This study explores the variety of research management structures, roles, tasks, responsibilities, activities and styles in relation to research management performance (RMP) and project success in the FP6 and FP7 research funding programmes of the European Commission. The key findings indicate six enabling factors of high RMP. The effects on RMP and project success of six management styles have been studied. Interestingly, management tools do not appear to influence project success. In about one third of the projects, the composition of the consortium changes along the way. The Commission would do well to focus on personal interaction with Project Coordinators rather than the administrative side of projects and create more flexibility, if it wants to improve the success of FP projects. Also, currently offered EC-provided project management tools and matchmaking instruments should be critically reviewed, as these do not work as intended. Finally, the Commission is cautioned that current frameworks for intellectual property are out of sync with international standards, such as those of the WTO. We make recommendations to the Commission (for implementation in the Horizon 2020 programme) and to Project Coordinators. We also provide suggestions for further research based on the findings of this exploratory study. Résumé Cette étude porte sur la variété des structures de gestion de la recherche, des rôles, tâches, responsabilités, activités et types de gestion, quant à la performance de la gestion de la recherche (PGR) et le succès des projets sous programmes de financement PC6 et PC7 de la Commission Européenne. Les principaux résultats indiquent six facteurs permettant une gestion performante. Les effets sur la PGR sous six types de gestion ont été étudiés. L'étude démontre que les outils de gestion n'influencent pas la réussite du projet. Pour environ un tiers des projets, la composition du consortium est modifiée lors du projet. La Commission devrait davantage se concentrer sur les interactions personnelles avec les coordinateurs de projets que sur leur côté administratif, et encourager sa flexibilité. Les outils de gestion de projet et instruments de rapprochement proposés devraient être soumis à un examen critique, puisqu'ils n'ont pas les effets prévus. Enfin, le système actuel de propriété intellectuelle n'est pas adapté aux normes internationales comme celles de l'OMC. Des recommandations pour la mise en oeuvre d' Horizon2020 sont développées pour la Commission et les coordinateurs de projet, ainsi que des suggestions pour de nouvelles recherches sur base des résultats de cette étude exploratoire.
Auditors often have prior information about the auditee before starting the substantive testing phase. We show that applying Bayesian statistics in substantive testing allows for integration of this information into the statistical analysis through the prior distribution. For example, an auditor might have performed an audit last year, they might have information on certain controls in place, or they might have performed analytical procedures in an earlier stage of the audit. Incorporating this information directly in the statistical procedure enables auditors to tailor their sampling plan to the auditee, thereby increasing audit transparency and efficiency. However, defining a suitable prior distribution can be difficult because what constitutes a suitable prior depends on the specifics of the audit and the auditee. To help the auditor in constructing a prior distribution we introduce five methodologies, consider their pros and cons, and give examples of how to apply them in practice.
The impact of statistical methods on the audit practice is growing because of the increasing availability of audit data and the statistical methods to analyze these data. A key aspect in the statistical approach to auditing is assessing the strength of evidence for or against a hypothesis. Unfortunately, the often-used frequentist statistical methods cannot provide the statistical evidence that audit standards demand directly nor easily. In this article we discuss an alternative approach that can provide this evidence: Bayesian inference. Firstly, we explore the philosophical differences between frequentist and Bayesian inference. Secondly, we discuss misconceptions in the interpretation of frequentist statistical evidence, and finally we discuss how Bayesian inference allows the auditor to obtain and interpret statistical evidence in line with audit standards via its alternative to the p value, the Bayes factor.
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