Below: the design of the survey instrument, the statistical machinery, the seven latent attitudinal factors, and the quality controls.
Pairing each survey respondent with their actual ride history lets temperature variance, multimodal fallback patterns, and revealed weather behaviour stand in for self-reports — and outperform them as predictors of intensive use.
Three stages connect raw data to inference: attitudinal measurement (EFA), data-driven segmentation (GMM), and predictive modeling (multinomial logit with random forest validation).
Two parallel surveys were distributed to ~32,000 registered users. After quality screening, 1,844 responses were retained; 1,743 were linked at individual level to the full operational trip history — combining stated preferences with revealed behavior.
EFA reduced 32 Likert items to seven attitudinal constructs — including weather tolerance, environmental self-identity, and multimodal complementarity. GMM clustering then identified three data-driven usage segments (low, medium, high intensity) without imposing arbitrary thresholds.
Multinomial logit predicted segment membership from demographic, geographic, and attitudinal predictors via backward elimination. Results were validated through 5-fold cross-validation and random forest feature importance rankings, which broadly corroborated the MNL findings.
Linking survey responses to trip histories enables triangulation between stated and revealed preferences. The key behavioral predictor — temperature variance across a user's ride history — captures demonstrated weather resilience independently of self-reports (r=0.05 with trip volume), confirming it as a genuine behavioral trait rather than a usage artifact.
Two parallel surveys (FFEBSS / DBEBSS), structurally identical, fielded in April 2025 in German, French, and English via Qualtrics. Six sections, ~80 items, with thirty-two Likert-scaled items reduced to seven latent attitudinal factors via exploratory factor analysis. Full instrument in Appendix A3 of the manuscript.
Driver's licences, PT subscriptions (GA, Half-fare, U-Abo, …), vehicle ownership, main mode by trip purpose, shared-mobility usage frequency, EBSS-PT integration patterns, mode substitution, weather likelihood, price sensitivity.
Theory-of-Planned-Behaviour-style items on environmental responsibility, EBSS as sustainable transport, hedonic evaluation, social support; plus policy preferences on infrastructure investment, subsidies, PT-app integration, private financing.
Perceived barriers — availability variance, price, convenience compared to private (e-)bike — plus geographic coverage feedback (open text) and intent to purchase a private e-bike within twelve months.
Year of birth, gender, country → postal code → tenure in Basel, education, employment status (workload + home-office share), nearest PT stop at work/education, household composition, income.
Explicit consent for anonymous linkage of survey responses to operational rental records, voucher choice, optional follow-up interview consent, optional comments on the survey itself.
Three response-quality filters were applied before retaining cases: a minimum-completion-time threshold (5 minutes), straight-line detection across Likert blocks, and a reCAPTCHA score floor. Net retention: 1,844 of 2,001 (92.2%).
An exploratory factor analysis (principal axis factoring, promax rotation κ=4) reduced the 32 attitudinal Likert items to seven interpretable latent constructs. Suitability confirmed by KMO=0.820 and a significant Bartlett's test (p<0.001). Eigenvalue cutoff 1.10 + reliability analysis retained the seven factors below.
| Factor | Construct | Items | Cronbach α |
|---|---|---|---|
| F1 | Weather-Independent Usage Propensity — stated willingness to ride in adverse conditions | 6 | 0.813 |
| F2 | Public Sector Intervention Attitudes — support for subsidies, infrastructure, policy integration | 5 | 0.532 |
| F3 | Hedonic Motivation & Social Support — enjoyment, comfort, social endorsement | 4 | 0.665 |
| F4 | Perceived Functional Superiority over PT — faster, more convenient than transit | 5 | 0.728 |
| F5 | Multimodal Complementarity Behavior — combining EBSS with PT, walking, fallback use | 4 | 0.598 |
| F6 | Service Reliability & Affordability Concerns — cost and availability barriers ⚠ marginal reliability | 4 | 0.401 |
| F7 | Environmental Self-Identity — environmental responsibility, EBSS sustainability beliefs | 2 | 0.633 |
Standardized factor scores (M=0, SD=1) computed via the regression method and merged into the analytical dataset. Factor 6 results interpreted with caution.
This page covers methodology and instrument design only. For findings, effect sizes, recommendations and the interactive data explorer, head back to the project overview — or jump straight into the key findings, the forest plot of effect sizes, or the spatial heatmap.