ArticleEntropy (Basel, Switzerland)2026
Entropy-Based Uncertainty-Aware Exploratory Factor Analysis for Ordinal Data: Application to Tramway Cultural Tourism Evaluation.
Article in Entropy (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundPerception-based evaluation using Likert-scale survey data is widely applied in tourism and transport research, yet conventional point-valued encoding imposes artificial precision and overlooks ambiguity between adjacent ordinal categories. This limitation is particularly relevant in experiential contexts, where subjective judgments often involve transitional evaluations.
methodsThis study develops a parameterized fuzzy-entropy exploratory factor analysis (FE-EFA) framework for uncertainty-aware analysis of ordinal perception data. The approach transforms ordinal responses into parameterized fuzzy membership distributions governed by a single effective uncertainty ratio, constructs a correlation structure in the five-dimensional membership space, and incorporates Shannon entropy and Jensen-Shannon divergence to characterize distributional dispersion and representation differences. The framework is applied to survey data from Chengdu Tramway Line 2 (
resultsUnder the Kaiser criterion (eigenvalues > 1), conventional EFA yields a seven-factor structure, whereas FE-EFA identifies an additional eighth factor located near the retention boundary. Under a unified factor specification, both approaches preserve a consistent high-level structure, while FE-EFA shows fewer cross-loadings and a more differentiated loading pattern in this empirical case under the adopted exploratory specification. From an information-theoretic perspective, FE-EFA produces higher entropy (average = 0.8688) and low Jensen-Shannon divergence (average = 0.0133), suggesting a limited redistribution of ordinal information without substantially altering the overall distributional structure. Entropy-adjusted weighting further reveals systematic shifts in indicator importance across key dimensions.
conclusionsThe FE-EFA framework extends conventional Likert-scale analysis by introducing an uncertainty-aware representation layer prior to factor extraction. It preserves overall structural stability while suggesting a more differentiated organization of latent constructs and indicator-level representations in this empirical context. The proposed approach provides an exploratory representation-level extension for perception-based evaluation and decision support in tramway cultural tourism development and related contexts.
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