Tourist Profile Segmentation through Symmetric Asymmetric Multivariate Structures: Integrating Biplot, Co-Inertia Analysis and Neutrosophic Psychology

Understanding tourist behaviour requires analytical frameworks capable of capturing both symmetric relationships among motivational constructs and asymmetric causal effects on behavioural intentions. Conventional segmentation approaches, particularly those based on structural equation modelling, primarily estimate directional relationships and provide limited insight into the multivariate interaction structures underlying tourist decision-making. To address this limitation, this study proposes an integrated analytical framework that combines GH-Biplot, Co-Inertia Analysis (COIA), STATICO, and Neutrosophic Psychology to analyse tourist motivations under uncertainty. The proposed framework was applied to a sample of 400 tourists participating in poverty-reducing tourism research. Measurement models were validated using confirmatory factor analysis and Partial Least Squares Structural Equation Modelling (PLS-SEM), while the proposed multivariate approach was employed to identify latent symmetric and asymmetric structures linking behavioural intentions, motivational constructs, and personal values. Results show that biospheric values exhibit the strongest association with intentions to participate in poverty-reducing tourism. More importantly, the proposed framework reveals multivariate relationships and behavioural patterns that remain hidden when conventional asymmetric causal models are applied independently. The incorporation of neutrosophic psychology further extends the analysis by explicitly representing indeterminacy in tourist motivations through truth, falsity, and indeterminacy components. The study contributes by introducing a novel analytical framework that integrates complementary multivariate techniques to improve tourism segmentation, enhance the interpretation of complex behavioural relationships, and support evidence-based decision-making for sustainable tourism management under uncertainty.