Social networking platforms have recently become instrumental in shaping consumer activities, particularly as public concerns about environmental sustainability and green consumption [1, 2]. Among the various determinants of consumer behavior, social influences have emerged as particularly impactful in shaping purchase decisions, especially in sectors like organic and eco-friendly products. Current global consumption trends, often characterized by unsustainable practices, have led to many environmental challenges, including pollution, depletion of essential natural resources, increased greenhouse gas emissions, and accelerated global warming. These ecological crises have catalyzed the global transition toward sustainable alternatives, drawing increased consumer attention to green products. The growing environmental consciousness among individuals and a heightened importance on personal health and well-being have spurred the demand for organic and environmentally responsible products. In this evolving consumer landscape, key opinion leaders, especially those active on social media, play a vital role in influencing how people perceive sustainability and make related purchase decisions [3, 4].

An influencer is an individual who shapes consumer decisions by creating engaging content on social media platforms. With the rapid growth of influencer marketing, collaborations between brands and content creators have become highly profitable. Simultaneously, growing concerns over pollution and global warming have increased public interest in sustainable consumption, giving rise to green influencers, also known as greenfluencers, who promote environmental responsibility. Greenfluencers are a niche group of social media influencers who focus on sustainability. They raise environmental awareness, encourage eco-friendly lifestyles, and advocate mindful consumption. Unlike traditional influencers, they are often perceived as more credible, as their motivations are seen as authentic rather than profit-driven. These influencers are vital in encouraging green purchase intentions by inspiring individuals to adopt environmentally responsible choices [5, 6, and 7]. Figure 1 illustrates the consumer’s decision-making process influenced by such sustainability messaging.

Figure 1: Understanding Consumer Behavior

Most existing research on green purchase intention (GPI) and consumer behavior was conducted in developed nations, such as the USA, the UK, Italy, and China. However, the impact of green influencers on consumer decisions remains underexplored, particularly in emerging markets like India. To the best of the author’s knowledge, there is a lack of empirical studies examining the role of green influencers in shaping green purchasing behavior among Indian consumers. Moreover, while green influencer marketing is rapidly gaining traction, most academic studies still focus on traditional promotional approaches [8, 9, and 10]. This gap highlights the need for a deeper understanding of how social media-driven environmental advocacy influences consumer intent in developing contexts. Accordingly, this study aims to investigate the impact of green influencers on green purchase intentions in India. The objectives are to examine whether awareness, trust, credibility, sustainable lifestyle promotion, and social norms significantly affect consumers’ intentions to purchase green products.

The structure of this paper is as follows: Section 2 includes a review of the existing literature. Section 3 describes the research methodology employed. Section 4 presents the results of the data analysis. The paper ends with the conclusion and implications elucidated, acknowledges limitati ons, and identifies avenues for future research. 

Anastasios Panopoulos et al. [11] examined the impact of environmental concern, eco-labeling, influencers, and user-generated content on green purchase intentions among Generation Z in the UK. Using a sample of 393 university students and Structural Equation Modelling (SEM) for analysis, the study revealed that environmental concern significantly influenced eco-labeling and user-generated content, while influencers positively affected the latter. However, the findings had limited generalizability due to the sample’s focus on Gen Z participants aged 18 and above from select UK universities.

Leelavathi and Satyanarayana S [12] investigated the effect of green labeling on consumers’ green purchase intentions, surveying 400 participants in Bangalore using purposive sampling. The study found a positive relationship between green labeling and purchase intentions, suggesting that increasing awareness through social media could motivate younger consumers to make ethical shopping choices. However, the study’s scope was geographically limited to Bangalore, which might affect the generalizability of its findings to other regions.

Yu-Shan Chen et al. [13] examined the influence of green brand affect on green purchase intentions by using a structured questionnaire administered to 365 randomly selected consumers. Structural Equation Modelling (SEM) was employed to assess mediation effects. The results indicated that the green brand effect did not directly influence green purchase intentions, while green brand associations and attitude fully mediated its impact. A key limitation was the study’s focus on Taiwanese consumers with experience limited to information and electronic products, restricting the applicability of findings to broader product categories.

Md. Nekmahmud et al. [14] explored how social media marketing (SMM) and social media usage (SMU) affected consumers’ green purchasing intentions and sustainable consumption behavior. Based on 785 valid responses from Hungarian consumers collected through a self-administered questionnaire, the study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) for analysis. Results revealed that attitude, subjective norms, perceived behavioral control, green thinking, and SMM were positively linked to green purchase intentions. However, the study focused on general green products, without examining specific product categories.

Rina Sugiarti et al. [15] examined how green marketing, product knowledge, and influencers impacted the purchase intention of Avoskin cosmetic products among educated Generation Z consumers in Jabodetabek. This study used a non-probability sampling method. The study found that green marketing and influencers positively influenced purchase intentions, whereas product knowledge had no significant effect. However, the study’s scope was limited, as it did not account for other relevant factors, such as economic considerations, brand trust, or consumer experience.

Nilna Muna et al. [16] investigated the direct and indirect effects of influencer marketing on consumers’ intentions to purchase eco-friendly products and services, highlighting the mediating roles of information credibility and positive emotions. Based on a quantitative survey of 246 individuals in Bali with prior knowledge of sustainable products, the study found that influencer marketing exerted a strong indirect impact on purchase intentions, though its direct influence was limited. However, the study’s findings were constrained by its geographic focus on Balinese consumers and the use of self-reported data, which might introduce social desirability bias.

Sheng-Hsiung Chang [17] explored the impact of green viral communication on green purchase intentions, focusing on the mediating role of consumers’ susceptibility to interpers onal influences. Based on data from 338 Taiwanese participants analyzed using Structural Equation Modeling, the study found that green viral communication positively influenced normative and informational interpersonal influences and green purchase intentions. However, only informational influence significantly affected purchase intention, while normative influence did not. A web-based survey introduced biases compared to in-person data collection methods.

The primary components of this section include descriptions of the research design and rationale for the research as well as the methodology that is used in the study. A research method is a step through which the analysis is performed, such as finding the data, choosing the data, collecting the data, and analyzing the data based on the survey. The study is based on research design, sample and sample size, sampling method, data collection, and analysis. Figure 2 represents the flowchart of the research method.

Figure 2: The flow chart of the research methodology

3.1 Research Design

A research design establishes the route to answering the research questions. Therefore, the research design is the primary plan, including all steps in the study that the researcher accomplishes. The most important step in any research project is the choice of a design appropriate for the research subject. A quantitative research method is used in this paper to test the relationships among the variables of importance. The quantitative research method is suitable for scenarios involving more subjects. This makes results generalization possible while using recognized standards, allowing for replication of the research. To better understand the purposes of this study, a conceptual framework is depicted in Figure 3.

Figure 3: Research framework

3.2 Questionnaire Design and Measures

A self-administered questionnaire is designed to test the proposed hypotheses and research framework. Measurement items for all variables are adapted from established studies and reviewed by academic experts to ensure content validity. Based on their feedback, minor revisions have been made. A pilot test is then conducted with a convenience sample of 50 Indian consumers to assess the reliability of the constructs. All scales demonstrate internal consistency with Cronbach’s alpha values exceeding the acceptable threshold of 0.70. The questionnaire is further refined based on participant feedback. A 5-point Likert scale ranging from 1 (“Strongly Disagree”) to 5 (“Strongly Agree”) is employed, and each construct is measured using five items.

3.3 Data Collection and the Sample Profile

Data for this study are gathered from both primary and secondary sources. Primary data are obtained via a structured survey questionnaire, while secondary data are collected from academic publications, journals, and relevant literature. Respondents are informed that their participation is voluntary, confidential, and intended for educational purposes. The survey questionnaire is distributed online, allowing for a broader geographic reach and enabling the enforcement of mandatory responses for all questions to ensure data completeness. This approach also facilitates diverse sampling across different demographic groups. The study employs a random online distribution method to circulate the questionnaire among Indian consumers. Out of 500 questionnaires, 430 are deemed valid after filtering out incomplete and inconsistent responses, resulting in a high response rate of 86%.

3.4 Tools and Statistics Used

This study research is conducted using the distribution of questionnaires. An online survey is created, which can be easily gathered and analyzed statistically. The questionnaire consists of closed-ended questions that best fit this study and include quantitative data to be analyzed. The primary data gathered through the questionnaire consists of demographic information, general information, and questions relating to testing independent and dependent variables concerning green purchase intentions. The statistical analysis is conducted using a quantitative method, including descriptive and statistical analysis.

3.5 Data Analysis

This study enables the researcher to collect accurate and relevant data for the research objective. Data from the surveys are subjected to statistical analysis to identify patterns and relationships. The responses are analyzed with descriptive statistics to get the statistical results from the respondents, and the studied variables are described in depth. The internal consistency, convergent validity, and discriminant validity are examined to test the suitability of the model constructs with the items. Data is collected using a random sampling technique. Then, the SEM is applied to determine the significance of the relationship between the variables used in this research

This section evaluates the measurement model of all the variables under study. Then, SEM results are applied to assess the outer measurement and inner structure models. The outer model’s goodness related to this study’s constructs is also established. Finally, the results of the hypothesis testing are offered.

4.1 Measurement Model Evaluation

At this stage, the reliability and validity of the latent constructs are evaluated through multiple criteria. Internal consistency is assessed using Cronbach’s alpha, while convergent and discriminant validity are examined through confirmatory factor analysis (CFA). To ensure the robustness of the measurement model, Structural Equation Modeling (SEM) is employed, enabling the evaluation of both the measurement and structural models in terms of construct validity and path relationships.

Table 1: Results of the validity test

Reliability Analysis: Composite reliability (CR) ranges from 0.828 to 0.910, greater than the 0.70 threshold value, indicating strong reliability between processes. Consequently, the survey instrument is valid in all areas of research design and is consistently free from random errors. Composite reliability, standardized factor loading, and average variance extracted are applied to calculate convergent validity.

Convergent Validity: The convergent validity is measured using the AVE, CR, and outer loadings. The results show that the AVE stretches from 0.508 to 0.530, which meets the convergent validity criterion. As shown in Table 1, the outer loadings, CR, and AVE values are satisfied (0.50), while CR is (>0.70).

Discriminant Validity: In measuring the discriminant validity, the Fornell-Larcker benchmark of comparing the square root of AVE with the constructs’ correlations is used. The AVE square roots of all the constructs are beyond the squared correlations among the constructs, and this indicates satisfactory discriminant validity. This is because all the items are loaded strongly than others.

Multicollinearity (inner VIF): The predictor variables’ variance inflation factor (VIF) ranges between 4.383 and 2.435, and this falls within the acceptable range, where VIF values less than 5.0 mean no multicollinearity problem and no negative effect between items or predictors. As a result, each construct is statistically distinctive and explains how discriminant validity is satisfactory.

4.2 Structural Model

The goodness-of-fit indicators from the structural equation model suggest that the model fits the data well. In brief, it is the supposed causation between the dependent and independent constructs, and this structural model has been used to test the hypotheses in this study. The χ2 test is used to estimate the goodness-of-fit indices (GFI) for measurement and structural models. In addition, the root mean square error of approximation (RMSEA) is also examined as an absolute fit index. The incremental fit index (IFI) and the comparative fit index (CFI) are used as incremental fit indices. The model fit indices of the structural model and the cut-off value of those fit indices are presented in Table 2.

Table 2: Model fit statistics

In Table 2, the goodness-of-fit statistics show that the structural model fits the data reasonably well. The RMSEA value of 0.03, which is below the threshold of 0.05, confirms that the model is an excellent fit for the data. Additionally, the Goodness of Fit Index (GFI) is 0.92 and the Adjusted Goodness of Fit Index (AGFI) is 0.90, both exceed the acceptable cut-off of 0.90, indicating that the model is a good representation of the data. The Comparative Fit Index (CFI) is 0.96, which is close to 1, demonstrating a near-perfect fit. In summary, the combination of these indices (RMSEA, GFI, AGFI, and CFI) provides strong evidence that the model accurately reflects the relationships among the variables, indicating a very good fit between the model and the data.

4.2.1 Hypothesis Testing and Analysis

The hypotheses of the study are formulated according to the needs, objectives, and importance of the study.

• H1: Raising awareness has a positive and significant impact on green purchase intention.
• H2: Enhancing trust and credibility has a positive and significant impact on green purchase intention.
• H3: Promoting sustainable lifestyles has a positive and significant impact on green purchase intention.
• H4: Influencing social norms has a positive and significant impact on green purchase intention.

Table 3: Results of hypothesis examination

*** p < 0.01.

It can be seen that there is a noteworthy significance between all the study variables. The results reveal that all four hypotheses are supported. “The path coefficient will be significant if the value is not zero within the confidence interval”. The effects of the path coefficients and p-values are shown in Table 3. Among the four hypotheses, the “enhancing trust and credibility” has a significantly positive impact on green purchase intention (β=0.75; p< 0.01). The rest of the path relationships are as follows: PSL→GPI (β=0.67; p< 0.01), RA→GPI (β=0.46; p< 0.01), and ISN→GPI (β=0.25; p< 0.01). These results suggest that enhancing trust and credibility is an important antecedent of green customers’ purchase intention compared to other green influencer impacts. Figure 4 illustrates the overall path coefficient analysis.

Figure 4: Structural Equation Modelling output

This paper’s starting point was to discuss the role of green influencers on green purchase intention. Then, it examined the significant relationship between influencing factors and green purchase intentions. The study’s results suggested that enhancing trust and credibility had the most important positive influence on green purchase intentions, whereas promoting sustainable lifestyles was the second-best predictor. Green influencers were crucial in shaping consumer attitudes and behaviors towards green purchasing by raising awareness, enhancing trust, promoting sustainable lifestyles, and influencing social norms. They achieved this by combining their credibility with the official validation of brands and by sharing educational content on sustainability. This combination increased consumer trust, the perceived value of eco-friendly products, and the intention to purchase. 

5.1 Implications

Current purchasing behaviors have negatively impacted the environment, creating a need for a change in purchasing and consumption patterns toward more green practices. Hence, this paper contributes to understanding how citizens can be encouraged to adopt green purchasing behaviors. Policymakers can build on these findings to facilitate green product knowledge, reduce green product skepticism, and build consumer awareness of the consequences of their purchasing and consumption behavior.

5.2 Limitations and Avenues for Future Research

As with any research, this study had several limitations. First, the study did not stipulate particular kinds of green products. Additionally, the reliance on self-reported data might introduce social desirability bias, potentially influencing the accuracy of the results. Future research should consider expanding the study’s scope to cover specific green products and include more diverse populations and geographic locations to address these limitations. Furthermore, incorporating longitudinal designs that track consumer behavior over time can provide more robust insights into the long-term impact of green influencers on purchasing decisions.

1. Arti Kumari Gurung and Chandra Mohan Singh, “The Impact of Social Media Influencers on Consumer Purchase Intentions for Organic and Green Products”, Frontiers in Health Informatics, vol. 13, no. 8, pp. 1-7, 2024.
2. Xiaoyu Zhao, Lidong Fan and Yuan Xu, “An investigation of determinants of green consumption behavior: An extended theory of planned behavior”, Innovation and Green Development, vol. 4, pp. 1-9, 2025.
3. Peter Ansu‑Mensah, “Green product awareness efect on green purchase intentions of university students’: an emerging market’s perspective”, Future Business Journal, vol. 7, no. 1, pp. 1-13, 2021.
4. Brahim Chekima, Syed Azizi Wafa Syed Khalid Wafa, Oswald AisatIgau and Sohaib Chekima, “Determinant Factors of Consumers’ Green Purchase Intention: The Moderating Role of Environmental Advertising”, Asian Social Science, vol. 11, no. 10, pp. 1-12, 2015.
5. Sarah Konig and Erik Maier, “Preaching to the choir: Do green influencers make a difference?”, Journal of Cleaner Production, vol. 447, pp. 1-14, 2024.
6. Francesca Conte, Francesca Sammartino, Alessandra Bertolini, Alfonso Sianoand Irene D’Onza, “The Role of Greenfluencers’ Communication in Overcoming Consumers’ Barriers to Circular Economy: An Explorative Study on @Eco.narratrice”, Sustainability, vol. 17, pp. 1-19, 2025.
7. Yu-Hsien Lin, “Determinants of Green Purchase Intention: The Roles of Green Enjoyment, Green Intrinsic Motivation, and Green Brand Love”, Sustainability, vol. 15, pp. 1-20, 2023.
8. Arani Rodrigo and Trevor Mendis, “Impact of social media influencers’ credibility on millennial consumers’ green purchasing behavior: a concept paper on personal and social identities”, Management Matters, vol. 20, no. 2, pp. 1-20, 2023.
9. Sanaz Saghati Jalali and Haliyana Binti Khalid, “The Influence of Instagram Influencers’ Activity on Green Consumption Behavior”, Business Management and Strategy, vol. 12, no. 1, pp. 1-13, 2021.
10. Shahahbuddin A. M, “Social Media Influencers’ Credibility On Green Purchase Behavior: Evidence From Bangladesh”, International Journal of Educational Research & Social Sciences, vol. 5, no. 3, pp. 372-378, 2024.
11. Anastasios Panopoulos, Athanasios Poulis, Prokopis Theodoridis and Antonios Kalampakas, “Influencing Green Purchase Intention through Eco Labels and User-Generated Content”, Sustainability, vol. 15, pp. 1-17, 2023.
12. Leelavathi S and Satyanarayana S, “Influence of Green Label's On Green Purchase Intention: A Study on Consumers In Bangalore”, Educational Administration: Theory and Practice, vol. 30, no. 5, pp. 1-9, 2024.
13. Yu-Shan Chen, Tai-Wei Chang, Hung-Xin Li and Ying-Rong Chen, “The Influence of Green Brand Affect on Green Purchase Intentions: The Mediation Effects of Green Brand Associations and Green Brand Attitude”, International Journal of Environmental Research and Public Health, vol. 17, pp. 1-17, 2020.
14. Nekmahmud, Farheen Naz, Haywantee Ramkissoon and Maria Fekete-Farkas, “Transforming consumers’ intention to purchase green products: Role of social media”, Technological Forecasting & Social Change, vol. 185, pp. 1-16, 2022.
15. Rina Sugiarti, Nenik Diah Hartanti, Ekaning Setyarini and Sri Kurniasih Agustin, “The influence of green marketing, product knowledge, and influencers on purchase intention”, International Journal on Social Science, Economics and Art, vol. 13, no. 3, pp. 1-6, 2023.
16. Nilna Muna, NiWayan Eka Mitariani and Ni LuhWayan Sayang Telagawathi, “Eco-Conscious Consumers and Influencer Marketing: Understanding the Path to Green Purchases through the Theory of Planned Behavior”, International Journal of Analysis and Applications, vol. 23, pp. 1-15, 2025.
17. Sheng-Hsiung Chang, “The Influence of Green Viral Communications on Green Purchase Intentions: The Mediating Role of Consumers’ Susceptibility to Interpersonal Influences, Sustainability, vol. 7, pp. 4829-4849, 2015.