An Integrated Triple–Trickle Fashion Adoption Model for Luxury Fashion in the National Capital Region of India: A JASP-Compatible SEM Path Analysis

This study re-estimates an integrated Triple–Trickle Fashion Adoption Model for luxury-fashion consumers in the National Capital Region (NCR) of India using a full latent structural equation model rather than standardized construct scores. The revision directly addresses measurement-error attenuation in the structural stage and extends Perceived Behavioural Control (PBC) with annual household income as an observed financial-resource proxy. Data from 800 NCR respondents and 68 item indicators across 14 latent constructs were analysed jointly with covariance-based maximum likelihood using a lavaan/JASP-compatible specification. The full latent model demonstrated excellent approximate fit (χ²=2400.434, df=2224, χ²/df=1.079, CFI=.9946, TLI=.9943, RMSEA=.0100, SRMR=.0336). Purchase Intention was significantly predicted by Trickle-Down (β=.257), Trickle-Up (β=.205), Trickle-Across (β=.135), Attitude (β=.248), Subjective Norm (β=.099), and PBC (β=.193), explaining 41.4% of its variance. Measurement-error adjustment modestly strengthened most structural coefficients and changed one earlier conclusion: Consumer Innovativeness no longer significantly predicted Attitude (β=.066, p=.067). Household income significantly predicted PBC (β=.138, p<.001); freeing this path improved fit by Δχ²(1)=13.699, p<.001 and increased PBC R² from approximately .034 to .052, although substantial unexplained variance remains. Brand Consciousness → PBC is retained only as an exploratory path. The study explicitly limits statistical generalization because the sample is non-probability and urban NCR-specific, and it identifies Cultural Drivers, Social Image, and Perceived Brand Reputation as unmeasured theoretical extensions for future research.

luxury fashion; trickle-down; trickle-up; trickle-across; full latent SEM; theory of planned behaviour; purchase intention; perceived behavioural control; NCR India

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