Keywords

Indian Consumers, Social Norms, Consumer Behaviour, Digital Influence, Influencer Marketing.

Introduction

The value of social media has increased during the decade. 45% of the world's population, or more than

3.4 billion people, enthusiastically use social media (Allam, 2019). These individuals inevitably look to social media experts for wise counsel. Social norms significantly influence people's behavioural choices. (Nayum & Thøgersen, 2022). People attempt to ensure that they perform professionally and that their acts are socially acceptable by following a social group's average and proper behaviours. (Freberg et al., 2011). The effective distribution of norms within social groupings is facilitated through communication. Influencers on social media are people who have an excellent status for being experts and skilful in a particular industry. (Bu & Go, 2008). They frequently publish on their favourite social media channels regarding that subject, which generates sizable fan networks of passionate, busy people who closely follow their observations. Social media experts are expected to give followers wise counsel. Brands adore influencers on social media because they may start trends and cheer their followers to purchase the goods they advertise. Of course, podcasters, YouTubers, and bloggers rarely rely on their current audiences to visit their websites and hope there is fresh content. Social media is where most influencers nowadays gain notoriety.

Celebrities were the original influencers, and even if their impact is waning, they continue to play an essential role in society. Celebrity endorsement leads to the emergence of influencer marketing. For a long time, companies have noticed that sales usually rise when a famous person advertises or supports their product. Businesses frequently use famous people as influencers, exceptionally high-end brands. (Yun et al., 2019). The only exception to this rule would be if a company created a product that an established individual already valued and used. In that situation, the well-known individual might be eager to use their platform to praise the goods. A celebrity could have a sizable following on social media and be very active there. However, it is debatable how much of an effect they have on those following them. Influencers need to change the way their followers behave. They have developed a reputation for education and competence in a particular field.

This strategy of enterprises to continue their business activities with the help of social media can be termed Social Media Marketing (SMM) (Shareef et al., 2019). The investigation is essential to determine whether the use of SMM can impact the growth of SMEs in India. In this scenario, this paper has made a holistic attempt to identify the factors that might affect SMM and investigate whether SMM can act as an effective instrument for the growth of SMEs in India.

Using SMM, it is a fact that SMEs can communicate with their valued customers comparatively, incurring less cost) (Chatterjee & Kar, 2020). These studies reveal that perceived usefulness, ease of use, facilitating conditions, cost (lent from the UTAUT2 model) and compatibility would impact the SMEs (Positively or negatively) to adopt SMM. The term "normative" refers to an evaluation standard. Normativity is classifying some behaviours or outcomes as desirable, desirable, or lawful while classifying others as undesirable, undesirable, or impermissible in human communities. According to normative social behaviour, the impact of descriptive norms on actions can be significantly enhanced by identifying with one's group identity, which consists of aspiration and perceived likeness with one's peers. Relationships between variables are described using structural equation models (SEMs). In addition to these fundamental advantages, SEMs also provide strategies for dealing with multi-co linearity and methods for accounting for the unpredictable nature of customer response data (Dey et al., 2020). The data was coded using observations of recurring themes and responses that expressed either favourable or unfavourable opinions toward influencers. To show the attitudes, subjective norms, perceived behavioural control, and intentions to reveal the theory of planned behaviour, SPSS AMOS for Structural Equation Model (SEM) were utilised in the datasets. This study advances our knowledge of the critical antecedents of credibility in social media-based influence strategies and management. Thus, research will examine the theory's practical applicability to help social marketers more successfully use it as a framework for creating persuading normative messages.

Problem Statement 1

Understanding the Role of Social Media Influencers in Shaping Normative Behaviors in Indian Consumers: The rapid rise in social media usage has led to a growing reliance on influencers for advice and recommendations. However, the extent to which virtual influencers shape normative behaviours among Indian consumers remains underexplored. There is a need to investigate how perceived credibility, group identity, and influencer-driven content influence consumer attitudes, subjective norms, and behavioural intentions in the Indian context.

Problem Statement 2

Examining the Impact of Normative Influence in Social Media Marketing on Indian SMEs: While social media marketing (SMM) offers a cost-effective means for Indian SMEs to connect with consumers, its effectiveness is influenced by the interplay between influencers' normative messages and consumer behaviour. This raises critical questions about how descriptive norms, perceived group identity, and influencers' credibility influence customer responses and adoption of SMEs' products and services in India.

Literature Review

Instagram is an excellent example of social media's benefits for fashion branding strategies because it enables users to read brand-related content regularly that is blended into the postings and experiences of well-known influencers (Casaló et al., 2020). The importance of influencer marketing in the fashion and beauty sectors has led to brands and agencies paying influencer specialists and internet services specifically to identify and authenticate trustworthy influencers (Bishop, 2021). Numerous studies have examined the branding of social media posts made by fashion influencers (as opposed to those made by commercial fashion brands), intending to identify the uniqueness of their content and the psychosomatic mechanisms underlying why people respond favourably to these influencers on social media (Jin et al., 2019). Marketers can discover how many individuals have looked, liked, commented on, and provided feedback on their goods and services. Influencer marketing differs from traditional word-of-mouth marketing because it gives marketers more knowledge and control over marketing outcomes. According to the analysis, influencer marketing has an 11 times higher return on investment than traditional advertising channels. Fashion customers, particularly those in generations Y and Z (Hassan et al., 2022), maybe more impacted by influencers in the context of the fashion industries since they frequently view them as friends and personal assistants. The widespread notion supports this among younger generations: people like themselves can be fashionable leaders. Social media influencers (Ki et al., 2020) are well- known users of Social Networking Services (SNS) who have gained a sizable following by being active and prominent in one or more relevant sectors primarily fashion, beauty, and lifestyle), which makes them credible and alluring to their admirers. Even though fashion brands typically appeal to younger consumers, these consumers regularly follow fashion influencers and find their comments and opinions more reliable than the advertisements for their products (Lou & Yuan, 2019). Unhealthy behaviours frequently accumulate in social networks, which are systems of interpersonal relationships and social interactions (Smith & Christakis, 2008). This demonstrates that social influence may be essential to developing and maintaining these behaviours (Ting et al., 2020). The qualities of the physical environment could communicate explicit and injunctive norms about tobacco use, which may contribute to the explanation of the association between teenage tobacco use and the presence of retail tobacco stores close to families and other communities (Davis et al., 2008). Since it implies that many people are using the product and that using it is socially acceptable, the physical accessibility of tobacco products may impact how descriptive norms are created. Social norms are an effective mechanism by which the environment influences conduct. A group's members' shared understanding of laws and principles that direct and constrain social behaviour without imposing laws constitutes social norms (RUNNING HEAD: YOUTUBE FITNESS VIDEOS 1 YouTube Fitness Videos: Just for Entertainment or Actually Healthful?, 2022). Being exposed to interpersonal and social cues from members of one's social network, such as those from family members and close friends, distal members (such as neighbours or classmates), and non-members, can lead one to significant sources of knowledge about social norms in their social environment (strangers). Youth smoking is strongly predicted by the modelling (Narang et al., 2020) and tobacco reinforcement by family, friends, and peers, with some of this effect resulting from the development of social norms. Norms about tobacco use's perceived prevalence and acceptability may be influenced similarly to physical exposure to smoking in the community (Unger et al., 2003). Tobacco- related litter, such as cigarette butts or surplus tobacco product packaging, encourages initiation and affects interruption. Influencer marketing is one of the new marketing approaches that emerged due to social media's launch and rising popularity (Shareef et al., 2020). Customers have always appreciated other people's opinions, but with the rise of social media platforms, regular customers can now better share their ideas and experiences with their peers (Subramanian, 2018). According to (Ali et al., 2019), physical environmental cues, such as the presence of trash, are adequate to transmit normative descriptions of the frequency of littering activity. These descriptive norms can then draw attention to the injunctive norms prohibiting littering and impacting people's behaviour (Cialdini et al., 1990).

Currently, the Theory of Planned Behavior (TPB), NAM, and Structural Equation Modeling (SEM) are used to conduct research related to the factors influencing altruistic behaviour. (Wang et al., 2020). NAM is a theoretical model for investigating altruistic behaviour based on the personal norm (PN). Pro- environmental behaviour is often associated with PN, and problem perception and attribution of responsibility (AR) in NAM have reliable explanatory power for pro-environmental intentions. Thus, NAM is widely used to explain altruistic behaviour and altruistic intentions. Related research topics include energy-saving (Li et al., 2022), water saving (Si et al., 2022), and electricity saving behaviours. Young people are typical pro-environmental altruistic behaviour that applies to NAM. (Zhang et al., 2014). Used 297 validated interviews in Jinan, China, and explored the mechanisms of individual subjective, external influences and willingness on EB. (Fu et al., 2021) Identified that cognitive attitudes influence people's desire to save electricity based on TPB and NAM. (Hien & Chi, 2020) Showed that primary factors (e.g., perceived behavioural control, subjective norms, attitudes, and personal ethics) and additional factors (perceived benefits) in TPB and NAM are essential factors that influence residents' willingness to save electricity. In addition, electricity-saving behaviour is influenced by willingness, perceived benefits, policy guidance, and social advocacy. (Du & Pan, 2021) indicated that PN significantly positively affects the desire to save energy. (Bae & Chang, 2021) SFE and perceived control significantly positively affected the intention to engage in habitual EB. The existing literature provides the foundation for exploratory research on EB, but at the same time, there has been insufficient attention to specific groups, such as younger age groups.

NAM is proposed to explain individual pro-social behaviours, which are usually related to morality. The underlying assumption of NAM is that personal character determines individual pro-social behaviour. (Chi et al., 2022). Its key concepts focus on an individual's inner moral considerations. It neglects the social environment where individuals live, which influences individual perception and behaviour. Specifically, employees are not alone and are involved in the organisation, which strongly affects their perception and behaviour. Prior organisational behaviour studies also acknowledged the effect of corporate social context on employees by proposing the concept of organisational climate (Forces et al., 2011). To better understand employee electricity-saving behaviour, it is necessary to consider both the internal individual factors and the extreme organisational climate (Xu et al., 2017). In this study, we develop the concept of organisational electricity-saving climate to capture this harsh organisational climate and examine how it can influence employee electricity-saving behaviour.

Theory of Normative Social Behavior

The theory of normative social behaviour (TNSB) makes the first attempts to integrate communication with these influencers by considering normative social impacts both generally and specifically. (Geber & Hefner, 2019). TNSB is a reference in our theory on a communication viewpoint on normative social implications. The TNSB utilises the distinction between two related concepts made by the descriptive and injunctive rules provided by (Nistor, 2022). Both varieties of norms are connected to specific reference groups and the members of those groups. While injunctive norms speak to the behaviour' s social acceptance by referent others, brilliant norms speak to a behaviour’s supremacy within a reference group. (Jacobs et al., 2012). As a result, the two sorts of norms might be conceptualised as rules about what is done (descriptive norms) instead of what ought to be done in the reference group (injunctive norms).

Research Methodology

Sequential exploratory research is a mixed-methods approach conducted in two distinct phases: qualitative and quantitative. This design is ideal for exploring phenomena, developing theories, and testing them. Sequential exploratory design is particularly beneficial and appropriate in the context of this research involving normative social behavior and social media influencers. Normative behaviour influenced by social media influencers, particularly in a culturally unique context like India, is still emerging. A qualitative phase allowed in-depth exploration of themes, perceptions, and nuances. Insights from qualitative data informed the formulation of robust, context-specific hypotheses (e.g., H1–H4 in the study). These were then quantitatively tested in the second phase. Combining qualitative insights with quantitative data strengthened the research's validity, ensuring a comprehensive understanding of the phenomena. Constructs such as awareness, perceived responsibility, content-following intentions, and reliance on influencers required initial qualitative exploration to ensure their accurate operationalisation for quantitative analysis.

Thus, in this research, different hypotheses were created and tested. If one hypothesis is significant, consecutive hypotheses are designed and tested. The hypotheses involved in the study were.

H1: Awareness of social media influencers among retail consumers to rely on them.

H2: Knowing about influencers' role & responsibility in promoting the brands to rely on.

H3: Following influencers on their intention to share the right content.

H4: Relying on influencers to perceive brands.

Sampling Design

A convenience sampling methodology was used for the study. Convenience sampling, a non-probability sampling technique, involves selecting participants based on their accessibility and willingness to participate. While it has limitations, its relevance to the study of virtual influence and normative behaviour among Indian consumers lies in its practicality and alignment with the research context. The study focuses on Millennials and Gen Z, active social media users who are easier to reach through digital platforms and educational institutions. Convenience sampling allows quick access to this tech-savvy demographic. Given the urban and semi-urban focus (e.g., Bangalore, Kolkata, Delhi, and outskirts), convenience sampling reduces logistical and financial constraints by leveraging easily accessible respondents within these regions. The study's emphasis on collegegoers and young professionals with moderate to high incomes aligns with convenience sampling, as these participants are more likely to respond to online surveys or participate in focus groups. It is helpful for exploratory studies where the objective is to identify patterns and relationships (e.g., reliance on influencers, perceived responsibility) rather than to generalise findings on a broader population.

The sample for this study was selected to provide insights into the interplay of virtual influence and normative behaviour among Indian consumers. The participants were predominantly from Generation Y (Millennials) and Z, aged between 18 and 40. These age groups are the most active social media users and are significantly influenced by digital content, making them relevant to the research objectives. Data was collected primarily from urban and semi-urban populations in Bangalore, Kolkata, Delhi, and their surrounding outskirts. These locations were chosen to capture diverse perspectives from major metropolitan and suburban areas where social media usage is prominent. Most participants were collegegoers or young professionals with moderate to high-income levels. This demographic group was targeted as they represent active consumers and frequent followers of social media influencers, thus providing valuable insights into the study's focus.

Structural Equation Modelling (SEM) is an analytical framework suited for testing complex theoretical models involving multiple variables and their interrelationships. In the context of this study on normative social behaviour and its interplay with social media influencers, SEM allows for the simultaneous testing of multiple interrelated hypotheses (H1 to H4). This is essential when exploring how awareness, knowledge, content-following behaviour, and reliance on influencers collectively contribute to consumers' perception of brands. Normative social behaviour often involves latent constructs such as "awareness of influencers," "perception of responsibility," "intention to share," and "reliance on influencers." SEM can model these unobservable constructs using observed variables, ensuring a nuanced analysis. The study integrates the Normative Theory of Social Behavior, emphasising the influence of descriptive norms and group identity on consumer actions. SEM is adept at modelling these normative influences, capturing the complex relationships between cognitive (internal) and media-driven (external) factors and facilitating the exploration of mediating or moderating relationships, such as how influencers' content mediates the link between their perceived role and consumer reliance, or how perceived group identity moderates the relationship between content-following behaviour and brand perception. Moreover, it distinguishes between the structural model (relationships among constructs) and the measurement model (link between constructs and indicators). This separation is critical in ensuring the reliability and validity of the constructs in normative social behaviour research. Normative social behaviour and consumer perceptions often involve interrelated variables. SEM can manage multicollinearity effectively, ensuring robust and interpretable results.

The hypotheses (H1-H4) aim to understand behavioural outcomes such as relying on influencers and perceiving brands. SEM is beneficial for modelling causal paths and predicting such intentions based on theoretical frameworks like the Theory of Planned Behavior. SEM is well-suited for this research as it captures the intricate interplay of variables, supports theory-driven hypothesis testing, and provides insights into normative social behaviour's cognitive and social dimensions in social media marketing.

Proposed Model

Results and Discussions

Statistical software SPSS Amos was used for data analysis. SPSS Amos is a potent structural equation modelling (SEM) program that extends standard multivariate analytic techniques like regression, factor analysis, correlation, and analysis of variance to assist your research and theories. Different criteria can be checked to evaluate the model's fitness. The following list includes some of the well-known and accepted standards Figure 1.

Hypothesis Testing

H₁: Awareness of social media influencers to rely on them.

The P-value obtained is 0.000 (< 0.005). To test the significance, we need to compare with p<0.005. On reaching, it is found to be significant. Thus, awareness of social media influencers has a positive and significant effect on relying on them Table 1.

Table 1 Descriptive Measures of the Variables
Hypothesis	Variables	Mean	Variance	Standard deviation	d.f.	T value	p-value	Result
1	Social media	4.14	0.53	0.73	999	6.31	0.000	Significant
	Rely on
2	Influencers	3.95	0.79	0.89	999	13.90	0.000	Significant
	Rely on
3	Follow influencers	3.81	0.72	0.85	999	3.10	0.000	Significant
	Share contents
4	Influencer	4.11	0.67	0.82	999	12.9	0.000	Significant
	Brand perception

H₂: Knowing about influencers to rely on them

After the t-test, the p-value achieved is 0.000. On comparing with p < 0.005, it is found to be significant. Hence, knowing about influencers is substantial and positively affects relying on them.

H₃: Following influencers on intention to share the content.

The p-value here is 0.000. Comparing the obtained p-value with p<0.005, we found it to be significant. Thus, we get to know the following influencers, which have a positive and significant effect on the intention to share the content.

H₄: Relying on an influencer to share the content.

The achieved p-value is 0.000. When comparing the threshold value p<0.005, it's significant. Therefore, relying on influencers significantly and positively affects the intention to share the content.

Normality Test

The normality test is performed using the Jarque-Bera test. The threshold value is p<0.05. All the p- values obtained are less than 0.05. Since all the p-values satisfy the condition, the values are said to be normally distributed. Cronbach α is calculated using the formula, (1- (MS of error/ MS of rows)), (1- (0.516/1.579) = 0.6732, α=0.6732. Since α is more significant than 0.67, the internal consistency is excellent Table 2. The data for all variables tested significantly deviates from a normal distribution, as indicated by the negative kurtosis, non-zero skewness, high JB statistics, and extremely low p-values Figure 2.

Table 2 Normality Measure of the Variables
Kurtosis	Skewness	Count	JB Stat	p-value
-1.100	-0.236	988	59.720	0.000
-1.241	0.543	988	113.333	0.000
-1.534	0.361	988	119.860	0.000
-1.241	0.543	988	113.333	0.000
-0.766	-0.005	988	24.428	0.000
-1.736	0.094	988	127.085	0.000
-1.594	0.143	988	109.223	0.000
-1.306	0.143	988	74.480	0.000
-1.306	0.143	988	74.480	0.000
-1.594	0.143	988	109.223	0.000
-1.346	0.621	988	139.765	0.000
-1.434	0.461	988	121.102	0.000
-0.794	0.292	988	40.487	0.000
-0.668	-0.509	988	61.779	0.000
-1.694	-0.284	988	133.018	0.000
-1.412	-0.245	988	93.095	0.000
-1.495	-0.218	988	101.080	0.000
-1.482	-0.169	988	96.316	0.000
-2.000	-0.064	988	167.334	0.000
-1.513	-0.221	988	103.518	0.000
-1.513	-0.221	988	103.518	0.000
-1.717	-0.535	988	170.569	0.000
-1.594	0.143	988	109.223	0.000
-1.594	0.143	988	109.223	0.000

The graph shows that the values are normally distributed.

Analysis of Variance of the Variables

ANOVA Table 3 depicts that Both rows and columns are significant contributors to the variability, as indicated by their F-statistics greater than their respective critical F-values and their P-values less than 0.05. Columns have a more pronounced effect than rows, as shown by their higher F-value (156.060 compared to 3.060) and larger mean square (80.557 compared to 1.579). These values suggest that the factors represented by the rows and columns significantly affect the dependent variable.

Table 3 Summary of Anova Table
Source of Variation	SS	df	MS	F	P-value	F crit
Rows	1577.840	999.000	1.579	3.060	0.000	1.076
Columns	3302.819	41.000	80.557	156.060	0.000	1.389
Error	21142.562	40959.000	0.516
Total	26023.221	41999.000

Types of Model Fit Criteria

Model Fitness

The research model Figure 1 shows all the hypotheses that will be tested.

Chi-Square Significant Levels

Table 4 depicts that with one degree of freedom, the model's chi-square test value for overall model fit is 157.271. Since the p-value of the chi-square test is .000, which is lower than the .05 level, the null hypothesis is rejected. In the above model, the CMIN/DF values are less than 3, showing the model fit Figure 3.

Table 4 Summary of Chi-Sq Significance
Model	C M I N	DF	P	C M I N /D F
Default model	0.154	1	0.00	0.154
Saturated model	0	0
Independence model	0.1001	10	0.00	0.1001

The χ2 value should range between 2 ≥ x ≥ 5. The significant χ ² value relative to the degrees of freedom indicates that the observed data does not support the suggested model. The fitness solely cannot depend on the chi-square value.

Goodness-of-Fit Index

In the Table 5, the first data is labelled as the used model, which contains the fit statistics for the model specified in the AMOS Graphics diagram. The GFI index is 0.944, and the AGFI index is 0.966, which indicates a suitable model fit since it is close to 1. The above provides a good fit for the data, as evidenced by favourable RMR, GFI, AGFI, and PGFI values. The independence model and saturated model serve as references, with the independence model indicating a good fit and the saturated model representing an idealised perfect fit. This comparison highlights the superiority of the default model in explaining the data.

Table 5 Summary of RMR & GFI of the Specified Path
Model	RMR	GFI	AGFI	PGFI
Default model	0.042	0.944	0.966	0.963
Saturated model	0	1
Independence model	0.0127	0.739	0.9609	0.893

Root Mean Square Error of Approximation

The sample's RMSEA value of 0.039 suggests an acceptable fit. The LO 90 and HI 90 indicate the significant range value—the value 90 marks a substantial level of 90%. The lower limit obtained is 0.034, and the higher limit is 0.045 Table 6.

Table 6 Summary of RMSEA Value of the Variables
Model	RMSEA	LO 90	HI 90	P CLOSE
Default model	0.039	0.034	0.045	0
Independence model	0.315	0.299	0.332	0

Parsimony-Adjusted Measures

The PNFI achieved through the analysis is 0.843, and that of P C F I is 0.843. The fit condition for PNFI and PCFI should be greater than 0.5. From the analysis, it is evident that the model fits the Parsimony Adjusted measures Table 7.

Table 7 Summary of Parsimony Adjusted Model
Model	P-RATIO	PNFI	PCFI
Default model	1	0.843	0.843
Saturated model	0	0	0
Independence model	1	0	0

Baseline Comparisons

The analysis obtained NFI, RFI, IFI, TLI and CFI values. The normed Fit Index (NFI) value is 0.965. The normal range is it should be greater than 0.9. Hence, the condition is satisfied, and the model fits the goodness. The relative Fit Index (RFI) value is 0.957. A value nearer to 1 is said to be fit. The incremental Fit Index (IFI) obtained is 0.944. The normal good fit value is supposed to be greater than 0.9. Thus, fitness is good. Tucker Lewis index (TLI) is 0.976. A value nearer to 1 gives a good value of fit. Thus, from all the deals, it is visible that the model fits the goodness test Table 8.

Table 8 Summary of Baseline Comparison for Model
Model	NFI	RFI	IF	TLI	CFI
	Delta1	rho1	Delta2	rho2
Default model	0.965	0.957	0.944	0.976	0.942
Saturated model	1		1		1
Independence model	0	0	0	0	0

The Table 9 herein shows that the 43 variables are grouped into nine components based on the Eigenvalues for analysis. The components whose Eigenvalues are more significant than one are used for research, while those of less than one are neglected. Nine factors are derived from 27 variables out of 43. Thus, the set of 27 variables with 1000 observations represents nine components.

Table 9 Summary of Factor Analysis and Clustering
Component	Eigenvalues	Variance %	Variables	Coefficients
Factor 1 (Product Knowledge Benefits)	13.716	31.899	1-4	0.975
Factor  2   (Influencer Reliability)	8.692	20.214	11-16	0.971
Factor  3   (Innovative Content)	5.003	11.635	17-20	0.936
Factor   4   (Injunctive Virtual Norms)	3.938	9.159	21-24	0.854
Factor 5 (Peer Influence)	2.791	6.491	25-27	0.722
Factor    6     (Opinion Leadership)	1.730	4.023	32-34	0.827
Factor 7 (Sharing Content)	1.495	3.477	37	0.905
Factor     8 (Promotional Benefits)	1.426	3.317	41	0.603
Factor    9     (Clicking	1.183	2.750	42	0.684
Product Ad)

Further, the extracted sum of squared holding % of variance depicts that nine components effectively represent all the characteristics or components highlighted by the stated 43 variables. The highest percentage of the variance of 31.899% is seen in 1-4 variables (Following Influencers helps identify the right product, Influencer's content affects buying decision, I share content on social media and follow and listen to a domain expert over social media rather than chatting or wasting time on any unproductive means); In comparison, the minuscule percentage of 2.750% is noted in the 42nd variable (Click product advertisement, if needed). The coefficient represents the variables that are highly related to framing the component. The higher the coefficient value, the greater the chance of framing as a component.

Discussion

The statistical analysis shows that awareness about social media influencers, the benefits of following them, the urge to stay informed and social belongingness play a pivotal role in developing the social acceptance of Influencer marketing among Indian consumers. Following influencers, their contents, and the factors that contribute to the success of the new form of marketing result from normative social influence. Further analysing the variables, nine prominent factors can be identified: the reasons behind the behavioural adoption of influencer marketing content among consumers.

Utilising the power of influential individuals on social media platforms influencer marketing aims to promote products or services by tapping into their popularity, credibility, and broad reach. Influencers should thoroughly understand the product they are promoting. (Vrontis et al., 2021). It encompasses the various aspects of the product, such as its features, benefits, target audience, and distinctive selling points. Without a deep understanding of the subject matter, their ability to effectively convey the product's value to their target audience will be compromised. The importance of authenticity cannot be overstated in influencer marketing. For influencers to effectively connect with their audience, they must genuinely believe in the products they promote (Andhini, 2017). This sincerity is what resonates with their followers. Individuals must clearly express their endorsement of the product and how it resonates with their lifestyle or values. It is essential for influencers to be open and transparent about their partnerships and to disclose any sponsored content they share. Ensuring transparency is crucial for maintaining trust with their audience and adhering to advertising regulations. Influencers can inform and educate their audience about the product. One way to approach this is by showcasing the product's usage, sharing real- life stories or testimonials, and addressing common inquiries. (Shobowale, 2022). Influencers need to be able to distinguish the promoted product from its competitors. It is essential to emphasise the product's unique qualities and explain why it is a superior choice compared to other options. A key aspect of effective influencer marketing campaigns is establishing a feedback loop that connects the Influencer, the brand, and the audience. It is essential for influencers to be open to feedback from both the brand and their audience and to effectively communicate this feedback to the brand to enhance the product or future

Marketing strategies. Cultivating enduring relationships with brands can prove advantageous for influencers. By immersing themselves in the field, individuals can gradually improve their understanding of the product, cultivating a sense of genuineness and reliability with their target audience. In addition, establishing lasting partnerships can result in more valuable collaborations and potentially increased compensation. Products and industries constantly evolve, so influencers must stay informed about any updates, changes, or new developments related to the products they promote. One way to stay informed is by attending product launches, participating in training sessions, and keeping up with industry news and resources. A solid understanding of the products is crucial for influencers to successfully promote them in influencer marketing campaigns. By establishing credibility, authenticity, and trust with their audience, they can provide value to the brands they partner with.

Understanding the impact of normative influence is crucial in influencer marketing, as it can shape behaviour and influence both influencers and their audiences. Perceived social norms or expectations regarding acceptable or appropriate behaviours within a specific context are called injunctive norms. Observing the behaviour of others, especially those seen as authoritative or influential, can significantly influence an individual's actions. This phenomenon, known as social proof, is often influenced by injunctive norms. Influencers promote specific products or behaviours. They shape social norms by indicating what is desirable or acceptable within their social circle or community. This can result in conformity among their followers, who might be inclined to imitate the behaviour promoted by the Influencer. The cultural and social context and the values and beliefs of a specific community or society shape norms regarding acceptable or unacceptable behaviour. Like a market research analyst, influencers deeply understand their audience's preferences and values, allowing them to cater to specific demographics or niches. Just by aligning their content and endorsements with these norms, influencers can strengthen their rapport with their followers and boost the effectiveness of their marketing efforts. Individuals are often influenced by injunctive norms, which create a sense of social pressure to conform to behaviours or expectations that are considered socially desirable.

Regarding influencer marketing, influencers often strongly emphasise maintaining authenticity, transparency, and ethical conduct in their promotional activities. Brand brands can shape consumer perceptions and behaviour by highlighting social responsibility or moral considerations in their collaborations with influencers. (Kamal & Himel, 2023). Understanding the impact of injunctive norms is crucial for assessing the accountability of influencers and brands in the influencer marketing ecosystem. Going against what people expect or consider normal can result in a substantial adverse reaction from the public, harm the Influencer's standing, and potentially adversely affect the brand's reputation. Thus, influencers and brands must be aware of the social norms and try to match their behaviour with the expectations of their audience and the wider community.

Tailoring influencer marketing strategies (leveraging Injunctive Norms) to regional cultural norms and values (localised campaigns) will be beneficial. For example, festivals and community-driven events in India offer a unique context for promoting products aligned with traditional practices and partnering with influencers who resonate with specific local audiences, emphasising collective benefits and social belongingness in messaging (community-centric messaging)—implementing co-promotions with reputable organisations or certifications to add credibility (enhancing influencer trust). For instance, food influencers collaborating with India's Food Safety and Standards Authority (Third-Party Endorsements) can boost confidence in health products. Encourage the use of transparent labels like "#Sponsored" or "#Ad" on posts, alongside periodic authenticity audits conducted by third-party firms to enhance consumer confidence (transparency tools). Conduct campaigns educating consumers on critically assessing influencer content and distinguishing genuine endorsements from promotional noise.

Studies in the Western world highlight similar emphasis on authenticity and transparency, with audiences increasingly sceptical of influencers perceived as overly commercial. Indian consumers, however, tend to prioritise trust in influencers' expertise and alignment with cultural values, making localised authenticity even more critical. Chinese influencer marketing has effectively integrated AI and virtual influencers, such as Xiaomi's digital brand ambassadors, to engage tech-savvy demographics. Adapting this in India could bridge gaps between urban and rural consumers, leveraging virtual influencers to cater to diverse linguistic and cultural groups.

Conclusion

This study has effectively demonstrated the applicability and efficacy of the norm activation theory model in understanding variables related to influencers, their followers, and their postings on social media. It sheds light on the critical role of awareness about social media influencers and how their perceived trustworthiness impacts consumer behaviour and social media-based marketing strategies.

The findings emphasise that awareness about influencers is a more significant factor than merely sharing their content. Establishing familiarity with influencers builds trust, encouraging users to share the insights and knowledge they gain with their social circles. This highlights the potential of influencers to shape consumer perceptions and behaviour effectively. This research highlights the pivotal role of influencers, brands, and marketers in shaping the effectiveness of influencer marketing through authenticity, transparency, and cultural alignment. Influencers must build trust with their audience by genuinely endorsing products and maintaining openness about sponsorships. Brands should prioritise collaborations with knowledgeable influencers and foster reciprocal feedback mechanisms to refine their strategies and product offerings. Meanwhile, marketers must strategically align campaigns with cultural and social norms, leveraging the power of normative influence to create relatable and impactful content. By integrating these practices, stakeholders can enhance the credibility and efficacy of influencer marketing, fostering deeper connections with audiences. This study contributes to the evolving discourse on digital influence in India, providing a foundation for more ethical, practical, and consumer-centric marketing approaches in the dynamic social media landscape.

This study underscores the growing significance of normative social influence in shaping consumer behaviour within the Indian digital ecosystem. By integrating authenticity, transparency, and cultural alignment into their strategies, stakeholders can harness the transformative power of social media influencers. These insights contribute to a deeper understanding of the evolving landscape of virtual influence, paving the way for more effective, ethical, and audience-centered marketing approaches in India's dynamic market.

Limitations

Participants may not fully represent the broader population of social media users, especially those from rural areas or lower-income groups. Results may not fully apply to all Indian consumers due to the urban- centric and younger demographic focus. Those who are more active on social media or interested in the topic may be overrepresented. The study uses self-administered surveys, which can lead to response bias. Participants might overestimate or underestimate their reliance on influencers or intentions to engage with brands. India's cultural diversity is immense and limiting the study to a few cities might not capture the varied social norms and consumer behaviours prevalent across different regions (mainly rural areas). The study captures data at a single point in time, making it difficult to assess how the influence of social media and normative behaviours evolve over time or with changes in the digital landscape. Longitudinal studies can be administered for further insights. While Structural Equation Modeling (SEM) is a robust tool for testing relationships, it relies on linearity and model fit assumptions, which may not fully capture the complexities of consumer behaviour influenced by social media. The research focuses exclusively on social media influencers and does not consider other sources of normative influence, such as family, friends, or traditional media. The study primarily examines descriptive and injunctive norms without delving deeply into different dimensions of normative influence, such as moral norms or situational factors that may impact behaviour.

Future Research Directions

The potential for AI influencers in India, particularly in tech-forward urban markets, needs to be explored, and their impact on engagement compared to traditional influencers needs to be assessed. Investigating how socio-economic and cultural disparities influence perceptions of influencer marketing and providing granular insights for brands targeting rural India can be beneficial. The growing role of influencers in promoting sustainable products and ethical consumerism, aligning with India's increasing focus on environmental consciousness, can be further studied.

Managerial Implications

Creating frameworks to measure the effectiveness of campaigns (reels, memes, short videos) based on metrics like engagement rates, conversions, and sentiment analysis may develop an innovative ROI model for the new-era marketers. Establishing guidelines for Influencer Partnerships will define clear expectations, including content quality, transparency, and periodic reporting of consumer feedback. Regulatory Standards should be introduced by mandating disclosure norms for influencer partnerships and establishing penalties for non-compliance to enhance transparency. The Support Educational Campaigns: Collaborate with industry associations to educate consumers and influencers on ethical practices.

Author Contributions

All authors contributed to the best of their abilities.

Acknowledgements

The author extends gratitude to all participants who completed the questionnaire.

Disclosure Statement

The author(s) report no potential conflicts of interest.

Data Availability Statement

This study is grounded in a primary dataset.

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Received: 07-Nov-2024, Manuscript No. AMSJ-24-15427; Editor assigned: 08-Nov-2024, PreQC No. AMSJ-24-15427(PQ); Reviewed: 20-Dec-2024, QC No. AMSJ-24-15427; Revised: 29-Dec-2024, Manuscript No. AMSJ-24-15427(R); Published: 18-Jan-2025