The evolution of an innovation policy in a local system of production. The case of the Regional Programme for Industrial Research, Innovation and Technology Transfer

Aim of this thesis is to analyse the evolution of a policy within a local system of production. We consider policy as standardised social technologies devoted to the coordination of the physical technologies of production. In this sense we can compare them to institutions: arising spontaneously as organic entities for the development of networks of production and then becoming pragmatic as formally embedded in laws and norms. To investigate this process we will employ as case study some industrial policies developed by Regione Emilia Romagna from the 1970s to the Regional Programme for Industrial Research, Innovation and Technology Transfer (PRRIITT) in 2003

How Outward-looking is Smart Specialisation? Results from a survey on inter-regional collaboration in Smart Specialisation Strategies (RIS3), S3 Policy Brief Series No. 16/2016

Smart specialisation (S3) emphasises the identification of niches, cross-sectorial innovation and solving societal challenges. With this comes a need for an outward-looking dimension, to find a region’s potential advantages in international markets, and to identify partners to help deliver new solutions and solve common challenges. This is the case not only for industry and academia, but also for regional policy-makers who need to engage in inter-regional collaboration processes. The purpose of the survey presented in this report was to increase our understanding of the factors underlying successful inter-regional cooperation within S3. It builds on an analytical framework to better understand the multiple dimensions of inter-regional collaboration, developed in a previous working paper (Uyarra et al., 2014). The objectives of this study were to increase our knowledge of inter-regional collaboration in research and innovation (R&I), with the aim of supporting regions and Member States in their collaborative efforts in S3, but also to inform the S3 Platform (S3P) and other European Commission (EC) services on how to best support inter-regional collaboration in R&I policy. The answers from the survey respondents indicate that the EU’s new cohesion policy has led some regions and Member States to change their behaviour in collaboration in R&I policy. More than half of the respondents reported having prior collaboration experiences, of which 67 % reported increased collaboration in the previous 2 years and 30 % reported a stable level of collaborative effort. The factors driving collaboration and the perceived benefits of collaboration include information sharing, meeting a new orientation of regional policy and supporting linkages between R&I and industry. Collaboration largely involves low-intensity activities that bring direct and immediate benefits. Collaboration is most prominent in the first steps of the RIS3 process, analysis, design and decision-making. The criteria underlying the choice of partners are in line with the RIS3 concept; they are based on industry composition (similar or complementary), research capabilities that are complementary or similar, as well as similar societal challenges. In contrast, the survey findings regarding the geographical location of partnering regions, as regions most often collaborate with other regions in their own country. The main barriers to collaboration seem to be inter-related and include lack of resources, insufficient political commitment, insufficient engagement of regional stakeholders and lack of clarity of objectives. One interpretation is that it is challenging to communicate clearly to stakeholders and politicians the outcomes of an intervention, with the result that stakeholders are unwilling commit or mobilise resources. The rationale for innovation policy interventions quite often is to support activities that provide indirect and dynamic benefits that are not easily measured, divisible or attributable to individual actors or activities. In contrast, the least problematic barriers are socio-cultural issues, legal or administrative barriers and lack of trust. It is recommended that regions and Member States better prepare the evidence base for their projects and improve the materials they use to communicate to stakeholders the potential benefits of collaboration and how to achieve them. Regions should also engage more with private sector actors and civil society. The paper indicates the importance of the EC communicating a more complex picture of the dynamics of inter-regional collaboration. An oversimplification of the message might lead to underinvestment and less intensive collaboration than that which is needed to address the larger challenges with potential for longer-term benefits for Europe. The recommendations for S3P include that it should focus on learning activities and support the initiation of collaborative processes. However, it appears that the regions and Member States want S3P support to implement thematic collaboration, but then to be left to themselves to carry it out. Likewise, respondents considered it important that S3P should provide guidance, act as a knowledge hub and offer expert assistance. This indicates that S3P should continue to develop knowledge around inter-regional collaboration and assist regions and Member States in establishing and developing this.JRC.J.2-Knowledge for Growt

Measuring the contribution of higher education to innovation capacity in the EU

The general goals of the study include the provision of evidence on the key factors determining the contribution of higher education institutions (HEIs) to innovation capabilities and expand the understanding of this contribution beyond traditional measures of the role of HEI on innovation capabilities. In this context, the general objective of the study could be verbalised as “to develop a more comprehensive model of the contribution of higher education to innovation capacity”. This objective has been operationalised into the following five specific objectives which define in detail the purpose of the study:  Specific Objective 1: Completion of a comprehensive literature review of existing means and methodologies used for capturing, interpreting and also applying data and evidence related to the contribution of higher education systems to innovation capacity;  Specific Objective 2: Critical assessment of the existing literature, including an identification of gaps and an assessment of the merits of different approaches used;  Specific Objective 3: Development of a new approach, that provides an alternative set of indicators to measure the contribution of HEIs to innovation capacity;  Specific Objective 4: Implementation of the prototype set of alternative metrics;  Specific Objective 5: Discussion of the feasibility of developing new proxies or metrics for capturing the contribution of higher education systems to innovation capacity at the EU level. In general, the objective of the project and its research tools is therefore to propose a set of indicators for future measurements of the innovation impacts of HE that is validated through the opinions of the different stakeholders in the field (through interviews, case studies and a survey)

Measuring the contribution of higher education to innovation capacity in the EU. Executive Summary

There has been a massive expansion of higher education in recent decades as part of attempts to create workforces with the skills to be able to compete successfully in the context of the knowledge based economy. This emerging context demands new kinds of skills and approaches from workers to feed into industries that are evolving rapidly. Economic strength in the knowledge-based economy is driven by innovation, taking existing resources and assets and using them to do new things better and increase overall welfare levels. Whilst innovation is necessary across government, business, and civil society, universities are at the heart of attempts to improve the overall knowledge capital endowments that provide the feedstock for innovation as well as a proving ground for future innovators. At the same time, there is widespread unrest that universities are failing to respond to these new demands and are continuing to act as ‘ivory towers’ outside of rather than driving forward society (Galan-Muros, 2016). Particular concern lies on perceptions that universities have tended to expand their existing activities rather than to create new courses, pedagogies, and learning environments that best meet these needs. Where universities contribute effectively to innovation, then they can create whole new industries and sectors, and transform the fortunes of particular places. But at the moment, these conflicting narratives make it hard for policy-makers to determine whether universities (and indeed, which kinds of universities) are a boost to or a drag upon innovation capacities. A key challenge for European policy-makers is therefore distinguishing the extent to which universities are realising their potential to contribute to the emergence of the knowledge-based economy. By distinguishing which institutions are and are not realising this potential, policy-makers can developed a more nuanced set of engagement stimuli that can help to maximise this contribution and optimise the returns that European societies receive for their substantial public investments in higher education. This means that are providing the necessary education and knowledge base to deliver the ambitions of Europe 2020 and support Europe’s transition towards a successful, just and sustainable economy. This requires dealing with the uncertainty of the extent to which universities’ contribute to supporting the development of the emerging knowledge economy. Here we define ‘innovation’ as the result of the set of activities by which different kinds of knowledge are combined to create solutions and interventions to solve problems, ultimately making society a better place (a form of Schumpeterian perspective). Those societal improvements may be through: (a) raising competitiveness and creating new markets and sectors, (b) improving the delivery of public services, particularly to vulnerable social groups, or (c) reducing our environmental impacts. We seek to understand the extent to which universities are supporting ‘innovation’ as here defined to distinguish between good and bad performances, as the first step in a process by which policy-makers actively intervene to improve the performance of universities overall

Employment in European high tech manufacturing SMES during the recovery (2009-2011)

ABSTRACTThe industrial policy debate in the EU is mostly centred on the importance of high-tech manufacturing in the recovery from the 2008/2009 crisis and for the future prosperity of Europe. This paper looks at employment in European high-tech manufacturing Small and Medium sized Enterprises (SMEs) during the recovery from the global financial and economic crisis: 2009 - 2011. Its aim is to study the relations between employment in said sector and macroeconomic, policy and structural factors. A simple regression is used to ascertain the influence of these factors on employment in high-tech manufacturing SMEs. Policy implications are also drawn.  ABSTRACT The industrial policy debate in the EU is mostly centred on the importance of high-tech manufacturing in the recovery from the 2008/2009 crisis and for the future prosperity of Europe. This paper looks at employment in European high-tech manufacturing Small and Medium sized Enterprises (SMEs) during the recovery from the global financial and economic crisis: 2009 - 2011. Its aim is to study the relations between employment in said sector and macroeconomic policy and structural factors. A simple regression is used to ascertain the influence of these factors on employment in high-tech manufacturing SMEs. Policy implications are also drawn.  RESUMENEl debate sobre la política industrial en la UE está fundamentalmente centrado en la importancia que deben tener los sectores manufactureros de alta tecnología en la recuperación de la crisis del 2008/2009 y para la prosperidad futura en Europa. Este artículo analiza el empleo en pequeñas y medianas empresas (SME en sus siglas en inglés) europeas que trabajan en sectores de alta y media tecnología y se centra en el periodo de recuperación de la crisis global y financiera del 2009-2011. El objetivo es estudiar las relaciones entre el empleo en dicho sector y la política macroeconómica y los factores estructurales. Se hace uso de una regresión simple para comprobar la influencia de dichos factores sobre el empleo en SME en sectores de alta tecnología. A partir de este análisis se extraen recomendaciones en materia de política económica.   &nbsp

Evolving missions and university entrepreneurship:Academic spin-offs and graduate start-ups in the entrepreneurial society

A recent call has urged to broaden the conceptualization of university entrepreneurship in order to appreciate the heterogeneity of contexts and actors involved in the process of entrepreneurial creation. A gap still persists in the understanding of the variety of ventures generated by different academic stakeholders, and the relationships between these entrepreneurial developments and university missions, namely, teaching and research. This paper addresses this particular gap by looking at how university teaching and research activities influence universities’ entrepreneurial ventures such as academic spin-offs and graduate start-ups. Empirically, we analyse the English higher education sector, drawing on institutional data at the university level. First, we explore the ways in which teaching and research activities are configured, and secondly, we examine how such configurations relate to academic spin-offs and graduate start-ups across different universities over time. Our findings suggest, first, that the evolution of USOs and graduate start-ups exhibit two different pathways over time; and second, that teaching and research both affect entrepreneurial ventures but their effect is different.JRC.B.4-Human Capital and Employmen

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis