We research on the overall social-economic and ethic impact of the innovation activities. Impact analysis is a component of the policy or programming cycle, where it can play two roles: A) Ex ante impact analysis. This is part of the needs analysis and planning activity of the policy cycle. It involves doing a prospective analysis of what the impact of an intervention might be, so as to inform policymaking – the policymaker’s equivalent of business planning; B) Ex post impact assessment. This is part of the evaluation and management activity of the policy cycle. Broadly, evaluation aims to understand to what extent and how a policy intervention corrects the problem it was intended to address. Impact assessment focuses on the effects of the intervention, whereas evaluation is likely to cover a wider range of issues such as the appropriateness of the intervention design, the cost and efficiency of the intervention, its unintended effects and how to use the experience from this intervention to improve the design of future interventions.
We adopt also foresight methodologies to outline potential long-term scenarios, among which: a) territorial and environmental foresight; b) economic scenarios; c) technological scenarios; d) resources scarcity.
We follow a systematic approach to co-design, test and validate sustainable and inclusive business models for our clients and innovation pilot scenarios. Our approach builds on the Triple Layered Business Model Canvas, a tool employed in practice to facilitate sustainable innovation. This helps us co-design and analyse alternative models from an integrated perspective (considering socio-economic and environmental aspects such as upcycling opportunities, gender issues, social benefits, etc.), while preserving the advantage of simplicity, so as to be easily understood and used by a broad range of stakeholders. Our methodology is grounded on evidence collected from our research and pilot activities. It unfolds in iterative steps for selecting the best value propositions with the help of Business Model Co-design workshops, before refining them based on the evidence we collect from testing as well as the opportunities, threats and constraints we discover in the process.
Responsibility by design is a collaborative approach promoting participatory and co-design methods to empower users and other stakeholders in design and in terms of ensuring successful outcomes by means of a deep understanding of the different stakeholder’s needs, values and circumstances in the context of the ecosystem. We analyse how to being aware of the human, societal and ethical values related to a particular design and reflecting them in the design. Our research targets at enlarging the RRI paradigm by extending the conceptual and procedural approach, and practical implications in order to make it even more usable and useful for innovation ecosystems. The scope is to extend the focus on the whole stakeholders’ supply chain by analysing policies and strategies in the context of structural and dynamic changes within a smart industrial environment hence providing an updated set of performance indicators. It reaches a preliminary understanding of the role of the social sphere into this multilevel ecosystem.
“An ecosystem is a set of actors with varying degrees of multilateral, non-generic complementarities that are not fully hierarchically controlled”. Innovation systems are multidimensional networks and are characterised by interconnections among actors in different organizational context (e.g. university, government and non-profit research institutions, and business enterprises). Knowledge creation and transfer processes within these contexts are characterized by uncertainty and controversy, particularly in the interactions among actors in the network and the exploration of knowledge.
The rationale about this research sector is based upon is that there are several similarities between evolutionary perspectives of interrelationships of organisms and their environment, and innovation processes. The flows of technology and information among people, companies and institutions are crucial to the innovative process, indeed innovation and technology development are the results of a complex set of linkages among actors producing, distributing and applying different types of knowledge. These actors are primarily universities, research institutes, business enterprise, and the people within them
The generic model we use in our research describes the innovation ecology as a complex system composed of many interlinked elements: time, organizational structure, physical space, tolerance of risk, strategy, recognition and incentive systems, virtual space, structured and spontaneous processes, knowledge management, financial capital, diversity, attention to the future, challenge and, finally, conversation. Each model is customised by defining the emergence of system-making connections and these connections typically flow from the need to solve specific innovation problems.
Organizations are at the centre of the sustainable transformation process of all human activities: social sustainability, economic sustainability, environmental sustainability. The challenge is to find a coherent balance among all the theoretical contributions of the multiplicity brought by the various members of an interdisciplinary environment and describe reliable and effective models solving the complexity of different sources of information and a set of ontologies to establish communication and elaboration rules. The research focus lies on the exploration of patterns and processes common to different perspectives expressed by the heterogeneous realms of the interdisciplinary environment. It focuses also on the definition of a common and multi-level model able to embed all the interdisciplinary requirements and to ensure shareable information format and methods for data analysis. As solution, we propose methodological frameworks able to “elaborate” and “talk” through the data heterogeneity and harmonise the various research and communication approaches into a shared interdisciplinary model.