Keywords

Narratives Advertising, Transportation Theory, Gratification Theory, Lexicon Analysis, Text Analytics.

Introduction

Nowadays marketers give importance to developing strong consumer brand relationship. They strive to create an image of their brands which connects with consumers’ emotions. In order to do so they continuously work towards right positioning of their brands in market. The advent of technology and social media has changed the way marketers advertise their products. Social media has allowed marketers to directly reach consumers. Brands are utilizing this mode of communication to engage with consumers by establishing their presence on the social network platforms (SNP). With this, new age advertising tools like online video narratives are becoming popular, where they are being used as a means to achieve desired engagement with end users. These narratives cast an impression on the viewers, which results in building perception towards the brand. They attempt to evoke the emotions of consumers with the help of an involving story and transporting them to another emotional state. Presently, online video narratives are becoming increasingly popular with brands. This is because the effects of an engaging online narrative are above and beyond the advertisement itself.

Since, the companies are increasingly investing in promoting their brands through online video narratives; this study aims to analyze this relatively new format as a tool for consumer engagement. It has made an attempt to understand the effectiveness of narrative advertisements in transporting viewers to intended emotional state. The study thus, sheds light on what may be the viewer’s emotional response to the advertisements. The work revolves around online journey of audiences, which starts from viewing the narrative advertisement to expressing their views. In the present study given its aggressive narrative based campaigning on Facebook, BIBA which is a women ethnic apparel brand has been chosen for the study. Comments on the brand’s Facebook page advertisements have been collected using text mining techniques. These comments were then analysed using emotion-based lexicon and text analytics. The results indicate, whether or not, themes of narrative contents resonate with the viewers’ responses to them.

The nature of the study conducted is diagnostic where researchers investigate the viewers’ brand experiences created through online video narratives. This is examined by studying the dialogue behavior of viewers pertaining to the online video advertisement. The study has been conducted in two phases. In first phase, the researchers have identified intended emotions of narratives by focus group ranking. And in second phase, viewers’ response is analysed through lexical analysis of comments. In the lexical analysis, the emotions evoked through these narratives are categorized into eight distinct emotions namely, ‘anger’, ‘anticipation’, ‘disgust’, ‘fear’, ‘joy’, ‘sadness’, ‘surprise’ and ‘trust’. After which the gap analysis between intended and evoked emotions have been carried out. The remaining of the paper is organised as follows. Section 2 discusses related research on experiential marketing. Section 3 introduces objective, data and methods used in the study. Section 4 discusses empirical results of the study and section 5 concludes the work.

Literature Review

Internet has become an integral part of everybody’s life today. It is estimated that by 2022, there will be 6 billion internet users worldwide (Morgan, 2017). The continuous breakthroughs in web and mobile technologies have transformed how marketers communicate with their customers. With the introduction of social media in consumers’ journey today, the game plan has changed for every brand. The arena of ever-evolving social media throws its own challenges, which has the potential of defying the ground rules and theories of traditional marketing.

Social network platforms (SNPs’) play a pivotal role in extending brand’s realm to online experiential marketing, which leads to enhancing the consumer’s brand relationship (Holt & Holt, 2004). Blackston (1993) and Fournier (1998) introduced the concept of consumer brand relationship in their respective research studies. Later Fajer & Schoutens’ article discussed and contributed to relationship theory in consumer research area (Fajer & Schouten, 1995).

Relationships can be categorized on the basis of functional connections and emotional connections. Consumers relate with brands as people and personalities. The concepts such as “brand love” etc are manifestation of mind share and degree of involvement a consumer has with brand (Blackston, 1993).

In the marketing literature the concept of customer engagement is still not established completely. Engagement with regards to brands was studied by Sprott and others (Sprott et al., 2009). A total engagement index was derived by Patterson which adds up all the values of engagement item like loyalty, duration, content clicked, recentness, duration, interactivity and subscription (Patterson et al., 2006). Customer engagement is conceptualized as a psychological state in the marketing literature. This concept is derived from parent disciplines such as social psychology and organizational behavior (Mitussis et al., 2006), which characterize customer engagement by a degree of dedication, absorption, vigor and interaction. According to Newman & Harrison, an online social platform customer engagement is defined as ‘the level of a customers’ emotional, cognitive, and physical presence in association with a particular online social platform (Newman & Harrison, 2008).

To further understand the social and psychological needs and pleasures derived by the consumer-brand-relationship, one must understand the kind of gratifications sought and obtained from these relationships. People actively seek specific media and content to satisfy specific needs. Social media is often opted for entertainment purposes or to sought emotional gratification (Bartsch & Viehoff, 2010).

One of the key motivational driver for social media usage is the desire to experience emotional gratification. Gratifications can range from different levels, from hedonic to complex gratifications such as the satisfaction of cognitive needs and social needs. Concepts such as narrative engagement (Busselle & Bilandzic, 2009) and transportation (Green & Brock, 2000) are examples of gratification of emotional experiences.

With evolving media space, the formats and ways in which advertising takes place are also changing. Gilmore & Pine, defined experiential marketing as one which engages consumers in events or experiences and makes them active participants (Gilmore & Pine, 2002). Such experiential marketing content appeal to their senses and at the same time provide the consumers with information to make decisions. There are many formats of online experiential marketing (e.g. chat rooms, blogs and virtual communities) but, narrative advertising has attracted considerable amount of attention.

The total Internet video traffic is predicted to be 80 per cent of all Internet traffic by 2021 globally (CISCO, 2019). Among so many content thrown by brands in social media space, internet based narrative advertising is considered an important and growing venue to engage consumers (Matzler et al., 2008).

Advertising is an essential part of communicating products and services to the consumers (Wells et al., 1989). It is majorly of two types (i) argumentative and (ii) narrative advertisements respectively (Boller & Olson, 1991). Argumentative advertising are based on logical arguments and present fact-based information (Deighton et al., 1989). Whereas, narrative advertising revolves around a story or share related experiences (Lien & Chen, 2013).

Narrative advertising persuades the consumer by appealing to their affective and emotional responses (Phillips & McQuarrie, 2010). The schematic design of this kind of advertising is rooted in drama and storytelling (Deighton et al., 1989). It evokes a person’s emotions, appealing to their cognitive and affective capacities (Matzler et al., 2008). Research has found that this advertising has better persuasive capacity, which facilitates changes in attitudes and beliefs towards brand (Chang & Liu, 2009). Green & Brock investigated elaboration likelihood model (ELM) which is one of the traditional persuasion models (Green & Brock, 2000). In one of the seminal studies conducted by Escalas, (2004) it is proposed that the narrative-based persuasion is driven by narrative transportation theory. Transportation is conceptualized as immersion of viewers’ while experiencing narrative’s plot. The literature suggests that by using storytelling as key ingredient in narratives, brands can captivate and mesmerize the audience through unfolding of event. The viewer is transported to the narrative world (Escalas, 2004b). The next step in the process of transportation is when the consumer begins to relate to the characters world (Escalas, 2004a). Padgett & Allen, describe the internet based narratives as the one which communicates an experience that unfolds in a dramatic sequence of events and captivates the audience (Padgett & Allen, 1997). They are also called drama ads or a slice-of-life (Mick, 1987, Swatman et al., 2006). These ads are popular for delivering an involved, self-relevant experience (Bruner, 1986, Padgett & Allen, 1997), while presenting the intangible benefits of the product (Batat & Wohlfeil, 2009).

In advertising research, transportation is also referred as an immersion process in which viewers of a narrative get deeply absorbed in the narrative world (Escalas, 2007; Green & Brock, 2002, Wang, 2006). Escalas in his studies concludes that narrative transportation uses the process of mental simulation which is construction of hypothetical scenarios in the cognitive minds of viewers and generating a higher transportedness (Escalas, 2004a). As studied by Green and Brock, it is the viewer’s cognitive attention to the events and their emotional involvement to characters with whom they identify that transportedness is evoked using mental imagery mechanism (Green & Brock, 2000).

The review of relevant literature on narrative advertising suggests that there are two key characteristics which contribute to the successful transportation of its viewers. The realism of a narrative (Escalas, 2004b) and direct experience consequently resulting in attitude change (Fazio & Zanna, 1981). Further research revealed that with state of highly transportedness among viewers, the chances of critical evaluations of facts and arguments are less, thereby resulting in favorable emotions (Chang & Liu, 2009).

Internet platforms by virtue of their nature and technical capacities support unique features (Jiang & Benbasat, 2007), including receptivity and interactivity between the consumer and marketer (Childers et al., 2001). In the new media of SNPs, users are having more control over their content viewing experiences in terms of what they view, how they view it, how long they view it (Zeff & Aronson, 1999).

In social media space, consumers are proactively exercising their powers also classified as customer-initiated behaviors such as interacting with brands or fellow consumers or potential consumers (Maslowska et al., 2016). Doorn et al. calls them customer engagement behaviors. These behaviors can be writing reviews of product and services, generating e-word of mouth (e-wom) or posting a comment (Van Doorn et al., 2010). Such behaviors assist the marketers in understanding viewer’s reaction to the brands initiatives and content.

There is a lack of literature with regards to consumer emotions evoked through internet based narrative. This study by using text mining techniques gives insight on gap between intended and perceived emotions of narrative advertisements. It is quite evident in marketing research that emotions affect attitudes and behaviors towards brands (Lin & Mattila, 2010).

Methodology

Objective

The objective of the study is to provide comparison between the two dimensions of the narrative advertisements a) intended or expected emotions to be evoked and b) emotions actually evoked by the narratives.

Data, Methods and Tools

In the present study given its aggressive narrative based campaigning on Facebook, BIBA which is a ladies ethnic apparel brand has been chosen for the study. Three potential narratives of the brand were selected. The narratives utilized for the study were published in the period of May, 2016-Aug, 2017. The data was captured from September, 2017-October, 2017. The narrative advertisement were identified and selected, by researchers primarily on following key criterion:

1. Narrative advertisements should be popular on social media.

2. Should have active participation from the social media users in forms of comments.

3. Should not offer any information regarding product or service in any particular way.

In first phase of the study, focus group ranking of intended emotions of narrative advertisements were conducted to understand the marketers’ perspective. Four academia and industry professionals, associated previously or currently in the field of brand management participated in the discussion. The captions of the campaigns performed as preliminary set of descriptors for the intended emotions by the brand refer to Table 1. The ranking was done on eight emotions namely, anger, joy, fear, trust, surprise, anticipation, sadness and disgust.

Table 1 Online Narrative Campaigns
S. No.	Online Narrative Caption	Date
1	Change the Perspective	7th May 2016
2	Change the Convention	13th Dec 2016
3	Change the Question	29th Aug 2017

Second phase of the study, allowed the researchers to compare between intended and evoked emotions’ ranking. For the second phase of the study, the researchers used and analysed the comments from Facebook page of the brand. The comments were posted on Facebook under the three online video narrative campaigns of BIBA. Online comments were selected for study because the nature of data in digital space is real time and not controlled by any scheme of design or experiment. In total a corpus of 1337 comments were gathered from three online narrative videos.

To quantify the comments, text analytics tools were used. Text analytics approach uses natural language processing (NLP) and text mining to mine sentiment, emotions and opinions toward the brands, services and products. Online comments were collected using data mining tool called web crawling. R software was used for acquiring the data from BIBA’s official Facebook page by creating a Facebook Application Programming Interface (API).

After collecting the comments, in the first step, data was cleaned to reduce the chances of ambiguity and repetitions. Cleaning the non-ASCII words is a necessary step, to remove all the non-English words. The data is further processed for a spell correction algorithm. Thereafter, using the Stanford Part of speech (POS) tagger module, all the words in comments were tagged by their corresponding POS. This process is also called stemming. Stemming is used to reduce modulated words to their root or base.

Stop words like is, as, an, a, the etc are filtered out before processing the comments for analysis. It is followed by tokenization. Tokenization breaks textual content like terms, words, symbols or some other meaningful elements into tokens.

The study uses National Research Council (NRC) predefined lexicon in R software for classifying the text corpus (comments) into eight distinct emotions. This lexicon comprises of 14,182 unigrams (words) which have 25,000 senses (contextual meanings). The NRC emolex helps in categorizing the words used in comments according to the emotions they represent. This lexical analysis of the corpus categorizes emotions embedded in comments of online narratives into eight emotional bands. They are ‘anger’, ‘anticipation’, ‘disgust’, ‘fear’, ‘joy’, ‘sadness’, ‘surprise’ and ‘trust’ (Mohammad & Turney, 2010). To quantify emotions in each comment, simple count of words related to a particular emotion has been carried out. For example, if in a comment words related to joy has come 3 times, the researchers have simply given the joy score of that particular comment as 3. Once all the comments have been quantified according to all eight emotions, one-way analysis of variance (ANOVA) has been used to determine whether there are any statistically significant differences between the means of a particular emotion across the three advertisements, e.g., anger scores across the three campaigns.

ANOVA gives result on significant differences among mean scores between two or more groups, which represents that at least one group differs from the other groups. Yet, this test is incapable in informing on the pattern of differences between the means, or in other words to rank the groups. In order to analyze the pattern of difference between means, the ANOVA is often followed by specific comparison test, i.e., Tuckey HSD test (Abdi & Williams, 2010). Tuckey developed a simple and frequently used pairwise comparison technique, which is named after him as Tuckey’s honestly significant difference (HSD) test. The main idea of the HSD test is to compute the honestly significant difference (i.e., the hsd) between two or more means using a statistical distribution (Abdi & Williams, 2010).

The researchers have conducted both ANOVA and Tuckey HSD test to rank actual emotions evoked by the three advertisements. The results revealed the dominant emotions carried in each of the three videos. This analysis helped in comparing the results between phase 1 and phase 2 of the study. In other words, comparing the results between expected versus perceived emotions by the viewers of online narrative advertisements.

Analysis and Results

The objective of the study is to understand the incongruity, if any, between the intended emotions supposed to be evoked and emotions actually evoked among the audience of online narrative advertisements.

This objective was achieved in two phases. First phase was designed to unveil the intended emotions of the campaigns. This was achieved by ‘focus group’ discussion among experts. Four experts were requested to rank the campaigns. These experts were chosen based on their earlier exposure in advertising field. The experts were asked to rank the campaigns in order of intended emotions evoked by the campaigns. They ranked the campaigns by looking first at the advertisement caption and then at the video. All three advertisements were ranked on all the eight emotions. These four experts first ranked individually and then they aggregated the ranking mutually. The results are summarized in Table 2.

Table 2 Interview Results
Emotions	Campaign1Change the Convention (Rank)	Campaign2 Change the Perspective (Rank)	Campaign3Change the Question (Rank)
Anger	1	2	3
Joy	2	3	1
Fear	1	2	3
Trust	1	2	3
Surprise	1	2	3
Anticipation	1	3	2
Sadness	2	1	3
Disgust	1	2	3

In second phase, the comments extracted from Facebook page of the brand were dispersed in different shades of emotions. In this phase the researchers analysed the receptivity of the narratives. This was achieved using text mining techniques through NRC emolex lexical analysis. The derived eight emotions were compared across three campaigns using one-way ANOVA. This test was conducted to compare the three online narrative campaigns’ mean scores in eight different emotions. The ANOVA results are summarized in Table 3.

Table 3 Anova Results
Emotions	Campaign1 (mean scores)	Campaign 2 (mean scores)	Campaign 3 (mean scores)	ANOVA Score (P values)	Interpretation
Anger	0.056	0.024	0.0179	0.0304	Significant
Joy	0.4456825	0.3143236	0.2276786	0.000684	Significant
Fear	0.1142061	0.1856764	0.08928571	0.00156	Significant
Trust	0.2952646	0.270557	0.2008929	0.213	Insignificant
Surprise	0.1364903	0.1193634	0.1026786	0.535	Insignificant
Anticipation	0.2674095	0.2374005	0.1651786	0.104	Insignificant
Sadness	0.1002786	0.1002786	0.09821429	0.00115	Significant
Disgust	0.02228412	0.01591512	0.02678571	0.597	Insignificant

Then post-hoc analysis of one-way ANOVA results was conducted using Tukey HSD. The results of which are encapsulated in Table 4.

Table 4 Tuckey HSD Rank-Wise Categorizations of Emotions Evoked Through Each Campaign
	Campaign	Ranks
Ranks	1	2	3
Category 1
Joy	Camp 1	Camp 2	Camp 3
Category 2
Fear	Camp 2	Camp 1	Camp 3
Sadness	Camp 1	Camp 3	Camp 2
Category 3
Anger	Camp 1	Camp 2	Camp 3

The findings are interesting and offer a post-facto glimpse of the effectiveness of the campaigns, particularly when video campaigns were originally designed and scripted with certain intended emotions whereas, emotional reception of the advertisements showed different patterns. In table 2, the intended emotions evoked by the three narrative campaigns are ranked according to intensity of emotions, i.e., 1^st being most intense and 3^rd being least intense. The campaigns were ranked on eight shades of emotions ranging from “anger” to “disgust”.

In Table 3, ANOVA results are summarized and interpreted. The ANOVA p-value represent the acceptance or rejection of the null hypotheses. If p-value is less than 0.05 we reject the null hypotheses and accept the alternate that there is significant difference in mean scores of that particular emotion across the three campaigns. These mean scores which are given in the table 3 have been generated from NRC emolex.

ANOVA generates the inter narrative comparison for each emotion. From the Table 4 it is clear that, p-values of only four emotions are less than 0.05, i.e., only four emotions’ mean scores are statistically different from each other. These emotions are joy, anger, fear and sadness. It also means that, only these four emotions are statistically different from each other across the three campaigns. In the next step Tukey HSD Post hoc test for significance of differences at 95 per cent confidence limit was conducted. The result is summarized in table. 4. From the results it can be seen that campaign 1 was most effective in evoking a kaleidoscope of emotions ranging from Joy to Sadness.

The study’s objective of understanding the incongruity, if any, between the intended and actual emotions evoked among the audience of the online narrative advertisements can be achieved by comparing Table 2 and Table 4. It can be seen that except anger all the three emotions’ ranking are different in the two phases. Which also means that only anger’s ranking given by experts and inferred from the comments of viewers are same. All the other seven emotions are not statistically ranked similar as experts ranking.

Conclusion

This study has contributed in extending our understanding on evaluating new format of consumer engagement, i.e., online narrative advertisement. This research work went through the viewer’s journey on online media platforms. It started with the process of viewer’s engagement on online media to comprehend the complex dynamics of emotional response. The manifestation of viewers’ response, which evolves from cognitive placement of brand, is reflected through their overt online behavior such as e-wom, comments or participation in other brand activities. The present study has investigated the new brand engagement tool, i.e., internet based narrative advertisements which touches the viewer’s cognitive and affective.

The content is king today, hence it is essential for every brand to convey the right message while striking the correct emotional chord. A lot of efforts go in curating the content for right brand communication which is in sync with brand positioning. Hence, this research work tried to bring out the findings which have considerable and important managerial implications for brand managers and creative industry. Through analyzing the three online video narrative of a popular ethnic apparel brand, the researchers were successful in showcasing the difference in intended and actual emotions evoked by the campaigns.

Results of the study are very promising in throwing light on the brand perception created via the narratives. The categorization of textual corpus in eight emotional shades, inter campaign comparison on these eight emotional categories and further rank wise distribution of the three campaigns reflect the robustness of the analysis performed to bring out the results with discrete details. This is a unique methodology which can be useful for other studies of consumer perception towards brands. At the same time given the fact that there are plethora of text data in form of comments on social media platforms and on e-commerce sites, brands can use this methodology to evaluate effectiveness of their marketing communication.

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