Going Viral on Advertising YouTube Video: Detecting the Influences

Viralizando em um Vídeo Publicitário no YouTube: Motivos de Influências

ABSTRACT

Objective:

this research aims to identify the key characteristics that influence viewers to watch and share ads.

Theoretical approach:

by exploring both technical and subjective attributes, this study provides valuable insights for advertisers to enhance their video performance. It farsighted to study the attributes that motivate the sharing video ads behavior using the characteristics present in the videos altogether.

Method:

the research employed a multi-method approach. The qualitative study analyzed 35 YouTube ads to identify potential attributes for evoking sharing behavior. Subsequently, an experimental study was conducted based on the derived qualitative propositions.

Result:

the propositions produced the attributes: narrative, purpose, interaction, real characters, celebrities, technical themes, humor, and market. With them, we formed 16 bundles, converted into ad visual scripts analyzed by a conjoint.

Conclusions:

the study highlighted that congruency of service announcements from service companies, addressing real problems with non-technical themes, have the highest potential for sharing, while ads focusing on technical topics with elements of humor performed poorly. This research sheds light on the discussion of two goals established by the United Nations - SDG 9 and SDG 12, providing valuable insights for advertisers.

Keywords:
viral advertising; digital marketing; advertising performance; ad attributes

RESUMO

Objetivo:

esta pesquisa tem como objetivo identificar as principais características que influenciam os espectadores a assistir e compartilhar anúncios.

Marco teórico:

ao explorar atributos técnicos e subjetivos, este estudo fornece insights valiosos para anunciantes aprimorarem o desempenho de seus vídeos. É previdente estudar os atributos que motivam o comportamento de compartilhamento de anúncios de vídeo usando as características presentes nos vídeos em conjunto.

Método:

um estudo multimétodo foi realizado. O estudo qualitativo analisou 35 anúncios do YouTube para identificar os atributos que poderiam evocar compartilhamento. Então, um estudo experimental utilizou as proposicoes vindas da pesquisa qualitativa.

Resultado:

as proposições produziram os atributos: narrativa, propósito, interação, personagens reais, celebridades, temas técnicos, humor e mercado. Com eles formamos 16 bundles, convertidos em 16 roteiros visuais de anúncios analisados por um conjunto.

Conclusões:

o estudo destacou que a congruência de anúncios de serviços de empresas de serviço, abordando problemas reais com temas não técnicos, tem o maior potencial de compartilhamento, enquanto os anúncios que se concentram em tópicos técnicos com elementos de humor tiveram um desempenho ruim. Esta pesquisa traz luz à discussão de duas metas estabelecidas pelas Nações Unidas - ODS 9 e ODS 12, fornecendo informação para quaisquer anunciantes.

Palavras-chave:
publicidade viral; marketing digital; desempenho publicitário; atributos do anúncio

INTRODUCTION

With so much content available to consumers daily, brands compete for attention with relevant ads, making consumers not only watch and be interested in the subject, but also engage, share, and build new stories from that dialogue (Tucker, 2015). Brands develop ads and content with the purpose of gaining high visibility from organic and spontaneous sharing (Hayes et al., 2016; Southgate et al., 2010). The way a narrative is presented (Chang, 2009; Yang & Kang, 2019), the presence of celebrities (Southgate et al., 2010), how the product appears (Christensen & Olson, 2002), the feelings that ads arouse (Berger & Milkman, 2012, and the influence of creativity (Moldovan et al., 2019) are some of the characteristics pointed out by the literature as influencing the performance of online content.

Some characteristics have already been addressed by the literature (e.g., Kulkarni et al., 2020), but their research aimed at specific targets (studies with college students or millenniums) (e.g., Nikolinakou & King, 2018), based on one or two campaigns (e.g., Hoffman et al., 2021) and focusing on one or few attributes of sharing motivations (e.g., Moldovan et al., 2019). In real life, consumers decide to share a video based on the entire video, considering all the characteristics holistically. Thus, it seems prudent to study this subject using the characteristics present in the videos altogether. With the goal of not focusing on one target audience or one campaign, we opted to identify the key characteristics in videos in different types of viral ads.

Following Moorman and Day’s (2016) concept of creating a table that presents research questions/hypotheses and future study suggestions, we developed Table 1. By reviewing the recent and significant studies of Voorveld, Araujo, et al. (2018), Shen (2021), Wen et al. (2022), and Segev and Fernandes (2023), the table highlights the importance of examining the characteristics of viral ads as a package of perceptions. It also reveals a gap in the literature on investigating the combined impact of these characteristics on video ad virality.

Table 1
Online media and viral marketing: Research questions and future research priorities.

Research Questions & Hypothesis	Future Research Priorities
Viral Advertising and It’s Main Drivers
What is viral advertising? How does the sentiments influence the viral advertising results? Is there a gender or a cultural difference in the sharing behavior?	• How does the actual sharing (other than the intention to share) works for viral content? • What determines a viral video campaign?
How does the creative drivers predict online viral metrics? Does claimed ‘likelihood to forward’ survey responses positively predict online viral viewing volume?	• Specific creative attributes and responses does the difference for the viral campaigns? • Surprise and excitement can be strong drivers of viral success?
Viral Advertising and Engagement
Do referrer-brand relationship strength and sharing motivations interact to affect likelihood of referral and likelihood of referral acceptance?	• What positive and negative effects does sharing viral advertising have on each type of relationship? How does the intertwining of the two impact each? • What does this mean for brand managers and marketing communication practitioners? • What is the influence of discrete emotions and their action tendencies in sharing videos and online content? • What is the impact of awe and affection on newer brands versus established brands, high involvement products versus low involvement products, and social cause advertising?
How does the engagement works through different social media platforms?	• How can the relationship the engagement be measured in terms of intensity or valence? • How are the conceptual differences such as modality, self-disclosure, media richness and privacy parameters perceived by users and how potential differences translate to digital engagement and advertising evaluations? • Does the engagement change differ between situations in which people use a social media platform in a passive way? Is there a difference between types of devices used to access the platforms?
Does creative ads works better than non-creative ads? What type of ad can elicit stronger changes in attitudes? Interactive advertising changes the individual attitude towards the brand?	• What’s kind of online advertising can generate immersion and a more intense experience with brands? • How can the advertising benefits both the received and the actor differently in its different ways? • Which are the main moderators that regulate the effectiveness of viral advertising? • How does these moderators influence the consumer relationship towards the brands? • Which is the relationship between content characteristics and the extent of virality?

• Note. Based on Baack et al. (2016); Voorveld et al. (2018);Shen (2021); Wen et al. (2022); Segev and Fernandes (2023).

Therefore, the purpose of this paper is to identify the advertising characteristics that impact altogether the viewing and sharing of ads. For that, we seek to answer (1) what are the common characteristics among the best performing videos and (2) considering all these characteristics, what is the importance of each one in the intention of consumer sharing?

This study aims to contribute to the marketing literature by (1) identifying key characteristics of online ads that can influence viewing and virality; (2) introducing new characteristics not previously addressed in the literature; (3) analyzing ad characteristics in an integrated manner to improve performance; and (4) providing market insights on the best characteristics to apply for viral ad objectives.

This paper is aligned with the Sustainable Development Goals (SDGs) set by the United Nations, which declares, “The ultimate goal of the SDGs is to promote a new worldview” (Gaffney, 2014). We aim to contribute to the objectives of SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production).

SDG 9 aims to build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation. In the context of the research, understanding the appeal of different approaches in video campaigns on the YouTube social network contributes to the development of innovative practices within the digital media industry. By identifying key characteristics that impact the viewing and sharing of ads, the research provides insights that can assist managers in developing innovative strategies and practices for digital media campaigns.

SDG 12 focuses on promoting responsible consumption and production patterns. The research contributes to this goal by analyzing the characteristics of viral ads and their impact on consumer behavior, particularly in terms of sharing and engagement. By identifying the attributes that influence the intention to share, the study highlights the importance of responsible content creation and consumption. This knowledge can guide managers in adopting responsible and sustainable approaches to digital media practices, such as creating ads that address real problems, using generic themes, and avoiding excessive resource consumption.

Therefore, this research paper aligns with SDG 9’s goal of promoting sustainable and inclusive industrial practices and with SDG 12 by promoting responsible consumption and sustainable production in the digital media industry.

After this introduction, we include the literature review, the qualitative research, the results of the study - propositions -, the online experiment, and the conclusion.

LITERATURE REVIEW

Advertising and viralization in the digital universe

The integration of social media into digital marketing plans is becoming increasingly prevalent due to its efficiency and influence in people’s daily lives (Ahmad et al., 2016; Alalwan et al., 2017; Hudson et al., 2016; Voorveld, 2019). Social networks have even displaced other forms of communication, leading advertisers to prioritize them to drive engagement (Voorveld, Noort, et al., 2018).

Companies recognize the potential of digital platforms in achieving objectives and mapping strategic opportunities, with communication being a primary focus (Alalwan, 2018; Hendijani Fard & Marvi, 2020). Digital communication of brands can generate engagement and potentially lead to engagement with the brand on other platforms (Unnava & Aravindakshan, 2021).

In digital, the relationship with the customer takes on new dimensions. The influence of brand communication initiated in one medium may reflect in other mediums and have a lasting impact (Hewett et al., 2016). With the popularization of social networks and the phenomenon of sharing, companies not only replicate ready-made campaigns, but also produce specific content for these channels: shorter, dynamic videos that enable interaction (Alalwan, 2018; Alalwan et al., 2017). In this format, the consumer is no longer just a receiver and starts to assume a leading role, establishing a dialogue with the brand and playing an important role in the viralization of the content (Casais & Pereira, 2021).

Viral marketing is the act of propagating marketing messages through the help and cooperation of individual consumers, spreading advertising messages in human networks through interpersonal contact (Cho et al., 2014; Liu-Thompkins, 2012). Companies have adopted viral marketing as a strategy to boost and engage online audiences (Berger & Milkman, 2012; Beverland et al., 2015).

To involve customers in marketing processes and track the relationship between companies and their customers, marketing activities have expanded to include customer relationship metrics, such as acquiring and retaining profitable customers (Srivastava et al., 1998). The ease with which users can share, comment, and promote a brand has resulted in aggressive metrics related to these viewing and sharing variables (e.g., Alalwan, 2018; Hewett et al., 2016; Teng et al., 2017; Wang et al., 2012; Xu, 2017; Zhu & Chen, 2015), making them relevant in digital advertising. Ads designed to go viral depend on consumer views, interactions, and sharing on social networks (Huang et al., 2013; Liu-Thompkins, 2012), and unlike TV ads, digital ads are measured by the number of people who viewed, shared, and reacted to them.

These metrics gain importance in digital advertising as they are linked to ad penetration and consumer engagement (Lou & Xie, 2021). With the vast amount of content available to users, traditional advertising challenges such as creating ads that spark interest and occupy consumers’ memory have taken on new dimensions (Hudson et al., 2016; Srinivasan et al., 2016; Wilson et al., 2012).

It is important to recognize the significance of digital platforms, particularly social media, in achieving communication objectives and mapping strategic opportunities, highlighting the increasing influence of social networks and the phenomenon of sharing in shaping consumer engagement, which is aligned with the goals of Sustainable Development Goals, mainly 9 and 12. That’s because, by focusing on the viralization of content and the metrics associated with viewing and sharing variables, the research acknowledges the importance of these aspects in digital advertising, which gives insights to industry, innovation, infrastructure, and responsible production.

Advertisements: Technical and subjective characteristics

When it comes to advertising, there are countless possibilities for brands to create materials that can communicate their message effectively (Jansen et al., 2021; Voorveld, 2019). Ads can be executed in different formats, using different characters, such as actors, consumers, and celebrities (Cho et al., 2014; Coker et al., 2021; Ghorbani et al., 2021; Kim et al., 2021). These characteristics, in addition to the main message, can also influence the user’s decision to share an advertising video (Chen et al., 2019; Jansen et al., 2021; Voorveld, Noort, et al., 2018).

Celebrities and digital influencers also play a crucial role in the effectiveness of ads. The presence of celebrities can increase the number of views and shares of ads, as their popularity can affect people’s perception of ads (Jin et al., 2019; Trivedi, 2018). Digital influencers, who have high engagement with their followers, have also become opinion leaders and are sought after by brands for collaborations (Pittman & Abell, 2021; Uzunoğlu & Kip, 2014). In some cases, influencers can even be more effective than celebrities or brands (Rosengren & Campbell, 2021).

Another important factor for the success of advertising is creativity, as creative ads can differentiate products and messages from competitors (Alegro & Turnšek, 2021; Baack et al., 2016; El-Murad & West, 2004). Marketers aim for creative advertising that is original, relevant, and strategic (Koslow et al., 2006), as it can capture the viewer’s attention and lead to greater engagement with the brand (Corrêa et al., 2020; Hendijani Fard & Marvi, 2020).

Beyond creativity, Koslow et al. (2006) propose strategy and art as two additional dimensions. The strategy factor refers to ads that meet consumer needs and expectations, while the art factor is related to the artistic expressiveness of the ad and whether it can be appreciated as an autonomous work of art. Southgate et al. (2010) sought to describe creative factors of viral campaigns that determined their performance and were able to verify that pleasure, involvement, and branding proved to be relevant for viewing the contents.

It is suggested that even the chosen soundtrack can play a relevant role in the performance of ads (Lourenço et al., 2023). The presence or absence of music, in addition to the tone of the chosen song, can have a positive effect on consumer brand recall. There is also the choice of ad narrative advertising (Escalas, 2004) being defined as advertising composed of narratives or an advertisement that tells a story (Chang, 2009; Escalas, 1998).

Narratives in ads often evolve around the experiences or consequences of using the product, and can take a dramatic or story-driven approach (Chang, 2009; Escalas, 2004). Other characteristics that can be considered relevant to the performance of offline ads are enjoyment and engagement (Brodie et al., 2011; Voorveld, Araujo, et al., 2018), and a similar effect has been proven in online content.

The emotional impact of content seems to be another factor in the success of viral marketing campaigns, influencing consumer attitudes toward the content and their propensity to share it (Kulkarni et al., 2020; Shen, 2021; Zhu & Chen, 2015). In an opposite result, Dobele et al. (2007) found that an emotional response to a campaign was not enough to trigger sharing, and that the content must include an element of surprise, such as awe. Still in this debate, Berger and Milkman (2012) suggest that people who experience high arousal are more likely to share information, even if it is neutral. However, Nelson-Field et al. (2013) classified 16 feelings, both positive and negative, of high and low arousal, which could be present in video content, including advertisements, and could help explain its performance. Regarding humor, Tucker (2015)found sentiment to be a relevant variable in video sharing, engagement, and viewing, highlighting the effectiveness of humorous and fun content in increasing views and sales. To make the situation more complex, Nikolinakou and King (2018) identified psychological motivations for content strategy, such as the stimulation of discussions, exclusivity of the topic, and the reactions they provoke, as key triggers for viralization of video ads that are not related directly to humor, or emotions, but to cognitive reactions.

Note that there are technical and subjective characteristics that can impact the consumer’s viewing and sharing of ads. Technical characteristics of digital advertising campaigns refer to the measurable and objective aspects of an ad, such as its format, size, placement, and targeting parameters. Subjective characteristics of digital advertising campaigns refer to the emotional and perceptual responses that the ad elicits from its audience, such as their attitudes, opinions, and feelings toward the brand or product being advertised. These subjective characteristics include factors such as creativity, emotional appeal, and storytelling. In the next section, we will summarize the main characteristics of digital advertising campaigns (Table 2), divided into technical and subjective characteristics.

Table 2
Analysis of characteristics of digital advertising campaigns.

Factors analyzed	Results	Source Reference
Technical characteristics
Bloggers / Celebrity	Research identified a positive correlation between the presence of celebrities and the number of views; they’re people with high level of engagement and, therefore, are considered opinion leaders.	Southgate et al. (2010); Uzunoğlu and Kip (2014); Brooks et al. (2021); Shen (2021)
Creativity	Factors such as pleasure, involvement, branding and distinction proved to be relevant for the visualization. Also, creative ads generated stronger shifts in attitudes toward the ad, attitudes toward the brand, and behavioral intentions.	Southgate et al. (2010)
Ad narrative	Positive correlation between narrative structure and user persuasion. Ads with chronology and causality hold more attention.	Escalas (2004); Chang (2009)
Storytelling	Brands communicate and build reputation from stories, making it easier for users to understand. Unlike narrative, storytelling is about the message itself, not the format. Digital video storytelling ads hook viewers more effectively than argumentative ads do.	Christensen and Olson (2002); Chang (2009); Coker et al. (2021)
Title (video name)	Campaigns whose title has a brand name and good keywords are easier to find.	Southgate et al. (2010)
Soundtrack	Music is a powerful tool for facilitating memory. Familiar songs as coming of age songs as can have a positive effect on consumer brand recall.	Anglada-Tort et al. (2022); Lourenço et al. (2023)
Subjective characteristics
Attitude towards the ad and the brand	The user’s attitude and perception towards the brand and the ad determine its sharing; engagement and advertising evaluations are related in a highly context-specific way because the relationship is highly contingent on the platform.	Huang et al. (2013); Voorveld, Noort, et al., (2018); Voorveld, Araujo, et al. (2018)
Humor	Ads that work with humor and relaxation have not only high power of engagement, but also increase the propensity to purchase the product.	Tucker (2015)
Intrinsic motivation	The user has personal motivations to recommend a brand. In the positive aspect, an improvement in self-image is expected and in the negative, it is the affirmation of identity that directs the consumer; empowerment is seen as an important motivation for using social media.	Muntinga et al. (2011); Alexandrov et al. (2013); Voorveld, Araujo, et al. (2018)
Feelings captured	The emotions experienced by the viewer (and the force with which they are felt) trigger the sharing behavior. Content that arouses high arousal, positive or negative, performs better and is shared more. Ads that arouse feelings of reverence and affection are more viral.	Dobele et al. (2007); Berger and Milkman (2012); Nelson-Field et al. (2013); Nikolinakou and King (2018)

• Note. Developed by the authors.

With the purpose of answering the research question, “What are the common characteristics among the best performing videos?” we present our first study as follow.

STUDIES

The purpose of this paper was to identify the key characteristics that impact the viewing and sharing of ads. To this end, two studies were conducted: a qualitative analysis, which sought to identify and measure the characteristics present in the ads through template analysis theory, followed by a quantitative survey using the conjoint analysis method.

Study 1

The first study sought to raise the common characteristics among the most viewed videos. For that, we did a content analysis of videos. Content analysis of videos is a qualitative research method that involves systematically analyzing the content and characteristics of videos (Stemler, 2015). It focuses on understanding the underlying themes, messages, and patterns present in the videos. Researchers examine the visual and audio elements, as well as the context in which the videos are presented, to gain insights and draw conclusions about the content being analyzed (Silverman, 2013).

In total, 35 videos were selected as a sample for this study. When choosing the videos, the idea was to have a generalization of the companies (financial for services, food for product, and technology for products and services). The main purpose was to seek the minimization of biases and to compare characteristics of ads in different markets. The brands chosen were: Itaú (financial sector), Samsung (technology), and Hellmann’s (food industry), chosen because they are among the largest in their sectors and have a wide range of brand awareness and dissemination in the digital environment.

After choosing the brands, all videos between 15 seconds and 1 minute long were selected. The time cut was made due to the limitation of social networks. Most of them (Facebook and Instagram) do not allow you to upload videos longer than one minute. As this is a qualitative research involving different execution criteria, less than 15 seconds would be insufficient for data collection. Videos that were not within this range were not selected. Another criterion was the date of publication: the campaigns were published for up to three years.

For the analysis of proprietary media (Colicev et al., 2018), only the content posted on the brand’s YouTube channel were chosen. Campaigns on other profiles or other communication vehicles were not considered. YouTube was the channel chosen for having assumed a leading role in the dissemination of organic and advertising content, facilitating the sharing and production of various materials.

Finally, the last criterion was the content itself. Some videos are part of a series, that is, a sequence of videos with similar format and characteristics for the promotion of the same product. In this case, a maximum of two advertisements per series was stipulated - the first two - in order to analyze heterogeneous contents with different characteristics.

The publication date was another important criterion for the selection of ads: they had to have been posted by 2017. This longer interval was stipulated so that it was possible to select different content. For videos that are born with the aim of going viral, the publication date does not significantly influence their performance. These videos attract an explosion of popularity at some point, but are not interesting enough to remain popular in the long term (Pinto et al., 2013).

The last criterion was the content itself. Some videos are part of a sequence of videos with similar format and characteristics for the promotion of the same product. In this case, a maximum of the first two ads per series was stipulated to analyze content with different characteristics.

With these definitions, 35 campaigns were selected: 10 from Hellmann’s, 12 from Itaú, and 13 from Samsung. Hellmann’s, a smaller brand compared to the others, had fewer campaigns that met the search criteria, which made it have a lower number of videos analyzed. Table 3 shows the complete list of ads, in descending order based on the number of views, in addition to the date the ad was aired. In videos whose brand does not appear in the title, it has been inserted next to it for easier viewing.

Table 3
List of advertisings from Itaú, Samsung, and Hellman’s brands.

Video title	Views
Service - Looking for time for what really matters? (Atendimento - Buscando tempo para o que realmente importa?) (Itaú)	12,782,278
Samsung | Galaxy A | Absurdly Galaxy (Samsung | Galaxy A | Absurdamente Galaxy)	9,961,819
Samsung | Your Galaxy connected this Christmas. (Samsung | Seu Galaxy conectado neste Natal.)	8,349,703
Hellmann’s turns leftovers into delicious dishes (Hellmann’s transforma o que sobrou em pratos deliciosos)	7,052,224
My Finances - Looking to organize your money? (Minhas Finanças - Buscando organizar sua grana?) (Itaú)	5,749,653
Itaú Vacations - Looking for tranquility to enjoy your vacations? (Férias Itaú - Buscando tranquilidade para curtir as férias?)	5,392,176
Samsung Pay & Anitta - Used, paid, won. (Samsung Pay & Anitta - Usou, pagou, ganhou.)	3,186,905
Creamy Macaroni Salad (Salada Cremosa de Macarrão) (Hellmann’s)	2,271,640
Itaú Loan - New Year’s Bills (Empréstimo Itaú - Boletos do Começo de Ano)	2,102,177
Hellmanns Ketchup Sweetened With Honey, Naturally Yummy! (Hellmanns Ketchup Adoçado Com Mel, Naturalmente Gostoso!)	2,008,914
The search for Marcos and Alice (A busca do Marcos e da Alice) (Itaú)	1,498,663
Samsung | Galaxy Note10 | Powerful as Vanessa Dutra (Samsung | Galaxy Note10 | Poderoso como Vanessa Dutra)	1,473,311
Samsung | Black Friday | Galaxy A	1,114,989
Samsung | S51 Pen | Two in one (Samsung | S51 Pen | Dois em um)	1,034,065
Samsung | Black Friday | Galaxy S	1,003,834
Hellmann’s Non-Food Restaurant (Restaurante sem Comida Hellmann’s)	990,539
Hellmann’s - 3 types of mayonnaise (Hellmann’s - 3 tipos de maionese)	728,151
Itaú Loan - Your money falls into the account even on the weekend (Empréstimo Itaú - Seu dinheiro cai na conta mesmo no fim de semana)	668,174
Walberg’s Quest | Itaú Customer (A Busca do Walberg | Cliente Itaú)	452,906
Samsung | Galaxy Note10 | Powerful as Vânia Goy (Samsung | Galaxy Note10 | Poderoso como Vânia Goy)	434,997
Samsung | New Galaxy A80 | Welcome to the Age of Live (Samsung | Novo Galaxy A80 | Bem-vindo à Era da Live)	250,890
Hellmann’s - Original	161,826
My Finances Itaú - My Package Service (Minhas Finanças Itaú - Serviço Meu Pacote)	35,056
Itaú Keyboard - Looking to split the bill? (Teclado Itaú - Buscando rachar a conta?)	31,686
[CES 2020] The Revelation: The Sero | Samsung ([CES 2020] A Revelação: The Sero | Samsung)	26,645
Consortium - How does the Itaú Consortium work (Consórcio - Como funciona o Consórcio do Itaú)	20,299
Samsung | S51 Pen | much safer (Samsung | S51 Pen | muito mais seguro)	20,096
Itaú Empresas - Learn about the benefits of issuing bank slips through Itaú (Itaú Empresas - Conheça os benefícios de emitir boletos pelo Itaú)	15,882
Hellmann’s - Oliva	12,615
Creamy Rice Cake (Bolinho de Arroz Cremoso)	10,469
Samsung | Odyssey 2 | No game challenges your machine (Samsung | Odyssey 2 | Nenhum jogo desafia sua máquina)	10,383
Virtual Card | What is it? (Cartão Virtual | O que é?)	9,917
Enjoy real food with Hellmann’s. #Instafood (Curta comida de verdade com Hellmann’s. #Instafood)	9,336
Hellmann’s - Light	8,040
Samsung | Promotion | Your Father Deserves Samsung (Samsung | Promoção | Seu Pai Merece Samsung)	4,818

• Note. Developed by the authors.

Data analysis

After downloading each video, a documentary of the campaigns was conducted. For each ad, an average of 2-3.5 pages of data ad description was written. Based on this material, content analysis was performed. The objective of this method is the systematic classification of materials to transform a large volume of content into a brief description, only containing the main elements (Bauer, 2000; Stemler, 2015). This classification takes place through coding, where content with similar characteristics is grouped to try to answer the research questions as proposed in the main objective (Silverman, 2013).

The analysis codes were defined according to the literature. These codes included the video title, presence or absence of celebrities, the context of the brand in the ad (whether it was selling a product or inserted in a story), storytelling (including the use of a narrator, presenter, or characters), and the feeling aroused in the consumer (high and low arousal in the positive and negative poles).

During the analysis, new characteristics proved to be relevant, as they frequently appeared in the videos collected, which enabled the creation of new analysis codes: presence or absence of real people; the veracity of the stories; the moment when the brand is mentioned in the ad; the way it appears in the ad; and verbal conjugation, that is, how the character interacts with the user.

By counting the frequency with the characteristics (codes) that appeared in each of the ads, it was possible to associate which characteristics seem to have the greatest impact on the performance of the ad. In this study, performance consists of the number of views of the ads.

It is worth mentioning that the analysis was carried out individually by two authors, and subsequently, a check was carried out, with one author having performed and the other validated. In addition, two specialists in the field of digital marketing validated the codes.

Results

Despite the documentary analysis being a qualitative methodology, the study counted the frequency with which the characteristics appeared in each of the ads, in a quantitative way, following the content analysis (Stemler, 2015). With this, it was possible to create research propositions (Bogdan & Biklen, 1994).

Regarding narrative storied, there were well-distributed variations among the videos with both good and poor performance. However, in general, it is almost unanimous that the method of exposure of the brand is related to performance. The following are the characteristics more causally related to performance based on the analysis of the videos.

In higher performing ads, the brand or product, in general, is contextualized within a narrative. The formats and executions are different, including narrated videos, presented videos and scenes of interaction between characters, but in all of them there is a major story of which the products are part. People naturally think in a narrative manner rather than an argumentative or paradigmatic way (Weick et al., 2005), which can explain the presence of this characteristic consistently. Campaigns that, on the contrary, presented the product or service directly, with a tone of service provision, performed worse and were less viewed. In this sense, the first proposition of the study is presented below.

P1: The presence of a narrative that contextualizes the main message, through a story, increases the viewing and sharing of the ad.

Research has shown that incorporating real facts or characters in advertising campaigns increases the perceived authenticity of the brand message (Fritz et al., 2017). Company ads are more influential in creating brand associations as consumers see those ads as conveying credible information (Ertimur & Gilly, 2012). Therefore, when consumers perceive an ad as genuine and relatable, they are more likely to engage with it, and share it with others. Based on our data we could see, for instance, that Itaú engages its own employees to interact with customers, to promote its customer service channels. This occurs in a direct and active manner. The employees are real, and their unscripted speech is reflected in the video with a human and relatable tone. The ending with the slogan ‘count on us’ exemplifies this approach. In this sense, we noticed that real stories, coherent purposes, and everyday people assuming the leading role seem to be some of the brand’s strategies of persuasion with the user. Therefore, proposition 2 is presented below.

P2: Real facts or characters bring the brand closer to the consumer thus increasing ad viewing and sharing.

Ads that focus on technical or specific topics may have limited relevance to the general audience (Alsina et al., 2015). If the content does not align with the interests or needs of the viewers, they are less likely to engage with it or see a reason to share it (Truong et al., 2010). In contrast, ads that touch on more universal themes or evoke emotions have a higher likelihood of being shared. Based on our data, it was observed, for example, that the “Creamy Rice Ball” video had lower sharing. This video reinforces the concept of food reuse - in this case, rice. Although the video is well produced, it does not evoke strong emotions in the consumer and has a rather limited audience, only appealing to those who are knowledgeable about and interested in cooking. This leads us to the following proposition:

P3: More technical or specific topics decrease ad viewing and sharing.

Storytelling helps communicate the benefits of the product, making tangible what the consumer can obtain with the brand (Woodside, 2010). Hellmann’s ads used a narrative that was causally related to its segment and set out to solve a real problem, inserting the product as a mediator of the food waste issue. On the other hand, Samsung ads chose a digital influencer to show how the brand’s device can help consumers create images and pieces for social networks. In this sense, the proposition 4 is created.

P4: Narratives that address real problems and insert the brand within a purpose increase viewing and sharing.

More than the presence of people, media characters can be an important part of performance. The only brand of the study that made use of this resource was Samsung, but the difference could be perceived, as indicated by the literature. Celebrities, as a source of entertainment and information, can make the content widely disseminated (Southgate et al., 2010), leading us to the following proposition:

P5: The presence of celebrities or digital influencers increases ad viewing and sharing.

Video humor was also a common characteristic in more popular campaigns. In general, videos with humorous content or high emotional appeal performed well, with a high number of views, reinforcing previous studies that already indicated the importance of humor in the content (Tucker, 2015). Content that elicited feelings of high arousal, especially positive, is usually viewed more (Belanche et al., 2017; Berger & Milkman, 2012; Nelson-Field et al., 2013). Based on this analysis, the following proposition is presented:

P6: Positive high-arousal feelings, especially humor, increase ad viewing and sharing.

It was also possible to observe the element of interaction as a relevant characteristic present in many ads. Videos with interaction between the characters, or the consumers themselves, in the form of a direct second person narrative, performed better, leading to the following proposition:

P7: Narratives with elements of interaction, that is, with characters that interact with each other or characters that directly talk to consumers, improve ad viewing and sharing.

Consumers tend to engage more with ads that are congruent with their cognitive frameworks and expectations. When an ad showcases a product that fits naturally into the consumer’s mental schema or existing knowledge structures, it requires less cognitive effort to process and comprehend (Hong & Zinkhan, 1995). This streamlined cognitive processing leads to positive perceptions of the ad, making it more enjoyable and memorable. We can observe this in many videos with a high number of shares. For instance, Itaú’s video showcases a company employee who introduces herself at the beginning and explains some functionalities of the application.

P8: Ads that marketed congruent products instead of incongruent improve ad viewing and sharing.

Note that propositions 1, 5, and 6 have been supported by the literature, as evidenced by Woodside (2010) for P1, Berger and Milkman (2012), Nelson-Field et al. (2013), and Belanche et al. (2017) for P5, and Tucker (2015) for P6. These propositions have already been tested in previous studies. Propositions 2, 3, 4, 7, and 8 add to the literature by introducing new aspects to the discussion. Summing up these propositions and findings, we present the Figure 1.

Figure 1
Viral advertising: Propositions and main findings.

Study 2

The objective of the second study was to understand how consumers observe the characteristics of the ads in a combined way. Thus, the characteristics present in the propositions of study 1 were used to form new advertisements. The new ads were then evaluated for their performance.

Since the study aims to jointly analyze the characteristics of the ads that may determine their view time and consumer-sharing intention, the conjoint analysis method was chosen, being the orthogonal bundle adopted as a technique. Conjoint analysis is a multivariate statistical technique used to understand how consumers structure their preference for products or services based on the global assessment of these alternatives (Green & Srinivasan, 1978; Hair et al., 2019). We used the full-profile conjoint analysis: this type of analysis presents respondents with several product profiles that include different levels of multiple attributes. The respondents then rate each profile, in this specific case, how consumers evaluate various attributes of a commercial relating them to their intention to share. After that, the analysis determines the relative importance of each attribute.

The characteristics present in the propositions of the study will be, from this point forward, named as attributes according to the nomenclature of conjoint analysis. The analyzed attributes were: narrative; purpose; interaction; the presence of real characters and/or stories; presence of celebrities; feeling of high arousal; how technical the theme is; and the market in which the brand operates as a variable that contextualizes the segment. Note that we used the attributes from the literature and from study 1. To form the bundles we used eight attributes, with two levels each, except interaction, that had three levels, as presented on Table 4.

Table 4
Attributes and levels used in the conjoint.

Attributes	Levels
Narrative	Brand contextualized in a story
	Explicit product selling or service provision
Purpose brands	Brands address real problems they are a part of
	Brands do not mention any purpose beyond product selling
Interaction	Interaction among characters
	Character/user interaction
	No interaction
Character	Yes
	No
Celebrities	Yes
	No
Technical themes	Campaigns with technical themes
	Campaigns with general or easily understandable themes
Positive high excitement (humor)	Yes
	No
Congruent product/brand	Ad incongruent with the brand
	Ad congruent with the brand

• Note. Developed by the authors.

After pre-tests, bundles (ads with attributes) were developed using Excel (add in - Conjoint Analysis), grouping the items in a balanced manner and ensuring that they all appeared more than once in different combinations. The number of bundles was defined by an orthogonal technique, which combines the attributes and their levels in a way that minimizes overlap and allows for the independent estimation of the relative importance of each attribute (Hair et al., 2019). We present on Table 5, as an example, the bundles of five ads.

Table 5
Example of how advertisements were created.

Attributes/Bundles	Advertisement (Bundle) 1	Advertisement (Bundle) 2	Advertisement (Bundle) 3	Advertisement (Bundle) 4	Advertisement (Bundle) 5
Narrative	Brand is contextualized in a story	Brand is contextualized in a story	Explicit sale	Explicit sale	Brand is contextualized in a story
Purpose	Brand solves real problems	Brand has no purpose	Brand solves real problems	Brand has no purpose	Brand solves real problems
Interaction	Interaction between characters	Interaction between characters	Interaction between characters	Interaction between characters	Character and consumer interaction
Real characters	Yes	No	No	Yes	Yes
Celebrities	Yes	Yes	No	No	Yes
Technical theme	Technical theme	Generalist theme	Technical theme	Generalist theme	Generalist theme
Humor	Yes	No	No	Yes	No
Congruent Product	Yes	No	No	Yes	No

• Note. Developed by the author.

After defining the bundles, storyboards were scripted and produced. Each bundle resulted in a storyboard that represents the ads. In total, 16 ads were developed. To eliminate biases, a fictitious tourism brand called Explorando Destinos was used. This approach is like that used by advertising agencies when creating content to be tested, with low-cost offers before large-scale production and replication. The stories and characters are identical in all ads, with only the attributes changing.

Pre-test

Before defining the finals storyboards, two pre-tests were conducted. One had the purpose of checking whether people recognized the celebrity’s presence (attribute) in the ad, which was conducted with 36 people who, by watching one of the ads, should indicate on a subsequent screen whether the ad had a celebrity. Users were mostly female (72%), belonging to the Southeast region (88%), with a monthly income ranging between R$ 5,700 and R$ 14,300 (70%) and self-declared white (86%). The result was 𝒳² = 12,857 and p-value < 0.001, confirming that people were able to identify the celebrity’s presence.

The other pre-test sought to validate whether the humor attribute created in the advertisement matched the humor perceived by people. Like the first pre-test, other 41 people read the visual ad scripts and responded on a scale proposed by Gorn et al. (2001) (on a seven-point Likert scale) to humor. Users were mostly male (51%), belonging to the Southeast region (91%), with a monthly income ranging between R$ 5,700 and R$ 14,300 (45%) and self-declared white (90%). With a value of t = -5.998 and p-value < 0.001, the variable humor was contemplated in the critical region and, therefore, there is statistical evidence to reject the null hypothesis, supporting the manipulation created.

Procedure

The present study employed an online data collection procedure to collect responses for a conjoint analysis research. Participants were recruited through Amazon Mechanical Turk (MTurk), a widely used crowdsourcing platform for academic research.

A task was posted on MTurk to collect 330 questionnaires, which described the research and invited participants to take part. The task included a link to the online questionnaire hosted on the Qualtrics platform. All the storyboard elements were inserted into Qualtrics for data collection and randomly assigned to research participants. The task was presented in Portuguese, and specific eligibility criteria were included to target individuals from Brazil who spoke Portuguese. Respondents were offered compensation of US$ 0.50 for their participation.

Upon accessing the survey, participants were provided with a clear and concise explanation of the conjoint analysis method. Specifically, they were asked to read the storyboard and answer the dependent variable, which was the intention to share the content. The question asked was, “Assuming you have watched this commercial, how likely are you to share this content with your friends?” Participants responded on a seven-point Likert scale, ranging from ‘zero probability’ to ‘high probability.’ To prevent mental exhaustion and difficulties in evaluating the ads, each respondent watched only four out of the sixteen ads. All of them are presented in the data/materials deposited online (see last page). After answering the main questions, participants responded to additional behavioral and demographic questions before being thanked for their participation.

Analysis

All the 330 participants answered all the questions. Each ad (bundle) was viewed by more than 80 respondents, ranging from 81 to 88 responses each. Users were mostly male (68.5%), belonging to the Brazilian Southeast region (54.9%), followed by Northwest (20.3%), with 30% on average monthly income ranging between R$ 2,800 and R$ 5,725, and with 40% traveling twice a year and 27.9% traveling at least once a year.

For the conjoint analysis, the database was adjusted following Hair et al. (2019): the dimensions of each attribute were converted into dummies with values of 0 or 1, where 0 refers to the absence and 1 to the presence of the characteristic. After that, we calculated an average value per ad, which measured the dependent variable intention to share. Then the linear regression was run, which provided the partial utilities of each dimension of the attributes.

Results

The Table 6 shows the result from the linear regression. In other words, it provides the partial utility for each attribute.

Table 6
Result from study 2.

Attribute	Dimension	Partial utility	Amplitude	Importance
Narrative	Brand is contextualized in a story	0.082	0.082	5.06%
	Explicit sale	0
Purpose	Brand solves real problems	0.39	0.39	24.09%
	Brand has no purpose	0
Interaction	Interaction between characters	-0.125	0.125	7.72%
	Character and consumer interaction	-0.105
	No interaction	0
Real Characters	Yes	-0.03	0.03	1.85%
	No	0
Celebrities	Yes	0.117	0.117	7.23%
	No	0
Technical theme	Technical theme	-0.225	0.225	13.90%
	Generalist theme	0
Humor	Yes	-0.27	0.27	16.68%
	No	0
Congruent product	Ad incongruent with the brand	-0.38	0.38	23.47%
	Ad congruent with the brand	0

• Note. Developed by the authors.

The attribute ‘narrative’ had positive effect in relation to the variable studied, indicated by 5.06% of importance. Ads whose brand was contextualized in the story showed positive utility (U = 0.082) compared to ads that sell the product more explicitly, indicating that the attribute positively interferes with the intention to share. The literature indicated that ads with chronology and causality hold the user’s attention more (Chang, 2009; Escalas, 2004), which was confirmed when we compared content with the brand contextualized in a story versus ads whose product or service sale took place more explicitly and directly.

The attribute ‘purpose’ was the one with the greatest utility, representing 24.09% of importance of the attributes of the bundle. Ads with this characteristic performed better compared to ads that did not have it. ‘Purpose’ proved to be relevant with importance greater than 24%. The result shows that ads that clearly present the company’s purpose are more likely to be shared. Purpose was observed in study 1 and already showed signs of being relevant, after observing the number of views of videos with this characteristic.

The literature on storytelling (Chang, 2009) concerns the reputation and connection of the brand with the consumer. To measure this attribute, the different methods of contact and interaction present in the ads were observed, comparing content with dialogues between the characters, dialogue between the characters and the viewer, and finally, narrated ads. This characteristic was analyzed and categorized as ‘interaction.’ Unlike expectations, narrated ads had positive effect in relation to the other elements of interaction. That is, content without interaction is more likely to be shared and increases ad performance. The attribute ‘interaction’ indicated importance of 7.72%. Ads without dialogue increase the intention to share, since the other dimensions indicated negative utility. Ads that showed interaction with the user (U = -0.105) and interaction between the characters (U = -0.125) had the opposite effect compared with ads that brought only one narrator.

The attribute that measured the presence of ‘real people’ was not significant, with only 1.85% of importance. The attribute ‘real people,’ also proposed based on study 1, has not shown to be relevant with importance lower than 2%. In addition, the effect was different than expected: ads with these characters indicated negative partial utility. According to the literature on storytelling, contents that promote a connection between the brand and the consumer are better perceived (Chang, 2009). Analyzing the veracity of the stories and characters presented was a way of analyzing this dimension. Although real consumers attribute veracity and representativeness to the stories, the attribute was not very expressive when compared to the others, indicating that, in general, it has little influence on the utility of the bundles.

The presence of ‘celebrities’ indicated importance of 7.23%, with positive utility (U = 0.117) in relation to ads without celebrities, indicating that the attribute also increases the intention to share. Confirming what the literature indicated (Southgate et al., 2010; Uzunoğlu & Kip, 2014), the presence of ‘celebrities’ has proven to be relevant for the performance variable under analysis. Although it did not indicate such high relative importance, ads with celebrities positively influenced the intention to share.

The attribute ‘technical theme’ indicated importance of 13.90%. As expected, ads with technical themes were of negative utility (U = -0.225) compared to content with more general themes and easy to understand. It is thus observed that ads with this characteristic increase the intention to share. Technical themes indicated the expected result and support the proposition of the study 1. The literature on storytelling states that content that informs, entertains, and inspires end up simplifying the story presented to the consumer (Hemsley-Brown & Alnawas, 2016; Hudson et al., 2016; Kim et al., 2019), and, in this sense, technical themes go in the opposite direction to this concept. The result showed that ads with technical themes perform worse and negatively influence the variable under analysis, with importance always above 13%.

The attribute ‘humor’ had a different result than expected, according to the previous literature (Tucker, 2015). Although humor was identified by the respondents in the pre-test, humorless ads increased the intention to share. In the case of this study, the attribute humor was combined with other variables with a quite different emotional appeal, such as purpose, which may have influenced the result, in addition to the segment of the chosen company. The attribute ‘humor’ had a relative importance of 16.68%. Ads without humor increase the intention to share, since the regression showed negative utility of ads with humor (U = -0.27) compared to neutral content.

As for the attribute 'congruent product' defined by ads with congruent or incongruent with brands, , it was observed that congruent ads increase the intention to share, since the regression demonstrated a negative utility to incongruent ads (U = -0.38). We can also see that the estimated relative importance of this attribute is high compared to the others, with 23.47% of the total. The attribute ‘congruent with product,’ which compared the advertisements that disclosed services and products and related it with the brand purpose, proved to be relevant for the variable under analysis: service ads indicated greater utility and positively impact the intention to share. Ads related to products, on the other hand, had negative effect.

CONCLUSION

The first study of this researched aimed to explore the workings of advertising and viralization, the customer-brand relationship, main characteristics used in ads, and the effectiveness of different attributes (summarized in Figure 1). We proposed that ads addressing real problems, featuring generic themes, and lacking humor were more likely to be shared by consumers, contributing to their viral spread. This exploratory study identified key characteristics used in ads, including purpose, congruency with consumer market perception, humor, technical theme, interaction, narrative, and celebrity presence. These factors seemed to play a significant role in shaping consumer responses and their propensity to share ads.

In the second study, a conjoint analysis using the attributes found in study 1, the attribute of ‘purpose’ was identified as the most important factor influencing viewer behavior, followed by congruency with consumer market perception. This highlights the significance of aligning ads with a clear purpose and meeting consumer expectations. The study also revealed that ads addressing real problems, featuring more generic themes, and lacking humor were more likely to be shared by consumers. Contextualizing the brand within a story and including a celebrity in the ad also had some influence on sharing behavior, although to a lesser extent. Furthermore, narrated stories, particularly those without character interaction, had a higher probability of going viral, indicating the importance of crafting compelling narratives in driving the shareability of ad content.

The findings from both studies provide valuable insights into optimizing ad viewing and sharing. Purpose-driven advertising that addresses real problems, uses generic themes, and lacks humor can effectively engage viewers and increase the likelihood of sharing. Contextualizing the brand within a narrative and incorporating celebrities in ads can also have an impact on sharing behavior, albeit to a lesser degree. Furthermore, the power of narrated stories, even without character interaction, in driving ad virality highlights the significance of compelling storytelling in capturing viewer attention.

This research provides valuable insights into the world of marketing by examining how various attributes influence the behavior of viewers in a combined manner. By observing these characteristics as a bundle of preferences, this study offers a unique managerial perspective on the topic, contributing to the field of marketing literature. Unlike previous studies that focused on one or two attributes contributing to viral ads, this research specifically explores the realm of advertisements, shedding light on the complex interplay of ad attributes and their influence on viewer behavior.

From a managerial standpoint, the research expands the understanding of the appeal of different approaches in video campaigns on the YouTube social network, providing valuable insights that can assist managers in developing innovative and sustainable digital media practices. The metrics used in this study are directly associated with the digital context, contributing to a more comprehensive understanding of consumer behavior in this domain. This research serves as a guide for marketing managers seeking to create effective video ads, highlighting the importance of brands communicating their social purpose, contextualizing products or services within a narrative, and presenting content with relatable characters known to consumers. By examining these characteristics in a combined manner, the study identifies their relative importance, providing marketing managers with powerful tools for constructing strategic content with high potential for engagement. By aligning with the objectives of SDG 9 (Industry, Innovation, and Infrastructure), this research contributes to the promotion of sustainable and inclusive industrial practices within the digital media sector. Furthermore, by considering the principles of SDG 12 (Responsible Consumption and Production), the findings of this study can guide managers in adopting responsible and sustainable approaches to content creation and consumption on digital platforms like YouTube. By integrating the principles of SDG 9 and SDG 12 into their strategies, managers can contribute to the development of a more sustainable and socially responsible digital media industry.

RESEARCH LIMITATIONS

Like all research studies, this paper has limitations. For study 1, we selected campaigns that met certain criteria such as duration and date of publication, in addition to being restricted to the three selected brands. Although the companies were chosen to eliminate bias, with different markets and strategies, other nuances related to the segment may have been suppressed. Another limitation refers to the metric chosen to measure ad performance: number of views and intention to share. We know that in the digital marketing context it is common for companies to boost their ads with a financial investment. Since this information is not public, we are unaware of whether the videos under analysis had any kind of boost and, therefore, had a higher number of views. In addition, campaigns were selected that met certain criteria such as duration and airing date, in addition to being restricted to the three selected brands. As much as the companies were chosen in an attempt to eliminate biases, other nuances referring to the segment may have been suppressed with this cut. Finally, the metric chosen to measure the performance of the ads in study 1: number of views. As this information is not public, we do not know if the analyzed videos had any kind of boost and, as a result, had a higher number of views.

In relation to study 2, a fictitious company was created, thus eliminating attitudes toward the brands. Although that was the best choice, as the perception of a brand could influence the analysis of the ads, it removes from the analysis this prior knowledge and the consumer expectations regarding a brand. The research also limited the study to only one context - travel agency and travel-related products. Finally, the ads were shown to the respondents as a storyboard. Although the choice was based on a routine of the communication agencies (Finn, 1988), and we believe that asking participants to choose between videos would have made the study long and tiring, the video format can bring different results and could thus be tested. It is worth mentioning that this format is used by marketers in the market, during the testing phase of the ads, implying very small losses when compared to the video format. Still in relation to the format, the soundtrack feature, which appeared in the theoretical framework, was not present and significant in study 1 and therefore was not included as a feature of the analysis.

FUTURE STUDIES

For future studies, it is recommended to explore new technical and subjective attributes present in viral ads. This could include investigating other technical elements like animation, sound effects, and visual effects, and their impact on ad performance. Comparing the effects of these attributes across different market segments and cultures would provide a more comprehensive understanding. Additionally, future research could examine how ad performance changes over time, considering factors such as seasonality and shifts in consumer behavior. Analyzing ads in video format, conducting field experiments, and collaborating with brands to minimize bias would also contribute to a deeper understanding of ad effectiveness.

To enhance the findings, a broader study could be conducted with a larger sample of videos and companies, offering additional insights into the tested characteristics and their influence on ad performance. Future research could also explore the performance of various ad formats, such as display ads, social media ads, or brand posts on social media. Furthermore, it would be valuable to investigate the impact of ad performance on brand equity or other performance metrics. By expanding the scope of analysis and considering different formats and metrics, researchers can gain a more holistic understanding of the advertising landscape and its implications for marketers.

REFERENCES

• Ahmad, N. S., Musa, R., & Harun, M. H. M. (2016). The impact of Social Media Content Marketing (SMCM) towards brand health. Procedia Economics and Finance, 37, 331-336. https://doi.org/10.1016/S2212-5671(16)30133-2
  » https://doi.org/10.1016/S2212-5671(16)30133-2
• Alalwan, A. A. (2018). Investigating the impact of social media advertising features on customer purchase intention. International Journal of Information Management, 42, 65-77. https://doi.org/10.1016/j.ijinfomgt.2018.06.001
  » https://doi.org/10.1016/j.ijinfomgt.2018.06.001
• Alalwan, A. A., Rana, N. P., Dwivedi, Y. K., & Algharabat, R. (2017). Social media in marketing: A review and analysis of the existing literature. Telematics and Informatics, 34(7), 1177-1190. https://doi.org/10.1016/j.tele.2017.05.008
  » https://doi.org/10.1016/j.tele.2017.05.008
• Alegro, T., & Turnšek, M. (2021). Striving to be different but becoming the same: Creativity and destination brands’ promotional videos. Sustainability, 13(1), 139. https://doi.org/10.3390/su13010139
  » https://doi.org/10.3390/su13010139
• Alexandrov, A., Lilly, B., & Babakus, E. (2013). The effects of social-and self-motives on the intentions to share positive and negative word of mouth. Journal of the Academy of Marketing Science, 41, 531-546. https://doi.org/10.1007/s11747-012-0323-4
  » https://doi.org/10.1007/s11747-012-0323-4
• Alsina, E. F., Rand, W., & Lerman, K. (2015, May-June). The success of question answering communities: How diversity influences ad hoc groups. Collective Intelligence 2015, Santa Clara, CA, USA.
• Anglada-Tort, M., Schofield, K., Trahan, T., & Müllensiefen, D. (2022). I’ve heard that brand before: The role of music recognition on consumer choice. International Journal of Advertising, 41(8), 1567-1587. https://doi.org/10.1080/02650487.2022.2060568
  » https://doi.org/10.1080/02650487.2022.2060568
• Baack, D. W., Wilson, R. T., Dessel, M. M. van, & Patti, C. H. (2016). Advertising to businesses: Does creativity matter? Industrial Marketing Management, 55, 169-177. https://doi.org/10.1016/j.indmarman.2015.10.001
  » https://doi.org/10.1016/j.indmarman.2015.10.001
• Bauer, M. W. (2000). Classical content analysis: A review. In M. W. Bauer, & G. Gaskell (Eds.), Qualitative researching with text, image and sound (pp. 132-151). SAGE. https://dx.doi.org/10.4135/9781849209731
  » https://dx.doi.org/10.4135/9781849209731
• Belanche, D., Flavián, C., & Pérez-Rueda, A. (2017). Understanding interactive online advertising: Congruence and product involvement in highly and lowly arousing, skippable video ads. Journal of Interactive Marketing, 37(1), 75-88. https://doi.org/10.1016/j.intmar.2016.06.004
  » https://doi.org/10.1016/j.intmar.2016.06.004
• Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192-205. https://doi.org/10.1509/jmr.10.0353
  » https://doi.org/10.1509/jmr.10.0353
• Beverland, M., Dobele, A., & Farrelly, F. (2015). The viral marketing metaphor explored through Vegemite. Marketing Intelligence and Planning, 33(5), 656-674. https://doi.org/10.1108/MIP-08-2014-0146
  » https://doi.org/10.1108/MIP-08-2014-0146
• Bogdan, R., & Biklen, S. (1994). Investigação qualitativa em educação: Uma introdução à teoria e aos métodos. Porto Editora.
• Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. (2011). Customer engagement: Conceptual domain, fundamental propositions, and implications for research. Journal of Service Research, 14(3), 252-271. https://doi.org/10.1177/1094670511411703
  » https://doi.org/10.1177/1094670511411703
• Brooks, G., Drenten, J., & Piskorski, M. J. (2021). Influencer celebrification: How social media influencers acquire celebrity capital. Journal of Advertising, 50(5), 528-547. https://doi.org/10.1080/00913367.2021.1977737
  » https://doi.org/10.1080/00913367.2021.1977737
• Casais, B., & Pereira, A. C. (2021). The prevalence of emotional and rational tone in social advertising appeals. RAUSP Management Journal, 56(3), 282-294. https://doi.org/10.1108/RAUSP-08-2020-0187
  » https://doi.org/10.1108/RAUSP-08-2020-0187
• Chang, C. (2009). “Being Hooked” by editorial content: The implications for processing narrative advertising. Journal of Advertising, 38(1), 21-34. https://doi.org/10.2753/JOA0091-3367380102
  » https://doi.org/10.2753/JOA0091-3367380102
• Chen, Q., Feng, Y., Liu, L., & Tian, X. (2019). ‘Understanding consumers’ reactance of online personalized advertising: A new scheme of rational choice from a perspective of negative effects. International Journal of Information Management, 44, 53-64. https://doi.org/10.1016/j.ijinfomgt.2018.09.001
  » https://doi.org/10.1016/j.ijinfomgt.2018.09.001
• Cho, S., Huh, J., & Faber, R. J. (2014). The influence of sender trust and advertiser trust on multistage effects of viral advertising. Journal of Advertising, 43(1), 100-114. https://doi.org/10.1080/00913367.2013.811707
  » https://doi.org/10.1080/00913367.2013.811707
• Christensen, G. L. & Olson, J. C. (2002), Mapping consumers’ mental models with ZMET. Psychology & Marketing, 19(6), 477-501. https://doi.org/10.1002/mar.10021
  » https://doi.org/10.1002/mar.10021
• Coker, K. K., Flight, R. L., & Baima, D. M. (2021). Video storytelling ads vs argumentative ads: how hooking viewers enhances consumer engagement. Journal of Research in Interactive Marketing, 15(4), 607-622. https://doi.org/10.1108/JRIM-05-2020-0115
  » https://doi.org/10.1108/JRIM-05-2020-0115
• Colicev, A., Malshe, A., & Pauwels, K. (2018). Social media and customer-based brand equity: An empirical investigation in retail industry. Administrative Sciences, 8(3), 55. https://doi.org/10.3390/admsci8030055
  » https://doi.org/10.3390/admsci8030055
• Corrêa, S. C. H., Soares, J. L., Christino, J. M. M., Gosling, M. de S., & Gonçalves, C. A. (2020). The influence of YouTubers on followers’ use intention. Journal of Research in Interactive Marketing, 14(2), 173-194. https://doi.org/10.1108/JRIM-09-2019-0154
  » https://doi.org/10.1108/JRIM-09-2019-0154
• Dobele, A., Lindgreen, A., Beverland, M., Vanhamme, J., & Van Wijk, R. (2007). Why pass on viral messages? Because they connect emotionally. Business Horizons, 50(4), 291-304. https://doi.org/10.1016/j.bushor.2007.01.004
  » https://doi.org/10.1016/j.bushor.2007.01.004
• El-Murad, J., & West, D. C. (2004). The definition and measurement of creativity: What do we know? Journal of Advertising Research, 44(2), 188-201. https://doi.org/10.1017/S0021849904040097
  » https://doi.org/10.1017/S0021849904040097
• Escalas, J. E. (1998). Advertising narratives: What are they and how do they work. In B. Stern (Ed.), Representing consumers: Voices, views, and visions (pp. 267-289). Routledge. https://doi.org/10.4324/9780203380260
  » https://doi.org/10.4324/9780203380260
• Escalas, J. E. (2004). Narrative processing: Building consumer connections to brands. Journal of Consumer Psychology, 14(1-2), 168-180. https://psycnet.apa.org/doi/10.1207/s15327663jcp1401&2_19
  » https://psycnet.apa.org/doi/10.1207/s15327663jcp1401&2_19
• Ertimur, B., & Gilly, M. C. (2012). So whaddya think? Consumers create ads and other consumers critique them. Journal of Interactive Marketing, 26(3), 115-130. https://doi.org/10.1016/j.intmar.2011.10.002
  » https://doi.org/10.1016/j.intmar.2011.10.002
• Finn, A. (1988). Exploring the effectiveness of exploratory research on print ad readership scores. Current Issues and Research in Advertising, 11(1-2), 113-137. https://www.tandfonline.com/doi/abs/10.1080/01633392.1988.10504931
  » https://www.tandfonline.com/doi/abs/10.1080/01633392.1988.10504931
• Fritz, K., Schoenmueller, V., & Bruhn, M. (2017). Authenticity in branding-exploring antecedents and consequences of brand authenticity. European Journal of Marketing, 51(2), 324-348. https://doi.org/10.1108/EJM-10-2014-0633
  » https://doi.org/10.1108/EJM-10-2014-0633
• Gaffney, O. (2014). Quiet green revolution starts to make some noise. Nature, 505, 587. https://doi.org/10.1038/505587a
  » https://doi.org/10.1038/505587a
• Ghorbani, Z., Kargaran, S., Saberi, A., Haghighinasab, M., Jamali, S. M., & Ale Ebrahim, N. (2021). Trends and patterns in digital marketing research: Bibliometric analysis. Journal of Marketing Analytics, 10, 158-172. https://doi.org/10.1057/s41270-021-00116-9
  » https://doi.org/10.1057/s41270-021-00116-9
• Gorn, G., Pham, M. T., & Sin, L. Y. (2001). When arousal influences ad evaluation and valence does not (and vice versa). Journal of Consumer Psychology, 11(1), 43-55. https://doi.org/10.1207/S15327663JCP1101_4
  » https://doi.org/10.1207/S15327663JCP1101_4
• Green, P. E., & Srinivasan, V. (1978). Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5(2), 103-123. https://doi.org/10.1086/208721
  » https://doi.org/10.1086/208721
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (18^th ed.). Cengage Learning EMEA.
• Hayes, J. L., King, K. W., & Ramirez, A. Jr. (2016). Brands, friends, & viral advertising: A social exchange perspective on the ad referral processes. Journal of Interactive Marketing, 36(1), 31-45. https://doi.org/10.1016/j.intmar.2016.04.001
  » https://doi.org/10.1016/j.intmar.2016.04.001
• Hemsley-Brown, J., & Alnawas, I. (2016). Service quality and brand loyalty: The mediation effect of brand passion, brand affection and self-brand connection. International Journal of Contemporary Hospitality Management, 28(12), 2771-2794. https://doi.org/10.1108/IJCHM-09-2015-0466
  » https://doi.org/10.1108/IJCHM-09-2015-0466
• Hendijani Fard, M., & Marvi, R. (2020). Viral marketing and purchase intentions of mobile applications users. International Journal of Emerging Markets, 15(2), 287-301. https://doi.org/10.1108/IJOEM-06-2018-0291
  » https://doi.org/10.1108/IJOEM-06-2018-0291
• Hewett, K., Rand, W., Rust, R. T., & Heerde, H. J. van. (2016). Brand buzz in the echoverse. Journal of Marketing, 80(3), 1-24. https://doi.org/10.1509/jm.15.0033
  » https://doi.org/10.1509/jm.15.0033
• Hoffman, L. H., Baker, A., Beer, A., Rome, M., Stahmer, A., & Zucker, G. (2021). Going viral: Individual-level predictors of viral behaviors in two types of campaigns. Journal of Information Technology & Politics, 18(2), 117-124. https://doi.org/10.1080/19331681 .2020.1814930
  » https://doi.org/10.1080/19331681 .2020.1814930
• Hong, J. W., & Zinkhan, G. M. (1995). Self‐concept and advertising effectiveness: The influence of congruency, conspicuousness, and response mode. Psychology & Marketing, 12(1), 53-77. https://doi.org/10.1002/mar.4220120105
  » https://doi.org/10.1002/mar.4220120105
• Huang, J., Su, S., Zhou, L., & Liu, X. (2013). Attitude toward the viral ad: Expanding traditional advertising models to interactive advertising. Journal of Interactive Marketing, 27(1), 36-46. https://doi.org/10.1016/j.intmar.2012.06.001
  » https://doi.org/10.1016/j.intmar.2012.06.001
• Hudson, S., Huang, L., Roth, M. S., & Madden, T. J. (2016). The influence of social media interactions on consumer-brand relationships: A three-country study of brand perceptions and marketing behaviors. International Journal of Research in Marketing, 33(1), 27-41. https://doi.org/10.1016/j.ijresmar.2015.06.004
  » https://doi.org/10.1016/j.ijresmar.2015.06.004
• Huh, J., Kim, H., Rath, B., Lu, X., & Srivastava, J. (2020). You reap where you sow: A trust-based approach to initial seeding for viral advertising. International Journal of Advertising, 39(7), 963-989. https://doi.org/10.1080/02650487.2020.1718823
  » https://doi.org/10.1080/02650487.2020.1718823
• Jansen, N., Hinz, O., Deusser, C., & Strufe, T. (2021). Is the buzz on? - A buzz detection system for viral posts in social media. Journal of Interactive Marketing, 56(1), 1-17. https://doi.org/10.1016/j.intmar.2021.03.003
  » https://doi.org/10.1016/j.intmar.2021.03.003
• Jin, S. V., Muqaddam, A., & Ryu, E. (2019). Instafamous and social media influencer marketing. Marketing Intelligence and Planning, 37(5), 567-579. https://doi.org/ 10.1108/MIP-09-2018-0375
  » https://doi.org/ 10.1108/MIP-09-2018-0375
• Kim, J. & Bae, K. & Hong, J. (2019, June). Consumption value system and storytelling for digital advertising. Proceedings of the 9th International Conference on Web Intelligence (WIMS2019), Mining and Semantics, Seoul, Republic of Korea.
• Kim, M., Song, D., & Jang, A. (2021). Consumer response toward native advertising on social media: The roles of source type and content type. Internet Research, 31(5), 1656-1676. https://doi.org/10.1108/INTR-08-2019-0328
  » https://doi.org/10.1108/INTR-08-2019-0328
• Koslow, S., Sasser, S. L., & Riordan, E. A. (2006). Do marketers get the advertising they need or the advertising they deserve?: Agency views of how clients influence creativity. Journal of Advertising, 35(3), 81-101. http://www.jstor.org/stable/20460742
  » http://www.jstor.org/stable/20460742
• Kulkarni, K. K., Kalro, A. D., Sharma, D., & Sharma, P. (2020). A typology of viral ad sharers using sentiment analysis. Journal of Retailing and Consumer Services, 53, 101739. https://doi.org/10.1016/j.jretconser.2019.01.008
  » https://doi.org/10.1016/j.jretconser.2019.01.008
• Liu-Thompkins, Y. (2012). Seeding viral content. Journal of Advertising Research, 52(4), 465-478. https://doi.org/10.2501/jar-52-4-465-478
  » https://doi.org/10.2501/jar-52-4-465-478
• Lou, C., & Xie, Q. (2021). Something social, something entertaining? How digital content marketing augments consumer experience and brand loyalty. International Journal of Advertising, 40(3), 376-402. https://doi.org/10.1080/02650487.2020.1788311
  » https://doi.org/10.1080/02650487.2020.1788311
• Lourenço, C. J., Isabella, G., Verbeke, Vo K., Dimoka, A., & Bagozzi, R. P. (2023). How songs from growing up and viewers’ attachment styles affect video ads’ effectiveness. Psychology & Marketing, 40(1), 209-233. https://doi.org/10.1002/mar.21725
  » https://doi.org/10.1002/mar.21725
• Moldovan, S., Steinhart, Y., & Lehmann, D. R. (2019). Propagators, creativity, and informativeness: What helps ads go viral. Journal of Interactive Marketing, 47(1), 102-114. https://doi.org/10.1016/j.intmar.2019.02.004
  » https://doi.org/10.1016/j.intmar.2019.02.004
• Moorman, C., & Day, G. S. (2016). Organizing for Marketing excellence. Journal of Marketing, 80(6), 6-35. https://doi.org/10.1509/jm.15.0423
  » https://doi.org/10.1509/jm.15.0423
• Muntinga, D. G., Moorman, M., & Smit, E. G. (2011). Introducing COBRAs. International Journal of Advertising, 30(1), 13-46. https://doi.org/10.2501/IJA-30-1-013-046
  » https://doi.org/10.2501/IJA-30-1-013-046
• Nelson-Field, K., Riebe, E., & Newstead, K. (2013). The emotions that drive viral video. Australasian Marketing Journal, 21(4), 205-211. https://doi.org/10.1016/j.ausmj.2013.07.003
  » https://doi.org/10.1016/j.ausmj.2013.07.003
• Nikolinakou, A., & King, K. W. (2018). Viral video ads: Emotional triggers and social media virality. Psychology & Marketing, 35(10), 715-726. https://doi.org/10.1002/mar.21129
  » https://doi.org/10.1002/mar.21129
• Pinto, H., Almeida, J. M., & Gonçalves, M. A. (2013, February). Using early view patterns to predict the popularity of YouTube videos. Proceedings of the sixth ACM international conference on Web search and data mining (pp. 365-374), Rome, Italy.
• Pittman, M., & Abell, A. (2021). More trust in fewer followers: Diverging effects of popularity metrics and green orientation social media influencers. Journal of Interactive Marketing, 56(1), 70-82. https://doi.org/10.1016/j.intmar.2021.05.002
  » https://doi.org/10.1016/j.intmar.2021.05.002
• Rosengren, S., & Campbell, C. (2021). Navigating the future of influencer advertising: Consolidating what is known and identifying new research directions. Journal of Advertising, 50(5), 505-509. https://doi.org/10.1080/00913367.2021.1984346
  » https://doi.org/10.1080/00913367.2021.1984346
• Segev, S., & Fernandes, J. (2023). The anatomy of viral advertising: A content analysis of viral advertising from the elaboration likelihood model perspective. Journal of Promotion Management, 29(1), 125-154. https://doi.org/10.1080/10496491.2022.2108189
  » https://doi.org/10.1080/10496491.2022.2108189
• Shen, Z. (2021). A persuasive eWOM model for increasing consumer engagement on social media: Evidence from Irish fashion micro-influencers. Journal of Research in Interactive Marketing, 15(2), 181-199. https://doi.org/10.1108/JRIM-10-2019-0161
  » https://doi.org/10.1108/JRIM-10-2019-0161
• Silverman, D. (2013). Doing qualitative research: A practical handbook (4^th ed.). Sage.
• Southgate, D., Westoby, N., & Page, G. (2010). Creative determinants of viral video viewing. International Journal of Advertising, 29(3), 349-368. https://doi.org/10.2501/ S0265048710201221
  » https://doi.org/10.2501/ S0265048710201221
• Srinivasan, S., Rutz, O. J., & Pauwels, K. (2016). Paths to and off purchase: Quantifying the impact of traditional marketing and online consumer activity. Journal of the Academy of Marketing Science, 44, 440-453. https://doi.org/10.1007/s11747-015-0431-z
  » https://doi.org/10.1007/s11747-015-0431-z
• Srivastava, R. K., Shervani, T. A., & Fahey, L. (1998). Market-based assets and shareholder value: A framework for analysis. Journal of Marketing, 62(1), 2-18. https://doi.org/10.1177/002224299806200102
  » https://doi.org/10.1177/002224299806200102
• Stemler, S. E. (2015). Content analysis. In R. A. Scott, & M. C. Buchmann, Emerging trends in the social and behavioral sciences: An interdisciplinary, searchable, and linkable resource (pp. 1-14). John Wiley & Sons.
• Teng, S., Khong, K. W., Chong, A. Y. L., & Lin, B. (2017). Persuasive electronic word-of-mouth messages in social media. Journal of Computer Information Systems, 57(1), 76-88. https://doi.org/10.1080/08874417.2016.1181501
  » https://doi.org/10.1080/08874417.2016.1181501
• Trivedi, J. P. (2018). Measuring the comparative efficacy of an attractive celebrity influencer vis-à-vis an expert influencer-a fashion industry perspective. International Journal of Electronic Customer Relationship Management, 11(3), 256-271. https://doi.org/10.1504/IJECRM.2018.093771
  » https://doi.org/10.1504/IJECRM.2018.093771
• Truong, Y., McColl, R., & Kitchen, P. (2010). Practitioners’ perceptions of advertising strategies for digital media. International Journal of Advertising, 29(5), 709-725. https://doi.org/10.2501/S0265048710201439
  » https://doi.org/10.2501/S0265048710201439
• Tucker, C. E. (2015). The reach and persuasiveness of viral video ads. Marketing Science, 34(2), 179-307. https://doi.org/10.1287/mksc.2014.0874
  » https://doi.org/10.1287/mksc.2014.0874
• Unnava, V., & Aravindakshan, A. (2021). How does consumer engagement evolve when brands post across multiple social media? Journal of the Academy of Marketing Science, 49, 864-881. https://doi.org/10.1007/s11747-021-00785-z
  » https://doi.org/10.1007/s11747-021-00785-z
• Uzunoğlu, E., & Kip, S. M. (2014). Brand communication through digital influencers: Leveraging blogger engagement. International Journal of Information Management, 34(5), 592-602. https://doi.org/10.1016/j.ijinfomgt.2014.04.007
  » https://doi.org/10.1016/j.ijinfomgt.2014.04.007
• Voorveld, H. A. M. (2019). Brand communication in social media: A research agenda. Journal of Advertising, 48(1), 14-26. https://doi.org/10.1080/00913367.2019.1588808
  » https://doi.org/10.1080/00913367.2019.1588808
• Voorveld, H. A. M., Araujo, T., Bernritter, S. F., Rietberg, E., & Vliegenthart, R. (2018). How advertising in offline media drives reach of and engagement with brands on Facebook. International Journal of Advertising: The Review of Marketing Communications, 37(5), 785-805. https://doi.org/10.1080/02650487.2018.1454703
  » https://doi.org/10.1080/02650487.2018.1454703
• Voorveld, H. A. M., Noort, G. van., Muntinga, D. G., & Bronner, F. (2018). Engagement with social media and social media advertising: The differentiating role of platform type. Journal of Advertising, 47(1), 38-54. https://doi.org/10.1080/00913367.2017.1405754
  » https://doi.org/10.1080/00913367.2017.1405754
• Wang, X., Yu, C., & Wei, Y. (2012). Social media peer communication and impacts on purchase intentions: A consumer socialization framework. Journal of Interactive Marketing, 26(4), 198-208. https://doi.org/10.1016/j.intmar.2011.11.004
  » https://doi.org/10.1016/j.intmar.2011.11.004
• Weick, K., Sutcliffe, K. M., & Obstfeld, D. (2005). Organizing and the process of sensemaking. Organization Science, 16(4), 327-451. https://doi.org/10.1287/orsc.1050.0133
  » https://doi.org/10.1287/orsc.1050.0133
• Wen, T. J., Choi, C. W., Wu, L., & Morris, J. D. (2022). Empowering emotion: The driving force of share and purchase intentions in viral advertising. Journal of Current Issues & Research in Advertising, 43(1), 47-67. https://doi.org/10.1080/10641734.2021.1937408
  » https://doi.org/10.1080/10641734.2021.1937408
• Wilson, R. E., Gosling, S. D., & Graham, L. T. (2012). A review of Facebook research in the social sciences. Perspectives on Psychological Science, 7(3), 203-220. https://doi.org/10.1177/1745691612442904
  » https://doi.org/10.1177/1745691612442904
• Woodside, A. G. (2010). Brand‐consumer storytelling theory and research: Introduction to a Psychology & Marketing special issue. Psychology & Marketing, 27(6), 531-540. https://doi.org/10.1002/mar.20342
  » https://doi.org/10.1002/mar.20342
• Xu, Y. (2017). Modeling the adoption of social media by newspaper organizations: An organizational ecology approach. Telematics and Informatics, 34(1), 151-163. https://doi.org/10.1016/j.tele.2016.05.002
  » https://doi.org/10.1016/j.tele.2016.05.002
• Yang, K. C., & Kang, Y. (2019). Narrative adverting and multi-platform storytelling: A critical review of current literature and best campaign practices. In R. Yilmaz (Ed.), Handbook of Research on Narrative Advertising (pp. 177-195). IGI Global.
• Zhu, Y. Q., & Chen, H. G. (2015). Social media and human need satisfaction: Implications for social media marketing. Business Horizons, 58(3), 335-345. https://doi.org/10.1016/j.bushor.2015.01.006
  » https://doi.org/10.1016/j.bushor.2015.01.006

• 
  # of invited reviewers until the decision:
  

  	1	2	3	4	5	6	7	8	9
  1st round
  2nd round

• Plagiarism Check

  RAC maintains the practice of submitting all documents approved for publication to the plagiarism check, using specific tools, e.g.: iThenticate.

• Copyrights

  The authors retain the copyright relating to their article and grant the journal RAC, from ANPAD, the right of first publication, with the work simultaneously licensed under the Creative Commons Attribution 4.0 International license (CC BY 4.0).

• Funding

  The authors thank Insper (Grant #300165/2020) for the financial support for this work.

• Peer Review Method

  This content was evaluated using the double-blind peer review process. The disclosure of the reviewers’ information on the first page, as well as the Peer Review Report, is made only after concluding the evaluation process, and with the voluntary consent of the respective reviewers and authors.

• Data Availability

  The authors claim that all data used in the research have been made publicly available through the Harvard Dataverse platform and can be accessed at:
  

  
  
  Isabella, Giuliana; Melo, Andressa Freitas de; Gonzalez, Marcela Carvalho, 2023, "Replication Data for: "Going viral on advertising YouTube video: Detecting the influences" published by RAC-Revista de Administração Contemporânea", Harvard Dataverse, V1.
  https://doi.org/10.7910/DVN/D3028Z
  RAC encourages data sharing but, in compliance with ethical principles, it does not demand the disclosure of any means of identifying research subjects, preserving the privacy of research subjects. The practice of open data is to enable the reproducibility of results, and to ensure the unrestricted transparency of the results of the published research, without requiring the identity of research subjects.

• JEL Code:

  M31, M310.

• Reviewers:

  George Rossi (Universidade de São Paulo, EACH, Brazil)
  Danilo de Oliveira Sampaio (Universidade Federal de Juiz de Fora, Brazil)
  Ramon Leite (Pontifícia Universidade Católica de Minas Gerais, Brazil)

• Peer Review Report:

  The Peer Review Report is available at this external URL.

Edited by

Editor-in-chief:

Marcelo de Souza Bispo (Universidade Federal da Paraíba, PPGA, Brazil)

Guest Editors:

Valter Afonso Vieira (Universidade Estadual de Maringá, Brazil)

Maria Carolina Zanette (Neoma Business School, France)

Marcos Inácio Severo de Almeida (Universidade Federal de Goiás, Brazil)

Data availability

The authors claim that all data used in the research have been made publicly available through the Harvard Dataverse platform and can be accessed at:

Isabella, Giuliana; Melo, Andressa Freitas de; Gonzalez, Marcela Carvalho, 2023, "Replication Data for: "Going viral on advertising YouTube video: Detecting the influences" published by RAC-Revista de Administração Contemporânea", Harvard Dataverse, V1.

https://doi.org/10.7910/DVN/D3028Z

RAC encourages data sharing but, in compliance with ethical principles, it does not demand the disclosure of any means of identifying research subjects, preserving the privacy of research subjects. The practice of open data is to enable the reproducibility of results, and to ensure the unrestricted transparency of the results of the published research, without requiring the identity of research subjects.

Publication Dates

• Publication in this collection
  15 Sept 2023
• Date of issue
  2023

History

• Published
  03 Aug 2023
• Received
  30 Nov 2022
• Reviewed
  27 June 2023
• Accepted
  28 June 2023