Statistical Modeling of Consumer Preferences for Eco-friendly Digital Products: A Data-driven Approach Toward Sustainable Consumption in India
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2025.16.10.14Keywords:
Sustainable consumption, consumer analytics, digital behavior, eco-friendly products, statistical modeling, circular economy, ESG marketingDimensions Badge
Issue
Section
License
Copyright (c) 2025 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
As environmental concerns intensify globally, consumer behavior is undergoing a paradigm shift, particularly within rapidly digitizing economies like India. In this context, understanding and statistically modeling consumer preferences for eco-friendly digital products is both timely and essential. This study offers a data-driven approach to decoding sustainable consumption patterns, focusing on key behavioral and demographic indicators influencing green purchase intent. Drawing from structured survey responses of over 350 urban Indian consumers, the research employs a suite of advanced statistical tools-including multiple regression, principal component analysis (PCA), logistic regression, and chi-square tests-to examine correlations between sustainability-driven choices and variables like age-of-consumers, education-of-consumers, income, digital literacy with prior exposure to environmental campaigns. The analysis reveals that awareness of sustainability issues is significantly associated with behavioral outcomes like trust in eco-brands, willingness to pay a premium, and digital engagement with green content. PCA effectively distilled 14 observed behavioral metrics into three principal components, accounting for 78% of the variance in sustainable decision-making. These components reflect digital influence, socio-demographic consciousness, and psychological affinity toward sustainability. The study contributes a novel statistical modeling framework that bridges consumer psychology with sustainability science. Its interdisciplinary approach supports SDG-9 (industry-and-innovation), SDG-12 (responsible-consumption), and SDG-13 (climate-action), while offering practical insights for marketers, digital strategists, and policymakers. By harnessing empirical evidence, the research informs ESG-aligned and circular economy marketing strategies that resonate with India’s digitally active and environmentally conscious consumer baseAbstract
How to Cite
Downloads
Similar Articles
- S. Deepa, I.S. Arafat, M. Sathya Priya, S. Saravanan, An improved spectrum sharing strategy evaluation over wireless network framework to perform error free communications , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Rudrapati Bhuvaneswara Prasad, Avutala Mallikarjuna Reddy, Edge properties of lexicographic product graphs of open neighborhood graphs , The Scientific Temper: Vol. 16 No. 01 (2025): The Scientific Temper
- Nalini S, Ritha W, Inventory model considering trade discounts and scrap disposal with sustainability , The Scientific Temper: Vol. 16 No. 04 (2025): The Scientific Temper
- Varsha Sharma, Krishna Kumar Gupta, Comparative accuracy of IOL power calculation formulas in nanophthalmic eyes undergoing cataract surgery , The Scientific Temper: Vol. 16 No. 07 (2025): The Scientific Temper
- Manisha Pallvi, Carlson’s Trophic State Index of Shatiya Wetland in Gopalganj District of Bihar , The Scientific Temper: Vol. 12 No. 1&2 (2021): The Scientific Temper
- Minas M. Ali, Farah H. Alenezi, Nora F. Alfayyadh, Sara Y. Alhassoun, Rahaf M. Alanzi, Waseem Radwan, Conservative esthetic dentistry in Riyadh – Saudi Arabia , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- Krishna P. Kalyanathaya, Krishna Prasad K, A framework for generating explanations of machine learning models in Fintech industry , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Muhammed Jouhar K. K., Dr. K. Aravinthan, An improved social media behavioral analysis using deep learning techniques , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- M. Yamunadevi, P. Ponmuthuramalingam, A review and analysis of deep learning methods for stock market prediction with variety of indicators , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- JOSHI GK, INDUSTRIAL IMPORTANCE OF HALOPHILIC BACTERIA , The Scientific Temper: Vol. 2 No. 1&2 (2011): The Scientific Temper
<< < 32 33 34 35 36 37 38 39 > >>
You may also start an advanced similarity search for this article.

