Harry Hillman Chartrand
Peter Phillips & George Khachatourians
DRAFT EDITIONThe Biotechnology Revolution in Global Agriculture: Invention, Innovation and Investment in the Canola Sector.
CABI Publishing, 2001.Chapter 2: Approaches and measurement of innovation
Canola is a product of innovation. From the very beginning, the development of rapeseed into a new plant variety whose products were suited to human and animal feeding purposes was a science-driven process (Juska and Busch, 1994). The public sector, and more recently the private sector, have invested significant resources to change the agronomic and end-use attributes of canola to increase the value created in the industry.
This chapter examines the evolution of the innovation process in the canola industry, starting from the early years when research and development was undertaken by the public institutions and moving into the recent period when privately-funded research and commercialisation is taking hold. The impetus for the research has clearly changed - initially in Canada public institutions sought new crops for Western Canadian farmers, in the mid 1980s seed and agrochemical companies endeavoured to create through plants and plant derived products new value for their shareholders, and now increasingly users of canola for animal or human consumption specify the attributes (e.g. fatty acid content and profile for humans or nutritative value and digestibility for animals) they seek from the seed. Furthermore, the innovation process, which has shortened from more than 15 years now to 10 years or less, would appear to have evolved and benefited from the non-traditional innovation model.
Ultimately, the challenge of examining innovation is in its quantification for its contributory value to rapidly evolving user needs and significantly better return on investment. After all innovations are the application of existing technical knowledge in more creative manner than the previous application so as to give its originators and exploiters a competitive edge. Innovations are ideas that are generated daily in creative minds and do not subscribe to the terms of diminishing returns. It is only possible to see them at discrete points in the system, such as when they are codified either in academic literature or in patents and when they move from the labs into the marketplace and are produced and marketed. This chapter will examine the practical problem of measuring the stocks and flows of innovations in the canola sector.
Data reflecting various measures of innovation will be examined to determine whether canola innovation has tended to concentrate in specific geographic areas where there are similar climate, physical soil characteristics, microbiology, hydrology and industrial structure. As noted in Chapter 1, if the final product is tradable, e.g. the canola oil or meal, but the innovation-based knowledge is a non-transferable intermediate factor of production (e.g. the canola seed may be such that it can only be grown in Western Canada, either due to regulatory hurdles or due to climatic conditions), then the fact that innovation begins in one jurisdiction could forever put that site on a higher R&D and new product development trajectory. As a result, because of innovation the contribution of canola as a product of high-technology to our share of GDP and exports will be higher than otherwise.
One manifestation of innovation is the way that it yields knowledge that exhibits a number of different traits in terms of how it can be used, who can use it and how widely or narrowly it can be applied. An examination of the innovation process and the types of knowledge and their characteristics provides some insight into cause and effect parameters, such as the types of knowledge the private sector may adequately provide against those where sustained or greater public effort may be required.
The classical innovation
process has been viewed as a linear process, starting with research and leading
through development, production and marketing phases (Figure 2.1). Although this may have made some sense in
earlier times when many innovations were simply the product of inventors’
ingenuity, it soon became clear that the more competitive companies and
industries were deploying a different strategy to develop and exploit
inventions. Creating newer
competitive intelligence needed a new model which turned incremental new
information of markets, utilities and value onto existing inventive steps to
generate intelligence, hence creating the non-linear nature of innovation and
the increasingly important role in the process for market knowledge
Klein & Rosenberg (1986) provide an approach that explicitly identifies the role of both market and research knowledge. Their ‘chain-link model of innovation’ (Figure 2.2) begins with a basically linear process moving from potential market to invention, design, adaptation and adoption but adds feedback loops from each stage to previous stages and the potential for the innovator to seek out existing knowledge or to undertake or commission research to solve problems in the innovation process. This dynamic model raises a number of questions about the types and roles of knowledge in the process. Some of the knowledge will be available inside the institution undertaking the innovation, or could be developed within or outside the firm.
Malecki provides a way of categorising the types of knowledge that helps to identify which route a firm or institution might go to acquire or develop knowledge needed to innovate. They identified four distinct types of knowledge: know-why, know-what, know-how and know-who (Table 2.1). Each type of knowledge has specific features (OECD, 1996).
‘Know-why’ refers to scientific knowledge of the principles and laws of nature, which in the case of plant breeding relates to the scientific domains of plant physiology, genetics (theoretical and applied), molecular biology, biochemistry and newer integrative disciplines of proteomics, bioinformatics and genomics. Most of this work is undertaken in publicly-funded universities and not-for-profit research institutes and is subsequently codified and published in academic or professional journals, making it fully accessible to all who would want it. This knowledge would be in the knowledge block in the chain-link model, having been created almost exclusively in the research block. In the most classical sense of scientific inquiry, very little of this knowledge would have been produced within firms. ‘Know-what’ refers to knowledge about facts and techniques: in the case of plant breeding, this includes the specific principles and steps involved in key experimental protocols of genetic crosses and selection of indicative traits after the transformation processes. This type of knowledge can often be codified and thereby acquires the properties of a commodity, being transferable through the commercial marketplace. In the case of canola, much of this knowledge is produced in private companies and public laboratories and increasingly is protected by patents and other property protection systems. The stock of know-what is in the knowledge block in the chain-link model, having been created in the research, invention, design and adoption blocks.
‘Know-how’ refers to the skills combination of intellectual, educational and physical dexterity, skills and analytical capacity to design a hypothesis driven protocol with a set of expected outcomes, which in the canola case involves the ability of scientists to effectively combine the know-why and know-what to develop new varieties. This capacity is often learned through education and technical training and perfected by doing, which in part generates a degree of difficulty for the uninitiated and makes it more difficult to transfer to others and, hence, more difficult to codify (in some cases videotapes can codify know-how). Know-how would be represented in the research block and also in the invention, design and adaptation stages. Marketing these innovations also takes a certain skill and expertise that is not codifiable but can realistically be viewed as knowledge. Finally, ‘know-who’, which “involves information about who knows what and who knows how to do what” (OECD 1996, 12), is becoming increasingly important in the biotechnology-based agri-food industry; as the breadth of knowledge required to transform plants competitively expands, it is necessary to collaborate to develop new products. In today’s context, know who also requires intellegensia and tracking of private sector knowledge generators who at times can hold back the flow of crucial and enabling information, expertise and knowledge. In extreme cases, know-who knowledge can be critical to successful innovation; if one does not know who to work with, they may stumble into scientific pitfalls and traps that could sabotage the chance of innovative success. Know-who knowledge is seldom codified but accumulates often within an organization or, at times, in communities where there is a cluster of public and private entities that are all engaged in the same type of research and development, often exchange technologies, biological materials and resources and pursue in staff training or cross training opportunities. This type of knowledge would be represented by the arrows in the chain-link model, as building relationships that lead to trusting networks of know-who is the basis for those flows. A major challenge in trying to examine innovation is finding some way to monitor and measure the stocks and flows of these different types of knowledge.
No definitive set of measures for knowledge has yet been developed. Nevertheless, there has been significant work undertaken in a number of areas using proxies for knowledge and transmission of knowledge. Taking the four types of knowledge, and the resulting products, one can construct a package of empirical measures that approximate the flow of innovations into the marketplace.
First, starting with know-why knowledge, it is clear that while it is quite difficult to identify the inputs to the research effort, one can look at ‘bibliometric’ estimates to measure the flow of knowledge from the initiators/originators, generally the universities, research institutes and private firms. There is general acceptance of the view that publications such as academic journals are the primary vehicle for communication of personal and institutional findings that become the vehicle for evaluation and recognition (Moed, et al, 1985). Hence, in general in the past, and to some extent even in current practices, most if not all of the effort put into a research area will be presented for publication. The common catch phrase, ‘publish or perish’ captures the essence of the past practice, while, the more progressive modality is ‘patent and then publish’, specially for a large number of research universities. There have been a number of efforts (by the National Science Board; Katz et al 1996; Industry Commission, 1995) to develop and use literature-based indicators to evaluate science effort. The 151 based evaluation system for connecting the scientific impact of anyone’s publication and a journal’s placement in the world of publications is becoming a more quantitative indicator, which is presently used for analysis of progress and evolution of science and innovative steps.
In the canola area, Juska
and Busch (1994), sociologists from
For the purposes of this study, Juska and Busch’s general “bibliometric” approach is adapted to a more refined database. Initially a manual search of the Institute for Scientific Investigations Scientific Citations Index for 1965-97 was undertaken. The manual search identified 3,646 articles over the period, with 648 in the 1965-80 period. The ISI was then contracted to undertake an electronic search of their databanks, which then covered the period from 1981 to July 1996, with a few entries in the following months. They were instructed to search their database, which included approximately 8,000 journals in the sciences and social sciences, for seven key words/phrases: brassica campestris, brassica napus, brassica rapa, canola, canola meal, rapeseed, and oilseed(s). The special tabulation identified 4,908 individual articles in 650 journals meeting the criteria (hereafter called the canola papers) produced by approximately 6,900 authors in approximately 1,500 organisations in 79 countries (see Table 2.2 for the types of papers).
Second, know-what knowledge
is most commonly examined using patent information. Trajtenberg (1990) argues that “patents
have long exerted a compelling attraction on economists dealing with technical
change... The reason is clear:
patents are the one observable manifestation of inventive activity having a
well-grounded claim for universality.” As of 1990, there were approximately four
million patents issued in the
For the purposes of the
canola study, two patent systems were searched. First, the
Third, know-how and
know-who types of knowledge, which as discussed above, are often inseparable and
are tricky to track at the best of times. Nevertheless, this type of knowledge can
be mapped by looking at a number of different sources. The regulatory systems in
In addition to investigating the regulatory records to determine who is working with whom, this study has also used the ISI canola papers to map capacities and linkages. The advantage of using the ISI database over the AGRICOLA or CABDATA systems is that the ISI databases provides the capacity to look both forwards and backwards from the target articles to determine where the key knowledge inputs come from and where the resulting knowledge is being used. The database identifies 17,995 papers from 1,294 journals, produced by approximately 28,800 authors in 3,816 organisations in 107 countries which were cited a total of 28,946 times by the 4,908 papers that relate to canola research. Although the average paper is cited only 1.6 times, approximately 300 papers were cited between 10 and 96 times. At the other end of the system, the 4,908 canola papers were cited 26,946 times, for an average citation rate of 5.49 and a median citation rate of 2. As a further point of reference, it is worth noting that the average citation rate for the 690,000 publications within the biological and natural sciences literature during 1992-96 was 4.1.
One-third of the papers were never cited in any other paper and as such represent either relationships that are quite distant to the mainstream of canola research or represent end point or discontinuities in particular research lines. The database can also be sorted and searched by author, institution, subject and country of the researcher, and then cross-tabulated for collaborations, allowing one to examine both the stocks and flow of knowledge. In this way, one can investigate the know-who linkages that underpin the innovation system.
Finally, the ultimate
measure of innovative success is market adoption. The challenge is that marketing
information is getting more difficult to find. Aggregate data for canola acreage and
yields are available nationally and through the FAO but production information
on specific varieties is difficult to obtain. Nagy and Furtan (1978) provide variety
market shares for
The following chapters use the chain link innovation model and the constructed data sources to examine the structure and impact of the innovation system respecting canola, looking for areas of stability or change. Rothwell (cited in Gibbons 1995) puts forward a paradigm for innovative development that defines five generations of sophistication. When applied to the case study of canola, it is possible to see those five ‘generations.’
The first stage, spanning 1944-71, involved a simple,
linear, technology-pushed innovation system (characterised by the model in
Figure 2.1), with markets simply receptacles for the resulting products. The second generation began in 1971 and
lasted until 1985. The key change from the earlier
period was that ‘need pull’ entered the system as a loop-back from the market to
the research level, so that the technology push, linear system was ultimately
being driven by market needs, especially the desire for a rapeseed with low
erucic acid and low glucosmolates. At the same time there was enough of a
momentum in research results and investment that the publicly supported
institutions (e.g., NRC and AAFC) and universities funded through the same
bodies had to make a strategic decision to continue or change their research
programs. The big change happened
after 1985, with the granting of generally regarded as safe (GRAS) status in the