Abstract
In our previous editorial, we positioned our perspective and introduced the acronym “ROTRUS” to characterise the science of managing for innovation as – Real world, Observable, Testable, Replicable, Uncertain and Social. Specifically, we argued that methods that draw on point-in-time beliefs, perceptions and de-humanised data in a complex and evolving social setting of innovation management pose a challenge for replicability. We warned innovation researchers to avoid the pitfalls that might foster pseudoscience and generalised assumptions from information that is still in the proto-science stage. Drawing on longstanding understanding in psychology of the whole human, we discussed the need to explore methods that capture brain, mind and behaviour aspects in innovation management, spanning the analysis from individual to group and societal levels. In this editorial, we move the discussions forward by focusing on one plausible methodological approach to advance the science of managing for innovation – behavioural experiments. In the following sections, we explain our methodological stance or in other words our world view followed by a brief review of behavioural experiments and their relevance to innovation research. We conclude with a foreword on our final editorial in the series titled the science of managing for innovation. (...)

Abstract
Some might argue that ever so nimble and responsive innovation paradigms can rarely be managed scientifically. We propose a more inclusive perspective. Science of managing for innovation has certain characteristics which we identify through the acronym “ROTRUS”- Real-world, Observable, Testable, Replicable, Uncertain and Social. Real-world refers to the notion that innovation happens in practical settings, be bound by resources and capabilities. This real-world is the context in which the observable events occur. To progress the understanding of formative predictors and their impact on innovation, the innovation events need to be observable. This may be challenging if we are to believe that much of the innovation is driven by heuristics (see e.g. Lopez-Vega, Tell and Vanhaverbeke, 2016; Nisch and Veer, 2018). Observable evidence in our perspective does not mean it needs to be capable of being observed but includes events or phenomenon that were observed. In this sense, managerial heuristics once actioned become observed evidence, such that observable evidence is any evidence that can be or has been experienced by one or many, regardless of whether this can be observed by a third party. (...)

Abstract
This study aims to conceptualise the risks of a co-creation relationship between providers and customers in the front end of innovation (FEI). The existing literature on the topic of the interactions between customers and providers is vast but mostly reflects the vision of each respective field of science addressing specific issues and dynamics. This review paper combines integrative and generative approaches (Post et al., 2020). The narrative overview methodology (Green et al., 2006) fits the purpose to integrate the contributions of scholars from different areas and with distinct perspectives. The authors present a comprehensive view of the complex and, sometimes, conflictual customer-provider relationship in a business-to-business (B2B) context applied to the FEI. The review, mostly focused in the service industry, included the identification of inconsistencies and poorly explained phenomena. This work contributes decisively to the innovation, management and organisation literature, advancing a model of the projected risks of the specific customer-provider relationship in the FEI phase.

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This paper describes results of work developed at INESC within the scope of the Esprit-5478 Shop-Control Project, as well as other related developments. The objective of the Shop-Control project1 has been to help SME's in reducing their operating expenses and enable them to deliver high quality products on schedule by providing them with an integrated shop floor control system. This system is built on an integrated set of shop floor application software packages interacting with the actual shop floor environment through factory data collection equipment and shop floor communication networks. The developed application packages include shop floor scheduling and dispatching, shop floor monitoring, supervision and alarm management, quality assurance and maintenance management. In this paper, the relevance of on-line shop floor data collection is put into focus and the underlying infrastructure is described. Information flow between interacting software applications is also presented, with a special emphasis for shop floor Supervision facilities. The integrated view on the shop floor which is achieved is of utmost importance in the support of shop floor management decision making in a wide range of end-user industries.

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