Clustering Digital Governance Adoption Patterns in the Metaverse Using K-Means and DBSCAN Algorithms

The rapid advancement of immersive digital environments has accelerated global interest in leveraging metaverse technologies as extensions of public governance systems. This study analyses citizen readiness and perception toward metaverse-based digital governance in The Gambia using two unsupervised machine learning algorithms: K-Means and DBSCAN, applied to a dataset of 115 survey responses. After preprocessing and feature standardization, the K-Means algorithm identified two distinct adoption clusters, consisting of Cluster 0 with 76 respondents and Cluster 1 with 39 respondents. The centroid projections in PCA space revealed a clear behavioural separation, with Cluster 1 exhibiting a substantially higher mean PC1 score (2.5270) compared to Cluster 0 (−1.2968), indicating stronger readiness, optimism, and trust among respondents in the former group. In contrast, DBSCAN produced a single dominant cluster of 107 respondents and identified 8 outliers, suggesting a generally cohesive perception landscape with a small number of respondents expressing atypical attitudes toward metaverse-enabled governance. Collectively, these findings demonstrate that while public sentiment toward metaverse governance is broadly aligned, significant intra-group differences exist, making behavioural segmentation crucial for informing policy strategies. The results underscore the need for tailored approaches that address both enthusiastic adopters and more cautious individuals to support equitable and inclusive metaverse governance adoption.