Influencer’s Presence in the Ad: Does It Really Work?

Influencer marketing demonstrates a significant change with almost 65 percent of top brands using this channel in 2012 and growing rapidly since then (Gutman, 2020; Martinez-Lopez et.al., 2020). It is proven that some factors are more important while choosing the influencer representing the brand. Some of the source factors such as attractiveness, trustworthiness, credibility, closeness, similarity, likeability and others could have a different effect on customers decision making, purchase intentions or attitudes toward brands (Lim et al., 2017, as cited in Pick, 2020, p. 3, Lou & Yuan, 2019; Taillon et al., 2020; Wiedmann & Mettenheim, 2020). Therefore, it is essential to understand what factors of influencers the brand chooses and their criteria to do so.

The growing niche of marketing with influencers is still focusing on direct influencer brand endorsement in their own social me dia channels, not including influencer amplification models (using influencers presence in company’s ads on social media) that could ensure more significant gains from influencer campaigns (Swan, 2020; Schouten et al., 2020). However, there is still no current research analysing the effectiveness of advertising using influencers in a brand’s social media ads in comparison with other ads that a company uses.

It is claimed that attention-grabbing is one of the biggest problems in contemporary advertising and a creative idea is needed to achieve this (Barreto, 2013; Bernardin et al., 2008). This is even more important since researchers suggest that companies can impact consumer’s brand image through impressions (impressions are the view count of an ad (Woods, 2016)) without even leading to clicks (Dahlen, 2001). Therefore, it is more challenging than ever for brands to grab the client’s attention and well-known presenters could be a way to achieve this goal (Bernardin et al., 2008). All these possible changes and methods are important targeting millennials’ (or Generation Y) market group, since they are now the most significant living adult generation (Gapper, 2018) with an estimate of 1.4 trillion of spendings in 2020 (Mullen, 2020). Millennials were reported to spend more time on digital buying than traditional channels (Escandon-Barbosa et al., 2020) and were also reported to have an influence on the decision-making of other age groups (Migacz & Petrick, 2018), making millennials one of the most critical decision-makers currently.

To sum everything up, even though there are previous research papers that analysed the influencers influence on purchases through their direct channels (Zhang et al., 2018), there is not enough research done to analyse how influencer presence in the brand ads can affect the awareness of the brand’s social media ads. Therefore, the goal in this research is to analyse how influencer’s presence in the brand’s digital ads affects the attention of the millennial audience, taking into consideration the source factors of the presented influencer.

The following section offers a review of current academic literature related to the variables of this research: influencers and influencer marketing, paid social media advertising, awareness of ads and their elements (attention, conscious preferences, implicit memory) and source factors (attractiveness, similarity, likeability and closeness).

Influencer Marketing

An influencer in this analysis is defined as an influential social media user who can make an impact on their followers’ decisions (Zhang et al., 2018) and who has grown a sizable network of followers (Veirman et al., 2017), usually by sharing a part of their personal lives (Martinez-Lopez et al., 2020). Influencers should not be mistaken with celebrities, who created their fame through music, film or sport industries. Influencer marketing, in this case, is a communication strategy to promote and sell products through influencer’s content by affecting their followers’ base (Lou & Yuan, 2019; Wiedmann & Mettenheim, 2020). Despite the recency of influencer marketing, influencer endorsements have been found to have a more significant impact on audience purchase decision making than ones from celebrities (Djafarova & Rushworth, 2017; Jin et al., 2019; Schouten et al., 2020), and what is more relevant is that they are perceived as more credible (Djafarova & Rushworth, 2017).

Despite the popularity of straightforward influencer endorsement in their channels, another way to use influencers in digital marketing, could be an amplification model (Swan, 2020), or in other words, influencer’s content and/or face usage in a brand’s social media ads. Celebrities were used in ads, billboards or posters by companies for decades (Zhang et al., 2018) but companies are still hesitant with the idea of influencer presence usage. Having in mind that influencers have a higher trust by their audience (Wiedmann & Mettenheim, 2020), their product endorsements are seen as more personal (Jahnke, 2018, as cited by Wiedmann & Mettenheim, 2020, p.1) and, most importantly, have an even higher impact than celebrities (Djafarova & Rushworth, 2017; Jin et al., 2019; Schouten et al., 2020), influencer’s usage in brand’s social media ads could transfer those benefits to the brand’s ads and lead to a significantly better digital performance. Associative learning theory claims that audiences could create positive associations between two events, in this case, influencers associative perceptions could be transferred to brand’s perceptions (Plotnik & Kouyomdijan, 2012; Till, 1998; Till & Shimp, 1998; as cited by Taillon et al., 2020). Of course, it should not be forgotten that while influencers created their following in the digital world, the effect of their presence also would be stronger on digital channels such as Facebook or YouTube.

Attention in paid social media advertising

Social media advertising is usually seen as an advertising platform to create stronger and more trustworthy relationships between companies and their audiences, which is achieved by different types of content placements and advertisement through these channels (Dao et al., 2014; Raji et al., 2019). Having such a huge industry as social media advertising, which reached 92.8 billion dollars market size in 2019 (Statista, 2020), means having even more noise and better solutions are needed to succeed with paid social media advertising. Researchers have already proven the phenomenon of banner blindness, which means that social media users avoid looking directly at ads (Barreto, 2013; Munoz-Leiva et al., 2018; Porta et al., 2013). Both of these problems - growing companies’ competition and banner blindness - could be a reason for continuously falling click-through rates (CTR), which fell from 2.6% in late 2018 to 2% in late 2019 (Cooper, 2020). This proves that attention is one of the most significant issues in contemporary advertising and creative solutions are the only way to address this (Bernardin et al., 2008).

Even though grabbing an audience’s attention has become more complex, researchers proved that a brand’s image could be impacted through impressions counts of ads (Woods, 2016) even if those ads have not led to clicks (Dahlen, 2001). Impressions, in this case, is the ratio of how often your ads are shown to the audience (Google, n.d.) or how many times it is placed to grab the audience’s attention. However, the placement does not mean a guaranteed interaction and with a short attention span that brands need to deal with, it is even more complicated to grab a client’s attention. Therefore, Bernardin et al. (2008) claim that known presenters or endorsers could be a way to achieve this goal since they are exceptional and extraordinary ads are still more effective (Bernardin et al., 2008). This is no surprise, having in mind that while clients avoid paid advertising on social media, they seek to get the information from influencers (Childers et al., 2019); therefore the endorser’s presence (Bernardin et al., 2008) and their ability to transfer their positive perceptions to social media advertising (Plotnik & Kouyomdijan, 2012; Till, 1998; Till & Shimp, 1998, as cited by Taillon et al., 2020) could become a new and more vital part of the influencers marketing industry.

Source factors with an influencer

While analysing influencer marketing, the source factors such as attractiveness and its sub-factors similarity and likeability could not be forgotten and are taken in this analysis as separate elements, while analysing each factor’s effect as it was done in the previous researches (Amos et al., 2008). Also, the additional source factor - closeness - was taken (Taillon et al., 2020), where it already proved to have a mediating effect on purchase intentions and a possibility to become a buffer in the decision making if other source factors do not show the effectiveness. Researchers have already proven source factors’ relationship with influencer marketing, analysing these factors’ effects on decision making, purchase intentions, attitudes (Lim, 2017, as cited in Pick, 2020, p. 3; Taillon et al., 2020). However, according to the author’s current knowledge, there was no research done to analyse how source factors of the influencer could affect awareness of the ad with the influencer’s presence in it.

Since influencers as a source factor could transfer different effects (Plotnik & Kouyomdijan, 2012; Till, 1998; Till & Shimp, 1998, as cited by Taillon et al., 2020), it is needed to interpret the attitude that the audience has toward influencer, including most of the source factors (Ohanian, 1990) like: attractiveness - analysing whether the influencer is attractive to the audience (Amos et al., 2008; Taillon et al., 2020); likeability - analysing, whether the audience has positive attitudes toward influencer (Amos et al., 2008; Taillon et al., 2020); similarity - analysing, whether audience found themselves similar or the same to the influencer (Lou & Yuan, 2019; Taillon et al., 2020) and finally, closeness - analysing, how close the audience feels to the influencer (Taillon et al., 2020). All these factors, positive or negative, could affect the ad performance with an influencer, having in mind that these attitudes could easily transfer from the source factor to the brand promotion.

The following framework was developed for the empirical research to test the relationship between main variables (Figure 1).

Following the conceptual model, the hypotheses were raised (Table 1).

For the analysis, the experimental within-subjects approach was taken, combining eye-tracking and implicit memory experiment analysis together with the survey. The combination of these methods was used to analyse different relationships within the presented theoretical model. In various research, these techniques are usually used together to analyse different relationships or even path modelling (Monteiro et al., 2019; Muñoz-Leiva et al., 2018; Muñoz-Leiva et al., 2019; Porta et al., 2013). The hypotheses were tested through a survey and an eye-tracking experiment using an online integrated Eye-tracking program Cool Tool and prior coded Facebook duplicate which could be adjusted to the experiment requirements.

Since in this research the elements of ad banners could strongly influence the experiment results, the preparation stage of the research was to evaluate the clearance of the ads and their attention attraction percentages using Attention Insight artificial intelligence analysis. Artificial intelligence could not recognize famous faces and evaluates the ads only based on the ad elements. Based on the analysis, both ads had almost similar clarity scores where an ad with an influencer had 67% and without an influencer had 69%. Also, all the elements, such as logo or product photo, of the ad got only a few percent difference which leads to a conclusion that both ads should get a similar effect if we are comparing them from elements perspective. Therefore, the validity of the ads was proven.

Pre-test

Having in mind that the experiment took place in the home en vironment of every participant, the pre-test was taken to check the clearance of the tasks and the suitability of the collected data for the further analysis. The pre-test was performed with 6 participants and questions were adjusted based on their insights. This data was not included in the final analysis.

Main study

Participants. For this research, the millennial audience is taken since this generation group is analysed to be the most influential and the most significant spending generation group nowadays (Migacz & Petrick, 2018; Mullen, 2020). Also, this is the biggest audience or one of the most prominent age groups on all the leading social media channels Instagram (Clement, 2020a), Facebook (Clement, 2020b), YouTube (Clement, 2020c). All of this makes the millennial age group the most affected by ads and the most present with influencers. It is tested that millennials’ decision-making is mainly influenced by the people they can trust (Goldgehn, 2004), while four out of ten millennials claim that their favourite creators are more relatable to them than their friends (O’Neil-Hart & Blumenstein, 2016). Considering this, the participants are taken from a millennial audience, who were born from 1980 to 2000 (Goldgehn, 2004; Hoekstra et al., 2015). So, the participants are taken of the age group from 21 to 41 years old.

Ninety-one participants went through the full main study consisting of eye-tracking and implicit memory experiment analysis together with survey analysis. Data of 23 participants was not included in the results due to the poor quality of their eye-tracking data and only partially possible analysis. From the 68 participants, one was eliminated after answering that he/she does not use Facebook, one of the necessary factors in the analysis. Therefore, for the final analysis, only the data of 67 participants were used. The gender split of the respondents was almost equal, having 33 female participants and 34 male participants, which accounted for 49.3 and 50.7 percentages accordingly and eliminated the biases of different gender visual perception (Boscolo et al., 2020).

Eye-tracking analysis. The beginning of the experiment asked participants to go through instructions of setting up the eye-tracking tool correctly in order to track the movement of eye pupils.

The eye-tracking experiment consisted of one out of four randomly selected Facebook feed options with varying placements of the same ad in the Facebook feed to avoid confounds of placement. Every of four different options has an ad with an influencer presence and an ad without influencer presence in it. The experiment takes place in a website programmed to look like a Facebook interface and even though the objects are not clickable, this creates the closest possible environment to the Facebook platform. Participants are asked to scroll through the Facebook feed their eye movements are tracked and several indicators calculated:
1. Number of fixations - what shows how many times fixations were tracked for the area of interest (AOI) marked.
2. Fixation duration - the period of time of how long participants were fixated to the AOI.
3. Time to the first fixation - the period of time of how long it takes from the AOI presence on the screen to participants’ eye-fixation on the AOI.

All of these measurements are used to analyse the participants’ awareness of the ad banner

Implicit memory analysis. After the eye-tracking experiment the questionnaire takes place, starting with implicit memory/ self-report analysis. The construct for self-report analysis is based on the model from Munoz-Leiva et al. (2019) research where the recall of the advertisement and the elements in it were used. Two closed questions are used in this construct “Do you remember having seen any advertisement during your time on Facebook feed?” and “In case you recall any ad, do you like it?” with possible answers a) yes; b) no; c) I don’t know. As well as the qualitative question was used “In case you recall any ad, please describe elements in the ad.”, where the elements of the answers helped identify and categorize what kind of ads participant recall, so the categories were: ‘remembered only ad with influencer’, ‘remembered only ad without influencer’, ‘remembered both’, ‘remembered none of them’.

Questionnaire analysis of source factors. Finally, the moderator of source factors is analysed using moderators of likeability, similarity, attractiveness and closeness. The questionnaire is used to analyse the moderating effect. All source factors were analysed using 7 point Likert’s scale where 1 point corresponded to ‘strongly agree’ and 7 points stood for ‘strongly disagree’. Perceived attractiveness was analysed using Ohanian’s (1990) 4 item’s 7 points semantic differential scale, using 7 points Likert’s scale in this analysis. The likeability of the source was analysed using a three-item, seven points semantic differential scale construct adopted from the Um (2008) research just changing the celebrity likeability factors to influencer likeability factors. The perceived participant’s similarity to the influencer presented was measured using four-item seven points semantic differentiation scale from the research done by Schouten et al. (2020) while analysing Celebrity’s source factors; therefore, it was adopted to this analysis changing the questions from celebrity to the influencer. The closeness between the influencer and the participant was analysed using a single item pictorial image as used in the construct of closeness as a moderator in the research of Taillon et al. (2020) used from the analysis of interpersonal connectedness from the research of Aron et al. (1992). The image shows two touching/overlapping circles showing the variation of closeness level between influencer and participant. Respondents are asked to pick the best representation of their closeness level based on this image.

Eye-tracking analysis results

Consumer fixation count, fixation duration on an ad is higher and time to first fixation is lower if there is an influencer presence in the ad. A paired t-test was performed to ascertain whether influencer presence in the ad is effective and the ‘FC with influencer’ mean is higher than ‘FC without influencer’, ‘FD with influencer’ mean is higher than ‘FD without influencer’ and ‘TFF with influencer’ is lower than ‘TFF without influencer’. As it could be seen in Table 2 for analysis of FC the mean of the difference (M = 2.492, SD = 7.171, N = 59) was significantly greater than zero, t(58) = 2.669, one-tailed p = 0.005, providing evidence that fixation count to an ad with an influencer is higher. The mean of the DF difference (M = 1.424, SD = 4.36, N = 59) was also significantly higher than zero, t(58) = 2.508, one-tailed p = 0.0075, showing the proof that fixation duration is affected by influencer presence in an ad. For the evaluation of the difference between the TFF means (M = 1.576, SD = 29.707, N = 59), the significance level is p = 0.34, which indicates that there is no significant difference between the means and hypothesis is not supported (Table 2).

Table 1:Research Hypotheses.

Table 2:Paired t-test Analysis.

Implicit memory test results

Consumer implicit memory of the ad is more likely if there is an influencer presence in the ad. Since implicit memory test data was provided in the qualitative data form, it was encoded to the categories: ‘remembered only ad with influencer’, ‘remembered only ad without influencer’, ‘remembered both’, ‘remembered none of them’. The data was distributed based on the frequencies and provided in Table 3, 2 participants have not indicated any elements so were not included, 15 participants have not remembered any of the ads or reported only the ads not involved in this analysis, 8 participants indicated both ads and also were not involved in the evaluation. Finally, while analysing the difference between a recall of an ad with an influencer (N = 18, 30.5%) and an ad without an influencer (N = 16, 27.1%), the hypothesis is not rejected, since more respondents were able to recall an ad with influencer directly or an elements from the ad (Table 3).

Table 3:Frequencies of Data in the Implicit Test.

Moderators test results

Consumer fixation count and fixation duration differences between the ad with the influencer and the ad without influencer is higher if the consumer has a stronger level of source factor of the influencer presented in the ad. To analyse whether the attractiveness, likeability, similarity or closeness source factors of the influencer affects the difference of ‘FC with influencer’ minus ‘FC without influencer’ and ‘FD with influencer’ minus ‘FD without influencer’, and to prove that the moderator strengthens the relationship by increasing the difference, multilinear regression analysis was used and the results of FC difference provided in Table 4 and Table 5, while the results of FD difference provided in Table 6 and Table 7. The matrix scatterplot has not shown any easily identifiable linear relationship between all of the moderators and FC or FD differences. Therefore, in multilinear regression analysis, all of the independent variables were used together. Based on the results provided in Table 4 and Table 6, the multilinear regression analysis results showed that ANOVA F-test for FC difference has a p = 0.811 and for FD difference has a p = 0.395, indicating that there is no strong model fit. The coefficient levels for FC difference provided in Table 5 (attractiveness - t(52) = -0.146, p = 0.885; likeability - t(52) = -0.230, p = 0.819; similarity - t(52) = -0.189, p = 0.851; closeness - t(52) = -1.171, p = 0.248) showed no significance to the model, therefore, should not be considered as the variables affecting the difference between ‘FC with influencer’ - ‘FC without influencer’. Meanwhile, coefficients of multilinear regression analysis of FD difference shown in Table 7 (attractiveness - t(52) = -0.520, p = 0.606; likeability - t(52) = 0.792, p = 0.432; similarity - t(52) = 0.350, p = 0.728; closeness - t(52) = -1.420, p = 0.162) also indicates no significance and source factors should not be considered as the moderator having an effect on the difference between ‘FD with influencer’ - ‘FD without influencer’ (Table 4,5,6,7).

Table 4:Multilinear Regression Analysis ANOVA for ‘FC with influencer’ - ‘FC without influencer’

Table 5:Multilinear Regression Analysis Coefficients Table for ‘FC with influencer’ - ‘FC without influencer’.

Table 6:Multilinear Regression Analysis ANOVA for ‘FD with influencer’ - ‘FD without influencer’.

Table 7:Multilinear Regression Analysis Coefficients Table for ‘FD with influencer’ - ‘FD without influencer’.

Consumer implicit memory of the ad is more likely if the consumer has a stronger source factor of the influencer presented in the ad. To evaluate the moderation effect of the attractiveness, likeability, similarity and closeness source factors on an implicit memory, where the dependent variable is in the nominal scale, multinomial logistic regression is used. The warning in the results indicated that Pearson and Deviance criteria for the model fit evaluation are not applicable, which is a common practice when a model has more than one regressors. In the ‘Case Processing Summary’ table none of the categories were dominating, which is important for reliable model information. In the ‘Classification’ table the model correctly identified 58.8% of the “only influencer ad” category, 33.3% “only ice cream ad” category and 46.7% of the “none of the ads” category correctly. However, McFadden pseudo R2 is all lower (p = 0.086) than 0.2, which indicates a poor model fit for data (good model fit is from 0.2 to 0.4) and this is ensured by model fitting significance level where p = 0.498, which is more than 0.05 and indicated no significance and no model fit, where none of the variables are affecting the dependent variable. This could also be seen from the Likelihood Ratio Tests table, where attractiveness (p = 0.118), likeability (p=0.751), similarity (p=0.985) and closeness (p=0.583) - all have no significance . Therefore, source factors as moderators are seen as not affecting the relationship between influencer presence in the ad and participant’s implicit memory of the ad.

To sum up, hypotheses H1, H2 and H4 were supported, while one of the main hypotheses H3 and all the moderating hypotheses with source factors had not shown significance and were qualified as not supported.

The analysis showed that influencer presence in ads could increase ad effectiveness while increasing the visual attention factors of the ad such as fixation count and fixation duration and only the data showing the effect on time to first fixation. While the current theory already shows the celebrity presence in the ads has an impact on ad effectiveness (Schouten et al., 2020), this research adds the findings that similar effects could be achieved using influencers instead of celebrities.

Secondly, the analysis proved that influencer presence in the ads has an impact on the working memory. It is already proven that unconscious brain motivation (Michael et al., 2019), as well as the unconscious awareness of ads (Hennessey et al., 2010; Mc- Clure et al., 2004), are important factors in the decision-making journey since the ability to recall the ad shows the higher impact on frontal and parietal area of the brain which is responsible for decision making (Balconi et al., 2014). The research done in this thesis added to the current influencer marketing academic theories information that influencer presence in the ads increases the ability to recall the ad, which means it affects the parietal and frontal brain areas more and affects the decision making.

Also, the analysis showed that even though the source factor as a moderator or a mediator has an impact on direct influencer marketing, it has not demonstrated any significance on the relationship between influencer presence in the ads and attention to the ads. This is a significant finding since this shows that very different factors are important while using influencers in various types of advertising, and one of the proposals for future research is to find those needed attributes of the influencer. Results of this research brought new information not only to the current academic literature but to practical marketing application. Since the current influencer marketing industry focuses on direct influencer marketing, this research showed that amplification of the influencers could be a new revenue stream for both creators and companies. Adding to this, due to banner blindness it gets harder for companies to become seen on social media, therefore this research showed that using influencers could become an easier approach to get the attention of the audience. Also, since recall of the ads is higher with the influencer presence in the ads, showing that it is more likely to stay in customer’s memory, so creating ads using influencers could become a branding factor for companies. Finally, having in mind that an ad with an influencer presence has a longer fixation duration could mean that while creating the ads, marketing managers could include more information in the ad and still expect it to be encoded by the audience.

Therefore, to the author’s knowledge, being the first neuromarketing research in the influencer marketing area and analysing how influencer presence could affect social media advertising brought a whole new topic for future research and practical application for companies.

To summarize this analysis, it could be concluded that the goal of this research was reached and the hypotheses analysed. Firstly, the analysis of the current academic literature about influencer marketing, paid social media advertising, attention of ads and source factors was analysed, and a link between those factors was found as well as the gap of the research regarding whether influencer presence in the ad has an impact on consumer attention. Based on the literature review, the conceptual model was created (Figure 1) together with research design and methodology on how to perform this research using eye-tracking analysis together with implicit memory test and survey to fulfill the research of all the hypotheses in the conceptual model. The experiment was performed for the sample of millennial audience and results analysed using paired t-test, regression, frequencies and multinomial logistic regression. After the analysis, it was concluded that the influencer presence in ads could affect the fixation duration, fixation count and ability to recall the ad; therefore these relationships were not rejected, differently than the relationship between influencer presence in ads and time to the first fixation on the ad. Also, the source factors were used to moderate these relationships and all the moderations were not supported, showing that these factors are not the ones affecting the relationships. Results of this research provided new information to the current research of influencer marketing and brought a new topic that could be deeper analysed in the future research. Also, analysis provided new practical methods for social media advertising and influencer marketing that could be used by managers to grab audience attention.

None.

No Conflict of interest.

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