Green Marketing, Brand Trust, and Perceived Misleading Green Claims in Purchase Intention

Ni Made Taman Sari; Made Pratiwi Dewi; Ni Made Santini

doi:10.61255/jeemba.v4i6.1585

Authors

Ni Made Taman Sari Universitas Warmadewa, Indonesia
Made Pratiwi Dewi Universitas Warmadewa, Indonesia
Ni Made Santini Universitas Warmadewa, Indonesia

DOI:

https://doi.org/10.61255/jeemba.v4i6.1585

Keywords:

Green Marketing Mix, Green Brand Trust, Perceived Misleading Green Claims, Purchase Intention, Signaling Theory

Abstract

Purpose – To examine pooled cross-category associations of the green marketing mix (GMM), green brand trust (GBT), perceived misleading green claims (PMGC), and purchase intention (PI), including indirect and interaction relationships.

Design/methodology/approach - A cross-sectional survey of 180 consumers in Denpasar and Badung, Bali, was analyzed using SmartPLS 4 with a reflective-formative GMM specification and refined measures. Robustness was examined with fully re-estimated alternative GMM specifications (four single-component models and a global reflective model) and an equally weighted fixed-score composite.

Findings/Results - GMM was positively associated with GBT and PI; PMGC was negatively associated with both. The indirect association through GBT was positive within a positive total association. Neither interaction was statistically detectable in the main model. Across six fully re-estimated alternative specifications, the direction and significance of all main and indirect associations were unchanged, and H8 remained null; only a Green Promotion-based model showed a borderline attenuation of the GMM–GBT association.

Originality/Value - The study separates level effects of perceived misleading green claims from slope-based boundary conditions in pooled cross-category perceptions: PMGC was associated with lower trust and intention without a detectable change in the marginal effects of marketing signals or trust. GBT is the principal observed indirect association in this same-source cross-sectional model. Component collinearity and possible common-method covariance prevent stronger causal or measurement claims.

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