The Evolution of Knowledge and the Dynamics of an Industry Network

The paper moves a step forward in the direction of establishing a connection between the structure and evolution of knowledge bases and the structure and evolution of organizational forms in innovative activities in a science-intensive industry. The paper has an explicit focus on the dynamics of the network of collaborative agreements in R&D in the pharma/biotech industry after the “molecular biology revolution”. Using a comprehensive dataset, built by the authors integrating several sources in the industry, the dynamics of the network over time is extensively analyzed. With regards to network structure, it is found that, while the size of the network increases over time due to net flows of entry, its topological properties remain relatively unchanged. The evolution of the network has occurred without relevant deformations in the core-periphery profile. With regards to age-dependent propensity to collaborate, the paper finds that the extent of inter-generational collaboration is much more significant than intra-generational collaboration. In addition, the propensity of firms of a given generation to enter into collaboration with firms of a different generation increases with the distance between the two, while the total number of intra-generational collaborations decreases over time and, moreover, tends to decrease for most recent generations. In the paper a unitary and coherent explanation of the evidence is developed, coming to reveal the existence of a striking isomorphism between structural properties of the dynamics of knowledge and of the evolution of network structure. This revised version was published online in July 2006 with corrections to the Cover Date.     

Machine learning and design of experiments with an application to product innovation in the chemical industry

Industrial statistics plays a major role in the areas of both quality management and innovation. However, existing methodologies must be integrated with the latest tools from the field of Artificial Intelligence. To this end, a background on the joint application of Design of Experiments (DOE) and Machine Learning (ML) methodologies in industrial settings is presented here, along with a case study from the chemical industry. A DOE study is used to collect data, and two ML models are applied to predict responses which performance show an advantage over the traditional modeling approach. Emphasis is placed on causal investigation and quantification of prediction uncertainty, as these are crucial for an assessment of the goodness and robustness of the models developed. Within the scope of the case study, the models learned can be implemented in a semi-automatic system that can assist practitioners who are inexperienced in data analysis in the process of new product development.     

Ownership structure,investors’ protection and corporate valuation: the effect of judicial system efficiency in family and non-family firms

Research on the effect of ownership structure on firm performance shows no convergent evidence concerning the sign and form of the above-mentioned relationship. Similarly, there is no homogeneous evidence documenting family ownership concentration is always positively or negatively correlated with firm value, or irrelevant. This paper analyses whether and how the de facto investor protection provided by the judicial system affects the relationship between corporate performance and ownership structure in 1314 firms operating in four European countries (Germany, France, Italy, and Spain) over a five-year period, 2010–2014. Moreover, we analyse whether judicial system efficiency influences if and how family firms in the controlling coalition collude for expropriating minority shareholders. Our findings show that the level of shareholder protection, derived from judicial efficiency, is relevant to the relationship between ownership structure and firm performance, thus corroborating literature in that institutional contexts matter in explaining such relations. The results suggest the need for more efficient external mechanisms of corporate governance to facilitate investment in equity capital, thus decreasing the country risk perceived by investors.     

A Generalized Estimating Equation in Longitudinal Data to Determine an Efficiency Indicator for Football Teams

Over the years football has attracted enormous interest from various fields of study, attracting attention both for its sporting and social aspects. Professional business operators consider football an important industry with enormous potential both in terms of its size and growth, and also because of indirect benefits due to the popularity gained by investors and management of football teams. The focus of the analysis has been on what characterizes most football clubs, and determines their particular economic and financial needs. The aim of this paper is to establish an efficiency measurement for football team financial resource allocation. In particular, we analysed the impact that the income statement, Net equity and Team value variables have on the points achieved by football teams playing in “Serie A” championship (Italian league). The method used in our study is a generalized estimating equation (GEE) for longitudinal count data. In addition we consider a coefficient of determination in the GEE approach based on Wald Statistics, and we propose a modified Mallow’s Cp for choosing the best model. Finally we propose an AFRSport index based on the differences between observed and theoretical points, in order to identify those teams that efficiently employ their financial resources.

     

Estimation of Feral Pigeon (Columba livia) population size using a novel Superimposed Urban Strata (SUS) method

A reference method to estimate Feral Pigeon (C. livia) population size in urban areas is missing: results of different surveys are difficult to compare due to the wide heterogeneity of approaches. A combined technique, the Superimposed Urban Strata (SUS) method, especially designed to estimate pigeon population size in urban areas, is proposed. SUS implements methods based on stratified sampling layers: its reliability was tested with a pilot study in Padua (Italy). The survey area was split into two superimposed strata and a different survey technique was applied to each stratum. The first stratum consisted of twelve topographic sampling units, identified as critical ornithic-density loci. The second stratum was obtained by selecting 10 % of the survey area with systematic sampling. In the estimate of the population size, different detection probabilities were assigned to the two strata. The SUS method was first applied in 2007, when three-year growth projections for the C. livia population were calculated, with a density-dependent model. In 2010, the survey was repeated to countercheck previous data. Estimated C. livia population size was of 2340 ±93 units in 2007, whereas in 2010 it was of 3679 ±161 units. The 2010 estimated population size resulted in fair accordance with the three-year projections, showing less than a 4 % mismatch. SUS proved to be an adequate estimate method for urban areas: so far it has been tested only in an average Italian urban area, but it could be easily adapted to other cities by calibrating parameters linked to the peculiar urban background.     

Construction,Co‐production,and Beyond. Academic Disputes and Public Concerns in the Recent Debate on Nature and Society

The debate on realism and constructionism has transferred to environment and science studies a long‐standing philosophical controversy over the constitution of, and human cognitive access to, nature. Quite lively years ago, the dispute has been gradually sidestepped by approaches that, without merging into a full‐fledged theory, counter the subject/object dichotomy and argue about the mutual constitution of knowledge and reality. As earlier, analysis and critique, academic questions and ‘public’ concerns, are closely intertwined. Co‐production scholarship has addressed a number of issues at the crossroads of science and policy, offering a reply to the alleged weaknesses of constructionism. Cutting‐edge approaches, with special reference to ‘new materialism’, are now moving forward, making a case for the liveliness and full agential role of matter. They build on different areas of scientific inquiry, where distinctions between living and non‐living and material and symbolic entities are increasingly blurring. They tend, however, also to align with an emergent way of regulating the interface with the material world, which can be ascribed to the neoliberal rationality of government.