Assessing the influence of green marketing on consumers’ word-of-mouth through the mediating effect of brand equity dimensions
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DOIhttp://dx.doi.org/10.21511/im.21(2).2025.07
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Article InfoVolume 21 2025, Issue #2, pp. 79-92
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Changes in consumer behavior are influenced by green marketing and brand equity dimensions. Green marketing places high concerns on consumers’ environmental attitudes as a determinant of purchases and word of mouth. Green marketing increases brand awareness, fosters consumer loyalty and enhances reputation. The study aims to assess the influence of green marketing on consumers’ word of mouth through the mediating effect of brand equity dimensions. This descriptive causal research establishes a cause-and-effect relationship between variables, employing a quantitative research method. The questionnaire was deployed to collect the data and was pre-tested using a pilot test. 495 Lebanese consumers were included in the sample, which was collected using the convenience sampling technique. The results validated that green marketing reinforces consumers’ word of mouth (β = 0.663). It has a strong influence on brand equity (β = 0.899), brand loyalty (β = 0.772), brand trust (β = 0.663) and perceived quality (β = 0.353). The results corroborated that brand trust has the strongest mediating in this study (SE: 0.950; CR: 6.602; p < 0.01). Brand loyalty, while not a significant mediator in this relationship (SE: 0.012, CR: 0.872, p: 0.383 > 0.005), still plays a crucial role in brand management. This study concludes that giving brands a voice will appeal to the target audience and keep them committed. It highlighted the need for managers to review their loyalty strategies.
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JEL Classification (Paper profile tab)M14, M31
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References46
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Tables6
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Figures4
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- Figure 1. Conceptual framework
- Figure 2. Structural equation modeling (second order)
- Figure 3. Path analysis with β (beta) coefficient
- Figure 4. Mediating effect of brand equity dimensions
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- Table 1. Demographic characteristics of respondents
- Table 2. Normality statistics
- Table 3. Correlation matrix
- Table 4. Component matrix
- Table 5. Summary of PCA
- Table 6. Regression weights
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