Demystifying the Effect of Social Media Advertising on Purchase Intention of Millennials: Role of eWOM and Privacy Concerns

Abstract

The substantial impact of businesses’ strong online presence on consumers’ spending habits, especially among decision-makers, cannot be overstated. One of the forms of online product reviews is electronic word-of-mouth (eWOM), and it has a significant impact on consumers’ attitude towards a product as well as their intentions to purchase. This article aims to provide a conceptual framework that assesses how eWOM and privacy issues affect Indian millennials’ attitude towards social media advertising and the subsequent effect of attitude on purchase intention. The data were gathered through non-probability sampling from 340 social network users utilising an online questionnaire through Google Forms. The adapted scales were validated using confirmatory factor analysis, followed by path analysis using SPSS AMOS 26.0 to examine numerous hypotheses that had been formulated. The results revealed significant relationships that are helpful in recognising the attitude and purchase intention of Indian millennials regarding social media advertisements. The study can be helpful to brand managers, marketing executives, and advertisers in generating social media commercials by incorporating key elements that can positively mould attitudes and further develop purchase intention.

Keywords

eWOM attitude privacy concerns purchase intention social media advertising

Introduction

Social media is one of the radical breakthroughs that have completely transformed our way of life by bringing people from all corners of the globe together by removing geographic constraints (Arora et al., 2020). Social media has been referred as any web-based apps that expand upon the conceptual and technical underpinnings of Web 2.0 and that permit the production and exchange of user-created content (Kaplan & Haenlein, 2010). One of the most widely utilised and prevalent tool for internet users is social media (Anderson et al., 2011). In April 2023, internet users accounted for 5.18 billion individuals or 64.6% of the global population. Overall, 4.8 billion of these individuals, or 59.9% of the world’s population, utilise social media. In terms of the number of social media users, China, India, and the United States surpassed all other nations (Statista, 2023a). India, the country with the second-highest number of social media users, is projected to reach 1.17 billion by 2027 from 755 million in 2022 (Statista, 2023b).

The fundamental nature of people which includes networking, sustaining connections, and expressing opinions regarding commercial content (Uitz, 2012), and because of ‘communication’, the key component facilitated by social media platforms, have fundamentally altered the mechanism of information diffusion. Social networking is vital as it allows not only individual connection but also group connection or connection within the community with rapid, informed, and low-effort exchanges (Gutierrez et al., 2023). In contrast to other forms of media like television, radio, and print media, social media has a substantial chance of reaching the public because of the information’s widespread transmission (Iosifidis, 2011). This novel medium is promoting consumer socialisation and altering consumer behaviour (Wilska et al., 2023).

The majority of people’s lives now include social media groups, blogs, and networks, which have arisen as low-cost forms of alternative communication that complement already-existing systems. Business organisations can monitor conversations about their brand on social media platforms and engage with their customers directly (Reyneke et al., 2011). Marketers today actively use social media to sell their products through social media marketing (Pandey et al., 2018). Social media is a great way to communicate with existing and potential customers, and it can potentially incremented by developing brand fan pages across various social media platforms (De Vries et al., 2012). The growth of social media sites like Facebook and Twitter has altered how word-of-mouth (WOM) is shared and received online (Rui et al., 2013). India is the leading nation with the greatest amount of 369.9 million active Facebook users (Datareportal, 2023). Consumers attempt to influence peers, acquaintances, or prospective buyers by promoting their suggestions and opinions about an item or product on their social networking profile pages. This is especially appropriate and accurate in the Indian context, where individuals seek the counsel and opinions of their close friends and family members (Mer & Virdi, 2021).

This can help them to influence the purchasing decisions of their networks. In light of this, social networking sites’ unique social nature offers an intriguing and appropriate setting for exploring electronic WOM (eWOM) behaviour. Social networking sites have advantages and disadvantages for people, just like any breakthrough. The advancement of communication technologies brings new worries and challenges, although it greatly improves our quality of life. Many social media applications used in daily life allow access, storage, and manipulation of personal information about individuals. Therefore, privacy issues are raised regarding the unauthorised usage of numerous forms of personal information about social media users. There are relatively few studies that consider negative aspects of social media advertising also. The social media marketing characteristics were recognised by Kim and Ko (2012) as interactivity, customisation, trendiness, entertainment, and WOM. The impact on luxury fashion brands was investigated, and it was discovered that social media marketing significantly influenced the creation of relationship equity, value equity, brand value, trust, and intention to purchase. Godey et al. (2016) examined the effectiveness of social media advertising activities across five variables, including WOM, customisation, modernity, amusement, and interactivity, and found that social media advertising has a favourable influence on growing brand equity. Afful-Dadzie et al. (2021) researched social media’s Web 2.0 value in the promotion of health and emphasised how social media for Web 2.0 improves WOM advertising. Our goal in this study is to address this research gap by incorporating privacy concerns with eWOM and examining their impact on attitude towards social media advertising which subsequently influences purchase intention. As one of the most populous social media users and a multicultural country like India, their perception of advertising messaging is very different from other economies (Arora & Agarwal, 2019).

Companies are investing a significant amount of money in using social media to market their goods, but the success of these initiatives has not been adequately evaluated. According to previous studies, consumers are avoiding traditional media like Television, radio, magazines, and other conventional media because they want more control over their information intake. They urge to be able to access the relevant data right away (Iosifidis, 2011). Consumes seek information on numerous social media channels and make buying decisions utilising these platforms (Ho et al., 2024). Social media advertising impact on consumers’ purchase decisions must therefore need to be investigated.

Literature Review and Hypothesis Development

Theory of Reasoned Action

One of the key elements that explain human conduct in a variety of situations is attitude (Yusuf et al., 2018). It is therefore characterised as the comprehensive assessments that reveal people’s propensity towards a certain thing or behaviour (Fishbein & Ajzen, 1975). In this aspect, the TRA implies that people may form behavioural intentions before action (Ajzen & Fishbein, 1977). Under normal circumstances, the conduct is typically under a person’s conscious command. In prior research, the TRA has been widely utilised on the interplay between eWOM and purchase intention (Cheung & Thadani, 2012; Erkan & Evans, 2016; Reichelt et al., 2013). Nevertheless, these researches have only utilised attitude and behavioural intention—the two TRA components. Since the purpose of this study is to investigate the impact of eWOM and privacy concerns on behavioural intentions, such as purchase intention, behavioural intention has been chosen rather than conduct. In theory of reasoned action, there is a substantial chance of conflating attitude for norms since attitude can be interpreted as norms and vice versa (Rannenberg et al., 2010). To reduce this conflation, only attitude is considered for this study.

eWOM

WOM is the term for individual conversations between a sender and a recipient (Lee & Youn, 2009). It is recognised for influencing how people make purchases (Katz & Lazarfeld, 2017). The ‘valence’ of reviews has been shown in numerous studies to have an impact on consumers’ purchasing choices. A review’s valence refers to its evaluative slant, which can be favourable or adverse (Lee & Youn, 2009). Users only rely on online reviews as a basis for decisions when they have faith in the reviewer (Xu, 2014).

The advent of social media sparked the growth of cyber consumers and electronic word-of-mouth (eWOM). It is defined as any favourable or adverse remark generated regarding a product or firm that is made available to numerous individuals or organisations over the internet by potential, actual, or past customers (Hennig-Thurau et al., 2004). It has been viewed as a crucial marketing tactic (Bickart & Schindler, 2001). To ensure they are making the appropriate purchase, consumers read the web reviews left by prior customers (Pitta & Fowler, 2005). Using blogs, discussion forums, review websites for the general public, and social media, the internet has helped in shaping public opinion (Cheung & Thadani, 2012). With their peer groups and friends, people can interact, discuss, and exchange information available on social media about a variety of services they have utilised (Chu & Kim, 2011). The surge in preference of social media avenues has increased prospects for learning about products and given the consumers a variety of ways to share their own suggestions through eWOM (online WOM) (Yaylı & Bayram, 2012). Online reviews assist in increasing sales by easing the cognitive load on consumers and facilitating decision-making (Ye et al., 2011). Previous research on eWOM has discovered that it affects consumers’ intentions to acquire or purchase a product (Bickart & Schindler, 2001; King et al., 2014).

As per the findings of a study based on online shopping, the relationship between online purchase intent and trust is strengthened by strong eWOM, and it also raises their perception of authenticity and attitude (Cheung et al., 2009). Social media platforms are very well recognised to be acceptable for eWOM (Canhoto & Clark, 2013; Erkan & Evans, 2014; Kim et al., 2014). Social networking sites are a powerful tool for eWOM among consumers, operating as a valuable resource for knowledge and opinions about products. These websites have altered how customers make purchases by enabling them to rapidly and easily share their opinions and information about products (Graham & Havlena, 2007). Through experimental research of consumers using online recommendations, eWOM’s ability to influence product choice has been investigated (Senecal & Nantel, 2004). As per Chatterjee (2001), eWOM lessens ambiguity and risk while making a purchase decision. Kudeshia and Kumar (2017) demonstrated that favourable eWOM on Facebook has a significant impact on brand attitude and purchase intention in the context of the smartphone sector in India. Khan et al. (2023) revealed that eWOM significantly and favourably affects Indian consumers’ intentions to make purchases of apparel.

Thus, the study proposes the following to assess Indian millennials’ attitude towards social media advertisements:

H ₁: eWOM has a positive influence on attitude towards social media advertisements.

Privacy Concerns

Concerns about privacy are feelings of anxiety about losing privacy owing to the information gathering practices used by several social media advertising agencies (Taylor et al., 2011). In addition to making purchases, personal data can also be collected when you use the internet and set preferences on your accounts (Mascarenhas et al., 2003). Additionally, the majority of individuals are unaware that websites are monitoring their online behaviour (Milne, 2000).

User-generated content and browser history are currently utilised by social media sites to develop tailored adverts. Privacy concerns thereby become a major element of these focused advertisements. When social media platforms have serious privacy issues, they express hostility towards social media marketing. Adhikari and Panda (2018) through their study revealed that when exposing their personal information, Indian Social Networking users feel vulnerable. Consequently, it is proposed that:

H ₂: Privacy concerns have a negative influence on attitude towards social media advertisements.

Attitude Regarding Social Media Advertisements and Purchase Intention

Social media communication has redefined attitude and perceptions of human behaviour. According to Mehta (2000), understanding how people feel about advertising is crucial as it impacts both their perception of brands and how likely they are to make purchases. Attitude can be defined as a person’s persistent positive or negative opinion, emotional state, and behavioural inclinations towards an idea or product (Kotler & Keller, 2006). A positive attitude results in people preferring the product and utilising the product. Contrarily, a negative attitude results in less brand preference and product usage. Previous research on consumers’ attitude towards conventional advertising technique demonstrates that advertising is viewed favourably by end-users (Bauer & Greyser, 1968; Shavitt et al., 1998). Consumers value the advertisement and anticipate its importance; the advertisement also assisted them in making a purchase decision (Shavitt et al., 1998). However, some studies have suggested that consumers are increasingly resistant to advertising due to the advertising clutter (Alwitt & Prabhakar, 1994), yet, this avoidance varies amongst advertising mediums. Internet advertising is perceived favourably by consumers compared to advertising through other forms of communication (Schlosser et al., 1999). The consumer’s attitude towards advertisements is one of the key indicators of advertisement efficacy since their feelings and thoughts influence their perceptions, which in turn influence their attitude towards advertisements (MacKenzie & Lutz, 1989; Mehta, 2000). In accordance with previous studies, consumers’ attitudes towards social media advertising and behavioural intentions are significantly correlated (Boateng & Okoe, 2015; Sun & Wang, 2010).

The psychological stage or evaluation phase that consumers undergo to determine whether there is genuine willingness to use a product or service is known as purchase intention (Wells et al., 2011). The likelihood of using the brand is another way to define the intent to purchase (Keller, 2013). Purchase intention raises the probability of making purchases, therefore having greater purchase intention makes that more likely (Schiffman & Kanuk, 2009). Since purchase intention changes a consumer’s behaviour in future, it is essential for organisations to better predict future consumer behaviour (Whan et al., 2010). Mukherjee and Banerjee (2019) showed how important it is for smartphone brands to have advertisements on social media in inciting favourable attitude and intention to buy the brand in the mind of Indian social media users. Natarajan et al. (2015) assessed Indian consumers’ attitude regarding social media advertising and discovered that the preferred social media sites, occupation, and income level of the consumer are the important variables influencing consumers’ perceptions of social media advertising. Studies in the past have shown that attitude has a significant impact on behavioural and purchase intentions (Fishbein & Ajzen, 1975). As per Kotler (2000), purchase intention is widely recognised tool for forecasting and assessing the effectiveness of behavioural responses. The goal of marketers is to arouse in consumers a desire to purchase the promoted goods. Understanding how consumers feel about social media advertising can help marketers to create the tactics they need to reach their target market since social media is being used as an interactive medium for advertising (Pandey et al., 2018). Consequently, the study proposes the following:

H ₃: Attitude towards social media advertisements has a positive influence on purchase intention of Indian millennials.

The model we utilised in this research was adapted from hierarchy effects model (Lavidge & Steiner, 1961) which suggests that beliefs influence attitudes, which then influence behavioural actions and expanded by including privacy concerns and eWOM (Figure 1). By doing this, we hope to demonstrate the impact, affirmative or negative, that privacy concerns and eWOM have on consumers’ purchase intention when it comes to social media applications.

Figure 1.

The Relation Among the Variables.

Research Methodology

Instrument Development

The measurement scales have been adapted based on existing research and are listed in Table 1. These constructs have undergone measurement using the Likert scale, which ranges from 1 for strongly disagree to 5 for strongly agree. In-depth discussions with researchers and experts in the disciplines of marketing and research were conducted to further refine the questionnaire. In addition to this, a pilot testing involving 40 participants was carried out to enhance the survey’s instrument clarity through participants’ responses and recommendations.

Table 1.

Sources of the Construct.

Constructs	Number of Items	Sources
eWOM	4	Bambauer-Sachse and Mangold (2011)
Privacy concerns	4	Yaakop et al. (2013) and Jung (2017)
Attitude	4	Pollay and Mittal (1993) and Ling et al. (2010)
Purchase intention	3	Jarvenpaa et al. (2000)

Data Collection

Millennial consumers were used as our sample comprised graduates, postgraduates, and research scholars of various universities and colleges from the Northern region of India. The descendants of the Baby Boomers are the millennials, sometimes referred as generation Y (Berndt, 2007; Dotson & Hyatt, 2005). ‘Millennials’ (Generation Y) are characterised as all individuals who were born in the period from 1981 to 1999 (Bolton et al., 2013). Due to their better propensity to adopt new technologies and their early exposure to the internet, millennials have been selected (Arora et al., 2018). The sample data were gathered through Google Forms using non-probability sampling. An online questionnaire was shared with 460 participants and a total response of 340 respondents was obtained after excluding invalid questionnaires.

Demographic Profile

To ascertain the profile of social media users, frequency analysis is utilised. Of the 340 respondents of the questionnaire, 184 were male and 156 were female. Overall, 127 respondents were of the age bracket 18–20 years. The majority of the participants (n = 158) were undergraduates, and after them, postgraduate students and others took the second and third places, respectively. Table 2 summarises the demographic information of participants.

Table 2.

Respondent Demographic Profile.

Characteristic	Frequency	Percentage	Cumulative Frequency (%)
Gender
Male	184	54.12	54.12
Female	156	45.88	100
Age
18–20	127	37.35	37.35
20–22	113	33.24	70.59
22–24 24–26 Education Undergraduate Postgraduate Others	64 36 158 118 64	18.82 10.59 46.47 34.71 18.82	89.41 100 46.47 81.18 100

Common Method Variance

Item preparation effects, consent biases, standard scale issues, scale length, and item characteristic effects were ruled out during the survey instrument design phase (Liang et al., 2007; Podsakoff et al., 2003). Across the data analysis stage, common method variance was verified using Harman’s one-factor test. The first element that addressed only 35.56% of the overall variation, therefore common method variance was not an obstacle.

Data Analysis

SPSS software was used for descriptive analysis and Structured Equation Modelling (SEM) through AMOS 26 was used for inferential analysis. To assess the reliability of research constructs, Cronbach’s alpha was used. SEM was chosen above other techniques as it combines several common techniques like correlation, factor analysis, and multiple regression into one data analysis tool. In addition, it permits the concurrent testing of hypothesised latent correlations, aiding the researchers in reducing measurement errors, and compares the theoretical model with the data, providing fit statistics and assisting in theory building.

Validity and Reliability Concerns

Confirmatory factor analysis was used in examining the validity, reliability, and evaluating the model fitness employing different goodness of fit and badness of fit metrics. The model fit (Table 3) demonstrates that all model constructs have a Cronbach’s alpha (α) reliability that falls within the range of 0.811–0.899, which is greater than the threshold of 0.6 (Loewenthal, 2004), therefore the model constructs’ internal consistency reliability is acceptable. For assessing the convergent validity, three different metrics were used: the confirmatory model’s factor loadings must be more than 0.5 (Steenkamp & Geyskens, 2006). All factor loadings are above the aforementioned limit, as seen in Table 3 and Figure 2, respectively. Composite Reliability (CR) must be higher than 0.7 and all constructs should have an average variance extracted (AVE) of greater than 0.5 (Hair et al., 2006). As it can be seen from Table 3, all three parameters for measuring convergent validity are satisfied.

Discriminant validity is attained when AVE is higher than the maximum shared squared variance (MSV) and is indicated by a low correlation between one measurement construct and other measurement constructs (Fornell & Larcker, 1981) so that square root of AVE for each construct is higher than the correlation between them. In Table 4, the square root of AVE is denoted in bold. The requirements for discriminant validity are also met as each variable’s square root of AVE is higher than its correlation with other constructs and all AVE is higher than MSV (see Table 3).

Table 3.

Scale Factor Loadings.

Construct	Items	Factor Loading	CR	AVE	MSV	Cron-bach’s α
eWOM	I usually read posts from friends and other consumers to make sure I choose the appropriate brand or product.	0.80	0.827	0.546	0.187	0.826
	I frequently read posts from friends and other consumers to find out what product/brand makes a good impression on others.	0.74
	I frequently read posts from friends and consumers to learn more about brands and products.	0.67
	I frequently check comments left by other customers or friends to feel more confident in my purchase.	0.74
Privacy concerns	I am comfortable sharing private information on social media platforms.	0.77	0.897	0.687	0.371	0.899
	I have the impression that everything about me will remain confidential.	0.76
	Social networking is a medium I use because it brings me to another world.	0.89
	I seek recommendations and guidance on social media platforms to help me making buying decisions.	0.90
Attitude	In general, I prefer social media advertising.	0.74	0.812	0.520	0.310	0.811
	In general, I think that social media advertising is beneficial.	0.76
	I think that advertising is beneficial since it highlights the latest products	0.66
	I am in favour of social media advertising since it influences my purchasing behaviour.	0.73
Purchase intention	I would buy the products and services promoted on social media.	0.73	0.827	0.615	0.371	0.823
	If I saw advertisements for products and services on social media, I would consider buying them over the course of the following three months.	0.76
	I will make my upcoming acquisition of products and services through social media.	0.86

Notes: AVE = Average Variance Extracted; CR = Composite Reliability; MSV = Maximum Shared Squared Variance.

Figure 2.

Proposed Model’s Standardised Regression Coefficients.

Table 4.

Correlation Matrix.

	eWOM	PC	ATT	PI
Electronic word of mouth (eWOM)	0.739
Privacy concerns (PC)	0.210	0.829
Attitude (ATT)	0.387	0.530	0.721
Purchase intention (PI)	0.486	0.723	0.510	0.784

Model Fitness

To assess the model’s fitness, several indices have been used, which indicate the goodness and badness of model fit. The suitability of the model and the data’s feasibility for further hypothesis testing are shown by the supplied indices in Table 5.

Table 5.

Fitting Metrics of the Model.

Fit Indices	Research Model	Threshold Limit
CMIN/DF	2.705	<3.0
CFI	0.936	>0.9
TLI	0.910	>0.9
GFI	0.915	>0.9
RMSEA	0.066	<0.08
NFI	0.907	>0.9
AGFI	0.845	>0.8

Results

The hypothesised relationships between the various constructs are examined using path analysis. Table 6 displays a summary of the findings from the hypothesis testing. Also, path diagram is shown in Figure 3. Three criteria were utilised in the hypothesis testing in order to assess the association between the constructs: significance level (p value must be less than .05), critical ratio (critical ratio must be greater than 1.96), and regression coefficients.

Figure 3.

Structural Model Test Results.

Table 6.

Results of Hypothesis Testing.

Path			Standardised Estimate	S.E.	Critical Ratio	P	Hypothesis
Attitude	←	eWOM	0.204	0.036	5.681	***	Supported
Attitude	←	Privacy concerns	0.280	0.030	9.231	***	Supported
Purchase intention	←	Attitude	0.924	0.097	9.530	***	Supported

As it can be seen from Table 6, the hypotheses from H₁ to H₃ are supported as p values are significant and less than .05. Values of the Critical Ratio are also higher than 1.96. The proposed model predicts attitudes about social media advertising with a total explained variance of 56.5% and purchase intention with a total explained variation of 62.8%. The findings indicate that attitude towards social media advertising is considerably predicted by eWOM and privacy concerns. Also, attitude has a significant effect on the millennials’ purchase intention.

Discussion and Implications

The advertising environment has changed as a result of the digital revolution, with many firms now utilising a variety of digital channels, with social media being one of the most utilised making it essential to evaluate these platfoms’ effectiveness for advertising (Alalwan, 2018; Arora et al., 2018). The purpose of this study was to examine how eWOM and privacy concerns on social media affect attitude regarding social media and, consequently influences millennials’ purchase intention. A model that was backed up, evaluated, and verified was established through this study. The findings of the analysis aligned with the Theory of Reasoned Action. Furthermore, this study validates the utility of TRA in elucidating the relationship between social media and purchase intention. TRA has been extended in the study with eWOM and privacy concerns. The findings showed a positive relationship between eWOM and attitude towards social media whereas privacy concerns and attitude are negatively related. With regard to purchase intention and attitude about social media advertising, H₁, H₂, and H₃ all are supported.

The results of the study indicate that eWOM is positively related to attitude towards social media and are consistent with the previous studies of Pandey et al. (2018) and Park et al. (2007). Kudeshia and Kumar (2017)showed that attitude and intention of consumers towards a brand are greatly influenced by favourable social media WOM generated by users. Yang et al. (2016) claim that reviews having a positive valence reduced the perceived purchase risk, which in turn enhances consumer attitude and motivates purchase intention. Arora et al. (2020) posited that attitudes towards social media were negatively correlated with privacy concerns. The findings are consistent with earlier research (Ohajionu & Mathews, 2019; Taylor et al., 2011). Relationship between the attitude and purchase intention has been demonstrated in the previous studies (Arora et al., 2020; Eze & Lee, 2012; Fishbein & Ajzen, 1975; Kamal & Chu, 2012; Kudeshia & Kumar, 2017; Wolin et al., 2002). Sun and Wang (2010) demonstrated that customers who have a favourable attitude on social media advertising have more probability to respond positively to it by purchasing a product that has been promoted on social media or looking up more information.

Theoretical Implications

This study made a contribution to existing literature in various ways. This study extends previous research by incorporating privacy concerns with eWOM in the context of social media. The interplay among eWOM, privacy concerns, attitude regarding social media advertising, and purchase intention provided a conceptual foundation for future research.

In this study, negative aspect of social media advertising is also included along with eWOM to comprehensively analyse the attitude of millennials towards social media advertising which subsequently influences purchase intention. The findings claimed that eWOM has a favourable influence on millennials’ attitude regarding social media advertisements whereas the influence of privacy concerns is negative. Attitude towards social media advertising have a positive effect on purchase intention. It is suggested through the findings that a favourable attitude regarding social media advertising is essential for inducing purchase intention among millennials. A positive eWOM and elimination of privacy concerns can lead to positive attitude towards social media advertisements.

Managerial Implications

This study has made a number of practical contributions. One consumer opinion has an influence on the other consumer’s buying intention. There must be awareness among the companies of this possible communication channel and should strive to capitalise by analysing and taking part in this type of eWOM communication in order to reap the benefits. WOM conversations were previously challenging for marketers to track, but in the age of social media, they are now able to not only analyse and track these interactions, but also administer them better by giving customers a forum to spread constructive criticism of the company. WOM serves as both a recommender and an informant on social media sites, providing feedback on products from previous customers (Park et al., 2007). Marketers should focus on ‘social care’, in which businesses should aim to seize any opportunity to offer customer help through various social media platforms. A powerful influencer’s reviews can significantly aid in converting a lead into a potential consumer. Managers are recommended to collect a repository of customers who can be classified as ‘influencers’ utilising applications such as Google Alert, Sysomos, and others. It is possible to later motivate these ‘social influencers’ or ‘market mavens’ on social networking platforms to disseminate favourable information regarding product (Chu & Kim, 2011).

Marketing professionals must design relevant ads in such a way that leads to removal of privacy concerns. The following tactics are recommended for marketers to use to encourage WOM: (a) posting a link of their e-commerce site on various social media platforms; (b) releasing updates of upcoming products; (c) creating posts/stories on social media of customers’ purchases to inspire others; (d) setting up a way to thank customers for the purchases made by them; (e) motivating customers for sharing their brand experiences; and (f) holding giveaways to encourage winners to post regarding their interactions with social media. Consumers are more likely to respond favourably by purchasing a product promoted on social media or looking for further information if they have a positive attitude about social media advertising.

The result implies that marketers need more sophisticated marketing campaigns while considering the privacy concerns of consumers to benefit from the robust and engaging features of social media as digital technologies are constantly upgrading and Internet and virtual world consumers are becoming more privileged.

Limitations and Directions for Future Research

Although millennials or generation Y constitutes the bulk of social networking sites users, they are not necessarily representative of all Social media users. Future research can be done on a different sample, such as generations X or Z, to ascertain their opinions and behavioural responses to social media advertising. Several advertising characteristics like personalisation, annoyance, and personalisation, can be regarded when deciding on attitude about advertising. To ensure consistency or variation in the findings from the current study, future studies can conduct research using a sample that is selected from various regions of India. Since cultural effect has not been considered, future study can compare perspectives of consumers from different cultural backgrounds to emphasise the differences in those perspectives.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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