Journal of Advertising ISSN: 0091-3367 (Print) 1557-7805 (Online) Journal homepage: https://www.tandfonline.com/loi/ujoa20 Integrating Advertising and Publicity Jooyoung Kim , Hye Jin Yoon & Sun Young Lee To cite this article: Jooyoung Kim , Hye Jin Yoon & Sun Young Lee (2010) Integrating Advertising and Publicity, Journal of Advertising, 39:1, 97-114, DOI: 10.2753/JOA0091-3367390107 To link to this article: https://doi.org/10.2753/JOA0091-3367390107 Published online: 04 Mar 2013. Submit your article to this journal Article views: 811 View related articles Citing articles: 8 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=ujoa20 Integrating Advertising and Publicity A Theoretical Examination of the Effects of Exposure Sequence, Publicity Valence, and Product Attribute Consistency Jooyoung Kim, Hye Jin Yoon, and Sun Young Lee ABSTRACT: Using several theories, including Information Integration Theory (IIT), Integrated Information Response Model (IIRM), confirmation effect, and contrast theory, this study investigates the combined effects of advertising and publicity under the varied conditions of exposure sequence, publicity valence, and product attribute consistency. Results obtained through a 2 (sequence: ad then publicity versus publicity then ad) × 2 (attribute variation: same attribute versus different attribute) × 2 (valence of publicity: positive versus negative) within-subjects factorial design show that advertising coupled with positive publicity induces confirmation effects regardless of sequence and attribute consistency, whereas negative publicity combined with advertising mostly produces contrast effects, with mixed results depending on the sequence and attribute consistency. Findings and implications are discussed. Wide attention has been given to integrated marketing communication (IMC) in the past decade, by both academia and industry, and the concept of IMC has found its way into the mainstream of marketing practice and literature (Duncan and Caywood 1996; Durkin and Lawlor 2001). Even though more and more companies are taking an IMC approach to corporate communication planning, relatively little empirical investigation has addressed its application (Eagle, Kitchen, and Bulmer 2007). Responding to this need, this study investigates the integration effect of advertising and publicity, two of the most widely accessible communication forms to consumers (Balasubramanian 1994). For researchers and practitioners, some guidelines are needed regarding how those two communication tools can be combined effectively. For example, how would consumers, after being exposed to an advertisement about the printing speed of a printer brand PRINTWELL react to the brand if they read a negative review of the brand’s printing speed? Would consumers react differently if the exposure sequence were reversed? The number of potential cases of advertising and publicity combinations extends far beyond these examples. Simply put, given an advertisement for a product focusing on attribute X, there are at least four Jooyoung Kim (Ph.D., University of Florida) is an assistant professor of advertising, Department of Advertising and Public Relations, Grady College of Journalism and Mass Communication, University of Georgia. Hye Jin Yoon (M.A., University of Georgia) is a doctoral student, Department of Advertising and Public Relations, Grady College of Journalism and Mass Communication, University of Georgia. Sun Young Lee (M.A., University of Georgia) is a doctoral student, School of Journalism and Mass Communication, University of North Carolina. possible combinations: ad and a positive publicity about the same attribute, ad and a negative publicity about the same attribute, ad and a positive publicity about another attribute, and ad and a negative publicity about another attribute. Because advertising and publicity can be exposed to consumers in many different ways, it is crucial to investigate the various combinations. Though rare, some studies provide research findings about the integration effect of advertising and publicity. Loda and Coleman (2005) examined whether there is a difference between dependent variables such as message acceptance and message response depending on the medium (advertising or publicity) and on the sequencing of message delivery (advertising then publicity or publicity then advertising). However, they only considered the situations when the messages in advertising and publicity are consistent and positive. Stammerjohan et al. (2005) investigated the interactions between different types of advertising media (radio and print) and the interaction between brand familiarity and message valence in publicity (positive or negative), but their study does not provide insight into how the combinations of advertising and various types of publicity (in terms of valence and attribute consistency) might work to form particular attitudes for each case. In addition, a few studies have focused on the joint effects of advertising and consumer experiences other than publicity (e.g., ad and trial [Smith 1993]; ad and negative word-of-mouth [Smith and Vogt 1995]; ad and product sampling [Marks and Kamins 1988]). Although there are some valuable findings in the literature, extant literature does not provide adequate empirical guidance or acceptable theoretical frameworks for marketers and researchers. Our study, therefore, attempts to investigate, both empirically and theoretically, the combination effects of Journal of Advertising, vol. 39, no. 1 (Spring 2010), pp. 97–113. © 2010 American Academy of Advertising. All rights reserved. ISSN 0091-3367 / 2010 $9.50 + 0.00. DOI 10.2753/JOA0091-3367390107 98 The Journal of Advertising advertising and publicity, focusing on three important conditional variables that are believed to serve as a simple but comprehensive set of conditions for advertising–publicity combinations: (1) exposure sequence, (2) publicity valence, and (3) product attribute consistency. Theoretical Framework and Hypothesis The literature shows that the virtue of integrating advertising and publicity is the synergistic effect generated between them. The synergy effect, which is the representative value of IMC, refers to the benefits of multiple communication tools (i.e., both promotional tools and media tools) delivering a unified message (Moriarty 1994; Naik and Raman 2003; Schultz 1996; Schultz 1996; Schultz and Kitchen 1997). In the same vein, there are times when consumers are exposed to multiple communication tools delivering ununified messages, or even worse, antagonistic messages, generating a more negative impact than being exposed to a single negative message. This type of negative effect is called the counter-synergy effect (e.g., Baker 2002). With this synergy effect of integration in mind, we developed our theoretical framework and hypotheses. The theoretical framework for understanding the collective effect of advertising and publicity can be developed by synthesizing some competing or supplementing theories found in the literature. The theories we used in the present study include Information Integration Theory (IIT) (Anderson 1971), Integrated Information Response Model (IIRM) (Smith and Swinyard 1982), contrast theory (Hovland, Harvey, and Sherif 1957), and confirmation effect (LaBella and Koehler 2004). Following a short review, each theory will be applied to several advertising publicity combinations. Hypotheses will be formulated based on the predictive directions in favor of the synergy concept of IMC or the counter-synergy concept against which IMC exists. Information Integration Theory Anderson (1971) developed the Information Integration Theory to explain how a person’s attitude is formed from integrating different pieces of information. The theory assumes that attitudes are formed and modified as people interpret new information and integrate it with their prior attitudes. It involves two basic operations: valuation and integration. Valuation refers to the determination of the meaning, importance (i.e., weight), and evaluation of the information. Integration is a process of combining those valued pieces of information. The integration process of advertising and publicity can be described in terms of a simple algebraic model, such as R = wad*sad + wpub*spub , where R = product evaluation after exposure to advertising and pub- licity, wad = weight given to the ad information, sad = evaluative position of message in the ad, wpub = weight given to the publicity information, and spub = evaluative position of information in the publicity. There are two competing models for the rules of integration: adding model and averaging model. The adding model assumes that people add each piece of information, which results in the increase of R when the information pieces are of the same sign. Therefore, each weight can range from 0 to 1. On the other hand, the averaging model assumes that people integrate each piece of information by averaging it with the prior set of integrated information. Therefore, R does not necessarily become larger, and each new weight is dependent on the weight of the information already integrated in the model. In the averaging model, the weights of the algebraic model above are interdependent, and the weights must sum to one (e.g., wad = 1 − wpub) (Anderson 1971). In addition, the weight (w) of each piece of information depends on its credibility and reliability, leading to the “weighted averaging” (Eagly and Chaiken 1993, p. 247) procedure. Accordingly, a consumer’s attitude toward the brand (Ab) after being exposed to both advertising and publicity would be the average of “Ab based on ad” and “Ab based on publicity,” where the weights of ad and publicity can be adjusted based on their credibility and reliability. The integration theory also provides a way of understanding the sequential effect of informational stimuli using the process of “inconsistency discounting” (Anderson 1971, p. 173) through which a new piece of information that is inconsistent with the existing set of information is given a decreased weight. Therefore, it is expected that consumers will discount the weight of advertising or publicity if it is inconsistent (in terms of valence) with what they were formerly exposed to. Integrated Information Response Model The Integrated Information Response Model is similar to the averaging model of IIT, but it specifically focuses on the combination of advertising and direct product experience. This model, developed by Smith and Swinyard (1982), using Fishbein and Ajzen’s (1975) expectancy-value theory (EV = Σ[bici ]ei , where EV = expectancy value, bi = belief confidence for attribute i, ci = belief strength for attribute i, ei = evaluation of attribute i), incorporates the effects of consumer cognition, affect, and behavior. The model suggests that consumers often have a low degree of information acceptance for advertising, for it is generally known to be a vested-interest source. Therefore, consumers often form weakly (belief confidence: ci ) held attitudes (EV) based on the lower-order beliefs (belief strength: bi ) created by the advertising exposure. Thus, consumer cognition will be based on lower-order beliefs that will result in a lower-order affect. Conversely, consumers who have direct product experience will put more trust in their Spring 2010 99 experience and form stronger beliefs that are higher-order beliefs (i.e., expectancy is closer to 1), which result in higherorder affect (Smith and Swinyard 1983, 1988). Because publicity is generally perceived as more credible than advertising (e.g., Loda and Coleman 2005), publicity might also result in a higher-order belief compared to advertising. When present, publicity, due to its higher-order belief, will be a greater influence on overall evaluation than advertising. Therefore, IIRM’s overall prediction would be that the combined effect of advertising and publicity (regardless of sequence) will be similar (or equal) to the effect of publicity alone. Confirmation Effect Although IIT’s weighted averaging process predicts that less credible information is given little to no weight and IIRM theorizes that a lower-order belief combined with a higherorder belief will have nearly zero weight (bi and ci ), some empirical studies have shown that probability judgments can still be influenced by nondiagnostic evidence (e.g., Nisbett, Zukier, and Lemley 1981; Zukier and Jennings 1983). For example, LaBella and Koehler (2004) found the so-called confirmation effect, where the combination of nondiagnostic evidence with diagnostic evidence increased the overall evaluation. That is, initial judgments can become more extreme with the introduction of either diagnostic or nondiagnostic evidence, when the evidence matches the person’s expectation, because “people are more sensitive to, or give greater weight to, evidence that confirms their currently held beliefs” (LaBella and Koehler 2004, p. 1083). For example, when a consumer tries a product and finds that it works as promised in an ad he or she has watched before, IIT and the IIRM will predict that the previous advertising effect will mostly disappear. Thus, the product evaluation based on an ad-trial condition and a trial-only condition should show no difference based on the IIT or the IIRM. Conversely, the confirmation effect theory will predict that an ad-trial condition (when ad and trial confirm each other) will show a higher evaluation than a trial-only condition because of the confirmation effect. As such, when ad is combined with publicity, IIT and IIRM predict that the advertising effect will mostly disappear, showing no differences from the publicity-only condition. Conversely, the confirmation theory predicts that the combined effect of ad and publicity will be stronger than either the ad-only or publicity-only condition due to confirmation effects. Contrast, Assimilation, and Assimilation-Contrast Effect Compared to confirmation effect theory, which focuses on the positive effect of an expected outcome between two experi- ences, contrast effect theory explains the surprise effect of an unexpected outcome (Oliver 1981). When expectations are not matched (either positively or negatively) by an actual product experience, contrast theory predicts that the contrast between expectations and actual experience will cause the consumer to inflate the discrepancy (Anderson 1973). Although there are other related theories, such as assimilation theory or a hybrid model called assimilation-contrast theory (see Eagly and Chaiken [1993] for a more detailed review), when advertising and publicity information (in terms of valence) is inconsistent, and thus unexpected, only the contrast effect theory is applicable. When consumers are exposed to either advertising or publicity that delivers inconsistent information (i.e., “negative publicity after ad” or “ad after negative publicity”), the contrast theory predicts that the combined effect will be much more negative than the publicity-only condition. In the same situation, IIT predicts that the combined condition and the publicity-only condition will result in a similar effect because, for the combined condition, the weight of advertising will be nearly zero (weighted averaging) or because any later inconsistent communication will be discounted (inconsistency discounting). Hypotheses for Advertising and Publicity Integration Theoretically, how might advertising and publicity work together? The traditional two-step model (e.g., Deighton 1984) of advertising explains that in the first step, an exposure to advertising induces the consumer to form a hypothesis about the product. Because advertising is viewed as a vested-interest source, the hypothesis should be tentative and beliefs might be weakly held. In the second step, if the consumer encounters any evidence, he or she will test the ad-based belief in order to confirm it and increase confidence. The model suggests that the effects of each step can interact so that the initial expectation can change in any direction after the second step (Deighton 1984). The strength of influence that the evidence has in the second step depends on the credibility of the content and source (e.g., Birnbaum and Stegner 1979; Eagly and Chaiken 1993). Generally, publicity is thought to be more credible and more influential than company-controlled forms of communications (Bond and Kirshenbaum 1998). Therefore, the power of publicity to influence purchase decisions over other forms of promotion would not be surprising (Chaiken and Maheswaran 1994; Sternthal, Dholakia, and Leavitt 1978). To lay the foundation for our theoretical framework and subsequent hypotheses, the following hypothesis was set forth first: H1: Believability of publicity will be significantly higher than that of advertising. How do consumers integrate the information from advertising and publicity? To answer this question, we characterize 100 The Journal of Advertising potential cases of ad (Ad) and publicity (Pub) combinations. Given an ad for a product focusing on an attribute X (i.e., Adx ), there are at least four possible cases of publicity: a positive publicity statement about the same attribute X (i.e., +Pubx ), a negative publicity statement about the same attribute X (i.e., –Pubx ), a positive publicity statement about another important attribute Y (i.e., +Puby ), and a negative publicity statement about another important attribute Y (i.e., –Puby ). These four different cases of publicity give us four Ad-Pub combinations: (1) Adx & +Pubx, (2) Adx & –Pubx, (3) Adx & +Puby, and (4) Adx & –Puby. In addition, the sequence of exposure needs consideration: Ad then Pub or Pub then Ad. Crossing all possible Ad-Pub cases with two possible sequences yield eight (4 × 2) combinations. In each case, the integration procedure that combines the pieces of information from both advertising and publicity will be different. For each case, we discuss and compare the relevant theories reviewed previously and formulate a hypothesis that is in favor of the synergy or counter-synergy effect, for or against which IMC exists. Hypothesized predictions and their competing or alternative predictions with respective theories are shown in Table 1. Adx & +Pubx This case combines an advertisement and a positive publicity statement that focus on the same attribute. IIRM predicts that publicity, due to higher credibility, will be more heavily weighted than the ad. The weighted averaging principle of IIT also predicts that the weight of advertising will be adjusted to accommodate the greater publicity weight. Furthermore, there should be no difference in total expectancy between the Ad/Pub or Pub/Ad sequence, for IIT does not predict the discounting effect for the presentation of consistent information. Conversely, alternative results can be predicted by confirmation effect theory when the information in advertising and publicity is consistent. All in all, IIT and IIRM theory predict that publicity will dominate the advertising effect and make it negligible, but the confirmation effect theory proposes an increase in evaluation that reflects a synergistic effect. In agreement with the confirmation effect theory predicting increased evaluation, the following hypotheses were proposed: H2a: Attitude toward the brand under the combined (Adx & +Pubx) communication condition in the Adx then +Pubx sequence will be significantly higher than that of the ad-only or publicity-only condition. H2b: Attitude toward the brand under the combined (Adx & +Pubx) communication condition in the +Pubx then Adx sequence will be significantly higher than that of the ad-only or publicity-only condition. Adx & +Puby When information between advertising and publicity is inconsistent in attribute (i.e., attribute X and Y) but consistent in valence (i.e., positive), IIT’s weighted averaging model suggests that +Puby might still gain higher weight but that Adx should not lose too much weight due to its unique piece of information (i.e., X). In this case, IIT’s adding model can also explain the additive effect of two pieces of information conveying different attributes. In a similar vein, within the IIRM principle, the consumer will simultaneously possess two separate stages of beliefs about the brand based on two pieces of information (advertising about attribute X and publicity about attribute Y), instead of exclusively choosing one information source that provides a higher-order belief. In addition, there should be no inconsistency discounting (because the attributes are not opposite in evaluation). Will the confirmation effect still be present even when the attributes are different? Previous studies suggest that the confirmation effect might still occur based on the affirmed valence, despite the lack of attribute confirmation. For example, in their interpersonal performance appraisal study, Robbins and DeNisi (1998) found that affect consistency influenced the weighting of recalled information and subsequent ratings. For this case of Adx & +Puby, accordingly, IIT and IIRM’s prediction would be that publicity will gain more weight but the advertising will also retain its own weight. In addition, the confirmation effect appears to be present because of the congruent positive valence. IIT’s and IIRM’s predictions are much closer to additive, while confirmation effect theory’s prediction is more synergistic. However, all three theories predict that the attitude from the Adx & +Puby situation will be more favorable than either the Adx-only or +Puby-only situation. H3a: Attitude toward the brand under the combined (Adx & +Puby ) communication condition in the Adx then +Puby sequence will be significantly higher than that of the ad-only or publicity-only condition. H3b: Attitude toward the brand under the combined (Adx & +Puby ) communication condition in the +Puby then Adx sequence will be significantly higher than that of the ad-only or publicity-only condition. Adx & –Pubx This case is widely prevalent in the marketplace and generally works against the advertiser (Ahluwalia, Burnkrant, and Unnava 2000). IIRM does not predict much in this case, for it does not have a theoretical property to explain how two conflicting pieces of information can be integrated. But IIT’s weighted averaging principle proposes that the weight of advertising will be adjusted to accommodate the greater weight Spring 2010 101 Table 1 Hypothesized and Competing Predictions Hypotheses and competing predictions Theories Prediction bases H2a A(Adx then +Pubx ) > A(Adx ), > A(+Pubx ) Confirmation effect Confirmation (a, v) Competing predictions: > A(Adx ), ≤ A(+Pubx ) IIT Averaging > A(Adx ), = A(+Pubx ) IIRM Expectancy weight of ad = 0 Confirmation effect Confirmation (a, v) H2b A(+Pubx then Adx ) > A(Adx ), > A(+Pubx ) Competing predictions: > A(Adx ), ≤ A(+Pubx ) IIT Averaging > A(Adx ) = A(+Pubx ) IIRM Expectancy weight of ad = 0 Confirmation effect Confirmation (v) H3a A(Adx then +Puby ) > A(Adx ), > A(+Puby ) IIT Adding IIRM Expectancy weight of ad > 0 H3b A(+Puby then Adx ) > A(Adx ), > A(+Puby ) Confirmation effect Confirmation (v) IIT Adding IIRM Expectancy weight of ad > 0 H4a A(Adx then –Pubx ) < A(–Pubx ) Contrast effect Contrast (a, v) Competing predictions: ≥ A(–Pubx ) IIT Weighted averaging > A(–Pubx ) IIT Inconsistency discounting Contrast effect Contrast (a, v) H4b A(–Pubx then Adx) < A(–Pubx ) Competing predictions: ≥ A(–Pubx ) IIT Weighted averaging ≥ A(–Pubx) IIT Inconsistency discounting Contrast effect Contrast (v) H5a A(Adx then –Puby ) < A(–Puby ) Competing predictions: > A(–Puby ) IIT Weighted averaging > A(–Puby ) IIT Inconsistency discounting Contrast effect Contrast (v) H5b A(–Puby then Adx ) < A(–Puby ) Competing predictions: > A(–Puby ) IIT Weighted averaging > A(–Puby ) IIT Inconsistency discounting Notes: A = attitude; a = attribute; v = valence; IIT = Information Integration Theory; IIRM = Integrated Information Response Model. Items in bold represent the hypotheses and their corresponding theories. of publicity, resulting in a nearly zero weight for advertising. In addition, IIT’s inconsistency discounting process predicts that the negative publicity will get a discounted weight for the Adx then –Pubx sequence because the information is inconsistent with the attitude formed by the original ad. Inconsistency discounting can also happen in the –Pubx then Adx sequence, for the ad information will be inconsistent with the evaluative belief formed from the negative publicity. In addition, this inconsistency discounting process suggests that the attitude from the Adx then –Pubx sequence will be higher than that from the –Pubx then Adx sequence. For a simple example, if attitudes based on Adx only and –Pubx only are 5 and 2 on a 1–7 scale, respectively, and the discount rate is 50%, the attitude from the Adx then –Pubx sequence will be (5 + 1)/2 = 3 and that from the –Pubx then Adx sequence will be (2 + 2.5)/2 = 2.25. Contrary to the predictions of IIT, different results are predicted by the contrast theory. For the Adx then –Pubx sequence, because expectation based on Adx is not matched by a more credible piece of information (–Pubx ), contrast theory predicts that the surprising contrast between two pieces of information will cause the consumer to magnify the discrepancy (Anderson 1973), resulting in a counter-synergy effect. Though smaller in magnitude, the contrast effect can also be present for the –Pubx then Adx sequence as the expectation based on –Pubx is not matched by a new piece of information (Adx ). The following hypotheses were formulated based on the contrast effect theory to predict the counter-synergy effects for a misintegrated combination of Adx & –Pubx. Since the ad-only condition provides only positive information, we would also expect this combined condition to be lower, but we are interested in a combined effect compared to the negative publicity alone, not compared to positive advertising alone. H4a: Attitude toward the brand under the combined (Adx & –Pubx ) communication condition in the Adx then –Pubx sequence will be significantly lower than that of the publicityonly condition. H4b: Attitude toward the brand under the combined (Adx & –Pubx ) communication condition in the –Pubx then Adx sequence will be significantly lower than that of the publicityonly condition. 102 The Journal of Advertising Adx & –Puby This case is an unfortunate one for marketers, but it might be less severe than Adx & –Pubx because of attribute inconsistency. When the information of advertising and publicity is inconsistent in both attribute and valence, IIT’s weighted averaging model suggests that –Puby might still gain more weight (due to credibility) than advertising, but that advertising is also likely to sustain its weight because of its unique piece of information. In addition, inconsistency discounting might occur because of the valence inconsistency. That is, the evaluations based on either –Puby in the Adx then –Puby sequence or Adx in the –Puby then Adx sequence will be discounted because they are opposite in valence. The contrast effect might also be observed. For the Adx then –Puby sequence, because the valence expectation based on Adx is not matched by a more credible piece of information (–Puby ), contrast theory might suggest that the surprising contrast between two pieces of information will cause the consumer to inflate the valence discrepancy. Though smaller in magnitude than it was in the case of the –Pubx then Adx sequence, the contrast effect might also be present for the –Puby then Adx sequence as the valence expectation based on –Puby is not matched by the new piece of information (Adx ). Accordingly, IIT’s prediction is much closer to subtractive than countersynergistic, while contrast effect theory’s prediction is more counter-synergistic. The following hypotheses reflect this counter-synergistic effect: H5a: Attitude toward the brand under the combined (Adx & –Puby ) communication condition in the Adx then –Puby sequence will be significantly lower than that of the publicityonly condition. H5b: Attitude toward the brand under the combined (Adx & –Puby ) communication condition in the –Puby then Adx sequence will be significantly lower than that of the publicityonly condition. Absolute Attitude Changes Lastly, using the confirmation effect and the contrast effect theory, we hypothesize that there will be different magnitudes of effects of exposure sequence on brand evaluations because the quality and strength of confirmation or contrast will be greater for the Ad then Pub sequence than for the Pub then Ad sequence. The confirmation effect difference was speculated by Deighton: “evaluative messages may be more prone to confirmation than factual messages, and so may interact more with evidence than do factual assertions” (1984, p. 764). For both Adx & +Pubx and Adx & +Puby conditions, the size of the confirmation effect would be different depending on the sequence of exposure because, as Deighton (1984) asserted, evaluative messages (Adx ) will be more prone to confirmation than factual messages (+Pubx or +Puby ). Therefore, the Ad then +Pub sequence condition will show a much stronger confirmation effect than the reversed sequence (+Pub then Ad). Regarding the contrast effect for negative publicity cases, Cohen and Goldberg’s (1970) finding shows that participants reversed choices when strong or weak preceding expectations were strongly disconfirmed but not when strong priors were weakly disconfirmed, implying that the initial attitude will negatively change in the Adx then –Pubx or the Adx then –Puby sequence, but it will not positively change in the –Pubx then Adx or the –Puby then Adx sequence. Our last hypothesis addresses this sequential effect difference; the absolute value of the attitude changes is relevant because it shows the degree of change independent of directionality (Marks and Kamins 1988). H6: Absolute attitude change will be significantly greater for the Ad then Pub sequence group than for the Pub then Ad sequence group. Method A 2 (sequence: Ad then Pub versus Pub then Ad) × 2 (attribute variation: same attribute versus different attribute) × 2 (valence of publicity: positive versus negative) within-subjects factorial design was used. Through pretests prior to the main experiment, four suitable product categories (printer, car insurance, highlighter, and disposable battery) and their important attributes to consumers were chosen. Although adding four product categories to a 2 × 2 × 2 design brings 32 combinations of conditions, 16 experiment cells were used by including two products in each cell. A total of 537 undergraduate students from a large U.S. university participated in the experiment for extra credit and were free at any time to opt out of participating. Because each participant evaluated stimuli of two product types, their responses produced 1,074 cases. The main experiment used a repeated-measures design, measuring the dependent variable (i.e., attitude toward brand) twice, once after exposure to advertising and once after exposure to publicity. A repeated-measures design was preferred because it allows for the examination of changes in participant attitudes during a sequence of stimuli exposures and can furnish more efficient use of subject resources, greater comparability of the conditions, and reduced error variance (Keppel and Wickens 2004). Although incidental effects (i.e., systematic differences incidental to the actual treatment manipulation, such as carryover effect) might be a limitation of the within-subject design, if the point of the study is the sequence of observations, the order of the stimuli is not an incidental problem to the study (Keppel and Wickens 2004). Spring 2010 103 Pretests Three pretests were conducted to determine (1) suitable product categories of high- and low-involvement products, (2) attributes that people might consider important for a given product category, and (3) the fictitious brand names for the products. First, we chose utilitarian products over hedonic products because the process of consumer attitude formation for utilitarian products is relatively more straightforward ( Johar and Sirgy 1991). Furthermore, product categories with different levels of involvement (i.e., high versus low) were considered because communication effects and effectiveness can vary as a function of product involvement (Marks and Kamins 1988; Smith and Swinyard 1982). Highlighter and battery were chosen as low-involvement products and laser printer and auto insurance were chosen as high-involvement products after pretesting their purchase decision involvement (Mittal 1989). The attributes were determined by free-association tests: long-lasting experience and grip comfort for highlighter, battery life and stable power output for battery, printing speed and toner longevity for printer, customer service and accident forgiveness for auto insurance. Fictitious brand names were selected to eliminate any possible confounds. The names generated were low in familiarity, low in implying product category benefit, neutral in quality, and neutral in liking: “Thompson Auto Insurance,” “Pendley Highlighter,” “Rizer Battery,” and “Kasler Laser Printer.” Main Experiment Stimuli (Advertisements and Publicity Articles) Stimulus materials were designed to resemble print ads and articles in a newspaper. The format of the ads was adapted from previous studies (e.g., Cline, Altsech, and Kellaris 2003). The ad consisted of the brand name, headline copy, and an image showing a close-up of the product. The size, layout, and background of the ads were identical across product types. The publicity stimuli were designed to resemble actual product review articles in newspapers. The publicity articles featured either positive or negative information and contained information either on the same attribute or a different attribute from the one addressed in the ad. A set of sample stimuli is shown in the Appendix. Experimental Procedures The experiment was conducted online. Links to the online survey were provided, and participants underwent a selfadministered survey. A random link generator randomly assigned participants to one of the 16 cells. Participants viewed a total of two ads and two publicity articles in random order (see Table 2). Stimuli were counterbalanced to control any undesired incidental (or nuisance) effects systematically (e.g., interactions between unrelated products, confirmation bias effects [Deighton 1984] between unrelated products, or context effects [Keppel and Wickens 2004]). For example, a negative review for Kasler printer was placed after a positive review for Pendly highlighter in cell no. 1, but it was placed before a positive review about Rizer battery in cell no. 6. Each participant viewed a mix of low- or high-involvement products, positive or negative reviews on either the same or different product attribute. Also, participants were specifically instructed to disregard previous brands and focus only on the stimulus brand at hand. After each stimulus was shown, attitude toward the message, message credibility, and attitude toward the brand were measured. Manipulation and Confounding Checks Manipulation checks were implemented for the following criteria: (1) attribute given in ad, (2) attribute given in publicity article, (3) valence of the publicity article, and (4) product involvement. Cases were deleted where incorrect answers were given on any one of the above manipulation criteria, resulting in 70 deleted cases from a total of 1,074 (=537 × 2). This adjustment reduced the total number of cases to 1,004. Product involvement was measured at the beginning of the experiment using the same purchase decision involvement (PDI) scale to confirm manipulation success in the experiment (Mittal 1995). Results showed that the product categories of highlighter (M = 2.99, SD = 1.49) and battery (M = 4.04, SD = 1.30) had lower mean scores of PDI than the high-PDI products, printer (M = 5.47, SD = 1.09) and auto insurance (M = 6.15, SD = .92). The difference between the means of high- and low-PDI products were significant, F(2, 1002) = 757.08, p < .001. Subsequent analyses for hypothesis tests, therefore, used pooled data of printer and auto insurance as “high-involvement product” (HI), and highlighter and battery as “low-involvement” product (LI). As a confounding check, participants’ general attitude toward advertising (see Pollay and Mittal 1993 for details about the scale) and general attitude toward publicity (in a reduced form of the scale from Yang and Oliver 2004) were measured at the beginning. Results showed no difference in each measure for Aad in general, F(7, 995) = . 893, p = .511 and for Anews (attitude toward the news) in general; F(7, 995) = 1.393, p = .205, across all cells and also no difference for any pair of post hoc comparisons (all ps > .193) between cells. In addition, attitudes toward the stimulus (i.e., advertising or publicity) were measured to ensure that all participants had equivalent attitudes toward the same stimulus, though in different cells. For example, attitude toward the Rizer 104 The Journal of Advertising Table 2 Order of the Stimuli Given and Mean of Ab in Each Condition Cell no. n (Cases) C1 74 C2 74 C3 70 C4 77 C5 66 C6 70 C7 66 C8 63 C9 48 C10 51 C11 57 C12 40 C13 57 C14 57 C15 59 C16 75 First stimulus Second stimulus Third stimulus Fourth stimulus P_AD (M = 3.88, SD = .72) P_AD (M = 3.70, SD = 1.03) P_AD (M = 3.79, SD = 1.02) P_AD (M = 3.71, SD = .98) P_+Y (M = 4.99, SD = .83) P_–Y (M = 2.95, SD = .72) P_+X (M = 4.64, SD = .74) P_–X (M = 2.79, SD = .64) I_AD (M = 3.30, SD = .80) I_AD (M = 3.54, SD = .92) I_AD (M = 3.50, SD = .76) I_AD (M = 3.23, SD = .70) I_+Y (M = 4.92, SD = .74) I_–Y (M = 2.91, SD = .63) I_+X (M = 4.90, SD = .75) I_–X (M = 2.54, SD = .72) H_+X (M = 4.88, SD = .76) H_–X (M = 2.63, SD = .78) H_+Y (M = 4.57, SD = .89) H_–Y (M = 3.13, SD = .83) B_–X (M = 2.90, SD = .71) B_+X (M = 4.99, SD = .70) B_–Y (M = 2.69, SD = .71) B_+Y (M = 5.03, SD = .69) H_AD (M = 5.01, SD = .78) H_AD (M = 4.44, SD = .70) H_AD (M = 4.99, SD = .84) H_AD (M = 4.79, SD = .76) B_AD (M = 3.62, SD = .59) B_AD (M = 3.90, SD = .71) B_AD (M = 3.84, SD = .95) B_AD (M = 3.91, SD = .84) P_–Y (M = 2.71, SD = .91) P_+Y (M = 5.07, SD = .94) P_–X (M = 2.21, SD = .86) P_+X (M = 5.11, SD = 1.07) P_AD (M = 5.10, SD = 1.02) P_AD (M = 3.21, SD = .82) P_AD (M = 4.91, SD = 1.14) P_AD (M = 2.71, SD = 1.11) I_+Y (M = 5.41, SD = 1.11) I_–Y (M = 2.26, SD = .83) I_+X (M = 5.25, SD = .94) I_–X (M = 2.02, SD = .79) I_AD (M = 5.31, SD = 1.02) I_AD (M = 2.64, SD = .78) I_AD (M = 5.59, SD = .89) I_AD (M = 2.39, SD = .83) H_AD (M = 5.57, SD = .92) H_AD (M = 2.44, SD = .84) H_AD (M = 5.21, SD = .96) H_AD (M = 3.42, SD = .90) B_A (M = 2.34, SD = .90) B_AD (M = 5.05, SD = 1.10) B_AD (M = 2.61, SD = 1.01) B_AD (M = 5.44, SD = .90) H_–X (M = 2.04, SD = .58) H_+X (M = 5.58, SD = .90) H_–Y (M = 3.27, SD = 1.16) H_+Y (M = 5.58, SD = 1.04) B_–X (M = 1.88, SD = .69) B_+X (M = 5.57, SD = 1.02) B_–Y (M = 2.04, SD = .83) B_+Y (M = 5.54, SD = .88) Notes: Ab = attitude toward the brand; P = printer; I = insurance; H = highlighter; B = battery; AD = advertising; + = positive publicity; – = negative publicity; X = publicity about product attribute consistent with ad; Y = publicity about product attribute inconsistent with ad; M = mean; SD = standard deviation. battery ad (attitude toward the ad itself, not toward the brand) should not be different between any pair of C13, C14, C15, and C16. It is important to note that in order to compare pure attitudes, only attitudes measured on the first stimuli were used in the analyses because the second stimuli could be contaminated by the previous stimuli for the same brand. For example, attitudes toward the Rizer advertisement in C5, C6, C7, and C8 cannot be considered as pure as those in C13, C14, C15, and C16. A four-item, seven-point semantic differential scale (i.e., bad/good, dislike/like, uninteresting/ interesting, irritating/not irritating) was used to measure the attitudes toward the ad and publicity article (Mitchell and Olson 1981). Post hoc pairwise comparison tests of analysis of variance (ANOVA) showed that there was no difference in the Aad measure (all ps > .05) for the same advertisements across a set of cells where the ad exposure came first (before seeing the same-brand publicity). For the comparison tests of Anews, we assumed that if the only difference between two publicity articles is the product attribute (X versus Y) and the attributes are regarded as equally important, Anews should not be different between Anews about X and Anews about Y . For example, the attitude toward the positive publicity article about attribute X of Rizer (in C6) should be the same as the attitude toward the positive publicity article about attribute Y of Rizer (in C8). Post hoc pairwise comparison tests of ANOVA showed that there was no difference in the Anews measure (all ps = 1.00) across the set of same-valence cells where the article exposure came first. All scales used were internally reliable. (See Table 3 for specific information on measures and scale reliabilities.) Spring 2010 105 Table 3 Measures and Scale Reliabilities Name of scale Items (all seven-point scales) Cronbach’s a Purchase decision Not care at all/care a great deal as to which brand I buy; involvement (PDI) Various types and brands in the market are all very alike/all very different ; (Mittal 1995) Not at all important/extremely important to make a right choice when buying product ; Not at all concerned/very much concerned about the outcome of choice .94 Aad in general (Pollay and Mittal 1993) Overall, I like advertising; My general opinion about advertising is unfavorable (RC); I consider advertising a good thing .90 Anews in general (Yang and Oliver 2004) Overall, I consider news to be valuable; Generally, I think news is trustworthy; I find news to be interesting .65 Message credibility (Hallahan 1999a) Not informative/informative; Untrustworthy/trustworthy; Inaccurate/accurate; Unconvincing/convincing; Not believable/believable Ad = .87; publicity = .90 Attitude toward brand (Simons and Carey 1998) Brand looks bad to me/brand looks good to me; I don’t like the brand/I like the brand; The brand is undesirable/the brand is desirable; I feel negatively about the brand/I feel positively about the brand Ad = .93; publicity = .98 Note: Aad = attitude toward the ad; Anews = attitude toward the news; RC = reverse coded. Results To test the hypotheses, comparison tests were performed on the relevant measures, but the measures from the exposure to first and second stimuli (whether advertising or publicity) were treated differently. As explained previously, when ad exposure comes prior to publicity, the ad measures assess the pure effects of the ad; the measures for publicity subsequent to the ad assess the effects of exposure to both the ad and publicity. The same logic applies to the opposite sequence. For example, in the Pub then Ad sequence, publicity will be the measure of the stimuli given first and will be treated as a control. Marks and Kamins (1988) successfully used this approach in their advertising-product sampling sequence study. Hypothesis 1 suggests that the credibility of publicity will be significantly higher than the credibility of advertising in all conditions. Credibility of advertising and publicity was compared in all eight combination conditions. As we had two sequences and two products, the total of 32 data blocks (16 cells × 2 products) were divided by 4 (2 × 2), resulting in eight conditions to be analyzed: HI+X, HI−X, HI+Y, HI−Y, LI+X, LI−X, LI+Y, and LI−Y, where HI, LI, +, −, X, and Y refer to high-involvement products, low-involvement products, positive publicity, negative publicity, consistent attribute, and inconsistent attribute, respectively. The results from every condition supported H1 by showing that the credibility of publicity is significantly higher than that of advertising (HI+X: t[137] = −12.31, p < .001; HI−X: t[104] = −11.93, p < .001; HI+Y: t[142] = −15.49, p < .001; HI−Y: t[105] = −13.02, p < .001; LI+X: t[145] = −10.44, p < .001; LI−X: t[101] = −9.81, p < .001; LI + Y: t[142] = −9.84, p < .001; LI−Y: t[120] = −6.63, p < .001). Because the data strongly supported H1, we could carry out the subsequent hypothesis tests. Hypothesis 2a proposed that the attitude toward the brand under the combined communication condition in the Adx then +Pubx sequence would be significantly higher than that of the ad-only or publicity-only condition. For this hypothesis and other subsequent hypotheses, a mean of combined condition (i.e., attitude measured after second stimuli) was compared with (1) the mean of the first condition within the same group, and (2) the mean of the first condition from the reversed sequence group, using a paired-samples t-test. We also compared the combined effect measure with an uncontaminated measure of +Pubx, which can only be found in the reversed sequence condition. A one-sample t-test, which examines whether the sample mean is significantly different from expected (i.e., null hypothesis mean), was used to compare the means. For H2a, the null hypothesis mean was the mean of “+Pubx only” from the +Pubx then Adx sequence. All variables used in the paired-samples t-test and the one-sample t-test conformed to the normality assumption (−1.96 < all skewness < 1.96, 106 The Journal of Advertising −1.96 < all kurtosis < 1.96). t-test results shown in Table 4 (HI/+X and LI/+X group) indicate that the attitude toward the brand after exposure to both ad and publicity in the Ad then Pub sequence was significantly higher than the means of both ad-only and publicity-only conditions, supporting H2a (all p < .002 for HI and LI conditions). Hypothesis 2b was also supported as the measures of attitude toward the brand under the combined (Adx & +Pubx) communication conditions in the Pub then Ad sequence were significantly higher than ad-only or publicity-only conditions (for both HI and LI conditions, all ps < .001), thus demonstrating a confirmation effect. Hypothesis 3a suggested that the measures of attitude toward the brand under the combined communication conditions in the Adx then +Puby sequence would be significantly higher than the ad-only or publicity-only conditions. Results in Table 4 show that the hypothesis was supported (all ps < .05 for HI and LI). Again, results for the different sequence were the same. Hypothesis 3b was also supported because the measures of attitude toward the brand under the combined communication conditions in the +Puby then Adx sequence were significantly higher than the ad-only or publicity-only condition (all ps < .05 for HI and LI). Hypothesis 4a hypothesized that the measures of attitude toward the brand under the combined communication conditions in the Adx then –Pubx sequence would be significantly lower than that of the publicity-only conditions. Results in Table 4 indicate that all conditions supported H4a (all ps < .001). Hypothesis 4b suggested that the measures of attitude toward the brand under the combined communication conditions in the –Pubx then Adx sequence would be significantly lower than the publicity-only conditions. Results in Table 4 show that for the Pub then Ad sequence, attitude toward the brand after exposure to negative publicity (–X) about the high-involvement product was not significantly different from the attitude based on the combined communication condition ( p = .40). On the other hand, the low-involvement condition supported H4b ( p = .005). Hypothesis 5a proposed that the measures of attitude toward the brand under the combined communication conditions in the Adx then –Puby sequence would be significantly lower than the publicity-only conditions. Results show that the hypothesis was supported for high-involvement products ( p < .003) but not for low-involvement products. As shown in Table 4, for the Ad then Pub sequence, attitude toward the brand after exposure to negative publicity (–Y) about the low-involvement product was not significantly different from the attitude based on the combined communication condition ( p = .13). The next hypothesis, H5b, suggested that the measures of attitude toward the brand under the combined communication conditions in the –Pubx then Ady sequence would be significantly lower than the publicity-only condition. This hypothesis was not supported. As indicated in Table 4, for the Pub then Ad sequence, attitude toward the brand after exposure to negative publicity (–Y) about both high- and low-involvement products was not significantly different from their respective attitude based on the combined communication condition ( p = .81 for HI; p = .28 for LI). Finally, our last hypothesis, H6, proposed that the absolute attitude changes would be significantly greater for the Ad then Pub sequence groups than for the Pub then Ad sequence groups. One-way ANOVA (for homogeneity of variance conditions) and Welch’s test of equality of means (for heterogeneity of variance conditions) were used for the analyses. Welch’s test was used in cooperation with ANOVA due to unequal sample size between groups and the heterogeneity of variance, which can be problematic for the t-test (Lewis-Beck, Bryman, and Liao 2003). Welch’s test (Day and Quinn 1989; Welch 1947; Wilcox 1993) uses adjusted degrees of freedom to protect against increased type 1 errors under variance heterogeneity (Quinn and Keough 2002). It is a powerful alternative to the t-test and F-test when sample sizes are unequal and heterogeneity of variance is present (Lewis-Beck et al. 2003; Maxwell and Delaney 2004). Results in Table 5 show that the hypothesis was supported in all conditions (all ps < .001). In addition, two post hoc analyses were performed to find any differences in the attitudes of the combined condition (1) between the Ad then Pub sequence versus reverse sequence for each group, and (2) between the attribute-consistent versus attribute-inconsistent condition under the same valence and same sequence. For the first analysis, the combined communication effects were compared between the Ad then Pub and the Pub then Ad sequences (i.e., between-sequence comparisons) for each group. This additional analysis was meant to verify and extend the recent findings of Loda and Coleman (2005), which showed that the Pub then Ad sequence produces higher attitude than the reversed sequence. One-way ANOVA and Welch’s tests produced interesting results. As shown in Table 6, regardless of the involvement level of product and product attribute consistency, there was no significant difference between the Ad then Pub sequence and the reverse sequence for positive publicity conditions, but the attitudes in the Ad then Pub sequence were significantly lower than those in the reverse sequence for negative publicity conditions. Another post hoc analysis was implemented to determine whether there was any difference in the attitudes of the combined condition between attribute consistent and attribute inconsistent under the same valence and same sequence (i.e., within-sequence comparison). Welch’s test was also jointly used with ANOVA for this analysis. Some of the results in Table 7 demonstrate that positive valence groups show no difference in attitude between attribute-consistent and attribute-inconsistent groups (e.g., HI/+X versus HI/+Y) regardless of sequence condition. However, other results in Table 7 show that when the attribute advertised was negatively featured in a publicity Not supported Not supported Not supported Supported Supported Not supported Supported Supported Supported Supported Supported Supported Supported Supported Supported Supported Hypothesis supported Notes: HI = high involvement; LI = low involvement; Ab_ad = attitude toward the brand based on ad only; Ab_pub = attitude toward the brand based on publicity only; Ab_ad+pub = attitude toward the brand based on the combination of ad and publicity. p < .001 p < .05 p < .001 p < .001 p < .001 p < .001 p < .001 p < .001 p < .001 p < .05 p < .001 p < .001 p < .001 p < .05 p < .001 p < .001 p < .001 p < .001 p < .001 p < .001 p < .001 p = .40 p < .001 p < .01 p < .001 p < .003 p < .001 p = .13 p < .001 p = .81 p < .001 p = .28 3.61 (.88) 5.18 (1.00) 4.77 4.16 (.75) 5.57 (.96) 4.93 3.61 5.25 (1.07) 4.77 (.75) 4.16 5.34 (1.03) 4.93 (.73) 3.53 (.95) 5.21 (1.02) 4.96 4.26 (.91) 5.56 (.94) 4.78 3.53 5.20 (1.02) 4.96 (.79) 4.26 5.32 (.93) 4.78 (.83) 3.62 (.96) 2.15 (.83) 2.65 4.26 (.97) 1.96 (.64) 2.75 3.62 2.53 (.97) 2.65 (.69) 4.26 2.39 (.86) 2.75 (.76) 3.73 (.82) 2.52 (.89) 2.94 4.44 (1.06) 2.68 (1.16) 2.93 3.73 2.97 (.84) 2.94 (.68) 4.44 3.07 (1.02) 2.93 (.81) H2a HI/+X Ad/Pub LI/+X Ad/Pub H2b HI/+X Pub/Ad LI/+X Pub/Ad H3a HI/+Y Ad/Pub LI/+Y Ad/Pub H3b HI/+Y Pub/Ad LI/+Y Pub/Ad H4a HI/–X Ad/Pub LI/–X Ad/Pub H4b HI/–X Pub/Ad LI/–X Pub/Ad H5a HI/–Y Ad/Pub LI/–Y Ad/Pub H5b HI/–Y Pub/Ad LI/–Y Pub/Ad –10.09 (67) 3.32 (67) –9.73 (62) 5.53 (62) 12.75 (69) –4.75 (69) 10.45 (82) –4.04 (82) –12.20 (69) 2.08 (69) –9.49 (67) 6.79 (67) 14.05 (72) –2.00 (72) 9.85 (74) –5.00 (74) 8.36 (42) –3.93 (42) 13.58 (38) –7.75 (38) –8.80 (61) .86 (61) –17.24 (62) 2.94 (62) 8.77 (47) –3.25 (47) 11.04 (49) –1.53 (49) –6.91 (57) –.25 (57) –11.29 (70) –1.09 (70) Ab_ad Ab_pub Ab_ad+pub t (df ) Significance Relevant hypothesis Group Sequence Table 4 Results by Analysis Group and Hypotheses Spring 2010 107 108 The Journal of Advertising Table 5 Absolute Attitude Changes Absolute attitude change Relevant Ad/Pub hypothesis Group sequence H6 HI/+X HI/+Y LI/+X LI/+Y HI/–X HI/–Y LI/–X LI/–Y Pub/Ad sequence Statistic Significance 1.65 (1.16) 1.82 (.92) 1.48 (1.07) 1.41 (.98) 1.54 (1.05) 1.29 (.86) 2.33 (1.05) 1.77 (1.11) .72 (.64) .74 (.76) .72 (.71) .81 (.69) .75 (.77) .78 (.56) .75 (.69) .77 (.69) W(1, 104.24) = 33.64 F(1, 141) = 58.28 W(1, 101.70) = 23.88 W(1, 118.21) = 17.26 F(1, 103) = 19.49 W(1, 77.78) = 14.62 W(1, 58.24) = 66.10 W(1, 75.13) = 32.07 p < .001 p < .001 p < .001 p < .001 p < .001 p < .001 p < .001 p < .001 Hypothesis supported Supported Supported Supported Supported Supported Supported Supported Supported Notes: HI = high involvement; LI = low involvement. Table 6 Comparisons Between Sequences by Analysis Group Ab_ad+pub Ad/Pub Group sequence Pub/Ad sequence HI/+X LI/+X HI/+Y LI/+Y HI/–X LI/–X HI/–Y LI/–Y 5.25 (1.07) 5.34 (1.03) 5.20 (1.02) 5.32 (.93) 2.54 (.97) 2.39 (.86) 2.97 (.84) 3.07 (1.02) 5.18 (1.00) 5.57 (.96) 5.21 (1.02) 5.56 (.94) 2.15 (.83) 1.96 (.64) 2.52 (.89) 2.68 (1.16) Statistic F(1, 136) = .17 F(1, 144) = 1.98 F(1, 141) = .01 F(1, 141) = 2.32 F(1, 103) = 4.48 W(1, 96.46) = 8.61 F(1, 104) = 6.93 F(1, 119) = 3.75 p-value Significance p = .68 p = .16 p = .94 p = .13 p < .05 p < .01 p < .05 p = .05 n.s. n.s. n.s. n.s. significant significant significant significant Notes: Ab_ad+pub = attitude toward the brand based on the combination of ad and publicity; HI = high involvement; LI = low involvement; n.s. = not significant. article, regardless of the sequence, participants evaluated the brand significantly less favorably than the different-attribute, negative publicity condition. Together with the first post hoc study result, this result implies that the strongest contrast effect might occur for the Adx & −Pubx condition. Discussion The purpose of this study was to examine the effects of varying combinations of advertising and publicity, in terms of the attribute consistency of publicity, valence of publicity, and the sequence of exposure, on attitude toward the brand. Our hypotheses were based on the basic assumption that publicity is generally thought to be more credible and more influential than other forms of company-controlled communication (Bond and Kirshenbaum 1998). Hypothesis 1 tested this assumption and was unanimously supported by all conditions. Theories of Information Integration (Anderson 1971), Integrated Information Response (Smith and Swinyard 1982), confirmation effect (LaBella and Koehler 2004), and contrast effect (Hovland, Harvey, and Sherif 1957) were used to propose and test the hypotheses. Overall, compared to IIT and IIRM, the confirmation effect theory was more useful and accurate in predicting the combined effects of positive publicity and advertising. In predicting the combined effects of negative publicity and advertising, however, the proposed usability of the contrast effect theory was only noteworthy for the Adx then −Pubx sequence (H4a), the low-involvement condition of the −Pubx then Adx sequence (H4b), and the high-involvement condition of the Adx then −Puby sequence (H5a). Taken as a whole, however, the results of this study demonstrate the overall ability of the confirmation effect (for synergy) and the contrast effect (for counter-synergy) to explain integrated communication conditions. The results show that for all positive publicity conditions, the combined communications condition produced a significantly higher attitude toward the brand than either LI/+Y 5.57 F(1, 129) = .02 p = .90 (.96) HI/–Y 2.52 F(1, 89) = 4.14 p < .05 (.89) LI/–Y 2.68 W(1, 79.23) = 14.04 p < .001 (1.16) LI/+X 5.57 (.96) HI/–X 2.15 (.83) LI/–X 1.94 (.64) LI/–X 2.39 (.86) HI/–X 2.54 (.97) LI/+X 5.34 (1.03) HI/+X 5.25 (1.07) LI/–Y 3.07 (1.02) HI/–Y 2.97 (.84) LI/+Y 5.32 (.93) HI/+Y 5.20 (1.02) F(1, 132) = 16.78 F(1, 118) = 6.66 F(1, 156) = .02 F(1, 141) = .63 Statistic Pub-Ad sequence Notes: Ab_ad+pub = attitude toward the brand based on the combination of ad and publicity; HI = high involvement; LI = low involvement. HI/+Y 5.21 F(1, 136) = .05 p = .83 (1.02) HI/+X 5.18 (1.00) Ab_ad+pubAb_ad+pub Means Statistic Significance Means Ad-Pub sequence Table 7 Comparisons Between Attribute-Consistent Versus Attribute-Inconsistent Condition p < .001 p < .02 p = .88 p = .79 Significance Spring 2010 109 110 The Journal of Advertising the advertising-only or publicity-only condition, regardless of product attribute consistency and exposure sequence, thus upholding the confirmation effect. For negative publicity and consistent product attribute (i.e., H4), the proposed contrast effects were supported in most conditions of the Ad then Pub sequence. In addition to the overall contrast effect, we further predicted that the magnitude of the contrast effect would be greater for the Ad then Pub sequence than for the reverse sequence. As predicted, for both high- and low-involvement conditions under the Ad then Pub sequence, the combination of negative publicity with advertising produced a significantly more negative attitude compared to the publicity-only condition, supporting the contrast effect of H4a. Under the Pub then Ad sequence, the low-involvement condition only demonstrated the contrast effect with a significantly more negative attitude in the combined condition, while no significant contrast effect was found for the high-involvement products in the −Pubx then Adx sequence. For negative publicity conditions and inconsistent product attribute, the proposed contrast effects were observed only for the high-involvement condition in the Ad then Pub sequence (partial support of H5a). These four unsupported cases provide useful insights. IIT’s inconsistency discounting process seems more typically and significantly evident for the −Pub then Ad sequence (except for the low involvement–consistent attribute condition) than the Ad then −Pub sequence, probably because the negative publicity was credible enough to discount the advertising effect. In addition, those unexpected results of H4b, H5a, and H5b might possibly result from primacy-recency effects (e.g., Lana 1963; Springbett 1958). Based on Lana’s (1963) finding that people with high interest in a topic exhibit the primacy effect, the unforeseen result (A[−Pubx then Adx] = A[−Pubx]) of the high involvement product situation of H4b might have been caused by the primacy effect, as the persuasion consequence of the initial communication (−Pubx) dominated the effect of Adx. In the same vein, the unpredicted result (A[−Puby then Adx] = A[−Puby]) of the high-involvement situation of H5b might also be explained by the primacy effect. On the other hand, lack of support for H5a (A[−Puby then Adx] = A[−Puby]) might be due to the recency effect, which is theorized to be present under low-involvement conditions (Brunel and Nelson 2003). We also found, from H6, that the absolute attitude change was significantly greater for the Ad then Pub sequence group than for the Pub then Ad sequence group, indicating a strong support for greater contrast and confirmation effect in the Ad then Pub sequence. As Marks and Kamins (1988) showed, the absolute value of the attitude changes is relevant because it shows the degree of change independent of directionality. Our two post hoc analyses added more findings. The first finding was that there was no significant difference in final at- titude between the Ad then Pub sequence and reverse sequence for high-involvement products, but the attitudes in the Pub then Ad sequence were significantly greater than those in reverse sequence, regardless of involvement level and product attribute consistency when negative publicity was combined. Contrary to the prediction of IIT’s inconsistency discounting process (that the later stimulus in sequence should get a discounted weight for both the Adx then −Pubx and −Pubx then Adx sequence), our result showed that the −Pub then Ad sequence generated significantly lower attitudes than the Ad then −Pub sequence. As we discussed briefly for H4 and H5, the contrast effect could be powerful enough to reverse the inconsistency discounting effect. In addition, the inconsistency discounting effect might happen retroactively if the former attitude stimulus has less credibility. This idea needs further theoretical investigation in future studies. The second post hoc analysis showed that the contrast effect was stronger and that participants evaluated the brand significantly less favorably for the combined condition of Adx & −Pubx than the Adx & −Puby condition, regardless of sequence. Together with the sequence effect finding from the first post hoc study result, this finding implies that the Adx & −Pubx condition will produce the greatest contrast effect. There are some limitations within which our results should be interpreted, such as the laboratory study conditions under which the data were collected, the limited generalizability to a larger population beyond college students, and the less controlled environment of an online experiment. In addition, some of our results may not successfully differentiate additive versus synergistic or subtractive versus counter-synergistic effects. For example, though the result of H2 showed that the combination of advertising and publicity produced more positive attitudes compared to either the advertising- or publicity-only condition, the result doesn’t sufficiently indicate whether a synergistic or a combined effect for advertising and publicity was the cause. Future research could extend our study and compare the combined effect to multiple exposures of the same ad or publicity. If, for example, the Adx & +Pubx condition generated a more favorable attitude compared to the double exposure condition of Adx or +Pubx, that result might be considered much stronger evidence for the confirmation effect. The same empirical test could be done for negative publicity. If, for example, the Adx & –Pubx condition generated a less favorable attitude compared to the double exposure condition of –Pubx, that result might be considered much stronger evidence for the contrast effect. Within our theoretical framework, however, our results at least upheld the confirmation and contrast effect over IIT and IIRM. Nonetheless, our study offers many insights into the current knowledge of advertising practice and theory regarding the combination effect of advertising and publicity in various conditions, which were relatively simple (compared to the real world) but reasonably comprehensive. Our overall finding is consistent with prior studies: Combined effects of various Spring 2010 111 communication tools are more significant than the effects of a single tool (Stammerjohan et al. 2005). Marketers might find our findings useful when they design a marketing plan with multiple communication tactics or when they react to a marketing or public relations crisis caused by negative publicity. When hit with negative publicity of a brand, which might convey information about a specific attribute, marketers and advertisers could decide to counter it by launching a campaign that addresses the positive qualities of that attribute or reinforces some other piece of information to decentralize the focus given to the negative publicity, ultimately balancing out the negative effects it might have on the brand. Our results suggest otherwise, however. Trying to balance out the negative publicity effect by showing ads that have attributes that are consistent or inconsistent with the publicity might cause even more damage to brand attitude. Instead of using advertising to counter negative publicity, it might be more beneficial to release publicity for damage control. Future studies should explore this strategy. On the other hand, when the brand receives positive publicity, our findings suggest that advertising will further boost consumer attitudes, regardless of timing of execution (before or after publicity). Future studies could investigate various other forms of communication integration. For example, the cross-promotion possibility between advertising and publicity (i.e., publicity featured in advertising and advertising in publicity) is a relatively unexplored yet worthwhile topic that merits future investigation. This type of integration might create a whole different level of synergy where the confirmation effect might occur on multiple levels. Future studies could also investigate how consumers might react to the combination of advertising and other types of communication (such as sales promotion and direct marketing) that might have similar levels of perceived credibility. 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Jennings (1983/84), “Nondiagnosticity and Typicality Effects in Prediction,” Social Cognition, 2 (3), 187–198. Spring 2010 113 Appendix Example of Ad and Publicity Stimuli (Pendley Highlighter) Ad +Pubx Publicity Examples (+Pubx) PENDLEY Highlighter Leads the Pack in Long-Lasting Experience ATLANTA (Feb. 6, 2007) – Consumer Reports announced today the results of a study that tested a variety of office supplies for their performance and functionality. Among similarly priced non-toxic highlighters, PENDLEY ranked highest in long-lasting experience. “Longevity has been an important feature of highlighters,” said Lance Jones, associate director of Consumer Reports. “This study showed PENDLEY to be superior in longevity compared to its competitors.” (–Pubx) PENDLEY Highlighter Lags Behind in Long-Lasting Experience ATLANTA (Feb. 6, 2007) – Consumer Reports announced today the results of a study that tested a variety of office supplies for their performance and functionality. Among similarly priced non-toxic highlighters, PENDLEY ranked the lowest in longlasting experience. “Longevity has been an important feature of highlighters,” said Lance Jones, associate director of Consumer Reports. “This study showed PENDLEY to be inferior in longevity compared to its competitors.” (+Puby) PENDLEY Highlighter Leads the Pack in Grip Comfort ATLANTA (Feb. 6, 2007) – Consumer Reports announced today the results of a study that tested a variety of office supplies for their performance and functionality. For highlighters, among products cleared as non-toxic and in the similar price range, PENDLEY ranked the highest in grip comfort. “Comfort in grip has been an important feature of highlighters. If the body of the highlighter is too thick or too thin, customers experience discomfort,” said Lance Jones, associate director of Consumer Reports. “This study showed PENDLEY to have just the right grip size, providing more comfort than its competitors.” (–Puby) PENDLEY Highlighter Lags Behind in Grip Comfort ATLANTA (Feb. 6, 2007) – Consumer Reports announced today the results of a study that tested a variety of office supplies for their performance and functionality. For highlighters, among products cleared as non-toxic and in the similar price range, PENDLEY ranked the lowest in grip comfort. “Comfort in grip has been an important feature of highlighters. If the body of the highlighter is too thick or too thin, customers experience discomfort,” said Lance Jones, associate director of Consumer Reports. “This study showed PENDLEY to have an inadequate grip size that causes grip discomfort compared to its competitors.”