Mercedes Bleda

Resources, capabilities and competences for eco-innovation

AbstractThe existing literature on the determinants for the development and adoption of ecoinnovations has generally focused on analysing the influence of business strategies and external drivers (public policy and stakeholder impacts) on innovation processes in firms. Internal factors to the firm such as resources, capabilities and competences (RCCs), which are important drivers of business strategies and innovation performance, are seldom considered in the literature. This paper builds an integrated framework that incorporates the impact of those firms internal factors and their interactions with external drivers on the development and adoption of eco-innovations. The relevance of those factors regarding several dimensions of eco-innovation is illustrated with case studies. It is shown that, while all RCCs are relevant for the development and uptake of ecoinnovations, their relevance differs across eco-innovation dimensions.

Entrepreneurship and the emergence of industrial clusters

Innovation and entrepreneurship are the most important catalysts of dynamism in market economies. While it is known that entrepreneurial activities are locally embedded, mutual effects of entrepreneurs and their local regional environment have not been adequately addressed in the existing literature. In this article, we use agent-based simulation experiments to investigate the role of entrepreneurship in the emergence of regional industrial clusters. We present fundamental extensions to the Simulating Knowledge Dynamics in Innovation Networks model Ahrweiler et al., Industry and Labor Dynamics: The Agent-based Computational Economics Approach; World Scientific: Singapore, 2004; pp 284-96 by using a multilevel modeling approach. We analyze the effects of changing entrepreneurial character of regions on the development industrial clusters in two simultaneously simulated regions. We find that an increase in the entrepreneurship of one region has a negative effect on the other region due to competition for factors of production and innovative outputs. The major policy implication of this finding is the limitation it posits on regional innovation and development policies that aspire to support clusters in similar areas of industrial specialization.

Theoretical Approaches to Dynamic Efficiency in Policy Contexts: The Case of Renewable Electricity

Dynamic efficiency (or the ability of a policy instrument to generate a continuous incentive for technical improvements and cost reductions in technologies) is central to the assessment and choice of environmental and energy policies in long-run scenarios where innovation lock-in is relevant. This is also the case in instruments that support electricity from renewable energy sources (RES-E). In contrast with effectiveness and static efficiency assessment criteria, the innovation effects of such support have received much less attention from both a theoretical and an empirical perspective. Several theoretical perspectives have paid some attention to these innovation effects, including the traditional economics approach, the systems of innovation perspective and the literature on learning effects. The aims of this chapter are to provide an overview of those perspectives and to build bridges between them.

Multilevel Analysis of Industrial Clusters: Actors, Intentions and Randomness Model

The literature on industrial clusters indicates a symbiotic relationship between innovation and geographical concentration of firms working in similar industries. Innovative processes require different forms of knowledge and expertise, which are distributed across individuals and organisations at different levels of industrial clusters. In this chapter, we present fundamental extensions to the SKIN model for representing such multi-level interactions. We introduce individual actors in addition to firms as agents. These agents are placed in a two-regions environment that simulates evolution of two competing regions. We also integrate elements of intentionality in addition to randomness in our model. Through subjective assessments of their managers, firms investigate and design research projects. These extensions help to open up the black box of the firm and relate firms to the creative agency of individuals in starting up new firms, establishing their research objectives and creating new knowledge. Within this broad range of issues we focus in this chapter on the role of entrepreneurship to illustrate how the extended model can be used. In experiments focusing on entrepreneurship, we generate the relative success of Silicon Valley in comparison to Boston in silico.

Economic Crisis, Turbulence and the Resilience of Innovation: Insights from the Atlantic Maritime Cluster

Innovation is related to economic cycles. Often seen as a procyclical phenomenon, many innovation actors try and succeed in maintaining (and even increasing) their innovation efforts to gain competitive advantage during the crises. In this chapter, departing from the recent developments in regional studies, which understand resilience as an evolutionary capacity of socio-economic systems, we suggest the notion of ‘resilience of innovation’ as the capacity of an innovation process to maintain its function at different levels of operation. Drawing upon the results from a survey on knowledge provision and needs of maritime cluster innovative actors in the European Atlantic Area, our analysis focuses on the evolution of innovation and knowledge services. We provide parametric and non-parametric evidence of the differences in the provision and utilisation of these services and provide econometric evidence of the main factors that influence the resilience of innovation at the organizational level.

Modeling Affect and Cognition: Opportunities and Challenges for Managerial and Organizational Cognition

Abstract In this chapter we examine some possibilities of using computer simulation methods to model the interaction of affect and cognition in organizations, with a particular focus on agent-based modeling (ABM) techniques. Our chapter has two main aims. First, we take stock of methodological progress in this area, highlighting important developments in the modeling of affect and cognition in other fields, including psychology and economics. Second, we outline how ABM in particular can help to advance managerial and organizational cognition by building and testing theoretical models predicated on the interaction of affect and cognition. We argue that using ABM for this purpose can improve the level of specificity of cognitive and affective concepts and their interrelationships in organizational theories, yield more behaviorally plausible models of behavior in and of organizations, and deepen understanding of the generative behavioral mechanisms of multi-level organizational phenomena. We highlight possibilities for using ABM to model affect–cognition interactions in studies of mental models, collective cognition, diversity in work groups and teams, and organizational decision-making.