The effect of micro-influencers on purchase intentions

Hele tekst


The effect of Micro-Influencers

on Purchase Intentions

Master Thesis

MSc in Business Administration - Marketing


Statement of Originality

This document is written by Student Laura Probstnerová who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.



The increased use of UGC as a marketing tool has led to industrialization of YouTube and redefined influencer marketing. As it is still a relatively new phenomena, there has not been much academic research done on this topic. This thesis was meant to investigate the relationship between the size of the influencer and purchase intention of the consumer, focusing on the mediating effect of source credibility and attitude. In accordance with the theoretical framework of the source credibility and the theory of reasoned action, the results of this study suggest that smaller influencers indirectly effect the purchase intention of the consumers via perceived credibility and attitude. Surprisingly however, the results also show that influencer size has no direct effect on source credibility, attitude, nor the purchase intention.

Key words: Word of mouth, influencer marketing, message source credibility, user generated content, YouTube


Table of Contents

1. Introduction ... 5

2. Literature Review ... 8

2.1. Word-of-Mouth ... 8

2.1.1. User Generated Content ... 9

2.2. Influencer Size ... 10

2.3. Purchase Intentions ... 13

2.3.1. Attitudes ...14

2.3.2. Theory of Reasoned Action ...15

2.4. Perceived Credibility ... 15 2.5. Conceptual Model ... 18 3. Methodology ... 19 3.1. Research Design... 19 3.2. Procedure ... 19 3.3. Measures ... 21 4. Results ... 23 4.1. Respondents ... 23 4.2. Reliability ... 24 4.3. Hypotheses testing ... 25 5. Discussion ... 28


5.1. Theoretical and Managerial Implications ... 28

5.2. Limitations and Future Research ... 30

References... 32




By the time you finish reading this sentence, more than 500,000 videos will have been watched on YouTube globally (Cohen, 2018). Meanwhile, statistics prove a constant decrease in TV viewership and simultaneously increasing trend of YouTube worldwide viewership, which already surpassed the time consumers spend on Facebook or Netflix (O’Neil-Hart & Blumenstein, 2016; Nielsen, 2017). This shift in the entertainment world and the loss of influence of traditional media calls marketers all over the world to arms. As more people start turning to peer endorsements and electronic word-of-mouth (e-WOM), seeking authentic opinions and reviews, it is necessary to find a less conspicuous and more effective alternative to advertisement.

Marketers are spending increasingly more for elaborate advertising campaigns and branded content on social media, in an attempt to be heard in the crowd, fighting for seconds of consumers’ attention (Holt, 2016). However, the immense overload of advertisement messages is making consumers skeptical and brands less relevant. People are even willing to pay for their services a little extra (Netflix, Spotify), just to avoid any advertisements. In an effort to escape advertisement, adblockers have developed that allow people to avoid ads by blocking them. Yet, the one thing that cuts through all the noise and makes up consumer’s mind is free and cannot be blocked – a word of mouth from a trusted source.

While consumers are less engaged with ads and seemingly disingenuous celebrity endorsements, they rather rely on recommendations expressed directly to them. A Nielsen global survey (2013) proved that word-of-mouth is the most trusted source of information, as well as most likely to be acted upon. Moreover, when deciding for a purchase, people tend to look for a review, comparison or a recommendation for a product themselves, trusting someone, whom they not


behind such endorsement (Weiss, 2014). These experts, or influencers, are sharing their opinions in the form of (usually) self-made posts, videos or photos, also called user-generated content (UGC). However, ever since the biggest influencers have been widely recognized, celebrated and praised in the traditional media, they have quickly transformed into mainstream celebrities, which according to Fristad & Wright (1994) triggers the persuasion knowledge and decreases the trust in the advertisement. UGC is now close to being professionally-produced, increasing the institutionalization of YouTube, as well as consumer’s skepticism as the intention behind influencer’s endorsement are being questioned (Kim, 2013).

A study by influencer marketing platform Markerly (2016) showed, that while smaller influencers (less than 1,000 followers) were reaching like rate of about 8 percent, the ones with up to 10,000 followers had a like rate of 4 percent. However, the study doesn’t stop there. They discovered, that as the follower base was growing in size, the like-to-follower ratio was decreasing, with the like rate of only 1.7 percent at the 1 million – 10 million followers level. They later continue to conclude, that so-called micro-influencers within 10 – 100 k followers range are perfect combination of reach and engagement.

These micro-influencers act as real people who happen to be everyday experts, with higher reach than average person and with highly targeted and niche follower base, but not big enough to have their credibility and approachability questioned (Berger & Keller Fay Group, 2016). Recent evidence points to the trend of small- to middle-sized influencers (micro-influencers) yielding better results in converting consumers and persuading strength of the advertising message (WOMMA, 2016). Moreover, using YouTube as a marketing tool might result in even higher customer engagement, since it is the second largest search engine in the world and people use


Micro-influencers find themselves in a research gap, between influencer marketing, which has in fact already become closer to a celebrity endorsement; and peer endorsement. Celebrities and likewise the biggest influencers are great at creating awareness. However, micro-influencers are better at the consideration and action phase of the marketing funnel, specifically purchase intention and conversion. Keeping in mind the limited scope of this study, only the former one will be tested.

With this study, I am aiming to contribute to existing scientific literature on user-generated content and influencer marketing on social media in several ways. Firstly, its goal is to prove that credibility and attitude towards the influencer impact consumer’s purchasing intention. Secondly, that number of followers and the scope of publicity affects the authenticity and credibility of an influencer. In practice, this research is meant to help managers and brands to decide for the right strategy and right influencers when creating social media marketing strategy plan.

Hence, my research question goes as follows:

What is the effect of micro-influencers compared to macro-influencers on purchase

intention and what is the mediating effect of perceived influencer credibility and attitude



Literature Review

Following pages will define main variables of the research question and explain how they are connected to each other. Starting with some background to influencer marketing, I will talk about word of mouth and content generated by consumers, and how are these two concepts related to influencer marketing. Next, I will explain why does the influencer size matter and how it can impact the attitude and purchase intention. Lastly, I will introduce source credibility as the mediator in this relationship.



As already proven by many studies, among which is also that of Jeong & Koo (2015), consumers find user-generated electronic word of mouth (e-WOM) to be more trustworthy and reliable than any communications from brands’ side. Complementary to their study, a research on social e-WOM via Facebook and its effects on brand attitude and purchase intention of brands has been conducted (Kudeshia & Kumar, 2017). The authors have found a direct and positive relationship between social e-WOM, brand attitude and purchase intention. Purchase intention, described as a cognitive behavior and intention to buy a particular brand (Shah et al., 2012), is therefore closely related to WOM. According to WOMMA (2010), internet-based WOM transmitted through social media has become a determining factor that drives return on investment. Similarly, Weiss (2014) in her article explains the rise of the skepticism over traditional marketing and media and how WOM is the most trusted source of information, as well as most likely to be acted upon. She also suggests that a powerful video on YouTube has the ability to raise


Word-of-Mouth on social media acts as a tool for providing product information, but also as a recommendation and a review given by experienced consumer, and is therefore seen as a necessary part of the marketing mix (Dichter, 1966; MacKinnon, 2012).


User Generated Content

Social media have over the time morphed into a marketing communication tool which allowed phenomena like viral advertising, buzz marketing and e-WOM to emerge. They are perceived to be more trustworthy than traditional media (Mangold & Faulds, 2009), mainly because the content is produced by users to express themselves and communicate with others (Lister et al., 2009), also called user generated content.

User Generated Content (UGC) works very similarly to electronic WOM, for both represent disseminating opinion through an online platform. Specifically, UGC is defined as any material created by a non-media user, having a greater influence on people and their consumption decisions, shared on social media such as Facebook, Instagram or YouTube. Similarly to e-WOM then, UGC stands for positive or negative opinion expressed by customer about a product or a company, available to anyone on the internet (Jonas, 2010).

Contrarily to UGC, Producer Generated Content (PGC) encompass any endorsers, celebrities and testimonials hired to speak about the advantages and benefits of a product or a brand (Verhellen et al., 2013). Nowadays, consumers are trying to find their way around the traditional forms of promotion and advertising and perceive UGC as more credible (Hassan et al., 2015). It is believed that it contains unbiased opinions based solely on the user’s own experiences, and as a result scores higher on trustworthiness (Verhellen et al., 2013). This perceived impartiality and no seeming commercial interest is the reason for other consumers trust in the UGC (Mir & Rehman,


2013), as well as their tendency to rely on the content generated by other users to assist them in making a purchase decision (Bae & Lee, 2011).

More than half of the consumers nowadays chose opinions online as a trusted source (Nielsen, 2015). Meanwhile, brands have been using social media trying to build their relationships with the consumers and as UGC started to gain on popularity, marketers saw an opportunity to reach their audience differently (Ertimur & Gilly, 2011), potentially exceeding PGC. Mainly due to its perceived authenticity, UGC has then become the new marketing tactic. However, with its increased use, consumers also started to gain on skepticism. The consumer skepticism towards traditional advertising stems from the premise, that PGC comes from the brand itself, intending to persuade people to buy its products. Over the years, consumers have eventually adapted and became immune to all new advertising practices there have been, forcing marketers to shift elsewhere to cut through the noise. In this case, consumers are starting to doubt the opinions of “professional influencers”, assuming they are driven by profit seeking motives, which decreases their authenticity and credibility (Brunel, Fournier & Lawrence, 2013).


Influencer Size

Contrarily to celebrities who are generally public figures with a lot of fans, influencers produce their own content (UGC) on social media, focusing on a niche audience genuinely interested in specific topics. Influencer marketing is considered a WOM marketing at scale where UGC is the message, WOM is the medium, and influencer marketing is the process (Berger & Keller Fay Group, 2016; Conick, 2016). The influencers are known for their ability to move a consumer from awareness to consideration to conversion, all in just one step. This influence


solely to the popularity. This level of interaction and a personal connection with audience is impossible with increasingly big follower base. The most popular influencers started to outsource part of their “job” – they hired video editors, managers and assistants, elevating their content to professional level (Kim, 2012; Lieber, 2014). This inevitably creates a gap in approachability between them and their audience and might in turn threaten their authenticity.

The term influencer marketing emerged as an idea of “identifying key communities and opinion leaders who are likely to talk about products and have the ability to influence the opinions of others” (WOMMA, 2016). Keller Fay Group and Berger identified micro-influencers as “a reliable and credible channel with a real impact in swaying consumer behavior” (2016, p.1).

Micro-influencers have been defined as people with above-average reach and impact through WOM in a relevant marketplace. They have changed the word of mouth from one-to-one to a one-to-many communication (McQuarrie, 2012). They are individuals, who are not the traditional celebrities, who are seen as knowledgeable, passionate and authentic and who are perceived as a trusted source for recommendation for purchase. In the research, these micro-influencers appeared more credible and believable, more knowledgeable, as well as better at explaining how the product works, compared to general population (Keller Fay Group & Berger, 2016).

YouTube creators commonly interact with their audience. They reply to comments, organize Q&A’s and create a degree of familiarity and intimacy which makes the community of followers feel more like friendship than a fandom. Compared to videos created by traditional celebrities, YouTube creators had 12 times as many comments and twice as big engagement in the form of likes, shares, clicks, etc. (O’Neil-Hart & Blumenstein, 2016). Researches also suggest, that information from someone who is similar and familiar elicits greater trust, seems more authentic


and is more likely to influence consumer’s purchase decision (Haremling et al., 2016). That is why 70% of YouTube subscribers relate to YouTube creators more than to traditional celebrities and 40% would claim that YouTube creator understands them better than their friend. Same study also found, that 6 in 10 YouTube subscribers would follow a purchase advice from their favorite YouTube creator over that of their favorite TV/ movie celebrity (O’Neil-Hart & Blumenstein, 2016).

Micro-influencer’s advantage compared to celebrity is the connection they have with their followers. Best of the influencers have created authentic bonds and relationships with their audience who trust them and readily turns to them for a brand and product recommendation (Nazeral, 2017). While celebrities are admired, consumers don’t really have a personal relationship attached to them. Celebrity endorsement has been historically used to build a brand image (Halonen-Knight & Hurmerinta, 2010). YouTubers (among other social influencers) on the other hand seem more relatable. Self-identification and self-expression with a brand (or in this case, a self-brand) are essential for relationship creation (Marwick, 2016). Moreover, transparency and two-way communication is what makes UGC and its source more credible and relatable (Tolson, 2010). Consumers trust the influencers, assuming they have no profit-driven motivations (MacKinnon, 2012).

However, greater size and professionality can make it harder for consumers to relate, making them more skeptical. In addition, the credibility, authenticity and trustworthiness of the source are compromised as the monetary intentions get more apparent (Brunel, Fournier & Lawrence, 2013; Campbell, 2011). This indicates, that these YouTubers are walking in the steps of mainstream celebrities (Kim, 2013).


Though the line between them is becoming blurry, celebrity marketing and influencer marketing are meant to offer different benefits for the brand. Celebrity marketing has historically focused on endorsement, sponsorship and product placement, while influencer marketing allows for deeper brand involvement and thematic integration, which is essentially far more effective (Nazerali, 2017). Additionally, macro-influencers have bigger reach due to the size of their follower base. However, to keep such a big audience, one needs to broaden his/her portfolio and to upload more often. These macro-influencers then tend to have diverse scope of interest, while micro-influencers are focused on their specific niche and very targeted audience.


Purchase Intentions

Purchase intention is one of the most talked about concepts in the marketing literature, mainly due to its close relationship with the actual purchase decision. It is regarded as “an individual’s conscious plan to make an effort to purchase a brand” (Spears & Singh, 2004, p. 56). The link that intention has with the actual behavior is built on an assumption that people are rational beings that make decision based on the information that is available to them. One’s intentions to perform certain behavior are then direct determinant of the actual behavior. Therefore, consumer’s purchase intentions relate to his behavior, perception and attitude and serve as a great predictor of the actual buying behavior (Brown et al., 2003).

Building on past studies and concepts, I expect the purchase intent to be higher when a smaller influencer is involved. A study found that the skepticism toward celebrity endorsement is a result of the message being perceived as a persuasion attempt, resulting in negative reactance towards such message (Verhellen et al. 2013). The same study also concluded, that celebrity endorser did not increase purchase intentions compared to an amateur. Moreover, an amateur


endorser did not raise any suspicion or trigger the persuasion knowledge of a consumer, like the celebrity endorser did, which essentially leads to feeling of manipulation and might result in lower purchase intention. This research however was comparing two extremes – a famous celebrity and an ordinary consumer, omitting the influencers who might lead to different result.

H1: The size of the influencer has a negative effect on the purchase intention of the consumer.

2.3.1. Attitudes

Attitudes are a great part of the evaluation part of the purchase decision process. These attitudes that consumers make towards different alternatives impact the final decision and purchase (Kotler & Armstrong, 2010). Defined as favorable or unfavorable feelings and evaluations of object, person or action (Ajzen & Fishbein, 1975), attitudes guide our thoughts, influence our feelings and most importantly, affect our behaviors.

Veirman et al. (2017) studied the marketing through influencers, specifically the impact of number of followers on brand attitude. They came to the conclusion, that although higher number of followers led to higher perception of popularity and likeability, it did not necessarily mean that those influencers were automatically perceived as opinion leaders. In this study, attitudes towards the influencer are meant to form the behavioral intentions of using the influencer’s recommendation for purchase decision. Positive attitudes towards the user generated content online increases consumer’s intention to consume such content (Daugherty et al., 2008).


2.3.2. Theory of Reasoned Action

Due to its close link to purchase intention, researchers have been trying to investigate the attitudes more closely. One of the dominant and most influential theories involving this construct is the theory of reasoned action (TRA) (Ajzen & Fishbein, 1980). This theory has been widely used to predict various human behaviors, on the premise that behavior is preceded by intentions and those intentions are formed based on one’s attitude. In this sense, intentions determine the actual behavior. The TRA model links beliefs, attitudes, intentions and behavior, and explains the process and relationship between attitudes and behavior (Ajzen & Fishbein, 1975). In the case of eWOM and social influencers, attitude towards them and towards intention of using their recommendations in decision making process might be influenced by their credibility.

Based on this framework, following hypothesis have been formed:

H2: The effect of the size of the influencer on purchase intention is mediated by the consumer’s

attitude towards him.


Perceived Credibility

Credibility is a trait that is capable of influencing the acceptance of an information (Ohanian, 1990) and includes trustworthiness, expertise and attractiveness. It could be argued that the credibility of an unknown source, which is what e-WOM along with UGC essentially is, is hard to evaluate. Nevertheless, it has been established that nowadays consumers prefer to trust content generated by other users and hold them more credible and honest, rather than one produced by the sellers (Jonas, 2010).


Many brands have been using celebrities in their social media and WOM marketing. It is therefore important to point out, that according to the persuasion knowledge model (Friestad & Wright, 1994), consumers learn to recognize celebrity endorsements as persuasion tactics that could be resisted. Federal Trade Commission, among many other institutions around the globe (Dutch Advertising Code, 2014), has developed a new regulation and guideline for celebrity endorsement of products, which now has to reveal any connection with advertisers when promoting a product on social media. The idea was to make consumers more aware of the commercial intent of sponsored posts.

A study on Effects of Sponsorship Disclosure (Boerman et al., 2017) found, that disclosing the commercial nature of a sponsored post helps consumers to recognize celebrity endorsement on social media as advertising and activate conceptual persuasion knowledge. This causes consumers to develop critical and distrusting beliefs about that post and leads to decrease in their intention to engage in e-WOM.

According to Dichter (1966), if the advertisement is perceived as a sales tool rather than an informational channel, the consumer feels threatened and will turn to WOM as a credible source for his purchase decision. However, if an advertisement elicits the friendly and unbiased environment of WOM, the consumer will relax and likely accept the recommendation. Whether a WOM is seen as a commercial promotion or a valuable information depends on several elements, one of which is communal norms. These communal norms, in the form of size of the follower base, their interest and social class, affect expression, transmission and reception of the message (Kozinets, et al., 2010). In other words, number of subscribers has the potential to influence whether consumers perceive the message source to be credible and how they react to it.


Though there is not much research done on influencer marketing yet, one major setback in most of these prevails. They don’t analyze the reason behind the big success of influencers, which is essentially their authenticity and credibility (Arnold, 2017). Conick (2016) wrote an article for American Marketing Association, describing how brands can find success in peer endorsements, specifically through influencers. While a celebrity might be successful in creating buzz with one-time low-effort expensive endorsement, influencers are the ones getting people to talk about products or brands and impacting sales. He proceeds to explain that these influencers appear real, authentic and more like friends the audience can relate to.

In an effort to seem more authentic, brands have shifted their marketing communication from celebrity endorsers to influencers, who among the consumers are perceived as “people like me” (Solis, 2016). This relatedness generates the highest form of influence (Nielsen, 2016). Influencers structure their personas by strategically sharing their personal information and carefully building their relationship with the audience. This so called para-social communication creates the illusion of having a real life relationship and a fake feeling of intimacy (Chen, 2014, Horton & Wohl, 1956).

However, for an influencer to remain influential, he/ she needs to remain genuine, humane and relatable (Chu & Kim, 2011). This cannot be said about most of the social media stars who have been talked about and covered in most of the mainstream media lately, gaining on popularity even more and ending up with multi-million follower base. They have transformed into typical celebrity, losing their leverage in authenticity, connection and closer relationship to their followers, as well as credibility (Brunel, Fournier & Lawrence, 2013).

On the other hand, micro-influencers might be what is left from the original idea of influencer marketing. As mentioned, credibility consists of trustworthiness, expertise and


attractiveness. Basing on the premise that smaller influencers score higher in trustworthiness (Verhellen et al., 2013), appear more knowledgeable and are better at explaining (Keller Fay Grouo & Berger, 2016), I expect source credibility to be higher in the micro-influencer condition. Additionally, sources considered to be more credible and trustworthy are more likely to influence people (Reichelt et al., 2014) and mediate the effect influencer size has on purchase intention.

H3: The effect of the size of the influencer on purchase intention is mediated by the influencer’s

perceived credibility.


Conceptual Model

Based on the aforementioned hypotheses, following conceptual model was developed.

Figure 1: Proposed model

Influencer Size Perceived Credibility Purchase Intention Attitude H3 H1 H2




Following empirical chapter will describe the methodology of this research. Firstly the design itself, next the procedure and finally the measurements and scales. All choices are explained and elaborated upon.


Research Design

The chosen approach is deduction, since the research will be mainly testing the theoretical propositions and hypotheses based on predictions. The study will be explanatory in nature, striving to explain the relationships and causality between stated variables. Considering limited timely and monetary resources, the data for the research will be collected via survey with random sampling, therefore quantitative data analysis is implied.

To answer the research question and test the conceptual mode, I have decided to conduct an online survey-based experiment with a between subject design to test my hypothesis. The independent variable in the form of influencer size will be manipulated to test whether this variation has an effect on dependent variable, which is purchase intention. The mediators in this case are source (influencer’s) credibility and consumer’s attitude towards him.



Participants were randomly assigned to two groups – a test-/micro-influencer and a macro-influencer group. All other factors being equal, the only differentiating component between the groups was the influencer size. This way, any other explanation of the effect or potential bias were eliminated.


First, the same influencer was introduced to both groups, only the number of followers was altered and clearly communicated. Both groups of participants were then shown the same video, in which the influencer recommends a certain restaurant. For this study, a service from travel industry has been chosen since the practice of influencer marketing became increasingly popular in tourism (Manap & Adzharudin, 2013).

Figure 2: Test condition

Figure 3: Macro-influencer condition

Afterwards, each group was asked to answer series of questions, aimed to find out, whether they would consider purchasing that service, and whether they think that what they are being shown is an authentic recommendation. Additionally, two bogus questions were implemented into the survey to check whether the subjects were paying enough attention, asking about the name of the restaurant (Q6) and the city in which the video takes place (Q7) and which is mentioned clearly in the video (whole survey in Appendix 9).




Message Source Credibility

To measure the extent to which consumer believes in the message spread by an influencer, a three dimensional source credibility scale by Ohanian (1990) has been used. Each sub-dimension was measured with five seven-point Likert scales, ranging from strongly agree (1) to strongly disagree (7). The three components determining believability in an ambassador are

trustworthiness, expertise and attractiveness. The more the source fits these criteria, the more likely he is to positively impact purchase intention.

Trustworthiness refers to the confidence and acceptance of the message and its source. It

directly effects persuasion and effectiveness of the message. The more the source is trustworthy, the more likely it is to influence. It is closely related to other factors like transparency, honesty and integrity.

Expertise is the professional knowledge the sender of the message has. It stems from the

competence and qualifications a communicator has. Endorsers are perceived as experts if they possess enough knowledge in a particular area.

Attractiveness is crucial for the initial judgement of the communicator (Baker & Churchill,

1977). Attractive endorsers have been found to have more impact and are more likely to lead to purchase aspiration (Wald, Loggerenberg & Wehmeyer, 2009).


Multiple studies have regarded attitude as a mediator for a behavior and an antecedent for a behavioral intention (Ajzen & Fishbein, 1980). Attitude is commonly regarded as a tendency to


evaluate an entity favorably/ unfavorably, expressed as a cognitive, affective and behavioral response. The attitude construct in this study refers to the attitude towards complying with the recommendation expressed by an influencer. To measure this construct, an attitude scale by Moon & Kim (2010) was adapted, with a 7-point semantic differential scale.

Purchase Intention

Purchase intention is defined as the probability that the consumer will purchase the product given certain conditions (Morwitz, Steckel, and Gupta, 2007). Though accepted that purchase intention does not match the actual purchase behavior, it has been established, that measures of purchase intention do hold a predictive usefulness (Brown et al., 2003).

As Warshaw (1980) has shown, conditional intentions relating to contextual behaviors lead to more accurate predictions of purchase than global intention measures. For this reason, the experiment subjects are introduced to a condition where they are to purchase certain product. Specific time constrain was placed on the purchase intention, improving the accuracy by limiting the temporal context of the behavior.

For measuring the probability of purchase, a well-established scale developed by Baker and Churchill (1977) to capture responses to advertising has been used. The scale contains three questions with a seven-point scale ranging from definitely yes (will purchase/try/seek) to definitely not. This scale is meant to measure the behavioral aspect of purchasing attitude.




Following chapter will present the results of the analysis of the data collected. Firstly, demographic data of the tested sample will be presented. Next, consistency of the measurements was examined through reliability check. Although all the scales were already validated in the previous researches, factor analysis was performed to determine shared variance between variables. For the sake of testing the hypothesis, new variables were calculated from the means of all items used to measure those variables. These are then presented along with standard deviations, Cronbach’s Alpha and the relationship between individual variables in the form of correlation analysis. Finally, one-way ANOVA and a regression in process were conducted to test the hypotheses.



The experiment was running from 6th May until 13th May. Out of 143 people who started the

survey, incredible 142 have finished it. However, out of these, 48 have answered at least one of the two bogus questions wrong. These two questions were asking the respondents to recall the name of the referenced restaurant and the city in which the video takes place. Nevertheless, before disregarding this respondent set completely, I have interviewed some of the respondents and found out, they had troubles recalling the name of the restaurant (Q6) or the city the video was taking place in (Q7). This does not necessarily mean they have not paid enough attention to the video or the survey, so I have decided to keep all the responses. Additionally, I have run the whole analysis with a sample excluding participants who answered either of the bogus questions incorrectly (results reported in Appendix 6, 7 and 8).


The distribution between genders was very equal, with 72 male and 70 female respondents. Majority (N=110) of the respondents were younger than 35 years, 38% being between 18 and 24 and 39% between 25 and 34 years old. Remaining 32 were over 35 years old.



All of the constructs used in the survey were adopted from previous studies and each scale has shown to have very high reliability, for all disposed of Cronbach’s Alpha higher than .70 (Purchase Intention: α = .878; Attitude: α = .934; Source Credibility: α = .952). The corrected item-total correlations suggest that all the items have a good correlation with the total score of the scale. Additionally, none of the items would substantially affect reliability if they were removed.

The scales were tested by the principal axis factoring analysis (PAF). The Kaiser-Meyer-Olkin measure proved the sampling adequacy for the analysis, with KMO = .916. Bartlett’s test of sphericity χ2 (171) = 2489.057, p < .001 suggested that correlations were large enough to perform

PAF. Firstly, an analysis was run to determine the eigenvalues. Both items had eigenvalues over the Keiser’s criterion, which is 1. Combined, the components explained 66,11% of the variance. Table shows the factor loadings after rotation. Results suggest, there is no significant cross-loadings for either of the factors.



Hypotheses testing

A simple one-way ANOVA test showed a statistically significant effect of the Influencer Size on Purchase Intention, F (1, 140) = 6.33, p < .05. The test revealed, that purchase intention was higher in the micro-influencer condition (p = .01). There was a total negative and statistically significant effect of the influencer size on purchase intention (c1 = -.548), comprised of direct negative, though insignificant effect and three negative indirect effects.

Despite mediation analysis proving that direct effect of influencer size on purchase intention (c1’ = -.270)is statistically insignificant (p = .10), further analysis of the indirect effect paths illuminate this relationship. After examining the first indirect path, the analysis showed a significant effect of the influencer size on purchase intent, when mediated by both source credibility and attitude (a1a3b2 = -.035). This translates into a tendency for people in micro-influencer condition to perceive the micro-influencer to be more credible (a1 = -.29, p = .11), which

Table 1: Means, Standard Deviations, Correlations

Variable M SD 1 2 3 4 5 6 7 1 Gender 1.49 0.50 - 2 Age 2.85 0.77 -0.77 - 3 Familiarity with the YouTuber 1.68 0.47 0.13 -0.04 - 4 Influencer Size 0.49 0.50 0.01 -0.02 0.13 - 5 Purchase Intent 46.72 1.32 -0.10 -0.07 -0.10 -0.21* (.88) 6 Attitude 3.11 1.39 -0.18* -0.01 -0.20* -0.12 0.49** (.93) 7 Credibility 3.08 1.06 -0.02 -0.12 0.24* -0.14 0.59** 0.30** (.95) *. Correlation is significant at the 0.05 level


showed to positively influence the attitude towards the source (a3 = .38, p < .001), and which subsequently impacts the purchase intention (b2 = .32, p < .001). Regardless of the first path of the effect (a1) being insignificant, it is still possible for the total indirect effect to be significant (Hayes, 2013). A bootstrap confidence interval for this indirect effect was entirely below zero (.103 to -.002), suggesting a significant negative relationship. Accordingly, hypothesis H1 was accepted.

However, remaining two indirect paths didn’t show the same statistically significant effect. The second indirect path is where attitude acts as a mediator. Even though the attitude had a significant positive impact on purchase intention (b2 = .32, p < .001), the negative effect of influencer size on the attitude (a2 =-.23) turned out to be insignificant (p = .32). This has caused the second indirect effect to be statistically insignificant and therefore, H2 was rejected.

The last indirect path was using source credibility as a mediator. In this case, the credibility of the source had a positive and significant effect on purchase intention (b1 = 59, p < .001). Nevertheless, the negative effect of influencer size on the credibility of the source (a1 = -.29) was missing a statistical significance (p = .11). Consequently, the third indirect negative effect was deemed insignificant and H3 was rejected.


Table 2: Results of hypotheses testing

Nr Hypothesis Status

H1 The size of the influencer has a negative effect on the purchase intention

of the consumer.


H2 The effect of the size of the influencer on purchase intention is mediated

by the consumer’s attitude towards him.


H3 The effect of the size of the influencer on purchase intention is mediated

by the influencer’s perceived credibility.


Figure 4: Proposed model with path coefficients

Influencer Size Perceived Credibility Purchase Intention Attitude (-.29) (-.23) H1 (.59**) ( -.04**) (.38**) (.32**) H3 (-.17) H2 (-.07)




Results of this research show that the hypothesized effect of the influencer size on the purchase intention is present, though it is not direct. Initial ANOVA test indicated that the intention to purchase was significantly higher in the micro-influencer compared to the macro-influencer condition. Further mediation analysis confirmed this by showing a strong indirect negative relationship between the size of the influencer and the purchase intention through perceived credibility and attitude as mediators. Despite this, the relationship between influencer size and perceived credibility or the attitude wasn’t found to be significant. The opposite was true for the remaining part of these paths. Perceived credibility of the influencer appeared to positively influence the attitude consumer holds towards him, and the attitude subsequently positively influences consumer’s purchase intention of the promoted product or service.


Theoretical and Managerial Implications

In an effort to jump on the bandwagon, companies have been pairing up with the largest bloggers, Instagrammers and YouTubers they could get their hands on. However, reach and fame have falsely been deemed critical when choosing an influencer for an ad campaign. Ever since the practice of influencer marketing and the use of UGC for advertisement purposes became more common, consumers started to be more skeptical. Influence represents the ability to affect an action, i.e. spur a purchase, not just popularity. Companies need to therefore look beyond follower count to determine the most effective and relevant influencers for their brand. Bigger reach may mean that the influencer is known across different demographics, but he may also lack proper


Since influencer marketing is still a relatively new phenomena, proper academic research on this topic is a scarce. Current study attempts to expand on this construct and contribute to the existing literature by examining the effect of influencer size on the intention to use his recommendation in a purchase decision process. One of the main contributions of this research is the confirmation of proposed hypothesis, that smaller influencers have higher chance of influencing the consumer in his purchase decision. The presence of this effect suggests a paradigm shift in the influencer marketing trend. In terms of theory, this result should shed some light on the effect number of followers has and highlight the importance of examining its impact on consumer’s purchase intention, as well as other variables.

From managerial perspective, this finding could significantly change the marketing strategy of a brand. Rather than using an influencer with the biggest reach for promotion, companies interested in social media and influencer marketing should instead consider finding couple of smaller influencers with highly targeted audience in their own niche. According to the results, this method would be more efficient as micro-influencers have higher chance to convert the consumers into customers, as well as more cost-effective since smaller influencers charge less. Moreover, a long term collaboration and strategic content co-creation with a micro-influencer is more likely to result in stronger buying conversion compared to a simple brand mention in a post/video.

Although hypotheses H2 and H3 were rejected, there are valuable insights to be drawn from this analysis too. Neither one of the mediators alone could explain the negative relationship between influencer size and purchase intention, mainly due to the absence of significant effect of the size of the influencer on both credibility and attitude. This reveals that there are other underlying constructs that mediate this relationship and that need to be explored in the future. Nevertheless,


the results suggest that the more the influencer was perceived to be credible, the more positive attitudes consumers formed about him and the more likely they were to follow his recommendation about the purchase. This outcome is in line with the source credibility theory and TRA model (Ohanian, 1990; Ajzen & Fishbein, 1980). This means that regardless of the influencer’s size, his credibility plays an essential role in consumer’s decision making process. In theory, these findings acknowledge the significance of credibility and attitude formation in the decision making process, and especially in the influencer marketing.

While monitoring WOM could have been a challenge for marketers in the past, in this era of social media networks, not only can brands monitor and analyze these communications, but they are also provided the opportunity to spur the e-WOM themselves by collaborating with the right opinion leaders. However, marketers are not the only ones being empowered by the advent of social media. Consumers are now able to research a product or service before its purchase, as well as look for reviews and user opinions in the form of UGC. This transparency made trustworthiness, honesty and credibility in marketing more important than ever. Therefore, the strong connection of credibility to purchase intent is something managers should consider when deciding for influencer and how the endorsement should be presented.


Limitations and Future Research

This research has several limitations. Firstly, the survey contained only one video of one influencer. Part of the scale measuring influencer’s credibility were questions concerning his attractiveness, which is obviously a very subjective matter. Therefore, using various influencers would be preferable in order to be able to generalize the results. Conversely, larger sample or a


results. Next, the study only focused on one specific segment in the travel industry. It would be interesting to see whether the effect would differ across different industries and product categories. Additionally, a source of a bias could lay in the way the ad is presented. Sponsorship disclosure is becoming a very controversial topic in this area and perhaps disclosing sponsorship would affect the source credibility. Lastly, the observed trends of decreasing like rate with increasing followership, on which this study was partially based, were academically captured only on Instagram insofar. Therefore, future research could look at the same effect on other platforms and compare the results.

Further research on this topic is necessary in order to fully understand the relationship. This study already established the presence of the effect of influencer size on the purchase intention. It did not however provide a strong supporting proof that credibility or the attitude is connected to the size of the influencer. This could be because of multiple reasons. For instance, both credibility and attitude showed to be correlated to the familiarity with the influencer. This could potentially mean a bias, in that people familiar with the YouTuber could have been more likely to deem him credible and hold more positive attitudes towards him.

Lastly, the lack of significant direct effect of influencer size on purchase intent could be explained by the fact, that at this stage it is only a beginning trend that will grow in the future, and it is therefore important to keep monitoring this phenomenon. Hence, future research should propose a different set of variables that would act as mediators and that would explain the relationship between the influencer size and consumers’ purchase intention.



Agrawal, T., Singhal, A. (2016). A comparative study of potential of various social networks for target brand marketing. International Conference on Information Technology, pp. 305-311.

Ajzen, I., Fishbein, M. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA, Addison-Wesley

Ajzen, I., Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior. Englewood Cliffs, NJ: Prentice Hall.

Armstrong, G., Kotler, P. (2010). Principles of marketing. Pearson Education.

Arnold, A. (2017) Why YouTube Stars Influence Millennials More Than Traditional Celebrities. Forbes. Available at:

Bae, S., & Lee, T, (2011). Product Type and Consumers’ Perception of Online Consumer Reviews. Electronic Markets, 21(4), 255–266.

Baker, M. J., and Churchill Jr, G. A. (1977). The impact of physically attractive models on advertising evaluations. Journal of Marketing Research, 14(4), 538-555.

Berger, J., & Keller Fay Group. (2016). Research shows micro-influencers have more impact than average consumers. Available at:


Boerman, S. C., Willemsen, L., M., Van Der Aa, E. P. (2017). “This Post Is Sponsored”, Effects of Sponsorship Disclosure on Persuasion Knowledge and Electronic Word of Mouth in the Context of Facebook. Journal of Interactive Marketing, pp. 82-92

Brown, M., Pope, N, Voges, K. (2003). “Buying or browsing? An expliration of shopping orientations and online purchase intention”, European Journal of Marketing, Vol. 37 Issue: 11/12, pp. 1666-1684

Brunel, F., Fournier, S., & Lawrence, B. (2013). When companies don't make the ad: a multimethod inquiry into the differential effectiveness of consumer-generated advertising. Journal of Advertising, 42(4), 292-307.

Campbell, C., Pitt, L.F., Parent, M. and Berthon, P.R. (2011). Understanding consumer conversations around ads in a web 2.0 world. Journal of Advertising, 40 (1), 87–102.

Chen, C. (2014). Forming digital self and parasocial relationships on YouTube. Journal of

Consumer Culture, (0)0, 1-23.

Chu, S., Kim, Y. (2011). Determinants of Consumer Engagement in Electronic Word-of-Mouth (eWOM) in Social Networking Sites. International Journal of Advertising, Vol.30(1), p.47-75

Cohen, D. (2018). Infographic: What Happens on the Internet in a Single Minute. Adweek. Available at:

Conick, H. (2016). ‘Word of Mouth on Seroids’: Brands Can Finds Success in peer Endorsements, Study Finds. American Marketing Association

Daugherty, T., Eastin, M.S., Bright, L. (2008). Exploring consumer motivations for creating user-generated content, Journal of Interactive Advertising, Vol. 8, No. 2, pp. 16-55


Dichter, E. (1966). How word-of-mouth advertising works. Harvard Business Review.

Federal Trade Commission. (2013). “com Disclosures. How to Make Effective Disclosures in Digital Advertising. Available at:

Friestad, M., Wright, P. (1994). “The Persuasion Knowledge Model: How People Cope with Persuasion Attempts,” Journal of Consumer Research, 21, 1, 1-31

Halonen-Knight, E. & Hurmerinta, L. (2010). “Who endorses whom? Meanings transfer in celebrity endorsement”, Journal of Product & Brand Management, Vol. 19, no. 6, pp.452–460.

Harmeling, C. M., Moffett, J. W., Arnold, M. J., Carlosn, B. D. (2016). Toward a theory of customer engagement marketing, Journal of the Academic Marketing Science, 45: 312-335

Hassan, S., Nadzim, S. Z. A., & Shiratuddin, N. (2015). Strategic Use of Social Media for Small Business Based on the AIDA Model. Procedia - Social and Behavioral Sciences, 172, 262–269.
 Hayes, A. F., (2013). Introduction to Mediation, Moderation, and Conditional Process Analysis : A Regression-Based Approach. United States: Guilford Publications Inc. ISBN10: 1609182308

Hayes, J.L. & King, K.W. (2014). The social exchange of viral ads: Referral and coreferral of ads among college students. Journal of Interactive Advertising 0(0), 1-12.

Holt, D., (2016). Branding in the Age of Social Media, Harvard Business Review, Available at:

Horton, D. & Wohl, R.R. (1956). Mass communication and parasocial interaction: Observations on intimacy at a distance. Psychiatry, 19 (3): 215-29.


Jeong, H. J., Koo, D. M. (2015). “Combined effects of valence and attributes of e-WOM on consumer judgment for message and product: the moderating effect of brand community type”, Internet Research, Vol. 25 No. 1.

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, 121–132.

Kim, J. (2012). The institutionalization of YouTube: from user-generated content to professionally generated content. Media, Culture & Society, 35(1), 53-67.

Kim, A. J., Johnson, K. K. P. (2016). Power of consumers using social media: Examining the influences of brand-related user-generated content on Facebook, Computers in Human Behavior, Vol.58, pp.98-108

Kozinets, R. V., de Valck, K., Wojnicki, A. & Wilner, S. J. S. (2010). Networked Narratives: Understanding Word-of-Mouth Marketing in Online Communities. Journal of Marketing, 74(2), 71-89.

Kudeshia, C., Kumar, A. (2017). Social eWOM: does it affect the brand attitude and purchase intention of brands? Management Research Review, Vol.40(3), pp.310-330

Langan, H. (2016). Research Shows Micro-Influencers Are Marketing’s Biggest Opportunity, WOMMA. Available at: influencers-marketings-biggest-opportunity/

Lieber, C. (2014). Clicks, likes, cash: How YouTube beauty stars threw the industry for a loop. Racked. Available at:

Lister, M., Dovey, J., Giddings, S., Grant, I., & Kelly, K. (2009). New media: A critical introduction (2nd edn). London: Routledge


MacKinnon, K.A. (2012). User generated content vs. advertising: Do consumers trust the word of others over advertisers. The Elon Journal of Undergraduate Research in Communications, 3(1), 14-22.

Manap, K. H. A., & Adzharudin, N. A. (2013). The Role of User Generated Content (UGC) in Social Media for Tourism Sector. Institute International Academic Conference Proceedings, pp. 52–58.

Mangold, W. G. & Faulds, D. J. (2009). Social media: The new hybrid element of the promotion mix. Business Horizons, 52(4), 357-65.

Markerly. (2016). Instagram marketing: Does Influencer Size Matter? Markerly Blog. Available at:

Marwick, A. (2016). You may know me from YouTube: (Micro-) Celebrity in social media. In Marshall, P.D., & Redmond, S. (Eds.), A Companion to Celebrity (pp. 333-350). West Sussex, UK: John Wiley & Sons, Inc.

Mir, I. A., & Rehman, K. U. (2013). Factors Affecting Consumer Attitudes and Intentions toward User - Generated Product Content on YouTube. Management & Marketing Challenges for the Knowledge Society, 8(4), 637–654.

McQuarrie, E. F., Miller, J., & Phillips, B. J. (2012). The megaphone effect: Taste and audience in fashion blogging. Journal of consumer research, 40, 136-159

Morwitz, V. G., Steckel, J. H., and Gupta, A. (2007). When do purchase intentions predict sales? International Journal of Forecasting, 23, 347-364.

Nazerali, S. (2017). How YouTube influencers are rewriting the marketing rulebook. Think with Google. Available at:

(38) (2015). Global Trust in Advertising. Available at:

Nielsen. (2016). Global Connected Commerce. Is E-tail Therapy the New Retail Therapy? Available at:

O’Neil-Hart, C., Blumenstein, H. (2016). Why YouTube stars are more influential than traditional celebrities. Think with Google. Available at:

Ohanian, R. (1990). Construction and Validation of a Scale to Measure Celebrity Endorsers’ Perceived Expertise, Trustworthiness, and Attractiveness. Journal of Advertising, 19(3), 39–52.

Reichelt, J., Sievert, J., Jacob, F. (2014). How credibility affects eWOM reading: The influences of 
expertise, trustworthiness, and similarity on utilitarian and social functions. Journal of Marketing Communication, 20, 65–81. 

Shah, H., Aziz, A., Jaffari, A. R., Waris, S., Ejaz, W., Fatima, M. and Sherazi., K. (2012). The Impact of Brands on Consumer Purchase Intentions, Asian Journal of Business Management 4(2): 105-110

Solis, B. (2016). The Influencer Marketing Manifesto: Why The Future of Influencer Marketing Starts With People And Relationships Not Popularity. Available at:

Spears, N., & Singh, S. N. (2004). Measuring Attitude toward the Brand and Purchase Intentions. Journal of Current Issues & Research in Advertising, 26(2), 53-66.


The Dutch Advertising Code, (2014). “The Dutch Advertising Code. With Information About the Working Procedures of the Advertising Code Committee and the Board of Appeal”. Available at:

The Nielsen Total Audience Report: Q2, (2017). The Nielsen Total Audience Report: Q2 2017. Available at:

Tolson, A. (2010). A new authenticity? Communicative practices on YouTube. Critical Discourse Studies, Vol.7(4), p.277-289

Verhellen, Y., Dens, N., De Pelsmacler, P. (2013). Consumer responses to brands placed in YouTube movies: the effect of prominence and endorser expertise. Journal of Electronic Commerce Research

Verhoef, P. C., Reinartz, W. J., & Krafft, M. (2010). Customer engagement as a new perspective in customer management. Journal of Service Research, 13(3), 247–252.

Wagner, A. (2017). Are You Maximizing The Use Of Video In Your Content Marketing Strategy? Forbes. Available at:

Waldt, D. Van Der, Loggerenberg, M. Van, & Wehmeyer, L. (2009). Celebrity Endorsements Versus Created Spokespersons in Advertising: A Survey Among Students. South African Journal of Economic and Management Sciences, Vol.12(1), pp.100-114

Warshaw, R. P. (1980). Predicting Purchase and Other Behaviors from General and Contextually Specific, Journal of Marketing Research, Vol. 17, No. 1, pp. 26-33


Weiss, R. (2014). Influencer Marketing. How word-of-mouth marketing can strengthen your organization’s brand, Vol. 34 Issue 1, p16-17. 2p.



Appendix 1: Demographics Variable Value # % Age 18 - 24 25 - 34 35 + 54 56 32 38 39.4 22.54 Gender Male Female 72 70 50.7 49.3 Education Student Unemployed Self-employed Employed full time Employed part time Retired 39 8 28 59 7 1 27.46 5.63 19.72 41.55 4.93 0.70 Income under 20,000 20.000 – 34,999 35,000 – 49,999 50,000 – 74,999 75,000 – 99,999 over 100,000 64 19 24 20 5 10 45.07 13.38 16.90 14.08 3.52 7.04 N=142


Appendix 2: Factor Analysis

Item Rotated Factor

Loadings Credibility Attitude

Following the YouTuber's recommendation is a (good/bad) idea .04 .91 (Wise/Foolish) idea .20 .80 (pleasant/unpleasant) idea -.08 .95 (positive/negative) idea -.04 .93

Do you consider the featured YouTuber to be

attractive .64 .00 classy .79 -.03 beautiful .75 -.17 elegant .79 -.19 sexy .63 -.07 dependable .72 .02 honest .70 .25 reliable .81 .19 sincere .70 .19 trustworthy .79 .09 .expert .82 -.08 experienced .82 .04 knowledgeable .84 .09 qualified .84 -.01 skilled .86 .05 Eigenvalues 9.56 3.01 % of Variance 50.29 15.82 Factor loadings over .40 appear in bold


Appendix 4: Multiple Mediation Analysis


SourCred (M1) Attitude (M2) PurchInt (Y)

Antecedent Coeff. SE p Coeff. SE p Coeff. SE p

InflSize (X) a1 -.29 .18 ns a2 -.23 .23 ns c’ .27 .17 ns SourCred (M1) --- --- --- a3 .38 .11 <.001 b1 .59 .08 <.001 Attitude (M2) --- --- --- --- --- --- b2 .32 .06 <.001 constant iM1 3.22 .12 <.001 iM2 2.05 .38 <.001 iY 44.03 .30 <.001 R2 = .018 R2 = .098 R2 = .470 F (1,140) = 2.614, p = ns F (2,139) = 7.520, p < .001 F (3,138) = 40.767, p < .001

Appendix 3: One-way ANOVA

SS DF MS F Sig. Influencer size 10.65 1 10.65 6.33 .01 Error 235.71 140 1.68 Total 246.36 141

Influencer size Mean SD N

Macro 46.44 1.19 70

Micro 46.99 1.39 72


Appendix 5: Coefficients


Total Effect c1 -.548 .218 <.05 -.979 -.117

Direct Effect c1’ -.270 .165 ns -.597 .057

Boot SE Boot LLCI Boot ULCI

H3 a1b1 -.170 .109 -.419 .016

H2 a2b2 -.073 .081 -.274 .052

H1 a1a3b2 -.035 .024 -.103 -.002

Appendix 6: One-way ANOVA (sample excluding wrong answers to bogus questions)

SS DF MS F Sig. Influencer size 2.42 1 2.42 1.47 .228 Error 150.69 92 1.64 Total 153.10 93

Influencer size Mean SD N

Macro 46.47 1.26 53

Micro 46.79 1.30 41


Appendix 7: Multiple Mediation Analysis (sample excluding wrong answers to bogus questions)


SourCred (M1) Attitude (M2) PurchInt (Y)

Antecedent Coeff. SE p Coeff. SE p Coeff. SE p

InflSize (X) a1 -.39 .214 ns a2 .11 .26 ns c’ -.01 .18 ns SourCred (M1) --- --- --- a3 .57 .12 <.001 b1 .73 .10 <.001 Attitude (M2) --- --- --- --- --- --- b2 .25 .08 <.001 constant iM1 3.41 .16 <.001 iM2 1.02 .46 <.05 iY 43.55 .34 <.001 R2 = .034 R2 = .194 R2 = .554 F (1,92) = 3.258, p < .05 F (2,91) = 10.930, p < .001 F (3,90) = 37.326, p < .001

Appendix 8: Coefficients (sample excluding wrong answers to bogus questions)


Total Effect c1 -.323 .266 ns -.852 .206

Direct Effect c1’ -.015 .184 ns -.381 .352

Boot SE Boot LLCI Boot ULCI

H3 a1b1 -.281 .160 -.641 -.004

H2 a2b2 .028 .063 -.091 .165


Appendix 9: Survey


Q1 Dear participant,

thank you for taking part in this research. This study is a part of my master thesis in the department of Economics & Business at the University of Amsterdam. For my thesis I am investigating the effects of influencer marketing. All the responses will be processed anonymously and used only for the purpose of this study. It will take around 5 minutes to answer the questions. Please, try to answer as honestly as possible. In case of any questions or comments, please feel free to contact me here:

Q2 In the following section you will be introduced to a YouTuber and see a short part of the video he made. Please read carefully and pay close attention to the video. Also note, that the channel on which the video is presented is not that of the actual YouTuber.


Q3 [macro-influencer condition]

Meet Mr Ben Brown!

About Channel

Film maker & photographer from London, uploading daily vlogs which are sometimes late due to projects, travel & life! I do my best to make my vlogs the best they can be, subscribe to joint my journey! :) Subscribers 2,003,523 Since Oct 2006


Q4 [test condition]

Meet Mr Ben Brown!

About Channel

Film maker & photographer from London, uploading daily vlogs which are sometimes late due to projects, travel & life! I do my best to make my vlogs the best they can be, subscribe to joint my journey! :) Subscribers 8,381 Since Oct 2006 Q5 [video]


Manipulation check

Q6 What was the name of mentioned restaurant?


Sea Breeze (1)


Breeze Street (2)


Fish & Chips (3)


Cape Town (4)

Q7 In which city is the video taking place?


Brighton (1)


London (2)


Cape Town (3)


Amsterdam (4)


Purchase Intention

Q8 Considering you would be going on a holiday in the chosen destination, would you

Extremely likely (45) Moderately likely (46) Slightly likely (47) Neither likely nor unlikely (48) Slightly unlikely (49) Moderately unlikely (50) Extremely unlikely (51)

like to try the recommended restaurant (1)








eat in the restaurant if you happened to see it (2)








actively seek out the recommended restaurant (3)











Good Bad

1 2 3 5 6 7

Following the YouTuber's recommendation is a(n) ____ idea ()


Wise Foolish 1 2 3 5 6 7

Following the YouTuber's recommendation is a(n) ____ idea ()



Pleasant Unpleasant

1 2 3 5 6 7

Following the YouTuber's recommendation is a(n) ____ idea ()


Positive Negative

1 2 3 5 6 7

Following the YouTuber's recommendation is a(n) ____ idea ()


Source Credibility

Q13 Do you consider the featured YouTuber to be

Strongly agree (1) Agree (2) Somewhat agree (3) Neither agree nor disagree (4) Somewhat disagree (5) Disagree (6) Strongly disagree (7) Attractive (1)








Classy (2)








Beautiful (3)








Elegant (4)








Sexy (5)









Q14 Do you consider the featured YouTuber to be Strongly agree (1) Agree (2) Somewhat agree (3) Neither agree nor disagree (4) Somewhat disagree (5) Disagree (6) Strongly disagree (7) Dependable (1)








Honest (2)








Reliable (3)








Sincere (4)








Trustworthy (5)












Gerelateerde onderwerpen :