Influence of consumer motivations and perception on the adoption of smart, green, and sustainable building materials

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The purpose of this study is to analyze the barriers to the widespread use of smart, green, and sustainable building materials in the construction industry by focusing on the perceptions, motivations, strategies, and challenges faced by consumers. The analysis employed an exploratory methodology and surveyed 385 respondents in Bangalore, India. The study result shows a significant positive partial correlation (r = 0.629, p = 0.001) between the challenges of adoption and the overall factors that influence adoption after controlling the annual income as a control variable. The higher mean score of personal values and ethics of 4.25 implies that moral values and ethics influence the decisions on the adoption of construction materials. The findings of multiple regression with robust standard error revealed perception of performance of smart, green and sustainable building materials is better compared to traditional building materials (p-value = 0.001), factors positively influencing adoption (p-value = 0.004), motivating factors of adoption (p-value = 0.001) and strategies that encourage adoption of smart, green, and sustainable building materials (p-value = 0.001). All of these have a substantial influence on how consumers evaluate the government’s efforts to increase the adoption of such materials. However, challenges in adoption showed a negative coefficient (B= –0.049) and a robust standard error of 0.024 (p-value = 0.048), demonstrating a negative influence on consumers’ perception. This research acts as a guiding beacon for green adoption policies by studying consumer motivations and perceptions toward adoption of eco-friendly building materials for the sustainable future.

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    • Figure 1. Research framework for H1, H2 and H3
    • Figure 2. Mean and standard deviation scores of factors positively influencing adoption
    • Table 1. Reliability analysis for measuring internal consistency
    • Table 2. Multiple regression with robust standard errors
    • Table 3. Multivariate analysis (MANOVA)
    • Table 4. MANOVA with Dunnet T3 post hoc analysis
    • Table 5. Partial correlation investigating relationship between challenges/hurdles in adoption (CIA) and overall factors affecting adoption (OFAA)
    • Conceptualization
      Rajendra P., Mohanasundaram T.
    • Data curation
      Rajendra P.
    • Formal Analysis
      Rajendra P.
    • Investigation
      Rajendra P., Mohanasundaram T.
    • Methodology
      Rajendra P., Mohanasundaram T.
    • Project administration
      Rajendra P., Mohanasundaram T.
    • Resources
      Rajendra P., Mohanasundaram T.
    • Supervision
      Rajendra P., Mohanasundaram T.
    • Visualization
      Rajendra P.
    • Writing – original draft
      Rajendra P.
    • Writing – review & editing
      Rajendra P.