Janelle de Weerd 11384425
Master’s Thesis: Graduate School of Communication Master’s Program Persuasive Communication Science
Supervisor: Daan Muntinga 30 June 2017
When the Medium is No Longer the Message:
The Role of Narrative Transportation in Brand Storytelling
Abstract
Brand storytelling has an unprecedented persuasive power to trigger individuals to adopt brand-aligned attitudes and intentions. Through a process called transportation, individuals are completely immersed into the brand story-world. The rise of new media and participatory culture has given firms more possibilities than ever before to convey their brand stories. Moreover, it has established a two-way flow of communication between individuals and brands. As a result, the internet – particularly social media - is full of brand sponsored videos and blog posts that are not only brand-generated, but also user-generated. These rapid
changes in the media landscape have created an unparalleled opportunity to study their impact. By expanding on previous studies that focus solely on medium or source effects, this study will take a cross-media approach to provide empirical evidence regarding the impact of brand stories that are shared in audiovisual (i.e., video) and text aided with photos (i.e., blog post) mediums. Furthermore, the role of source (user-generated versus brand-generated content) in such mediums is investigated. Narrative transportation is focused on to explain the persuasive effects of the content. The results of an experiment (N = 228) indicate that there are only marginal positive effects of using narrative audiovisual mediums on the responses of consumers towards brands over text aided with photos. No significant results were found regarding source impacts, however, significant relationships between
experiencing transportation and consumer responses were found. This indicates that medium and source do not affect transportation but hinge on the particular story that is being told. These results put pressure on practitioners to formulate brand stories that facilitate immersion into the narrative.
When the Medium is No Longer the Message: The Role of Narrative Transportation in Brand Storytelling
In 2014, Always, a female hygiene brand, started a movement to reject the negative
connotation of the phrase “Like a Girl” (Always, n.d.). The Like a Girl campaign started as a video, and since, has expanded to a variety of online platforms including a thread of blog posts and series of videos on their website. The impact of the campaign has been widespread, in fact, after one year a study found that 70% of women and 60% of men claimed a positive opinion of their campaign phrase (Campaign, n.d.). To add to the impact, the campaign has inspired individuals around the world to contribute to the conversation by creating their own blogs and videos revolved around female empowerment (e.g., Fatherly, 2016; Sarah, n.d.).
The success of this brand campaign can be attributed to the power of storytelling. Always was able to emotionally engage a global audience to not only listen to the story that they had to tell, but to also encourage individuals to share their opinions through
user-generated content. This form of brand storytelling is effective because of its ability to trigger narrative transportation into a story that may not relate directly to a given brand or product (Green & Brock, 2000). Transportation is the psychological response to narratives that leads individuals to suspend disbelief and immerse into a story-world (Dal Cin, Zanna, & Fong, 2004). Due to this immersion, individuals adopt positive attitudes toward the advertisement, which are then transferred to the brand sharing the story (Escalas, 2004, 2007).
Firms have used storytelling to convey their message for decades. Recently, the frequency has increased with the continued rise of new media and the participatory culture, allowing brands to reach consumers globally (Fournier & Avery, 2011). The changes in consumer trends has led to an influx of online videos that aim to persuade viewers through strong narratives and sense simulating visuals (Hsieh, Hsieh, & Tang, 2012). The impact of these narrative videos can be seen from a data-standpoint, with 75% of online traffic
attributed to videos (Syndacast, 2014). However, there is a distinct lack of empirical findings that explain the effects of storytelling in different mediums on a deeper level. Since videos can be costly to produce (George, 2015), it is of relevance to provide more insight into the difference in persuasive effects when compared to lower cost mediums, such as blog posts. Studies in this line of research have found that narrative videos generally do facilitate transportation to a great degree (Pera & Vigilia, 2016), but have not explicitly tested the effects in a cross-media comparison.
Adding to this, the rise of user-generated content is further altering the brand
communication landscape. Consumers are now taking an active role in defining, creating, and sharing brand stories online (Gensler, Völckner, Liu-Thompkins, & Wiertz, 2013). With this change, brands have been forced to invite their fans to co-create their stories (Smith, Fischer, & Yongjian, 2012). The impact of users as the source for this form of brand communication has been cited as more persuasive than brands as sources (e.g., Jonas, 2010). However, the effects of source on facilitating a greater level of transportation has not been explicitly studied, despite recent calls to do so (Gensler, Völckner, Liu-Thompkins, & Wiertz, 2013).
This study seeks to examine how storytelling effectiveness is influenced by medium and source, and investigates the role of narrative transportation on influencing consumer responses such as brand attitudes, campaign attitudes, and forwarding intentions. The study contributes to the growing body of narrative transportation literature in two ways. First, it is the first study to investigate the effect of medium on narrative transportation by comparing audiovisual media (i.e., video) and text aided with photos (i.e., blog posts). Second, it
investigates the role of the brand-related story’s source to understand if and why content that is user-generated or brand-generated is more effective. In the following sections, relevant marketing communication theories will be elaborated on to formulate hypotheses, which are
subsequently tested in an empirical study. The results and implications for theory and managerial practice are then discussed.
Theoretical Framework The Role of Narratives in People’s Lives
Every day, people make sense of the world around them (Hirschman & Holbrook, 1982). To aid this sense-making, the mind indexes itself in a story-based fashion (Schank, 1999). Thus, everything that is indexed, stored, and retrieved in one’s mind takes the form of a narrative (Woodside, 2010; Woodside, Sood, & Miller, 2008). In this view, individuals are story builders who do not document their memories in isolated occurrences (Bublitz et al., 2016). Rather, they organize and understand situations and their emotional responses to them in a narrative, or a sequential story with a recognizable beginning, middle, and end (Hinyard & Kreuter, 2007). The story-oriented manner of thinking, otherwise known as narrative processing, is an automatic process (Mick, 1987) and comes naturally to individuals (Bublitz et al., 2016). It is, therefore, logical that when an individual engages in narrative processing, the manner of indexing and storing given information occurs with more ease than if it were analytically processed. As a result, stories tend to have greater persuasive effects than facts and figures about brands and products (Chang, 2009; Nielsen & Escalas, 2010).
The intrinsic, automated response to retrieve narratives is thought to come from one’s subconscious desire to experience what Aristotle called proper pleasure, in other words, archetypical myths (as cited in Woodside et al., 2008). Jung (1969), psychiatrist and founder of analytical psychology, describes archetypes as “Forms or images of a collective nature which occur practically all over the earth as constituents of myths and at the same time autochthonous, individual products of unconscious origin” (p. 53). Simply put, archetypes are themes that stem from society’s collective memory, traditions, and values. They are
(Batey, 2015; Mark & Pearson, 2001). By enacting a specific archetype through narratives, marketers can provide a deeper meaning to products (Mark & Pearson, 2001), as individuals will perceive the communicated archetype to be embodied in the product (Holt, 2003). This, in turn, enables brands to play an essential role in aiding consumers to achieve their proper pleasure (Woodside et al., 2008). In other words, brands help consumers to construct their desired self and build their social identity (Black & Veloutsou, 2017).
The consumer brand archetypical relationship goes further than brand created stories. Consumers also seek to engage in storytelling as a way to experience archetypical myths and make sense of the meaning of certain events (Woodside et al., 2008). Individuals use brands to construct their self-narratives, therein expressing how one wishes to represent themselves (Escalas, 2004; Schembri, Merrilees, & Kristiansen, 2010). This experiential influence of brand stories allows products to transcend their functional benefits (Lundqvist, Liljander, Gummerus, & Van Riel, 2012) and contribute to the meaning making process that individuals engage in (Batey, 2015; Hirschman & Holbrook, 1982).
Modality Effects in Text and Audiovisual Mediums
Firms have been quick to incorporate narratives into their strategy, each attempting to stand out of the clutter of brand noise. From communicating brand stories in social media (Kozinets, De Valck, Wojnicki, & Wilner, 2010), to mobile phones (Karapanos, Barreto, Nisi, & Niforatos, 2012), new media play a large part in a brand’s marketing. Especially in the last decade, web blogs (Woodside et al., 2008) and online videos (Pera & Viglia, 2016) have moved to the forefront of brand communication. Despite this surge in popularity, the modality effects of blogs and videos have not been explicitly researched. Modality refers to the channel(s) that are engaged in media, namely employing the audio and/or visual channel (Mayer & Moreno, 2003). It is argued that engaging more than one modality will result in
greater recall (Bol, Van Weert, De Haes, Loos, & Smets, 2015; Slater & Rouner, 2002) and attention (Frick, 1984).
The influence of modality can be further explained by the Cognitive Theory of Multimedia Learning (CTML), which contends that individuals learn better when they are provided with both words and pictures (Mayer, 2002). This theory is based on three
arguments: the dual channel assumption, limited capacity assumption, and active processing assumption. The dual-channel assumption posits that the human mind has two channels of processing (Paivio, 1991; Baddeley, 1998). The first processes auditory and verbal
information, thus reading or hearings words, and the second processes visual and pictorial representations, hence pictures and other visualizations (Mayer & Moreno, 2003). The limited capacity assumption argues that individuals have a finite amount of cognitive resources that can be used to process information in each channel at one time (Lang, 2000; Mayer, 2002). If this limited capacity is exceeded, the respective channel(s) will be
cognitively overloaded and unable to process additional information (Lang, 2000). Lastly, the active processing assumption states that an individual actively uses the incoming information to organize and build mental representations, which is then integrated with previous
knowledge (Mayer, 2002). This process can be done in both channels individually or together simultaneously.
Relating back to narrative processing, which also involves the process of indexing and storing information in one’s mind (Woodside, 2010). The CTML explains that this process can be better facilitated by allocating the narrative’s information over the two processing channels, therein avoiding cognitive overload (Mayer, 2002). In other words, by providing an audience with material that can be processed using both the auditory and visual channels, it is more likely that the receiver will be able to appropriately organize, store, and retrieve the information. Various studies have found support of this theory, concluding that the inclusion
of relevant pictures with text have an advantage over purely pictorial (Stone & Glock, 1981) or textual formats (Levie & Lentz, 1982). Using visual aids such as videos or photos to support marketing texts have also been found to generate positive brand attitudes (Lee & Gretzel, 2012), higher forwarding intentions (Lee & Tussyadiah, 2010), greater social media engagement (Lei, Pratt, & Wang, 2017), and better recall (Bol et al., 2015).
Multimedia Effects in Text and Audiovisual Mediums
When studying which medium is more effective in changing attitudes and intentions, the multimedia effect (Hsieh et al., 2012) should be considered. The multimedia effect refers to the level of richness of a specific medium. Media richness, sometimes referred to as information richness, is the ability for a medium to change an individual’s understanding of a concept in a certain timespan, or in other words, the learning capacity of communication (Daft & Lengel, 1986). Therefore, when a form of communication can clarify concepts and change one’s comprehension in a timely manner, it is rich, which is often the case with audiovisual media. In contrast, if the communication is seemingly denser, requiring a longer period to reach comprehension, it is considered lower in richness; as one may experience with visually oriented media, such as when reading a textbook.
The level of vividness, or the extent of sense stimulation (Steuer, 1992), is another key factor that explains how media effects can differ (De Vries, Gensler, & Leeflang, 2012). Vividness is established using animations, pictures, and contrasting colors (De Vries et al., 2012; Goldfarb & Tucker, 2011), and its impact on advertising and brand evaluations will increase based on the senses that are engaged (Coyle & Thorson, 2001). For example,
audiovisual is more vivid media compared to textual formats, as they engage multiple senses, i.e., sight and hearing. This vividness also contributes to a greater multimedia effect (Hsieh et al., 2012), as individuals can cognize information faster when exposed to audiovisual media
with synchronized narration and animation than they would when exposed to text with pictorial aids (Mayer & Moreno, 2003).
The multimedia effect and vividness have been found to influence attitudes and intentions regarding advertisements as well as websites (Hsieh et al., 2012; Steuer, 1992). For instance, increased vividness in marketing messages that include video, text, and sound leads to improved processing of the information (Cook & Coupey, 1998). Audiovisual product reviews on websites have also been found to generate more positive attitudes and higher intentions compared to their textual and visual counterparts (Appiah, 2006). Furthermore, vivid and rich audiovisual media lead to more positive attitudes toward the media (Xu, Oh, & Teo, 2009), encourage forwarding behavior (Hsieh et al., 2012), and social media
engagement (De Vries et al., 2012). Based on these theoretical explanations and empirical findings, narratives expressed through audiovisual media are expected to be more effective in generating positive brand and campaign attitudes, as well as result in a higher forwarding intentions, than a text aided with photos medium.
H1: Narratives in an audiovisual media medium will result in a) more positive campaign attitudes, b) more positive brand attitudes, and c) higher forwarding intentions in consumers than those in a text aided with photos medium.
Influence of Narrative Mediums on Transportation
Narrative-based audiovisual media have been found to facilitate transportation, which is the underlying process of narrative persuasion (Pera & Viglia, 2016). Narrative persuasion is defined as “the effect of narrative transportation, which manifests itself in story receivers’ affective and cognitive responses, beliefs, attitudes, and intentions from being swept away by a story and transported into a narrative world that modifies their perception of their world of origin” (p. 301; Van Laer et al., 2014). In this vein, transportation refers to an integrative blending of imagery, attention, and feelings based on events of a story that causes the viewer
to suspend disbelief and lose access to real-world facts (Dal Cin et al., 2004; Green & Brock, 2000; Van Laer et al., 2014). Similar to multimedia learning’s active processing assumption, transportation occurs when the viewer builds a mental representation of the information that was presented and stores it with previously indexed information and memories (Green, 2004; Mayer, 2002).
Transportation consists of three key parts: emotional engagement, mental imagery, and cognitive engagement (Green, 2004). Emotional engagement refers to the emotions that receivers have concerning the characters and events in a story, either feeling for the
characters (sympathy), or feeling their emotions (empathy; Busselle & Bilandzic, 2009). Additionally, this type of engagement can come in the form of a positive mood that stems from the plot of the story, positive identification with the characters, or arousal (Appel & Richter, 2010). Emotional content in narratives is suggested to positively influence
transportation (Appel & Richter, 2010) and brand attitudes (Escalas & Stern, 2003); as well as increase the likelihood of a brand narrative to go viral (Dobele et al., 2007). There are certain advantages of different mediums that enable emotional engagement (Green et al., 2008). In audiovisual media, for example, the use of audio to characterize voices and
facilitate music can result in a heightened emotional experience, whereas textual formats can provide more emotional content regarding the internalized emotions of the characters (Green et al., 2008).
The second element of transportation, mental imagery, is the internal visualization that a viewer has to the events and characters in a story (Van Laer et al., 2014). This process is facilitated through indices, which are image-evoking touchpoints in a story that relate to the audiences’ personal experiences (Woodside et al., 2008). For example, imagine listening to someone explain in vivid detail their favorite childhood summer memory of eating
description will trigger memories for the average person, which will then be visualized in the individual’s mind. When these images are evoked internally, the associations that are built in the mind of the receiver will be strengthened, which in turn makes it more likely that the viewer or listener will store and recall the story.
It should be noted that while mental imagery is present in both textual and audiovisual media, the nature of it is changed based on the medium (Green et al., 2008). Specifically, whereas text allows the reader to create a mental representation based on a description, audiovisual media provides this vivid representation for the audience. Since the former mental representation takes more cognitive effort, the level of transportation could be greater than when watching audiovisual media, as the individual is using more cognitive resources to build the mental representation (Busselle & Bilandzic, 2008).
Increased effort in building mental representations relates directly to cognitive engagement. When an individual constructs a mental representation of the story’s events, a considerable amount of cognitive resources is required (Hamby, Brinberg, & Daniloski, 2017). Since individuals only have a limited capacity of cognitive resources (Lang, 2000; Mayer, 2002), the cognitive effort that is spent on building a mental representation consumes many of the available resources (Green & Brock, 2000). In fact, advertisements that require more cognitive resources to comprehend have led to improved product evaluations due to the lessened effect of skepticism (Fransen, Verlegh, Kirmani, & Smit 2015; Nielsen & Escalas, 2010). Thus, when there is an increased amount of cognitive resources used to decipher a story, fewer resources are left for persuasion resistance mechanisms such as counterarguing (e.g., Dal Cin et al., 2004; Fransen et al., 2015). As a result, the information provided in the story is automatically accepted as true, only to be disproved when additional cognitive resources are freed (Gilbert, 1991). Therefore, by being absorbed into a story, the message
sender’s persuasive intent may not be registered by the receiver (Dal Cin et al., 2004), making it more likely that narrative-consistent beliefs occur (Escalas, 2004).
Narrative persuasion studies have demonstrated that when individuals experience transportation, they are significantly more likely to adopt the narrative consistent beliefs, in other words, positive attitudes toward the message (Braddock & Dillard, 2016; Escalas, 2004, 2007). This occurs due to the affective responses and decreased resistance that is generated (Escalas, 2007), which often leads to positive brand attitudes, advertisement attitudes (Chiu, Hsieh, & Kuo, 2012; Escalas, 2004), and higher intentions to share the campaign (Dobele et al., 2007). Especially in the case of narratives in vivid (Ching, Tong, Chen, & Chen, 2013) or verbal (Lien & Chen, 2013) media; transportation is strengthened, resulting in improved attitudes toward products. These findings lead to the proposition that experiencing transportation into the narrative will mediate the main effects of narrative medium. H2: The level of transportation mediates the effects of narrative medium on a) campaign attitudes, b) brand attitudes, and c) forwarding intentions in consumers, such that brand-related stories in an audiovisual medium lead to higher levels of transportation than a text aided with photos medium.
Source Cue Effects on Narrative Transportation
Brand stories are not only told by the brands themselves. Instead, consumers play a large part in the creation and sharing of brand stories (Woodside, 2010). With the rise of user-generated content, the role of the consumer in brand communication is growing in importance
(Creamer, 2007; Smith et al., 2012). The Interactive Advertising Bureau (2008) defines user-generated content as “any material created and uploaded to the Internet by non-media professionals.” This can be in the form of brand parodies or fan videos that are made
independently from the brand (e.g., Gensler et al., 2013). User-generated content differs from the similar but broader, concept, electronic Word-Of-Mouth (eWOM). In that eWOM refers
to the conveying of brand-related information, and user-generated content refers specifically to the content generation (Bhandara & Rodgers, 2016). The current study will focus
exclusively on consumer-generated content (CGC), meaning content that is created by consumers for brands (Christodoulides, Jevons, & Bonhomme, 2012).
In general, CGC has been cited to be more persuasive than brand-generated content (BGC; Jonas, 2010; Lee, Lee, & Hansen, 2016). This discrepancy is largely caused by the source’s level of perceived credibility (Hautz, Füller, Hutter, & Thürridl, 2014; Jonas, 2010). Credibility is made up of the two sub-dimensions: trustworthiness and expertise (Lee et al., 2016; Tormala, Briñol, & Petty, 2007). Of these, trustworthiness has been found to play a profound influencing role for CGC, showing higher effects related to increased forwarding intentions (Hautz et al., 2014), as well as more positive brand and campaign attitudes (Lee et al., 2016). Trustworthiness refers to whether an individual can accept or approve a message without further investigation (McCole & Palmer, 2002) and the belief that the sender is providing information with fair, honest, and sincere intentions (Ohanian, 1991). Consumers tend to view BGC as having primarily commercial interests that seek to persuade the
consumer to buy their products (Verlegh, Ryu, Tuk, & Feick, 2013). CGC, on the other hand, is assumed to be based on the creator’s experiences and free of commercial intent (Hautz et al., 2014), thus more trustworthy (Berthon, Pitt, & Campbell, 2008). As a result, consumers are more likely to believe and value the opinions of other consumers over brands (e.g., Jonas, 2010; Lee et al., 2016)
The impact of source on transportation has been scarcely researched in the CGC-related body of literature. Most studies to date focus on narrative formats in eWOM, such as product reviews (Hamby, Daniloski, & Brinberg, 2015; Liang & Tukachinsky, 2017). These studies generally find that narrative formats aid transportation, and in turn, improve attitudes (Liang & Tukachinsky, 2017) and intentions (Hamby et al., 2015). Other studies that look at
source effects in transportation are situated in the health communication line of research (Lu, 2013; Stavrositu & Kim, 2015), suggesting that expert sources facilitate transportation to a greater degree than layman sources (Boiarsky, Rouner, Long, 2013; Hove, Paek, &
Issaacson, 2011). Although marketers can be deemed as experts in their field (Min & Mentzer, 2000), these results cannot be generalized to marketing communication, as individuals tend to have less trust for brands than they do for health experts (Fogg et al., 2001). In fact, as previously established, individuals are more inclined to trust other consumers when sharing brand information than brands (Jonas, 2010). Based on this, opposite effects would be expected to occur, in that CGC would be considered more trustworthy than BGC.
Although empirical findings are inconclusive, theory in this line of research suggest that source plays a role in producing trust for the content. Therefore, a hypothesized
moderating effect of source between narrative medium and transportation will be posed. H3: Source will moderate the effects of narrative medium and transportation showing increased transportation effects for consumer-generated content.
Figure 1. Conceptual model showing the relationship of narrative medium with positive campaign and brand attitudes, as well as forwarding intention, mediated by transportation, and moderated by source credibility.
Method
To test the hypotheses, a 2 (narrative medium: textually-aided versus audiovisual) x 2 (source: CGC versus BGC) between-subject factorial experiment was conducted. All
participants were randomly assigned to one of four experimental conditions regarding narrative medium (written blog post with photos versus video) and type of source manipulations (consumer-generated story versus brand-generated story).
Participants and Procedure
In total, 228 participants (64% female) took part in an online experiment. The participants ranged in age from 18 – 65 years (M = 34.92, SD = 14.21) with the majority of participants holding a bachelor’s degree. Of these, 46.5% were American, 37.7% Dutch, and 15.8% “other”. Participants were recruited via social media. Due to the nature of this
research, which considers one’s likeliness to forward a message, recruitment via social media ensured that those included in the sample would be social media users who could be inclined to forward a message. Upon entering the online experiment and providing consent,
participants started the experiment. Following the manipulation, a questionnaire gauged their transportation level, brand attitude, campaign attitude, and forwarding intention. Lastly, several questions regarding the participants’ demographics were asked.
Stimulus Material and Pretests
A video from the content marketing campaign of the beer brand New Belgium Brewing was chosen as the main source of stimulus material for this study (New Belgium Brewing, 2015). This brand was chosen because beer has been found to be subject to few differences in attitudes regarding gender and age (Arias-Bolzmann, Ghakraborty, & Mowen, 2000), and craft beer is experiencing rapid growth in popularly amongst individuals globally (GlobalData Consumer, 2017). Furthermore, the video met technological requirements, by choosing a high-quality video the external validity was increased, as low-quality videos may
lead to decreased levels of credibility and, subsequently, lower brand evaluations (Hautz et al., 2014). The approximately three-minute video was part four of New Belgium Brewing’s Zealots of Zag Series and featured a local artist. It showed the process that the artist went through after being hired by the brand to come up with a brand-inspired art project (see Appendix A).
To avoid confounding effects, pre-tests were required to check the stimulus material. For the sake of preventing effects from existing brand attitudes, it was necessary to choose a brand that was relatively unknown to the study’s participants. Therefore, the first pre-test tested brand attitude and familiarity. This sample consisted of 20 individuals (60% female) ranging in age from 21 – 58 years old (M = 27.25, SD = 10.53) with the majority holding a bachelor’s degree. Of these respondents, 60% were American, 25% were Dutch, and 15% other. Both items were set on seven-point Likert scales. On average, when asked if the respondents were familiar with the brand, low scores were reported (M = 2.20, SD = 2.12); brand attitude levels were correspondingly low (M = 2.20, SD = 2.48). These results indicated that the brand was relatively unknown with few preconceived brand attitudes, showing that it was an appropriate brand to use as the stimulus for the main experiment.
The stimulus for this study consisted of four conditions, namely two video conditions and two written conditions, both made up of a CGC and BGC condition. The BGC video condition’s stimulus was left unchanged, as this was originally created and released by the brand. The CGC video, however, required small edits to be used for the CGC condition to ensure it appeared as it was created by a consumer and not a brand. These edits included a frame that was added at the end of the video, which told viewer that the artist was inspired by the brand, but the video was made by him independently. Additionally, a direct reference to being hired by New Belgium Brewing was edited out.
For the written conditions, the video was transcribed and re-rewritten into two
corresponding blog posts with photos and text. These posts were manipulated to suggest that it was either CGC or BGC (see Appendix B). In both conditions, the blog posts were
approximately 750 words and included the same visuals and text, except for three sections throughout the blog post where the manipulation of the conditions was embedded. The manipulation sections relayed that the artist was either hired by New Belgium Brewing (BGC) or was inspired by the brand and did the project voluntarily (CGC). To maintain internal validity, the overarching story and stills in the blog post were taken directly from the video.
A second pretest was administered to check the content of the written conditions. The CGC and BGC conceptual blogs were sent to 10 individuals (60% female) with the majority holding a bachelor’s degree and ranged in age from 21 – 58 years old (M = 30.20, SD = 14.21). The nationality of the respondents was, 50% American, 40% Dutch, and 10% other. After reading one of the blog entries, participants were asked to what extent they agreed with the following statements, measured on seven-point Likert scales: “The blog post had a beginning, middle, and end” (M = 5.60, SD = 1.08), “The blog post involved at least one character” (M = 6.10, SD = 0.74), “The blog post told a story” (M = 6.10, SD = 0.74)., “The blog post was engaging” (M = 5.40, SD = 1.51), and “It was clear the author was hired/did it voluntarily” (yes, N = 10). Based on these results, the blog text was slightly adjusted to improve its engaging nature and structure by adding more context to the written version of the story.
In the final version of the study, to further express the correct manipulation, additional descriptions were added to each condition. For instance, the blog posts’ banners were
adjusted to feature a branded image for the BGC condition and an image featuring the artist in the CGC condition. Furthermore, in the experiment instructions of both the video and blog
of the BGC condition, it was clearly indicated that the artist was hired to do a project on the behalf of the brand. These statements were also included in the CGC conditions, stating that the project was done voluntarily due to being inspired by the brand and sent to New Belgium Brewing after completion. To further stress these manipulations the words “hired” and “voluntarily” were italicized and underlined.
Measures
Transportation. To measure Transportation, Appel, Gnambs, Richter, and Green’s (2015) Transportation Scale-Short Form (TS-SF) was used. This scale is based on Green and Brock’s (2000) original 12-item scale and has been found to be a reliable, shorter alternative (Appel et al., 2015; Gnambs, Appel, Schreiner, Richter, & Isberner, 2014). The
Transportation Scale has been found to be the most reliable scale in measuring transportation and has been used in various studies (e.g., Van Laer et al., 2014). The TS-SF consists of five items, which measure the cognitive, imaginative, and emotive impacts of transportation (all items were posed on a seven-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (7); see Appendix C). A principle component analysis of these items showed that the scale was unidimensional, with only one component with an Eigenvalue above 1 (eigenvalue = 2.90, EV = 58%), and was internally consistent (Cronbach’s α = .82, M = 4.74, SD = 1.26).
Brand and campaign attitude. Brand attitude was measured using a single item measured on a seven-point Likert scale that was adapted from Berger and Mitchel (1989). This item posed, “After seeing this video/blog post, I […] this brand”. This scale was anchored by “Extremely dislike” (= 1) to “Extremely like” (= 7; M = 5.04, SD = 1.07). The participants’ campaign attitudes were measured using a seven-point semantic differential scale. It posed, “In general, I think that this campaign is…” and was anchored with
Forwarding intention. The intention to forward the campaign was measured using one item, adapted from Smith, Coyle, Lightfoot, and Scott (2007) and Van Noort, Antheunis, and Van Reijmersdal (2012). This item was measured on a seven-point semantic differential scale and stated, “I would forward this video/blog to someone in my network on social media,” and was anchored with unlikely (= 1) / likely (= 7; M = 3.06, SD = 2.09).
Manipulation check. To ensure the stimuli were successfully manipulated, a
manipulation question regarding the source (CGC versus BGC) of the stimuli was asked. The item posed, “Was the artist Jimmy Kuehnle hired by New Belgium Brewing to complete this project?” with the possibility of answering “Yes, the company hired him” or “No, he was inspired by the brand”.
Results Manipulation Check
One hundred sixty-one of the participants (71%) answered the manipulation check question correctly. Specifically, 80.6% of those in the CGC conditions (n = 79) and 63.6% (n = 82) of those in the BGC conditions. Since the first research question refers to source
differences, only those who answered correctly were included in the moderation analysis. Randomization
The four experimental groups did not differ regarding gender χ2 (3) = 0.91, p = .822, education χ2 (18) = 17.04, p = .521, nationality χ2 (60) = 58.86, p = .518, or age F (3, 224) = 1.43, p = .236, η2= .019. This indicates that the randomization was successful. Therefore, these variables will not be controlled for in the analyses as they cannot be expected to influence the effects.
Direct Effect of Narrative Medium on Attitudes and Intentions
To test hypothesis one and two, Hayes’ (2013) PROCESS macro (model 4) was used. This method estimates the regression coefficient paths, generating 95% bootstrap confidence
intervals for both the direct and indirect effects with 5,000 bootstrap samples estimating bias corrected and accelerated confidence intervals (BCACI).
The first hypothesis (H1) stated that an audiovisual medium would have a positive direct effect on brand attitudes, campaign attitudes, and forwarding intentions. The results, however, show that while the effect is in the expected direction, it is not significant for brand attitudes (SE = 0.12, p = .785, BCACI [-0.21, 0.28]), campaign attitudes (SE = 0.17, p = .594, [-0.25, 0.44]), or forwarding intentions (SE = 0.23, p = .606, BCACI [-0.34, 0.58]), thus rejecting H1 (see Figure 2). This means that audiovisual media narratives do not generate more positive attitudes or behavioral intentions than textually aided media.
Mediated Effect of Narrative Medium on Consumer Responses via Transportation Concerning the second hypothesis (H2), which posed that transportation would mediate the effects of medium and the dependent variables, mixed results were found. Namely, the results demonstrate that the overall mediation effect was not significant for brand attitudes (SE = 0.14, p = .290, BCACI [-0.13, 0.42]), campaign attitudes (SE = 0.21, p = .174 [-0.13, 0.69]), or forwarding intentions (SE = 0.28, p = .182, [-0.18, 0.91]), therefore rejecting H2. Further investigation of the model showed that narrative medium had a
marginally significant influence on transportation (SE = .17, p = .100, BCACI [-0.05, 0.61]), in that audiovisual mediums facilitates transportation marginally better than text aided with photos mediums. Additionally, significant relationships were found between experiencing transportation and positive brand attitudes (SE = 0.05, p = < .001, BCACI [0.33, 0.52]), campaign attitudes (SE = 0.07, p = < .001, [0.56, 0.83]), and increased forwarding intentions (SE = 0.09, p = < .001, [0.72, 1.09]). This means that while the effects of medium on
transportation are marginal, there is a significant relationship between transportation and experiencing more positive brand attitudes, campaign attitudes, and forwarding intentions (see Figure 2).
Figure 2: Summary of mediated model showing narrative medium’s direct effect and mediated effect of transportation on campaign attitude, brand attitude, and forwarding intention.
* p < .05. ** p < .001.
Moderating Effect of Source
The third hypothesis (H3) posed that there was a moderating effect of source of the narrative medium and transportation. In this analysis, only respondents who answered the manipulation check correctly were included (n = 163). Therefore, a moderated mediation (i.e., a full test of the conceptual model) could not be performed. Hayes’ (2013) PROCESS Model 1 was used to test for moderation, again using 5,000 bootstrap samples to estimate BCACI with 95% bootstrap confidence intervals for direct and indirect effects. No significant moderation source effects were found regarding CGC (b = 0.43, SE = 0.28, p = .121, BCACI [-0.11, 0.97]) or BGC (b = 0.27, SE = 0.28, p = .340, [-0.29, 0.82]). Therefore, H3 was also rejected. Meaning that the source of the content does not moderate the relationship between narrative medium and transportation.
Discussion
Shining light on an increasingly popular but academically overlooked phenomenon of storytelling in online communication, the current study exposed the effects of the medium and source on several consumer responses. Specifically, it examined the effects of narrative
mediums (i.e., audiovisual media vs. text aided with photos) on consumers’ brand-related attitudes and forwarding intentions, and the moderating effect of source cues (i.e., brand versus consumer-generated content) on these relationships. Moreover, it examined the process underlying these effects, narrative transportation.
Multimedia Effects in Audiovisual and Textually-Aided Media Narratives Based on current trends in media (Syndacast, 2014) and a substantial body of academic literature (e.g., De Vries et al., 2012; Hsieh et al., 2012; Mayer, 2002; Xu et al., 2009), the current study expected to see a main effect of narrative medium on an individual’s brand attitudes and buying intentions. The findings, however, indicate that the two mediums do not differentially impact attitudes or intentions. This suggests that viewing content in an audiovisual medium does not produce more positive brand and campaign attitudes, or increased forwarding intentions than viewing content in a text aided with photos medium.
These findings contrast predictions based on the cognitive theory of multimedia learning (CTML; Mayer, 2002) and the multimedia effect (Hsieh et al., 2012). Several reasons may account for this. First, the insignificant difference between narrative mediums could be attributed to the fact that both text aided with photos and audiovisuals use images and/or animation, which contribute to making content higher in vividness (e.g., De Vries et al., 2012). Although this study makes the case that audiovisual media are higher in vividness than text aided with photos (Coyle & Thorson, 2001), by contrasting two essentially highly visual and vivid media, the differences between both narrative formats become too small to detect. This means that whether an individual is exposed to a video or a blog with visual aids, both media benefit equally from these visuals with no substantial differences.
Additionally, whereas CTML is primarily studied in learning environments where the information provided requires many cognitive resources to comprehend (Mayer, 2002), the current study is framed in a context that is easy to imagine and understand. This means that
the information that is provided focuses on mental imagery more than complex subjects that need to be cognized. Therefore, the viewer is not required to fully process and comprehend new concepts and information. Rather, they create associations with experiences or events that have already been indexed and stored in their memory (Schank, 1999; Woodside, 2010; Woodside et al., 2008). The CTML is largely revolved around reducing the strain of each channel (Mayer, 2002). However, in the current study, it is likely that the division of
information between the two channels did not contribute to a greater understanding or liking. Theories such as the multimedia effect (Hsieh et al., 2012) and modality effect (Russell, 2002) may explain the marketing implications to a greater extent. These theories focus more on sense stimulating elements that are present in marketing communication such as media vividness (De Vries et al., 2012), richness (Daft & Lengel, 1986), and modality effects (Russell, 2002), rather than cognition of new, complex information.
Transportation into Audiovisual and Textual Narratives
Regarding the proposed mediating role of transportation, it was expected that narrative medium would affect transportation, which in turn would affect brand attitudes, campaign attitudes, and forwarding intentions. However, these effects were not observed. The results show that although narrative medium marginally affects transportation, transportation does have a substantial and positive effect on brand attitudes, campaign attitudes, and forwarding intentions (Braddock & Dillard, 2016).
While these findings suggest that audiovisual media do not hold any benefits over text aided with photos when it comes to their ability to stimulate transportation (Dal Cin et al., 2004), the most interesting finding here is that it is that the level of transportation determines the persuasive effects. This is in line with various other studies in the narrative transportation field (Ching et al., 2013; Chiu et al., 2012; Escalas, 2004, 2007; Green & Brock, 2000). The persuasive effect of narrative transportation is caused by the increased level of cognitive
resources that is required to build a mental representation of the story (Green & Brock, 2000), leaving fewer resources available to produce negative attitudes such as counterarguing or skepticism (Dal Cin et al., 2004). Therefore, those who experience transportation are more likely to experience brand aligned attitudes (Escalas, 2004) and a higher likelihood to forward on the campaign (Dobele et al., 2007).
While the insignificant mediation effect results were unexpected, they add additional strength to transportation theory. They show that regardless of the medium used to tell the story, desired persuasive effects will occur if the level of transportation is high (Dal Cin et al., 2004; Green et al., 2008). With this in mind, there are several possible explanations for the marginal differences between the audiovisual media and text aided with photos that this study detected. First, the results could be the outcome of the aspects of equivalence between the mediums (Green et al., 2008; Hsieh et al., 2012). For example, factors such as the plot and identification with the main character, which have been found to contribute to greater transportation, remained consistent between both conditions (Appel & Richter, 2010; Van Laer et al, 2014). In fact, the main character that was featured in all conditions was the source of the consumer-generated content (CGC). Meaning that the positive or negative attitudes that were produced could be a result of identification with this character. Additionally, the equivalence of the conditions could also be attributed to the similarity in terms of vividness (De Vries et al., 2012).
A second explanation relates to the idea of tailoring media to the audience’s need for cognition. Need for cognition refers to one’s tendency to engage and enjoy enhanced
cognitive activity (Petty & Cacioppo, 1986) and has been found to positively moderate narrative persuasion (Braverman, 2008; Thompson & Haddock, 2012). In fact, Green and colleagues (2008) suggest that when individuals are matched to their preferred need for cognition medium, their level of transportation increases compared to individuals without
their preferred need for cognition. Accordingly, it is likely that those with a higher need for cognition would transport greater in text conditions, which consume more cognitive
resources, and vise-versa. However, additional research is required to test this hypothesis. The final and potentially most interesting explanation considers that the medium is not always the message, as is a common assumption (e.g., Berger & Iyengar, 2013; Van Noort & Willemsen, 2012). Moreover, a specific medium does not guarantee certain
behaviors or attitudes any more than a specific message. It is a combination of both the media and the story that drive persuasion. The findings of this study show the importance of
formulating a story that allows individuals to experience proper pleasure in terms of their identity goals (Black & Veloutsou, 2017; Woodside et al., 2008). By creating an engaging story that the audience can fully engage in, and using that story to determine the appropriate media, the effectiveness of a marketing campaign can be improved (Van Laer et al., 2014). The concept of transmedia storytelling (Jenkins, 2003) uses this principle when creating media campaigns for the creative industry. Rather than adapting one message across several mediums, transmedia storytelling argues that the message and the medium need to come together in a way that can be understood and enjoyed but is also self-contained. This study finds similar conclusions; however, these findings are dependent on using the capabilities of new media by implementing visual aids to guide the audience. This shows that the decision to produce audiovisual over textual media because of a general perceived preference may not have the effects that would typically be expected.
Source Cue Effects on Narrative Transportation
With regard to the moderating source cue effects, no significant differences were found, suggesting that source cues play no part in moderating the effects of narrative medium and transportation. In the study, the status of the source as a volunteer (consumer-generated content) or hired by the company (brand-generated content; BGC) was emphasized several
times throughout the text. Nevertheless, the source of the content remained largely unnoticed, as indicated by the fact that many respondents answered the source manipulation check incorrectly.
As an explanation, this could be attributed to respondents not paying enough attention to this information, which might have been an artefact of the research method. Alternatively, however, participants’ inability to correctly name the source could very well be related to their level of absorption into the story. Unlike dual-process models such as the heuristic-systematic model (Chaiken, 1980) and the elaboration-likelihood model (Petty & Cacioppo, 1986), which consider heuristic cues such as source, persuasive for individuals with low-involvement in the message, transportation leads to persuasion through immersion (Green, Brock, & Kaufman, 2004). By reducing negative cognitive responses, narratives feel like real-life to the receiver, making cues such as source irrelevant to their story-world experience and, thus, unrecallable. Due to this, the so-called sleeper effect occurs (Pratkanis, Greenwald, Leippe, & Baumgardner, 1988). The sleeper effect refers to the difficulty people experience when trying to recall the source of a message. More specifically, when individuals process information, their memory trace slowly degenerates if it is not frequently reactivated (Hintzman & Block, 1971). Commonly, the source of a message is more difficult to
remember and degenerates more quickly than the message itself. This results in easier recall of a message and one’s attitudes toward it, but not the source by which the message was conveyed. In this vein, the results of this study once again support transportation as being an all-encompassing merging of imagery, emotional engagement, and cognitive engagement that results in individuals losing access to real-life facts such as the logical evaluation of what a source means to the credibility of a message (Green & Brock, 2000; Van Laer et al., 2014). From this theoretical vantage point it makes sense that the expected effect does not occur.
It should be considered that due to the nature of experimental studies, broad assumptions of source’s relevance to narrative transportation cannot be made.
Trustworthiness, which is one of the key drivers of positive CGC effects (Hautz et al., 2014; Lee et al., 2016), is difficult to establish for the participants, as they do not know background information about the source (Thompson & Malaviya, 2013). Therefore, individuals are unable to develop a basis of trust for the CGC source. Studies have found that when
individuals cannot identify a source, CGC becomes less trustworthy than when the source can be identified (Jin, Bloch, & Cameron, 2002; Smith, Menon, & Sivadumar, 2005). For
instance, in the possible case where the respondent knows a brand and not the user creating the content.
Limitations and Future Research
As with any study, there are various limitations. As previously mentioned, certain potential mediators are present that could further explain one’s produced attitude and
intention regarding given stimulus, including a respondent’s need for cognition (Green et al., 2008) and identification with the characters (Van Laer et al., 2014). These factors should be integrated into future research designs in hopes of providing more context to the reasons why specific mediums and/or stories appeal to individuals.
Second, this study used the CTML (Mayer, 2002) and multimedia effect (Hsieh et al., 2012) to predict the effects of transportation. Unfortunately, these effects did not accurately explain text aided with photos versus audiovisual medium effect differences. This, in part, could be attributed to the casual environment of the stimulus, as opposed to applying the theory in a learning environment (e.g., Mayer, 2002; Mayer & Moreno, 2003). Future research could test the relevance of this theory in regard to transportation by examining how the modality effects differ given more dense material, such as a narrative-based explainer
video for a technology product, as opposed to a blog post that is similar to the one presented in this study.
Related to this, the current study observed small differences regarding the two different mediums, which could be attributed to similar levels of vividness. These insignificant effects were not predicted on a basis of the multimedia effect (Hsieh et al., 2012). In order to detect the influence of vividness and an increased multimedia effect on receivers, follow-up studies should expand on this thought by including a text-only condition. This would indicate whether visual aids do, indeed, impact one’s attitudes and intentions.
A fourth limitation of this study concerns the low power score that was produced (brand attitude: .24; campaign attitude: .37; forwarding intention: .31). These results increase the likelihood that one or more null hypotheses were incorrectly accepted (Lakens, 2013). In other words, a hypothesis in the current study could have been falsely rejected. Future studies should ensure that their power score is high enough, guaranteeing an accurate acceptance or rejection of a hypothesis. This will also help in detecting the large effect that is proposed in this line of research. These results would facilitate more meaningful and comprehensive findings regarding the given population.
Finally, narrative transportation is a dynamic field of research that only continues to evolve as new communication methods emerge (e.g., Farace, Van Laer, De Ruyter, & Wetzels, in press). The momentum that is expressed in this study should be expanded upon, as these results are increasingly noteworthy to both academic and practicing professionals. To make the results of future studies more impactful, researchers should investigate real
behavior, rather than behavioral intentions, by using online data to analyze click-through and engagement (e.g., likes, comments, and shares) rates of content marketing videos versus blog posts on social media. Elements such as source (user versus brand) could also be integrated into this type of experiment to analyze if and when these cues may have an impact on
behavior. By using this “real-life” data, researchers would be given the valuable chance of seeing true behavior without the constraints of an experimental environment. These findings would not only bring valuable insights to the field but also actionable evidence that could be used in multiple industries.
Managerial and Theoretical Implications
Today’s market is full of noise, crowded with many brands fighting for the attention of the consumer. In order to break through that noise, practitioners need to stay vigilant in finding optimal ways to communicate to their target audience. This study shows that while some methods of communication may be trendy, as is the case with videos (Syndacast, 2014), that does not ensure that they are the most effective manner in reaching an audience. The rise of new media allows brand communication many possibilities. While brand videos do serve an important purpose in today’s media landscape, it does not mean that video is king. Brands should continue to invest in blog content that uses visual aids to guide the reader in their experience. Before this, thorough research of brands’ audiences should be conducted to understand their media preferences and consumption habits so content can be based on these findings. Ultimately, the marginal differences between video and blog formats should come as good news to marketers, as videos can cost at least ten times the amount of blog posts (George, 2015).
The results of this study put additional pressure on marketing departments to create stories that resonate with their audience. The relationship between transportation and positive campaign attitudes, brand attitudes, and increased forwarding intentions suggest that as long as an individual is able to transport into the story-world, persuasive effects will occur. When practitioners can employ imagery and emotion provoking content in their stories (Green & Brock, 2000), those viewing the content will experience subdued counterarguing and skepticism in response to the persuasive intent (Dal Cin et al., 2004). This finding is
especially interesting as it indicates that factors such as medium choice and heuristic cues do not always contribute to a greater transportation experience. Rather, the story that is told is the most important factor. Practitioners need to find a way to bring the medium and story together in a manner in which they complement each other.
While this study did not find significant results in regard to its hypotheses, it offers a major contribution to the transportation theory. It shows that the impact of transportation is hinge on the quality of the story and not the medium or source by which it is communicated. It provides additional evidence to the effects of transportation, except this time in a marketing cross-media comparison. These results add to the wealth of knowledge in this area of
academic research by indicating areas where transportation effects do not differ. With the insights generated by this study, future researchers and practitioners know where to invest their time and energy in, and where it is not necessary.
References
Always. (n.d.). #Likeagirl: How it all started [Web page]. Retrieved from https://always.com/en-us/about-us/likeagirl-how-it-all-started
Appel, M., Gnambs, T., Richter, T., & Green, M. C. (2015). The transportation scale–short form (TS–SF). Media Psychology, 18(2), 243-266.
doi:10.1080/15213269.2014.987400
Appel, M., & Richter, T. (2010). Transportation and need for affect in narrative persuasion: A mediated moderation model. Media Psychology, 13(2), 101-135.
doi:10.1080/15213261003799847
Appiah, O. (2006). Rich media, poor media: The impact of audio/video vs. text/picture testimonial ads on browsers' evaluations of commercial web sites and online products. Journal of Current Issues & Research in Advertising, 28(1), 73-86. doi:10.1080/10641734.2006.10505192
Arias-Bolzmann, L., Chakraborty, G., & Mowen, J. C. (2000). Effects of absurdity in advertising: The moderating role of product category attitude and the mediating role of cognitive responses. Journal of Advertising, 29(1), 35-49.
doi:10.1080/00913367.2000.10673602
Baddeley, A. (1998). Human memory. Boston, MA: Allyn & Bacon.
Batey, M. (2015). Brand meaning: Meaning, myth and mystique in today's brands. New York, NY: Routledge.
Berger, I. E., & Mitchell, A. A. (1989). The effect of advertising on attitude accessibility, attitude confidence, and the attitude-behavior relationship. Journal of Consumer Research, 16(3), 269-279. doi:10.1086/209213
Berger, J., & Iyengar, R. (2013). Communication channels and word of mouth: How the medium shapes the message. Journal of Consumer Research, 40(3), 567-579. doi:10.1086/671345
Berthon, P., Pitt, L., & Campbell, C. (2008). Ad lib: When customers create the ad. California Management Review, 50(4), 6-30. doi:10.2307/41166454
Bhandara, M. & Rodgers, S. (2016). Electronic word-of-mouth and user-generated content: Past, present, and future. In R. E. Brown, V. K. Jones, & M. Wang (Eds.), Branding, content, and consumer relationships in the data-driven social media era (pp. 175-201). Santa Barbara, CA: Praeger.
Black, I., & Veloutsou, C. (2017). Working consumers: Co-creation of brand identity, consumer identity and brand community identity. Journal of Business Research, 70, 416-429. doi:10.1016/j.jbusres.2016.07.012
Boiarsky, G., Rouner, D., & Long, M. (2013). Effects of responsibility attribution and message source on young adults' health attitudes and behaviors. Journal of Health
Communication, 18(7), 881-894. doi:10.1080/10810730.2012.757389
Bol, N., Van Weert, J. C. M., De Haes, J. C. J. M., Loos, E. F., & Smets, E. M. A. (2015). The effect of modality and narration style on recall of online health information: Results from an online experiment. Journal of Medical Internet Research, 17(4), e104. doi:10.2196/jmir.4164
Braddock, K., & Dillard, J. P. (2016). Meta-analytic evidence for the persuasive effect of narratives on beliefs, attitudes, intentions, and behaviors. Communication
Monographs, 83(4), 446-467. doi:10.1080/03637751.2015.1128555 Braverman, J. (2008). Testimonials versus informational persuasive messages.
Communication Research, 35(5), 666–694. doi:10.1177/0093650208321785 Bublitz, M. G., Escalas, J. E., Peracchio, L. A., Furchheim, P., Grau, S. L., Hamby, A., ...
Scott, A. (2016). Transformative stories: A framework for crafting stories for social impact organizations. Journal of Public Policy & Marketing, 35(2), 237-248. doi:10.1509/jppm.15.133
Busselle, R., & Bilandzic, H. (2008). Fictionality and perceived realism in experiencing stories: A model of narrative comprehension and engagement. Communication Theory, 18(2), 255–280. doi:10.1111/j.1468-2885.2008.00322.x
Busselle, R., & Bilandzic, H. (2009). Measuring narrative engagement. Media
Psychology, 12(4), 321-347. doi:10.1080/15213260903287259
Campaign. (n.d). Always, Procter & Gamble, and Leo Burnett #likeagirl [Webpage]. Retrieved from http://www.campaignlive.co.uk/article/case-study-always-likeagirl/1366870
Chaiken, S. (1980). Heuristic versus systematic information processing and the use of source versus message cues in persuasion. Journal of Personality and Social Psychology, 39(1), 752–766. doi:10.1037/0022-3514.39.5.752
Chang, C. (2009). "Being hooked" by editorial content: the implications for processing narrative advertising. Journal of Advertising, 38(1), 21-34. doi:10.2753/JOA0091-3367380102
Ching, R. K., Tong, P., Chen, J. S., & Chen, H. Y. (2013). Narrative online advertising: Identification and its effects on attitude toward a product. Internet Research, 23(4), 414-438. doi:10.1108/IntR-04-2012-0077
Christodoulides, G., Jevons, C., & Bonhomme, J. (2012). Memo to marketers: Quantitative evidence for change. Journal of Advertising Research, 52(1), 53-64.
doi:10.2501/JAR-52-1-053-064
Chiu, H., Hsieh, Y., & Kuo, Y. (2012). How to align your brand stories with your products. Journal of Retailing, 88(2), 262-275. doi:10.1016/j.jretai.2012.02.0
Cook, D. L., & Coupey, E. (1998). Consumer behavior and unresolved regulatory issues in electronic marketing. Journal of Business Research, 41(3), 231-238.
doi:10.1016/S0148-2963(97)00066-0
Coyle, J. R., & Thorson, E. (2001). The effects of progressive levels of interactivity and vividness in web marketing sites. Journal of Advertising, 30(3), 65-77.
doi:10.1080/00913367.2001.10673646
Creamer, M. (2007). Ad age agency of the year: the consumer. Advertising Age. Retrieved from http://adage.com/article/news/ad-age-agency-year-consumer/114132/
Daft, R. L., & Lengel, R. H. (1986). Organizational information requirements, media richness and structural design. Management Science, 32(5), 554-571.
doi:10.1287/mnsc.32.5.554
Dal Cin, S., Zanna, M. P., & Fong, G. T. (2004). Narrative persuasion and overcoming resistance. In E. S. Knowles & J. A. Linn (Eds.), Resistance and persuasion (pp. 175– 191). Mahwah, NJ: Erlbaum.
De Vries, L., Gensler, S., & Leeflang, P. S. (2012). Popularity of brand posts on brand fan pages: An investigation of the effects of social media marketing. Journal of
Interactive Marketing, 26(2), 83-91. doi:10.1016/j.intmar.2012.01.003
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. doi:10.1016/j.bushor.2007.01.004
Escalas, J. E. (2004). Imagine yourself in the product: Mental simulation, narrative transportation, and persuasion. Journal of Advertising, 33(2), 37-48.
Escalas, J. E. (2007). Self-referencing and persuasion: Narrative transportation versus analytical elaboration. Journal of Consumer Research, 33(4), 421-429.
doi:10.1086/510216
Escalas, J. E., & Stern, B. (2003). Sympathy and empathy: Emotional responses to advertising dramas. Journal of Consumer Research, 29(1), 566–578. doi:10.1086/346251
Farace, S., van Laer, T., Ruyter, K. D., & Wetzels, M. (in press). Assessing the effect of narrative transportation, portrayed action, and photographic style on the likelihood to comment on posted selfies. European Journal of Marketing. Retrieved from
http://www.emeraldinsight.com/journal/ejm
[Fatherly]. (2016, April 28). Fight like a girl [Video file]. Retrieved from https://www.youtube.com/watch?v=6AHJxM0Prn0
Fogg, B. J., Marshall, J., Laraki, O., Osipovich, A., Varma, C., Fang, N., ... Treinen, M. (2001, March). What makes web sites credible: A report on a large quantitative study. In Proceedings of the SIGCHI conference on Human factors in computing
systems (pp. 61-68). ACM. doi:10.1145/365024.365037
Fournier, S., & Avery, J. (2011). The uninvited brand. Business Horizons, 54(3), 193-207. doi:10.1016/j.bushor.2011.01.001
Fransen, M. L., Verlegh, P. W., Kirmani, A., & Smit, E. G. (2015). A typology of consumer strategies for resisting advertising, and a review of mechanisms for countering them. International Journal of Advertising, 34(1), 6-16.
doi:10.1080/02650487.2014.995284
Frick, R. W. (1984). Using both an auditory and a visual short-term store to increase digit span. Memory & Cognition, 12(5), 507-514. doi:10.3758/BF03198313
Gensler, S., Völckner, F., Liu-Thompkins, Y., & Wiertz, C. (2013). Managing brands in the social media environment. Journal of Interactive Marketing, 27(4), 242-256.
doi:10.1016/j.intmar.2013.09.004
George, E. (2015, September 22). Corporate video costs, do you know how much you should be paying? [Blog post]. Retrieved from
https://www.b2bmarketing.net/en- gb/resources/blog/corporate-video-costs-do-you-know-how-much-you-should-be-paying
Gilbert, D. T. (1991). How mental systems believe. American Psychologist, 46(2), 107–119. doi:10.1037/0003-066X.46.2.107
GlobalData Consumer. (2017, January 30). Craft beer growth set to continue in 2017 [Webpage]. Retrieved from
http://www.drinks-insight-network.com/features/featurecraft-beer-growth-continue-2017-5726749/
Gnambs, T., Appel, M., Schreiner, C., Richter, T., & Isberner, M. B. (2014). Experiencing narrative worlds: A latent state–trait analysis. Personality and Individual
Differences, 69(1), 187-192. doi:10.1016/j.paid.2014.05.034
Goldfarb, A., & Tucker, C. (2011). Online display advertising: Targeting and
obtrusiveness. Marketing Science, 30(3), 389-404. doi:10.1287/mksc.1100.0583 Green, M. C. (2004). Transportation into narrative worlds: The role of prior knowledge and
perceived realism. Discourse Processes, 38(2), 247-266. doi:10.1207/s15326950dp3802_5
Green, M. C., & Brock T. C. (2000). The role of transportation in the persuasiveness of public narratives. Journal of Personality and Social Psychology, 79 (5), 701– 721. doi:10.1037/0022-3514.79.5.701
Green, M. C., Brock, T. C., & Kaufman, G. F. (2004). Understanding media enjoyment: The role of transportation into narrative worlds. Communication Theory, 14(4), 311-327. doi:10.1111/j.1468-2885.2004.tb00317.x
Green, M. C., Kass, S., Carrey, J., Feeney, R., Herzig, B., & Sabini, J. (2008). Transportation across media: Print versus film comparisons. Media Psychology, 11(4), 512–539. doi:10.1080/15213260802492000
Hamby, A., Brinberg, D., & Daniloski, K. (2017). Reflecting on the journey: Mechanisms in narrative persuasion. Journal of Consumer Psychology, 27(1), 11-22.
doi:10.1016/j.jcps.2016.06.005
Hamby, A., Daniloski, K., & Brinberg, D. (2015). How consumer reviews persuade through narratives. Journal of Business Research, 68(6), 1242-1250.
doi:10.1016/j.jbusres.2014.11.004
Hautz, J., Füller, J., Hutter, K., & Thürridl, C. (2014). Let users generate your video ads? The impact of video source and quality on consumers' perceptions and intended
behaviors. Journal of Interactive Marketing, 28(1), 1-15. doi:10.1016/j.intmar.2013.06.003
Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach [eBook]. Retrieved from
https://www.ebscohost.com
Hintzman, D. L., & Block, R. A. (1971). Repetition and memory: Evidence for a multiple-trace hypothesis. Journal of Experimental Psychology, 88(3), 297.
doi:10.1037/h0030907
Hinyard, L. J., & Kreuter, M. W. (2007). Using narrative communication as a tool for health behavior change: A conceptual, theoretical, and empirical overview. Health
Hirschman, E. C., & Holbrook, M. B. (1982). Hedonic consumption: Emerging concepts, methods and propositions. The Journal of Marketing, 46(3) 92-101.
doi:10.2307/1251707
Holt, D. B. (2003). What becomes an icon most? Harvard Business Review, 81(3), 43-49. Retrieved from https://hbr.org/
Hove, T., Paek, H. J., & Isaacson, T. (2011). Using adolescent eHealth literacy to weigh trust in commercial web sites. Journal of Advertising Research, 51(3), 524-537.
doi:10.2501/JAR-51-3-524-537
Hsieh, J. K., Hsieh, Y. C., & Tang, Y. C. (2012). Exploring the disseminating behaviors of eWOM marketing: Persuasion in online video. Electronic Commerce Research, 12(2), 201-224. doi:10.1007/s10660-012-9091-y
Interactive Advertising Bureau. (2008). IAB platform status report: User generated content, social media, and advertising – an overview. Retrieved from:
http://www.iab.net/media/file/2008_ugc_platform.pdf
Jenkins, H. (2003, January 15). Transmedia Storytelling. MIT Technology Review. Retrieved from https://www.technologyreview.com/s/401760/transmedia-storytelling/
Jin, Y., Bloch, P., & Cameron, G. T. (2002). A comparative study: Does the word-of-mouth
communications and opinion leadership model fit epinions on the internet? Honolulu,
HA: 1st Annual Hawaii International Conference on Social Sciences.
Jonas, J. R. O. (2010). Source credibility of company-produced and user-generated content on the internet: An exploratory study on the Filipino youth. Philippine Management Review, 17(1), 121-132. Retrieved from http://ovcrd.upd.edu.ph/pmr
Jung, C. G. (1969). Dogma and natural symbols. In C. G. J. (Ed.), Collected works of C.G. Jung, volume 11 (pp. 34-63). Princeton, New Jersey: Princeton University Press.
