Why is sharing not enough for brands in video ads? A study about commercial video ads' value drivers

Flavia Braga Chinelato (CENTRUM Catolica Graduate Business School, Lima, Peru and Pontificia Universidad Catolica del Peru, Lima, Peru)
Cid Gonçalves Filho (Fumec University, Belo Horizonte, Brazil)
Daniel Fagundes Randt (Fumec University, Belo Horizonte, Brazil)

Spanish Journal of Marketing - ESIC

ISSN: 2444-9695

Article publication date: 11 July 2023

Issue publication date: 2 November 2023

1601

Abstract

Purpose

The main goal of viral marketing is to affect brands positively. But most studies concern the causes of an ad going viral, not its impact on brands. In this sense, this study aims to demonstrate and compare video ads' value drivers on brands and sharing, determining which antecedents maximize results on each, enabling the best ad performance for advertisers.

Design/methodology/approach

A survey was conducted with 368 respondents who watched viral video ads from five global companies on YouTube. The proposed model was tested using structural equation modeling in SmartPLS4.

Findings

The results of this study demonstrated that product category involvement is essential for viral advertising. Furthermore, the entertainment value is the most relevant antecedent of sharing, but it does not affect brand equity; it is the social value responsible for brand equity.

Practical implications

Marketing managers should create ads that simultaneously generate entertainment and social values, maximizing sharing and branding effects. However, if only one of the two effects (brand/share) is achieved, then the advertiser will fail to obtain maximum performance.

Originality/value

The mainstream of viral marketing research is focused on antecedents of sharing. However, sharing is not enough to provide brand effects and return on investment of advertisement. This study reveals that different consumers’ values drive sharing and brand equity, suggesting that firms should consider a dual value generation strategy regarding the performance of viral video ads. On the other hand, this research conciliates the extant literature about the phenomena with the importance of product category involvement.

Propósito

El objetivo principal del marketing viral es influir positivamente en las marcas. Pero la mayoría de las investigaciones se refieren a las causas de que un anuncio se vuelva viral, no a su impacto en las marcas. En este sentido, esta investigación tiene como objetivo demostrar y comparar los impulsores de valor de los anuncios de video en las marcas y su viralización, determinando qué antecedentes maximizan los resultados en cada uno, permitiendo el mejor rendimiento publicitario para los anunciantes.

Diseño/metodología/enfoque

Se realizó una encuesta con 368 participantes que vieron anuncios de video virales de cinco empresas globales en YouTube. El modelo estructural se analizó mediante ecuaciones estructurales basada en mínimos cuadrados utilizando SmartPLS4.

Hallazgos

Los resultados demostraron que la participación en la categoría de productos es esencial para la publicidad viral. Además, el valor de entretenimiento es el antecedente más relevante de compartir, pero no afecta el valor de la marca; es el valor social responsable del valor de la marca.

Implicaciones practices

Los gerentes de marketing deben crear anuncios que generen simultáneamente entretenimiento y valores sociales, maximizando los efectos de uso compartido y de marca. Sin embargo, si solo se consigue uno de los dos efectos (marca/participación), el anunciante no conseguirá obtener el máximo rendimiento.

Originalidad/valor

La corriente principal de la investigación de marketing viral se centra en los antecedentes de compartir. Sin embargo, compartir no es suficiente para proporcionar efectos de marca y ROI de publicidad. Este estudio revela que los diferentes valores de los consumidores impulsan el intercambio y el valor de la marca, lo que sugiere que las empresas deberían considerar una estrategia de generación de valor dual con respecto al rendimiento de los anuncios de video virales. Por otro lado, esta investigación concilia la literatura existente sobre los fenómenos con la importancia de la participación de la categoría de productos.

目的

病毒式营销的主要目标是对品牌产生积极的影响。但大多数研究关注的是广告走红的原因, 而不是它对品牌的影响。在这个意义上, 本研究旨在证明和比较视频广告对品牌和分享的价值驱动因素, 确定哪些前因能使每一个因素的结果最大化, 为广告商带来最佳的广告效果。

设计/方法/途径

对368名受访者进行了调查, 他们在YouTube上观看了五家全球公司的病毒视频广告。在SmartPLS4中使用结构方程模型 对提议的模型进行了测试。

研究结果

结果表明, 产品类别的参与对于病毒式广告来说是至关重要的。此外, 娱乐价值是分享的最相关前因, 但它并不影响品牌资产; 对品牌资产负责的是社会价值。

实践意义

营销经理应该创造同时产生娱乐和社会价值的广告, 使分享和品牌效应最大化。然而, 如果只实现两种效果(品牌/分享)中的一种, 广告商将无法获得最大的绩效。

原创性/价值

病毒式营销研究的主流是关注分享的前因后果。然而, 分享并不足以提供品牌效应和广告的投资回报率。本研究揭示了不同消费者的价值观对分享和品牌资产的推动作用, 表明企业应该考虑关于病毒视频广告表现的双重价值产生策略。另一方面, 本研究将现有的文献与产品类别参与的重要性结合在一起。

Keywords

Citation

Chinelato, F.B., Gonçalves Filho, C. and Randt, D.F. (2023), "Why is sharing not enough for brands in video ads? A study about commercial video ads' value drivers", Spanish Journal of Marketing - ESIC, Vol. 27 No. 3, pp. 407-426. https://doi.org/10.1108/SJME-10-2022-0214

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Flavia Braga Chinelato, Cid Gonçalves Filho and Daniel Fagundes Randt.

License

Published in Spanish Journal of Marketing - ESIC. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode


1. Introduction

Companies spend over US$100bn annually on online research and advertising worldwide (Statista, 2021). They produce and disseminate their content with different objectives, such as promoting their products and services, increasing brand recognition and value and improving consumer interaction (Arica et al., 2022; Liu et al., 2021). These contents are considered commercial and are different from consumer-generated content. Typically, consumer-generated content focus on free media and commercial content using both free and paid media (Souki et al., 2022).

Several content formats have emerged to be more effective and interactive (Belanche et al., 2017). In the search for differentiation and dissemination of content, commercial videos have proved to be a more satisfactory alternative in advertising than texts or static photos. Duffett (2022) argues that YouTube alone is responsible for over 1 billion hours of video views daily.

Currently, marketers face a considerable challenge in creating content that piques the audience's interest and goes viral. Content goes viral when it is liked, commented on, publicized and shared quickly in a short time (Sung, 2021; Styven et al., 2020). According to Reichstein and Brusch (2019), viral marketing is dynamic and evolutionary and maintains the objective of encouraging honest communication between consumer networks. In addition, viral marketing creates strategies for users to produce and share content exponentially. This rapid sharing is related to electronic word of mouth (eWOM), defined as performing specific actions like seeking, giving and passing opinions via online networks (Casaló et al., 2017a). In the literature, the terms viral marketing and eWOM are often treated as synonyms, although there is a discussion about cause and effect. Viral marketing is the cause that generates eWOM, while eWOM is the effect (Reichstein and Brusch, 2019).

More than discussions about the definition of these concepts, current researchers have raised questions about the impacts that eWOM can generate for brands (Sung, 2021; Petrescu et al., 2020), assessing the possible consequences for companies and the effects on image, reputation and brand equity (Jiao et al., 2018). After all, content can go viral because of negative consumer perceptions that it wants to share to promote some demonstration, boycott or discourage the use and consumption of a brand. Thus, it is possible that sharing is not connected to positive brand outcomes, has no effect or is even more harmful to the brand image, negatively affecting brand equity (Warren and McGraw, 2016).

In this sense, research on the antecedents of video sharing and virality is recurrent in the literature (Liu and Liu, 2020; Lee and Youn, 2021; Lee and Youn, 2021). Besides the objective of advertising is to add value to brands and create impact by promoting its products and services, the mainstream studies regarding viral ads focus on determining what properties of the ads lead to maximize sharing and generate virality. In this sense, it is observed studies on ad appeals (Liu and Liu, 2020; Akpinar and Berger, 2017), typology of emotions (Reichstein and Brusch, 2019; Nelson-Field et al., 2013), purchase risk and operationalization elements (Tellis et al., 2019) and perceived value of the ad, including the social, entertainment and functional dimensions (Casaló et al., 2021; Casaló et al., 2020; Tellis et al., 2019; Casaló et al., 2017a; Berger, 2014; Berger and Milkman, 2012; Taylor et al., 2012) and consumer’s involvement with product category (Taylor et al., 2012).

Likewise, despite this massive investment in research to explain sharing and create ads that deliver earned impressions, there is no research and consensus on how to create ads that are both shared and enable positive impact for advertisers' brands (Duffett, 2022; Tellis et al., 2019), leaving a first relevant and fundamental gap to explain this phenomenon. In addition, the type of message that consumers are more likely to share is another topic without consensus in the literature with different recommendations from previous research (Berger, 2014; Akpinar and Berger, 2017; Casaló et al., 2017b), which leaves a second gap to be filled. The third gap was about the influence of product category involvement as a relevant antecedent of sharing, no conclusive study on its impact (Souki et al., 2022; Taylor et al., 2012). The fourth gap concerns the differences and relevance of branding-sharing alignment in viral marketing (Souki et al., 2022). Therefore, this research aims to provide scientific evidence about this field of study, revealing the properties of ads that add value to brands and maximize the sharing of commercial videos online. Also, in an unprecedented way, it highlights consumer involvement with product category as a driver of virality.

Accordingly, the following research objectives were proposed:

  • to verify the contribution of product category involvement in sharing of video ads;

  • to empirically demonstrate the impact of value drivers of video ads on sharing;

  • to identify the influence of value drivers on advertiser's brand equity; and

  • to compare the effects on both constructs, revealing differences and the relevance of branding-sharing alignment in viral marketing.

This study, thus, contributes to the previous research in the following ways. First, we extend the viral marketing research to explain sharing antecedents, revealing the differences regarding advertisers' brand consequences. Second, we demonstrate that sharing and ad virality do not necessarily generate brand equity, indicating the specific antecedents that can maximize sharing, brand equity or both. Finally, we demonstrate the relevance of product category involvement to generate value for viral ads and develop managerial and theoretical insights from our research, contributing to forging better viral communication strategies and improving the phenomenon's comprehension.

The article is structured as follows. The introduction is presented in Section 1, and the conceptual background and development of the research hypotheses are provided in Section 2. The methodology is in Section 3. The results analysis is described in Section 4. Finally, Section 5 sets forth the final considerations, presented as managerial implications, research limitations and future research directions.

2. Theoretical background and development of research hypotheses

2.1 Relationship between consumer values and sharing

For companies to succeed in their marketing efforts, it is crucial to understand how consumers behave and what motivates them in their purchase decisions. According to Souki et al. (2022), there are three principles:

  1. Consumer choice is a function of multiple consumption values.

  2. Consumption values make contributions to any choice.

  3. Consumption values are independent.

Consumers derive many types of value simultaneously based on their decisions and the kind of activities they perform. Generally, the values influencing consumer behavior are functional, social and entertainment (Khan, 2017). However, consumers can create combinations of values to suit their needs and wants and are willing to accept earning less of a specific value to gain more of another (Souki et al., 2022).

On the other hand, the advances of the internet and the increasing use of new technologies by consumers allow the use of digital platforms as a resource to obtain necessary information about brands and, from there, create their impressions about products and services (Riskos et al., 2021; Flavian et al., 2017). In this context, Lim et al. (2020) argue that companies should use available online resources such as digital platforms and social media to build relationships, promote brands and improve their products. Thus, video ads emerge as an alternative for companies to achieve these goals (Wang, 2021; Reichstein and Brusch, 2019; Belanche et al., 2017).

In addition, through socio-networking sites the same time, consumers can collect information can also make content about their experiences and perceptions available to other users, making it possible for consumers to share this information and perceptions about a brand (Arica et al., 2022;). Berger (2014) argues that consumers are likelier to share social and functional value content. On the other hand, Taylor et al. (2012) found in their studies that the entertainment value increases the probability of consumers sharing content. Meanwhile, Casaló et al. (2017b) suggest that there must be a combination of content characteristics and the number of publications to impact the consumer experience. Thus, more empirical studies have become relevant to explain this behavior.

2.2 The relationship between viral marketing and brand equity

The objective of the companies is that the developed marketing communications can go viral. However, the consumer can also be motivated to share content that he intends to protest, encouraging the boycott or sanctions and restrictions against the brands, known as negative viral marketing (Styven et al., 2020; Reichstein and Brusch, 2019).

On the other hand, what drives content to go viral (positive or negative) is eWOM. Just as viral marketing can be positive or negative (depending on the type of comment and reasons that lead consumers to share content), eWOM can also be considered positive or negative (Casaló et al., 2017a). It is positive eWOM when what is shared and widely viewed is for admiration, agreement and consumer enthusiasm for the brand wanting to share their experiences and impressions (Petrescu et al., 2020; Styven et al., 2020). However, negative eWOM is when the goal of consumers is to spread to their network that they should not consume a particular brand or spread some message of repudiation, retaliation or boycott, often motivated by negative experiences with the brands (Arica et al., 2022; Petrescu et al., 2020).

Therefore, companies should develop marketing strategies that go viral but positively. Consequently, it is essential that companies know the antecedents of content sharing and evaluate the impacts that this sharing can generate on brands. Ratna et al. (2017) and Flavian et al. (2017) argue that this viral marketing content can affect purchase intention and brand equity. It is not because the content is shared that it can bring value to brand equity. According to Liu et al. (2017), brand equity is a significant marketing asset that creates competitive advantages and improves firms' financial performance. However, in the same logic, if a brand is highly shared negatively, then this may subtract brand value. Hence, brand equity becomes a critical organizational resource (Ou et al., 2020).

Hence, there are relevant foundations in the literature to understand these relations. Companies should consider the impacts of eWOM communication on brand image, reputation, market share, product sales and brand equity (Duffett, 2022; Petrescu et al., 2020). In addition to identifying what motivates consumers to share content, it is essential to understand why some commercial ads go viral and others do not (Arica et al., 2022). In this sense, it is necessary to check the antecedents of viral marketing (Casaló et al., 2017a) and its impacts on brands and identify the characteristics that can predict whether the content will be highly shared. Many studies aimed to verify how the brand contributes to content sharing, but few have evaluated how ads impact brand equity (Duffett, 2022; Akpinar and Berger, 2017). However, we do not know that no empirical evidence demonstrates how value drivers of viral marketing can also contribute to brand equity.

Thus, to fill this gap, the present study identified and analyzed the antecedents of video sharing and brand equity, comparing its effects. The hypothetical model of the current research considers the product category involvement (Taylor et al., 2012), entertainment value (Taylor et al., 2012; Souki et al., 2022), the social value (Izawa, 2010; Sweeney and Soutarb, 2001) and the functional value (Izawa, 2010; Akpinar and Berger, 2017) as antecedents of likelihood to share (Taylor et al., 2012) and brand equity (Yoo and Donthu, 2001). Therefore, a hypothetical model was proposed and presented in Figure 1.

2.3 Value antecedents of online video sharing and advertising’s brands

The literature states that consumers can have different levels of involvement with a product or service (Taylor et al., 2012) based on their perceptions of importance and risk (Peng et al., 2019).

Consumers are highly involved when they are more willing to research extensively and invest greater economic importance and resources. On the other hand, the low-involvement product category is those that consumers routinely buy, carrying low risk or low value (Kim and Chao, 2019). According to Peng et al. (2019), for products that induce high involvement, customers tend to make purchasing decisions based mainly on their cognitive attributes, while low involvement primarily relies on affective characteristics.

Previous studies suggest that depending on the level of consumer involvement with a product category, there is an impact on the relationship with the brand (Taylor et al., 2012), as well as generating greater attachment, identification or trust (Kim and Chao, 2019), greater purchase intent and electronic word of mouth (Peng et al., 2019). In the study by Peng et al. (2019), the authors suggest that consumer engagement with a product category is closely related to the value perceived by the customer. On the other hand, the literature considers that through social networks, consumers can collect information and make content about their experiences and perceptions available to other users (Arica et al., 2022). Therefore, companies should use available online resources to build relationships, promote brands and improve their products (Lim et al., 2020; Orús et al., 2017), and video ads emerge as an alternative for companies to achieve these goals (Wang, 2021; Reichstein and Brusch, 2019). However, there is still no consensus in the literature about which type of message consumers are more likely to share. Akpinar and Berger (2017) believe they are more emotionally embedded content (as joy), while Berger (2014) believes they are contents of social and functional value. Casaló et al. (2017b) recommend combining content characteristics and the number of publications to impact the consumer experience.

Thus, it is plausible to assume that, according to the consumer's level of involvement with the product category, there is an impact on the perception of the value dimensions of an ad (entertainment, social and functional). Thus, the following hypotheses are proposed:

H1.

Product category involvement has a positive impact on (H1a) entertainment value; on (H1b) social value; and on (H1c) functional value in online commercial video.

In an infinity of content generated by users and companies at all times, to go viral, brands must know which content generates greater engagement and sharing on social networking sites (Chen et al., 2022; Arica et al., 2022). According to experts in interactive marketing, the generated content needs to be exciting, inviting, funny and develop active user participation (Wang, 2021). The study by Casaló et al. (2021) revealed the important role that perceived creativity and, to a lesser extent, positive emotions play in user engagement. Consumers are more motivated to share more fun content and create entertainment (Souki et al., 2022; Taylor et al., 2012).

Entertainment value is the perceived usefulness of an alternative to arouse feelings or affective states in consumers (Casaló et al., 2017a). According to Kim et al. (2016), consumers tend to share more videos online on their SNSs when these contents are more fun. Recently, Souki et al. (2022) found that consumers share funny videos with their contacts on social networks. Entertainment stimulates the emotions and feelings of viewers that foster the intention and desire to share videos. According to Casaló et al. (2020), consumers will share content, as they have common interests and needs.

On the other hand, studies also suggest a relationship between entertainment value and its impact on brands (Akpinar and Berger, 2017). Lou and Xie (2021) observed that the entertainment value of branded video content and the perceived functional value positively impact their experiential evaluation of the brand, which leads to greater brand loyalty. In this sense, Souki et al. (2022) found that entertainment value is an antecedent of the likelihood of sharing that, in sequence, impacts brand equity and attachment. Given the above, it is plausible that the entertainment value precedes the intention to share and has an effect on brand equity, which led to the following hypotheses:

H2.

The online commercial video’s entertainment value has a positive impact on (H2a) the likelihood to share it and on (H2b) the brand equity.

The influences that affect consumers' purchase decisions are related to a product's or service's functional attributes and social value (Wu et al., 2018). Social value refers to the impact that certain products and services can have on individuals' social relationships with their groups and how it can increase their status and self-esteem (Previte et al., 2019). The research developed by Jiao et al. (2018) argues that consumers join groups and communities with similar goals on social networks to share content and seek interaction, as individuals need belonging and affiliation. People achieve social value by being socially connected and satisfying their needs for belonging and cognition with others who share norms, ideals and interests (Jiao et al., 2018). Souki et al. (2022) identified that social values encourage consumers to leave the passive position and use these resources to improve their image with friends, family and others on social networks. In this sense, Berger (2014) states that consumers tend to share online content that generates a perception that they are more intelligent, funny and entertaining. In addition, Souki et al. (2022) point out that somehow social values can impact the likelihood of sharing, which affects brands. In this aspect, Jiao et al. (2018) found that social value, on the one hand, increases value for the consumer, but on the other hand, it increases brand equity. Lim et al. (2020) also found a significant relationship between social value and brand equity. Therefore, considering that social value can impact both likelihood to share and brand equity, the following hypotheses were formulated:

H3.

The online commercial video’s social value has a positive impact on (H3a) the likelihood to share it and on (H3b) brand equity.

Functional value refers to consumers' perceived usefulness of a product or service and its ability to provide good functional, instrumental or physical performance (Petrescu et al., 2020; Khan, 2017). These functional attributes dominate the consumer's decision-making when purchasing utility items (Previte et al., 2019; Wu et al., 2018). For Berger (2014), the functional value of products and services is a significant antecedent of virality. Research suggests that the desire for social interaction with groups motivates SNS users to share the content of interest with specific groups (Khan, 2017; Kim et al., 2016). Thus, consumers are expected to share videos containing content important to society and other users (Souki et al., 2022).

On the other hand, studies suggest that functional values also impact brand equity (Kato, 2021). The research developed by Kato (2021) conducted in Japan indicates that functional values affected brand preference for car consumers. Lou and Xie (2021) verified that for involvement products, consumers' entertainment and social value of branded content, as well as the functional value of its YouTube channel, jointly affect consumers' experiential evaluation, which subsequently contributes to increased brand loyalty. Similarly, Yang et al. (2019) also found that functional values have a positive relationship with brands, suggesting that, to some degree, functional values can affect consumers' perception of certain brands. Thus, considering the functional value as an antecedent of the probability of sharing videos online and that there is an impact on brands, the following hypotheses are proposed:

H4.

The online commercial video’s functional value has a positive impact on (H4a) the likelihood to share it and on (H4b) the brand equity.

3. Methodology

3.1 Procedure

Commercial communication spending on digital platforms will reach 4.5 billion consumers and grow to US$37bn by 2022 (Duffett, 2022). Only YouTube is responsible for 25% of all this content, being an important market to study. Brazil is the third country with the most users on YouTube and the seventh country with the most advertising spending, reaching a value of $12.83bn in 2019 (Souki et al., 2022).

The researchers of this study selected five commercial videos broadcast on YouTube Brazil by large companies from different economic sectors that were listed with the highest access rate on the platform (in the data collection period), with more than 14 million views. The videos are of consolidated brands of products and services from different segments of the Brazilian economy. Even so, these brands were not direct competitors – this procedure was intended to prevent respondents from comparing the products or services presented in the videos. Table 1 details information about the selected videos.

3.2 Measurements

This research is quantitative, descriptive and transversal. An electronic questionnaire was created from scales with statistically validated items from previous studies. The probability of sharing was operationalized with the same five-item scale proposed by Taylor et al. (2012). Brand equity was measured using the original four-item scale by Yoo and Donthu (2001). Category involvement applied the five-item scale by Taylor et al. (2012), initially derived from Beatty and Talpade (1994). However, one item was eliminated because of low commonality, and the final scale comprised four items. Functional value was measured using the same four-item scale proposed by Izawa (2010). Finally, the social value was operationalized with a five-item scale consisting of two items offered by Isawa (2010) and three items obtained from the social value scale proposed by Sweeney and Soutarb (2001). Response scales ranged from 1 to 7 points, with “one” (completely disagree) to “five” (completely agree), plus the option “DK/NA” (do not know/does not apply). In addition, the survey also included questions about the sociodemographic profile of respondents, the level of internet access and the frequency of sharing videos on YouTube, Facebook, Twitter and WhatsApp.

3.3 Data collection

The questionnaire was translated from English into Portuguese, and qualified bilingual professionals conducted the linguistic validation process. Subsequently, ten respondents participated in a pre-test to check for flaws in the questionnaire. After completing the development of the questionnaire, data collection took place electronically. Furthermore, to create a research environment as close to natural as possible, university students individually watched videos displayed on interviewers' tablets on five university campuses, sampled by accessibility and convenience. According to Taylor et al. (2012), student samples are appropriate for this type of research. They represent the population of interest and are generally more likely to engage in online video sharing. In addition, student samples tend to be more homogeneous, favoring theory extraction and reducing errors compared to more heterogeneous samples (Souki et al., 2022). There were 368 valid questionnaires considered adequate based on the parameters suggested by Hair et al. (2019).

A frequent question in studies that adopt the quantitative approach involves sample size. The criteria Hair et al. (2017) suggested calculating the sample size for a statistical power of 80% were analyzed. Accordingly, the recommended minimum sample is 145 respondents (sig. level = 1%; min. R2 = 0.1 and max. arrows = 3). A post hoc verification of the sample size adequacy was evaluated by calculating the statistical power using the software G*Power (Prajapati et al., 2010). We followed the procedures recommended by Ringle et al. (2014). The sample presented a statistical power of 98%, higher than the 80% recommended threshold (Hair et al., 2017).

Common method bias was evaluated using the single-factor test proposed by Harman (Hyman and Sierra, 2012). Therefore, unrotated exploratory factor analysis was accomplished, revealing a multi-factor structure. The first factor explained variance was 44%, below the threshold of 50%, implying that common method bias was not a concern (KMO = 0.936; Barlett p = 0.000; χ2 10,577.996; and gl = 351).

3.4 Data processing

The data obtained were statistically treated using the SPSS ™ software recommended by Hair et al. (2019). SmartPLS4 software tested structural equation modeling.

4. Data analysis

4.1 Description of the sample

Table 2 presents the socio-demographic characteristics of the respondents that compound the survey’s final sample:

The results show that the final sample is consistent with the profile of Brazilian undergraduate students according to data from the Brazilian Ministry of Education (MEC/INEP, 2019).

4.1 Exploratory analysis

Concerning the missing data, no missing was observed as the online questionnaire made it mandatory to answer all questions. On the other hand, we sought to verify the existence of outliers. The Mahalanobis distance method (D2) was used, and five multivariate outliers were removed. Regarding multicollinearity, when there is a possible redundancy in the database (Kline, 2015), all the scales presented an adequate fit except for one item. It was observed that the second item of the probability of sharing scale presented a correlation greater than 0.90 in absolute terms with the third indicator and variance inflation factor > 10. Therefore, the second share probability indicator was deleted, eliminating this issue. In addition, a normality analysis was performed. Concerning univariate normality, no variable has asymmetry greater than 3, and the absolute value of kurtosis of all variables were less than 10 (Kline, 2015). Thus, it was observed that although there are variables are not normal, the deviations from normality are moderate and acceptable (Kline, 2015; Byrne, 2010).

4.2 Measurement model

This section it is described the validation of the measurement model. The first task is to check if each construct is formed by only one factor: one-dimensional. Thus, exploratory factor analyses were performed for each construct that makes up the hypothetical model. According to the analysis, all the constructs showed adequate commonality, factor loadings higher than 0.4, no cross-loadings and explained variance over 60% and one-dimensionality. Also, all correlations between the construct indicators were statistically significant at the 95% level. The Bartlett Sphericity Test value reached a p-value equal to 0.000, and all KMO values were above 0.600. Likewise, it is also necessary to check the reliability of each scale used to measure each construct. The values obtained for Cronbach's alpha are above the value of 0.700 recommended by Malhotra et al. (2017). Regarding convergent validity, the measurement model results indicated that all the items presented significant loads (p < 0.01) on each construct, except the fifth indicator of the product category involvement scale, which was excluded. Two additional parameters contribute to checking the convergent validity: the average variance the extracted (AVE > 0.5) and the composite reliability (CR > 0.7) (Hair et al., 2019). In this sense, according to Table 3, it is concluded that the constructs have adequate reliability. The following analysis is on discriminant validity, which indicates whether the constructs are distinct from each other. In this case, the verification occurred using the criterion of Fornell and Larcker (1981).

Then, the psychometric properties of the scales are presented in greater detail in Table 4.

4.3.1 Structural model: Antecedents of video sharing and brand equity.

In this section, a hypothetical structural model was analyzed considering value antecedents of likelihood to share and brand equity, using partial least squares estimation. Partial least squares path modeling is regarded as a valid tool for structural equations and is helpful for testing hypotheses, mainly in complex path models in an explorative approach modeling (Rigdon, 2016). SmartPLS4 software was adopted, and the results are presented in Figure 2.

In Table 5, the path coefficients and its significances are presented.

Concerning hypothetical adjustment, the original SRMR value was 0.047, lower than the threshold of 0.08 in the saturated model, suggested by Hu and Bentler (1999), and 0.10 proposed by Ringle et al. (2014). Normed-Fit Index values range between 0 and 1, with Bentler and Bonnet (1980) endorsing values greater than 0.90, indicating a good fit. The model presented an Normed-Fit Index of 0.901, considering an χ2 = 807.387, representing a good fit (Ringle et al., 2014). The model was able to explain 48.3% (R2) of ad sharing and 35.7% (R2) of brand equity.

5. Results

Hypotheses H1a, H1b and H1c postulated that product category involvement is a relevant driver in the model, impacting all value dimensions. The results indicated that H1a impacted entertainment value (β = 0.329; p < 0.000) and explained 10.7%. H1b affected the social value (β = 0.492; p < 0.000) and explained 24%. H1c, on the other hand, impacted the functional value (β = 0.481; p < 0.000) and explained 23%. So, these hypotheses were supported and the results reveal that social and functional sharing correlates more to a category. Thus, this suggests that it is more possible for social/functional sharing of consumer segments involved with the category. Also, that entertainment is probably more share because of the ad's capacity to entertain in a way that is more independent from product category involvement.

According to the results, the entertainment value is the most relevant antecedent of the probability of sharing (β = 0.489; p < 0.001), supporting H1a. Social value also significantly impacted the likelihood of sharing, with a lower level than entertainment value (β = 0.209; p < 0.001), supporting H2a. Finally, the functional value did not significantly affect the sharing probability (β = 0.126; p = 0.068), not supporting hypothesis H3a. On the other hand, the results demonstrate that the antecedents of brand equity were different. Social value is the unique antecedent of brand equity (β = 0.477; p < 0.001), as functional value presents no significant impact (β = 0.132; p = 0.063), and entertainment value also has no significant impact on brand equity (β = 0.027; p = 0.0608).

It also observed the indirect effects of product category involvement, mediated by the three dimensions of value (entertainment, social and functional), which demonstrated an impact of β = 0.318 (p < 0.000) on the likelihood to share and of β = 0.294 (p < 0.000) on brand equity. Therefore, the research data demonstrates different antecedents for sharing and brand equity and the relevance of product category involvement.

6. Conclusion

This research demonstrates and conciliates the dilemma of viral video ads and creating positive impacts on brands. It also reconciles the literature regarding viral content and product category involvement, as previous research could not relate or explain it adequately (Taylor et al., 2012; Souki et al., 2022).

Hence, it empirically demonstrates the impact of value drivers of video ads on content sharing. In this sense, this study proposes a comprehensive model involving sharing drivers (entertainment, social and functional) and demonstrates that entertainment value is the most relevant antecedent of sharing. The second most relevant driver is social value. Thus, we observe that people like to share funny videos that cause positive impressions on others and serve as social currency, creating value (Berger, 2016; Berger and Milkman, 2012).

Second, it identified the influence of value drivers on advertisers’ brand equity; it was observed that product category involvement indirectly impacts brand equity. This study reveals that entertainment value does not significantly impact brand equity. Social value is the most relevant and impactful antecedent of brand equity. Berger (2016) argues that social value is like a social currency. People talk about things that make them look good, intelligent and exceptional. So the content should create value for them and the people who receive the viral message sharing it as a social currency.

Third, this study demonstrated the differences among these drivers on sharing and brand equity, revealing how differences and similarities would contribute to aligning branding and communication strategies. In this sense, the results suggest that to maximize sharing and brand effects, a firm should consider a mixed strategy: provide entertainment value with social value and consider the product category involvement. A high level of entertainment would maximize sharing, especially if mixed with social value. However, suppose consumers perceive higher levels of social value in the ad. In that case, a utility is observed, acquired from their desires to associate with social groups and the impression that the shared content can create on other individuals, enhancing their self-esteem or acceptance within a social group. Therefore, the research data suggests that social + entertainment would be a successful formula for viral video ads, especially for brands with positive previous consumer attitudes (Lien and Cao, 2014). Thus, firms should look for a communications strategy that maximizes sharing and promotes brand enhancement; otherwise, they would lose opportunities. However, it does not necessarily happen, as demonstrated. So a matrix is presented comparing sharing intentions and ad effects on brand equity.

Figure 3 shows that the best strategy would be to develop an ad with specific properties (perceived value) that positively affect the brand and sharing rates (maximum positive viral effect). Therefore, according to the results, strategies should focus on social value (increase brand equity and sharing) and entertainment value (increase sharing rates). If the ad caused positive brand effects and no sharing, then it does not obtain earned media and impressions, and we entitled this case as Lost Opportunities of Brand Enhancement I.

However, suppose an ad creates neutral brand effects and presents high sharing rates (possibly a high entertainment ad without social value). In that case, the brand will perhaps increase its awareness but not enhance brand equity (Lost Opportunities of Brand Enhancement II). Finally, if the ad does not present brand and sharing effects, then the firm is probably losing the money invested in the ad campaign (Loss of Money invested in the drive). Therefore, this research reveals that sharing is part of an effective viral marketing campaign, demonstrating that it is not enough to bring brand equity.

Finally, it is relevant to cite that consumer’s involvement with a product category is, according to this research, an appropriate driver of virality. In this sense, managers that drive brands with higher product category involvement should consider viral strategies in their brand communication portfolio.

6.1 Theoretical implications

This study contributes to interactive marketing by empirically demonstrating the impact of video advertising value drivers on content sharing. The proposed hypothetical model involved the sharing drivers (entertainment, social and functional) and showed that the entertainment value is the most relevant antecedent of sharing, and the second is the social value. Furthermore, it reinforces the literature on viral content and product category engagement, as previous research could not adequately link or explain it (Souki et al., 2022; Tseng et al., 2021).

Another theoretical contribution is about the influence of value drivers on advertisers' brand equity, as it was observed that product category involvement indirectly impacts on brand equity. Additionally, it revealed that social value is the most relevant and impactful antecedent of brand equity, while entertainment value does not significantly affect brand equity.

6.2 Managerial implications

The managerial implications of this study are related to the properties of video that companies should use to obtain maximum results in a volume of sharing and brand equity. This study reveals that different consumer values drive sharing and brand equity, suggesting that companies should consider a dual strategy of driving value concerning viral video ad performance. According to the results, product category involvement directly affects entertainment, social and functional values and indirectly in likelihood to share and brand equity. Thus, commercial videos must focus primarily on social value, which creates brand equity and positively affects sharing. Businesses should research which category and brand-related messages to provide to enhance consumers' self-image and brand equity. Second, managers should consider entertainment as a secondary strategy to improve share rates, but remember that social value is key to a successful branded viral marketing strategy.

In addition, marketers should consider elements that can increase viral effects, including specific details in ads, multi-channel distribution and social, digital and broadcast goals, reducing costs (Arica et al., 2022; Chen et al., 2022; Lee and Youn, 2021; Casaló et al., 2020). However, the power of viral videos is in their message and execution, as demonstrated by numerous cases of virality in low-budget campaigns. In this sense, this research suggests the existence of a brand-share paradox, as it indicates that sharing is not enough to impact brand equity in viral marketing. In other words, the company can consider aligning brand-share communication strategies and planning for success in its digital ecoverse.

Therefore, in addition to the relevance of earned media and viral content, this research reinforces the importance of analyzing all stages of consumer information processing. Furthermore, it includes understanding and effectively accepting the ad message and aligning elements of commercial execution to produce results of communication programs that increase share rates and brand enhancement (Table 6).

6.3 Limitations and suggestions for future studies

This study is not exempt from limitations, which can motivate further research. First, this research was limited to real videos that were shared. Therefore, the intensity of each value considered (entertainment, social and functional) was limited to its presence in the ads used. So we suggest using future experiments to ensure variance in the sample or accomplish new surveys with a higher number of ads (about 10 or 20).

Second, it is also suggested to test models of noncommercial ads to reveal antecedents of sharing of individual/personal posts and content and compare to research focused on ads.

Third, we recommended the proposal of more comprehensive models that would integrate constructs related to consumer–brand relationships to verify how virality and video sharing impact the brands, improving the comprehension of the phenomena.

Finally, we suggest studies that explore the brand-share paradox, expanding the knowledge about the phenomenon, including positive and negative emotions as an antecedent of brand-sharing analysis and negative brand messages. Thus, its relations with perceived value dimensions should be mapped, generating a broader view of effective brand-share strategies in viral marketing antecedents and consequences.

Figures

Hypothetical models

Figure 1.

Hypothetical models

Value antecedents of video sharing

Figure 2.

Value antecedents of video sharing

The brand-share matrix of virality

Figure 3.

The brand-share matrix of virality

Commercial videos from YouTube Brazil contemplated in this survey

Company Sector Message Views
Vivo Telecoms With emotional background music the message says: Real or digital, it doesn't matter. What matters is to live all the moments that deserve to be shared. Live it all 22,014,472
Lacta Food and beverage Campaign for a brand of chocolates to make a raffle and share it among friends 18,231,081
Smartphone Asus With an action song, the video features a famous Brazilian actress kidnapped, but when she is saved, she also fights to save the brand's new phone model 16,521,392
Nissan Automotive industry Video of a car brand valuing the attributes of a lacquering of a new vehicle model, highlighting its value and advantages 15,320,815
Amil Health insurance plan Health company concerned with childhood obesity stimulated the campaign: Resist! Say not to childhood obesity 14,137,667

Respondents’ socio-demographic characteristics

Variable Cases (%)
Gender
Female 165 (45,10)
Male 203 (54.9
Age
Between 18 and 22 years 163 (44.3)
Between 23 and 27 years 88 (23.9)
Between 28 and 32 years 42 (11.4)
Between 33 and 37 years 25 (6.8)
Between 38 and 42 years 18 (4.9)
Above 42 years 32 (8.7)
Marital status
Single 273 (74.2)
Married 84 (22.8)
Divorced 11 (3)
Widowed 0 (0.00)
Household income
Less than US$2,000.00 176 (47.8)
Between US$2,001.00 and US$4,000.00 89 (24.2)
Between US$4,001.00 and US$6,000.00 55 (14.9)
More than US$6,000.00 48 (13.1)

Reliability, validity and discriminant analysis

Construct Cronbach's alpha
(CA)
Composite reliability
(CR)
AVE 1 2 3 4 5 6
1. Brand equity 0.933 0.957 0.882 0.939
2. Entertainment value 0.908 0.936 0.785 0.296 0.886
3. Functional value 0.930 0.955 0.877 0.513 0.464 0.936
4. Product category invol 0.844 0.903 0.677 0.637 0.327 0.479 0.823
5. Likelihood to share 0.949 0.967 0.908 0.369 0.637 0.513 0.369 0.953
6. Social value 0.935 0.954 0.837 0.590 0.435 0.770 0.490 0.518 0.915
Notes:

The values in the main diagonal of the table represent the value of the square root of AVE. Off-diagonal are correlations

Measurement items, FL factor loadings and psychometric properties

Item description λi t
Product category involvement α = 0.844, AVE = 0.677 and CR = 0.903
Source: Taylor et al. (2012)
In general, (product) is very important to me 0.931 94,169
In general, (product) matters a lot to me 0.939 108,296
In general, I have a strong interest in (product) 0.897 55,448
In general, (product) is very relevant to me 0.879 42,183
Entertainment value α = 0.908, AVE = 0.785 and CR = 0.936
Source: Taylor et al. (2012)
This message is entertaining 0.889 73,480
This message was fun 0.770 28,046
This message was amusing 0.855 42,774
I enjoyed this message 0.920 113,741
This message was pleasant 0.841 43,555
Social value α = 0.935, AVE = 0.837 and CR = 0.954
Adapted from Izawa (2010) and Sweeney and Soutarb (2001)
Sharing this video will make other people happy 0.865 52,618
Sharing this video will make my friends grateful 0.912 82,546
Sharing this video would improve the way I am perceived 0.928 98,301
Sharing this video would make a good impression on other people 0.905 71,589
Sharing this video would give its owner social approval 0.880 58,472
Functional value α = 0.930, AVE = 0.877 and CR = 0.955
Source: Izawa (2010)
This video is useful to me 0.819 32,967
This video is useful to my friends 0.944 114,927
This video is useful to other people 0.941 116,845
This video is important for society 0.879 54,732
Likelihood to share α = 0.949, AVE = 0.908 and CR = 0.967
Source: Taylor et al. (2012)
Unlikely-likely 0.934 89,502
Probably would not-probably would 0.968 209,974
Definitely would not-definitely would 0.956 114,741
Brand equity α = 0.933, AVE = 0.882 and CR = 0.957
Source: Yoo and Donthu (2001)
It makes sense to buy X instead of any other brand, even if they are the same 0.876 51,428
Even if another brand has the same features as X, I would prefer to buy X 0.922 76,232
If there is another brand as good as X, I prefer to buy X 0.917 67,867
If another brand is not different from X in any way, it seems smarter to purchase X 0.922 74,181
Notes:

α = cronbach’s alpha coefficient; AVE = average variance extracted; CR = composite reliability

Standardized weights – video sharing

Hypotheses Path coefficient Standard deviation t-statistics
(|O/STDEV|)
p-values
Social value → likelihood to share 0.209 0.071 2.924 0.003
Social value → brand equity 0.477 0.074 6.402 0.000
Product category involvement → social value 0.492 0.045 10.924 0.000
Product category involvement → functional value 0.481 0.045 10.763 0.000
Product category involvement → entertainment value 0.329 0.056 5.878 0.000
Functional value → likelihood to share 0.126 0.068 1.861 0.063 ns
Functional value → brand equity 0.132 0.079 1.690 0.091 ns
Entertainment value → likelihood to share 0.489 0.048 10.224 0.000
Entertainment value → brand equity 0.027 0.052 0.513 0.608 ns

Conclusions, theoretical and managerial implications

Conclusions Theoretical and managerial implications
  • People like to share funny videos that cause positive impressions on others and serve as social currency, creating value

  • The entertainment value is relevant to initiate the process, impulse ad sharing and maximize impressions, increasing its exposure

  • The entertainment value does not significantly impact brand equity. Social value is the most relevant and impactful antecedent of brand equity

  • Managers should focus on social value commercial videos to generate brand equity

  • Product category involvement has an indirect impact on brand equity

  • To maximize sharing and brand effects, managers should consider a mixed strategy: provide entertainment value with social value and consider the product category involvement

  • Sharing is not enough to impact brand equity in viral marketing. So the company can consider aligning brand-share communication strategies and planning for success in its digital ecoverse

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Acknowledgements

The authors wish to thank the SJM-ESIC editor Carlos Flavian and the anonymous reviewers for their constructive reflections and comments to enable the publication of this paper.

Dr. Gustavo Quiroga Souki for his contributions. He is a Business Strategy Professor and researcher at ISMAT/TRIE - Lusófona and a Researcher at CinTurs, Faculty of Economics, University of Algarve (Portugal).

Corresponding author

Flavia Braga Chinelato can be contacted at: fchinelato@pucp.edu.pe

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