From whence it came

From whence it came

Understanding source effects in consumer‑generated advertising Peter Steyn

Luleå University of Technology

Michael T. Ewing Monash University

Gené van Heerden

Luleå University of Technology

Leyland F. Pitt

Simon Fraser University

Lydia Windisch

Monash University

Web 2.0 technologies are empowering consumers to co-produce online brand communications and thereby co-create brand meaning. As both consumers and marketers are increasingly using video-sharing websites to showcase their brand communication efforts, viewers of these ads are inadvertently becoming part of the co-production process as they create context around the ads (in the forms of reviews, comments and ratings). The environment in which such online advertisements are viewed has significant effects on consumer perceptions of the ad message, and ultimately impacts the persuasive properties and efficacy of the ad. This study reports on research conducted to test the source effects of consumer-generated advertising. Schlinger’s Viewer Response Profile (VRP) is used to assess the impact of three source variables: ad creator, ad popularity and motivation for creation of the ad. Findings confirm the importance of popularity ratings on consumer ad evaluation, and also suggest that certain source effects result in consumers being more critical in their evaluation of the ads.

Introduction The advertising industry has been significantly affected by recent developments in technology and media use. Technologies increasingly allow for more personal and well-targeted marketing communications that International Journal of Advertising, 30(1), pp. 133–160 © 2011 Advertising Association Published by Warc, www.warc.com DOI: 10.2501/IJA-30-1-133-160

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now extend well beyond traditional concepts of organisation-generated, paid and ‘one-to-many’ marketing messages (Muñiz & Schau 2007a). Consumers are taking advanced technologies in their stride and, acting independently of marketers and agencies, they are creating and disseminating ‘many-to-many’ brand-related online content, which often resembles advertising about the brands that they love (Flight 2005) or hate (Berthon et al. 2008). This ‘customer evangelism’ has also been referred to as ‘open source’ branding (Garfield 2005) and ‘vigilante marketing’ (Ives 2004; Muñiz & Schau 2007a). In this paper, we refer to it as ‘consumergenerated advertising’ (CGA). It has become apparent that the creation of ads is no longer the prerogative of marketers and their advertising agencies (Berthon et al. 2008), which means that brands are no longer proprietary (Cromie & Ewing 2009). In fact, the ascendancy of empowered consumers is now a marketplace reality (Ives 2004; Flight 2005) and the emergence of consumer-generated ads suggests revolutionary changes in how advertising is defined and practised (Muñiz & Schau 2007b). Consumers who craft their own ads can easily reach a global audience by broadcasting online. In response, marketers still seem undecided, and to some extent uncertain, about the benefits of adding this source of new content to their creative arsenals. Whether marketers like it or not, consumer-generated advertising will be around for a long time. If anything, one could argue that astute marketers should embrace this new reality and seek to align their traditional media with consumer-generated content. However, they first need to know what drives consumers to generate their own ads and, second, what effect these ads have on their audiences. Both ‘source effects’ and ‘framing effects’ theories suggest that the effectiveness of an advertisement is influenced by the consumer’s perception of the source of the ad (e.g. Wilson & Sherrell 1993), and the framing of cues and stimuli (e.g. Tversky & Kahneman 1986). Marketing scholars have investigated source effect variables such as media vehicle source effects (Aaker & Brown 1972), various spokesperson effects, such as attractiveness (Kang & Herr 2006), ethnicity (Green 1999), celebrity status (McCracken 1989) and credibility (Lafferty & Goldsmith 2004), among others. However, in the digital age, new source effect variables are emerging that are not under the direct control of the marketer, their advertising agencies, their media planners or even the media owners.

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While marketers may not have the opportunity to control or directly influence the source effects of every type of online advertisement, consumers often, inadvertently, create and distribute these effects on websites such as YouTube (Berthon et al. 2008). These source effects may have a significant impact on consumers’ subsequent evaluation of advertising. This study investigates whether consumers’ knowledge or perceptions about the source of an online ad affects their evaluation of the ad. The study focused on three key research questions. 1. First, do consumers evaluate an ad differently if they are aware that it was created by a typical consumer, or by a professional ad agency? 2. Second, if consumers are aware that a consumer-generated ad is awardwinning and very popular among their fellow consumers, does this knowledge have an impact on their evaluation of the ad? 3. Third, would the fact that the ad was created by a consumer in response to a contest, or as an expression of consumer creativity, affect consumers’ evaluation of the ad? These are all important issues for both marketers and marketing scholars, as they will inevitably affect both strategy development and future research. The article begins with a literature review on consumer-generated advertising, source effects and framing theory. We then introduce and substantiate our research questions and develop specific hypotheses. Next, we outline our research approach before discussing the study findings. We conclude by considering the managerial implications of the research, acknowledging the limitations of our approach and offering suggestions for future research.

Literature review Consumer-generated advertising Consumer-generated advertising is broadly defined to include any ‘usergenerated brand-related content’, in the form of online brand testimonials, product reviews and user-generated commercials (Salwen & Sacks 2008, p. 199). The present study focuses on the latter category: ‘advertisements’



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created by consumers in an online environment and the possible impact of consumer content created around ads posted on social media such as YouTube. Participants in consumer-generated content are not a homogenous group – van Dijck (2009) argues that there are varying levels of participation, including, creators, passive spectators and even amateurs showcasing their creative skills with hopes of gaining the attention of possible employers. Consumers can access, and drive, a powerful perspective on brands and products that sometimes can be contrary to the perceptions that the manufacturers and firms wish to create for their brands and products (Christodoulides 2009). Increasingly, firms are not in control of the message (Berthon et al. 2008). Thus, in order to embrace this phenomenon, firms will have to devise appropriate response strategies (Berthon et al. 2008), rather than control strategies for consumer-generated advertising. To do so, marketers need to develop their knowledge about the influences and variables that affect the impact of consumer-generated content. Several authors have investigated the possible motivations for consumers to engage in the production of consumer-generated advertising. These range from emotional and psychological needs for consumers to seek out activities that decrease self-doubt and increase their sense of community (Daugherty et al. 2008) to providing entertainment for a smaller audience of family and friends (van Dijck 2009), through to the more activist, political motivations of changing perceptions about brands, products or contemporary issues (Berthon et al. 2008). It increasingly attracts the attention of marketers for a number of reasons. First, consumers can generate content at a fraction of the cost of professional agencies (Klein 2008). Second, feedback in the form of consumer perceptions of brands by opinion leaders can be invaluable to brand management (Klein 2008). Third, these ‘non-conventional’ ads stand a good chance of breaking through the clutter of conventional advertising in markets increasingly suffering from advertising overload syndrome (AOS) (Ouwersloot & Duncan 2008). Fourth, increasing brand awareness through electronic word-of-mouth (eWOM) can be much faster than traditional advertising (Muñiz & Schau 2007a). Fifth, consumers can be quite skilled in their creation of brand-relevant communications and may offer the most compelling marketing messages from the perspective of brand leaders (Garfield 2005; Muñiz & Schau 2007a). Finally, ads developed by consumers may well exceed the effectiveness

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achieved by traditional advertising with their ‘real’ feel and ‘peer created credibility and trustworthiness’ (Muñiz & Schau 2007a). Indeed, Cheong and Morrison’s (2008) in-depth qualitative study of online recommendations within user-generated content indicated that consumers feel greater trust in the product information created by other consumers than in the information provided by manufacturers. All in all, the consumer-generated content audience is indeed ‘an attractive demographic’ to marketers (van Dijck 2009, p. 47).

Source effects and the Elaboration Likelihood Model Given the exponential rate of evolution of online environments, how consumers react to online advertisements is of perpetual concern to both marketing scholars and practitioners. Academic and applied knowledge of online environments must be updated constantly in order to keep pace with technological advancements. To understand consumers’ reaction to advertising, it is necessary to understand the effect of knowledge on attitude-behaviour and persuasion. The Elaboration Likelihood Model’s (Petty & Cacioppo 1986) peripheral processing route indicates that the presence of cues and inferences, such as source credibility and quality, will affect the formation of attitudes (Petty et al. 1983). Research indicates that the knowledge that is available during the advertising evaluative processing stage strongly influences attitudinal judgements and attitudebehaviour (Kisielius & Roedder 1983; Kamins & Marks 1987). Two sets of factors have been suggested to influence knowledge: prior productrelated experiences and stimulus factors. While advertisers seldom have the opportunity to manipulate the product-related experiences that the viewers of an advertisement have, they can, and they do, manipulate stimulus factors that drive consumer knowledge and effect persuasive elements (Kamins & Marks 1987). The present research focuses on the specific influences of the source effects that are argued to be utilised in the peripheral processing route. The large amount of research that has been conducted on advertising effectiveness has greatly enhanced our understanding of the elements of persuasive communication (e.g. Aaker et al. 1986; Ohanian 1991; Wilson & Sherrell 1993; Petty et al. 1997). To analyse persuasive communication one needs to consider two sets of factors, namely the independent variables­



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that can be manipulated by advertisers and the dependent variables that are the various aspects of the persuasion process that may occur as a result, and in response to the communication. Among the major independent variables are source, message, audience, medium and target behaviour variables (Oskamp & Schultz 2005). The term ‘source effect’ is commonly used in communication research and attitude psychology, where a distinction is made between ‘source variables’ and ‘message variables’ (Verlegh 2002), although it is not too exceptional for a single stimuli to perform both roles at the same time (Petty et al. 1997). The source variables are characteristics of the source of the message with far-reaching attitudinal implications such as the spokesperson’s credibility, expertise, trustworthiness, physical attractiveness and ideological similarity (Ohanian 1991; Wilson & Sherrell 1993). Vehicle source has also received much attention among scholars (e.g. Aaker & Brown 1972; Assmus 1978), and is defined by Aaker and Brown (1972, p. 11) as ‘a measure of the relative value of an ad exposure as a function of the exposed vehicle’, such as the media in which the ad appears. Furthermore, research on traditional media revealed that media context, or the communication environment in which an advertisement is displayed, can have significant effects on consumer persuasion (Chaiken & Stangor 1987; Malthouse et al. 2007). The same advertising media vehicle delivering the same message to the same audience might produce different persuasion effects depending on the communication context where the message appears (Chook 1985). One of the primary explanations for this is context effect, which is due to stimuli, cues or any materials surrounding an advertising message that can have a significant impact on the advertising message, its interpretation and the resulting persuasion effects (Chook 1985; Meenaghan 2001). Prior research has comprehensively examined source and context effects in traditional media (Wilson & Sherrell 1993). With regard to online source effects, credibility has probably been the most widely studied. Research has revealed that online advertising placed on credible websites produces better ad effects than ads placed through less credible vehicles (Shamdasani et al. 2001) by increasing ad credibility (MacKenzie & Lutz 1989). In the digital age, where consumers are now able to generate content around an embedded ad, there are increasingly more source and context

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effects at play that have received less scholarly attention. In particular, there is reason to believe that in online advertising (both firm and consumer generated) there are certain context effects that may pose a significant impact on consumer persuasion and the effectiveness of the ad. Previous academic research has indicated that the consumer’s perception of who created the ad will affect their perception of the ad (Cheong & Morrison 2008). Additionally, there is anecdotal evidence of suspicion of companies manipulating the content of online user-generated spaces (see the Anheuser-Busch/SeaWorld and Exxon-Mobil/Alaska oil-spill online incidents in Hafner 2007). Thus, it could be argued that whether the ad was created by a professional ad agency or a consumer will affect consumers’ evaluation of the ad. Given the breadth of motivations for producing consumer-generated content (Berthon et al. 2008; Daugherty et al. 2008; van Dijck 2009) it is also of interest as to whether the reason for the creation of the ad will also affect consumers’ perceptions of the ad. Finally, given the highly social and interactive nature of the online environment, and the demonstrated effects of eWOM (Muñiz & Schau 2007a; Lee et al. 2009), the peer evaluation of an ad should also be considered as a focus for research on the contextual influences. Video-sharing websites such as YouTube may provide clues to answer these questions, as well as evidence for what drives consumer perceptions. Video-sharing websites have gained much popularity since the launch of YouTube in February 2005. A multitude of similar websites are now available, such as Yahoo! Video, Google Video, and Vimeo, to name just a few. While initially used by consumers to post and share their favourite professional agency created ads as well as to showcase their own ‘home movies’, these websites are increasingly being used by consumers to flaunt their own creations of brand communications. Most of these websites also allow viewers to post comments about any of the videos – professionally created ads as well as consumer-generated ads. The consumer-generated content has both source and context effects on those who subsequently view these ads. These websites are not only used by consumers. Brand marketers and their agencies also use these websites as free platforms to reach their target audiences (Sinclair 2009). The question remains as to what effect consumer-generated content has on the effectiveness of online advertisements. How does consumer content, created around an online advertisement, impact the effectiveness­



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of the ad and how does it influence consumer attitudes towards the advertised brand? These are important questions for marketing scholars and practitioners alike. By identifying which variables will induce more favourable consumer responses to online advertisements, marketers can better understand the perils and promise of consumer-generated online content for their brand communications. Framing theory Framing theory is useful in understanding the importance of source effects, as it can explain some of the consumer’s reaction towards consumer-generated advertisements. Tversky and Kahneman (1986) provide evidence that consumers are systematically affected by the manner in which advertising messages are worded or framed. Since the original framing theory was developed more than two decades ago, much research has focused on the framing of messages, and it was found that consumers’ responses vary when information is framed differently, e.g. either positively or negatively (Smith 1996; Shiv et al. 1997; Ferguson & Gallagher 2007). Research in behavioural decision theory suggests that people use reference points as the basis for judging and comparing the value of decision alternatives (Puto 1987). These reference points, which activate knowledge, influence not only their judgements and decisions, but also their attitude-behaviour and persuasion (Kisielius & Roedder 1983). In the research reported in this article, framed messages are recognised as the messages posted on video-sharing websites in which the pictorial information provided by the online advertisement is re-stated (by viewers) around the ad in verbal form. By processing these framed messages, it is argued that the knowledge gained from these stimuli impacts the viewer’s attitude-behaviour.

Formulation of hypotheses Utilising the source effects subset area of the Elaboration Likelihood Model in conjunction with framing theory, the present study considered the effects of different sources, and positive and negative framings of the messages about those sources. For the source effects, the importance of word-of-mouth and brand references by other consumers indicates that

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consumers are more likely to assign a higher credibility to an advertising message created by other consumers than those created by the advertisers. Thus, if a viewer of an online ad believed that another consumer (as opposed to an ad agency) created the ad, would that affect their evaluations of the ad? Based on these considerations, the first hypothesis is: H1:

Consumers have more positive feelings towards an ad when aware that other regular consumers created it.

Given the influence of framing effects, when consumers know that an ad is popular among other consumers and has won awards, would this knowledge influence their evaluation of the ad? Thus, by incorporating the framing dimension, the second hypothesis is: H2:

Consumers have more positive feelings towards a consumergenerated ad when aware that it is an award-winning and popular consumer-generated ad.

Finally, when consumers know that another consumer created the ad purely to showcase their creativity, rather than being created in response to a contest, would this knowledge influence their evaluation of the ad? Consequently, the third hypothesis is: H3:

Consumers have more positive feelings towards a consumergenerated ad when knowing that it was created purely on internal creativity rather than in response to a contest.

Consumer-generated advertising is growing rapidly and, in so doing, is leading marketers into previously uncharted territory. In an attempt to understand how consumer-generated content of brand communication will affect consumers, we turned to the literature on source effects, which provides ample evidence that the media context, or the communication environment in which an advertisement is displayed, can have significant effects on consumer persuasion. We developed three hypotheses that are based on the framing theory that stipulates that consumers use reference points that activate knowledge that influences their attitude-behaviour and persuasion.



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For each of the above hypotheses, two bipolar statements were created to manipulate the three selected source effects (the independent variables). We then report their impact on our measurement scale (the dependent variables).

Method In describing our approach, we first address the research design, including the procedure and criteria for selecting the test ad. We then discuss the questionnaire and measurement scale employed, and the sample design and data collection procedure. Research design According to Assmus (1978), vehicle source effects can be measured efficaciously through controlled experimentation, by comparing the response of respondents who saw the advertising in one vehicle to the response of a matched sample of respondents who saw the same ad in a different vehicle. To test the three hypotheses, we designed an experiment with six test and one control group. All seven groups were exposed to exactly the same 30-second online commercial (from now on referred to as the ‘ad’). However, each of the six test groups was exposed to a different source effect (independent variable) in the form of a label displayed above the ad describing its source. This label was also displayed above the measurement scale items (dependent variable) that followed after the respondent viewed the ad. The seventh group (control) served as the benchmark measure and viewed the ad without any labelling cues (see Table 1 for the labels of each group). The demographic profile of respondents across the seven independent groups was matched to allow for cross-group comparisons. Demographic matching was achieved through the survey mechanism, whereby, as participants completed their demographic details, they were automatically placed in the required group. Once sufficient numbers of respondents had been recruited to each of these groups, the questionnaire access was progressively closed until the final group was completed and the data collection was completely closed. Across the six groups, we compared three source-effect variables: CREATOR – Consumer vs Professional agency created (Groups 1 and 2);

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Table 1: Group labels provided as cues to respondents Group 1 Group 2 Group 3 Group 4 Group 5 Group 6 Group 7

‘This ad was created by a professional advertising agency based on their client’s brief’ ‘This ad was created at home by a consumer and then posted on YouTube’ ‘This is a popular ad created by a consumer that has been viewed by over 1 million people on YouTube and has won several awards in advertising competitions’ ‘This ad, created by a consumer, has not won any awards in advertising competitions, and after being posted on YouTube, very few people watched it’ ‘This ad was created by a consumer in order to enter an advertising competition’ ‘This ad was created by a consumer to express their creativity’ No label (control group)

POPULARITY – Popular vs Unpopular peer reviews (Groups 3 and 4); and MOTIVATION – Creation was motivated to express creativity vs to enter a contest (Groups 5 and 6). Each of the above included a comparison against the control group (Group 7), which then allowed for a three-way comparison by means of an analysis of variance. Selection of the ad for the study Research has demonstrated an association between attitude and usage in several product categories (Achenbaum 1966). It has also been demonstrated that current users of a brand are more likely to have positive attitudes towards that brand, while former users have negative attitudes. Those who have never used the brand are more likely to have neutral attitudes (Stout & Rust 1993). In addition, Schlinger (1982) found that ad evaluation is highly correlated with brand usage, and that ad evaluation has a stronger correlation with brand usage than with either demographic or any situational variables. She also found that users of the advertised brand react more favourably to a commercial for that brand. Taking these findings into account, we specified three criteria to adhere to in the selection of ad to employ. The first criterion was that the brand advertised in the ad should have a negligible likelihood of ever being used by any of the respondents. This requirement increased the likelihood that the various source effects would be ‘believed’ by participants – e.g. participants would have been hard pressed to believe framing information that indicated ‘nobody liked the ad’ around a well-known, popular ad. Second, the ad (or the brand advertised) should not have any predetermined level



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of empathy or familiarity among the respondents. The third criterion in this selection procedure was that the ad, viewed without any source labelling, should appear to be neutral (or bipolar) on all the source-effect variables of the six test groups. Most importantly, the selected ad had to appear to be either agency or consumer created, and should be fairly neutral on overall likeability. A small advisory panel of consumers was drawn on to carefully review several shortlisted online ads on these three criteria. Through a process of elimination the advisory panel selected a 30-second commercial developed for the First Community Bank, which is a relatively small New Mexico (US) bank with a few branches across New Mexico, Arizona, Colorado and Arizona. It was expected that none of our Australian-based respondents would be current or past customers of this bank, and that awareness and familiarity would be very low or non-existent. Questionnaire Schlinger’s Viewer Response Profile (VRP) was developed to quantify participants’ subjective feelings about television commercials. We chose Schlinger’s (1982) VRP because the scale has received much attention by both academics (Olson 1985; Stout & Rust 1993; Ewing et al. 2005; Strasheim et al. 2007) and commercial research agencies, such as Millward Brown. The VRP has been linked with other behavioural variables such as the focal and emotional integration of advertising (MacInnes & Staymen 1993), as well as attitudes to websites (Chen & Wells 1999). The original 32-item scale has been reduced to a more usable, 14-item semantic judgement scale (Strasheim et al. 2007). These 14 revised items still represent the original seven dimensions that consumers use to evaluate advertising: entertainment value, confusion, brand reinforcement, relevant news, empathy, familiarity and alienation. Each dimension consists of two items that are rated on a five-point Likert-type scale anchored on 1 (strongly disagree) to 5 (strongly agree). Recognising that overall ad likeability is an important measure of advertising effectiveness (Du Plessis 1994), and that it has been found to be a better predictor of sales effectiveness than other copy test measures (Haley & Baldinger 1991), we included an overall advertising likeability measure as a single-item semantic scale anchored on 1 (‘I do not like the ad at all’) to 7 (‘I like the ad a lot’). Single-item measures of likeability are

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commonly used within viewer response research (Du Plessis 1994) and have been shown to validate multi-item measures of likeability (Strasheim et al. 2007). This measurement was also used to provide external validity of the VRP ratings. Moreover, Bergkvist & Rossiter (2007) make a compelling case that supports the use of single-item measures in marketing. Data collection Data collection was conducted by a professional online research firm using its online consumer panels in Australia. A non-probabilistic sample of respondents was selected using a quota applied to the demographic characteristics of age and gender. The target for this experiment was defined as internet users aged 18 to 39 years old. A total of 12,000 online panel members were invited via an email invitation to participate in the experiment. Following Daugherty et al.’s (2008) approach, as soon as a usable number of participants responded, access to the experiment was closed. Thus, as this study utilised a purposive sampling technique, response rates were not applicable. Of the 558 individuals who responded, 92 were rejected because they did not qualify on the specific age or gender quotas set for each group. No monetary incentive was offered, however panel members who completed the experiment qualified for a small number of member points, which accumulate over time and can then be redeemed for prizes. A total of 466 usable questionnaires were analysed. Each respondent evaluated one online ad embedded in the online questionnaire, and then indicated his or her agreement with the 14 VRP statements on a five-point rating scale ranging from ‘strongly disagree’ to ‘strongly agree’. Depending on the group to which the respondent was randomly assigned, a different label showed above the ad (for test Groups 1 to 6), while no label appeared for the control group (Group 7). The final item was an overall likeability measure of the ad using a seven-point semantic differential scale anchored at 1 = ‘I do not like the ad at all’ to 7 = ‘I like the ad a lot’. Matched demographic groups Even though Schlinger (1982) found in her examination of the relationship between respondent characteristics and attitude scales that ad



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evaluation­ has a significantly stronger correlation with brand usage than with either demographic or any situational variables, she did report that women and older consumers may respond relatively more positively than men or younger consumers. According to McGuire (1969), women could be more susceptible to persuasion than men. As a result, each of the seven independent groups of respondents in our experiment was matched on selected demographic variables. The overall sample comprised nearly equal numbers of males (48.7%) and females (51.3%), with an equal split between the two age groups: 18–29 (49.4%) and 30–39 (50.6%). Within gender, the age groups were equally split to render four equal demographic groups of close to 25% of the sample in each. Similar proportions were achieved through quota enforcement across all the groups. A chi-square test of goodness-of-fit was performed to determine whether the demographic variables across each of the seven groups equally matched. The results indicated that the demographic profiles (gender, age, education) did not significantly differ across the seven groups: GENDER (6, n = 466) = 1.927, p < 0.926, AGE (6, n = 466) = 1.102, p < 0.981, EDU 6, n = 466) = 3.481, p < 0.746. We therefore concluded there were seven matching groups. A Kruskal–Wallis test achieved similar results.

Results Descriptive statistics and scale reliability The 14 VRP items were modelled as the dependent variables of the six independent source variables (three positively framed and three negatively framed statements). The individual VRP items were then grouped into the six factors, or dimensions, as proposed by Schlinger (1982) and Strasheim et al. (2007), i.e. Relevant News, Brand Reinforcement, Stimulation, Empathy, Familiarity and Confusion. To compute an index for each dimension, a combined mean was obtained for the two scale items loading on each of six factors. The means were calculated by summation and then divided by the number of factor loading items. Table 2 presents a list of the 14 individual statements used to develop each of the seven evaluative dimensions and their basic descriptive statistics. The structure of these dimensions has been well established in previous researchers (Strasheim et al. 2007).

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Overall

Alienation

Familiarity

Empathy

Brand reinforcement

Relevant news

Confusion

Entertainment

Dimensions

2.652 0.872 2.781 0.917 2.781 1.000 2.394 0.975 2.672 0.894 2.761 2.565 0.992 2.672 0.778 2.656 0.946 2.348 0.886 2.597 0.871 2.582 2.594 1.089 2.453 0.925 2.844 0.979 2.273 1.016 2.463 1.146 2.567 2.406 1.034 2.328 0.944 2.625 1.016 2.409 1.007 2.388 0.984 2.403 2.420 1.130 2.313 0.871 2.297 1.003 2.197 0.932 2.493 0.911 2.418

That’s a good brand and I wouldn’t hesitate recommending it to others

I know that the advertised brand is dependable and reliable

I felt that the commercial was acting out what I feel at times

I felt as though I were right there in the commercial experiencing the same thing

This kind of commercial has been done many times. It is the same old thing

I have seen this commercial so many times I am tired of it

The ad didn’t have anything to do with me or my needs 3.261 1.133 3.156 1.130 3.109 1.086 3.409 1.052 3.284 1.139 3.060

V17

V18

V20

V21

V24

V25

V28

n/a

Overall evaluation

The commercial did not show me anything that would make me want to use one of their services. (R)

2.652 1.135 2.484 1.023 2.969 0.992 2.606 1.149 2.463 1.064 2.582

During the commercial I thought how that service might be useful to me

V16

V29

2.652 1.186 2.547 0.991 2.859 0.924 2.485 1.011 2.582 1.047 2.507

4.116 1.605 3.813 1.816 4.516 1.553 3.909 1.652 3.985 1.692 4.164

3.232 1.165 3.250 1.039 3.125 0.984 3.409 1.081 3.284 1.098 3.179

1.986 1.007 2.109 0.875 2.219 1.119 1.985 1.088 2.000 0.969 2.104

2.333 1.172 2.375 1.031 2.359 1.104 2.439 0.979 2.463 1.035 2.522

The commercial told me about a service and I think I’d like to try it

Mean

V15

SD

2.246 1.104 2.359 1.045 2.266 1.130 2.394 1.149 2.433 1.090 2.478

Mean

It was too complex. I was not sure what was going on

SD

V10

Mean

3.203 1.195 3.109 1.183 3.531 1.038 3.136 1.135 3.239 1.349 3.269

SD

I thought it was clever and entertaining

Mean

It required a lot of effort to follow the commercial

SD

V2

Mean

V9

SD

3.290 1.226 3.188 1.233 3.469 1.154 3.152 1.180 3.269 1.298 3.060

Mean

SD

Group 7: Control

1.629 4.000 1.627

1.058 3.159 1.133

1.043 3.145 1.019

0.971 2.333 1.080

0.907 2.536 1.065

0.871 2.681 0.978

0.908 2.841 1.024

0.762 2.855 0.896

0.780 2.855 0.791

1.061 2.884 0.993

0.894 2.884 0.963

0.975 2.594 1.089

0.959 2.710 1.164

1.123 3.261 1.184

1.099 3.159 1.184

SD

Group 5: Group 6: CGA contest CGA creativity

Mean

Group 4: CGA flop

The commercial was lots of fun to watch and listen to

Group 3: CGA popular

V1

Group 2: CGA

Schlinger reference Schlinger items (reduced: Strasheim 2008)

Group 1: Ad agency

Table 2: Descriptive statistics of the 14 VRP items across the seven dimensions

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The reliability of the scale was assessed using Cronbach’s (1951) alpha (α), a widely applied index of internal consistency (Peterson 1994). While a coefficient alpha of 0.7 is generally regarded as exemplary (Nunnally 1978; Carman 1990; Hair et al. 1998), for the combined 14-item scale (see Table 3), the alpha was 0.85. Subsequently, reliability of the scale for each of the seven dimensions was assessed. With the exception of Alienation, which is marginal at 0.65, all other dimensions were well above 0.70. However, Kline (1999) reports that although 0.7 is generally held to be desirable for tests of ability, realistically, given the diversity of constructs, values below 0.7 are to be expected. To test for the external validity of the VRP scores, we measured the criterion validity (Graham 2003), by correlating all 14 VRP items with a criterion measure known to be valid. Our known criterion is the single-item measurement of ‘overall likeability’. We found an acceptable and positive correlation of 0.80. To test our hypotheses, we examined the relationships between source effects (independent variable) and the scores on the VRP dimensions (dependent variable) by conducting a series of one-way ANOVA procedures. As a precursor to the ANOVAs, the statistical independence of the observations, the homogeneity of variance, and the normality were all checked for violations. Based on our research design and sampling, all test and control groups were statistically independent so this requirement­ Table 3: Tests of variance homogeneity (Levene’s test) and scale reliability (Cronbach’s alpha)

Dimension Entertainment Confusion Relevant news Brand reinforcement Empathy Familiarity Alienation Overall

Creator Levene statistic Sig. 0.076 0.927 0.591 0.555 2.757 0.066 1.007 0.367 1.748 0.177 0.407 0.666 2.381 0.095 1.885 0.155

* Cronbach’s alpha across all 14 items

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Source effect variable Popularity Motivation Levene Levene statistic Sig. statistic Sig. 0.414 0.662 3.257 0.041 0.775 0.462 0.887 0.413 1.637 0.197 0.737 0.480 1.975 0.142 0.781 0.459 1.485 0.229 1.283 0.280 0.090 0.914 0.598 0.551 1.601 0.204 1.436 0.240 0.384 0.681 0.208 0.813

Cronbach’s alpha 0.891 0.828 0.831 0.810 0.795 0.733 0.646 0.850*

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was not violated. Levene’s test was used to assess variance homogeneity, which is a precondition for parametric tests such as the t-test and ANOVA. The resulting F-values of Levene’s test (see Table 3) suggested that homogeneity of variance was not violated at p > 0.05 except for the ‘Entertainment’ dimension on the Motivation source-effect variable (p = 0.041). However, the Games–Howell, Brown–Forsythe and Welch t-test (recommended for variables with unequal variances) delivered no significantly different results, so we concluded that the homogeneity of variance of this single dimension was acceptable. The normality of the dependent variables was tested with box plots, stem-leaf diagrams, PP/QQ plots and a Kolmogorov–Smirnov goodness-of-fit test. We found that the distribution for all variables could be considered normal. Therefore, the means and standard errors obtained are deemed stable and valid to be applied in further tests. To test our first hypothesis (see Table 4a), which measured the source effect of the ‘creator’, we compared the scores on the seven VRP dimensions across the three groups: Control (no label), Ad agency-created, and Consumer-generated ads. Three of the seven comparisons indicated a significant difference: Confusion: F (2, 199) = 2.342, p = 0.099; Relevant News: F (2, 199) = 2.360, p = 0.097; and Empathy: F (2, 199) = 2.976, p = 0.053. While we did detect significant differences between the scores of ad agency-created and consumer-generated, and therefore rejected our null hypothesis, the Tukey–Kramer HSD post hoc comparisons of the three groups revealed that consumers seem to be more critical towards the ad when labelled as either agency or consumer-generated, compared with the unlabelled ad (control group). On the Empathy and Relevant News dimensions, the ad scored significantly higher among the control group than in the two test groups. On Confusion, the test groups scored significantly higher than the control group, which means ‘less confusion’ when the ad was labelled agency-created and/or consumer-generated. To test the second hypothesis (see Table 4b) that measured the source effect of ‘popularity’, we compared the scores on the seven VRP dimensions across the three groups: Control (no label), CGA popular ad, and CGA non-popular ad. Four of the seven comparisons indicated a significant difference: Relevant News: F (2, 196) = 3.371, p = 0.036; Brand Reinforcement: F (2, 196) = 5.849, p = 0.003; Empathy: F (2, 196) = 4.335, p = 0.014; and Familiarity: F (2, 196) = 2.243, p = 0.093.



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Independent variable Control Ad agency CGA Control Ad agency CGA Control Ad agency CGA Control Ad agency CGA Control Ad agency CGA Control Ad agency CGA Control Ad agency CGA Control Ad agency CGA Mean 3.21 3.25 3.15 3.35 3.71 3.63 2.88 2.65 2.52 2.86 2.61 2.73 2.76 2.50 2.39 3.57 3.80 3.79 2.85 2.75 2.80 4.00 4.12 3.81

Std. Deviation 1.123 1.146 1.171 1.065 1.076 0.948 0.904 1.099 0.959 0.805 0.822 0.786 0.902 0.985 0.809 0.955 0.929 0.811 0.896 1.052 0.876 1.627 1.605 1.816

*Significant at p < 0.10; **Significant at p < 0.05 Note: Confusion, Familiarity and Alienation were reversed-scored in this analysis

Overall

Alienation

Familiarity

Empathy

Brand reinforcement

Relevant news

Confusion

Entertainment

Dependent variable Std. Error 0.135 0.138 0.146 0.128 0.130 0.119 0.109 0.132 0.120 0.097 0.099 0.098 0.109 0.119 0.101 0.115 0.112 0.101 0.108 0.127 0.110 0.196 0.193 0.227

95% Confidence Interval for mean Lower bound Upper bound 2.94 3.48 2.97 3.52 2.86 3.44 3.09 3.60 3.45 3.97 3.40 3.87 2.67 3.10 2.39 2.92 2.28 2.76 2.66 3.05 2.41 2.81 2.53 2.92 2.54 2.98 2.26 2.74 2.19 2.59 3.34 3.79 3.57 4.02 3.59 3.99 2.63 3.06 2.50 3.01 2.58 3.02 3.61 4.39 3.73 4.50 3.36 4.27

Table 4a: H1 – Creator: Consumer-generated ad vs. Agency created

0.0055

0.0017

0.0144

0.0290

0.0160

0.0232

0.0230

0.0012

R2

0.5477

0.1712

1.4519

2.9765

1.6169

2.3603

2.3416

0.1233

F

0.5791

0.8428

0.2366

0.0532*

0.2011

0.0970*

0.0988*

0.8841

Sig.

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Independent variable Control CGA popular CGA flop Control CGA popular CGA flop Control CGA popular CGA flop Control CGA popular CGA flop Control CGA popular CGA flop Control CGA popular CGA flop Control CGA popular CGA flop Control CGA popular CGA flop Mean 3.21 3.50 3.14 3.35 3.69 3.58 2.88 2.91 2.55 2.86 2.72 2.37 2.76 2.73 2.34 3.57 3.74 3.91 2.85 2.88 2.59 4.00 4.52 3.91

Std. Deviation 1.123 1.016 1.059 1.065 1.052 0.931 0.904 0.857 0.952 0.805 0.899 0.829 0.902 0.913 0.945 0.955 0.951 0.924 0.896 0.844 0.984 1.627 1.553 1.652

*Significant at p < 0.10; **Significant at p < 0.05 Note: Confusion, Familiarity and Alienation were reversed scored in this analysis.

Overall

Alienation

Familiarity

Empathy

Brand reinforcement

Relevant news

Confusion

Entertainment

Dependent variable

Table 4b: H2 – Popularity: CGA popular vs. CGA flop Std. Error 0.135 0.127 0.130 0.128 0.132 0.115 0.109 0.107 0.117 0.097 0.112 0.102 0.109 0.114 0.116 0.115 0.119 0.114 0.108 0.105 0.121 0.196 0.194 0.203

95% Confidence Interval for mean Lower bound Upper bound 2.94 3.48 3.25 3.75 2.88 3.40 3.09 3.60 3.42 3.95 3.35 3.81 2.67 3.10 2.70 3.13 2.31 2.78 2.66 3.05 2.49 2.94 2.17 2.57 2.54 2.98 2.51 2.96 2.11 2.57 3.34 3.79 3.50 3.98 3.68 4.14 2.63 3.06 2.67 3.09 2.35 2.83 3.61 4.39 4.13 4.90 3.50 4.32 0.0266

0.0202

0.0224

0.0424

0.0563

0.0333

0.0196

0.0204

R2

2.6735

2.0184

2.2433

4.3354

5.8486

3.3707

1.9581

2.0407

F

0.0715*

0.1356

0.0930*

0.0144**

0.0034**

0.0364**

0.1439

0.1327

Sig.

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On the additional item to measure overall likeability of the ad, the ANOVA indicated significant differences, F (2, 196) = 2.673, p = 0.072. Four of the seven dimensions as well as the overall likeability measure indicated significantly more favourable attitudes towards the consumer-generate ad labelled as ‘Popular’, compared with the consumer generated-ad labelled as ‘Unpopular’. The Tukey–Kramer HSD post hoc comparisons of the three groups indicated that both the control group and the ‘popular’ groups scored significantly higher than the ‘unpopular’ on the dimensions of Relevant News, Brand Reinforcement and Empathy. On the Familiarity dimension, the ‘unpopular’ scores are significantly higher than those of the control group, which suggests that when the ad is labelled as ‘not popular’ and ‘not viewed by many people’, respondents are also more likely to claim to have not seen the ad. For the third hypothesis (see Table 4c), which tested the source effect of the ‘motivation’, a comparison was made across the three groups: Control (no label), CGA created for a contest, and CGA created to show individual creativity. Two of the seven comparisons indicated a significant difference: Relevant News: F (2, 200) = 3.222, p = 0.042; and Empathy: F (2, 200) = 2.727, p = 0.068. The Tukey–Kramer HSD test revealed that consumers seem to be more critical towards the ad when labelled as either created for contest or to show creativity, compared with if not labelled (control group). On both the Empathy and Relevant News dimensions, the ad scored significantly higher among the control group than in the two test groups.

Conclusions and managerial implications Consumer-generated advertising and consumer-generated content created around online advertising is increasingly influencing both brand communication and brand meaning. To understand how to respond to this phenomenon, marketers need to know what effect this has on their target audiences. We tested the effect of the manipulation of three variables: knowledge or perception of who created the ad, the popularity of the ad, and the motivation for the creation of the ad. While we did not find overwhelming evidence that CGA is preferred over agency-created ads, our findings suggest that consumers seem to be more critical towards an ad when they are exposed to cues that inform

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Independent variable Control CGA contest CGA creativity Control CGA contest CGA creativity Control CGA contest CGA creativity Control CGA contest CGA creativity Control CGA contest CGA creativity Control CGA contest CGA creativity Control CGA contest CGA creativity Control CGA contest CGA creativity Mean 3.21 3.25 3.16 3.35 3.55 3.50 2.88 2.52 2.54 2.86 2.63 2.67 2.76 2.43 2.49 3.57 3.75 3.74 2.85 2.72 2.88 4.00 3.99 4.16

Std. Deviation 1.123 1.283 1.074 1.065 0.970 0.888 0.904 0.990 0.903 0.805 0.819 0.705 0.902 0.942 0.839 0.955 0.809 0.827 0.896 0.978 0.858 1.627 1.692 1.629

*Significant at p < 0.10; **Significant at p < 0.05 Note: Confusion, Familiarity and Alienation were reversed-scored in this analysis.

Overall

Alienation

Familiarity

Empathy

Brand reinforcement

Relevant news

Confusion

Entertainment

Dependent variable Std. Error 0.135 0.157 0.131 0.128 0.118 0.108 0.109 0.121 0.110 0.097 0.100 0.086 0.109 0.115 0.103 0.115 0.099 0.101 0.108 0.119 0.105 0.196 0.207 0.199

Table 4c: H3 – Motivation: CGA contest vs. CGA creativity 95% Confidence Interval for mean Lower bound Upper bound 2.94 3.48 2.94 3.57 2.90 3.43 3.09 3.60 3.32 3.79 3.28 3.72 2.67 3.10 2.28 2.76 2.32 2.77 2.66 3.05 2.43 2.83 2.50 2.84 2.54 2.98 2.20 2.66 2.28 2.69 3.34 3.79 3.56 3.95 3.54 3.94 2.63 3.06 2.48 2.95 2.67 3.09 3.61 4.39 3.57 4.40 3.77 4.56 0.0024

0.0061

0.0099

0.0265

0.0155

0.0312

0.0078

0.0010

R2

0.2441

0.6096

0.9982

2.7269

1.5743

3.2221

0.7893

0.0993

F

0.7837

0.5446

0.3704

0.0679

0.2097

0.0419**

0.4556

0.9055

Sig.

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them who created the ad. Similarly, we find that when consumers are made aware of the motivation for creating the ad, they seem to be more critical in their evaluation. Therefore, being exposed to prompts about the creator and motivation for creation of the ad may lead to a more critical evaluation of the ad. Significant evidence was found that prompts suggesting that an ad is not popular, or was a ‘flop’ (opposed to being a popular and awardwinning ad), generated a negative evaluation of the ad. These findings hold a number of implications for marketers. First, our research suggests that source effects have a significant impact on the way consumers evaluate an ad. Marketers should be concerned about consumer-generated content around both their online ads as well as those generated and posted by consumers. Second, any negative comments posted about the ad and the brand may have a significantly negative impact on the evaluation of the ad, which suggests that marketers should monitor consumer-generated content and react appropriately to negative comments. Third, even though the results did not provide significant evidence, marketers should keep in mind that when an ad has won awards or has been viewed a great number of times, consumers should be informed about this as it may affect the subsequent evaluation of the ad as well (much like with box office movies). Lastly, consumers should be encouraged to assist in creating such ads, as this communicates a ‘realness’ that resonates favourably with other consumers.

Limitations and directions for future research As with all research, the study described in this article has a number of limitations. First, this experiment manipulated only three independent variables. If more source effect variables were manipulated, more insights would have been gained on how consumers react to different variables. Second, the selection of the type of advertisement used in this study may potentially have biased the results. A different ad in a different category may have delivered slightly different results. Indeed, the presentation of the ads could also be more stringently controlled, by utilising different presentations of the labelling of the ads and different wording of the cue labels. Third, only a single ad was tested. Testing more than one ad could have provided external validation. Fourth, the panel members were all based in Australia, and all were between the ages of 18 and 39 years old.

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If the experiment were conducted in a different country, the results may have been different. Similarly, older-aged respondents may respond differently to the variables tested in this experiment. Interestingly, the unlabelled control ad appeared to perform better than the treatment ads on almost all the dimensions tested in this experiment. The actual presence of cueing information appears to have a detrimental influence on evaluations of ads. Whether this is due to an underlying cognitive information-processing mechanism or an artefact of the experimental conditions is fertile ground for future research in this area. A number of avenues for future research arise from this study. It would be worthwhile to conduct similar experiments for comparisons among different age groups, different countries, by testing different source and context variables, and by using test ads from different product or services categories. An interesting aspect that should be explored in greater detail is the fact that when respondents are told something (anything) about the ad it causes them to think more critically about the advertisement. This is an important aspect that needs greater explanation and investigation. The results of this study can be used as a starting point for future studies aimed at investigating the online environment and consumer-generated content in greater detail. Finally, any differences or similarities between online and offline consumer-generated advertising would provide greater depth to the understanding of the influence of these media.

Implications for theory development The results of the present study are limited in effect size. However, given the limited amount of research that has examined source and framing effects in consumer-generated content, these results may indicate where the examination of theoretical constructs should continue. For the source effects component of the Elaboration Likelihood Model, it would appear that source effects are influential. Who created the ad and why they did so appears to be important in consumers’ evaluation of an ad, which aligns with previous research (Berthon et al. 2008; Cheong & Morrison 2008) and anecdotal media evidence. Interestingly, when there was no information present, evaluations of the ad were less critical, compared with those labelled as user- or agency-generated, or with the motive for creation as creativity or as a contest entry. Source effects are argued in the Elaboration



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Likelihood Model to be effective through the peripheral route of content processing. Given that the presence or absence of information appeared to have an effect, this would argue for another, underlying, mechanism that is affecting the peripheral route when evaluating the ad. Were the differences simply between user-created versus agency-created, it could be argued that consumers were making a more conscious, high-elaboration approach to evaluating the ad. The evidence for supporting elements of source theory was clearly demonstrated in that the negative evaluations from other users also affected the ad evaluation. While this aspect requires further investigation to establish the influence of specific phrasing, these results nonetheless provide initial evidence that such source effects can be important for evaluations of consumer-generated content.

Acknowledgement The authors would like to acknowledge the data collection sponsored by Toluna Ltd, a global provider of online consumer panels.

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About the authors Peter Steyn is a post-doctoral researcher at Luleå University, Sweden, and a research consultant based in Hong Kong. His research interests are in online marketing research with particular reference to brands, sponsorship and communities. Michael T. Ewing is Professor and Head of the Department of Marketing at Monash University. His research interests include advertising evaluation, the technology-communications interface, health promotion and brand management.



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Gené van Heerden is Assistant Director Specialised Degree Programmes, Rotterdam School of Management, Erasmus University, Rotterdam, The Netherlands. She recently completed her PhD degree in Marketing at the Luleå University of Technology, in Luleå, Sweden. Leyland F. Pitt is Professor of Marketing and the Dennis F. Culver EMBA Alumni Chair of Business at the Segal Graduate School of Business at Simon Fraser University, Vancouver, Canada, and is also a Senior Research Fellow of the Leeds University Business School in the United Kingdom. Lydia Windisch is currently completing her doctoral thesis in psychology and is a Research Fellow in the Department of Marketing at Monash University. Address correspondence to Peter Steyn, Division of Industrial Marketing, eCommerce and Logistics, Luleå University of Technology, Luleå, Sweden. Email: [email protected]

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