Hindsight to foresight: an AI-powered analysis of future events in EJFR in 10 years

Abstract

Abstract The world is changing increasingly faster, bringing uncertainty and complexity to many processes, and this acceleration leads to the rise of demand for studies regarding the future. Futures Studies have been widely used to support decision-making, emphasizing forecasting technologies’ timing, diffusion, and life cycle. Therefore, evaluating previous forecasts to understand their proposed scenarios better is often necessary. This work analyzes all articles published until the end of 2023 in one of the most relevant journals of this field of study, the European Journal of Futures Research (EJFR). The EJFR is among the main contributors to fostering dialogue among scholars, practitioners, and policymakers interested in anticipating and shaping the future. We streamlined the uncovering of future events written on EJFR articles using NERMAP, a software we designed to semi-automate the roadmapping process. We built five simple scenarios and associated each of the 214 future events found into a scenario. Then, we analyzed the 52 events that were supposed to happen until 2023 to discover how the expected possible events did occur in real life. Therefore, we expect to provide insights, better understand how expected possible events occur in real life, and write a novel way to understand the journal’s history. The methodology used in this study is novel and applicable to other studies to reduce the time and risk of errors when developing large-scale roadmaps. This work provides valuable information for decision-makers in organizations and governments. The Futures Research scientific community can use the results to improve their research frameworks.

Keywords

• Futures studies
• Roadmapping
• Scenarios
• Decision-making process
• Fact-checking
• European Journal of Futures Research