developed, which try to use carefully simulation models in studying specified, empirical ‘histories’ of individual industries, for example, the models on the history of the computer industry with a special focus on the role of IBM (Malerba et al., 1999, 2001) and the recent history of pharmaceuticals and biotechnology (Malerba and Orsenigo, 2002). The “History-friendly models” (HFM) are formal evolutionary economic models. They aim to capture – in stylised form – the mechanisms and factors affecting the evolution of various industries, technological and institutional change (Malerba et al., 2001). But HFM need further examination through historical evidences.

Here I will offer another descriptive “history-friendly” research method, but not in formalised mathematical models, for studying the evolution of an industrial cluster. This qualitative history-oriented research method is characterized by a mixture of the methods of business history case studies and ethnographic methods. My approach is closer to what Nelson and Winter (1982) labeled “appreciative theorizing”, i.e. non formal explanations of observed phenomena based on specific causal links proposed by the researcher. The common points of HFM and my approach at least includes: (1) they both try to reproduce stylized facts in accordance with an evolutionary explanation; (2) their main purposes are to broadly explore the logic of evolutionary economic processes; (3) they both recognize the richness and importance of history, and give

more attention to time series and specific sequences of events (for the point of HFM, see Malerba et al., 2001). But my approach is different form HFM in the following aspects: (1) HFM are quantitative theories, while my approach is mainly based on qualitative analysis; (2) HFM are more deductive but my approach is more inductive;

(3) HFM believe that formal models play a crucial role for the development of more general theories of industrial evolution, but I think that my mixed methods also can identify variables of industrial evolution and relationships and test causal mechanisms.

Among the three research methods I will employ to study the evolution of an industrial cluster, business history is the base one for the other two in the sense that the materials and data used in the latter two methods are collected through the first method.

The approach of business history was often used in early evolutionary work, for example, of both Joseph Alois Schumpeter and Alfred D. Chandler. Business history is an approach which moves beyond a “pure” empirical and historical approach to economics, and led Schumpeter to a uniquely powerful understanding of modern capitalism (McCraw, 2006, p: 261). Alfred D. Chandler employed the business history case study method to engage with a broader question, namely the importance of the large managerially directed enterprises (see Lamoreaux et.al, 2008). In a nutshell, business history case study could be seen as an interpretative history-friendly method, based on long-range empirical and historical data on (i) entrepreneurs (their behavior, decision-making rules, and interactions); (ii) individual companies, and (iii) the environment in which they operate, and other particular parameters that are likely to have been important in generating the observed history. The value-added of this method is that it offers detailed historical materials, but it is silent on the historical relationship between firms over time, i.e. the evolution between different firm generations. So we need other research methods to complement the business history approach, when we want to explore the evolution of co-located firms.

The genealogical method and the approach of “generative relationships” obviously can fill this gap. The genealogical method is a well-established ethnographic research method and was developed in anthropology in the late nineteenth century, by which ethnographers can symbolize an evolutionary connection between kinship, descent, and marriage. This genealogical method can be applied to industrial (cluster) evolution, because it is helpful to understand the “kinships” (of firms, technologies), by testing the effect of inheritance on any individual trait and variation, based on the collected materials and data through business history method. For example, in practice, we can

record connections of kinship, descent and merger and reorganization of firms with diagrams and symbols, based on individual firm’s histories, which can be collected through interview surveys with firm founders and/or key consultants, and second-hand data (e.g. enterprise autobiography) as well.

In light of the theories of complex systems and coevolution, however, an entity (for example firm organization) is not fixed, but constantly changing (a complex adaptive system itself); at the same time, it is also a member of a higher-order complex adaptive system comprising the focus entity itself and the others with which it interacts.

This means that the changes in one entity are not just elements of its own evolution (path-dependent processes), but, rather, are influenced by heterogeneous and unpredictable contingent factors. For example, the final particular form of technological development we observe is not only the result of technical necessity, but is influenced by social, economic factors and, to some extent, political and institutional factors as well. Often, interactions between particular sets of entities take place in recurring patterns that persist over time, and these interactions may give rise to relationships between the participants (Lane et al., 1996, p: 59) that we can call “generative relationships” (GRs). The notion of GRs was put forward by Lane and Maxfield (1996) and was defined as “a relationship that can induce changes in the way the participants see their world and act in it and even give rise to new entities, like agents, artifacts, even institutions” (Lane and Maxfield, 1996, p: 215). GRs has two important characteristics: (i) generative: interactions amongst the participants in a GR can give rise to something new, which one of the members of the relationship could not have produced alone; (ii) unpredictable: the loosely coupled reciprocal relations and their results could not have been foreseen in advance. It was created by the interaction between the parties (for extended discussion of generative relationships, see Lane and Maxfield, 1996; Lane et al., 1996, p: 59; Russo and Hughes, 2002). This approach, as Russo and Hughes (2002) pointed out, is consistent with the definition of innovation suggested by Schumpeter (1934).

The combined approach I will use is not new in the evolutionary study of an industrial cluster. A good example is that Patrucco (2005) explicitly employed the notion of “generative relationships” and the ethnographic approach, and implicitly used the approach of business history case study as well, in studying the emergence of technology systems in the Emilian plastics district, Italy. With this method, a detailed analysis on the individual paths of main entrepreneurs, both specific and idiosyncratic,

can be made. The historical and in-depth analysis of formation and transformation of firms is extremely important for grasping the historical relationships among firms at any point in time in the local pharmaceutical sector. It is notable that the mixed method only provides some rough lines implying the material succession between enterprises, therefore, it is indispensable to examine how the knowledge, especially the knowledge of industrial technology and business management, flows through personal movement in this local industry. In a nutshell, the mixed method enables longitudinal and evolutionary studies, in particular in the case of small studies, in which the number of firms is relatively small. If the amount of firms is sufficiently large, the work on data collection and depicting generative relationships of clustered firms is so much that it is impossible to be well finished.