Using Machine Learning to Optimize Green Influencer Marketing Strategies: A Study of Consumer Behavior Trends
In the backdrop of increasing sustainability awareness among Indian consumers, this study explores the role of machine learning (ML) in optimizing green influencer marketing strategies to drive eco-conscious purchasing. While eco-friendly consumer behavior and influencer marketing have gained tracti...
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| Format: | Article |
| Language: | English English |
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INTI International University
2025
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| Online Access: | http://eprints.intimal.edu.my/2175/ http://eprints.intimal.edu.my/2175/1/jobss2025_6.pdf http://eprints.intimal.edu.my/2175/2/724 |
| _version_ | 1848766984793096192 |
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| author | Neetu, Sharma |
| author_facet | Neetu, Sharma |
| author_sort | Neetu, Sharma |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | In the backdrop of increasing sustainability awareness among Indian consumers, this study explores the role of machine learning (ML) in optimizing green influencer marketing strategies to drive eco-conscious purchasing. While eco-friendly consumer behavior and influencer marketing have gained traction, there remains limited empirical evidence on how ML-enabled recommendation systems can enhance green influencer effectiveness in India. Employing a quantitative research design, data were gathered through surveys of 1,500 users of an Indian e-commerce platform. Respondents provided insights on their interactions with green influencers, their perceptions of influencer authenticity and transparency, and the impact of ML-driven recommendations on purchase intent. Factor and correlation analyses examined the relationships among perceived authenticity, consumer trust, and purchase behavior. Findings reveal that influencer trustworthiness, particularly authenticity and transparency, significantly drives consumer engagement with green products. Most respondents expressed willingness to purchase green products when the messaging was authentic and well-targeted. Moreover, ML algorithms were instrumental in identifying top-performing influencers, segmenting audiences by green preferences, and personalizing recommendations, which enhanced engagement and conversion rates. Positive correlations were observed between influencer authenticity, trust, and purchase intention. This study fills a regional gap by offering India-specific, empirical evidence on the synergy between ML-driven marketing and green consumer behavior. Its practical implications are twofold: marketers can leverage these insights to enhance influencer selection and recommendation strategies, while policymakers and researchers gain a data-informed perspective to promote sustainable marketing practices. The study demonstrates that ML-augmented green influencer marketing can effectively elevate sustainability and commercial performance within the Indian e-commerce context. |
| first_indexed | 2025-11-14T11:59:50Z |
| format | Article |
| id | intimal-2175 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:59:50Z |
| publishDate | 2025 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-21752025-09-09T09:58:06Z http://eprints.intimal.edu.my/2175/ Using Machine Learning to Optimize Green Influencer Marketing Strategies: A Study of Consumer Behavior Trends Neetu, Sharma H Social Sciences (General) HD28 Management. Industrial Management HF Commerce In the backdrop of increasing sustainability awareness among Indian consumers, this study explores the role of machine learning (ML) in optimizing green influencer marketing strategies to drive eco-conscious purchasing. While eco-friendly consumer behavior and influencer marketing have gained traction, there remains limited empirical evidence on how ML-enabled recommendation systems can enhance green influencer effectiveness in India. Employing a quantitative research design, data were gathered through surveys of 1,500 users of an Indian e-commerce platform. Respondents provided insights on their interactions with green influencers, their perceptions of influencer authenticity and transparency, and the impact of ML-driven recommendations on purchase intent. Factor and correlation analyses examined the relationships among perceived authenticity, consumer trust, and purchase behavior. Findings reveal that influencer trustworthiness, particularly authenticity and transparency, significantly drives consumer engagement with green products. Most respondents expressed willingness to purchase green products when the messaging was authentic and well-targeted. Moreover, ML algorithms were instrumental in identifying top-performing influencers, segmenting audiences by green preferences, and personalizing recommendations, which enhanced engagement and conversion rates. Positive correlations were observed between influencer authenticity, trust, and purchase intention. This study fills a regional gap by offering India-specific, empirical evidence on the synergy between ML-driven marketing and green consumer behavior. Its practical implications are twofold: marketers can leverage these insights to enhance influencer selection and recommendation strategies, while policymakers and researchers gain a data-informed perspective to promote sustainable marketing practices. The study demonstrates that ML-augmented green influencer marketing can effectively elevate sustainability and commercial performance within the Indian e-commerce context. INTI International University 2025-09 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2175/1/jobss2025_6.pdf text en cc_by_4 http://eprints.intimal.edu.my/2175/2/724 Neetu, Sharma (2025) Using Machine Learning to Optimize Green Influencer Marketing Strategies: A Study of Consumer Behavior Trends. Journal of Business and Social Sciences, 2025 (06). pp. 1-16. ISSN 2805-5187 http://ipublishing.intimal.edu.my/jobss.html |
| spellingShingle | H Social Sciences (General) HD28 Management. Industrial Management HF Commerce Neetu, Sharma Using Machine Learning to Optimize Green Influencer Marketing Strategies: A Study of Consumer Behavior Trends |
| title | Using Machine Learning to Optimize Green Influencer Marketing
Strategies: A Study of Consumer Behavior Trends |
| title_full | Using Machine Learning to Optimize Green Influencer Marketing
Strategies: A Study of Consumer Behavior Trends |
| title_fullStr | Using Machine Learning to Optimize Green Influencer Marketing
Strategies: A Study of Consumer Behavior Trends |
| title_full_unstemmed | Using Machine Learning to Optimize Green Influencer Marketing
Strategies: A Study of Consumer Behavior Trends |
| title_short | Using Machine Learning to Optimize Green Influencer Marketing
Strategies: A Study of Consumer Behavior Trends |
| title_sort | using machine learning to optimize green influencer marketing
strategies: a study of consumer behavior trends |
| topic | H Social Sciences (General) HD28 Management. Industrial Management HF Commerce |
| url | http://eprints.intimal.edu.my/2175/ http://eprints.intimal.edu.my/2175/ http://eprints.intimal.edu.my/2175/1/jobss2025_6.pdf http://eprints.intimal.edu.my/2175/2/724 |