LATENT HETEROGENEITY IN DECISION MECHANISMS FOR FUTURE ELECTRIC VEHICLE PURCHASE AMONG CURRENT OWNERS: A FIMIX-PLS ANALYSIS

Fredy Jhon Philip Sitorus; Leksmono Suryo Putranto; Ade Asmi

doi:10.21009/jpensil.v15i3.67703

Authors

Fredy Jhon Philip Sitorus Department of Civil Engineering & Center for Urban Studies, Faculty of Technology and Design, Pembangunan Jaya University
Leksmono Suryo Putranto Department of Civil Engineering, Tarumanagara University
Ade Asmi Department of Civil Engineering, Bakrie University Indonesia

DOI:

https://doi.org/10.21009/jpensil.v15i3.67703

Keywords:

Electric Vehicle, Future Purchase Intention, Current EV Owners, Latent Heterogeneity, FIMIX-PLS

Abstract

Future electric vehicle (EV) purchase intention among current owners may be formed through different decision mechanisms even when an aggregate structural model produces stable average coefficients. This study examines unobserved heterogeneity in a survey of 521 current EV owners in Jabodetabek, Indonesia. The aggregate PLS-SEM model is retained only as the analytical baseline, whereas finite mixture partial least squares (FIMIX-PLS) is used to compare latent structural solutions with two, three, and four segments. The three-segment solution was retained because BIC (2677.79) and CAIC (2739.79) reached their lowest values at three segments, and the estimated mixture proportions (53.0%, 37.8%, and 9.2%) were less imbalanced than the two-segment solution (89.9% and 10.1%). AIC3 favoured four segments, whereas entropy and NFI favoured two; the retained solution is therefore a model-selection compromise rather than a uniquely determined partition. In the two larger segments, perceived behavioural control had the largest proximal coefficient in Segment 1 (β = 0.497), whereas subjective norm had the largest coefficient in Segment 2 (β = 0.468). The smallest segment had near-perfect explained variance for purchase intention (R² = 0.9998) and a residual variance of 0.001, so its coefficients are interpreted cautiously. The analysis adds segment-specific information to the aggregate model but does not establish statistically significant differences between segment coefficients.

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