Influence of eco-awareness and price sensitivity on bridging the intention behavior gap in sustainable consumption

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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.

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    • Figure 1. Structural model and path coefficient
    • 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
    • Conceptualization
      Parveen Yadav, Arun Yadav, Neelika Arora
    • Investigation
      Parveen Yadav, Neelika Arora, Vinay Kumar
    • Supervision
      Parveen Yadav, Neelika Arora, Sumanjeet Singh
    • Writing – original draft
      Parveen Yadav, Arun Yadav, Abhinav Thakur
    • Data curation
      Arun Yadav, Sumanjeet Singh, Abhinav Thakur
    • Formal Analysis
      Arun Yadav, Vinay Kumar, Abhinav Thakur
    • Writing – review & editing
      Neelika Arora, Vinay Kumar, Sumanjeet Singh
    • Methodology
      Vinay Kumar, Sumanjeet Singh, Abhinav Thakur