Abstract:Responsible research and innovation (RRI) is an approach to research and innovation governance aiming to ensure that research purpose, process and outcomes are acceptable, sustainable and even desirable. In order to achieve this ambitious aim, RRI must be relevant to research and innovation in industry. In this paper, we discuss a way of understanding and representing RRI that resonates with private companies and lends itself to practical implementation and action. We propose the development of an RRI maturity model in the tradition of other well-established maturity models, linked with a corporate research and development (R&D) process. The foundations of this model lie in the discourse surrounding RRI and selected maturity models from other domains as well as the results of extensive empirical investigation. The model was tested in three industry environments and insights from these case studies show the model to be viable and useful in corporate innovation processes. With this approach, we aim to inspire further research and evaluation of the proposed maturity model as a tool for facilitating the integration of RRI in corporate management.
Errors, in particular human errors, play an important role in many aspects of human life, from day-to-day activities to extraordinary situations. This paper describes the theoretical background in the context of Distributed Cognition and the practical design process of an error trapping and mitigation system for supporting seniors' (including disabled seniors) use of public transportation.Data available from the ASSISTANT project support the basic assumption that there are not many things that typically go wrong when people from this target group use buses, trams, trains etc., but that these classes of errors cover a majority of instances. An error model, being a first approximation of a rulebased error capturing and mitigation system, is proposed that is adequate for sparse data and available before the initial use of the system.Several error types, sources of information coming from a Personal Navigation Device and reasonable conclusions are presented and discussed. Furthermore, some examples of an error trapping and mitigation class tree are provided, as well as some aspects of implementing these systems in earlier projects.The on-going ASSISTANT project addresses especially mitigation types and error type classification, which can lead to easier implementation and broader acceptance in a near future.
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State of the Art and Previous WorkConsidering the role that human error plays in both day-to-day life and extraordinary situations, there has been a relative paucity of work done in this field. Early research
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