The cross-platform synergies of digital video advertising: Implications for cross-media campaigns in television, Internet and mobile TV

Computers in Human Behavior 48 (2015) 463–472

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Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh

The cross-platform synergies of digital video advertising: Implications for cross-media campaigns in television, Internet and mobile TV Joon Soo Lim a,⇑, Sung Yoon Ri b, Beth Donnelly Egan c, Frank A. Biocca d,e a

Department of Public Relations, S.I. Newhouse School of Public Communications, Syracuse University, 215 University Place, Syracuse, NY 13244, United States YTN (Korea’s 24-hour News Channel), Department of News Production II, 76 Sangamsanno, Newsquare, Mapo-gu, Seoul 121-904, Republic of Korea c Department of Advertising, S.I. Newhouse School of Public Communications, Syracuse University, Syracuse, NY 13244, United States d Media Interface and Network Design (M.I.N.D.) Labs, S.I. Newhouse School of Public Communications, Syracuse University, United States e Interaction Science, Sungkyunkwan University, Republic of Korea b

a r t i c l e

i n f o

Article history:

Keywords: Media planning Cross-media Synergy Mobile advertising Internet advertising Mobile TV

a b s t r a c t This study examines the synergy effect of digital video advertising through television, mobile TV, and the Internet on general outcomes of advertising effectiveness. In a 3 (paired media conditions for ad repetition)  2 (product involvement) mixed factorial design, we examined empirical outcomes of the crossmedia synergy effect. The results show that participants exposed to repetitive ads on paired media of television, Internet, and mobile TV have greater perceived message credibility, ad credibility, and brand credibility than counterparts exposed to repetitive ads from a single medium. The multiple-media repetition also generated more positive cognitive responses, attitude toward the brand, and higher purchase intention than the singlemedium repetition. Finally, the cross-platform synergy effect remained robust for different levels of product involvement. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction Marketers have long used multiple channels to deliver impactful levels of reach to their campaigns. Each medium was carefully planned to optimize reach and frequency within that medium and then aggregated to deliver a total, synergistic impact to the consumer. Abernethy, Cannon, and Leckenby (2002) note that advertisers in this era of integrated marketing communications are making more effort to drive synergy across media campaigns. The benefits of harnessing synergy across multiple media to build brand equity of products and services have been extensively examined in marketing research on media planning (Lin, Venkataraman, & Jap, 2013; Naik & Raman, 2003). With the advent of the Internet and satellite technology, the ability for advertising firms to engage with consumers who serially consume multiple media (Lin et al., 2013) is increasingly important. These consumption patterns have generated a variety of new media viewing habits ranging from a serial viewing of small, incomplete chunks of multimedia called media multiplexing (Lin et al., 2013) to a non-linear viewing of multimedia on portable devices such as mobile TV,

⇑ Corresponding author. Tel.: +1 315 443 8046. E-mail addresses: [email protected] (J.S. Lim), [email protected] (S.Y. Ri), [email protected] (B.D. Egan), [email protected] (F.A. Biocca). http://dx.doi.org/10.1016/j.chb.2015.02.001 0747-5632/Ó 2015 Elsevier Ltd. All rights reserved.

tablets, and smartphones. The consumption of television and commercial messages these days increasingly takes place on non-linear media through mobile TV, a system that conveys the television content to the mobile phone via wireless or cellular networks (Jung, Perez-Mira, & Wiley-Patton, 2009). According to Nielsen’s Cross-Platform Report for Q2 2014 (Nielsen, 2014), 72% of Americans own a smartphone and 39% own a tablet device. Total time spent watching video is increasing but driven solely by the increase in digital video viewing. The adoption of mobile TV is especially widespread in Asian countries where the smartphone penetration rate is greater than that of other countries and the mobile phone has become the dominant communication tool. Developed in South Korea in 2005 as the next-generation digital mobile television system, Digital Multimedia Broadcasting (DMB) has broadened a concept of mobility in consuming television content, allowing viewers to store the recipe while watching a cooking television program and to pay for the order made through homeshopping channels (Shin, 2009). In a study by Kim and Jun (2008), it was reported that about 80% of the South Korean population is equipped with DMB devices. As the importance of mobile advertising is growing, DMB is wielding considerable leverage as a key player for effective advertising on mobile platforms (Kim & Jun, 2008). Despite the importance of non-linear media in media and commercial message consumption, the relative impact of

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potential synergy between DMB, Internet, and traditional media is not well understood. As with provision of new media, multiple uses of media have become increasingly common. Nowadays, one watches TV at home while he or she watches DMB on the move and can surf the Internet anytime and anywhere. As consumers are getting skillful at simultaneous media use, advertisers are growingly interested in the impact of synergy of effective cross-platform campaigns. As this shift is occurring, it is becoming increasingly imperative to understand how messages across these media interrelate. The current study is interested in examining the effect of advertising synergies between linear (i.e., television) and non-linear (i.e., Internet and DMB) media platforms. Examining the effect of DMB in the context of synergy is important because previous research implied that the synergy effect could be affected by the screen size (Varan et al., 2013). Although numerous studies (Chang & Thorson, 2004; Havlena, Cardarelli, & De Montigny, 2007; Jessen & Graakjær, 2013; Sheehan & Doherty, 2001; Stolyarova & Rialp, 2014; Varan et al., 2013; Voorveld, Smit, & Neijens, 2013) examined the synergy effect of advertising on cross devices, little is known about whether the synergy effect can also be found in cross-media advertising employing mobile devices (Bart, Stephen, & Sarvary, 2014). In a replication of Chang and Thorson’s (2004) study, we examined the synergy effect of cross-advertising when an ad of both a high and a low-involvement product was repeated on a unichannel vs. on multiple channels of Internet, mobile TV (i.e., DMB) and television.

2. Theoretical background 2.1. From mere exposure theory to two-factor theory The question that repeated exposure to the advertising message without involvement or learning process would result in sales via attitude change has been the long-sought research agenda (cited more than 1817 times) in advertising (Belch, 1982; Campbell & Keller, 2003; Hawkins & Hoch, 1992; Krugman, 1965; Malaviya, 2007) since Krugman’s arresting question in his address to the American Association for Public Opinion Research in 1965. The main thesis in Krugman’s thought-provoking question was how advertising could change consumer attitude followed by purchase behavior despite the fact that most messages are easily forgotten over time. This big question was followed by a groundbreaking study in which Zajonc (1968) demonstrated what Krugman (1965) described as the effect of ‘‘much of advertising content . . . learned as meaningless nonsense material’’ (p. 351) in his mere repeated exposure experiment. This seminal study by Zajonc (1968) in fact provided theoretical grounds on the persuasive effect of mere exposure in inducing positive attitude change. In proposing the two-factor theory, however, Berlyne (1970) demonstrated that an exposure to a noble message initially increased attitude toward the product due to the positive learning factor. But repeated exposures to a homogenous stimulus resulted in diminished positive attitudes. The first positive affect results from positive habituation, while the decreased affect is due to the boredom factor. Several studies that examined the relationship between repetition and attitudes found that the inverted U-relationship between the ad repetition and attitudes could be moderated by a message’s complexity (Anand & Sternthal, 1990; Cox & Cox, 1988), an audience’s cognitive responses (Batra & Ray, 1986), and brand familiarity (Machleit & Wilson, 1988). Other researchers (Cacioppo & Petty, 1979; Rethans, Swasy, & Marks, 1986) showed that the repeated exposures to an ad could generate

more cognitive responses that eventually mediated the repetition effect on persuasion. Chang and Thorson (2004) changed the course of ad repetition research by addressing the synergistic effects of ad repetition on general outcomes of advertising effectiveness. This line of research on the synergy effect is increasingly important as the scope and reach of cross-media advertising is ever increasing. 2.2. The synergy effect of cross-media advertising Synergy is a fundamental concept in media planning (Lin et al., 2013) and understanding the impact of synergy has become increasingly important in the age of media convergence (Voorveld et al., 2013). Sheehan and Doherty (2001) once asserted that the ‘‘strategic synthesis of strategy and tactics across multiple channels is the hallmark of integrated marketing communication (IMC)’’ (p. 48). The synergy effect is important because IMC fundamentally pursues the maximization of ‘‘the benefits of harnessing synergy across multiple media to build brand equity of products and services’’ (Naik & Raman, 2003, p. 1). Media synergy arises when the combined effect or impact of a number of media activities creates added value beyond the sum of their individual effects on individual consumers (Schultz, Block, & Raman, 2011). Among such media synergies, television and Internet advertising synergies are regarded as the most popular fit. In the case of the Internet, advertisers are starting to exploit the communication potential of this technology. This is because the Internet is most effective in motivating consumers to participate in information processing with a lot of cognitive effort (Sheehan & Doherty, 2001). On the other hand, television is most effective in influencing consumers without a lot of cognitive effort. Such complementary traits of two different media can contribute to create a synergy effect. In this regard, Chang and Thorson (2004) examined the advertising effect when the ad was simply presented with repetition as opposed to when it was presented in synergistic conditions such as an Internet ad followed by a television ad. For instance, participants in their experiment were exposed to either an Internet or television ad only twice while counterparts in the synergetic conditions viewed the same ad on television followed by on the Internet or vice versa. They found that individuals who were exposed to the ad on the synergetic conditions paid more attention to the ad than those who viewed it in repetition. The synergy effect was also found across different measures such as message credibility and the number of cognitive thoughts. However, they failed to find the direct effects of television–Internet ad synergy on brand credibility, ad credibility, and attitude change. Rather, the effect of television–Internet synergy on persuasion occurred as a result of differential processing routes. In other words, participants in the synergy condition exhibited attitude change via central processing of the ad message in that the persuasive ad effect from the multiple media was mediated by increased attention and cognitive responses to the message itself. In contrast, attitude change among participants exposed to repetitive ad conditions (i.e., only to Internet ads or television ads with repetition) was observed through the focus on a peripheral cue (i.e., advertiser credibility) instead of message credibility. These results support Harkins and Petty’s (1987) theoretical assumption that the multiple sources would enhance message-centric processing and ultimately lead to persuasion via central processing of the message. A substantial body of empirical research provided theoretical underpinnings of the potential effect of the cross-media synergy (Naik & Peters, 2009; Naik & Raman, 2003; Voorveld, 2011; Voorveld & Valkenburg, 2014). They are (1) the multiple-source effect, (2) differential attention hypothesis, (3) forward encoding hypothesis, and (4) repetition-variation theory.

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2.2.1. The multiple-source effect Chang and Thorson (2004) first tested the synergy effect on the theoretical background of the multiple-source effect. They revealed that advertising through multiple sources can bring about more effects such as more positive thoughts and information processing through a central processing route than repetitive ads in a single source. However, under the condition of repetitive ads in a single source, information was processed through a peripheral route: ad credibility affected by advertiser credibility elicited positive thoughts (Chang & Thorson, 2004). Even though TV and Internet synergies influence a consumer’s cognition, they exert less influence on affective and conative states (Chang & Thorson, 2004). Particularly, in cases of low-involvement ads of products such as soda or a cleaning product, impact on cognition is insufficient to induce greater attitudinal and behavioral effects. According to the Elaboration Likelihood Model (ELM hereafter), a person’s elaboration likelihood is high when the person has the motivation to scrutinize the argument. Thus, when elaboration likelihood is high, a person will be attentive to message arguments and the person’s attitude will change via a central route to persuasion. When elaboration likelihood is low, a person will be attentive to peripheral cues such as advertiser credibility and the person’s attitude will change via a peripheral route to persuasion (Petty, Cacioppo, & Schumann, 1983). When information is processed through a central route, people concentrate on the perceived merits of the ad’s message and related ad contents and consider them thoroughly (Petty & Cacioppo, 1996). This leads to an increase in ad credibility. Research findings also suggest that the use of cross-media integration enhances perceived media engagement of overall advertising messages (Tang, Newton, & Wang, 2007; Wang, 2011) such that multi-channel communication tends to enhance message credibility of an ad. Thus, we posit the following three hypotheses:

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than an ad repeated in a different execution. The decreased attention is the result of repetition wearout that occurs when individuals do not perceive anything new in the coming advertising message (Naik, Mantrala, & Sawyer, 1998). Recent studies have demonstrated that cross-media advertising can slow down the process of repetition wearout because varying the media can reduce the repetition wearout phenomenon by motivating individuals to pay more attention (Voorveld et al., 2013), reducing boredom and reviving consumer interest in the ad (Grass & Wallace, 1969). As a result, varied repetition of a promotional message in two different media resulted in higher attention from the audience than repetitive promotion from a single medium (Tang et al., 2007). Previous research revealed that the varied repetition of an ad, as opposed to the same repetition of a single ad, made individuals undertake more effortful processing of the repeated message and resulted in more recall of the message (Unnava & Burnkrant, 1991).

Researchers have explained the mechanism that might explain why a cross-media campaign results in more persuasion than a repetitive campaign through a single medium. When individuals are exposed to an ad message on multiple media, they tend to perceive each medium as an independent source of information. Because messages from multiple independent sources are perceived to be more credible, multiple sources can enhance the credibility of the brand as well as the ad (Chang & Thorson, 2004; Harkins & Petty, 1987; Voorveld, 2011). Therefore, we proposed the following hypotheses:

2.2.3. The forward encoding hypothesis Previous studies have demonstrated that the mere exposure to ad messages was effective in inducing more cognitive responses (Belch, 1982; McCullough & Ostrom, 1974). Synthesizing the previous research on the information processing of cross-platform advertising, Voorveld and his colleagues (Voorveld, Neijens, & Smit, 2011) proposed the so-called forward encoding hypothesis. The forward encoding hypothesis is a cognitive account for Zajonc’s (1968) mere exposure effect. In a nutshell, the forward encoding hypothesis assumes that the ad aired in the first device may leave a memory trace for the ad that will be presented in another device. Voorveld et al. (2011) compared this function to the role of a teaser in that the ad ahead of the second exposure can grab the audience attention and interest. The forward encoding hypothesis is grounded on the memory trace hypothesis in previous research (Edell & Keller, 1989; Keller, 1987). Edell and Keller (1989) said: ‘‘. . .when a person is exposed to an ad for a second time, the ad may serve as a retrieval cue for the stored ad memory trace or as a second encoding opportunity’’ (p. 150). A closely connected but slightly different theoretical account for the synergy effect is the so-called the encoding-variability hypothesis. This hypothesis postulates that the same ad message from multiple media yields greater memory than from a single medium (Stammerjohan, Wood, Chang, & Thorson, 2005). This enhanced recall performance occurs due to the fact that the varied encoding via different medium adds a new piece of information to episodic representation of the advertised message (Jin, Suh, & Donavan, 2008). Although this forwarding encoding hypothesis accounts for the effect of repeated exposure in general, Voorveld et al. (2011) suggested that the forward encoding would give a greater advantage for the sequential exposure from multiple devices than from repeated exposure from a single device. In explaining the differential advantage, they reasoned that the repeated exposure to an ad from a single device would make individuals less motivated to process the repeated message (Batra & Ray, 1986), especially when there is a low level of involvement in the promoted message (Cacioppo & Petty, 1979). Therefore, we posit H5:

H4. Participants exposed to an ad in multiple media will have higher perceived brand credibility than those who viewed the same ad in a single medium.

H5. Participants exposed to the same ad in multiple media will have more positive cognitive responses than those who viewed the same ad in a single medium.

2.2.2. The differential attention hypothesis Unnava and Burnkrant (1991) also raised a possibility that individuals’ attention paid to a single ad that is repeated will be lower

2.2.4. The repetition-variation theory The repetition-variation theory accounts for the effect of varying the information modality on attitude toward brand

H1. Participants exposed to an ad in multiple media will have higher perceived message credibility than those who viewed the same ad in a single medium. H2. Participants exposed to an ad in multiple media will have higher perceived advertiser credibility than those who viewed the same ad in a single medium. H3. Participants exposed to an ad in multiple media will have higher perceived ad credibility than those who viewed the same ad in a single medium.

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(Schumann, Petty, & Clemons, 1990; Stammerjohan et al., 2005). This theory postulates that information modality can be varied either by cosmetic variation or substantive variation. The former is about the variation in nonsubstantive features of the ad, whereas the latter deals with the variation in message content (Stammerjohan et al., 2005). The repetition-variation theory is a useful theoretical framework since it can explain that the positive attitude change for brand is not so much the number of exposures to an ad as the modality of ad presentation. On the basis of the repetition-variation theory, a growing body of research suggests that when people are exposed to a message in multiple media, instead of being exposed to a message in the same medium repetitively, they have more positive affective reactions to the brand (Voorveld, 2011). In an experimental study, Voorveld et al. (2011) found that participants in a cross-media campaign condition had a greater interest in processing the message than those who were in the TV–TV condition, which in turn led them to have more positive attitudes toward the brand. Janiszewski (1993) earlier demonstrated how the incidental exposure to a brand name can increase positive affect as a function of availability of stored representation in memory representation. According to Janiszewski (1993), the effect of mere exposure is influenced by how much the incoming message matches the stored memory representation formed in previous incidental exposure, which is called the match-activation hypothesis. As the traditional cognitive response theory posits, such positive affect, including positive thoughts, wields influence over attitude toward brand (MacKenzie, Lutz, & Belch, 1986; Maclnnis & Jaworski, 1989). Thus, we proposed the following hypothesis: H6. Participants exposed to an ad in multiple media will have more positive attitude toward the brand than those who viewed the same ad in a single medium. From a perspective of IMC approach, the combined effects of multimedia activities can exceed the sum total of their individual contributions (Naik & Raman, 2003). Thus, such a multiple source effect is able to exercise influence on brand credibility and purchase intent. In ELM, repeated ad exposure effect was explained in terms of peripheral processing. In other words, those who are exposed to repetitive ads focus and elaborate on peripheral signals such as advertiser credibility (Petty & Cacioppo, 1996). Ad credibility that influences brand credibility is determined by advertiser credibility (Chang & Thorson, 2004; MacKenzie & Lutz, 1989). Higher ad credibility leads to more positive thoughts through a lower level of information processing such as the pure affect transfer or heuristic evaluation (Maclnnis & Jaworski, 1989). Together with ad credibility, positive thoughts induce more positive brand credibility (Fishbein & Ajzen, 1975) and increase purchase intention. H7. Participants exposed to an ad in multiple media will have a higher purchase intention than those who viewed the same ad in a single medium.

2.3. Summary of the constructs and relationships among them The aforementioned hypotheses aimed to examine general outcome measures of the synergetic advertising repetition that have been examined in various empirical studies. Table 1 showcases the selected research results that tested the synergistic repetition effects on various outcomes for advertising effectiveness. The measures can be identified in three types of responses: credibility measures, cognitive responses, and attitudinal measures. Credibility measures include ad credibility, message credibility, brand credibility and advertiser credibility. When

involvement is controlled, the ad credibility is influenced by ad message credibility and advertiser credibility (MacKenzie & Lutz, 1989). Cognitive responses are both positive and negative ad-related thoughts that are generated while watching the advertisement. Previous research has revealed that the message credibility affects the cognitive responses especially when the consumer is highly involved in the product (Chang & Thorson, 2004). The cognitive response is also affected by ad credibility particularly under the low-involvement condition (Petty, Cacioppo, & Schumann, 1983). Finally, attitudinal measures are composed of the most pivotal two constructs (Spears & Singh, 2004): attitudes toward the brand and purchase intent. Several studies (Rethans et al., 1986) have measured the cognitive and attitudinal measures for advertisements as outcomes of synergetic advertisement repetition. According to MacKenzie and Lutz (1989), the ad perception (i.e., ad credibility) is one of the important antecedents to observing the attitude toward brand and purchase intention. The ELM explains that the ad credibility is influenced by the message credibility when an individual engages in central processing and that ad credibility is affected by the advertiser credibility under peripheral processing. Finally, in the classic attitude toward ad framework (MacKenzie & Lutz, 1989; Spears & Singh, 2004), attitude toward the brand has a direct impact on purchase intent. 2.4. Multiple-source effect under different levels of involvement An important factor that can affect the overall impact of the cross-media advertising on persuasion is consumers’ product involvement (Voorveld, Neijens, & Smit, 2012). As demonstrated by numerous studies, product involvement tends to moderate the influences of advertising (Petty et al., 1983). An emerging question is whether the level of involvement would moderate the multiple-source effect. Voorveld et al. (2012) found that a TV–Internet commercial sequence was effective for inducing persuasion for both high- and low-involvement products while Internet–TV sequence was only effective for highinvolvement products. Their findings indicate that TV commercials are most effective for a low-involvement product, while they can also exert influence on consumers who process a high-involvement product. In contrast, Internet advertising requires consumers to be involved in a certain level of ‘‘active, conscious, and cognitive information process’’ (Cho, 1999, p. 36) such that it may limit the effect for a high-involvement product. We assume that the way that DMB advertising works for inducing persuasion would be similar to how TV advertising works since it allows individuals to consume the message on the go and spend less cognitive effort than they do to process Internet advertising. So, we can predict that the DMB–Internet sequence might be effective for either high- or low-involvement products as the TV– Internet sequence has proven to be effective for both high- and low-involvement products (Voorveld et al., 2012). Following the study from Voorveld et al. (2012), we also assume that the Internet– DMB sequence will be effective only for a high-involvement product. Although DMB and TV have considerable common denominators, the advertising effectiveness between the DMB–TV sequence and the TV–DMB sequence can be different. Equipped on a smartphone, DMB enables users to personalize the content on the go, which inherently increases individuals’ cognitive effort. Therefore, the DMB–TV sequence can be effective for the high-involvement product because advertising on DMB devices can enhance advertising effectiveness by reducing ‘‘the ad clutters or consumer ad avoidance’’ (Kim & Jun, 2008). On the basis of key attributes of DMB devices, we assume that a DMB commercial may exert less influence than a TV commercial for a low-involvement product.

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Table 1 Selected studies on the cross-media synergy effect. Author(s) (year), [Method]

Dependent variables

Media

Major findings

Edell and Keller (1989), [Experiment]

CR, AB, PI

T+R

Confer and McGlathery (1991), [Experiment]

AB, PI

T+P

Campbell and Keller (2003), [Experiment] Chang and Thorson (2004) [Experiment]

T+W T+W

Dijkstra, Buijtels, and van Raaij (2005), [Experiment]

CR, AB AC, AB, PI, BC, MC, CR CR AB

The T–R and R–T elicited significantly fewer cognitive responses than did the T–T but more responses than the R–R condition AB and PI: only significant main effect of media exposure. T yielded greater effect than did R. No cross-media exposure effect was found The combination of T + P produces greater attitudinal change than a single-medium repetition Brand familiarity moderated the effect of repetition on AB Synergy effects on MC, CR No synergy effects on AC, AB, PI, BC, and ATC CR: T was superior in evoking CR AB and PI: No significant main effect of media type. Only the main effect of involvement was witnessed

Stammerjohan et al. (2005), [Experiment] Tang et al. (2007), [Experiment]

PI AB MC, AB

P+R T+P

Voorveld et al. (2011) [Experiment]

AB, PI

T+W

Voorveld (2011), [Experiment]

AB, PI

W+R

Voorveld et al. (2012), [Experiment] This study, [Experiment]

MC AC, AB, PI, BC, MC, CR, ATC

T+W T+W+M

T + P+ W

The main effect of media on AB was found only in one condition of two experiments The coordinated T and P repetition led to higher perceived MC and more positive AB compared to a single-medium repetition Cross-media repetition yielded a more positive AB and a higher purchase intention than a repetition in the Internet Combining W and R ads resulted in more positive AB and PI than using only one medium Significant interaction effect of the cross-media repetition and involvement on MC Synergistic repetitions from paired media of T, W, and M yielded greater scores for all dependent variables than single medium repetitions

Note: AC = Ad Credibility, AB = Attitude toward the Brand, PI = Purchase Intention, BC = Brand Credibility, MC = Message Credibility, CR = Cognitive Response, ATC = Advertiser Credibility. T = Television, R = Radio, P = Print, W = Internet, M = Mobile.

When consumers are first exposed to a TV commercial, however, the TV–DMB sequence can also be effective for both high- and low-involvement products since the television ad first triggers interest and enhances attention as evidenced by previous research (Voorveld et al., 2013). In the current study, we replicated the previous research on the synergy effect by varied repetition by adding mobile TV advertising. While adding DMB in cross-media advertising for examining the synergy effect along with television and the Internet, we treated involvement as a within-subject factor. Though Voorveld et al. (2012) tested the interacting role of media sequence and product involvement in cross-media campaigns, they only used two multichannel (exposure to multiple media) conditions of TV–Internet and Internet–TV sequences. In the current study, we also added another condition that was not considered in Voorveld et al.’s (2012) study, which is to examine the varied multiple-repetition effect when the order of the ad presentations was reversed. Since there is scant empirical evidence of the interaction effect of varied multiple repetitions and involvement on persuasion, we propose a research question: RQ1. Will the persuasive effect of varied multiple repetition predicted in H1–H7 be different for a high- vs. a low-involvement product? In other words, will there be differences in the number of positive thoughts, message/source/ad/brand credibility, attitude toward brand, and purchase intention between repetition on a single device and varied repetition under different level of involvement?

Chang and Thorson’s (2004) experiment on television and Internet advertising synergies. The design was a 3 (paired media condition for ad repetition: repetition on a single medium vs. repetition on multiple media vs. reversed repetition on multiple media)  2 (level of product involvement: high vs. low) mixed factorial design. The paired media condition was a between-subjects factor with exposure to a commercial on a single medium [TT (television–television), DD (DMB–DMB), and WW (Internet–Internet)] vs. multiple media [TD (television–DMB), DW (DMB–Internet), and TW (television–Internet)]. The exposure to multiple devices was replicated by changing the order of a multiple repetition condition [DT– WD–WT]. The level of product involvement was a within-subject factor by which each subject was exposed to each commercial in both high- and low-level of product involvement. Two hundred eighty-two undergraduate students in a large private university in South Korea who had experience using TV, DMB, and Internet were recruited for the current study in return for research participation credit for an introductory marketing class. Participants were randomly assigned to one of three paired media conditions in which they were simultaneously asked to watch two 30-s commercials that were high vs. low in product involvement for about 1 min. Then participants filled in a paper-and-pencil questionnaire. Ninety-eight participants took an experiment under the repetitive ad condition (TT = 37, DD = 31 and WW = 30), 91 participants under the multiple-device condition (TD = 31, DW = 30, TW = 30) and 93 under the multiple-device condition in a reversed order of media (WT = 32, DT = 31 and WD = 30). Two hundred eightytwo participants’ mean age was 23 years old and 53.9% of participants were male.

3. Method 3.2. Experimental stimuli 3.1. Design, participants, and procedure An experiment was conducted to analyze how media conditions and involvement of advertised products impacted on consumers’ thinking and attitude. In the first experiment, we replicated

One news story and two television commercials were presented to participants in each of the three experimental conditions. In order to create a natural viewing atmosphere for the commercials, a news story of YTN, Korea’s news channel, was included. The story

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was a tidbit of the Ukrainian couple who did not wash for sixteen years. The news story and commercials for Internet and DMB were identical to the original television commercial. A pretest was conducted to select a commercial of a high-involvement product and a commercial of a low-involvement product. For the commercials to be presented on the Internet, a website of YTN was professionally simulated from an existing YTN site. Following Chang and Thorson (2004), participants in the TW condition first watched the news and commercials on a 42-inch television and then they were presented with the stimuli on a 19-inch computer screen. Participants in the Internet–DMB condition first viewed the stimuli on the Internet through a 19-inch computer monitor, followed by viewing on their 3.5-inch DMB-equipped smart phone. 3.3. Measures Table 2 presents key constructs, scale items and reliability of the measured items in different involvement levels. The constructs of the study can be identified in ad- and brand-related credibility, cognitive responses and attitudes. The following sections summarize the operational definitions for each construct. 3.3.1. Ad- and brand-related credibility measures 3.3.1.1. Ad message credibility. The measures for ad-related perceptions include ad message credibility, advertiser credibility and ad credibility. Ad message credibility is the perceived believability and trustworthiness of an advertisement’s message (Brennan & Bahn, 2006; Chang & Thorson, 2004). 3.3.1.2. Advertiser credibility. Advertiser credibility is defined as ‘‘the perceived truthfulness or honesty of the sponsor of the ad’’ (MacKenzie & Lutz, 1989, p. 51). 3.3.1.3. Ad credibility. Ad credibility refers to ‘‘the extent to which the consumer perceives the message in the ad to be believable’’ (MacKenzie & Lutz, 1989, p. 51). 3.3.1.4. Brand credibility. Brand credibility was defined as ‘‘the extent to which the consumer perceives claims made about the brand in ads to be truthful and believable’’ (MacKenzie & Lutz, 1989, p. 51). According to MacKenzie and Lutz (1989), the ad-related credibility influences brand-related perception (i.e., brand credibility). 3.3.2. Cognitive response As reviewed earlier, cognitive response is an important measure to understand the repetition-induced persuasion in advertising research (Cacioppo & Petty, 1979). We adopted the same technique that Chang and Thorson (2004) used to elicit cognitive thoughts as a response to ad exposures. Chang and Thorson’s technique to measure cognitive responses is composed of two stages: first, participants were asked to list all the thoughts that came to mind while watching the commercials of a laptop and a headache pill; then they rated each of the elicited thoughts as positive, negative, or neutral. 3.3.3. Attitudinal measures Spears and Singh (2004) reviewed that two attitudinal constructs of attitude toward the brand and purchase intention are most popular and pivotal in the marketing research based on the hierarchy of effect model rooted in attitudes. 3.3.3.1. Attitude toward the brand. Spears and Singh (2004) define attitude toward the brand as ‘‘a relatively enduring,

unidimensional summary evaluation of the brand that presumably energizes behavior’’ (p. 55). 3.3.3.2. Purchase intention. The purchase intention in this study is referred to as ‘‘an individual’s conscious plan to make an effort to purchase a brand.’’ 4. Results 4.1. Manipulation check To check participants’ involvement in two products promoted in two commercials, four statements adapted from Petty and Cacioppo’s (1996) study were asked on a Likert scale anchored by ‘‘1’’ = ‘‘strongly disagree’’ and ‘‘7’’ = ‘‘strongly agree’’. The four statements were: (1) I carefully evaluated the product’s advantages and disadvantages; (2) The product is important to me; (3) I am interested in the product; (4) The product means a lot to me. Participants rated a laptop as a high-involvement product and a painkiller as a low-involvement product. The mean difference of a summed involvement index for two products was statistically significant. Mean for a high involvement product was 3.92 (SD = .79), and mean for a low-involvement product was 2.17 (SD = .88), t = 26.70, df = 281. 4.2. Hypothesis testing 4.2.1. Ad message credibility A one-way analysis of variance (ANOVA) was performed for assessing message credibility of the three experimental conditions. The analysis was performed by splitting the results for commercials for the high and low-involvement products. Table 3 presents the overall results for the hypothesis tests using a one-way analysis of variance (ANOVA). The ad repetition in the multiple media conditions yielded higher message credibility than in the repetition condition on a single device for both high- and low-involvement products. In a high-involvement condition, the message credibility score was higher for repetition on multiple devices (M = 3.11, SD = .79) and reversed repetition on multiple media (M = 2.96, SD = .72) than for a single medium (M = 2.63, SD = .74), F (2/279) = 10.43, p < .001. For a low-involvement product, participants also rated higher message credibility in repetition from multiple devices (M = 3.04, SD = .87) and a reversed repetition from multiple devices (M = 2.77, SD = .95) than a repetition from a single device (M = 2.42, SD = 0.78), F (2/279) = 12.28, p < .001. A post hoc analysis using Tukey’s b further demonstrated that there was no mean difference between two multiple-media repetitions, whereas there were significant mean differences between multiple-media conditions and single-medium conditions. Therefore, H1 was supported. 4.2.2. Advertiser credibility To test H2, a one-way ANOVA was performed. As predicted in H2, advertiser credibility was perceived higher in multiple-media repetition conditions than single-medium repetition conditions for both high- and low-involvement products. A post hoc Tukey’s b pairwise comparison revealed that there was slight difference of means between two multiple-media conditions [MMultiple-media repetition = 3.32 (SD = .65), MReversed multiple-media repetition = 3.05 (SD = .79)] for a low-involvement product, while there was also a significant difference between multiple-media repetition and single-medium repetition [MSingle-medium repetition = 2.78 (SD = .82)], F (2/279) = 11.89, p < .001. For a high-involvement product, both multiple-media repetitions yielded higher advertiser

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J.S. Lim et al. / Computers in Human Behavior 48 (2015) 463–472 Table 2 Key constructs, measurement items, and item reliability for composite index. Construct

Question

Items

Cronbach’s a

Message credibility (Chang and Thorson, 2004)

How reliable [. . .] was the information of the ad you watched?

High Involvement Low Involvement

.836 .917

Advertiser credibility (MacKenzie and Lutz, 1989)

How convincing [. . .] did you think the advertiser of the ad you watched?

High Involvement

.840

Ad credibility (MacKenzie and Lutz, 1989)

How convincing [. . .] did you feel that the ad was?

Low Involvement High Involvement Low Involvement

.880 .848 .872

Brand credibility (Chang and Thorson, 2004)

How would you rate advertised brand along these scales?

Not reliable/reliable Not credible/credible Not believable/believable Not convincing/convincing Biased/unbiased Not believable/believable Not convincing/convincing Not believable/believable Biased/unbiased Not reliable/reliable Not credible/credible Not believable/believable Not likable/likable Unpleasant/pleasant

High Involvement Low Involvement

.901 .897

High Involvement

.880

Bad/good Not appealing/appealing Not likely/likely Improbable/probable Impossible/possible Unwilling/willing

Low Involvement

.867

High Involvement

.859

Low Involvement

.847

Attitude toward the brand (Chang and Thorson, 2004; Spears and Singh, 2004)

Purchase Intention (MacKenzie et al., 1986)

Please describe your overall feelings about the brand featured in the ad you just watched

How likely is it that you will try the advertised product if it becomes available in your area?

Note: All measures are based on a 5-point semantic-differential scale: the higher the composite score is, the more positive is the assessment for each measure. Each individual’s responses to the above scales were averaged to produce the single index of measured construct.

Table 3 Tests of proposed hypotheses: results of one-ways ANOVAs for different levels of product involvement. Variable (Hypothesis)

Involvement

Repetition on a single device

Repetition on multiple devices

Reversed repetition on multiple devices

F (2, 279)

Message credibility (H1)

High Low High Low High Low High Low High Low High Low High Low

2.63a (.74) 2.42a (.78) 2.89a (.85) 2.78a (.85) 2.60a (.66) 2.42a (.74) 2.29a (.74) 2.31a (.82) .72a (1.08) .42a (.82) 2.30a (.84) 2.22a (.79) 1.88a (.80) 2.00a (.86)

3.11b (.79) 3.04b (.87) 3.25b (.70) 3.32c (.65) 3.11b (.77) 2.95b (.85) 2.85b (.79) 2.82b (.92) 1.93b (1.53) 1.32b (1.32) 2.90b (.80) 2.60b (.91) 2.39b (.77) 2.50b (.91)

2.96b 2.77b 3.16b 3.05b 2.97b 2.80b 2.80b 2.76b 1.73b 1.16b 2.70b 2.55b 2.36b 2.48b

10.43*** 12.28*** 5.82** 11.89*** 12.62*** 11.35*** 13.43*** 10.55*** 20.23*** 14.39*** 13.17*** 5.73** 10.56*** 9.60***

Advertiser credibility (H2) Ad credibility (H3) Brand credibility (H4) Number of positive thoughts (H5) Attitude toward the brand (H6) Purchase intention (H7)

(.72) (.95) (.68) (.79) (.75) (.79) (.95) (.81) (1.58) (1.51) (.86) (.87) (.99) (.91)

Note: Repetition on a single device indicates repeated exposure of the same ad on TT (TV–TV), DD (DMB–DMB), and WW (Web–Web). Repetition on multiple devices indicates exposure to the same ad in cross-media in sequence of TD, DW, and WT. Reversed Repetition on multiple devices is the replication of the ‘‘Repetition on multiple devices’’ condition, by changing the presentations order (e.g., DT, WD, and TW). For each condition, N = 93. Means with different superscript letters are significantly different according to Tukey’s b post hoc test (p < .05). The numbers in parentheses are standard deviation. ⁄ p < 0.05. ** p < 0.01. *** p < 0.001.

credibility than a single-medium repetition, F (2/279) = 5.82, p < .01. These results corroborate H2.

single-medium repetition regardless of product involvement for the ad. Therefore, H3 was also supported.

4.2.3. Ad credibility When it comes to ad credibility, both multiple-media conditions yielded significantly higher ad credibility than the single-medium repetition condition for both high- and low-involvement products. For the ad exposure to high-involvement product, MMultiple-media repetition was 3.11 (SD = .77), MReversed multiple-media repetition was 2.97 (SD = .75), and MSingle-medium repetition = 2.60 (SD = .66), F (2/279) = 12.62, p < .001. For the ad exposure to a low-involvement product, MMultiple-media repetition was 2.95 (SD = .85), MReversed multiple-media repetition was 2.80 (SD = .80), and MSingle-medium repetition = 2.42 (SD = .74), F (2/ 279) = 11.35, p < .001. These results indicate that ad credibility is perceived higher for cross-media repetition than for a

4.2.4. Brand credibility To test H4, a one-way ANOVA was conducted for the brand credibility index. As a result, brand credibility was higher for multiple-media repetitions than for a single-medium repetition for both high- and low-involvement products. The mean of multiplemedia repetitions and reverse multiple-media repetition was 2.85 and 2.80 respectively for a high-involvement product, F (2/ 279) = 13.43, p < .001. The mean of multiple-media repetition and reverse multiple media repetition was 2.82 and 2.76 respectively, and the mean of single-media repetitions was 2.31 for a low-involvement product, F (2/279) = 10.55, p < .001. Consequently, H4 was also corroborated.

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4.2.5. Cognitive response As for the cognitive response measure by ad repetition, both multiple-media conditions resulted in a significantly greater number of positive thoughts than single-medium repetitions. For a high-involvement product commercial, MMultiple-media repetition was 1.93 (SD = 1.53), MReversed multiple-media repetition was 1.73 (SD = 1.59) whereas MSingle-medium repetition was 0.72 (SD = 1.08), F (2/279) = 20.23, p < .001. For a low-involvement product, MMultiple-media repetition was 1.32 (SD = 1.32), MReversed multiple-media repetition was 1.16 (SD = 1.51) whereas MSingle-medium repetition was 0.42 (SD = .82), F (2/279) = 14.39, p < .001. A post hoc using Tukey’s b method also revealed that the mean differences between multiple-media repetition and single-media repetition were statistically significant under the p < .05 level. Therefore, H5 was also supported. 4.2.6. Attitude toward the brand H6 was tested by performing a one-way ANOVA for the index of attitude toward the brand. As predicted, both multiple-media conditions showed higher attitude toward brand scores than single-medium repetitions for both product commercials in different levels of product involvement. For high-involvement products, participants showed more positive attitude toward the brand when exposed to the commercial in multiple media [MMultiple-media repetition = 2.90 (SD = .79), MReversed multiple-media repetition = 2.70 (SD = .86)] than in single medium [(MSingle-medium repetition = 2.30 (SD = .84)], F (2/279) = 13.17. For a low-involvement product, multiple-media repetition conditions also resulted in more positive attitude toward the brand than single-media repetition, F = (2/279) = 5.73. Therefore, H6 was also supported. 4.2.7. Purchase intention In H7, we also predicted that multiple-media repetitions of a commercial would yield higher purchase intention than singlemedium repetitions. The result of the ANOVA test revealed that two multiple-media conditions yielded higher purchase intention than single-media repetition for both a high-involvement product [F (2/279) = 10.56, p < .001] and a low-involvement product [F (2/ 279) = 9.60, p < .001]. Thus, H7 was supported. In RQ1, we asked a research question (RQ1) regarding whether the predicted advantage of multiple-media repetitions over singlemedium repetition could be different for a high- vs. a low-involvement product. As reported for hypothesis testing, the advantage of multiple-media repetitions over single-media repetitions was robust across different dependent measures. Therefore, we conclude that the multiple-media repetitions are more effective than singlemedium repetitions in achieving intended goals of advertising. 5. Discussion This study expanded the knowledge on the synergy effect that came from the sequential presentation on multiple devices of different screen sizes. Consistent with findings from the previous research, we evidenced that ad repetition on multiple devices had a greater advantage in achieving intended ad effects than the repetition on a single device. In summary, the repetition of ads on multiple media induced significantly higher credibility perception, cognitive responses, attitude toward the brand, and purchase intention than repetitions on single devices of television, DMB, and the Internet. Extending the earlier study by Chang and Thorson (2004), this study also considered the product involvement as a potential moderator. Unlike Voorveld et al.’s (2012) study in which product involvement was treated as a between-subject factor, this study treated it as a within-subject factor. It is noteworthy that the product involvement condition did not make any impact on the main effect of cross-media advertising on the ad effectiveness. In conclusion, it was confirmed that multiple-media conditions were

superior to single-medium repetitions for the most important outcomes measures of advertising effectiveness. 5.1. Theoretical implications There is little research on the effects of mobile video advertising. This research suggests that the mobile ad environment operates similarly to the much studied Internet environment. The robust effects of synergistic ad repetition on general outcomes of perceived credibility, cognitive responses, and attitude shed light on the cross-media synergy effects for digital advertising. In this study, the original television commercial was digitized and presented in three different media of different screen sizes. Any combinations of cross-media presentation yielded greater and more positive results for intended measures than simple repetitions on a single media. In proposing the hypotheses, we reasoned four theoretical underpinnings for the cross-media synergy effect. The greater perceived credibility was postulated based on the multiple-source effect. The more positive cognitive responses were predicted based on the differential attention hypothesis and forward encoding hypothesis. Finally, attitudinal change was grounded on repetition-variation theory. Therefore, findings of the current study can provide useful theoretical accounts for the cross-media synergy effect. Findings of this study are the first empirical results that exhibited the synergy effect of the Internet–television–mobile advertising on inducing positive perceptions, cognition, and affects. One notable thing is that the varied ad repetition in our study exhibited positive effects on ad credibility, brand credibility, attitude toward the brand and purchase intentions, which was not found in Chang and Thorson’s (2004) study. We note that participants in this study were exposed to digitized commercials that were presented in starkly different screen sizes (42 vs. 19 vs. 3.5 inches respectively). Reeves et al. (1999) revealed the display size effect on the audience’s arousal. Although little evidence has been available regarding the repetition effects through varied screen sizes, the varied repetition in different screen sizes from this study could have generated more striking variation. We presume that this helped to yield the effects that the forward encoding hypothesis and the repetition variation theory postulate. In other words, it is probable that participants in our study may have formed more positive credibility perceptions and attitudes due to much greater variations in synergistic repetition than Chang and Thorson’s experimental condition (similar size of television and computer screen). Because the higher level of encoding affected the participants’ attitude toward the brand and purchase intention (Voorveld et al., 2011), more positive attitude toward the brand and higher purchase intention in multiple-source repetition than in a single media repetition can be explained. Also, screen size can increase attention and arousal, which provide variations (Reeves et al., 1999). Repetition with such variations connects to a significant positive effect on the viewer’s cognitive response activity including ad credibility and brand credibility (McCullough & Ostrom, 1974). Consequently, this research found intriguing results that were different from Chang and Thorson’s research. An explanation for the different findings is that the ad repetitions on multiple-media of distinctively different screen sizes are likely to invoke higher ad/ brand credibility, more positive attitude toward the brand, and higher purchase intention than the repetitions on two screens (i.e., television and computer) of the same size. 5.2. Managerial implications The current study provides managerial implications in regards to the synergistic repetition of digital advertising. First, synergistic repetition through variations can help increase more positive

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advertising effects than a single-media based repetition. In doing so, professionals need to consider the so-called three-screen (television–Internet–mobile) variation strategy (Truong, McColl, & Kitchen, 2010) to maximize the influence of distinct types of variation (Haugtvedt, Schumann, Schneier, & Warren, 1994; Schumann et al., 1990). Secondly, advertisers, in an interactive media age, need to adapt the non-interactive nature of television spots to address the synergistic effects of digital media platforms. In this regard, Assael aptly states: ‘‘As applied to cross-media, an IMC approach would require an integrative media plan capable of measuring the interactive effects of media components on exposure, attention, and behavior’’ (Assael, 2011, p. 7). Political campaigns in the U.S. have taken unique advantage of the cross-media opportunities by chasing the potential voters at home and on the go. As showcased in a recent New York Times report of the 2014 election (Corasaniti & Parker, 2014; Corrigan, Powell, & Michaels, 2013), implementing advertising on multiple devices including mobile devices is not merely an option but an integral part of today’s media planning. Thirdly, airing the ad on mobile TV may not make any difference in the synergy effect of cross-media advertising. In other words, the screen size may not yield a difference in the ad synergy effect since the ad presentation on DMB in different sequential order did not result in any statistical difference for the dependent variables of the current study. This does not mean that the screen size does not entirely matter. When examined separately in a post hoc analysis of the comparison of multiple repetitions from a single device, an ad delivered on a DMB device yielded the lowest score across different measures. 5.3. Limitations and suggestions for future research While this study found the effectiveness of cross-media advertising over repetition of advertising in the same media, some limitations should be applied to make a generalization of the repetition effect. Firstly, the condition in which participants were exposed to advertising in repetition may be slightly different from the real-life situation since participants’ viewing behavior was controlled in a laboratory experimental setting. For instance, consumers may watch the mobile advertising typically on the move, which may affect their attention and make their information processing less rich than when they watch it on a larger screen. Future researchers for cross-media advertising may consider the condition that can simulate more realistic viewing behavior. Future research also needs to replicate the results obtained from the current study, possibly adding more conditions for screen size. In this regard, Edell and Keller (1989) once pointed out that the screen size effect could be affected by the cognitive capacity to process ad messages. Researchers who aim to incorporate the cognitive capacity condition into the synergy effect by different screen size can also consult with Janiszewski’s (1993) previous study. Secondly, the findings of the current study indicated that the adverse effect of additional ad exposures on attitude could be moderated by the variation in ad repetition through cross-media sequential presentation. As the two-factor theory postulates, however, the frequency of repetitions would eventually reduce the positive affect of varied repetition as the number of repetition increases (Rau, Zhou, Chen, & Lu, 2014; Rethans et al., 1986). Future research needs to examine how many varied repetitions would start to generate consumer wearout with each additional repetition diminishing positive affect. It may be of some benefit to the industry if future research can formulate repetition wearout rates under differential synergistic combination. Thirdly, the synergistic repetition in this study is based on only two-paired media. As suggested in Truong et al.’s (2010) research, however, the future trend of cross-media synergy for digital adver-

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