Preparing, conducting, and analyzing Delphi surveys: Cross-disciplinary practices, new directions, and advancements.

Delphi is a scientific method to organize and structure an expert discussion aiming to generate insights on controversial topics with limited information. The technique has seen a rise in publication frequency in various disciplines, especially over the past decades. In April 2021, the term Delphi method yielded 28,200 search hits in Google Scholar for the past five years alone. Given the increasing level of uncertainty caused by rapid technological and social change around the globe, collective expert opinions and assessments are likely to gain even more importance. Therefore, the paper at hand presents technical recommendations derived from a Delphi study that was conducted amid the outbreak of the COVID-19 pandemic in 2020.•The paper comprehensively demonstrates how to prepare, conduct, and analyze a Delphi study. In this regard, it combines several methodological advancements of the recent past (e.g., dissent analyses, scenario analyses) with state-of-the-art impulses from other disciplines like strategic management (e.g., fuzzy clustering), psychology (e.g., sentiment analyses), or clinical trials (e.g., consensus measurement).•By offering insights on the variety of possibilities to exploit Delphi-based data, we aim to support researchers across all disciplines in conducting Delphi studies and potentially expand and improve the method's field of application.

Transfer market activities and sportive performance in European first football leagues: A dynamic network approach.

Professional football is a globalized game in which players are the most valuable assets for clubs. In this study, we explore the evolution of the football players' transfer network among 21 European first leagues between the seasons 1996/1997 and 2015/2016. From a topological point of view, we show that this network achieved an upper limit expansion around season 2007/2008, thereafter becoming more connected and dense. Using a machine learning approach based on Self-Organizing Maps and Principal Component Analysis we confirm that European competitions, such as the UEFA Champions League or UEFA Europa League, are indeed a "money game" where the clubs with the highest transfer spending achieve better sportive performance. Some clubs' transfer market activities also affect domestic performance. We conclude from our findings that the relationship between transfer spending and domestic or international sportive performance might lead to substantial inequality between clubs and leagues, while potentially creating a virtuous (vicious) circle in which these variables reinforce (weaken) each other.