New innovation models

Based on the above critique of the linear model, the scientific research community has developed new and more interactive interpretations of how innovation is developed in society and within firms (Gibbons. et al., 1994; Kline, 1985; Kline and Rosenberg, 1986; Leydesdorff and Etzkowitz, 1996; Stokes 1997). These models and ways of thinking (e.g. Mode 2 knowledge production, Triple Helix, Chain-linked model, National Innovation System) re-thinks the processes and the actors involved into a more interactive interpretation. The models range from micro to macro-economic perspectives. Many of them also gave new meaning to how innovations are created and the knowledge production process which leads to it.

Other innovation models, such as Pasteur’s Quadrant (Stokes, 1997), The stage Gate Model (Cooper, 1990, 1993) also makes valuable contributions to the innovation literature.

Complementary arguments have also been made in evolutionary economics about Schumpeterian competition in an industry (Nelson and Winter, 1982). However, these models do not add significantly to this thesis as it develops its arguments from new venture firms with limited routines and decision rules.

In the following, a review is made of a sample of such non-linear models - models which have been a key source of inspiration. The content of these models will later be useful for deriving at new perspectives for the early phases of corporate venturing. Common to these models is that they all in different ways provide a counter reaction to the linear approach, by implying dynamics to the knowledge creating process of innovation.

Mode 2 knowledge production

One of these new interactive approaches was introduced by authors such as Gibbons. et. al (1994) and et al. Nowotny (2001). The approach was called Mode 2 science (as opposed to the linear Mode 1). This model has already been introduced in the methodology chapter of this thesis, as it dealt with the role of science in society. In the context of this thesis, the understanding of Mode 2 grasps a broader set of actors and their role in creating knowledge for innovations. This approach argues that no individual or single firm holds all relevant knowledge and expertise needed to facilitate input to significant innovations. Cooperation between a greater number of participants therefore increases the chances of new and innovative developments. The sample of participants has to be trans-organizational, relying on activities in networks between firms, universities, consultants, customers, suppliers, national laboratories, media etc. From a Mode 2 perspective it will have to become common knowledge that complex research results are no longer robust when developed in academic communities of homogeneous character. Nowotny et al. (2001) and Gibbons et al. (1994) argue that the growth of complexity arising from the abandonment of industrial age meta-narratives has developed a distinctive society in which the development of more open systems of knowledge production and the growth of social complexity has increased uncertainty in both knowledge production and society. As a general consequence of this shift, the boundaries between basic research, oriented basic research and applied research have become blurred.

Within the traditional linear model (Mode 1) the process of innovation corresponded to a vertically and serially structured supply chain of basic research, oriented basic research and applied research. By contrast, within the new model (Mode 2), and more especially in the field of innovation, research is based on recursive interaction processes and networks of heterogeneous actors that are provided with heterogeneous knowledge resources. This has developed a position where knowledge is carried out in a context of application. It has made knowledge creation more socially accountable and reflexive. The core processes have involved transforming knowledge claims into trustworthy, socially robust and usable knowledge. This tightly links to what Carlile and Rebentisch refers to as knowledge “transformation” of knowledge into novel application (Carlile and Rebentisch, 2003).

Those who were in favour of Mode 1 and Mode 2 claim that this approach provides a more suitable framework for explaining modern societies in which the roles of knowledge, research and education are characterised by increasing distribution, complexity and significance. It is argued by Hellström et al. (2003) that commentators on Mode 2 knowledge production have often focused on the problem of justification. Here it is argued, that it does not seem sufficient to recognize the need for a formalized and internally received phase of justification in academic knowledge creation (Weingart, 1997). Justification of knowledge is connected to disciplinary institutions such as departments and journals. Hence the argument often replicates itself, and it is only through these institutions that knowledge has its validity. Hellström et al. (2003) further argues that the problem with this critique is that it is based on a naive “truth as correspondence with nature” understanding of the product of academically produced knowledge, and, further, on an idealistic conception of the political economy of science. This correspondence and presentious understanding is problematic from a socialized perspective because it presupposes a conception of nature prior to and disconnected with the knowledge that is to be tested about it.

To take one well-known example, it takes for granted that knowledge is tested through replication, Collins (1985) however has shown that it is not. This standpoint has been grounded in the tradition of academic society for some time, but the significance has not yet attained meaning in the practice of corporate venturing. This becomes problematic because one of the premises for Mode 2 knowledge production in the formation of new venture firms is that knowledge has been tested and challenged in the context of application. It is presumed that corporate venture firms have participated in the process.

Triple helix

Another approach to the analysis of the innovative process was introduced as the Triple Helix model. The Triple Helix model was developed in 1996 by Leydesdorff and Etzkowitz and describes the process as a transition from the linear model of scientific progress to an interactive structure, which appears as a "triple helix" of science, policy and industry (Leydesdorff and Etzkowitz, 1996, 1998; Etzkowitz and Leydesdorff 1997, 1998; Etzkowitz, 2002, 2005; Ernø-Kjølhede, 2001). Contrary to the Mode 2 model, the triple helix model deals with specific institutions. The model explains the changing relationship between government, university and industry and thereby challenges the assumption that the public and private spheres are separate.

According to Etzkowitz and Leydesdorff (1997), the three institutional helixes public, private, and academic, which formerly operated at arm’s length, are increasingly working together to create new knowledge and value from the innovation generated within each helix. Leydesdorff and Etzkowitz (1996) take this argument further and state that a triple helix of academia,

industry, and government relations is likely to be a key component of any national or multinational innovation strategy in the late 20th century. In arguing for the changed relationship between these three actors, Etzkowitz and Leydesdorff (1997) emphasize that the boundaries and roles of the helixes are undergoing significant changes and are being replaced by a web of ties.

These network ties will later be analysed in more detail.

Etzkowitz and Leydesdorff (1997) further claim that the challenge of creating new knowledge flows has today become an academic challenge in cooperation with government and private firms, which has changed the structure and role of the university from the traditional linear model. Their primary focus is thereby directed towards the changing role of the university in relation to the other helixes (Martin and Etzkowitz, 2000:13). The university is increasingly becoming involved in tasks such as technology transfer, commercialization, and the creation of new ventures, tasks that originally have been held by private firms. This moves university research closer to application and the authors argue that commercialization is supported by, e.g., new hybrid organizations, ventures, incubators, etc. (Martin and Etzkowitz, 2000). This way of analysing knowledge production has challenged established notions of institutional roles and identities more so than the earlier critique of the linear innovation model.

Chain-linked model

An alternative innovation model developed by Kline (1985), and Kline and Rosenberg (1986) propose an interactive process approach to the development of innovations. Their model was also developed as a counter reaction to the linear model. Contrary to the linear model of research, Kline (1985), and Kline and Rosenberg (1986) launched the so-called “chain-linked model” of innovation processes. The model was first introduced by Kline (1985) and a year later further elaborated with Rosenberg. This model explains the relation in most countries between basic research and the innovative firm. It also explains the activities that an innovative firm undertakes. The model of Kline and Rosenberg (1986) carries the logical implication that, in commercially successful radical innovations, the research solutions in one arena are influenced by the ideas and opportunities in other areas. In this connection Kline and Rosenberg (1986) argue that:

“...it is a serious mistake to treat an innovation as if it were a well-defined, homogeneous thing that could be identified as entering the economy at a precise date – or becoming available at a precise point in time…” (p. 283)

In contrast to the linear sequential model, that is, the “scientific push” argument, according to which scientific discoveries naturally drift towards the market and are instinctively adopted.

Kline (1985), and Kline and Rosenberg (1986) emphasize the effects of “loops” and feedback on the flows and transfers of information within the firm. The chain-linked model takes into account the loops and feedback between the roles of different functions within the firm. These includes design, manufacturing, marketing, sales etc. Hage and Hollingsworth (2000), argue that:

“Obviously, a product that does not have desired attributes and a certain level of customer-preferred quality is unlikely to do well, although, products are frequently developed without much research concerning the needs of customers.” (p. 977)

While on this model, as on the linear model, innovation still emerges from a process, it is assumed that scientific research is not only a source of inventive ideas but is used to solve problems along the chain of innovation:

“In this model [the Chain-linked model], the “central chain of innovation” begins with design and moves toward development and production to sales. Each step is linked together via feedback loops and all are side-linked to research” (Mehta, 2002: 270)

Science profits directly and indirectly from the products of innovative activities such as the tools and instruments made available by technology (Kline and Rosenberg, 1986). Moreover, special emphasis is put on the important role of the actors who learn with the context of the innovation process and who cooperate and participate in the process.

National innovation system

There has also been a counteraction to the linear model from a macroeconomic perspective: “The innovation system” approach (Cooke, 1998; Lundvall, 1992; Niosi, 2000). A dominant part of the literature from the systems approach focuses on the national level and the central theoretical and empirical contributions within this approach have been published in recent decades by the work of Freeman (1988), Nelson (1988) and Lundvall (1988), setting forth a framework allowing for a systems approach in understanding the possibilities and opportunities in innovation (Lundvall, 1992, 2000). The analysis of the innovative capacity of nations has become widely diffused and is now an integral part of the analytical frameworks of such organizations as OECD and the European Commission. The national system of innovation has been defined by Lundvall (1992) as:

“…the elements and relationships which interact in the production, diffusion and use of new, and economically useful, knowledge… either

located within or rooted inside the borders of a nation state” (Lundvall, 1992: 2)

This concept has emerged in recent decades, especially in work that seeks to define the composition of innovation actors. Other strands within the systems approach are represented by, e.g., sectoral systems of innovation (Malerba, 2002), regional systems of innovation (Cooke, 1996, 2002) and technological systems of innovation (Carlsson et al., 2002).

The system rests on the ability of all actors to collaborate and interact. Interaction is assisted through nearness and collaborative initiatives; joint research activities and licensing agreements between public and private sector actors. In the system approach, private sector actors can access and exploit the pure science competencies generated in public organizations and institutions and the public sector can realize the transfer and application of its technology into new commercial products. The national system of innovation framework argues that such a system creates, stores, and transfers information, knowledge and skills to technologies and new innovative projects.

Although academics and policy makers employ different definitions and perspectives to the system approach, the basic premise is interaction. This includes understanding the linkages between the actors involved in innovation.

After reviewing the innovation models, which counteracts and complements the linear approach in different ways, an obvious next step is to analyse the early phases of corporate venture firms in a different, non-linear light. This draws on these critical frameworks to different degrees, and creates new analytical approaches to the early phases of the corporate venture process by these means.