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
- Bhavya Sathenapalli, Kali Charan Sabat, Unleashing entrepreneurial spirit: Driving innovation and growth in a rapidly changing world , The Scientific Temper: Vol. 16 No. 06 (2025): The Scientific Temper
- M. Ragul, A. Aloysius, V. Arul Kumar, Enhancing IoT blockchain scalability through the eepos consensus algorithm , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- Karthik Baburaj, Navaneeth kattil Madathil, Roshini Barkur, NLP Based Voice Assistant Usage on Consumer Shopping , The Scientific Temper: Vol. 16 No. Spl-2 (2025): The Scientific Temper
- Dhara B. Makwana, Adwait Mevada, Diversity and Green Synthesis of Various Metal Nanoparticles (MNPs) , The Scientific Temper: Vol. 16 No. 11 (2025): The Scientific Temper
- Abbasova Sona Jamal, Aliyev Sabit Shakir, Mahmudov Elmir Heydar, Museyibli Emin Bakir, Nadirkhanova Dilshat Adalat, Econometric analysis of grain yields (using the example of the Republic of Azerbaijan) , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- V. Babydeepa, K. Sindhu, Piecewise adaptive weighted smoothing-based multivariate rosenthal correlative target projection for lung and uterus cancer prediction with big data , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- G. C. Sowparnika, D. A. Vijula, Modeling and control of boiler in thermal power plant using model reference adaptive control , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- P S Renjeni, B Senthilkumaran, Ramalingam Sugumar, L. Jaya Singh Dhas, Gaussian kernelized transformer learning model for brain tumor risk factor identification and disease diagnosis , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- Kinjal K. Patel, Kiran Amin, Predictive modeling of dropout in MOOCs using machine learning techniques , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Joji John Panicker, Ancy Elezabath John, Nair Anup Chandrasekharan, A tapestry of tradition: Revitalization of Indian Heritage and Folk Art , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.

