No abstract

We survey the economic literature, both theoretical and empirical, on the choice of intellectual property protection by firms. Our focus is on the trade-offs between using patents and disclosing versus the use of secrecy, although we also look briefly at the use of other means of formal intellectual property protection. (JEL D82, K11, O31, O34)

A surprisingly small number of innovative firms use the patent system. In the UK, the share of firms patenting among those reporting that they have innovated is about 4%. Survey data from the same firms support the idea that they do not consider patents or other forms of registered IP as important as informal IP for protecting inventions. We show that there are a number of explanations for these findings: most firms are SMEs, many innovations are new to the firm, but not to the market, and many sectors are not patent active. We find evidence pointing to a positive association between patenting and innovative performance measured as turnover due to innovation, but not between patenting and subsequent employment growth. The analysis relies on a new integrated dataset for the UK that combines a range of data sources into a panel at the enterprise level.JEL classifications: L21, L25, O34.

The concept of Big Data has become very popular over the last decade, with large technology companies successfully building their business models around its exploitation. The public sector in the United Kingdom has tried to follow suit and local governments in particular have tried to introduce new models of service delivery based on the routine extraction of information from their own Big Data. These attempts have been hailed as the beginning of a new era for the public sector where service delivery and commissioning are shaped by data intelligence on local needs and by evidence on the outcomes. In this article we assess this claim and the extent to which it captures the way local governments in the United Kingdom use intelligence from Big Data in light of the structural barriers they face when trying to exploit their data. We also present a case study on the development and deployment of an integrated data model for children services in a large county council in the South-East of England.

Absorptive capacity is one of the most influential concepts in the management and innovation 8 literature. First introduced by Cohen and Levinthal (1989), it is typically defined as a set of 9 organisational routines and processes that allow firms to assimilate, transform and exploit 10 external knowledge. An aspect that has been ignored by the literature on absorptive capacity is 11 the nature of the knowledge being absorbed. This paper suggests that the learning strategies 12 underpinning absorptive capacity adapt to the type of external knowledge they are more likely to 13 get exposure to and as a result, not all the firms appear to benefit from the same type of external 14 knowledge for the same level of absorptive capacity. To this purpose, we explore how firm-level 15 absorptive capacity mediates the relationship between rent and pure R&D spillovers on the one 16 hand and firm-level turnover on the other in three economic areas (Europe, Japan and US). The 17 empirical analysis uses a dataset (sourced from the EU R&D investment scoreboards) made of 18 879 worldwide R&D-intensive manufacturing firms. Given the panel data structure of the 19 sample, econometric techniques that deal with unobserved heterogeneity as well as weak 20 exogeneity are employed. The empirical results suggest for the same level of absorptive capacity, 21 firms in economic areas that are closer to the world technology frontier tend to benefit more 22 from pure (knowledge) spillovers than from rent spillovers. Vice versa, firms located in areas that 23 are not on the technology frontier appear to benefit mostly from rent spillovers that travel along 24 the supply chain. These results suggest that absorptive capacity changes with the type of 25 knowledge they may get exposed to.

This paper analyses the impact that financial constraints have on women"s entrepreneurial choice. The empirical analysis is based on the data provided by the Household Survey of Entrepreneurship database that surveys individuals" intentions of becoming self-employed in England, UK. We do find evidence that women are less likely to seek external finance for business start-ups. This suggests that women in the general population perceive stronger financial barriers to business start-up than men, and this may be discouraging them from seeking external financial support. We find no evidence, however, that once women do seek finance for start-ups they are any less likely to obtain it than men.

We survey the economic literature, both theoretical and empirical, on the choice of intellectual property protection by firms. Our focus is on the tradeoffs between using patents and disclosing versus the use of secrecy, although we also look briefly at the use of other means of formal intellectual property protection.

The purpose of this paper is to discuss the opportunities talent analytics offers HR practitioners. As the availability of methodologies for the analysis of large volumes of data has substantially improved over the last ten years, talent analytics has started to be used by organizations to manage their workforce. This paper discusses the benefits and costs associated with the use of talent analytics within an organization as well as to highlight the differences between talent analytics and other sub-fields of business analytics. It will discuss a number of case studies on how talent analytics can improve organizational decision-making. From the case studies, we will identify key channels through which the adoption of talent analytics can improve the performance of the HR function and eventually of the whole organization. While discussing the opportunities that talent analytics offer organizations, this paper highlights the costs (in terms of data governance and ethics) that the widespread use of talent analytics can generate. Finally, it highlights the importance of trust in supporting the successful implementation of talent analytics projects.