Elsevier

Research Policy

Volume 31, Issue 7, September 2002, Pages 1053-1067
Research Policy

Determinants of innovation capability in small electronics and software firms in southeast England

https://doi.org/10.1016/S0048-7333(01)00176-7Get rights and content

Abstract

The paper explores determinants of innovation capability in small UK electronics and software firms. An experimental innovation index is used alongside conventional proxies of innovative performance. These indicators are correlated with variables capturing a range of potentially important internal sources—such as education, prior work experience and R&D effort—as well as measures of intensity of external interactions and proximity in network relations. The findings support the importance of R&D, the key role played by the regional science base in nurturing high-tech spin-offs, and proximity to suppliers. However, no support is found for the current policy fashion of encouraging regional networks revolving around firms in similar business activities and close customer relations.

Introduction

Small high-technology firms have lately received much attention among researchers and policy-makers world-wide. Since the mid 1980s, regional networks of dynamic small firms started to emerge, which began to make inroads into the hegemony of large industrial corporations based on mass production. This led to a new belief in the economic viability of small-scale production, and in its ability to contribute—not just to employment and income creation—but to innovation, productivity and competitiveness (e.g. Porter, 1990, Audretsch, 1998, Best, 1990, Becattini, 1989, Camagni, 1991, Piore and Sabel, 1984, Steiner, 1998, Storper, 1993, Storper and Harrison, 1991). In the UK, small firms operating in the field of newly emerging technologies, especially ICT, biotechnology and high-tech electronics, are expected to hold particularly promising potential as agents of industrial regeneration. This has made them a central element in recent government policies to build a ‘knowledge-driven economy’ (DTI, 1998).

One would hope that the policies that were set up in the course of the 1990s to nurture the innovative performance of these companies would be informed by insights based on sound empirical research. However, despite several innovation surveys (for instance, those reported in Pavitt et al., 1987, Centre for Business Research, 1996, Thomas and Jones, 1998), there is still little empirical evidence about how companies improve their innovation capacity. The difficulty of quantifying technological performance remains a major hurdle to solid statistical research. Indeed, a recent review of innovation and technology studies in small- and medium-sized UK firms noted that even in the authoritative 1992 Cambridge survey (SBRC, 1992), which collected a variety of data from more than 2000 small companies, ‘… the data collected and presented in the section on technology and innovation is largely qualitative, based on subjective perceptions of the SMEs; and the analysis, though suggestive of some useful broad correlations, does not quantify innovative investment’ (Hoffman et al., 1998, p. 42).

The aim of this paper is to make a modest contribution towards filling this gap in the literature. The paper reports on a small pilot survey of 33 small software development and electronics manufacturing companies in southeast England held in 1998. The survey elicited detailed information about the companies’ innovative performance as well as a large range of internal and external factors that might have contributed to that performance. The interviews were conducted in the Thames Valley and along the M4 corridor as well as in more rural parts of Berkshire and Oxfordshire, an area with high concentrations of small high-tech software and precision electronics companies.

Different proxies for innovation performance and determinant factors are constructed in the paper, and the links between them are analysed statistically.1 The performance indices include commonly used innovation measures as well as a more experimental index, which is intended to circumvent some of the problems associated with conventional indicators. Qualitative case-study material from the interviews is used to help interpret and illustrate the statistical patterns.

Obviously, a small pilot survey cannot come up with firm conclusions about the driving forces behind innovation in small high-tech firms in England’s southeast as a whole. However, it can throw some new light on policy-relevant issues that are also being discussed in other studies on the subject, thereby generating key pointers that contribute to the ongoing policy debate and help to give direction to further research on the subject. Another contribution of the paper is to provide some input into the ongoing methodological discussion about innovation measurement.

In Section 2, we discuss relevant literature that forms the conceptual basis for the statistical analysis. It is also shown that some of the issues discussed in that literature have a close bearing on current UK policies towards promotion of innovativeness in small high-technology firms. The conceptual model and indicators used in the paper are discussed in Section 3. The data analysis and a discussion of the findings are in Section 4, followed by conclusions in Section 5.

Section snippets

Review of relevant literature

The point of departure for this paper is a body of literature in which firm-level technological advancement is conceptualised as a learning process (Garvin, 1993, Malerba, 1992, Dodgson, 1991, Dodgson, 1993, Hitt et al., 2000, UNCTAD, 1996, Lall, 1992, Cohen and Levinthal, 1989). Learning results in technological capability—knowledge and skills needed for firms to choose, install, operate, maintain, adapt, improve and develop technologies.

Conceptual model and variables

The main analytical concepts and the relationships between them that are to be explored in the paper are set out schematically in Fig. 1. The oval at the top represents the innovation capability of a firm, which accumulates as a result of the various internal and external inputs discussed above. For the purpose of the data analysis, these inputs have been reorganised under a few main headings. Potentially important internal sources include: (a) the initial educational background and prior

Main findings

The average size of the companies in our sample is 34 employees. The large majority are small (fewer than 50 people) rather than medium in size.6 The largest company employed around 166 people, the smallest one just 5. Average gross value of plant and equipment is £ 633,000. None of the firms were majority-owned by another non-small or medium-sized entity. They had

Conclusions

A range of internal and external factors were found to be statistically significantly related to the innovative performance of the electronics and software development firms that were analysed in this paper. Among the internal factors, the importance of prior experience in a scientific environment stands out. A prevalence of staff with science and engineering degrees in the enterprise was also found to have a positive effect.

These results point towards the importance of specialised knowledge

Acknowledgements

This paper is an output from the research programme ‘SMEs in Europe and East Asia: Competition, Collaboration and Lessons for Policy Support’, coordinated by Edinburgh University and financed by the TSER programme of the European Union. The authors wish to thank Mike Albu for his contributions to conceptualisation and fieldwork, Sanjaya Lall for coordinating the research, and three anonymous reviewers for comments on an earlier draft.

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