What makes SMEs more likely to collaborate? Analysing the role of regional policy

The last twenty years have witnessed the diffusion of regional innovation policies supporting networks of innovators. The underlying aim of these policies is to encourage firms, particularly SMEs, to undertake collaborations with organisations possessing complementary knowledge. Focusing on a set of SMEs that have participated, over time, in several innovation networks funded by the same regional government, the paper investigates how their relationships have evolved with respect to the following aspects: (i) reiteration of pre-existing relationships as opposed to experimentation of new relationships; (ii) collaboration with organisations possessing complementary rather than similar knowledge and competencies; (iii) creation of local relationships rather than experimentation of extra-local collaborations; (iv) reliance upon intermediaries to connect with other organisations. Our findings reveal that the involvement in these policy-supported networks changed the firms’ relational patterns, leading them to collaborate with a wider variety of agents than those with whom they were linked before the policies. Sectoral heterogeneity had a negative effect on the probability to collaborate, while co-localisation increased the likelihood to collaborate. Mutual involvement with intermediaries also had a positive effect. However, in the case of firm-to-university relationships only specialized intermediaries were likely to perform a positive role and, therefore, encourage networking

A functional-cognitive framework for attitude research

In attitude research, behaviours are often used as proxies for attitudes and attitudinal processes. This practice is problematic because it conflates the behaviours that need to be explained (explanandum) with the mental constructs that are used to explain these behaviours (explanans). In the current chapter we propose a meta-theoretical framework that resolves this problem by distinguishing between two levels of analysis. According to the proposed framework, attitude research can be conceptualised as the scientific study of evaluation. Evaluation is defined not in terms of mental constructs but in terms of elements in the environment, more specifically, as the effect of stimuli on evaluative responses. From this perspective, attitude research provides answers to two questions: (1) Which elements in the environment moderate evaluation? (2) What mental processes and representations mediate evaluation? Research on the first question provides explanations of evaluative responses in terms of elements in the environment (functional level of analysis); research on the second question offers explanations of evaluation in terms of mental processes and representations (cognitive level of analysis). These two levels of analysis are mutually supportive, in that better explanations at one level lead to better explanations at the other level. However, their mutually supportive relation requires a clear distinction between the concepts of their explanans and explanandum, which are conflated if behaviours are treated as proxies for mental constructs. The value of this functional-cognitive framework is illustrated by applying it to four central questions of attitude research

Prato: The Social Construction of an Industrial City Facing Processes of Cultural Hybridization

This chapter deals with a widely studied case, that is, Prato, a middle-sized city with rooted industrial traditions, in the Centre of Italy. Prato is a textile industrial district embedded in the so-called Third Italy—an area characterized by the presence of small firms spread throughout the territory, linked together in supply and subcontracting relationships—which, in the last twenty years, has undergone a profound transformation as a consequence of the crisis of textile and immigration, leading to the formation of a large Chinese community. The related changes brought with them problems of social cohesion and sustainable development. The authors address these issues by analyzing both academic and public discourses on Prato. Their basic idea is that common stereotypes act as drivers of a public discourse that prevents the city to re-negotiate its identity. The analysis concludes that different forms of hybridization—particularly cultural hybridization—are occurring, which would need further investigations

The Triple Helix in the context of global change: dynamics and challenges

Understanding how economies change through interactions with science and government as different spheres of activity requires both new conceptual tools and methodologies. In this paper, the evolution of the metaphor of a Triple Helix of university–industry–government relations is elaborated into an evolutionary model, and positioned within the context of global economic changes. We highlight how Triple Helix relations are both continuing and mutating, and the conditions under which a Triple Helix might be seen to be unraveling in the face of pressures on each of the three helices – university, industry, and government. The reciprocal dynamics of innovation both in the Triple Helix thesis and in the global economy are empirically explored: we find that footlooseness of high technology manufacturing and knowledge-intensive services counteract the embeddedness prevailing in medium technology manufacturing. The geographical level at which synergy in Triple Helix relations can be expected and sustained varies among nations and regions

Towards estimating computer users' mood from interaction behaviour with keyboard and mouse

The purpose of this exploratory research was to study the relationship between the mood of computer users and their use of keyboard and mouse to examine the possibility of creating a generic or individualized mood measure. To examine this, a field study (n = 26) and a controlled study (n = 16) were conducted. In the field study, interaction data and self-reported mood measurements were collected during normal PC use over several days. In the controlled study, participants worked on a programming task while listening to high or low arousing background music. Besides subjective mood measurement, galvanic skin response (GSR) data was also collected. Results found no generic relationship between the interaction data and the mood data. However, the results of the studies found significant average correlations between mood measurement and personalized regression models based on keyboard and mouse interaction data. Together the results suggest that individualized mood prediction is possible from interaction behaviour with keyboard and mouse

NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics

NeuroBench:Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics