In this article we report on the design, prototyping and results of a research effort aimed at identifying whether and how trust affects the innovativeness of a partnership between two players. The methodology combined an experiment and two questionnaires. The research aimed to increase our understanding of trust and its impact on the innovative outcome of cooperation and to derive some guidance for economic actors, namely R&D managers and executives who intend to build innovation‐oriented relationships with their business partners. Specifically, we investigated the effect of trust on partners' creativity and willingness to invest financially in a joint development. Our results show that more trustful partners invest higher amounts in the alliance, while there seems to be an optimum amount of mutual trust between partners who maximize their joint creativity and innovativeness; if the level of mutual trust is below or above this threshold, their joint creativity seems to increase less or even to decrease. Our findings suggest that joint development projects should always include explicit trust development activities at the beginning of the project, and that the amount of trust in the joint team should be monitored to avoid the negative consequences of excessive trust.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. In this article we report on the design, prototyping and results of a research effort aimed at identifying if and how trust affects the creativity of a partnership between two economic agents. The methodology combines an experiment and two questionnaires. The purpose of the research is to increase our understanding of trust and its impact on the outcome of cooperation, and to derive some guidance for economic actors, namely R&D managers and executives who want to build trustful innovation oriented relationships with their business partners. Specifically, we investigate the effect of trust on partners' creativity and willingness to invest financially in a joint development. Our results show that more trustful partners invest higher amounts in the alliance, while there seems to be an optimum amount of mutual trust between partners to maximize their joint creativity; if the level of mutual trust is below or above this threshold; their joint creativity seems to decrease. Terms of use: Documents in

No abstract

The dynamics of relational quality in co-development alliances Author(s):* Francis Bidault, ESMT Co-development alliances are formed to create new capabilities (technologies, products, services, processes, etc.) that partner organizations need in order to reach their goals. They involve the combination of competencies, and other intangible assets. These alliances typically face a high level of risks in terms of undesired leakages of confidential knowledge or failure to achieve the expected development. Relational quality, an important consideration in all alliances, is particularly key. Without it, partners might not be open enough to combine their knowledge effectively with the partners'. This article proposes a framework for defining, assessing, and monitoring relational quality in co-development alliances.

Granting a short-term loan is a critical decision. A great deal of research has concerned the prediction of credit default, notably through Machine Learning (ML) algorithms. However, given that their black-box nature has sometimes led to unwanted outcomes, comprehensibility in ML guided decision-making strategies has become more important. In many domains, transparency and accountability are no longer optional. In this article, instead of opposing white-box against black-box models, we use a multi-step procedure that combines the Fast and Frugal Tree (FFT) methodology of Martignon et al. (2005) and Phillips et al. (2017) with the extraction of post-hoc explainable information from ensemble ML models. New interpretable models are then built thanks to the inclusion of explainable ML outputs chosen by human intervention. Our methodology improves significantly the accuracy of the FFT predictions while preserving their explainable nature. We apply our approach to a dataset of short-term loans granted to borrowers in the UK, and show how complex machine learning can challenge simpler machines and help decision makers.