Min Shuai, Louvain Research Institute in Management and Organizations
Benoît Gailly, Louvain Research Institute in Management and Organizations
Nadia Steils, University of Liege
Summary
Crowdsourcing has emerged as a powerful open innovation model, enabling firms to tap into global knowledge and creativity at reduced costs. However, despite its potential, many crowdsourcing initiatives fail to translate submitted ideas into implemented solutions, leading to stakeholder dissatisfaction and distrust. While prior research has examined crowdsourcing success from external (crowd and idea-level) perspectives, internal firm-level factors—particularly corporate strategy and idea perception—remain underexplored. This study addresses this gap by investigating how companies' strategic positioning of crowdsourcing (as either an inspirational marketing tool or a necessary innovation driver) and their perceptions of idea durability, reliability, and quality jointly influence implementation intentions. Through a survey of 804 Moroccan marketing managers and cluster analysis, we reveal how distinct configurations of strategy and perception shape firms' willingness to act on crowdsourced ideas. Our findings contribute to crowdsourcing literature by (1) conceptualizing two strategic approaches to crowdsourcing adoption and (2) demonstrating their interplay with idea evaluation in implementation decisions. The study offers practical insights for managers seeking to align crowdsourcing initiatives with organizational goals while mitigating implementation failures.
Flore Vancompernolle Vromman, Louvain Research Institute in Management and Organizations
Summary
In a context where artificial intelligence (AI) plays an increasingly prominent role in society, this project explores its potential to predict well-being based on individuals' behaviors and lifestyle habits, with a specific focus on explainability as a key principle of responsible AI. Following an initial data collection, we have developed a rigorous machine learning pipeline to generate well-being predictions from lifestyle and behavioral data. The second phase of our research centers on explaining these predictions to users. To achieve this, we test five explanation techniques—visual, textual, numerical, interactive, and contextual—through an online survey to evaluate their effectiveness in increasing participants' awareness of their well-being. Ultimately, this project aims to enhance users' understanding of their mental health and could contribute to developing accessible educational and preventive tools, such as the Datagotchi Santé platform.