Sep 2026· EUR Research Repository (Erasmus University Rotterdam)
Abstract
Modern businesses increasingly rely on big data and artificial intelligence. However, behind every data point lies a human decision. To be successful, managers must also understand the psychological processes that drive decision-making. Traditional research methods, such as surveys and focus groups, provide valuable insights, but may fail to capture the subconscious processes that influence decisions. This dissertation demonstrates how neuroscientific methods can generate novel business insights by measuring these processes in the brain. Using functional magnetic resonance imaging (fMRI), this dissertation studies brain activity in three distinct domains of business. In marketing, it shows that storytelling advertisements persuade consumers not because they share a character’s feelings, but because they engage brain networks involved in understanding a character’s intentions. In finance, it finds that reward-related brain activity of professional investors is associated with future stock performance, even when their explicit market forecasts are not. In organizational management, brain activity reveals how biases shape social judgments: while expectations of cooperation are based on perceived similarity and attractiveness, actual cooperation depends primarily on the explicit commitments that people make. Together, the findings of this dissertation leverage neuroscientific methods to reveal psychological processes underlying economic decision-making in business contexts. By illuminating the hidden mechanisms of narrative persuasion, financial intuition, and interpersonal cooperation, brain activity informs business practices in ways that self-report studies cannot. Even as artificial intelligence continues to transform businesses, these findings prove that the biological mind remains an indispensable source of insights.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.
Jinhe Bi, Yifan Wang, Danqi Yan et al.· arXiv.org· 73 citations· ⚡4
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
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Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al.· Advances in Neural Informati...· 59 citations· ⚡8
An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.
Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al.· arXiv.org· 56 citations· ⚡6