Influence of eco-awareness and price sensitivity on bridging the intention behavior gap in sustainable consumption
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DOIhttp://dx.doi.org/10.21511/im.22(2).2026.19
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Article InfoVolume 22 2026, Issue #2, pp. 274-288
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Type of the article: Research Article
Abstract
Growing environmental degradation and unsustainable consumption have intensified the need to understand the determinants of sustainable purchasing behavior, particularly the persistent intention-behavior gap. Using the Theory of Planned Behavior, this study aims to bridge the intention behavior gap by extending it to include eco-awareness and price sensitivity. Data were collected using a structured questionnaire administered to students and employees in higher education institutes in Northern India (Delhi NCR, Rajasthan, and Jammu & Kashmir). The responses were analyzed using partial least squares structural equation modelling. The results indicate that eco-awareness (β = 0.237, p < 0.001), consumer attitude (β = 0.182, p < 0.001), social norms (β = 0.487, p < 0.001), and perceived behavioral control (β = 0.315, p < 0.001) have significant positive effects on sustainable purchase intention. Purchase intention had a direct influence on sustainable purchase behavior (β = 0.657, p < 0.001), confirming its central role in behavioral execution. In contrast, price sensitivity did not have a significant direct effect on purchase behavior (β = 0.012, p = 0.720), although its interaction with purchase intention showed a weak but statistically significant moderating effect (β = 0.076, p < 0.05). These findings indicate that purchase behavior is driven primarily by social and psychological factors, while economic considerations play a limited and conditional role in the intention-behavior relationship. Marketers should design demographic specific awareness campaigns by recognizing variations in consumer behavior.
- Keywords
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JEL Classification (Paper profile tab)D12, M31, Q01
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References69
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Tables10
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Figures1
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- Figure 1. Structural model and path coefficient
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- Table 1. Demographic characteristics of respondents
- Table 2. Descriptive statistics
- Table 3. Construct reliability and validity
- Table 4. Heterotrait-Monotrait ratio (HTMT)
- Table 5. Fornell-Larcker criterion
- Table 6. R-square
- Table 7. PLS predict LV summary
- Table 8. Model fit summary
- Table 9. Results of structural equation model
- Table A1. Summary of constructs and measurement items
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