Technological regimes, Schumpeterian patterns of innovation and firm-level productivity growth

The article investigates the relationships between technological regimes and firm-level productivity performance, and it explores how such a relationship differs in different Schumpeterian patterns of innovation. The analysis makes use of a rich dataset containing data on innovation and other economic characteristics of a large representative sample of Norwegian firms in manufacturing and service industries for the period 1998–2004. First, we decompose TFP growth into technical progress and efficiency changes by means of data envelopment analysis. We then estimate an empirical model that relates these two productivity components to the characteristics of technological regimes and a set of other firm-specific factors. The results indicate that: (i) TFP growth has mainly been achieved through technical progress, while technical efficiency has on average decreased; (ii) the characteristics of technological regimes are important determinants of firm-level productivity growth, but their impacts on technical progress are different from the effects on efficiency change; (iii) the estimated model works differently in the two Schumpeterian regimes. Technical progress has been more dynamic in Schumpeter Mark II industries, while efficiency change has been more important in Schumpeter Mark I markets.

Incremental learning from stream data

Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries. Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process. In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance. Detailed system level architecture and design strategies are presented in this paper. Simulation results over several real-world data sets are used to validate the effectiveness of this method.

The role of proximity in university-business cooperation for innovation

The potential for universities to contribute positively to business innovation has received much attention in recent years. While the determinants of university-business cooperation have been examined extensively, less attention has been given to the mediating influence of proximity in this relationship. The analysis in this paper builds on theUKbusiness innovation survey (2002–2005) by incorporating measures of the university research environment for each of the 16,500 businesses surveyed. These measures allow us to look beyond business-level characteristics as determinants of the geography of university cooperation and account for the character of the local university environment. Measures include the distance from each business to its nearest university, the quality of local university research and the density of the university research environment. The findings suggest that significant differences exist between those businesses that cooperate with local universities and those that cooperate with non-local universities. These differences relate to business size, sales profile, location, absorptive capacity and innovation activity. In addition, we also find that if a business is located close to a research excellent university, cooperation tends to remain local, however, the distance between businesses and the nearest university is not a significant determinant of university-business cooperation and further, the higher the concentration of universities in the business locale, the more likely businesses are to cooperate with non-local universities.

Catalysing open innovation through publicly funded R&D: A Comparison of University and Company-based research centres

Public funding of university and company-based R&D centres of excellence is widespread both in core and more peripheral regions. What is less well-known is whether these R&D centres can catalyse multi-directional, multi-actor and iterative innovation. Based on data from a real-time monitoring study, this article explores the development of 18 R&D centres’ external connections. University-based R&D centres establish more new connections than company-based centres and are more likely to be interacting with small or micro-firms. However, there is a general bias towards links with larger firms; micro, small and medium-sized enterprises also are less likely to be involved in collaborative R&D with research centres than other types of relationships. The results suggest the potential for R&D centres to act as a catalyst for open innovation but emphasise the need to ensure that the focus of the R&D being conducted is relevant to the needs of smaller firms.