Identifying subfields by integrating journal co-citation, topic co-occurrence, and interlocking editorial relationships

Integrating multiple journal similarity relationships to identify subfields can provide more comprehensive information. However, a major issue with existing research is that it has not adequately addressed the conflicts between different similarity relationships. To fill this gap, this study introduces an evidence theory-based fusion approach, which excels in handling uncertainty and ambiguity. First, using the tourism field as a case study, we collected co-cited information, keywords, and editorial board information from 52 tourism journals between January 2019 and April 2024 to establish journal co-citation, topic co-occurrence and interlocking editorial relationships. Second, we applied a multi-relation fusion approach based on evidence theory to integrate the three journal similarity relationships. Third, clustering evaluation using Silhouette values and factor analysis was conducted to confirm the applicability of the integrated relationship. The results show that the integrated relationship outperforms individual relationships and those fused using other approaches in identifying subfields. Following the rule of eigenvalues greater than 1, the integrated journal relationship achieves the highest explained variance at 81.57%, indicating that the integrated data exhibits the greatest variability and information content among all relationships. These findings confirm the effectiveness of the evidence theory-based fusion approach in identifying subfields.