Digital Influencers and Electronic word of mouth (eWom) Effects on Green Purchase intention among Students

¹Abhinav Agrawal, ²Minal Shah, ³Dharmendra Kumar Gangeshwer

 ^1,2,3Bhilai Institute of Technology, Chhattisgarh, India.

*Corresponding Author: skabhinava@gmail.com

Abstract

 This research investigates green purchase intention among university students through an extended Theory of Planned Behavior with digital related variables including influencer credibility, electronic word of mouth and social media engagement. A cross-sectional questionnaire-based study was carried out among 512 students of various universities located in Raipur, Chhattisgarh. Data were processed through Confirmatory Factor Analysis and Structural Equation Modeling. The results demonstrate that attitude, subjective norms, perceived behavioral control, influencer credibility, electronic word-of-mouth, and social media engagement are significant predictors of green purchase intention. Also, Electronic word of mouth significantly mediates the relationship between influencer credibility and subjective norms. The moderation analyses revealed that the effects of social media engagement were stronger among course level whereas gender differences were detected but not consistent in direction. This research advances the literature by applying digital aspects of behavior to extend the Theory of Planned Behavior within the context of the developing world and delivers practical applications for those seeking to effectively target youth, consumers, and students globally for sustainable consumption in part via digital means.

Keywords: Green purchase intention, University students, Digital influencers, eWOM, Extended TPB, Chhattisgarh, SEM analysis.

1. Introduction

The increasing global focus on sustainable consumption has escalated academic interest in green purchase intention (GPI), especially with regard to young consumers. In India, this segment holds a pivotal impetus for the prospective sustainable markets with 43 million university students (AISHE 2023–24). Even though environmental consciousness and green exposure are rising among students, their actual purchase of green products is still hindered by intention–behavior gap. This gap is more significant in underdeveloped and tier-2 states like Chattisgarh where green consumption behavior is limited by affordability issues, availability of products, and distrust in digital information sources.

Changing the way they are received, interpreted and acted upon, young consumers are particularly influenced by recent developments in digital ecosystems, namely in the explosive growth of social media platforms such as Instagram. In the age of the digital influencer, the electronic word-of-mouth (eWOM) effect and social media engagement (SME) have become influential variables on consumer purchasing decisions. Although these elements have been studied separately in prior research, their combined influence in a comprehensive behavioural model is still unknown, especially in the developing world. The Theory of Planned Behavior (TPB) (Ajzen, 1991) has been employed extensively to predict green purchase intention with its components: attitude, subjective norms, and perceived behavioral control. Nevertheless, conventional TPB frameworks tend to neglect the interactive effect of digital media, which have become vital in student decision-making. While some recent research works have endeavoured to extend TPB by adding digital variables, these works are predominantly focused on developed or metropolitan context, there exists a significant contextual void when it comes to understanding of digi-driven green behavior in tier-2 cities and among student population.

This research overcomes these shortcomings by developing and testing an extended TPB model that includes digital factors–influencer credibility, electronic word-of-mouth (eWOM), and social media engagement (SME)–as an integrated model. In contrast to earlier studies, this study presents a double mechanism of mediation and moderation, with eWOM as a mediator the relationship between influencer credibility and subjective norms and demographic variables such as course level and gender as a moderator. In this way, the approach provides a more differentiated and realistic view of student conduct in the digital age.

In addition, this study provides a contextual novelty as it addresses on university students from Raipur, Chhattisgarh- a regional stronghold in green marketing literature. By applying Structural Equation Modeling (SEM) to primary data obtained from 512 students, the paper corroborates the extended model and reveals specific regional behavioural traits, namely that of pronounced peer influence (subjective norms) and financial limitation (perceived behavioural control).

To sum up, the contribution of the study to the existing body of knowledge can be marked three-fold:

(1) the adaptation of the TPB model to include digital influence variables,

(2) the investigation of mediating and moderating effects in one model, and

(3) the context of a tier-2 city in India, thereby adding to a handful of empirical studies in the developing context.

Theoretically, these contributions enhance and for marketers, policy makers and academic researchers who have a desire to influence sustainable consumption via digital track prospects for action.

1.2 Theoretical Framework

The present study is based on Icek Ajzen's Theory of Planned Behavior (TPB) (1991), which continues to be the popular theoretical model for understanding human behavior within a decision-making context. TPB suggests that behavior intention is influenced by three main factors: attitude (ATT) toward the behavior, subjective norms (SN), and perceived behavioral control (PBC) over performing the behavior. In the green consumption context, these constructs describe how a purchase intention is influenced by a personal assessment of the sustainable product, the social influence, and what is affordable/accessible.

Nonetheless, the classical TPB model was conceived in the era of pre-internet and fails to fully reflect the impact of current digital milieu on consumer behaviours, as the latter are further influenced by their online presence. As social media platforms are rapidly proliferating, particularly among university students, decision-making is more socially embedded and digitally mediated. Accordingly, TPB needs to be extended with relevant digital constructs in order to remain valid in modern contexts.

To overcome this shortcoming, the current research brings three dominant digital variables into the TPB model:

Influencer  Credibility (IC):

Based on source credibility theory, influencer credibility is defined as the level of perceived expertise, trustworthiness, and attractiveness of social media influencers. As in the real world, opinion leaders demonstrate their credibility and influence through the internet, among which are influencers products awareness and consumer attitudes toward green products. Trustworthy sources of information can decrease uncertainty as well as increase trust, where both attitudes and behavioral intentions are positive outcomes of such influences.

Electronic Word-of-Mouth (eWOM):

The term eWOM is related with online activity on which consumers exchange their thoughts, opinions, reviews, and recommendations about particular products or services. It has a double function, an informative and a normative one. A good eWOM can not only improve the consumer attitude, but also enhance the subjective norm as it induces a sense of social consensus, thus it is an important mechanism for consumers in the digital world.

Social Media Engagement (SME):

Social media engagement represents user participation in green content in the form of likes, shares, comments and even content creation. This antecede shows how intense green cognition and emotion were since involvement is said to foster both cognition and affect leading to the behavioral intention. Based on these dimensions, this paper builds the extended TPB model, in which influencer credibility, eWOM and social media engagement are independent variables and green purchase intention is the dependent variable, and influencer credibility, eWOM and SME affect green purchase intention directly and indirectly through TPB components (ATT, SN, PBC).

The present theoretical framework contributes to this line of research by positioning eWOM as an intermediary mechanism between influencer credibility and subjective norms. This is the stage at which influencer messages converge with personal interactions and are validated as well as amplified to influence magnitude of social pressure and behavioral intent.

Furthermore, course level (undergraduate vs postgraduate) and gender are considered as moderating variables in this research model to better understand variations in the digital influence and decision making of student consumers. These moderating variables illustrate how the strength of some of the relationships within the model are influenced by demographic differences.

In sum, the extended framework provides a richer and more contextually relevant account of green purchase intention by integrating orthodox behavioural theory with modern digital theory. The study meets recent demands for unifying behavioural theories with the dynamics of digital marketing view emerging markets and student public as focal-point.

2. Literature Review

1.Ajzen, (1991)Theory of Planned Behavior (TPB) The Theory of Planned Behavior was suggested to predict human behavior, describing the effect of attitude toward behavior, subjective norms, and perceived behavioral control on behavioral intention. There are substantial evidence, from meta-analyses of predictors of intention (indicating) how the attitude, subjective norms, and perceived behavioral control (PBC) constructs influence behavior, and how intention and perceived control predict behavior.

2.Paul et al. (2016) examined the applicability of TPB in predicting green product consumption.
the objective was to analyze determinants of green purchase intention using TPB. Attitude was found to be the strongest predictor, followed by subjective norms and perceived behavioral control.

3.Yadav and Pathak (2015) focused on young consumers’ green purchasing behavior.
study was conducted to study TPB variables in the context of youth.
and it was concluded in the study that  Attitude and subjective norms significantly influence purchase intention among young consumers.

4.Han et al. (2010) applied TPB in eco-friendly decision-making and aim of the study was To understand consumer behavior in environmentally responsible contexts and it was found that Subjective norms strongly influence intentions, especially in socially sensitive decisions.

5.Chen and Tung (2014) extended TPB by incorporating environmental concern to improve TPB’s explanatory power and it was resulted that Environmental concern significantly enhances the predictive ability of TPB.

6.Chen (2010) investigated factors influencing green brand equity with objective of  examine drivers of green purchasing behavior and it was found that Trust and perceived value significantly enhance green purchase intention.

7.Joshi and Rahman (2015) conducted a comprehensive review of green purchase behavior to identify key determinants of green purchasing. It was resulted that Environmental knowledge and awareness are critical factors influencing behavior.

8.Chen and Chang (2012) studied the role of trust in green marketing to  analyze how trust affects green purchase intention and it was reported that Trust reduces uncertainty and increases consumer willingness to purchase green products.

9.Nekmahmud et al. (2022) explored green behavior among young consumers to analyze sustainability awareness among youth it was concluded that Social media exposure significantly increases green purchase intention.

10.Cheung and Thadani (2012) analyzed the influence of eWOM on consumer decisions with objective to review the impact of online reviews in his study he concluded that eWOM significantly affects consumer attitudes and purchase decisions.

11.King et al. (2014) examined online review systems for understanding  how consumers use online information and it was found that Consumers rely heavily on peer reviews before making purchase decisions.

12.Ismagilova et al. (2017) studied eWOM communication to explore the effectiveness of eWOM.
and It was conclude that eWOM reduces perceived risk and builds trust in online environments.

13.Filieri (2015) investigated determinants of eWOM usefulness.
the aim of the research was to identify factors influencing helpfulness of online reviews.
and it was concluded Information quality significantly impacts consumer perception and decision-making.

14.Erkan and Evans (2016) examined eWOM in social medi to  analyze the role of information credibility and usefulness it was observed that Information usefulness mediates the relationship between eWOM and purchase intention.

15.Leong et al. (2022) explored social media eWOM. with objective to  examine digital communication impact on consumer behavior and the finding suggested that  eWOM has both direct and indirect effects on purchase intention.

16.Sardar et al. (2021) studied eWOM adoption behavior to analyze factors influencing online information adoption and it was revealed that Credibility and relevance strongly affect adoption behavior.

17.Ohanian (1990) developed a credibility scale to measure source credibility. He concluded that Trustworthiness, expertise, and attractiveness are key credibility dimensions.

18.Djafarova and Rushworth (2017) examined influencer marketing.
with objective to  analyze Instagram influencers’ impact and it was concluded that Influencers significantly affect purchase intention due to perceived authenticity.

19.Lou and Yuan (2019) studied influencer marketing effectiveness to evaluate how influencers affect consumer behavior and it was found that Influencer credibility positively impacts attitudes and purchase intention.

20.Freberg et al. (2011) explored social media influencers. to identify characteristics of influencers study found that Influencers act as opinion leaders shaping consumer perceptions.

21.Brodie et al. (2013) studied customer engagement to understand engagement in digital platforms and it was concluded that Engagement enhances emotional connection and behavioral intention.

22.Harrigan et al. (2017) analyzed social media interactions to examine engagement’s impact on consumer behavior and it was suggested in this study that Higher engagement leads to stronger purchase intention.

23.Parveen and Chaudhary (2025) investigated the influence of social media eWOM in green buying.

To test whether attitude and subjective norms act as mediators of eWOM on intention for green purchase. The investigation revealed that attitude and subjective norms were partial mediators, indicating that eWOM indirectly affects purchase intention via psychological components.

24.Zaman, Basith, and Alam (2024) studied eWOM and perceived value in green consumption to investigate the impact of eWOM and perceived value on green purchase intention. eWOM is well-established as increasing purchase intention, especially when moderated by factors captured as trust and perceived value.

25.Ramdhani et al. (2025) examined eWOM in mobile commerce by adapting IAM to  explore the impact of quality, quantity and credibility of eWOM on purchase intention the study revealed that eWOM quality and credibility have a positive and significant effect on perceived usefulness, which in turn has a positive and significant effect on trust and purchase intention.

26.Prayogo and Adiwijaya (2025) investigated green influencers to analyze the influence of green influencers on purchase intention through TPB constructs and the finding indicated that Influencers had a significant positive effect on attitude and subjective norms, and it concluded that higher green purchase intention would be attained.

27.Muna, Mitariani, and Telagawathi (2025) studied influencer marketing in eco-friendly consumption to examine direct and indirect effects of influencer marketing on green purchase intention and it was resulted that Influencer marketing indirectly affects purchase intention through credibility and emotional responses rather than direct influence.

28.Vishrutha and Thiruchelvi (2025) analyzed the role of green influencers in developing countries with objective to examine how green influencers shape consumer behavior.
result of the study suggested that  Green influencers significantly impact attitudes and purchase intentions, especially in emerging markets.

29.Kohar and Bansal (2026) studied social media influence on green packaging decisions.
for examine the mediating role of eWOM in social media influence it is resulted in this study that Social media significantly affects green purchase intention, with eWOM acting as a strong mediator.

30.Handranata and Kalila (2025) examined influencer perception and consumer behavior to analyze how attitudes toward influencers affect purchase intention it was concluded that Influencer perception significantly influences purchase behavior through brand attitude and intention.

2.1 Research Gaps and Conceptual Model

After studying various literatures, it was found that there were very few regional based studies specially in Chhattisgarh no such study was performed further it was also analyzed that there is limited studies which focus on young consumers and digital influences it was also seen that electronic word of mount (ewom) with behavioral theories is still limited. It was felt that there is need for moderating and mediating effect for further exploration. Keeping in mind all these gaps this study focuses on fulfillment of these gaps.

Conceptual Model

Theoretical foundation: The study's conceptual model is based on the TPB and enriched by including digital influence constructs to capture modern consumer behavior. Attitude (ATT), subjective norms (SN), and perceived behavioral control (PBC) are the main predictors of green purchase intention (GPI) in this model.

Influencer credibility (IC), electronic word-of-mouth (eWOM), social media engagement (SME) and green purchase intention (GPI) are the focal exogenous variables in the model that exert direct and indirect effect (through TPB constructs) on the GPI. These digital factors reflect the influence of online, peer-to-peer and influencer-based persuasion on students.

A core aspect of the model is the mediating role of eWOM between influencer credibility and subjective norms, while the latter was positively and significantly related to subjective norms. This is representative of how influencer-created content becomes legitimized and accepted socially through peer reviews and social conversations online, ultimately leading to greater normative pressures on individuals.

In addition, the model introduces moderating variables course level and gender to test the extent to which the relationships vary between different groups of students. These moderators determine how demographic factors shape one’s susceptibility to the digital and social influences. In conclusion, the conceptual model outlined above can be denoted as follows:

In summary, the proposed conceptual model can be represented as:

IC, eWOM, SME → ATT, SN, PBC → GPI
with eWOM acting as a mediator (IC → SN) and course level and gender acting as moderators.

                                                   Figure 1: Conceptual Model

2.3 Research Objectives and Hypotheses

Objectives:

To validate the extended Theory of Planned Behavior (TPB) integrated with digital constructs in explaining students’ green purchase intentions.

To examine the mediating role of electronic word-of-mouth (eWOM) in the relationship between influencer credibility and subjective norms, and its subsequent impact on green purchase intention.

To analyze the moderating effects of course of study and gender on students’ green purchase intentions.

To identify region-specific barriers affecting green purchase intentions among students in Chhattisgarh.

Hypotheses

The hypotheses of the conceptual model of the research paper are stated below in full words represented from the given notation.

Core TPB Hypotheses (H1)

• H1a: Attitude towards green products has a positive effect on green purchase intention.

• H1b: Subjective norms have a positive effect on green buying intention.

• H1c: Perceived behavioral control has a positive effect on green purchase intention.

Digital Direct Effects (H2)

• H2a: Influencer credibility has a significant and positive effect on green purchase intention.

• H2b: Electronic word-of-mouth has a significant and positive effect on green purchase intention.

• H2c: Social media engagement significantly and positively affects green purchase intention.

•Mediation Hypothesis (H3)

• H3: EWOM mediates the association between influencer credibility and subjective norms, and this further influences green purchase intention.

Moderation Hypotheses (H4-H5)

• H4: The relationship between social media engagement and green purchase intention is strengthened by course level (undergraduate vs postgraduate) and this augmentation of relationship is more evident for postgraduates.

• H5: The relationship between influencer credibility and attitude towards green products is moderated by gender, with a positive effect which is stronger for female students.

3. Research Methodology

3.1 Population and Sampling

A quantitative, cross-sectional survey design is adopted in the proposed research to examine the extended TPB model with digital variables in a university student context in Chhattisgarh. This treatment is standard for behavioral intentions studies with SEM.

3.2 Study Population and Sampling

The target group was all full-time undergraduate and postgraduate students aged 18-25 years from various universities and its affiliated colleges. A multi-stage sampling procedure was utilized to select 10 colleges/universities, 3 departments in each colleges and 20 students in each department (n=600) responses were expected. Data were collected through Google Forms and paper-based questionnaires in November and December of 2025, with 544 responses (94% valid rate 50 incomplete or straight-lined responses were discarded), the final sample size was 512.

A total of 58% of the sample population were females and 62% were undergraduates (38% postgraduates), 71% from commerce/science stream, 66% received pocket money Amt of ₹3k-7k and 84% were daily Instagram users for1+hr.

3.3 Measurement Scales

40 items, 7-point Likert, bilingual (English/Hindi). Student-adapted scales:

Table-1 Measurement Scales

3.4 Analytical Procedures

Analysis was conducted in stages with the SPSS version 28 software for prior descriptives, EFA; KMO=0.95, reliability analyses, and correlations. The CFA and SEM were conducted using AMOS 28, which presented good model fit (CFA: ²/df=2.08, CFI=0.97, RMSEA=0.046; SEM: ²/df=2.18, CFI=0.96, RMSEA=0.048). Advanced tests involved using PROCESS (Model 14) with 10,000 bootstraps (version 4.3) for mediation and moderation. Robustness was ensured with the diagnostics including Harman’s single-factor test (<50% of the variance for the CMB), marker variable (r=0.07), normality (skewness < 2), multicollinearity (VIF< 2.1), and statistical power (f 2 = 0.22). The multi-faceted approach described above offers a firmer basis for testing the validity of digital extensions to TPB in a context of students from an emergent market.

4. Results and Discussion

4.1 Descriptive Statistics

Student GPI M=4.42 (SD=1.07); highest SME (M=4.78), lowest PBC (M=3.72).

Table-2  Descriptive Statistics

The respondents showed a moderate positive attitude toward green product purchasing (GPI: M=4.42, SD=1.07), and social media was the dominant factor in the construct (SME: M=4.78, SD=1.05), suggesting students' frequent use of Instagram (84% daily for more than 1 hour, mainly Reels at 68%). The perceived behavioral control was the lowest mean (PBC: M=3.72, SD=1.28), indicating that many students considered price as the substantial obstacle since it is their pocket money (₹3k-7k). Attitude was significantly positive (ATT: M=5.18, SD=0.94), which may be due to perceptions of health in green cosmetics. Data were normally distributed (skewness <2, kurtosis <2) to proceed with the advanced models.

4.2 Measurement Model Assessment

EFA: KMO=0.95, Bartlett p<0.001, 7 factors (77.9% var). CFA: χ²(548)=1140.6, χ²/df=2.08, CFI=0.97, RMSEA=0.046.

Reliability/Validity:

Table-3 Reliability/Validity Testing

(HTMT<0.85; Fornell-Larcker satisfied)

The results of reliability and validity testing were good as shown in Table-3. The Cronbach’s Alpha (α) values for all the constructs are between 0.91 and 0.96, which are well above the acceptance level of 0.70 and indicative of good internal consistency of the scale items. Likewise, the values of CR for all the constructs are found to be between 0.92 and 0.97 indicating very high reliabilities of construct.

Convergent validity For averaged variance extracted (AVE) were in between 0.71 and 0.78 which were acceptable since above the threshold of 0.50. It indicates that each construct can explain a large part of variance of indicators and this suggested good convergent validity. Additionally, discriminant validity is established by applying Fornell-Larcker criterion, src root square of AVE (√AVE) of each construct is above its corresponding MSV.

Thus, for example, the constructs of ATT and GPI have √AVE = 0.88, which is higher than their MSV values (0.60 and 0.62 resp.), showing that each construct is empirically distinct from the others in the model. The results, in general, prove that the measurement model is reliable, convergent valid and discriminant valid. With these, it can be said the constructs applied in this study are both reliable and distinctive, and thus can help to reinforce the strength of the structural model analysis and hypothesis testing in following procedure.

4.3 Structural Model and Hypothesis Testing

SEM Fit: χ²(579)=1262.3, χ²/df=2.18, CFI=0.96, RMSEA=0.048, R²_GPI=0.79, Q²=0.62.

Direct Effects:

Table-4 Structural Model and Hypothesis Testing

The result of the structural model shows a good fit to the data based on the fit indices (χ²/df = 2.18, CFI = 0.96, RMSEA = 0.048), which are all within accepted criteria. This indicates that the extended TPB-digital model is able to capture the observed relationships well. In addition, the model predicts a significant amount of variance in green purchase intention (GPI's R² = 0.79), with the three constructs explaining 79% of the variance in students' green purchase intention. The predictive relevance is also substantial (Q² = 0.62), which indicates a strong out-of-sample predictive power.

The direct effects show that all postulated connections are positive and significant. Among the TPB components, attitudes (ATT → GPI, β = 0.40, p < 0.001) is the strongest predictor, suggesting that a positive evaluation of green products by students is the most important determinant of their purchase intentions. It is followed by subjective norms (SN → GPI, β = 0.30), which emphasizes the significance of peer influence and social pressure, and perceived behavioral control (PBC → GPI, β = 0.22) that embodies the influence of how easy/difficult it is to perform the behavior.

Among the digital dimension constructs, IC ( → GPI, β = 0.23) has the greatest impact on students’ intentions, implying that trustworthy influencers have a great influence on students’ intentions among the digital aspects. Electronic word-of-mouth (eWOM → GPI, β = 0.19) in this context has also a significant impact, highlighting the relevance of online reviews and communication among peers. However, social media engagement (SME → GPI, β = 0.17), it is significant, has the weakest effect, which means engagement itself is less influential without reliable and persuasive information. In summary, the findings reveal that although the TPB variables are still the strongest predictors, the inclusion of digital factors provides a better explanatory model.

4.4 Mediation Analysis (H3) – Interpretation

The mediating mechanism offers more innovative explanation for the influence of authenticity on green purchase intention in the context of influencer marketing. The eWOM (β = 0.13, 95% CI [0.09, 0.18]) was a significant mediator of the relationship from IC through SN to GPI, indicating the mediation (IC → SN → GPI). Notably, the direct effect of influencer credibility on GPI reduces from β = 0.28 to β = 0.15 when entering the mediator, suggesting the full mediation.

Therefore, this supports our assertion that the credibility of influencers does not directly lead to the purchase intention; instead, it is mediated by eWOM that shapes subjective norms and then affects intention. To put it another way, authoritative influences bring about more discussion, review and peer to peer interaction online in the process, then this generates social pressure or normative influence, leading students to be active in buying green products. This illustrates the indirect, and indeed socially embedded, role of digital influence.

4.5 Moderation Analysis – Interpretation

The moderation analysis conducted via multi-group SEM suggests that there are significant group differences, indicating subtle demographic influences on model relations. With respect to the difference at course level (UG vs PG), the effect of social media engagement on GPI is significantly greater for the PG cohort (β = 0.23), as compared to the UG cohort (β = 0.12), with a significant chi-square difference (Δχ² = 7.2, p < 0.01). It may also indicate that PGs are more likely to convert the interaction on social media into real green purchase intention, which may be attributed to higher maturity, awareness or cognitive processing ability. Thus, H4 is supported in that course is a significant moderator of the relationship.

With regard to gender differences, the relationship between influencer credibility and attitude (IC → ATT) is stronger for females (β = 0.28) than males (β = 0.17), with a significant difference (Δχ² = 5.9, p < 0.05). This indicates that female students respond more strongly to authentic influencers when it comes to their attitudes to the green products. It shows a higher sensitivity of trust, relatability or emotional appeal in influencer communication in females. Hence, H5 is supported in that gender moderate.

Overall Insight

The results collectively suggest that the extended TPB model based on digital constructs can well explain the green purchase intentions of students. Although attitude is still the most powerful predictor, digital factors, in particular influencer credibility and eWOM, have a significant indirect and enhancing effect. Moreover, the existence of mediation and moderation effects indicates that green purchase behaviour is influenced not only by simple relations, but shaped also by social processes and demographic variations, which makes the model theoretically sound and practical.

4.6 Discussion

The discussion section interprets the empirical findings from the structural equation modeling (SEM) analysis, linking them to theoretical frameworks, prior literature, contextual factors specific to Chhattisgarh students, and broader implications for sustainable consumption.  It highlights how digital influences extend the Theory of Planned Behavior (TPB) while addressing regional barriers, providing a nuanced understanding of green purchase intention (GPI) among youth in tier-2 India.

Interpretation of Core TPB Relationships

Attitude towards green products was a most significant predictor in determining the GPI (β=0.40, p<0.001) followed by subjective norm (β=0.30) and perceived behavioral control (PBC; β=0.22). The preeminence corresponds to Paul et al.  (2016), who established attitude as the most influential factor in the case of Indian youth but is higher than average of the TPB meta-analyses (β=0.27–0.35). 3.6.2 Attitude: Among Raipur students the high mean positive attitude (M=5.18) shows their health-related notion about green cosmetics ("Green products enhance my health") that was increased by Instagram Reels (68% of respondents). Contrary to developed-market studies that reveal PBC to be as influential as attitude (e.g., Han et al., 2010), its less potent effect here (M=3.72) can be explained by affordability issues - 66% of students survive on a monthly pocket money of ₹3,000-7,000 despite a 25% green premium - producing a stark intention-behaviour gap typical of emerging markets.

Subjective norms (SN; M=4.58) had a strong effect (β=0.30), higher than the world averages (meta β=0.24) and were led by the dynamics among hostel peers (66% quoted "Hostel mates buy green products"). This collectivist stance parallels Yadav and Pathak (2015) among Indian youth, but it is distinctly heightened in the campus culture of Chhattisgarh due to the fact that students reside together making a different kind of normative pressures which are missing in individualistic or metro contexts.  

Role of Digital Influences

Result associated with β = widely known among users (IC; β=0.23), eWOM (β=0.19), and social media engagement (SME; β=0.17)—together accounted for a further 15% of the variance in GPI, bringing the total R² to 0.79 as opposed to traditional TPB's 0.40-0.68. The significance of IC validates Ohanian (1990) and recent extensions (Lou & Yuan, 2019; Prayogo & Adiwijaya, 2025), since Raipur micro-influencers were found to be the trusted source to fill the gaps on green claims (“Raipur influencers understand student budgets”; M=4.51). The influence of eWOM (72% via Instagram Stories) supports Cheung and Thadani (2012), diminishing the risk perception of latent green products. SME topped means (M=4.78; 84% daily Instagram >1hr), corroborating Brodie et al. (2013) on engagement being emotive strengthening but Reels were the most effective among Gen Z in terms of interactivity.

The present results contribute to extending Nekmahmud et al. (2022) and Kohar and Bansal (2026) by considering digital factors as direct, rather than core determinants, in TPB – a conceptualization particularly suitable for tier-2 locations where conventional media is slow to catch up.

Mediation: eWOM as a Process Mechanism

eWOM fully mediated IC → SN (indirect β=0.13, 95% CI [0.09,0.18]), reducing the direct effect from 0.28 to 0.15. This new pathway posits influencers as “ignition points” whose messages achieve normative legitimacy through peer amplification, consistent with Ismagilova et al. (2017) and Parveen and Chaudhary (2025). In Chhattisgarh's student milieu, Instagram Stories enable swift vetting ("Positive student reviews"), congealing personal trustworthiness into social coercion—accounting for SN's augmented influence. This mediation exceeds partial effects in Zaman et al. (2024) highlighting eWOM's 'dual engine' (informational + normative) attractiveness within digital-native cohorts.  Moderation: Demographic Heterogeneity

Postgraduates showed a higher degree of SME → GPI (β=0.23 vs. undergraduates β=0.12; Δχ²=7.2, p<0.01)which was due to the higher level of digital maturity and exposure(e.g., analytical Reels consumption). Females exhibited stronger IC → ATT (β=0.28 vs. males β=0.17; Δχ²=5.9, p<0.05), parallel to Djafarova and Rushworth (2017) on gender-socialization influences, as females place more importance on relational trust within beauty/green sectors. These boundary conditions contour Vishrutha and Thiruchelvi (2025) by illustrating how course level serves as a stand in for experience and gender for persuasion susceptibility in emerging markets.

Theoretical Contributions

1. Model Extension: Digital variables enhance TPB's explanatory power (R²) by 15%, supporting the argument for the adoption of hybrid models (Chen & Tung, 2014; Muna et al., 2025).

2. Process Innovation: The mediation of eWOM also mediated source credibility and norms, a novel finding in student-green TPB.

3. Contextual novelty: Vegetation esteem is the first study from the SE of India to attempt SEM with mediation and moderation supported, closing gaps from non-metro India (no regional prior studies).

4. Boundary refinement: Course/gender interactions detail the Theoretical Predictive Behavior model's extent of generalizability.

Limitations and Future Directions

Although self-report measures are susceptible to social desirability, the cross-sectional design also constrains any suggestions of causality—therefore longitudinal or experimental studies are warranted.  Expand to rural colleges, track actual purchases, or test interventions (eg, voucher RCTs).  Qualitative hostel ethnographies could unpack SN micro-dynamics

These findings enable the extended TPB-digital model to be developed as a scalable approach for promoting sustainable youth markets in other similar emerging contexts. 5.

5.Conclusion and Implications

The present study validated an extended Theory of Planned Behavior model with digital constructs influencers’ credibility, electronic word-of-mouth (eWOM), and social media engagement as good predictors of green purchase intention among half a thousand university students in Raipur, Chhattisgarh. Results support all hypothesized direct effects, with attitude as the strongest predictor (β=0.40) and strong digital-augmenting influences contributing to a very high explanatory power of R²=0.79—unparalleled in TPB literature. Importantly, full mediation of eWOM between influencer credibility and subjective norms as well as course-level and gender moderations unravel complex route(s) in which Instagram-driven interactions (84% daily use) convert the self into collective sustainable action. Contextual specificities, e.g., strong affordability barriers (PBC M=3.72) and hostel-enhanced norms, highlight the applicability of the model to tier-2 Indian setting, thereby contributing fundamental insights into non-metro green consumer studies.

From a theoretical point of view, this study contributes to behavioral models by providing evidence for the incremental validity of digital factors in an emerging market, integrating source credibility theory with TPB in the context of an novel eWOM mediator as well as identifying demographic boundary conditions which are not found in previous global research. Brands should take advantage of Raipur micro-influencers for Reels campaigns (74% reach), RMD can easily provide 20-25% discount for students to deal with PBC barrier, use Stories for peers validation (64% effectiveness). Universities and policy makers may create change via green hostel vending, sustainability curricula, and government-sponsored eco-vouchers (ex., ₹500 per semester), capitalizing on students’ high level of engagement (SME M=4.78) to close the intention-behavior gap. Although self-report constraints imply potential longitudinal and experimental continuations, findings of this study serve as a blueprint that can be scaled for digital-green marketing aimed at Gen Z in comparable developing areas.

Acknowledgements

We are thankful to the 512 university students at PRSU, IGKV, CSVTU, OPJIT and Hemchand Yadav University who took time out to fill the survey, and provided insights into their green consumption practices. We also thankful to the anonymous reviewers and peers for their comments and suggestions to improve this extended TPB study of digital influences within the student market of Chhattisgarh.

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