Deficits of knowledge concepts

The high-technology perspective is attractively simple for nations and communities that wish to develop knowledge-based economies. An example is a report of the Irish Science Technology and Innovation Advisory Council (1995): Even though the report – Making Knowledge Work for Us – espouses the national system of innovation as a basis for the development of policy, its main focus is on science and advanced technology, to be achieved through increasing R&D. Another example is the annual report of the German Ministry for Education and Research with its exclusive focus on the developmental perspectives of knowledge intensive economic sectors (cf. BMBF, 2002). That in itself 1 All of the data here is drawn from OECD (1997a).

motivates a more thorough discussion of the high-tech/low-tech concept and its analytical foundation.

Firstly, the indicators of knowledge intensity (i.e. R&D intensity) that are typically used are not robust, in the sense of being consistent across industries and technologies. There is strong variation in the extent to which industries and technologies use R&D to create knowledge. On the one hand knowledge formation is organised differently across technologies and industries – we suggest that whether science (or “research”) related activities are more efficient or more growth inducing than other forms of knowledge is an empirical question and should not be postulated a priori. On the other hand there are differences in how industries identify their activities in relation to R&D – it is important to note that R&D statistics focus on the direct R&D expenditure of a firm (whether the expenditure leads to research carried out internally or externally). Non-R&D performing firms may nevertheless be participating in R&D via collective organisations or other indirect forms (such as monitoring university R&D results). As a result, S&T indicators may show a strong variation over industries and technologies as regards the real knowledge intensity as well as its character in a general sense. This issue of data validity also has implications for a correct understanding of industries’ and technologies’ growth prospects (cf. Laestadius, 1996, 1999; Palmberg, 2001).

Secondly, several successful design oriented firms belong to industries classified as low-tech. The “design” concept is vague – as is the concept “innovation” – and may be in need of a taxonomy of its own. For the moment design is not necessarily classified within the

“D” in “R&D” according to the Frascati Manuals but there are reasons to believe that a taxonomy starting with design may catch other activities (and still exclude others) in comparison with the activities included in the present formulae. From a practical point of view, the definitions of R&D in the OECD's Frascati Manual, which structure R&D data collection in OECD economies, exclude a wide range of activities that involve the creation or use of new knowledge in innovation.² The OECD’s Oslo Manual explicitly includes

2 The development definition, on any reasonable interpretation, should include more or less all activities related to innovation. However the Frascati Manual contains a substantial list of

exclusions. The most important of these are summarised in Chapter 2.3 and summarised in Table 2.3, which gives guidance on how to divide R&D from non-R&D. Prototypes are basically included in R&D. Both pilot plants and industrial design are only included if 'the primary purpose is R&D'.

We would argue that very little pilot or design activity is aimed at R&D, and therefore that most of these central innovation activities are excluded. All improvements in production processes are excluded from R&D. On the other hand, trial production is included 'if it implies full scale testing

17 design activity and the surveys based on it collect data on design expenditures. Expenditure on design turns out to be an important predictor of innovation performance at firm level.

IKEA, Benetton and H&M are good illustrations of the importance of design. IKEA – belonging to the very low-tech end of OECD classification of industries – has created a capability in design (for manufacturing and for use) and logistics. Benetton and H&M also combine design, marketing and logistics in new forms within the framework of “mature”

industries.³ This innovativeness outside the realm of the S&T paradigm may be illustrated by a recent “airport best-seller” by Kelley and Littman (2001). Although not scholarly, this book invites the reader to a world dominated by creativity and high competence among highly educated innovators in a Silicon Valley based design firm. Most of their design solutions are far from the S&T frontier, yet in fact they are useful, profitable – and low-tech.

Thirdly, the character of the specific knowledge created in several of low-tech industries, and its relevance for innovative capabilities has not been given the attention it deserves.

This is important for companies, regions and indeed entire economies. Significant parts of that knowledge may be characterised as predominantly “practical” or “application-oriented”, distinct from “theoretical” or “scientific” knowledge, and resembling what Michael Polanyi (1966) has termed “implicit knowledge” in contrast to “explicit knowledge”. It may be also argued that the very essence of engineering activity is design oriented. Design can be understood as an intention to create artefacts or technical solutions rather than understanding, and is therefore not part of R&D. We might argue that design and engineering development are focused on the specificity rather than the general (cf. eg.

Vincenti, 1990 and Petroski, 1996), but this does not make these activities less technologically or economically significant. The complexity of knowledge formation in technology and industry is thoroughly analysed by Wendy Faulkner (1994; cf. also Faulkner and Senker, 1995) who also provides a typology of knowledge used in innovation, where (experimental) R&D is just one family of knowledge among others (Faulkner, 1994, p.

447). One conclusion that may be drawn from the work of Faulkner is that analysis of knowledge formation in industry and technology has to start in direct empirical research and subsequent further design and engineering'. Trouble shooting, patent and licence work, market research, testing, data collection and development related to compliance with standards and

regulations are all excluded. Obviously there are difficult boundary prblems for defining R&D. But an important point arising from this is that many innovation-related activities in LMT industries asre likely to be excluded from measured R&D (OECD 2002a, pp.34-50).

3 Indeed, it could be argued that much of the clothing industry, and certainly the designer

capturing the variety across different realms of technology rather than in indirect collection of R&D data.

Fourthly, a recent study on the dynamics and characteristics of firms’ relations to external repositories of knowledge (Hales et al., 2001), demonstrates that a distinction between knowledge as furnished by external repositories or ‘knowledge bases’ and the productive competence underpinning firm-level innovation and behaviour is essential for understanding the ‘learning processes’ of innovating firms. Rather than ‘knowledge intensity’, this implies that the relevant driver is ‘competence intensity’. Although formulated somewhat differently this perspective is present in several discourses on knowledge formation and creation of firm capabilities. Cohen and Levinthal (1990) for example use the concept

“absorptive capacity” and Teece et al. (1997) and Zollo and Winter (2002) use “dynamic capability” to address these issues. The competences and capacities are not necessarily R&D-based, and may involve many non-technological dimensions.

Case studies on competence intensity – facing similar problems of measurement and taxonomy – reveal that the intersection of knowledge intensive and competence intensive industries is far from total. This is still more obvious if the analysis is extended outside the manufacturing sector. For example, even with very conservative criteria normal hospitals show low knowledge intensity (R&D is done elsewhere) and high competence intensity including a very high skilled staff. In the extension of this family of arguments we face the complexity of knowledge formation in networks, in supply chains and in qualified purchasing relations. The dynamics and synergies within these structures and collaborative relations are far from easy to capture and locate to specific actors/industries when using traditional S&T indicators (cf. Coombs et al., 1996; Laestadius, 1996).

Finally, we might question the validity of any knowledge indicators or knowledge analysis that are not sensitive to context conditions. Can we adequately analyse the specific features of innovation and production processes through a more or less isolated approach to

“knowledge” or should knowledge be related to its context? Our view is that knowledge formation is highly context specific.⁴ This means that if we seek to reveal the specific type and form of knowledge and its relevance to technology and industry, we must focus on its clothing sector, is based entirely on innovative design.

4 In this sense, knowledge is to be comprehended as a socially determined phenomenon and should not be mixed up with pure data and information (cf. Nonaka, 1994, pp. 15; Willke, 1998, pp.

19 connection with the action and work context in each case. Such studies have been done on the level of laboratories and breakthroughs of new technological solutions (cf. Latour, 1987). With respect to “traditional” manufacturing, the work by Böhle et al. (1992) should be mentioned. It demonstrates the relevance of the informal side of work organisation and experience-based knowledge to the efficiency of highly standardised and automated production processes. As for the investigation of the seemingly low knowledge-intensive, low-tech work processes, one may come to the conclusion that only the analysis of the whole production and work process makes it possible to draw conclusions on the question whether – and if so – which forms of knowledge are really constitutive of them.

In short, there is a need to reconsider the prevailing understanding of the dynamics of technology and industry. In other words, the black box called knowledge has to be opened and analysed seriously across industries. In the following, some preliminary steps will be taken in this direction focusing on three domains of core importance to the “low-tech discourse”.