Integrating neurophysiological sensing and group-based multi-criteria decision-making for fourth-party logistics platform selection

The selection of an optimal fourth-party logistics (4PL) platform is a critical yet complex multi-criteria decisionmaking (MCDM) task in cross-border e-commerce (CBEC) operations. Existing studies often rely on subjective judgments from industry experts, failing to fully capture the psychological states of key stakeholders involved in such a knowledge-intensive and operationally complex decision. To address this limitation, this study proposes a neuro-informed collaborative multi-criteria decision support system (Neuro-IC-MCDSS) that integrates human expertise and neurophysiological insights. The pairwise comparison following the Best Worst Method (BWM) is employed to elicit the preferences of domain experts, while the engagement level of each expert is detected correspondingly to provide continuous feedback. The user experience-related stress levels of one frontline platform operator are also collected and incorporated into the final ranking using the Group Technique for Order of Preference by Similarity to Ideal Solution (Group TOPSIS). This hybrid approach allows for the fine-tuning and explanation of the final decision, ensuring alignment with the tacit knowledge and cognitive feedback of key stakeholders. The feasibility and effectiveness of the Neuro-IC-MCDSS are demonstrated through a real-world case study involving a manufacturing company seeking a 4PL platform partner. This study contributes to the literature by pioneering the integration of EEG-embedded MCDM process, providing a potential pathway for future decision support applications that seamlessly blend human expertise and neurophysiological sensing within the decision science domain.