Intangible assets, absorbing knowledge and its impact on firm performance: theory, measurement and policy implications

Abstract

The use of intangible assets (IA) is widely recognised as a key driver of enterprise performance. A concept that is closely linked to IA is absorptive capacity, which is defined as the ability to exploit knowledge that is embodied in IA. The main objective of this paper is to explore what is meant by absorptive capacity, before examining the empirical relationship between absorptive capacity and various dimensions of firm performance. The latter is not straightforward because there is no agreed approach to measuring absorptive capacity. The approach taken here is to use data on whether firms sourced knowledge or collaborated externally from the UK Community Innovation Survey. This allows us to show that there is a clear and important link between absorptive capacity and various dimensions of firm performance. In this paper, we aim to contribute to the special issue by focusing on the role mobilising knowledge can play in improving the performance of firms in the private sector, which has a particular resonance in these difficult economic times. Our central message is that for firms to perform better in hard times, they need to mobilise their absorptive capacity. Government must therefore consider whether they should focus their efforts on helping firms directly to increase their own absorptive capacity or on improving the flow of (local) knowledge through supporting networks. Our view is that, while maintaining existing policies that aim to increase connections and encourage collaborations between firms, there should be a greater emphasis on the firm because evidence shows that unless firms have sufficient absorptive capacity, they will not be able to fully internalise the benefits of any knowledge spillovers, no matter how large such spillovers may potentially be.

Keywords:

• intangible assets
• absorptive capacity
• firm performance

Notes on contributors

Richard Harris currently is employed by Durham University Business School. Before that he held the positions at Glasgow University, Newcastle, Durham and Portsmouth in the UK and Waikato in New Zealand. He has published over 130 journal articles, book chapters and books. He has undertaken a significant amount of public policy research, including extensive work for UK Trade & Investment (UKTI). In 2012–2013 he was a member of the BIS/Foresight Lead Expert Group on The Future of Manufacturing in the UK and also a special advisor to the Parliamentary Commission on Banking Standards.

John Moffat holds an undergraduate degree from the University of Edinburgh, an MSc from the University of Warwick and a PhD from the University of Glasgow. Prior to joining the Economics department at Swansea University, he worked for the Spatial Economics Research Centre at the University of Glasgow and on the Impact of Higher Education Institutions on Regional Economies initiative at the University of Strathclyde. His main research interests lie in the application of econometric techniques to firm/plant-level panel datasets.

Notes

1. In concrete terms, intangible assets have been classified by some ‘… into economic competencies (i.e., invest in skills, advertising and branding and organisational structure), scientific and creative property (i.e., R&D and “innovation” more generally) and Information and Communications Technology’ (BIS, 2012; emphasis in original).

2. To some extent, the issue here is between the existence of stocks of IA and their use (i.e. the flow of knowledge obtained from them), which we discuss below.

3. The exception is the impact of R&D on performance – e.g. see Hall, Mairesse, and Mohnen (2009). A longer version of this paper discusses more fully the literature covering the economic impact of IA on firm performance (Harris & Moffat, 2012a).

4. As pointed out in MERITUM (2002), intellectual capital can be both the product of R&D activities and the enabler for creating greater value from R&D.

5. Defined as returns in excess of their opportunity costs, to distinguish them from monopolistic rents when firms restrict output.

6. Due to space constraints, a fuller discussion of the literature covering dynamic capabilities is available in the longer version of this paper.

7. The longer version of this paper sets out in more detail the information available in CIS (see also Table 1).

8. However, Metcalfe and Georgiou (op. cit.) do recognise that much of this technology infrastructure is about sharing information of a non-proprietary kind, and thus it is generic (e.g. pre-competitive, far-from-market product and process developments involving shared R&D). Thus, Metcalfe and Georgiou (op. cit.) acknowledge that the knowledge transfers they are referring to are ‘not the defining element in the generation of competitive advantage’, since they also state that ‘the value to the firm of acquired knowledge clearly depends on how complementary that knowledge is to the firm's existing knowledge base’. They also admit that this emphasis on joint-learning has the possibility of ‘lock-in’ to inferior outcomes (cf. network failures), and therefore argue that innovation infrastructures are more appropriate for incremental innovation (presumably technology diffusion) and less good with more radical innovation (presumably this covers examples like new products and processes involving the creation of new markets).

9. This implies that firms with high levels of absorptive capacity are more likely to engage in networking (through collaborative arrangements and other ways of sourcing external knowledge); the reverse is not necessarily the case if firms need to (a priori) have higher absorptive capacity to benefit from networks.