The rapid growth of digital technologies has changed the way businesses communicate with consumers. Traditional advertising is no longer the only way to reach audiences, as people now spend a significant amount of time on social media platforms (Kaplan & Haenlein, 2010). Within this context, viral marketing has become an effective strategy because it spreads messages quickly and encourages consumers to engage with brands at a low cost (Leskovec et al., 2007).

Viral marketing works by designing content that motivates people to share information with their peers. This sharing process not only extends the reach of a brand but also creates trust, as consumers often value recommendations from others more than direct advertising (Trusov et al., 2009). Prior research has shown that factors such as entertainment, informativeness, and interactivity play an important role in shaping how people respond to online content (Huang et al., 2013; Izquierdo & José-Cabezudo, 2011). In addition, electronic word of mouth (EWOM) has been identified as a powerful driver of consumer behavior, especially in digital environments where peer influence is strong (Cheung & Thadani, 2012).

Although many studies highlight the benefits of viral marketing, there is still a need to better understand which message elements have the strongest impact on consumer attitudes and how these attitudes translate into purchase intentions. This is particularly relevant in highly competitive markets, where brands seek cost-effective ways to attract and retain customers. Addressing this gap, the present study focuses on testing the effects of five key viral marketing message elements—entertainment, informativeness, customization, interactivity, and EWOM—on consumer attitudes and purchase intentions. This provides new insights into how firms can design more effective viral marketing strategies.

Although viral marketing has gained wide attention, gaps remain in understanding exactly which message features most strongly shape consumer attitudes and drive purchase intentions. Prior studies have often looked at these factors in isolation, but less work has examined them together in a single framework. Moreover, customization—often highlighted as a strength of digital marketing—has shown mixed evidence in past research, leaving uncertainty about its actual effect on attitudes. This makes it important to re-examine the role of entertainment, informativeness, interactivity, EWOM, and customization within a structural model that links message design to consumer responses. By doing so, this study not only tests the relative importance of these factors but also provides actionable insights for marketers seeking to design more impactful viral campaigns. This sets the tone for a closer review of existing research and the development of hypotheses to guide this study.

Theory of Reasoned Action

This study is grounded in the Theory of Reasoned Action (TRA) proposed by Fishbein and Ajzen (1975), a robust and widely applied framework for examining consumer behavior, particularly in relation to attitudes and purchase intentions. Prior research highlights the relevance of TRA in explaining how viral marketing messages influence consumers’ evaluations, which subsequently drive their purchase decisions. Empirical studies have applied TRA directly to the social media context, demonstrating that viral marketing content significantly affects users’ attitudes and purchase intentions (Fard & Marvi, 2019; Gunawan & Huarng, 2015; Jovlin & Belgiawan, 2024). Specifically, elements such as argument quality, source credibility, and information quantity have been shown to enhance perceptions of usefulness and ease of use, thereby fostering favorable attitudes toward the product and increasing the likelihood of purchase (Fard & Marvi, 2019; Jovlin & Belgiawan, 2024).

The TRA has been widely and successfully applied to examine purchase intentions across diverse sectors such as mobile applications, luxury fashion, and green products, consistently identifying attitude as a key predictor of behavioral intention (Fard & Marvi, 2019; Lau et al., 2022; Belleau et al., 2007; Paul et al., 2016; Rabidas & Bowen, 2019; Jiang et al., 2019; Jovlin & Belgiawan, 2024). The model’s flexibility also allows for extension with additional constructs—such as perceived risk and social identity—to better capture the nuances of viral marketing’s influence on consumers (Gunawan & Huarng, 2015; Lau et al., 2022; Paul et al., 2016). Within the viral marketing context, informativeness, entertainment, and source credibility emerge as critical drivers of favorable attitudes, with credible and informative messages more likely to elicit positive responses, while irritation has been found to exert little to no significant effect in certain settings (Zernigah & Sohail, 2012; Nordin et al., 2019; Jaidon, 2019). Furthermore, studies indicate that customization in advertisements and branding enhances purchase intentions through the mediating role of attitude toward the ad (Petrescu et al., 2015; Linardi et al., 2023).

In addition to content-related factors, positive electronic word-of-mouth (EWOM) and interactive messaging on social media platforms also play a crucial role in shaping consumer behavior. Research shows that positive EWOM on platforms such as Facebook significantly enhances brand attitudes and strengthens purchase intentions, particularly in categories like consumer electronics and among millennial consumers (Kudeshia & Kumar, 2017; Garima & Sheokand, 2024). Similarly, interactivity—through opportunities for dialogue, feedback, and two-way communication—has been found to foster more favorable brand attitudes and increase consumers’ willingness to purchase (Ko et al., 2005; Jiang et al., 2010). Building on these insights, the present study conceptualizes a model that integrates five key viral marketing strategies—entertainment, informativeness, customization, positive EWOM, and interactivity—to examine their effects on consumer attitudes, which subsequently influence purchase intentions.

Entertaining VM Strategies

Entertainment value of viral marketing such as being enjoyable, fun, and interesting significantly enhances both consumer attitudes toward brands and their intention to purchase. (Ismail et al., 2022; Choshaly & Mirabolghasemi, 2020; Adis & Kim, 2013; Petrescu et al., 2015). A favorable attitude serves as a crucial determinant of consumers’ willingness to both share advertisements and consider purchasing the promoted product (Petrescu et al., 2015; Choshaly & Mirabolghasemi, 2020; Ismail et al., 2022; Liu & Wang, 2019). Empirical evidence consistently demonstrates that the perceived entertainment value of viral marketing messages is positively associated with purchase intentions. Specifically, when consumers perceive an advertisement as entertaining, they are more inclined to form the intention to buy the featured product (Choshaly & Mirabolghasemi, 2020; Ismail et al., 2022; Liu & Wang, 2019; Jovlin & Belgiawan, 2024).

The influence of entertaining content on purchase intention is frequently mediated by consumers’ attitudes toward the advertisement. When an advertisement is perceived as more entertaining, it fosters a more favorable attitude, which in turn enhances both the likelihood of sharing the ad (viral intention) and the intention to purchase the product (Petrescu et al., 2015; Ismail et al., 2022; Liu & Wang, 2019). Thus, entertaining viral marketing messages exert a significant effect on consumer attitudes and subsequent purchase decisions. In line with prior research, this study proposes the following hypothesis:

H1: entertaining viral marketing strategies significantly and positively affect attitude towards viral marketing brand.

Informative VM Strategies

Informativeness in viral marketing such as delivering useful, accurate, and timely information significantly increases consumers’ positive attitudes and purchase intentions. When consumers perceive viral content as informative, they are more likely to trust the brand and consider purchasing the product (Ismail et al., 2022; Choshaly & Mirabolghasemi, 2020; Nawacatur et al., 2024; Zernigah & Sohail, 2012).viral marketing messages delivering valuable, clear, and credible information, tend to cultivate a more favorable attitude toward both the advertisement and the brand (Fard & Marvi, 2019; Petrescu et al., 2015; Ismail et al., 2022; Prajogo & Purwanto, 2020; Amperawati et al., 2024; Tien et al., 2019; Liu & Wang, 2019). This attitude toward the ad functions as a central mediator, such that informative content not only strengthens positive attitudes but also increases the likelihood of viral sharing and ultimately enhances purchase intentions (Petrescu et al., 2015; Prajogo & Purwanto, 2020). Perceived informativeness, source credibility, and usefulness of viral marketing messages have been consistently associated with stronger purchase intentions across diverse product categories, including mobile applications, eco-labeled products, and cosmetics (Fard & Marvi, 2019; Choshaly & Mirabolghasemi, 2020; Ismail et al., 2022; Amperawati et al., 2024; Tien et al., 2019; Liu & Wang, 2019; Jovlin & Belgiawan, 2024). Informative content enhances consumers’ perceptions of usefulness and trust, which in turn stimulate favorable attitudes and drive their intention to purchase (Fard & Marvi, 2019; Choshaly & Mirabolghasemi, 2020; Ismail et al., 2022; Tien et al., 2019; Jovlin & Belgiawan, 2024). Thus, informative viral marketing messages exert a significant positive effect on both consumer attitudes and purchase intentions. Accordingly, this study proposes the following hypothesis:

H2: informative viral marketing strategies significantly and positively affect attitude towards viral marketing brand.

Customised VM Strategies

Customised viral marketing strategies such as tailored messages spread via social media or digital platforms impacts consumer attitudes and purchase intentions (Fard & Marvi, 2019; Widyadhana et al., 2025; Jovlin & Belgiawan, 2024; Alsamydai, 2016). Customized advertisements have been shown to generate more favorable attitudes, particularly among consumers with high self-esteem, while even those with lower self-esteem respond positively to customization, leading to improved attitudes toward both the ad and the brand (Linardi et al., 2023). Moreover, attitude toward the ad and the brand serves as a critical mediator between message customization and purchase intention, suggesting that positive attitudes formed through effective customization significantly enhance consumers’ willingness to purchase (Petrescu et al., 2015; Linardi et al., 2023; Jovlin & Belgiawan, 2024; Liu & Wang, 2019). Customized viral marketing messages have been found to directly enhance purchase intentions, with the effect becoming stronger when the content is perceived as credible and personally relevant (Linardi et al., 2023; Jovlin & Belgiawan, 2024; Amperawati et al., 2024). Such messages exert a significant influence on both consumer attitudes and purchase intentions, as personalization and message quality foster more favorable attitudes toward ads and brands. These positive attitudes not only increase the likelihood of sharing the message but also translate into stronger purchase behavior. Hence, customized viral marketing emerges as an effective strategy for shaping consumer responses and driving purchase decisions. Accordingly, this study proposes the following hypothesis:

H3: customised viral marketing strategies significantly and positively affect attitude towards viral marketing brand.

Electronic Word of Mouth

Electronic word of mouth (EWOM) in viral marketing significantly affects consumer attitudes and purchase intentions, with both positive and negative impacts depending on message quality, credibility, and valence. Positive EWOM increases trust, emotional appeal, and perceived value, leading to higher purchase intent, while negative EWOM can damage brand image and reduce purchase likelihood (L, 2025; Teng et al., 2017; Huete-Alcocer, 2017; Kansal & Kaushik, 2024; Kuo & Nakhata, 2019; Nasiruddin et al., 2016; Siregar et al., 2024; Kocic & Radaković, 2019; Erkan, 2015). Positive EWOM on social media platforms such as Facebook has also shown to significantly enhance brand attitudes and strengthen purchase intentions, particularly in the context of consumer electronics and among millennial consumers (Kudeshia & Kumar, 2017; Garima & Sheokand, 2024). EWOM messages that are persuasive, informative, and originate from trusted sources are more likely to be accepted by consumers, thereby increasing their likelihood of making a purchase (Tien et al., 2019; Erkan & Evans, 2016). Moreover, the quality, credibility, and perceived usefulness of EWOM emerge as critical factors influencing both the development of positive attitudes and the strengthening of purchase intentions (Verma et al., 2023; Tien et al., 2019; Erkan & Evans, 2016).

The adoption of EWOM information serves as a critical mediator in shaping consumer behavior: when consumers perceive EWOM as credible and useful, they are more inclined to adopt the message, which subsequently enhances their purchase intention (Sardar et al., 2021; Verma et al., 2023; Tien et al., 2019; Erkan & Evans, 2016). Furthermore, consumers’ attitude toward EWOM, along with the perceived relevance and usefulness of the information, reinforces its influence on purchase decisions (Verma et al., 2023; Erkan & Evans, 2016; Patel et al., 2023). Meta-analytical evidence highlights that argument quality, message valence, and trust in EWOM are among the strongest predictors of purchase intention, whereas factors such as message volume or source credibility alone exert relatively weaker effects (Ismagilova et al., 2019; Albayrak & Ceylan, 2021). For marketers, this implies that designing campaigns that stimulate credible, informative, and positive EWOM—while fostering trust in consumer-to-consumer communications—can substantially improve purchase intentions (Sardar et al., 2021; Garima & Sheokand, 2024; Tien et al., 2019; Erkan & Evans, 2016). Thus, EWOM viral marketing messages emerge as a key driver of consumer attitudes and purchase intentions. In line with this, the study proposes that:

H4: EWOM promoting viral marketing strategies significantly and positively affect attitude towards viral marketing brand.

Interactive VM Strategies

Interactivity in viral marketing such as features that allow consumers to engage, comment or share generally enhances consumer experience, brand perception, and can increase purchase intention, though the effects are nuanced and context-dependent (Kim, 2021; Gu, 2025). Interactivity on social media platforms enhances user engagement, which fosters more positive brand attitudes and stronger purchase intentions (Ko et al., 2005; Jiang et al., 2010). By encouraging active information exchange, interactivity shapes consumer behavior and purchase decisions in meaningful ways (Gu, 2025). Rather than mere passive reception, consumer interaction with viral marketing messages is critical for strengthening brand equity and driving purchase intentions (Liu & Wang, 2019). Moreover, interactivity helps build brand awareness and trust, further supporting positive purchase outcomes (Gu, 2025; Liu & Wang, 2019). The effectiveness of interactivity, however, may vary depending on product type, user motivations, and the nature of the interaction—for instance, reciprocal communication tends to be more effective for functional products compared to expressive ones (Jiang et al., 2010).

Attitude toward the product or service often mediates the relationship between interactivity and purchase intention, as interactivity influences consumer attitudes, which in turn shape purchase decisions (Ooi et al., 2023; Ko et al., 2005; Jiang et al., 2010). While interactivity in viral marketing can yield both positive and negative outcomes depending on its design and context, higher levels of interactivity generally enhance consumer engagement, strengthen brand attitudes, and increase purchase intentions. In line with prior research, this study proposes that:

H5: interactive viral marketing strategies significantly and positively affect attitude towards viral marketing brand.

Attitude and Purchase Intention

Multiple studies confirm that a positive attitude toward viral marketing ads and the brand itself leads to higher purchase intentions. Attitude toward the ad acts as a direct predictor of purchase intention and also mediates the effect of ad content and appeal on purchase behavior (Prajogo & Purwanto, 2020; Dharmasaputro & Achyar, 2021; Manaf & Lee, 2014; Alsamydai, 2016).

Positive attitudes toward viral marketing not only enhance consumers’ willingness to share and forward messages (Izquierdo & José-Cabezudo, 2011; Huang et al., 2013; Modiri-Delshad, 2015), but also directly strengthen business competitiveness and performance. Moreover, such attitudes serve as a mediator, linking viral marketing messages to improved organizational outcomes (Tandijaya & Hatane, 2021). Consumer attitudes toward viral ads and brands, fully mediates the relationship between viral marketing and purchase intention, highlighting the importance of psychological factors (Fard & Marvi, 2019; Fitriani et al., 2022; Jovlin & Belgiawan, 2024; Gu, 2025), while the attitude towards viral marketing is affected by message characteristics such as informativeness, entertainment, customisation, interactivity and EWOM messages as theorised in this study. In accordance with the literature this study proposes that:

H6: attitude towards viral marketing brand significantly and positively affect consumer purchase intention.

Drawing from the above discussion, this study develops a conceptual model where five dimensions of viral marketing strategies (VMS) – informative, entertaining, customised, EWOM and interactivity shape consumers’ attitudes toward advertising (ATT), which in turn drive their purchase intentions (PI).

Figure 1: VM Conceptual Model

A structured questionnaire was administered to the respondents to collect the required data. The sample was collected employing convenience sampling from the customers in India who have already used social media platforms (Dwivedi and Irani, 2009). The required data was primarily collected from management post graduate students who represent typical online customers over the period from March 2025 to May 2025 from five major cities in Uttar Pradesh (Lucknow, Kanpur, Allahabad, Agra and Varanasi). Respondents were either approached at their study places (i.e. universities, colleges, and institutes) or a google form link was sent with the help of faculty members and friends. The questionnaire was also administered to students’ friends and relatives who should have an account on social media platforms. Initially 258 responses were received which were then scanned for any errors and incomplete responses. After screening, 236 responses were found to be eligible to be entered into the software for data analysis in SmarPLS. The considered sample size was in accordance both with the rule of thumb rule of 5-10 times of the indicators of latent variables employed in the model. This study has a total of 18 items hence a sample of 90-180 would be deemed as sufficient. The sample size was determined based on the guideline of Hair (2014), which recommends that in SmartPLS, the minimum sample should be at least ten times the maximum number of structural paths directed toward any single construct in the model.

As far as the measures are concerned, the constructs of entertainment and informative VM strategies (ENT & INF) were measured with three items each adapted from Trivedi (2017), the next constructs of customized, electronic word of mouth and interactive VM strategies were measured using three items each adapted from Kim and Ko (2010), the dependent constructs of attitude towards VM brand and purchase intention were measured through three items each adapted from Trivedi (2017) as sown in Table 1. A five-point Likert type scale with a set of items rated on five-point scale from strongly agree to strongly disagree was used to measure each of the item. To ensure an adequate level of validity and reliability prior to conducting the main survey, the researcher applied a pilot study with 30 target profile respondents. Most of those respondents reported that the language used was clear and straightforward and that the length of the questionnaire was reasonable. All factors were also able to have an acceptable value of Cronbach’s alpha higher than 0.70 as suggested by Nunnally (1978).

Measurement Model – Reliability and Validity Testing

The proposed conceptual model was tested through structural equation modelling (SEM) technique since it involved series of inter-relationship. The SEM was performed employing Anderson and Gerbing (1988) two-step method. First the measurement model was estimated or confirmatory factor analysis was done and reliability and validity of the constructs were established. This was followed by the path analysis of structural model to determine the path coefficients. SmartPLS was used to estimate the SEM model proposed in this study. The results of confirmatory factor analysis are presented in Table 1. The convergent validity is established through reliability or internal consistency (Cronbach Alpha), composite reliability (CR), average variance extracted (AVE) and the factor loadings of the items onto their respective constructs. It may be observed Cronbach Alpha for all constructs was > 0.7, ranging from 0.758-0.864, all the CRs were > 0.7, ranging from 0.881-0.917. The AVE by the items of each construct was well above the threshold of 0.50 (Bagozzi & Yi, 1988), ranging from 0.815-0.904. Each indicator loaded significantly onto its expected latent construct, with factor loadings between 0.815-0.904 all significant with p < .05, (Anderson & Gerbing, 1988).

Table 1: Measurement Model – Reliability and Convergent Validity

The discriminant validity, was established through the Fornell-Larker criteria and heterotrait-monotrait (HTMT) ratio between the constructs. As per the Fornell-Larker criteria, the square root value of AVE for a construct (shown in bold in Table 2a) is greater than any of its correlation with other constructs in the respective row, this is observed for all the constructs, indicating that values for all constructs are greater than the squared correlations between constructs; thus, no significant relationships exist between the constructs that would jeopardize the validity of the results (Fornell & Larcker, 1981). The Fornell-Larker criteria may be sensitive sometimes so the values of HTMT ratio for all the constructs (shown in Table 2b) were also observed and it was found that all the values were lower than the cut-off value 0.85 (Hair et al. 2012).

Table 2: Discriminant Validity and Correlation

After establishing the reliability and validity of the constructs, the correlations among the constructs are observed (shown in Table 2c) and all the correlations were substantial to move for structural model. The multicollinearity among the constructs was determined through the results of VIF tests conducted in SmartPLS, it was found that all the inner VIF values were way less than the recommended value of 10 (Kock, 2015). The results of measurement model conclude that all the constructs were reliable and validity is also achieved.

Structural Model – Hypothesis Testing

Considering the series of relationships, this study has constructed a structural equation model to test hypotheses. The hypothesized causal paths were estimated using SmartPLS 4.0. The Table 4 below displays the standardized path coefficient, path significance, and explained variance (R2) for the dependent variables of ATT and PI. Among six hypotheses proposed, five were supported while the data could not support one proposed hypothesis. The results of the structural model illustrated the vital role of entertainment, informativeness and EWOM and interactivity in determining consumer attitudes towards VM and brand leading to purchase intention. EWOM (β4 = 0.348, p < 0.001) was found to be most important factor with largest β in determining ATT. ENT was found to have second largest impact on ATT (β1 = 0.246, p < 0.001). INF and INT were also found to have similar significant effect on ATT (β2 = 0.147, p < 0.05 and β5 = 0.143, p < 0.05 respectively) hence hypothesis H1, H2, H4 and H5 were supported through the observed data.

Table 3: Structural Model – Path Coefficients

However, the effect of CUS on ATT was found insignificant (β1 = 0.058, p > 0.05), thus H3 was not supported. Finally, a positive and strong relationship was observed between ATT and PI (β6 = 0.679, p < 0.001 supporting H6. It was revealed that the four independent variables explained 57.2% of the variance in ATT and in turn ATT explains 46.1% variance in PI. Hence, a substantial amount of variance is explained in ATT and PI.

This study shows that some viral marketing message features are more important than others in shaping consumer attitudes and purchase intentions. Among them, EWOM came out as the strongest factor (β = 0.348, p < 0.001). This means that when people talk positively about a brand online, it has a powerful effect on how others feel about the brand. This supports earlier findings by Izquierdo and José-Cabezudo (2011) and Huang et al. (2013), who showed that word-of-mouth builds trust and encourages sharing. Entertainment was the second strongest factor (β = 0.246, p < 0.001). This highlights that fun and engaging content can capture attention and make people view the brand more positively. Informativeness (β = 0.147, p < 0.05) and interactivity (β = 0.143, p < 0.05) also mattered, which agrees with earlier studies (Modiri-Delshad & Halimi, 2015). Consumers want ads that not only entertain but also give useful information and allow them to engage with the brand.

However, customization did not play a significant role (β = 0.058, p > 0.05). This result is different from what some earlier studies suggested. One reason may be that people today are cautious about too much personalization, as it sometimes feels like an invasion of privacy. Finally, this study found a strong link between attitude and purchase intention (β = 0.679, p < 0.001). This confirms the Theory of Reasoned Action (Fishbein & Ajzen, 1975), which posits that positive attitudes lead to stronger intentions to act. The model explained 57.2% of the variation in attitudes and 46.1% in purchase intention. This means the framework offers a solid explanation of how viral marketing messages affect consumer behavior.

The findings of this study offer several practical lessons for marketers. First, since EWOM was the strongest driver of consumer attitudes, managers should focus on creating campaigns that encourage customers to talk about and share their brand experiences. Strategies like referral discounts, contests, or social sharing incentives can help spread positive messages widely. Entertainment was also shown to have a strong effect, which means that marketers need to invest in fun, creative, and engaging content such as short videos, memes, or storytelling formats that capture consumer attention. At the same time, campaigns should not only be entertaining but also informative. Providing useful product details or educational content alongside creative elements can help build credibility and trust.

The role of interactivity also deserves attention. Features like polls, quizzes, or live chat options can make campaigns more engaging and create a sense of two-way communication, which strengthens consumer connections with the brand. On the other hand, customization was not found to be significant in this study. This suggests that while personalization has often been promoted as effective, marketers should be cautious in how they use it, as consumers may see it as intrusive or irrelevant. Instead, focusing on contextual relevance, such as delivering the right message at the right time, may be more effective. Finally, because attitudes were found to strongly predict purchase intention, managers should design viral marketing campaigns that not only build positive attitudes but also guide consumers toward making purchase decisions. In this way, campaigns can achieve both engagement and sales outcomes.

Scope for Future Research

While this study provides valuable insights, it also opens up several directions for future research. First, the study focused on entertainment, informativeness, interactivity, EWOM, and customization as the main viral marketing message elements. Future work could explore additional factors such as emotional appeal, credibility of the message source and trust which may also shape consumer attitudes and purchase intentions. Second, this research was conducted within a specific consumer group and context. Future studies could test the model across different industries, product categories, or geographic markets to check if the results remain consistent.

Another promising direction is to examine the role of mediators and moderators. For instance, consumer engagement, trust, or brand loyalty could be tested as mediating variables that explain how viral marketing messages influence attitudes and behaviors. Similarly, demographic factors like age, gender, or digital literacy could be used as moderators to see whether certain groups respond differently. Finally, this study relied on cross-sectional survey data analyzed using SmartPLS. Future researchers may employ longitudinal designs or experimental approaches to better capture changes in consumer behavior over time and strengthen causal inferences.

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