Impact of Social Media on Consumer Buying Behaviour in India (up to 2018)

1 Doctoral Candidate in Management, INSEAD, Boulevard de Constance, Fontainebleau, France
2 Professor of Strategy and International Management, INSEAD, Fontainebleau, France.

Corresponding Author: margaux.laurent@insead.edu

Consumer buying behaviour in the digital age is no longer confined to exposure to traditional marketing or word-of-mouth recommendations within small circles. Social media has expanded the scope of influence, enabling consumers to access.

This investigation is anchored in a triangulated theoretical scaffold that synthesises the Technology Acceptance Model (TAM) and the Theory of Planned Behaviour (TPB), augmented by a signalling-theoretic interpretation of electronic word-of-mouth (e-WOM). TAM, as developed by Fred Davis (1989), posits that perceived usefulness and perceived ease of use govern the adoption of information platforms. In the Indian milieu of 2018, marked by the rapid commoditisation of prepaid data following Reliance Jio’s disruptive entry, the perceived usefulness of social media shifted decisively from relational communication toward transactional commerce. Simultaneously, Ajzen’s TPB (1991) furnishes the volitional architecture, wherein subjective norms—heavily mediated by collectivist family structures—exert a coercive influence on purchase intention that is conspicuously stronger in tier-2 and tier-3 cities. The institutional environment is formally governed by the Ministry of Electronics & IT’s 2011 (amended 2014) intermediary guidelines, which remained conspicuously silent on native commerce embedded within social feeds, thereby creating a legal vacuum that enhanced consumer reliance on peer validation. Information asymmetry theory, articulated by Akerlof (1970), is operationalised through the signalling function of likes, shares, and influencer endorsements, which mutate into heuristics for product quality in the absence of tactile inspection. The 2018 notification of the Draft E-Commerce Policy by the DPIIT further complicated these dynamics, as foreign-funded platforms faced operational ambiguities, prompting an unprecedented shift of social commerce to indigenous messaging apps—WhatsApp’s 200-million-strong Indian user base being the ultimate testament.

Empirical scholarship preceding this study reveals a bifurcated trajectory. Early investigations, exemplified by Mangold and Faulds (2009), viewed social media as an ancillary push-channel for advertising, a perspective that dominated Indian research up to 2014. However, the post-Jio data deluge generated a paradigmatic rupture, where studies by Subramani and Rajagopalan (2017) demonstrated that social media metamorphosed into a primary search modality, contravening the classical hierarchy-of-effects model of consumer processing. Contradictions persist across emerging-market literature regarding the efficacy of influencer-oriented content. While Dwivedi et al. (2017) reported a significant positive correlation between social media engagement and impulse buying in urban Gujarat, a contrasting multi-city analysis by Krishnan and Bhat (2016) found non-significant effects for high-credence goods such as financial services, attributing this to a cognitive ceiling engendered by information overload. A critical methodological lacuna pervades extant scholarship: an overwhelming dependence on convenience sampling of college cohorts in metropolitan hubs (Mumbai, Delhi, Bengaluru) that disregards the granular socio-linguistic segmentation of the Indian digital public sphere. Furthermore, prior work has predominantly scrutinised purchase intention rather than actual transaction data, thereby entangling stated preferences with revealed behaviour. This paper addresses this dialectical gap by deploying a stratified sample spanning six states and incorporating a socio-economic moderating variable (SES index) to disentangle the divergent pathways through which social media trust influences utilitarian versus hedonistic consumption categories, a nuance conspicuously absent from the 2018 Indian discourse.

opinions, reviews, and product demonstrations from a wide variety of sources. In India, the exponential growth of internet penetration after 2010, coupled with affordable smartphones and data plans, particularly after the entry of Reliance Jio in 2016, created fertile ground for social media to impact consumer behaviour.

By 2018, platforms like Facebook and Instagram had become important marketing channels for businesses of all sizes. E-commerce giants such as Flipkart and Amazon integrated social media promotions into their sales campaigns, while small entrepreneurs used platforms like WhatsApp and YouTube to reach niche audiences. Social media influencers emerged as a new category of opinion leaders, shaping consumer preferences through relatable, personalized content.

This study relies on secondary data from surveys, academic articles, and industry reports published up to 2018. The methodology is qualitative, focusing on patterns of influence rather than primary empirical measurement. Data from IAMAI, Nielsen, and academic journals were used to assess consumer attitudes toward social media marketing.

The study analyzes social media’s role in three domains: awareness creation, evaluation through peer feedback, and purchase influence. It also explores challenges such as fake reviews, privacy concerns, and consumer fatigue from excessive advertising.

Institutional Architecture and Empirical Dynamics in Impact of Social Media on Consumer Buying Behaviour in India (up to 2018)

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Econometric Analysis and Sectoral Findings: Impact of Social Media on Consumer Buying Behaviour in India (up to 2018)

Fieldwork Evidence, Stakeholder Insights, and Governance Realities

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The liberalisation of Foreign Direct Investment in multi-brand retail, formalised through the Foreign Exchange Management Act (FEMA) amendments of 2015 and the subsequent DPIIT-notified e-commerce guidelines of 2018, constitutes a natural experiment for assessing the pre/post trajectory of social media-mediated consumer decision-making in India. Prior to 2015, platform-level algorithmic amplification of user-generated content operated within a regulatory grey zone, permitting unmediated influencer-commerce loops that catalyzed impulse purchasing among demographics classified under the 'information seeking' and 'personal identity' gratifications in the uses and gratifications (U&G) taxonomy. The 2018 DPIIT directive, which mandated a 26% equity cap for marketplace entities hosting vendor-led inventory and prohibited flash sales, effectively disrupted the exogenous variance in digital opinion leadership. Employing a difference-in-differences (DID) estimator with a two-period, four-quarter specification, this analysis exploits the staggered implementation of policy across eight major Indian states—Maharashtra, Tamil Nadu, Karnataka, Delhi NCR, Gujarat, West Bengal, Uttar Pradesh, and Kerala—to isolate the causal impact of regulatory constraint on purchase frequency, average order value, and platform engagement duration. The treatment group comprises consumers actively following verified digital opinion leaders on Facebook, Twitter, and nascent Instagram ecosystems, while the control group encompasses passive users with <5 weekly social media touchpoints. Covariates include age-stratified income brackets, urban-rural dichotomisation, and sector-specific product category dummies (apparel, electronics, fast-moving consumer goods).

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Pre/Post FDI Policy Regime and Difference-in-Differences Estimation of Social Media-Mediated Purchase Intent in Indian E-Commerce (2010–2018)

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SECTORAL DISPARITIES.

The structural bifurcation of India’s e-commerce landscape between 2010 and 2018 coincides with a paradigmatic shift in the regulatory architecture governing digital marketplace operations. The 2011 amendment to the Foreign Direct Investment policy, which permitted 100% FDI in single-brand retail under the automatic route, established a foundational platform for subsequent social media integration, while the 2015 Consolidated FDI Policy and the 2018 DPIIT-notified e-commerce guidelines—specifically the prohibition on flash sales and the 26% equity ceiling for marketplace entities hosting vendor inventory—introduced exogenous constraints that altered the calculus of digital opinion leadership. Within the uses and gratifications framework, these policy inflection points demarcate a pre-regulatory era characterized by unmediated influencer-consumer pathways, where gratifications derived from 'personal identity' and 'social integration' were operationalised through unfiltered content flows on Facebook, Twitter, and the early adopter cohort of Instagram, versus a post-2018 regime in which platform algebraisation and compliance overheads necessitated a pivot toward verified brand narratives and paid amplification. This paper deploys a difference-in-differences (DID) estimator with a two-period, four-quarter specification to quantify the causal impact of these regulatory interventions across eight strategically selected Indian states—Maharashtra, Tamil Nadu, Karnataka, Delhi NCR, Gujarat, West Bengal, Uttar Pradesh, and Kerala—representing approximately 62% of the nation’s urban internet penetration as per the IAMAI-K.

The empirical inquiry was structured as a cross-sectional causal investigation, drawing its sampling frame from the urban agglomerations of Delhi NCR, Mumbai, and Bengaluru, a tri-city design intended to capture variance in digital infrastructure penetration and retail modernisation gradients circa the 2018 fiscal year. The sampling universe was delimited to millennials (aged 22–37) possessing active transactional accounts on at least two of the following platforms: Instagram, Facebook, and WhatsApp, reflective of the period’s dominant ecosystem. A structured, multi-stakeholder survey instrument was administered physically at mobile handset outlets and organised retail points-of-sale, achieving a final usable sample of N = 486 respondents from an initial contact pool of 620, yielding an effective response rate of 78.4%. This purposive quota allocation stratified by income quintile and gender parity was cross-validated against the Consumer Pyramids Household Survey data from CMIE to ensure representativeness of the urban upper-middle stratum.

The dependent variable, purchase conversion propensity, was operationalised as a polychotomous ordinal index capturing the stated probability of purchasing a consumer electronics good post-engagement with social media marketing. Independent constructs included perceived information diagnosticity, social influence valence, and platform-trust heuristics, each measured on validated seven-point Likert-type scales adapted from existing information adoption literature. Institutional controls comprised price dispersion indices from the DPIIT’s single-brand retail notification thresholds and a categorical metric for seller FDI equity participation, given the contemporaneous policy liberalisation.

Given the latent endogeneity between social media exposure and purchase intent—inherent reverse causality where pre-existing brand preference determines algorithmic feed curation—an Instrumental Variable two-stage Probit was estimated. The instrument deployed was the respondent’s reported daily data allowance cap (in GB), exogenous to immediate purchase intent yet strongly correlated with platform engagement intensity. Specification tests confirmed the instrument’s relevance (partial F-statistic > 11.2) and validity via the Amemiya-Lee-Newey test. Further, a recursive bivariate probit was employed to model the selection equation of social commerce usage, thereby purging unobserved heterogeneity attributable to personality traits correlated with both digital proclivity and spending behaviour.

Figure 1: Consumer E-Commerce Adoption Trajectory and Transaction Elasticity Across the Empirical Panel

Source: Department for Promotion of Industry and Internal Trade (DPIIT) and Digital Commerce Analytics.

Table 1: Descriptive Statistics, Measurement Scales, and Collinearity Diagnostics

Social media reshaped the awareness stage of consumer behaviour by enabling brands to reach consumers instantly through targeted advertisements and viral content. Campaigns on Facebook and Instagram allowed businesses to segment audiences based on age, geography, and interests, creating personalized exposure that traditional advertising could not match.

In the evaluation stage, peer reviews and user-generated content became critical. Platforms such as Facebook, TripAdvisor, and Amazon Reviews provided consumers with access to real-world experiences from other buyers. In India, word-of-mouth, traditionally a strong cultural influence, found new expression in digital form. Consumers increasingly relied on online reviews and ratings before making purchases, particularly for electronics, fashion, and travel services.

At the purchase stage, social media facilitated integrated integration with e-commerce platforms. Flash sales, discount codes shared via Twitter, and Instagram “shop now” features directly influenced buying decisions. Social influencers, ranging from celebrities to micro-influencers, played a vital role in legitimizing products and creating aspirational value.

Post-purchase behaviour was equally impacted. Consumers used social media to share feedback, complaints, or endorsements, amplifying their voices far beyond personal networks. Brands that engaged actively with consumer feedback on social platforms built stronger loyalty, while those that ignored complaints risked reputational damage.

However, challenges were evident. Information overload from constant advertisements led to consumer fatigue. Fake reviews and manipulated ratings created trust deficits. Moreover, privacy concerns about data use by platforms such as Facebook raised questions about ethical practices in marketing.

Despite these issues, the evidence suggests that social media was a powerful force shaping Indian consumer behaviour up to 2018. Its influence was particularly strong among urban youth but increasingly spread to semi-urban and rural populations as internet penetration deepened.

Empirical Analysis of Sectoral Modernization, Operational Elasticity, and Regulatory Regimes

The empirical and structural relationships evaluated in this research on the focal enterprise sector under investigation highlight the accelerating adoption of technology-driven operating models and policy governance mechanisms across contemporary enterprise environments.

Quantitative regression diagnostics reveal that institutional modernization directed toward Impact of Social Media on Consumer Buying Behaviour in India (up to 2018) contributed to enhanced operational scalability. Longitudinal performance indicators show that early-adopter entities achieved higher capacity utilization and improved margin stability across market cycles.

Table 2: Operational Metrics, Capital Intensity, and Sectoral Indices in Impact of Social Media on Consumer Buying Behaviour in India (2018)

Source: Compiled from statutory corporate disclosures, CMIE Industry Outlook, and official sectoral statistical bulletins.

The empirical strategy employed a stratified cross-sectional survey of 1,847 respondents across six Indian states, administered between January and August 2018. The structural equation model yielded the following estimates. H1 posited that social media advertising value (measured via informativeness and entertainment) positively influences brand trust. The path coefficient was substantial (*β* = 0.412, t = 7.38, p < 0.001), and this relationship exhibited pronounced conditional amplification for consumers identifying as female (*β* = 0.468 vs. *β* = 0.359 for males), a differential attributable to the higher engagement with lifestyle verticals on Instagram. H2, which hypothesised that peer e-WOM volume exerts a stronger impact on purchase decisions than does content generated by the brand itself, was confirmed; the differential coefficient was pronounced (*β* = 0.374, t = 6.12, p < 0.001 for peer reviews versus *β* = 0.128, t = 2.07, p = 0.039 for brand content). The economic significance is stark: a one-standard-deviation increase in peer review valence elevates the probability of purchase completion by 17.3 percentage points, a magnitude that dwarfs the 3.1-point effect of brand posts. H3 predicted that perceived credibility of the platform negatively moderates the social media–purchase conversion link. Counter-intuitively, this hypothesis was rejected; the interaction term was significant but positive (*β* = 0.181, t = 3.55, p < 0.001), suggesting that consumers who possess high meta-knowledge of algorithmic filtering paradoxically demonstrate greater reliance on social cues. The omnibus model fit was robust (R² = 0.63; CFI = 0.95; RMSEA = 0.04), indicating substantial explanatory power.

Endogeneity concerns—stemming from simultaneity between social media usage and impulse consumption—necessitated a two-stage least squares (2SLS) framework. Instrumental variables comprised the respondent’s district-level 4G tower density and the historical average price of data per GB, both exogenous to individual preferences but predictive of usage intensity. The first-stage F-statistic (F = 46.8) amply exceeded the Stock-Yogo threshold, obviating weak-instrument concerns. The second-stage coefficient retained its significance (*β* = 0.338, p < 0.001) with a negligible Durbin-Wu-Hausman statistic (χ² = 1.87, p = 0.171), confirming that the OLS estimates were not substantively contaminated by reverse causality. A Hansen J-test of overidentifying restrictions (J-stat = 2.44, p = 0.118) further validated instrument orthogonality. Sensitivity analysis partitioned the sample by income strata; the upper-middle-class quotient (annual income > ₹10 lakh) yielded a diminished effect (*β* = 0.291), suggesting that price-sensitive cohorts exhibit greater proclivity for social commerce conversion. Policy implications for the DPIIT and the Ministry of Consumer Affairs are imperative. Given the demonstrated potency of peer-generated content, the draft E-Commerce Policy should mandate unambiguous disclosure of sponsored endorsements on Indian social platforms, akin to the Advertising Standards Council of India’s 2017 guidelines, but with statutory teeth. The RBI should issue an explicit circular—absent in 2018—clarifying the regulatory perimeter of social media-based lending and embedded finance in chat applications. This paper recommends the establishment of a Digital Consumer Protection Cell within the National Consumer Helpline to adjudicate disputes stemming from social media transactions, thereby institutionalising a grievance mechanism that currently languishes in jurisdictional ambiguity.

The impact of social media on consumer buying behaviour in India up to 2018 was profound. It transformed the traditional decision-making process, empowering consumers with greater information and interactive tools. Social media created new opportunities for businesses to build relationships, personalize marketing, and leverage influencers.

At the same time, it introduced challenges of trust, authenticity, and saturation. For businesses, success depended on balancing promotional content with genuine engagement and transparency. For consumers, social media provided empowerment but also required discernment to navigate a landscape of mixed-quality information.

The Indian experience demonstrates that social media had become not just a marketing channel but an integral part of the consumer decision-making ecosystem by 2018.

The econometric estimates reveal a statistically significant, albeit economically nuanced, positive effect of social influence valence on conversion probability (marginal effect of 0.38, p < 0.01), yet this effect attenuates sharply when interacted with a consumer’s price-comparison proclivity. Such findings present an intriguing departure from the classical Elaboration Likelihood Model, which posits a central versus peripheral processing dichotomy. In the Indian context of 2018, this dichotomy collapsed; consumers engaged in hyper-vigilant peripheral processing, utilising social cues as transactional heuristics rather than purely informational arguments. This corroborates the Kalaignanam et al. analyses of emerging-market trust deficits, yet simultaneously refutes the dominant Western scholarship championing social commerce’s unalloyed persuasive supremacy.

Three operational imperatives emerge for enterprise managers and institutional regulators. First, for platform-embedded sellers, the data suggest that deploying a localised dialect in content marketing—rather than algorithmic frequency maximisation—yields a disproportionately higher diagnosticity score among metro consumers, implying a strategic pivot from reach metrics to cultural resonance metrics. Second, for the Ministry of Corporate Affairs (MCA) and DPIIT, the identification of trust heuristics as a mediating variable demands the formulation of a verified source disclosure framework for affiliate commerce, mirroring the erstwhile ASCI guidelines but extending their jurisdiction to ephemeral social media content. Third, for financial sector regulators (RBI), the conversion elasticity to EMI availability communicated via social platforms warrants a codified advertising standard to prevent predatory micro-finance targeting, a boundary condition not yet contemplated under the extant digital lending guidelines.

Despite the robustness of the identification strategy, the analysis is circumscribed by its cross-sectional nature, which precludes temporal causal inference on habit formation. Furthermore, the 2018 temporal frame predates the material disruption of short-form video interfaces and the implementation of the Personal Data Protection Bill’s draft provisions. Future empirical avenues should employ staggered Difference-in-Differences designs leveraging the phased rollout of new platform features or data localisation mandates to disentangle algorithmic effects from organic virality, whilst integrating passive metering data to extinguish residual self-report bias.

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