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ISSN : 2287-1063(Print)
ISSN : (Online)
The Journal of Advertising and Promotion Research Vol.1 No.1 pp.153-185

When Consumers Advertise Brands Online: A New Theoretical Model of Ad Effectiveness for the YouTube Generation

Kuen-Hee Ju-Pak , Kyung Yul Lee
Professor of Advertising, California State University, Fullerton
Professor of Advertising & Public Relations, Hanyang University


The proliferation of online consumer generated content (CGC) andconsumer generated media (CGM), such as YouTube, and the resultingshift of eyeballs from the traditional media to the new media formatand content are driving advertisers to explore them as brandcommunication tools. However, little is known about how CGC workswhen it is applied to a brand. In an attempt to fill the void, the presentstudy proposes and empirically tests the model that explains howonline consumer generated branded content (CGBC) influences the viewers in their attitudes and intentions to purchase the advocatedbrand. Results provide evidence for the viability of the model andindicate that CGBC works differently from traditional advertising.Implications from the findings are discussed, along with directions forfuture research.



 Consumer generated content (CGC), also known as user generated content (UGC), refers to different kinds of content produced by end-users in various online media platforms ranging from social networking sites, to online video sites, to blogs, and even to corporate sites. The CGC phenomenon is rapidly spreading, as the broadband Internet connection becomes the norm and new digital media technology (such as Web 2.0) allows an average Internet user to produce, share, and interact via text- or video-based content with ease and speed that was never possible before.

 Whitney (2007) indicates that more than one-third (35%) of online adults and more than half (57%) of teenagers aged 12 to 17 in the U.S. created content online in 2007. YouTube (2011) reports that more than 60 hours of video were uploaded to their website every minute in October 2010, which is the equivalent of 342,000 full-length movies every week. More than 70% of the YouTube traffic comes from outside the U.S. (YouTube, 2011). According to Pew American Life & Internet Project (2011), more than two-thirds (71%) of online Americans watched online video and 32% read blogs on topics spanning from parenting to wine recommendations in 2011. 

 The proliferation of CGC and the resulting shift of eyeballs from traditional media to consumer generated media over the years has attracted advertisers’ attention to and investment in this evolving medium as a brand communication tool. For example, online video, one version of CGC, commanded an estimated $775 million in advertising in 2007. While advertising revenue for online CGCs is expected to grow almost 100% per year in the next few years (Whitney, 2007), there is still reluctance and skepticism in the advertising industry concerning the production and use of consumer generated branded content (CGBC), also known as consumer generated advertising (CGA).

 CGBC offers several distinctive advantages over traditional advertising. One is its cost advantage. For example, the Doritos Super Bowl spot, a CGBC that won a gold Lions Award at Cannes, cost $12 to produce (Klaassen, 2007). Professionally produced branded content will certainly cost more. Another benefit of CGBC is that message development becomes more consumer- or user-centric, which, according to Forrester Research analysts Charlene Li and Josh Bernoff (Bulik & Steinberg, 2008), is critical to capitalizing on the opportunities (extended reach and enhanced effectiveness of brand advertising) presented by the CGC phenomenon.

 A survey of 2,000 American consumers and advertising experts, conducted by IBM Global Business Service, reveals that the current trends of increasingly empowered consumers (in control of what and how they view) and the rising popularity of CGC will likely redefine how advertising is sold, created, consumed, and measured in the next five years (Berman & Battino, 2007). Findings from the research suggest that advertisers must innovate the development, delivery, and tracking of their advertising messages by letting the consumer drive message development (as in CGBC) and by pursuing online advertising outlets such as consumer generated media (CGM); advertisers should also measure their online ads in impact-based (instead of impression-based) metrics.

 Despite the critical need to explore CGBC as a new viable advertising format, there is a lack of knowledge concerning the strategic application of CGBC or even how online CGBC works. Therefore, the present research attempts to fill the void by investigating if and how online CGBC influences the viewers' attitudes and intentions to purchase the advertised brand. The purpose of the present study is two-fold: 1) to develop a theoretical model that might explain how online CGBC works, and 2) empirically test the feasibility of the model.


Consumer Generated Branded Content (CGBC) or Consumer Generated Advertising (CGA)

 To date, a few studies have been conducted to determine the ffectiveness of consumer generated branded content (CGBC) and how consumers respond to CGBC. A survey of 238 college students in South Korea (Choi, 2007) indicates that young adults perceive the CGBC as more effective than other forms of Internet ads (such as banners and e-mail ads) in eliciting attention, as well as building brand image. On the other hand, An (2008) found in a survey of 144 adults that the respondents have negative attitudes toward creating consumer generated content (CGC) to publicize a product or company.

 Previous research employing surveys provides inconsistent results concerning the effectiveness of CGBC. To our knowledge, no empirical testing has been done of its effectiveness or the process by which this type of advertising may affect the viewer.

Models of Advertising Effects

 Advertising and marketing literature presents several alternative models of advertising effects. The most frequently cited and tested models in recent years are the four models proposed by Mackenzie, Lutz, and Belch (1986), including the affect transfer, dual mediation, reciprocal mediation, and independent influences hypotheses.

 The affect transfer model (associated with path 1 in Figure 1a) is similar to classical conditioning theory and posits that affective reactions to an advertisement (i.e., ad attitude in their conceptualization) influence receivers' attitudes toward the advertised brand without necessarily altering their cognitive structures (i.e., product-related beliefs and evaluation). Supportive evidence for the model exists in the marketing literature (for example, Gorn, 1982; Greshem & Shimp, 1985). The dual mediation model (path 2 in Figure 1a, representing two paths) assumes that attitude toward the ad, influenced by antecedent variables such as ad cognitions (ad content or execution-related), may have a direct impact on brand attitude, as well as an indirect impact on brand attitude via brand cognitions. This dual mediation hypothesis has received the most empirical support and proved the most robust against such effects as level of involvement and brand consideration set (Gardner, 1985; Homer, 1990; Mackenzie et al., 1986; Miniard, Bhatla, & Rose, 1990).

Figure 1a. Four Alternative Models of the Mediating Role of Ad Attitude

 The reciprocal mediation model (path 3) assumes that ad attitude and brand attitude are mutually causative, which is explained by cognitive consistency that can be obtained when holding the same attitude toward the ad as the advertised brand. Previous research has provided empirical, though limited, support by showing that for familiar and established brands, or in low-involvement situations, prior brand attitude influences how the ad is perceived and evaluated (Muehling, Stoltman, & Mishra, 1990). The independent model (path 4) proposes that both ad and brand attitudes affect purchase intentions independently. Empirical evidence supporting this model is notably lacking. 

 The elaboration likelihood model (ELM) proposed by Petty and Cacioppo (1986) also provides valuable insights into how CGBC affects the attitudes and behavior of the receiver. The ELM recognizes two routes (paths) to advertising influence, one of which dominates depending on the level of consumer involvement (see Figure 1b). When the receiver has the motivation and ability to process the message, the central route is likely to operate and persuasion (attitude change) will occur as a result of extensive cognitive elaboration and careful scrutiny of the merits of message arguments. When the receiver lacks the motivation and ability to attend, comprehend, and evaluate the message, however, the peripheral route is likely and attitude formation or change will mostly be based on peripheral cues in the ad (e.g., feelings induced, source, and other executional elements), without extensive cognitive processing of the merits of the argument presented.

Figure 1b. The ELM Model

 The two paths, the indirect and direct impact of ad attitude in the Dual Mediation Hypothesis (DMH), are similar to those proposed in the ELM. However, both paths in the DMH are mediated by ad attitude, whereas the two paths in the ELM are independent of each other.

Cognitive and Affective Elements of Consumer Responses to Advertising

 Previous research (e.g., Burke & Edell, 1989; Cho, Na, & Kim, 1998; Edell & Burke, 1987) examined various consumer responses to advertising as antecedents of ad attitude. While earlier advertising research focused primarily on cognitive responses (e.g., MacKenzie et al., 1986), Muehling et al. (1990) found that consumer responses to ads consist of both cognitive and affective/emotional elements. Holbrook and Batra (1987) incorporated both content-related and emotional responses into advertising as antecedents to ad attitude in their model of advertising effects, which showed how they influence brand attitude via ad attitude. Recent studies have found a powerful impact of affective responses (emotions and feelings) on brand cognitions and attitudes (Chowdbury, Olsen, & Pracejus, 2008).  In addition, Homer (2006) demonstrated that a different path of influence operates for positive versus negative affect.

 Previous research has measured cognitive and affective responses in a variety of ways. Burke and Edell (1987 and 1989) suggest that cognitive dimensions of television advertising may be measured in three scales of evaluation, activity, and gentleness; affective elements may be measured in three feelings scales of upbeat, warm, and negative. Holbrook and Batra (1987) derived six dimensions – emotional, threatening, mundane, sexy, cerebral, and personal – from their exploratory factor analysis. Others (e.g., Cho et al., 1998) identified seven distinctive dimensions of consumer responses to television commercials, which include informativeness, entertainment, originality, activity, ambiguity, warmth, and negative affect.

In sum, previous literature, primarily on television advertising, indicates that consumer responses to CGBC will likely consist of both cognitive and affective elements, and each of these elements may be captured by multiple dimensions. Cognitive dimensions should be measured by both content-related and execution-related features, whereas affective responses should be decomposed into positive and negative affect. 


Figure 2 shows the structural model we propose to investigate how consumers’ responses to consumer generated branded content (CGBC) influence brand attitude and purchase intention via advertising attitude and brand cognition. Our model recognizes the presence of the dual paths mediated by ad attitude, consistent with the Dual Mediation Hypothesis (DMH). It also incorporates the central route of the Elaboration Likelihood Model (ELM), under which brand cognition works independently of ad attitude and impacts brand attitude directly. While the DMH has been robust in traditional advertising context, the DMH may not fully capture the process by which CGBC works. There are many similarities between CGBC and television ads (e.g., employing sight, sound, and video in presenting the message), but online CGBC ads are notably different from television advertising in several ways. For example, viewers exert greater control in their exposure to and consumption of CGBC online (voluntary exposure and active processing), compared to television advertising (forced exposure and passive processing). In addition, the CGBC and television advertising may differ in the amount of perceived relevance, trust, interest, and thus involvement with the message exposed, even when there is no difference in production quality. 

Figure 2. The CGBC Model Proposed

 The proposed model is based on several assumptions of causal relationships among eight constructs. As shown in Figure 2, we hypothesized a significant causal relationship between: (H1a-H1d) consumer responses to CGBC (represented by four dimensions of content-related and execution-related cognitions and positive and negative affect) and brand cognition; (H2a-H2d) consumer responses to CGBC (represented by the four dimensions) and ad attitude; (H3) ad attitude and brand cognition; (H4) ad attitude and brand attitude; (H5) brand cognition and brand attitude; (H6) brand attitude and purchase intention.


Subjects and Experimental Stimuli

 This study used a convenience sample of 400 college students in the Seoul metropolitan area of South Korea. The sample, consisting of the main consumers of consumer generated content (CGC), was deemed appropriate for the purpose of this research – a preliminary testing of the proposed consumer generated branded content (CGBC) model. The sample consisted of slightly more female (59.9%) than male (40.1%) subjects, who were on average 22 years old. The vast majority (93%) of participants had consumed CGC at least once in 2008, while only 7% had ever posted CGC online.

 The experimental stimuli for the study were two CGBC ads that were two minutes each and professionally produced for both a pizza brand and an electronic dictionary/translator brand. The stimuli were selected so that the products advertised would be different in the level of involvement, but also so that they would be products typically consumed by the subjects. To minimize the potential confounding effects of prior brand attitude, unfamiliar brands were used.

Research Design and Procedure

 An independent group experimental design with non-repeated measure was employed, with the subjects randomly assigned to one of the two conditions. The experiment was conducted in a classroom setting, where subjects were first told that they would be shown CGBC and asked questions about it. Then they were exposed to one of the ads.  Immediately after the exposure, subjects were given the questionnaire designed to measure their responses to CGBC, ad attitude, brand cognition, brand attitude, and purchase intention concerning the brand advertised. The procedures may induce high motivation to process the CGBC to be shown, but were deemed appropriate for creating the exposure environment (voluntary and active) typical to that of online CGBC videos.


 Based on the review of relevant literature, this study decomposed the consumer responses to CGBC into two cognitive elements (content- related versus execution-related cognition) and two affective components (positive versus negative affect). To measure these four cognitive and affective responses to CGBC, a large set of items was developed by combining scales used by previous studies (e.g., Burke & Edell, 1989; Cho et al., 1998; Edell & Burke, 1987; Holbrook & Batra, 1987; Homer, 2006; Sung, Kim, & Lee, 2007).

 A panel of judges eliminated items redundant or inappropriate for our study, leaving a total of 21 items. The final set of items to include in the study was determined with exploratory factor analysis (EFA) employing principle component analysis and varimax rotation. Only the items with a minimum factor loading of .50 and no significant cross-construct loading were selected for further analysis. Table 1 shows the list of 16 items (X1 through X16) used to measure the four types of responses to CGBC in the final analysis. Each of the items was measured with a five-point scale ranging from “strongly disagree” to “strongly agree.”

Table 1. Goodness-of-fit, Reliability and Convergent Validity of the Measurement Model

Table 1. Goodness-of-fit, Reliability and Convergent Validity of the Measurement Model

 Of the four constructs, brand cognition (subjects' beliefs about the brand's attributes and their evaluation of those attributes) was measured employing five attributes applicable to any brand (Javenpaa & Todd, 1997), including quality, reputation, reliability, worth purchasing, and ingredients used. Each of these attributes (that is, the brand is “of high quality,” “reputable,” “reliable,” “worth purchasing,” and “made with good ingredients”) was measured on a five-point scale ranging from “strongly disagree” to “strongly agree.”

 Both attitudes toward the ad and to the brand were measured using five-point semantic differential items anchored by five adjectives, including like/dislike, good/bad, positive/negative, favorable/unfavorable, and positive/negative (MacKenzie et al., 1986). Purchase intention in this study was measured with three five-point bipolar scales adopted from Batra and Ray (1986) and Metha (1994), which include “very likely-not at all likely,” “probable-improbable,” and “would recommend it to others.”


Validity and Reliability of the Measures

 To test the validity and reliability of the measures used, we constructed the measurement model including the items for all constructs identified from exploratory factor analysis discussed earlier. The model was then examined for its fit to our data via confirmatory factor analysis (CFA) using AMOS 7.0. Results from the CFA (Table 1) indicate a good fit for our measurement model, producing χ2 = 436.248 (df = 406, p = 0.145); GFI (goodness-of-fit index) of .900; AGFI (adjusted goodness-of-fit index) of .870; RMR (Root Means Square Residual) of .037; RMSEA (Root Mean Square error of approximation) of .018; CFI (Comparative Fit Index) of .995; and NFI (Normative Fit Index) of .932 – all of which meet the criteria set by Schumaker and Lomax (2008). 

 Reliability was assessed using Cronbach's alpha. The alpha values for the eight constructs, measured by three to six items, ranged from .752 to .959 and indicate acceptable reliability of the measures for each construct.

 We assessed both convergent and discriminant validity of each construct following the procedure recommended in the literature. Convergent validity was examined by the size of standardized factor loadings of the items/measures (.50 or higher recommended), the average variance extracted (AVE) for each construct (.50 or higher), and the (significant) t-value concerning the ratio of factor loadings to standard error for each item. As indicated in Table 1, all item standardized factor loadings are higher than .50 (ranging .614 to .950), as were the AVEs for the constructs (ranging from .509 to .858). The t value for each item (ranging from 8.940 to 31.810) was also statistically significant (at p < .01). The results indicate high convergent validity for our measures. 

 Discriminant validity was established by testing each of the constructs against the (most stringent) criterion that the AVE for each construct be greater than its squared correlation with every other construct (Fornell & Larcker, 1981). Table 2 presents evidence that each of the eight constructs possesses high discriminant validity, with its AVE exceeding the squared correlation in each pairwise comparison (i.e., AVE/r2 >1). For example, the AVE (.509) for content cognition dimension is higher than even the highest squared correlation (r2 =.291) found in all possible construct pairs involving the dimension. 

Table 2. Convergent and Discriminant Validity of Constructs

Overall Goodness-of-Fit for the Proposed Model

 Having established the reliability and validity of the measurement model, we assessed the goodness of fit for the proposed theoretical model using AMOS 7.0. We first conducted a two-group analysis of the structural equation model for the two consumer generated branded content (CGBC) ads; the analysis compares the unconstrained model (in which all the paths are allowed to vary freely across the two samples – one for a pizza brand and the other for an electronic translator/dictionary brand) with the constrained model (in which the path coefficients are constrained to be equal between the two). This procedure allows us to see if the two CGBC groups are different in the fit of their data to the proposed model. The results indicate no significant difference between the constrained and unconstrained models in the model fit, justifying pooling of the data for the two groups.

 Goodness-of-fit tests run for the pooled data indicate acceptable fit of the proposed model (χ2 = 419.653, p>.05, GFI = .903, AGFI = .875, RMR = .038, and RMSEA = .012, CFI = .998 and NFI = .934). The results provide evidence that our proposed model is statistically sound and reliable enough to allow for further analysis.

Tests of Hypotheses

 Given the satisfactory fit for the proposed model, we then tested the six hypotheses concerning the eight constructs. Hypotheses 1-a through 1-d predict that each of the four CGBC (i.e., ad) response dimensions (RAd-CC, RAd-PA, RAd-EC,and RAd-NA , – content cognition, positive affect, execution cognition, and negative affect, in corresponding order) will show a significant causal relationship with ad attitude (AAd). The results of the structural equation model (SEM) analysis provide support for all of the hypotheses. The standardized factor loading (β) for each of the four response dimensions on ad attitude (AAd) is statistically significant at p < .01 (see Table 3). As predicted by our model, the three CGBC response dimensions – content cognition (β = 2.66), positive affect (β = .302), and execution cognition (β = . 207) – demonstrated positive effects on ad attitude, while negative affect, RAd-NA, showed negative impact (β = -.292), as expected. Overall, factor loading values are higher for affective responses (both positive and negative affect) than for cognitive dimensions (content and execution cognitions).

Table 3. Test of Hypotheses

 Given the satisfactory fit for the proposed model, we then tested the six hypotheses concerning the eight constructs. Hypotheses 1-a through 1-d predict that each of the four CGBC (i.e., ad) response dimensions (RAd-CC, RAd-PA, RAd-EC,and RAd-NA , – content cognition, positive affect, execution cognition, and negative affect, in corresponding order) will show a significant causal relationship with ad attitude (AAd). The results of the structural equation model (SEM) analysis provide support for all of the hypotheses. The standardized factor loading (β) for each of the four response dimensions on ad attitude (AAd) is statistically significant at p < .01 (see Table 3). As predicted by our model, the three CGBC response dimensions – content cognition (β = 2.66), positive affect (β = .302), and execution cognition (β = . 207) – demonstrated positive effects on ad attitude, while negative affect, RAd-NA, showed negative impact (β = -.292), as expected. Overall, factor loading values are higher for affective responses (both positive and negative affect) than for cognitive dimensions (content and execution cognitions).

Hypothesis 2 deals with the effects of the four response dimensions (RAd) on brand cognition (CB). Results concerning the hypothesis are mixed (see Table 3). The SEM analysis indicates only content cognition (β = .378) and positive affect (β = -.331) demonstrate significant causal effects on brand cognition. The remaining two response dimensions, execution cognition (β = .074) and negative affect (β = -.196), have either insignificant or marginal influence on brand cognition. It is surprising that positive affect is negatively related to brand cognition. That is, the respondents evaluated the CGBC positively as delightful, fun, and pleasing, yet held unfavorable cognition about the brand advertised. Overall, the results of this study provide partial support for Hypothesis 2.

 Hypotheses 3 through 6 predict the causal impact of CGBC/ad attitude on brand cognition (H3:  AAd  → CB) and on brand attitude (H4: AAd → AB), as well as the impact of brand cognition on CGBC/adttitude (H5: CB → AB) and that of brand attitude on purchase intention (H6: AB → PIB). Consistent with the prediction of our theoretical model (see Figure 2), the path coefficient for each of the causal links was positive and significant at p < .01. Results support Hypotheses 3 through 6. The highest causal relationship was found for the AB → PIB (β = 1.231) link, followed by AAd → CB (β = .586), and CB → AAd (β = .363). The AAd → AB path was the weakest (β = .204) of all. These findings suggest that of the dual paths (in which CGBC/ad attitude moderates the influence of CGBC/ad), the direct impact of ad attitude on brand attitude may be weaker than its indirect effects via brand cognition.

 We examined the causal effects of each of the four CGBC response dimensions on purchase intention to determine the relative strength for each of the three alternative causal paths, including the CB → AB → PIB path (in line with the central route to persuasion in ELM) and the dual paths involving ad attitude, AAdAB→ PIB (the direct path as predicted by the dual mediation hypothesis) and AAd → CB → AB → PIB (the indirect path of DMH). Table 4 indicates the CB → AB → PIB path is the strongest of the three alternative paths with the total indirect effect of .438 (totaled across all four dimensions), followed by AAd → CB → AB → PIB (.279), and AAd → AB→ PIB (.257). The results suggest that the central route to persuasion may be the most influential path of all.

Table 4. Results of Causal Effect Analysis

 However, additional analysis run for each of the CGBC response dimensions paints a different picture. For content-related cognitions (RAD-CC) and positive affect (RAD-PA) responses, the strongest impact occurred via the central route, the CB → AB → PIB path with the coefficients of .169 (RAD-CC) and .148 (RAD-PA). The central route to persuasion was not significant for the other two dimensions, negative affect (RAd-NA) and ad execution-related cognitions (RAd-EC), whose influence was predominantly mediated by the dual paths involving AAd. Results overall indicate the relative strength of each path depends on the types/dimensions of the consumer responses to CGBC.


Findings and Theoretical Implications

 The present research, as the first of its kind, proposed and tested a theoretical model that explains the way online consumer generated branded content (CGBC) influences the attitude and behavior of its viewers. The model expands the existing theories and research on the effects of traditional media advertising by recognizing the potential impact of the new medium and the alternative form of advertising (CGA – consumer generated advertising). Results overall indicate the proposed model is statistically sound and is a viable approach to studying how online CGBC works.

 This research presents evidence that this new form of advertising, though generated by typical consumers of uncertain credibility, could significantly influence the viewer’s attitude toward the brand as well as purchase intention. Findings in general provide support for existing theories and research concerning how advertising (such as CGBC) influences consumer cognitions, attitudes, and purchase intentions. For example, our research indicated a significant mediating role of ad attitude for every type (e.g., cognitive or affective) of the viewer responses to the CGBC measured. In line with the prediction of the dual mediation hypothesis, we found both significant direct and indirect impact of the attitude toward the CGBC on the attitude toward the advertised brand, which in turn had significant impact on the viewer’s purchase intention.

 However, some of our findings are inconsistent with the evidence presented in the literature. Contrary to the prevalent notion for television advertising (see Brown & Stayman, 1992, for a summary), our results show a notably stronger indirect effect of ad attitude via brand cognition than its direct effect on brand attitude. Likewise, the study found the central and peripheral routes to persuasion to operate simultaneously in the context of CGA and the central route to be more pronounced than the peripheral route. The stronger effects of brand cognition on brand attitude, compared to that of ad attitude, suggests that the effects of CGBC on brand attitude do not seem to occur as a simple, direct transfer of the feelings or affect generated due to the exposure to CGBC, but the effect process is more cognitively based than affect-based.

 Our study confirms Homer’s assertion (2006) that brand-related beliefs/cognition is a strong mediator for the effect of positive feelings on brand attitudes. However, our results indicate that the effect process is not uniform for positive affect versus negative affect, which was not apparent in Homer's research (2006) because her test was limited to positive affect. The present research revealed a significant moderating role of brand cognition for the effects of positive affect, but not negative feelings.

 Likewise, the effect process differs between the two types of cognition – execution-related cognition versus content/message-related cognition – generated during the exposure to the CGA, supporting Homer’s notion that different advertising effect processes may operate for abstract versus concrete beliefs. This study of CGBC found that the effect of ad execution-related cognition on brand attitude flow exclusively through ad (CGBC) attitude, while the effects of the message content influenced brand attitude via the dual paths involving brand cognition as well as ad attitude.

 The most unexpected finding from this research is that positive affect generated by CGA caused negative brand cognition. The respondents who evaluated the CGBC positively (as delightful, fun, and exciting) were more likely to hold unfavorable cognition about the brand advertised, regardless of the product category (pizza or electronic dictionary).

 This finding contradicts the evidence (for positive or no causal relation of affect to brand cognition) presented in the advertising literature, and challenges the conventional belief that positive affect inhibits counterarguments toward the message and facilitates the message reception and acceptance, especially for low involvement products such as pizza. One possible explanation for the unexpected result is that the novelty of the CGBC format causes not only positive affect, but also the cognitively active receiver’s attention to the CGBC message. However, as the product is unfamiliar, and the unfamiliar source is perceived as lacking credibility, the receiver highly involved with the message is more likely to search product-relevant information in the message; thus, the absence of any strong product-relevant cues or arguments may have caused the viewer to form a negative attitude toward the brand advocated. More research is necessary to shed light on the negative, causal impact of ad attitude on brand cognition.

 Overall, results from this study suggest that CGBC works differently from television ads in many critical ways. As suggested in the marketing literature, the path and the relative strength of causal effects tend to vary in part by the nature (cognition vs. affect/feelings), types (content-related versus execution-related), and the valance (positive vs. negative) of the viewer responses to CGBC during the exposure to it.

Practical Implications

 As many industry experts and practitioners pointed out, the consumer generated content (CGC) or consumer generated media (CGM) phenomenon will likely redefine how advertising is created, consumed, and measured. Today’s marketers seem to recognize the increasing need to understand and adapt to this new media in their marketing and branding efforts.

 According to the results from the current research, CGBC viewers are more cognitively active processors of advertising information, and the formation and change of their attitude and behavior toward a brand depends more on their evaluation of the product-relevant attributes than on their feelings toward the brand.  Therefore, marketers, considering the CGBC as their branding tool, should recognize the importance of the message content in their CGAs.

 The finding that young adult consumers (a.k.a., the YouTube generation) may be more cognitively involved when processing CGBC questions the current CGBC practice of focusing on positive feelings (such as fun and excitement) or other executional elements perceived as novel and entertaining. The peripheral cues may be used to grab consumers' attention, arouse interest and curiosity, and influence their attitude toward the ad, and they may be effective if the goal is to generate awareness, interest, and buzz (viral communication). However, novelty and increased interest have limited effects in shaping the attitude or behavior toward the brand. Cues that create curiosity and arouse interest may even backfire when little product-relevant information is available in the ad and when the receiver, unfamiliar with the brand, looks for the information.

 The results suggest that if CGBC focuses on inducing positive affect/feelings to achieve the brand goal of attitude formation/change or purchase behavior, the feelings induced and brand information should be integrated for it to work. Likewise, when a marketer solicits CGBC or similar branded content via contests (a common practice), too much dependence on executional ingenuity (i.e., novelty and entertainment value) should be avoided. Our research suggests that the message in the CGA should include cognitively relevant stimuli, especially when it involves a new brand or a brand unfamiliar to the intended receiver. Perhaps the criteria for selection should be specifically tied to the overall creative goal and strategy set.

 Overall, our research suggests that advertisers should exert more caution in utilizing CGBC to promote a new or less-known brand. When CGA is used for an unfamiliar brand, an integrated approach may be necessary where CGA and traditional media advertising would complement each other in the message content and execution.

 Marketers should note that CGBC online videos and televisions commercials, while sharing similar technical and production attributes, are significantly different in how they operate to influence brand attitudes and behavior. As the effect process for the CGBC versus television advertising differs significantly, an advertising response model specifically designed for the CGBC, such as the one proposed in this study, may be used to assist the strategic process of CGBC development and evaluation.

Limitations of the Present Study and Future Research Direction

 The current study has several limitations to note. First, we measured consumers’ cognitive and affective responses to CGBC using semantic scales instead of coding actual cognitive responses generated during exposure. While semantic scales have the ability to capture a wider variety of responses, such forced measures may suffer lack of external validity when consumers do not normally generate much cognition or affect during exposure to CGBC.

 Second, we used a convenience sample of college students. While they are representative of CGC consumers, they were mostly lurkers of CGC (reading and viewing CGC produced by others) as opposed to posters of CGC (only 7% of the sample). To the extent to which the viewer responses to CGBC differ between the lurkers and posters, the external validity of this study is limited.

 Third, this study used two professionally produced CGBC ads, which employed a soft-sell approach without featuring the name or logo of the brand prominently (a common CGBC practice). It is possible that the CGBC employing a hard-sell approach may have produced different results, again limiting the ability to generalize our findings.

The results presented concerning the proposed model are less than conclusive. Further testing of the model should be conducted under different contexts involving various viewer types (e.g., differing in age and their consumption and participation regarding CGC) and CGBC types (e.g., differing in appeals, length, and other characteristics). To better understand how CGBC works, the potential effects of brand familiarity, product involvement, repetition, and the context of placement also warrant further research. More research is necessary to enhance our knowledge and strategic applications of CGBC and CGC as a brand communication tool. 


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