A MODEL OF ARTIFICAL INTELLIGENCE AND GREEN BUYING BEHAVIOUR

Abstract

The sense of urgency toward the escalating environmental issues has heightened interest in green buying behavior in which consumers will target buying environmentally friendly and ethically produced products. The following study probes into the application of AI in shaping the green buying behavior through an integrated model that considers five AI-based factors: AI personalization, AI-based consumer education, social influence through AI, AI-powered eco-labeling, and sentiment analysis. The model examines how these variables interact to shape consumer decisions, reduce barriers to sustainable consumption, and promote environmentally conscious behavior. AI personalizes recommendations of environmentally friendly products according to the customers' preferences to make relevance and convenience enhanced. AI education empowers consumers with knowledge in making informed decisions while AI-powered eco-labels enhance trust through credible and transparent environmental information. Social influence through AI uses the following to internalize norms for sustainable consumption: peer recommendations, trends, and virtual communities. Public perceptions as captured by sentiment analysis empower businesses to make strategies more responsive and offerings better aligned with consumers' expectations. This model suggests that the variables create synergies for green buying behavior in general. By integrating technological advancements with sustainability goals, the research contributes to academic discourse on sustainable consumption and offers practical insights for businesses and policymakers. The findings underscore AI’s transformative potential to encourage environmentally conscious behavior, providing a roadmap for leveraging technology to advance sustainability in the digital age.

Specification

Description:A new approach towards promoting ecologically conscious consumer behaviors is marked by the union of AI and sustainability. Green buying behavior, which seeks to buy products that are environmentally responsible and ethically produced, can no longer simply come from traditional marketing. AI has significantly changed how consumers perceive, interact with, and then decide on green products. AI can help businesses associate themselves with environmentally conscious consumers in greater ways. For instance, through AI-driven personalization, AI can make tailored recommendations to individuals about green products, making choices of ecologically friendly products more attractive and accessible. Similarly, AI-based consumer education empowers consumers fully to understand the environmental implications of their choices, hence an enhanced ability to make informed purchase decisions.
Another vital dimension is social influence by AI wherein algorithms and virtual communities emphasize sustainable consumption. Eco-labeling through AI also fuels transparency by providing understandable and trustworthy information on the environmental merits of products. Ultimately, AI-based sentiment analysis enables the business to measure consumer perception towards green products so that their strategies may be corrected and perceived in concert with expectations and values of customers. The proposed model develops the relationship of various AI-driven factors with Green Buying Behaviour, thus offering a glimpse into how artificial intelligence influences the consumers' disposition towards the purchase of green products. The discussion of each independent variable along with the linkage to the dependent variable is as under:
1. AI Personalization and Green Buying Behaviour
Personalization through AI improves the shopping experience. Recommendations get targeted on preferred choices of a consumer, based on past purchases and browsing behaviour. The probability of buying green products increases when AI personalises suggestions for eco-friendly products and aligns these options with those of its consumers' values and convenience. More relevant and desirable are perceived ecofriendly choices due to personalized recommendations that offer a connection between the consumer and the product.
Relationship Hypothesis: AI-based personalization enhances green buying behavior because it reduces fatigue from too much choice and makes green choices more appealing.
2. AI-Based Consumer Education and Green Buying Behavior
AI platforms would present thorough educational material on the various environmental and social impacts of products. Truly, AI affords a customer the opportunity to make a judgment in aligning with their values and ethical conduct while being environmentally sensitive with factual and timely information. Green product-related questions answered by AI chatbots and virtual assistants in real time would in a great way create trust and awareness.
Hypothesised Relationship: AI-based consumer education has the effect of enhancing knowledge and strengthening decision-making forces for green buying behavior.
3. Social Influence Through AI and Green Buying Behaviour
Social influence, amplified by the AI, is crucial for prompting green buying. AI-based platforms mainly seek to commend products that are green in nature based on popular trends, reviews, or the purchasing behaviors of peers and influencers. Virtual communities created through algorithms developed by AI-based ideas tend to amplify collective attitudes toward sustainable consumption, creating a sense of social normativity around green purchasing.
Hypothesized Relationship: Social influence through AI positively affects green buying behavior by posing to individuals peer pressure and reinforcement of environmentally friendly choices.
4. AI-Powered Eco-Labels and Green Buying Behaviour
Eco-labels supported by AI suggest transparent and reliable information about the environmental situations of products. It can translate obscure data like carbon footprint or sourcing into understandable information for consumers. By being an objective source of information and validating claims, AI-supported eco-labels build confidence for consumers to choose green buying rather than conventional buying.
Hypothesized Relationship: AI-supported eco-labels positively influence green buying behavior: increasing transparency, this minimizes skepticism towards green products.
5. Sentiment Analysis and Green Buying Behaviour
Sentiment analysis with the help of AI collects and deciphers consumer opinion in relation to green products, which exist on social media, reviews, and forums. It adjusts marketing strategy and handles problems or issues brought up by customers and promotes the product if the positivity scores dominate the negative; consumer sentiment enhances loyalty and trust, which are highly valued for green buying.
The hypothesized relationship is as follows: indirect support for green buying behavior by shaping marketing strategies according to consumers' values and expectations.
These variables can be interlinked. For instance, AI-based consumer education improves the efficacy of AI-driven eco-labels due to the provision of a deeper understanding of environmental certifications. Similarly, social influence through AI can boost the analysis of emotions through the influence of consumer opinions on peer recommendations. All of these factors together provide a holistic ecosystem of AI that fosters and sustains green buying behavior. These connections represent the transformative potential that AI holds in regards to sustainable consumption and offer a firm framework with which mechanisms leading to green buying behavior can be understood .
The proposed model examining the connection between AI-driven factors and green buying behavior pertains highly to addressing problems with contemporary environmental challenges. Growing urgency to reduce climate change and promote sustainability provides insight into the impact artificial intelligence can have on consumer behavior, making this study very valuable for academia, businesses, and policymakers. Among the most important hurdles to green buying is consumer reluctance, which is fueled most significantly by limited awareness, lack of trust, and perceived higher cost or inaccessibility of green products. This model addresses these barriers by leveraging AI to provide personalized recommendations, educating consumers, and enhancing product transparency. AI-driven personalization ensures that eco-friendly products are introduced before consumers in ways related to their preferences and needs, making this decision-making process easier and more appealing for them.
Consumer knowledge and trust play a central role in green buying behavior. The model fundamentally insists on both through AI-based consumer education and AI-powered eco-labels. The efficacy of AI-driven, targeted educational content educates consumers to make sound decisions; the AI-powered eco-label builds trust, through transparent and credible insights into the environmental impact of a product. These constituents altogether empower the consumer with informed and confident choice, thus reducing skepticism toward green products.

The model also includes social influence, where AI can analyze and amplify trends, and it is apt to produce a powerful influence on consumer behavior. Therefore, through peer recommendations, influencer endorsements, and virtual communities, AI promotes green products, which in turn gives rise to collective responsibility and social norms in sustainable consumption and thus plays an effective role among environmentally conscious, socially activist consumers who agree upon the shared goals of environmental responsibility. Sentiment analysis, among other things, allows businesses to measure consumer perceptions of green products, thus engaging a feedback loop that can help improve marketing efforts, alleviate concerns, and line offerings up with consumer expectations. This interaction creates a dynamic marketplace wherein consumer feedback and business strategies mutually reinforce sustainable consumption.
The beauty of this model lies in its holistic approach. All AI-based drivers interact synergistically to form an overall framework that drives consumer behavior at various dimensions. For instance, consumer education using AI-based eco-labels provide contextual background for various certifications. AI-based social influence amplifies positive sentiment through sentiment analysis. Therefore, the intensity of AI towards green buying habits increases the sustainability of individual consumers' choices and overall market trends. From an academic standpoint, the model contributes to the evolving discourse on sustainability and technological innovation by integrating AI technologies with behavioral science. For businesses, it offers actionable insights into leveraging AI to align environmental objectives with economic goals. Policymakers can also use this framework to design effective interventions that encourage sustainable consumption through technological advancements. In conclusion, this model will express the transformational potential of AI in green buying behavior and will overcome consumer barriers by building trust and fostering social norms toward sustainability, thereby providing a roadmap for advancing sustainability in the digital age.






, Claims:We claim that
1. AI driven personalization have positive influence on Green Buying behaviour.
2. AI based consumer education have positive influence on Green Buying behaviour.
3. Social influence through AI have positive influence on Green Buying behaviour.
4. AI powered eco label have positive influence on Green Buying behaviour.
5. Sentiment analysis have positive influence on Green Buying behaviour.

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