Linear model of innovation

I recently came across a fascinating paper: The Linear Model of Innovation: The Historical Construction of an Analytical Framework by Benoit Godin.

The basic linear model of innovation posits a process that moves from one stage to another:

Basic Research --> Applied Research --> Development --> Diffusion/production.

There are countless variations, which differing descriptions and titles for the stages. There are also variations that employ feedback mechanisms. But they are essential the same: science goes in and products (innovations) come out.

In the paper, Godin argues that the linear model is the construct of managers, business schools and economists -- not derived from science and technology policy report such as Vannevar Bush's Science: The Endless Frontier.

One would be hard pressed, however, to find anything but a rudiment of this model in Bush's manifesto. Bush talked about causal links between science (namely basic research) and socio-economic progress but nowhere did he develop a full-length argument based on a sequential process broken down into its elements, or that suggests a mechanism whereby science translates into socioeconomic benefits.

Rather, he argues, the linear model of innovation originally gained acceptance and continues to this day because of the availability and power of the statistics used in the model. The model took hold as part of the general mindset - and the statistics were development to reinforce the model. No other rival concept had (and has) the available statistic to operationalize the model.

Thus, our view of research owes more to the continuing power of "scientific management" than to the actual study of science.

On another point, I noted with interested that early definitions of what counted as "research" were vague -- just like today's measure of "innovation" are criticized as too vague. The 1963 adoption of the OECD Frascati Manual on science and technology metrics helped standardize the definitions. Maybe the latest OECD Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data, 3rd Edition (part of the Frascati series) may help provide the needed clarifications.

However, Godin goes on to argue that the current "innovation" surveys - with their inclusion of new product development measures as the end point - actually reinforce the linear model.

In a sense, we owe this continuity to the very simplicity of the model. The model is a rhetorical entity. It is a thought figure that simplifies and affords administrators and agencies a sense of orientation when it comes to thinking about allocation of funding to R&D. However, official statistics are in fact more important in explaining the continued use of the linear model. By collecting numbers on research as defined by three components, and presenting and discussing them one after the other within a linear framework, official statistics helped crystallize the model as early as the 1950s. In fact, statistics on the three components of research were for a long time (and still are for many), the only available statistics allowing one to "understand" the internal organization of research, particularly in firms. Furthermore, as innovation came to define the science-policy agenda, statistics on R&D were seen as a legitimate proxy for measuring technological innovation because they included development (of new products and processes). Having become entrenched in discourses and policies with the help of statistics and methodological rules, the model became a "social fact".

Bottom line: our current model is a remnant of our industrial age mind set. As Lord Keyes Keynes stated, "practical men, who believe themselves to be quite exempt from any intellectual influences, are usually the slaves of some defunct economist." In this case, we are slaves to a defunct idea.

Time to change the mind-set, and the statistics, and the methodological rules.