How rich is too rich? Visual design elements in digital

International Journal of Research in Marketing xxx (xxxx) xxx

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International Journal of Research in Marketing

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How rich is too rich? Visual design elements in digitalmarketing communications

https://doi.org/10.1016/j.ijresmar.2021.06.0080167-8116/� 2021 Elsevier B.V. All rights reserved.

⇑ Corresponding author.E-mail addresses: [email protected] (Y. Bashirzadeh), [email protected] (R. Mai), corinne.faure@grenoble-em

Faure).

Please cite this article as: Y. Bashirzadeh, R. Mai and C. Faure, How rich is too rich? Visual design elements in digital marketing cnications, International Journal of Research in Marketing, https://doi.org/10.1016/j.ijresmar.2021.06.008

Yashar Bashirzadeh a,⇑, Robert Mai b, Corinne Faure b

aRennes School of Business, 2 Rue Robert D’Arbrissel – CS 76522, 35065 Rennes, FrancebGrenoble Ecole de Management, 12 Rue Pierre Semard, 38000 Grenoble, France

a r t i c l e i n f o

Article history:Received 22 July 2019Available online xxxx

Keywords:Digital marketingPictographsAnimationField experimentClutterEnrichment

a b s t r a c t

Companies are increasingly including innovative visual design elements such as anima-tions and pictographs in digital communication. While both elements can be beneficialin exchanges with their customers, we propose that combining them can have negativeeffects on communication effectiveness. Animations and pictographs enhance digital com-munication, essentially through increased perceptions of enrichment, but these elementsalso raise perceptions of clutter. As they enrich a message in unique ways, processing thesedifferent types of visual design elements requires distinct cognitive resources such that,when combined, clutter perceptions dominate the recipient’s perceptions and behaviors,thus paradoxically offsetting their positive effects. This interplay may not only underminemessage outcomes but even spill over to downstream behavioral outcomes. In a large-scalerandomized field experiment in cooperation with a mobile app company, we find thatincluding animations (GIFs) and pictographs (emojis) together damages message outcomes(increasing unsubscriptions) and downstream outcomes (reducing in-app time) comparedwith what happens when these elements are deployed separately. We elaborate on the pro-cessing of the text and visual elements from this field experiment in two lab experiments,including an eye-tracking study. Finally, in two further online studies, we seek to establishwhether the proposed mechanisms depend on the number of visuals or the types of pic-tographs employed.

� 2021 Elsevier B.V. All rights reserved.

1. Introduction

Spurred on by advances in technology that facilitate richer communication, marketers increasingly make use of ever moreintricate visual design elements in their digital communication. This type of communication commonly includes animationssuch as GIFs (Graphics Interchange Format images, i.e., frames of an image shown in succession to give the impression ofanimation) and pictographs such as emojis (i.e., small icons or symbols conveying ideas, actions, or emotions). Within emails,the use of animated GIFs and emojis increased dramatically by 90% and 775%, respectively, from 2015 to 2016 (Finn, 2017).In the business world, Dell, for instance, reports that including a GIF in an email campaign lifted click-through rates by 42%

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Y. Bashirzadeh, R. Mai and C. Faure International Journal of Research in Marketing xxx (xxxx) xxx

and revenues by 109% (Banko, 2014). Although some visual design elements have received attention in the marketing liter-ature (e.g., Das, Wiener, & Kareklas, 2019; Li (Shirley), Chan, & Kim, 2019; Luangrath, Peck, & Barger, 2017), the implicationsof using visual elements in digital business communication are understudied (Sample, Hagtvedt, & Brasel, 2020; Steinhoff,Arli, Weaven, & Kozlenkova, 2019).

To stand out in a crowded digital environment, marketing managers might be inclined to include multiple visual ele-ments in their digital messages. This research proposes that, while employing animations or pictographs alone can be ben-eficial, using them simultaneously may be counterproductive because the design elements in focus can have distinct andpotentially countervailing effects through their effects on complexity perceptions, especially perceived enrichment (i.e.,bringing new features into a message) and clutter (i.e., interrupting the processing flow of a message). Instead of benefitingfrom enriching their message, companies may unintentionally hamper processing and hence communication effectivenesswhen combining several very different types of visual elements. The simultaneous processing of these elements by con-sumers requires distinct cognitive resources, overtaxing processing abilities so that clutter perceptions take over in guidingperceptions and behavior. This is a pressing problem because digital communication is increasingly carried out on mobiledevices (Van Rijn, 2018), where complex visual presentations are particularly detrimental because of limited screen size(Zhou, Tian, Mo, & Fei, 2020). The implications of using various design elements may be so pervasive that they even carryover to downstream outcomes such as product usage or purchases.

Drawing on visual processing research (Berlyne, 1974; Mai, Hoffmann, Schwarz, Niemand, & Seidel, 2014; Pieters, Wedel,& Batra, 2010), this paper develops a framework that explains how the use of innovative visual design elements (i.e., anima-tions and pictographs) separately or together in digital communication can affect message and downstream outcomes. Wetest this framework through a randomized field experiment (N = 10,701 users) that uses rich pre- and post-campaign userdata from a mobile game app company. We manipulate the presence of animations (GIFs) and pictographs (emojis) in anemail campaign to quantify the impact of using these elements alone or together on message outcomes (click-throughand unsubscription rates) and downstream outcomes (update, in-app time, and in-app purchase). We show that employingGIFs and emojis separately can have favorable implications for unsubscription rates, time spent using the marketed app (i.e.,in-app time), and in-app purchase, while their simultaneous use reduces or even eliminates these benefits. In further exper-iments that include eye-tracking and self-reported perceptions, we tap into the mechanisms underlying the observations ofthe field experiment. Specifically, we seek to corroborate our theoretical assumption that animations and pictographs stim-ulate distinct perceptual processes and that the induced clutter perceptions take over and dominate judgment-formationwhen combining these elements. We apply our general theory to distinct domains (freemium apps, online retailing) andexplore how specific properties of the visual elements affect the findings (e.g., the number of elements involved or the speci-fic subtypes).

Our findings contribute to the literature on marketing communication in the digital world. We identify conditions underwhich the implementation of widely used innovative visuals in business communication has the potential to offset their ben-efits, contradicting the conventional ‘‘more is better” wisdom to which marketing managers may cling. We show that theseso-far overlooked implications can have quantifiable effects that reduce the benefits for companies. In this way, we con-tribute to the literature by showing that the effects of visual design elements in digital communication (especially emails)are so powerful that they can even transfer to the actual usage of a marketed product or service.

2. Theoretical background

2.1. Digital marketing effectiveness

The effectiveness of a digital marketing campaign is often assessed by reference to two separate types of outcomes: mes-sage outcomes resulting from the interaction of a recipient with the communication channel and downstream outcomesresulting from interactions with the products or services. Message outcomes take various forms depending on the commu-nication channel involved. For emails, key message outcomes include open rates (i.e., the proportion of emails opened), click-through rates (i.e., the proportion of emails in which recipients click on embedded links), and unsubscription rates (i.e., theproportion of emails for which recipients ask to be taken off an email list), and the extent to which recipients pay attention toan email (Hong, Thong, & Tam, 2004).

Downstream outcomes focus on reaching marketing campaign objectives and may include relationship duration and cus-tomer profitability (Reinartz, Thomas, & Kumar, 2005), judgments about a company and its offers (Li et al., 2019), purchaseintentions (Das et al., 2019; Smith & Rose, 2020), and brand engagement (McShane, Pancer, Poole, & Deng, 2021). Down-stream outcomes can also include more specific objectives such as word-of-mouth recommendations or updating to anew version and therefore depend on the concrete objective of the digital communication.

Empirical research rarely includes both message and downstream outcomes. A few exceptions are worth noting: Sahniet al. (2018) study click-through, open, and unsubscription rates as well as sales leads, whereas Zantedeschi, Feit, andBradlow (2017) address click-through rates and sales. We investigate both message and downstream outcomes in thisproject.

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2.2. Visual design in marketing communication

Visual design is a valued tool that marketers use to grab recipients’ attention and to shape behaviors (Feld, Frenzen, Krafft,Peters, & Verhoef, 2013). Visual design contributes to communication success in various marketing areas, such as advertising(Yoo & Kim, 2005), social marketing (Choi, Li, Rangan, Yin, & Singh, 2020), and direct marketing (Feld et al., 2013). It is thushardly surprising that visual design also plays a crucial role in digital communication. For example, including a video in anelectronic message generates product interest (Scheinbaum, Hampel, & Kang, 2017), while digital smiles (i.e., smiley-faceemojis) in electronic messages improve relationship strength and strengthen future purchase intentions (Smith & Rose,2020). Still, additional knowledge is needed to better understand the impact that commonly used design elements, suchas animations (especially GIFs) and pictographs (such as emojis), can have. This urgency is reinforced by a recent boost inthe use of visual design elements in private and business communication. With this research, we focus on design elementsthat complement written language through (i) animations and (ii) pictographs. These nonverbal cues convey meaning andserve as surrogates for missing social cues (Luangrath et al., 2017). Animated GIFs, for example, can showcase new featuresor products, thereby offering novel benefits to a communication channel. Pictographs are used in electronic messages to sig-nal emotions or convey personal attitudes and are increasingly common in company–consumer interactions (e.g., Li et al.,2019).

Despite their widespread use, there is a lack of attention to animations and pictographs in the digital marketing literature.Notably, the use of combinations of distinct types of design elements has been ignored thus far, even though this commonlyoccurs in marketing practice (see some real-world examples in the Web Appendix). It remains, therefore, unclear whethermultiple design elements incrementally enhance a message because of their additive benefits or whether they interact witheach other and, if so, what the underlying mechanisms are. It is our central premise that distinct types of visual elementsinteract and that their interplay may trigger mechanisms that can only be detected through an integrated theoreticalapproach.

From a methodological standpoint, prior research has typically been conducted in laboratory settings, which, unfortu-nately, do not facilitate theorizing or testing of spillover into downstream outcomes. By combining a field experiment withlaboratory and online studies, this research addresses actual user behavior and the mechanisms underlying that behavior.More formally, we investigate the impact of the simultaneous (vs. separate) application of animations and pictographs onmessage and downstream outcomes. We thereby also test for the unique perceptual effects that these visual design elementselicit.

2.3. Animations and pictographs

Animations. An animation consists of a sequence of images that create a perception of movement. Animations are widelyemployed in marketing contexts (for instance in advertising (Bruce, Murthi, & Rao, 2017) or brand logos (Brasel & Hagtvedt,2016)), and have been found to shape communication effectiveness with regards to consumer engagement (Cian, Krishna, &Elder, 2014), and focused attention (Hong et al., 2004).

GIFs that encompass short looping sequences of movement have become popular in online communication (Highfield &Leaver, 2016) and are increasingly used in marketing to enhance textual messages and express emotions visually. Whenannouncing ‘GIF’ as the ‘‘USA Word of the Year” in 2012, the Oxford dictionary acknowledged the business relevance of GIFs(Oxford, 2012). GIFs convey visual representations that typically align with the given context and complement written textby augmenting various levels of meaning (Miltner & Highfield, 2017). GIFs can transmit or visualize concrete information,such as when presenting a new product or demonstrating a new product feature. The looping repetition of the same anima-tion sequence reinforces the first viewing and enables users to absorb more of the details of the image (Miltner & Highfield,2017).

Pictographs. Communication based on written texts is often unable to capture the core traits of face-to-face interactions,including gestures or facial expressions (Kahai & Cooper, 2003). To convey these aspects, electronic messages can beenriched with pictographs. Smiley faces, emoticons, and emojis (in Japanese, ‘‘picture words”) are all examples of pic-tographs. These miniature images are interspersed throughout the text and serve as nonverbal cues to express emotions,ideas, or gestures (Luangrath et al., 2017). While smiley faces or emoticons serve primarily as a visual kinesics paralanguage,emojis are broader and enhance a message by conveying expressions, gestures, objects, and concepts. Emojis are highlydiverse (in 2019, more than 3000 different emojis were identified; Buchholz, 2019) and widely used in private interactionsas well as firm–user communication. On Facebook messenger, five billion user–user emojis are used each day (Buchholz,2019).

2.4. The effects of animations and pictographs on enrichment and clutter perceptions

Our conceptualization (Fig. 1) builds on visual complexity as a theoretical foundation to explain the effects of animationsand pictographs and their mode of presentation (presented together or seperately). The number of distinct design elementsand the diversity of these elements in digital communication trigger perceptions of visual complexity (Deng & Poole, 2010), acentral property of human-computer interactions. While the terminologies used vary, there is agreement that visual com-plexity can be decomposed into separate perceptual processes (Deng & Poole, 2010; Mai et al., 2014; Pieters et al., 2010).

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Fig. 1. Conceptual Framework.


Following this research stream, our framework distinguishes two types of perceptions, namely enrichment perceptions andclutter perceptions. Enrichment perceptions capture the visual complexity that resides in basic message features (Mai et al.,2014; Pieters et al., 2010) and can be defined as a recipient’s assessment that the design element introduces some new fea-tures to the message or conveys new meaning. Clutter perceptions relate primarily to structural variation (Mai et al., 2014)and can be defined as a recipient’s assessment that the processing of the message has been interrupted. Clutter perceptionstap into the cognitive demand involved when processing a message as well as the mental overhead required for makingsense of it. Note that, within our framework, we chose to conceptualize the actual perceptions that recipients develop ratherthan the intrinsic characteristics of a message (e.g., Pieters et al., 2010). It is the central premise of our framework that ani-mations and pictographs can increase both enrichment and clutter perceptions. Importantly, we argue that these effects areunique, in that the two types of visual elements enhance a message but for distinct reasons.

The benefits of using two very different types of visuals, such as animations and pictographs, can be explained by refer-ence to dual coding (Paivio, 1971). Humans are believed to process verbal and visual information through distinct perceptualchannels; messages are processed and learned more effectively when represented by verbal association and visual imagery.While traditional text-based messages rely on a (single) channel to process verbal code, elements such as animations andpictographs trigger visual imagery, activating spatially separate brain regions (Kaye, Malone, & Wall, 2017). Given their abil-ity to complement the processing of verbal code and to facilitate dual coding, these design elements should thus be perceivedas enriching digital communication. As we will argue, however, the processing of these elements elicits a second perceptualeffect, with offsetting implications.

Animations and pictographs are inherently different forms of visual elements, with distinct cognitive mechanisms beingactivated when decoding them. The expected effect of animations on enrichment perceptions rests on its ability to enhancedigital communication through dynamism and visualization. Animations attract a recipient’s attention because humans havethe inherent tendency to respond spontaneously and more intensely to moving than to static visual input (Abrams & Christ,2003). Animations should therefore improve perceived enrichment as it adds novelty to a message. Yet, this design elementalso requires other processing capacities than those required by reading a text, which also elicits clutter perceptions.

Pictographs, in contrast, enable emotions and other nonverbal concepts to be conveyed and therefore enrich a messagethrough the evocation of these concepts rather than through movement. In the literature, pictographs are also referred to as‘‘paralanguage” because they contribute to a message’s meaning in a nonverbal manner (Luangrath et al., 2017). Accordingly,pictographs, such as emojis, have been shown to trigger effects that can even carry over to brand-consumer relationships(Luangrath et al., 2017; McShane et al., 2021), judgments about a company and its offers (Li et al., 2019), and purchase inten-tions (Das et al., 2019; Smith & Rose, 2020). These characteristics imply that pictographs increase enrichment and clutterperceptions because the conveyed emotions and related aspects introduce novel input to the reader. Yet the exercise of deci-phering the meanings of pictographs interrupts or detracts from the processing of the message they accompany, interferingwith reading the text or other verbal input. We therefore postulate the following research proposition (RP):

RP1a,b: Animations (i.e., GIFs) and pictographs (i.e., emojis) elicit (a) enrichment perceptions and (b) clutter perceptions in addi-tive ways

Note that some design elements may be more likely than others to trigger clutter perceptions. Animation elements, suchas GIFs, are often inserted separately from the text, whereas pictographs are typically interspersed throughout the text,which, arguably, more likely disrupts the reading of the message and, ultimately, impairs message processing. Emojis, in par-ticular, are used to either substitute for or complement words in the text by adding emotions or other meanings to these

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words (Ge & Gretzel, 2018; McShane et al., 2021). This rhetorical function implies that emojis are typically inserted irregu-larly in the text (a central property of clutter) so that they directly interrupt deciphering the text. It is therefore plausible thatpictographs are more likely to trigger clutter perceptions than animations are.

RP2: Pictographs are more likely than animations to strengthen clutter perceptions

The two perceptual processes are central to our theory because enrichment and clutter perceptions are known to elicitopposite consequences for judgment-formation and behavior (Mai et al., 2014; Pieters et al., 2010). While higher levels ofperceived enrichment make digital communication more engaging and interesting with favorable outcomes, clutter percep-tions engender confusion that hinders communication effectiveness. The (aggregate) effects of visual design elements areconsequently not merely linear but instead follow a convex curvilinear shape that stems from the distinct imprints of bothperceptions (aesthetic theory; Berlyne, 1974). It is therefore important to note that the relative weight of the opposite effectsof these perceptions matters for the message and downstream outcomes (Fig. 1). Generally, companies should be interestedin employing visual design elements for which the positive enrichment effects dominate in shaping consumer perceptions sothat the negative clutter effects do not override the enrichment benefits.1 With this research, we propose that the use of mul-tiple visual design elements together can tilt the balance between these opposite effects, resulting in a negative offsetting effecton communication effectiveness.

2.5. Separate or simultaneous use of visual design elements

Considering their unique benefits, one could assume that the simultaneous implementation of several design elementswould lead to positive effects through even more enrichment of the digital message. This assumption may be misleadingbecause animations and pictographs should also strengthen clutter perceptions in unique ways, thereby activating mecha-nisms that interfere with and detract from processing the medium and its message. Our framework (Fig. 1) therefore sug-gests that the effects of animations and pictographs are not merely additive, as they can interact, potentiallyundermining their favorable impact. Specifically, we propose that a simultaneous presentation of animations and pic-tographs dampens the positive enrichment effects of these design elements while reinforcing the negative clutter effects.

Cognitive resources are limited (Kahneman, 1973), and humans find it difficult to process multiple tasks simultaneously,which results in inferior performance (Lang, 2000). Under the worst conditions, performance failures are possible whenhumans try to master two or more activities at the same time, even in computer-mediated environments (Jenkins,Anderson, Vance, Kirwan, & Eargle, 2016). This has been widely confirmed for the (parallel) processing of verbal stimuli(Cherry, 1953) and should also apply to processing diverse visual elements in digital communication. Integrating multipleelements, such as animations and pictographs that differ widely in their effects likely hurts processing ability when humansare forced to process them at the same time, so animations and pictographs may not achieve their intended purpose (i.e.,their positive effects of enriching the firm–user interaction are switched off). Eye-tracking studies have shown that combi-nations of animated and static visual elements in ads are more distracting than static or animated elements alone (Simola,Kuisma, Uusitalo, & Hyönä, 2011).

Considering these arguments, the relative weight of the countervailing perceptual effects triggered by animations andpictographs should shift when these design elements are employed together (vs. separately). We therefore expect the extentto which enrichment and clutter perceptions guide the message and downstream outcomes to be moderated. Once anima-tions and pictographs are combined, this is expected to shift the balance between the countervailing effects, predominantlyincreasing the clutter effect. Although these visual elements still improve the perceived enrichment of the digital message,the corresponding perceptions quickly hit a ceiling so that the incremental (i.e., additional) improvement in enrichment per-ceptions remains limited when combining different types of design elements. Processing movement (animations) and non-verbal cues (pictographs) requires very different cognitive processes, though, such that the simultaneous processing of theseelements requires attentive effort and increases the perceptual load (Kahn, 2017). The combination of several very differentdesign elements in digital communication is therefore expected to increase the relative weight of clutter perceptions. As aconsequence, the enrichment effects will strengthen only slightly (if at all), whereas the induced clutter effect should inten-sify and even outweigh the enriching benefit of visual design elements. Put differently, when animations and pictographs arecombined, perceptions of enrichment do not make up for the inflated clutter perceptions, which then dominate consumerjudgments and, in turn, digital marketing effectiveness.

This proposed interplay between the design elements extends beyond message outcomes and may even spill over todownstream outcomes. Emails, for example, have been shown to evoke lasting effects that materialize in concrete purchases.The effectiveness of an email discount offer can last longer than the discount period itself (Sahni, Zou, & Chintagunta, 2017);emails have also been found to impact purchases even if customers do not click on embedded links (Kumar, Zhang (Alan), &Luo, 2014). For these reasons, the proposed interplay may also affect the downstream outcomes.

1 Apart from presentation mode (together vs. not), it is also possible that a high number of design elements or the use of specific types of elements separatelycan tilt the balance in the relative weight of both countervailing effects and in particular intensify the clutter mechanism. We explore this possibility in Studies3A and 3B.

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RP3a,b. The use of animations and pictographs together (vs. separately) in email communication has an interactive effect. Includ-ing both design elements together (vs. separately) elicits negative effects on (a) message outcomes and (b) downstreamoutcomesRP3c,d. The negative (positive) impact of clutter (enrichment) perceptions on (c) message and (d) downstream outcomes) is mod-erated by the use of animation and pictograph elements together (vs. separately) in digital communication such that when theseelements are used together, clutter perceptions dominate, and enrichment perceptions weaken.

3. Overview of studies

In a series of studies, we test our theoretical framework in the context of email marketing. Marketers increasingly useemails to interact with customers (Emma, 2017) because customers retain information delivered through this channel moreeffectively than information they draw from any other channel (Li & Kannan, 2014). When designing email campaigns, com-panies strive to optimize message outcomes, such as maximizing open and click-through rates while minimizing unsub-scription rates as well as improving downstream outcomes.

We start with a field experiment that tests for the effects of including distinct types of visual design elements—animation(GIF) and pictographs (emojis)—separately or together on message outcomes and whether this can spill over to actual down-stream outcomes. We then report the results of two experiments (2A and 2B) that test the stimulus materials used in thefield experiment in a controlled laboratory environment, including eye-tracking. These studies allow us to elaborate onour premise that the design elements enrich and clutter a message in unique ways and that combining them hurts the pro-cessing of the message. In two studies (3A and 3B), we seek to establish whether these effects depend on the number of visu-als employed in an electronic message and whether they emerge for different subtypes of pictographs (specifically, emojisrepeating the preceding word vs. not). To ensure that the context and the specific stimuli are not driving the results, ourframework is tested in two separate contexts (online gaming, online retailing) and includes multiple visuals. We presentthe summary of the studies and the details of the stimulus materials in the Web Appendix.

4. Field experiment (study 1): The effects of animations and pictographs on message and downstream outcomes

In this field study, we explored the effects of the interplay between animation and pictographs on message outcomes anddownstream behavioral outcomes. We conducted a large-scale randomized field experiment with users of a mobile gameapp who had given permission to the company to contact them. Field experiments investigate behaviors in relevant naturalenvironments, which helps to establish causality and obtain behavioral measures that are less biased than self-reports(Gneezy, 2017; Gordon, Zettelmeyer, Bhargava, & Chapsky, 2019). In this manner, the main study allows us to rule outdemand effects and enhance ecological validity (Harrison & List, 2004).

4.1. Objective and context

We conducted our field experiment in the context of permission-based email marketing for a freemiummobile game app.Game apps represent a significant industry (Rutz, Aravindakshan, & Rubel, 2019). Freemium apps generate revenues not onlythrough in-app purchases that some users make but also, and especially, through the in-app time that all users spend (Appel,Libai, Muller, & Shachar, 2020; Rutz et al., 2019). In the mobile app industry, emails are particularly valued as catalysts forcustomer engagement (e.g., increasing in-app purchase and in-app time) (Leanplum, 2018). In cooperation with an app com-pany, we designed an email campaign inviting the app’s users to benefit from some new game features that were integratedinto the latest app version. The marginal costs of emails are low, making this an attractive tool that firms use to interact withcustomers. Specifically, in a freemium app context, emails can help app providers reach customers who have disabled noti-fications or even deleted an app. Firms in this domain often seize the opportunity to announce a major change in an app asjustification for contacting users via email, which was also the case in the context of this study. The email campaign intro-duced new features and the company used the call for updating as an opportunity to contact the app’s users. Note that, withfreemium apps, the impact of an email can unfold in various ways because such a message can impact downstream out-comes even without noticeable message outcomes. Quite often, companies are more interested in downstream outcomes,such as updates and revenues (through in-app purchases and time spent in the app), than solely in message outcomes, whichis why this field experiment considers both classes of outcomes.

4.2. Design of the field experiment

Wemanipulated two design elements on two levels: the presence (vs. absence) in an email of (1) an animated GIF and (2)emojis. We used the same subject line for all email conditions to ensure constant open rates across the experimental con-ditions because Feld et al. (2013) found that, with offline direct mail, visual design elements on envelopes are the primaryfactors that drive recipients to open the mail.

To manipulate the GIF factor, a central pictorial element (i.e., an image with a ship representing the most critical featurein the new version of the app) appeared as a static image (JPEG) or as an animation (GIF). In the GIF condition, the ship was

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shown sailing towards a harbor. In the static condition, the same ship was shown in the same environment without anima-tion (in fact, the static image consisted of a single frame of the animation). To manipulate the emoji factor, the messageeither did or did not contain emojis that were scattered throughout the body of the email. Beyond the manipulated factors,the email body and text were kept constant across all experimental conditions. The specific emojis (and their number), theGIF, and the email text were specified together with the cooperating company to maintain their usual communication styleand avoid creating bias (i.e., novelty effects). As the email asked users to update to the newest version of the app, two updatelinks were included within the email body. We provide the email and the stimulus materials in the Web Appendix.

4.3. Sample and data

To test the proposed effects on users for whom the email was relevant, we used data from 10,701 customers who hadpreviously given permission to be contacted and who owned the version of the app that needed updating (we also receiveddata from 2863 users who did not need to update their app and analyzed data from these users as a robustness check). Theusers were assigned to one of the 2 � 2 conditions. Given this random assignment, any systematic difference in userresponses to the email and ensuing app usage must reflect user perceptions of the manipulated visual design elementsand, more precisely, whether a GIF and/or emojis are present in (or absent from) the email body. To investigate overall emaileffectiveness, we also used data from 1396 users in a control group who did not receive the email.

We merged two rich datasets. First, for each user, the email provider data include multiple communication metrics toinvestigate behavioral reactions to experimental factors. The email data indicate whether a recipient clicked on either ofthe two update links (Click-through Rate) or asked to be taken off of the subscription list (Unsubscription Rate). Second, in-app data provided by the game app company includes daily downstream outcomes such as whether users updated theapp (Update), howmuch time they spent in the app (In-app Time,measured through the sequence of activities in which usersengaged within the app after receiving the email), and how much money they spent in the app (In-app Purchase). The in-appdata were available at the individual level for 127 days before and three weeks after the email was sent.

The dependent variables encompass message outcomes (i.e., Click-through and Unsubscription Rates) and downstream out-comes (i.e., Update, In-App Time, and In-App Purchase). Data for all dependent variables were aggregated over a 3-week timeframe after the email was sent (we test for various time frames in the robustness-check section and find similar results).

To account for potential confounding factors, we measured several control variables: Recency (number of days betweenthe last usage and the date of the email), Tenure (number of days between the first usage and the date of the email), In-appTime Before per Day (in-app time, aggregated for the 127 days before the date of the email and divided by the number of dayson which a user was active), and In-app Purchase Before per Day (money spent, aggregated for the 127 days before the date ofthe email and divided by the number of days on which a user was active). The continuous variables were highly right-skewed, so we log-transformed them. We report the descriptive statistics for the variables in Table 1.

We ran several checks to assess whether the randomization was effective. We observed no differences (all ps > 0.05) withrespect to any of the pre-experimental variables across the experimental groups (we present randomization checks in theWeb Appendix). As expected, we observed similar open rates across the four treatment conditions (all ps > 0.05). Theseresults rule out the possibility that the effects of the experimental factors reflect bias introduced by assignment to theconditions.

4.4. Modeling framework of the field experiment

The analysis investigates the impact of the design elements and the control variables on the message and downstreamoutcomes. Given the data format, we employed multiple techniques to analyze the dependent variables. First, we estimatedthe effects on the continuous variables (in-app time and a second-stage model of in-app purchase) by ordinary least squares(OLS) regression. Second, given that click-through and unsubscriptions were (dichotomous) rare events with a large numberof non-events (i.e., no clicks or unsubscriptions), we used rare event logit regression (ReLogit) (King & Zeng, 2001). ReLogitestimates a standard logistic regression but corrects for logistic coefficient bias owing to the rarity of an event; ReLogit henceavoids underestimating the probability that rare events occur. All models include the two experimental factors and theirinteraction term as well as the control variables.

To model in-app purchase, we took measures to accommodate not only the rarity of purchase events but also the left-censoring of the purchase amount (negative values impossible). We followed prior research and used two-stage modelingto account for both issues (Donkers, Franses, & Verhoef, 2003 for the set-up and estimation). To further account for rarityand maximize the informativeness of the data, we oversampled the events (i.e., decisions to purchase) such that they rep-resent 20% of the new sample, which is the lowest acceptable proportion (Cramer, Franses, & Slagter, 1999), and took a ran-dom sample of the non-events (i.e., no purchase) representing 80% of the sample. To correct for bias that might be introducedby oversampling, we used a Logit with an offset correction as the binary choice model in the first stage. After estimating thefirst-stage model, we computed the Pseudo Inverse Mills Ratio and ran the second-stage censored regression model, includ-ing the Pseudo Inverse Mills Ratio as an additional variable. This censored regression equation includes the same variables(except for recency) and the Pseudo Inverse Mills Ratio. We provide further details elaborating our modeling framework (e.g.,model specifications and equations) in the Web Appendix.

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Table 1Descriptive Statistics for the Treatment Groups (Study 1).

Mean St. Dev. Min Max

Communication OutcomesOpen Rates a 0.13 0.34 0 1Click-through Rates 0.02 0.13 0 1Unsubscription Rates 0.002 0.04 0 1

Downstream outcomesUpdates 0.05 0.23 0 1In-app Time b 1.73 3.44 0 12In-app Purchase b 0.07 0.65 0 9

Control VariablesRecency 47.90 37.56 1 127Tenure 86.51 33.16 2 128In-app Purchase Before Per Day b 0.25 0.90 0 7In-app Time Before Per Day b 5.97 1.48 0 9.21

Notes: a We use open rates only for the randomization check because the subject line did not vary across treatment conditions. b Variable is log-transformed.


4.5. Results

In the first step, we investigate the impact of the four experimental conditions on (i) message outcomes and (ii) down-stream outcomes. Note that the GIF and emoji variables in our analyses are dummy-coded such that they are equal to 1 if thevisual is present and 0 otherwise.

Message outcomes. In the first two columns of Table 2, we report the effects of the design elements (GIF, emoji) on mes-sage outcomes (click-through and unsubscription rates). While the design elements did not affect click-through (Model 1),GIF and emoji exerted main and interaction effects on unsubscription (Model 2). Including emojis and a GIF separately hadfavorable consequences as they reduced unsubscription rates (BGIF = �1.88; Bemoji = �1.37), but including both togetherincreased unsubscriptions (Bboth = 2.40), consistent with RP3a. Although we followed a rigorous statistical approach (i.e.,the Relogit regression) to account for the rarity of unsubscriptions (17 unsubscribers), we acknowledge the limits of theseanalyses and use these results only to complement our other findings. We conducted a robustness check on a larger sampleof users, including those who did not need to update to the latest app version, and the results were consistent with those ofthe main analysis (Web Appendix). The pattern of the interplay between the two design elements is visualized in Fig. 2. Wedid not observe significant effects on click-through rates (Table 2).

Downstream outcomes. Regression results for downstream outcomes (i.e., actual in-app behavior) are reported in columnsthree through six of Table 2. In terms of updating (Model 3), the main and interaction terms are directional only but generallyfollow our expectations in RP3b (i.e., we find a directional positive trend for GIF and emoji, but their combination has a neg-ative coefficient, albeit one that is not significant). Consequently, there is no significant effect of including the visual ele-ments on the stated objective of the email campaign, namely, updating. The results obtained with Model 4 show asignificant interplay between the design elements on in-app time. Including a GIF increased in-app time by 19.7% (= (exp(0.18) � 1) * 100; because of the log transformation, the coefficient’s exponential indicates the change). Adding emoji along-side GIF, though, substantially decreased in-app time by 13.2%, such that combining the elements was not significantly dif-ferent from including neither. The lower panel of Fig. 2 visualizes this interplay between both visuals.

As the variable in-app purchase involves a high number of non-purchases, we used a two-stage approach in which thefirst stage (Model 5-1) captures whether or not users make in-app purchases while the second stage captures the amountof money spent on these purchases (Model 5-2). We find that email design elements did not evoke effects on the (binary)decision to participate in in-app purchases (Model 5-1) but, conditional on this decision, animation (GIF) significantlyincreased the in-app purchase amount. There were no additional main or interaction effects with emoji (Model 5-2). Regard-ing the control variables, the results obtained with Models 5-1 and 5-2 further show that users with higher in-app purchasesbefore receiving the email were also more likely to purchase and spend more money after receiving the email. Also, userswho had played more recently were more likely to purchase in the app. This illustrates the managerial relevance of recencyas a proxy for consumer engagement (Lobschat, Osinga, & Reinartz, 2017). It is particularly important for marketers to under-stand which customers are most likely to be influenced by the visual design elements we deployed. As such, we conducted apost-hoc target-group analysis (reported in the Web Appendix), which shows that the offsetting effect of using multiplevisuals appears to particularly hurt current users (i.e., users who have played most recently). For this segment, we evenobserve an interaction effect on updating, which was the stated goal of the email communication.

Robustness checks.We conducted four separate robustness checks, varying the sample considered, the aggregation rule forthe dependent variables, and the estimation models used. In our main analysis, we focused on users who did not have theupdated version. For our first robustness check, we also checked for the effects on all email receivers, including those whodid (n = 10,701) and those who did not need to update the app (n = 2863). The results remained consistent with this largersample (detailed results in the Web Appendix). Second, we aggregated the dependent variables for varying time frames afterthe email campaign (one, two, and three weeks after the campaign). Again, the results remained stable regardless of the

8

Table 2Effects of Design Elements on Message and Downstream Outcomes (Study 1).

Message Outcomes Downstream Outcomes

Click-through Rate Unsubscription Rate Update In-app time In-app purchase

Model 1(Relogit)

Model 2(Relogit)

Model 3 Model 4 Model 5-1(Decision)

Model 5-2(Amount)

Emoji .24 �1.37+ .19 .10 .52 �.07(p = .30) (p = .08) (p = .13) (p = .19) (p = .28) (p = .79)

GIF .33 �1.88+ .14 .18* .37 .49*(p = .15) (p = .08) (p = .27) (p = .02) (p = .43) (p = .05)

Emoji*GIF �.13 2.4+ �.11 �.25* �.34 �.21(p = .68) (p = .08) (p = .54) (p = .02) (p = .60) (p = .53)

In-app Purchase Before Per Day .15** .24 .02 .11*** .80*** .39***(p = .01) (p = .34) (p = .70) (p = .00) (p = .00) (p = .00)

In-app Time Before Per Day .43*** �.21+ .11** .29*** .18 .02(p = .00) (p = .09) (p = .01) (p = .00) (p = .34) (p = .88)

Tenure .005* �.02 .01*** .02*** �.001 �.002(p = .03) (p = .16) (p = .00) (p = .00) (p = .84) (p = .42)

Recency �.02*** .01 �.03*** �.06*** �.05***(p = .00) (p = .15) (p = .00) (p = .00) (p = .00)

pseudo Inverse Mills Ratio .14(p = .62)

Constant �6.81*** �3.77*** �3.37*** .77*** �3.13* 4.61***(p = .00) (p = .00) (p = .00) (p = .00) (p = .02) (p = .00)

Observations 10,701 10,701 10,701 10,701 630 126R2 .38 .37Adjusted R2 .37 .33AIC 1696.59 251.23 3994.27 51818.06 221.72

Notes: GIF and Emoji are dummy-coded: 1 if the visual is present, and 0 otherwise, +p < .1; *p < .05; **p < .01; ***p < .001.


aggregation levels. Third, although investigating overall email success was not our objective, our experiment makes it pos-sible to compare emailed users (in the four treatment groups) with non-emailed users (the control group). In response to theemail, the likelihood of updating the app three weeks after emailing was 1.33-times (=exp(0.29)) greater for emailed than fornon-emailed users. The email thus served its stated purpose of inducing users to update to the latest version. For a finalrobustness check, we modeled click-through and unsubscription rates with regular logistic regressions and found results(e.g., unsubscription: BGIF = �2.33, p = .03, Bemoji = �1.57, p = .04; Bboth = 2.97, p = .03) that are consistent with using rareevent logistic regressions as in our main specification (Web Appendix).

4.6. Discussion

The field experiment yields evidence for our premise that the use of multiple types of visual design elements together canreduce the effectiveness of digital communication, offsetting their positive effects when used separately. The results are con-sistent for unsubscription rates but not for click-through rates. Taking advantage of the rich in-app data the company madeavailable, the field experiment even shows that the effects of design elements extend beyond message outcomes and arecapable of spilling over to the actual usage of the product. Importantly, we find support for the interplay between animationand pictographs on in-app time. Combining distinct types of design elements mitigates the improvements in in-app timethat can be obtained when these elements are used separately. For updating, we find an effect of receiving the email (con-trasting emailed users to non-emailed users), but the effect of the interplay between animation and pictographs did notreach significance (except for the subgroup of current users). This might be due to the fact that users can update the gamedirectly on their mobile devices without opening the email (which still serves as a reminder and therefore evokes an effect).The in-app metrics provide ample evidence that GIFs can have positive effects, even for money spent within the app. Like-wise, the field experiment attested to the distinct nature of the impact that is induced through pictographs, with the maineffect of emoji not reaching statistical significance. Having demonstrated the relevance of the interplay between the distinctdesign elements in a field experiment with high ecological validity, we next shed light on the underlying mechanismsthrough a series of lab and online experiments.

5. Study 2A: Effects of visual elements on eye gaze

We ran a series of studies to gain a deeper understanding of the mechanisms governing the behaviors observed in thefield experiment. In Study 2A, we employed eye-tracking to delve more deeply into the processing of animations and pic-

9

Fig. 2. Effects of Design Elements on Message and Downstream Outcomes (Study 1).


tographs. The study adopts the 2 (GIF absent vs. present) � 2 (emoji absent vs. present) design and the stimuli used in thefield experiment.

5.1. Procedure

In total, 148 business students were recruited for the experiment that took place in the behavioral lab of a major Frenchbusiness school. The participants were asked to imagine that they had provided their email address to a game company andhad just opened an email they had received from this company. To make the study stimuli more realistic, the email was pre-sented in a widely used email system (Gmail) and participants saw both the subject line and text in standard email format.The email invited users to download an update of the game to the latest version (mirroring the field experiment setting). Theparticipants’ attention focus was tracked using a portable wireless eye-tracking device (Tobii�), recording their eye move-ments in real-time. The video recorded for each participant was analyzed frame by frame to code the eye-tracking metrics.On average, participants focused on the email for 34.68 seconds (SD = 4.95) and with 65.99 fixations (SD = 20.83). As a mea-sure of focused attention, we calculated the average fixation duration (M = 597.26 ms, SD = 269.72).

5.2. Results and discussion

We examine the impact of the experimental factors on focused attention on the email (regression analysis with robuststandard errors). We find positive main effects of GIF (B = 152.64 ms, SE = 54.33, t = 2.810, p = .006) and emoji (B = 139.14 ms, SE = 64.92, t = 2.134, p = .034). These results imply that both types of visuals increased the average duration of fixations

10


when being presented separately. When the two types of visuals occurred together, however, we find a strong negative effect(B = �223.74 ms, SE = 86.76, t = �2.579, p = .011), as visualized in Fig. 3. These results reveal a pattern that is similar to whatwe observed in the field experiment. Evidently, animation and pictographs affect the processing of the email, as reflected inlonger average fixations among the participants. Yet, both of these effects are offset once the two design elements are com-bined, in support of RP3a. We ran a robustness check in which we analyzed the average duration of fixations on the emailbody without the visual elements (i.e., the email except for the pictorial element and the emojis). This analysis produced verysimilar results (interaction effect: B = �250.44 ms, SE = 92.89, t = �2.696, p = .008); we report detailed results for other areasof interest in the Web Appendix.

6. Study 2B: Differential perceptions of animations and pictographs

6.1. Objective and procedure

Study 2B aims to corroborate our proposition that animations and pictographs affect the processing of a digital messagebecause both enrich the message in unique ways but also provoke clutter perceptions. We used, again, the 2 � 2 design andthe stimuli from the field experiment. Two hundred and nineteen students (Mage = 20.9 years, SD = 1.62, 53% women) at amajor French business school participated in a controlled laboratory experiment in exchange for course credit. After readingthe email, they completed a brief questionnaire measuring enrichment perceptions (‘‘high variety”, ‘‘interactive”, M = 3.24,SD = 1.38, a = 0.68) and clutter perceptions (‘‘crowded”, ‘‘overwhelming”, M = 4.11, SD = 1.50, a = 0.68). These two types ofperceptions were only weakly correlated (r = 0.35, p < .001). We also measured overall complexity perceptions (‘‘The email iscomplex”; M = 3.37, SD = 1.79). As a proxy for message processing, we measured focused attention, which is known toprecede judgments about a company (Mai et al., 2014), with a four-item scale adapted from (Novak, Donna, & Yung,2000), (e.g., ‘‘I concentrated fully”; M = 3.57, SD = 1.52, a = 0.87).


First, we explore whether animations and pictographs shape certain complexity-related perceptions in unique ways.ANOVAs were conducted with clutter and enrichment perceptions as the dependent variables and the design elements asindependent variables. The results confirm that animations and pictographs both enrich a message; enrichment perceptionswere driven by the presence of both GIF (F(1, 215) = 4.554, p < .05) and emoji (F(1, 215) = 15.232, p < .001; interaction:p > .05). Clutter perceptions, by contrast, were triggered predominantly by emoji (F(1, 215) = 14.124, p < .001) and much lessby animation (GIF or interaction: ps > 0.05). Study 2B consequently supports our reasoning that animations and pictographsenrich a digital message, but for unique reasons. While both amplify enrichment perceptions in support of RP1, it is primarilypictographs dispersed in the text that increase clutter perceptions, supporting RP2. This is a notable observation becausethese perceptions are known to generate countervailing implications (e.g., Mai et al., 2014), which we explore in Study 3A.

For overall complexity, the analysis shows incremental main effects of GIF (F(1, 215) = 4.080, p < .05) and emoji (F(1,215) = 6.532, p < .05), but no interaction effect (F(1, 215) = 1.251, p > .05). Consequently, animation increases perceived com-plexity in distinct ways and thus in addition to the impact of pictographs. This boost in visual complexity is a consistent find-ing across our studies. This overall perception, also very consistent, is governed by both clutter (b = 0.48, t = 7.945, p < .001)and enrichment perceptions (b = 0.18, t = 2.978, p < .01).

With regards to focused attention, we find results that are similar to those obtained for eye-tracking (Study 2A), implyingthe occurrence of a detrimental effect of including animation and pictographs together, in support of RP3a. The cross-overinterplay between GIF and emoji (F(1, 215) = 3.512, p = .06) is visualized in Fig. 3. In the final stage of our research, weexplore whether the capacity of animation and pictographs to enrich and clutter a message varies with the number (Study3A) or type (Study 3B) of visuals included.2

7. Study 3A: Varying the number of animations and pictographs

In Study 3A, we test whether the effects of animations and pictographs depend on the number of included visuals. Wealso explore the consequences of the evoked perceptual processes more deeply. To ensure generalizability, we used a differ-ent context (retailing) and email message than in the previous studies.

2 To test for several alternative explanations, we also assessed annoyance with a four-item scale adapted from Fennis and Bakker (2001), e.g., ‘‘the email isannoying” (M = 4.72, SD = 1.35, a = 0.80), and intention to report as spam through a single item (M = 4.52, SD = 2.10). The varying stimulus conditions did notdiffer in negative (annoyance, spam) sentiments; we observed neither main effects (ps � 0.395) nor interactive effects (ps � 0.395). We replicated thisexperiment, conducted in a controlled lab environment, with a larger sample of 480 U.S. participants recruited through Prolific Academics. The results wereconsistent with the main findings of Study 2B, with both GIF (p = .017) and emoji (p < .001) increasing perceived enrichment, but especially emoji raising clutterperceptions (p < .001; GIF: p = .153; interactions ps � 0.392). Importantly, combining GIF and emoji evokes the negative interaction effect on focused attention(p = .012). No such interaction, again, was observed for spam (p = .283) or annoyance (p = .615).

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Pictographs: present (emoji)absent (no emoji)

Animation

STUDY 2AFocused attention

(average fixation duration in ms)

500

600

700

absent (static) present (GIF)

Animation

STUDY 2BFocused attention

3.0

3.5

4.0


Animation

STUDY 2BEnrichment perceptions

2.5

3.0

3.5

4.0


Animation

STUDY 2BClutter perceptions

3.5

4.0

4.5

5.0


Animation

STUDY 2BVisual complexity

2.5

3.0

3.5

4.0


Fig. 3. Perceptual Effects of Design Elements (Studies 2A and 2B).


7.1. Procedure

We conducted a 3 (GIF: absent, moderate vs. high) � 3 (emoji: absent, moderate vs. high) experiment. Eight-hundred andtwenty-four participants (Mage = 34.5 years, SD = 12.7, 50% women) recruited through the Prolific Academic platform pro-vided usable data.3 For pictographs, we included a moderate (6) and rather high (15) number of emojis. For animation, wemanipulated a moderate (1) and higher (2) number of GIFs in the message. Participants were asked to imagine receiving anemail from an online retailer fromwhom they regularly make purchases. The online retailer email invited participants to benefitfrom a sales promotion.

After exposure to the email, participants completed a questionnaire measuring their enrichment perceptions (‘‘notinteractive—interactive”, ‘‘visually poor—visually rich”, ‘‘without visual variety—with lots of visual variety”, M = 4.36,SD = 1.28, a = 0.67) and clutter perceptions (‘‘visually clutter-free—visually cluttered”, ‘‘not visually overwhelming—visuallyoverwhelming”, ‘‘with too few visual elements—with too many visual elements”, M = 4.37, SD = 1.59, a = 0.88).4 Regardingmessage outcomes, we assessed email attractiveness through a five-item scale adapted from Pieters et al. (2010) (e.g., ‘‘Theemail is attractive”, M = 4.65, SD = 1.45, a = 0.92) and focused attention as in Study 2B (M = 5.09, SD = 1.50, a = 0.91). Weassessed specific reactions to the email with single items, assessing how likely a user would be to ‘‘click on the links”(M = 4.33, SD = 1.92) or ‘‘unsubscribe from the email” (M = 3.10, SD = 1.99). We further assessed trust in the company usinga three-item scale adapted from Sirdeshmukh, Singh, and Sabol (2002) (e.g., ‘‘I feel the company is competent”, M = 4.99,SD = 1.36, a = 0.94).

3 In the online studies (Studies 3A and 3B), we eliminated data from respondents who took longer than the mean + 3 SD to complete the study, who did nottake the study on the specified device (those who used tablets or mobile phones), and those who failed an attention check.

4 Note that we improved the measures for enrichment and clutter perceptions compared with those used for Study 2B.

12



Perceptions triggered by the number of visuals. In the first step of this analysis, we examine the impact of the two designelements on the key perceptions of enrichment and clutter. Regarding perceived enrichment, the analysis reveals, again, amain effect of GIF (F(2, 815) = 20.991, p < .001) and a weaker effect of emoji (F(2, 815) = 7.875, p < .001), supporting RP1. Thedesign elements did not interact (F(4, 815) = 0.677, p = .61). Planned contrasts confirm that raising the number of animationshits a ceiling in terms of a user’s capacity to enrich a digital message, as visualized in Fig. 4. While enrichment perceptionsimprove when including one GIF, regardless of the number of emojis present (ps � 0.01), adding another GIF was not per-ceived as enriching the message further (ps � 0.45).

In terms of clutter perceptions, the results showed a main effect of GIF (F(2, 815) = 15.199, p < .001) and a much strongereffect of emoji (F(2, 815) = 70.992, p < .001). Additionally, the two types of visuals interacted (F(4, 815) = 3.454, p < .01). Whileclutter perceptions increased with elevating levels of animation when no or few emojis are present, these perceptions wereparticularly strong when the message contained a greater number of emojis. Mirroring the findings of Study 2B, emoji wasthe predominant driver of clutter perceptions, lending further support to RP2.

Consequences of enrichment and clutter perceptions. We examined how the distinct perceptions mentioned above translateinto message and downstream outcomes. Regression analysis confirms again that these perceptions trigger countervailingconsequences. While, for example, enrichment perceptions generally improved email attractiveness (b = 0.50, t = 19.88,p < .001), clutter perceptions had a detrimental impact on this variable (b = �0.54, t = �21.56, p < .001). We find consistentevidence for these countervailing effects across the various outcomes.

Based on our theoretical reasoning, we further argue that the countervailing implications of enrichment and clutter per-ceptions are more or less influential, depending on whether the design elements are combined or not. We thereforeregressed the message outcomes (e.g., email attractiveness) and downstream outcomes (e.g., company trust) on both com-plexity perceptions as independent variables and the presentation mode as a moderator (with the levels together vs. not).5

The weights of the enrichment and clutter effects under both modes are presented in Table 3. Especially for moderate numbersof visuals, the impact of clutter perceptions appeared to vary depending on whether the visuals occurred together. Email attrac-tiveness, for example, was damaged to a greater extent by clutter perceptions when both types of design element were pre-sented together (B = �0.55, t = 7.66, p < .001) compared with when they were not (B = �0.32, t = 6.83, p < .001) and thisdifference was significant (p < .01). We found similar differences in the magnitude of the clutter effect for unsubscriptionand clicking intentions. Apart from this, the positive enrichment effect appeared to weaken when the two visual types werecombined, although this moderation did not reach significance in several cases. For example, the improvement in focused atten-tion and the reduction in unsubscription intentions associated with the enrichment effect were offset (i.e., turned nonsignifi-cant) when the visuals were combined. The full regression results are presented in the Web Appendix.

Conditional indirect effects. Thus far, we have demonstrated that animations and pictographs can elicit clutter and enrich-ment perceptions. We have also shown that these perceptions, in turn, have countervailing consequences for the messageand downstream outcomes but that the negative consequences are more pronounced when both visuals occur at the sametime. In this step of the analysis, we assessed these differential mechanisms (Fig. 1). We estimated parallel mediation of thevisuals through perceived enrichment and clutter under the distinct presentation modes (i.e., together vs. not), as proposedin RP3c and RP3d. The results reported in Table 4 show the conditional indirect effects of the visuals on the outcomes. To illus-trate how the combination of very different design elements shifts the balance between the positive enrichment process andthe negative clutter process, we also report their net effect.

Especially for moderate numbers of the design elements, the impact through clutter perceptions varied depending onwhether the visuals were presented separately or together. With regards to email attractiveness, for example, we observedthat the combined use of the design elements (moderate numbers) reinforced the detrimental indirect effects operatingthrough clutter perceptions for GIF (�0.23 vs. �0.13) and emoji (�0.36 vs. �0.21). Also, for clicking intentions and unsub-scription intentions, we observed a significant increase in this negative mechanism. In addition, the indirect enrichmenteffect was significant for all outcome variables when animations or pictographs were used separately but turned nonsignif-icant for several outcomes when these visual elements were combined (or significantly reduced, as was the case for focusedattention). For unsubscription intention, the message-enriching effects of both GIF (�0.14) and emoji (�0.09) were switchedoff when both visuals occurred together (GIF: �0.07, emoji: �0.04, p > .10). Further, the increased clutter perceptions follow-ing a combined use of GIF (0.27 vs. 0.16) and emoji (0.42 vs. 0.26) increased unsubscription intentions. For high numbers ofthe design elements, these effects materialized primarily in reduced company trust. These differential mechanisms henceenhance our understanding of the detrimental impact of the combined use of animations and pictographs that was observedin the field experiment (esp. for in-app time and unsubscription). Finally, we assessed alternative mechanisms that mightexplain the results of the field experiment (i.e., annoyance, perceived spam, perceptual fluency; Web Appendix). The resultsconfirm that moderate numbers of GIF and emoji (which are more likely in professional digital marketing) operate primarilythrough the perceptions of clutter and enrichment.

5 In the Web Appendix, we also report this moderation on three levels: (i) no design elements, (ii) GIF or emoji presented separately, and (iii) both designelements presented together. The conclusions remain consistent.

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Pictographs: moderate (6)absent (no emoji)

Enrichment perceptions

2.5

3.5

4.5

5.5

absent moderate high

Animation

absent moderate high

high (15)

Clutter perceptions

Fig. 4. Effects of Design Elements on Enrichment and Clutter Perceptions (Study 3A).

Table 3Effects of Enrichment and Clutter Perceptions on Downstream Outcomes (Study 3A).

Emailattractiveness

Focusedattention

Clickingintention

Unsubscriptionintention

Companytrust

Moderate number of design elementsClutter effect when used. . .separately �0.32*** �0.20** �0.26** 0.40*** �0.20***together �0.55*** �0.28** �0.58*** 0.65*** �0.28***

Enrichment effect when used...separately 0.58*** 0.42*** 0.37*** �0.27** 0.31***together 0.50*** 0.22 0.29+ �0.12 0.23*

High number of design elementsClutter effect when used...separately �0.50*** �0.16** �0.48*** 0.60*** �0.38***together �0.54*** �0.36*** �0.43*** 0.57*** �0.46***

Enrichment effect when used...separately 0.66*** 0.45*** 0.52*** �0.47*** 0.42***together 0.52*** 0.25* 0.44** �0.34* 0.25**

Notes: Significance of coefficients: +p < .1; *p < .05; **p < .01; ***p < .001, coefficients in bold indicate different weights between presentation modes (p < .1).

Table 4Conditional Indirect Effects of Design Elements on Downstream Outcomes Operating through Enrichment and Clutter Perceptions (Study 3A).

Dependent variable

Email attractiveness

Focused attention Click Unsubscription

intentionCompany

trustNET NET NET NET NET

Moderate number of design elements-.13 .20 .23 .15 -.11 .11 .16 .02 .09-.23 .06 .12 .01 -.23 -.07 .27 .20 .01

-.21 -.01 .01 -.17 -.04 .17 -.02-.36 -.18 -.11 -.37 -.27 .38 -.10

High number of design elements.23 .10 -.04 .11 .05 .00 -.08 .07.18 .02 -.10 -.02 .03 .05 -.13 -.04

-.23 -.12 -.04 -.23 .35 -.22 .08 -.14-.33 -.28 -.24 -.26 .39 -.37 .05 -.32

Notes: Confidence intervals bootstrapped with PROCESS (5,000 sample draws, Hayes 2018). CLU: clutter perceptions; EN: enrichment perceptions; NET: neteffect operating through clutter and enrichment perceptions. Coefficients in grey are not significant (90% cofidence interval include 0). Coefficients in bold:conditional indirect effects differ between the presentation modes (i.e., index of moderated mediation significant).


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8. Study 3B: Varying types of pictographs

8.1. Objective and procedure

This last study tests for the generalizability of our theory for varying types of pictographs. Thus far, the messages includedpictographs of various types, but there is evidence that these types can generate differential effects (McShane et al., 2021). Totest for this potential confound, we specifically manipulated two kinds of pictographs, namely emojis that repeat the verbalcode (i.e., preceding words) in a message and emojis that add a new input to the meaning of the preceding word (e.g.,emotions). The study adopts a 2 (GIF absent vs. present) � 3 (emoji absent vs. repeat vs. add) design. To enable the exper-imental manipulation of distinct emoji types, we slightly modified the email body from Study 3A. Both emoji conditions hadthe same moderate number of emojis (i.e., 6 emojis), which were interspersed in the same positions (that is, after the samewords) in the text. In the repeat condition, the emojis used directly repeated the words that preceded them (for instance,(globe showing Americas emoji) followed ‘‘entire world”, (wrapped gift emoji) followed ‘‘special offers”, and (emailemoji) followed ‘‘reminder email”), whereas in the add condition, the emojis used conveyed emotions evoked by the wordsthat preceded them (for instance, (blue heart emoji) followed ‘‘entire world”, (face with hand over mouth emoji)followed ‘‘special offers”, and (nerd face emoji) followed ‘‘reminder email”). We were interested in testing whether theinterplay between the distinct visuals found in the previous studies would still occur for different types of pictographs.Five-hundred-and-forty-four U.S. American consumers from the Prolific platform provided usable data (M = 35.09 years,SD = 13.55; 45.6% females). We used the same measures of perceptions and communication effectiveness as in Study 3Aand included further downstream outcomes related to word-of-mouth communication (forwarding) and purchase.


For enrichment perceptions, and consistent with RP1, we found positive main effects for both emoji types (Brepeat = 0.565,p = .001; Badd = 0.391, p = .020) as well as for GIF (B = 0.497, p = .003). We also found significant main effects of both emojitypes on clutter perceptions (Brepeat = 0.618, p = .005; Badd = 0.876, p < .001) and a weaker main effect of GIF (B = 0.417,p = .036). These results thus provide evidence for RP1 for both repetitive and additive emojis, and the latter type in particularincreases clutter perceptions, lending support to RP2.

Furthermore, we again observed interaction effects for several measures of communication effectiveness for the addi-tive emojis but these did not reach significance for the repetitive emojis. For example, the additive emojis reduced focusedattention when being paired with GIF (Badd = �0.826, p = .009; Brepeat = �0.461, p = .135) and reduced intentions to for-ward (Badd = �0.797, p = .017; Brepeat = �0.390, p = .242) or to purchase (Badd = �0.635, p = .058; Brepeat = �0.269, p = .423),therefore providing support for RP3a and RP3b for additive emojis. For email attractiveness, trust perceptions, intentionsto click, and unsubscription, the interactions were directional and consistent with previous results but did not reachsignificance.

We also tested whether these results were affected by language barriers. In a post-hoc test, we therefore limited the sam-ple to native English speakers (N = 498). In addition to the interactions reported earlier, the negative interaction effect turnsmarginally significant for trust in the repeat condition (Brepeat = �0.447, p = .071; Badd = �0.272, p = .297) in support of RP3b.Finally, the field experiment as well as Studies 2A and 2B sampled rather young consumers. We therefore conducted post-hoc analyses for a younger subsample of respondents; our reasoning here was to focus on a sample that is more closelyaligned with our field experiment in terms of age and that younger consumers may respond less negatively to animationsor pictographs as they are, arguably, more accustomed to using or encountering these visuals. We therefore zoomed in onyounger subjects; 40 years of age or lower (N = 383). In this sample, the interaction effect on focused attention occurred forboth types of emojis (Brepeat = �0.839, p = .027; Badd = �1.133, p = .003) as well as for the concrete downstream outcomesrelated to clicking (Brepeat = �0.924, p = .047; Badd = �0.716, p = .104) and purchase (Brepeat = �0.688, p = .099; Badd = �0.827,p = .038; forwarding: ps > 0.05).

This final study hence confirmed that the complexity-related perceptions of clutter and enrichment are triggered fordifferent types of visuals (here, different kinds of pictographs). We found that emojis that repeat a word and those thatadd new pieces of information can both enrich a digital message but also elicit clutter perceptions. We again observeda detrimental interaction effect when animations and pictographs occur together for some of the dependent variablesand especially among younger consumers who might be more familiar with such visual elements in digital communica-tion. We therefore conclude that the offsetting effect that we observed in the field experiment is robust to differences inpictograph type.

9. General discussion

9.1. Combining visual design elements

This research has focused on the current trend towards employing visual design elements in digital communication, espe-cially animations and pictographs. Marketers embrace animations such as GIFs because of their relatively small file size and

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the ease with which they can be included in marketing communications (Stacey, 2017). Marketers also increasingly use pic-tographs, such as emojis.

Our research proposes and empirically tests a theory of countervailing effects that can culminate in an offsetting effect,which we show affects not only message outcomes (i.e., processing of a message) but also spillovers to the actual usage of amarketed product. Design elements such as animations and pictographs are believed to improve communication success—anassumption that has been partly confirmed by our field experiment. Indeed, our results demonstrate a favorable effect ofapplying either animations or pictographs separately on certain message outcomes (unsubscription) and consequent usagebehavior, especially for GIFs (e.g., in-app time and purchase). Yet, as suggested by our framework, the combination of mul-tiple design elements of a conceptually differing nature does not produce additive benefits, contradicting a more-is-betterphilosophy.

We have theorized, then, that animations and pictographs elicit unique perceptual processes and consequently requiredistinct mental capacities to be processed. Our series of studies show consistently that animation, such as GIFs, can be ben-eficial to digital communication because consumers perceive enrichment of email messages, which positively impacts down-stream outcomes. Pictographs (emojis) can have similar benefits for different reasons; but, in addition to enrichment, theypredominantly amplify clutter perceptions as they are irregularly scattered across the text. More importantly, when digitalcommunication includes animations and pictographs together, both perceptual processes are activated, so their combineduse shifts the extent to which these mechanisms govern consumer judgment-formation and behavior. While the use of bothtypes of visual design elements dampens the positive enriching mechanism, the negative clutter mechanism is reinforced.For this reason, animations and pictographs interact predominantly in a negative manner.

We establish this offsetting effect and its underlying mechanisms in a series of field, lab, and online experiments inwhich we apply several methodological approaches. Apart from behavioral data in the field and from self-report data, wefind evidence for our premise by studying eye gaze (Study 2A). We also excluded alternative explanations (Study 3A); formoderate numbers of design elements, we did not observe significant indirect effects of alternative mediators on any ofthe dependent variables. In Study 3A, we also tested higher numbers of design elements from the same type. Whileenrichment perceptions appear to hit a ceiling with larger numbers of the same design elements, the harmful clutter per-ceptions increase in a linear fashion. For these reasons, a positive enrichment effect can be expected to occur primarily inthe presence of moderate numbers of visual design elements, with an offsetting effect when used together. For highernumbers, however, consumer judgments are already dominated by the negative clutter effect whether the elementsare presented separately or together. Similarly, in Study 3B, we show that interaction between the visual elements occursprimarily with pictographs that add meaning to a text and which noticeably increase clutter perceptions. This interplaywas directional but less strong for pictographs that repeat the text, likely because these are cognitively less taxing. Notethat the results obtained in the other four studies (using a mix of repetitive and additive emojis) were consistent withthose obtained for the additive emojis, suggesting the possibility that these dominate when used together with repetitiveemojis.

Based on this set of results, we advise marketers to use design elements in their digital communication that elicit pre-dominantly enrichment perceptions (e.g., GIFs) and to cautiously manage those design elements that provoke clutter percep-tions (e.g., emojis). Managers are well-advised to avoid excessive numbers of these elements and, most importantly, torefrain from combining multiple design elements of a very different nature.

9.2. Spillover effects on downstream outcomes

In digital marketing environments, most design studies have focused either on the user–message response or on the user–product response (see Feld et al., 2013 for a review). One strength of our field experiment with an app provider was that wewere able to test the real-world effects of the design elements on actual customer responses to the message (i.e., messageoutcomes) as well as responses to the marketed product (i.e., downstream outcomes). Our results emphasize the importanceof complementing message outcomes with downstream outcomes and drawing conclusions based on both sets of criteria.Indeed, our field experiment shows that email marketing can have consequences for customer behavior without necessarilymaking a detectable imprint on all message outcomes (e.g., we did not detect a significant effect on click-through). Evidently,not all users who updated the app did so by clicking on the email link. This can plausibly be explained by users reading theemail through a different medium. For example, some may have read the email on their PCs but play the game on theirmobile phones. The downstream effects obtained in the field experiment are particularly noteworthy because the focal emaildid not explicitly encourage game users to play or spend money but asked them to update the app to access new features.The test was therefore particularly conservative, and one may expect even stronger effects for more traditional communi-cation focused on promoting user activity or purchases.

From a managerial viewpoint, it is important to account for the research context of our field experiment when consider-ing the observed effects. The freemium app context has average in-app purchase rates of less than 5% (Günzel-Jensen &Holm, 2015) and average 3-week retention rates of less than 10% (Leanplum, 2018). Compared with these numbers, theobserved effect sizes are not only significant but also substantial. These results imply that managers should carefully con-sider and pretest the message designs they contemplate using.

By studying both message and downstream outcomes, we also add to the digital marketing literature by showing that theeffects of visual design elements can translate into in-app behavior. When evaluating email campaigns, managers may find it

16


difficult to detect signals given the rarity of certain message outcomes (e.g., click-through rates, unsubscription). Our find-ings suggest that an email can guide actual customer behavior even without interaction with the embedded links. We con-sequently propose that managers should not rely solely on message outcomes to evaluate email campaigns but shouldinvestigate downstream outcomes as well.

9.3. Limitations and avenues for further research

This investigation is subject to limitations that pave the way for future research. Our field experiment contrasts two levelsfor each of the design elements. While we investigate varying numbers of animations and pictographs in Study 3A, estab-lishing the optimal number of design elements was not a goal of this project. Future research could investigate the optimalnumber of animations and pictographs, for which the positive enrichment effects dominate consumer perceptions while thecluttering effects do not offset or override these enrichment benefits.

Our research propositions address the effectiveness of including animations and/or pictographs in digital communica-tions. Across our studies, we found systematically that including animations and pictographs together disrupts digital com-munication. The detrimental effect of including animations and pictographs together did not always, however, reachsignificance on the same message and downstream outcomes across our studies. Mai et al. (2014) have shown that the coun-tervailing effects due to clutter and enrichment perceptions tend to differ between upstream message outcomes and moredownstream behavioral outcomes. Importantly, however, the general patterns were consistent across studies, and we neverobserved a beneficial impact when including these visual elements together. We are therefore confident with our generalrecommendation that marketing managers should not combine these visual elements in digital communication. Neverthe-less, future research could investigate the effects of combining visual elements on specific outcomes and attempt to explainthe differences across outcomes.

We tested our theory in the context of email marketing, which is an important communication tool, especially in themobile app industry where emails make it possible to reach users who do not receive push notifications, or in online retail-ing, where it enables consumers to be brought back to the website. In the field experiment, we found that email receiverswere more likely to update to the newest version than were members of the control group, indicating that emails are indeeda useful tool in this context. We should note, however, that our theoretical framework is by no means limited to email mar-keting. The offsetting effects and the spillover to downstream behaviors may occur in other digital contexts in which man-agers combine various types of message-enriching design elements, thereby amplifying the clutter effects. Follow-upresearch, for example, may also explore whether our conceptual framework can be applied to cross-selling email marketing,text messages, or push notifications as well as to other types of digital business communication. Furthermore, while our the-ory was tested for GIFs and emojis as archetypes of animations and pictographs, it may also extend to other (current andfuture) design elements used in digital communication (see Luangrath et al., 2017 for a discussion of novel design elements).

We chose to focus on animations and pictographs because these are two widely used visual design elements in digitalmarketing, therefore ensuring managerial relevance. The animations used in our studies were proprietary GIFs related tothe service or the promoted event. In day-to-day communication, it is possible to choose from a variety of existing GIFsto illustrate a given topic with a small animated film. We replicated our results with a variety of animations across our stud-ies. Although their positions in the messages varied (on top in Studies 1, 2A, and 2B, within the text in Studies 3A and 3B), wedid not study these effects or those of their size, color, duration, or content systematically. Given that companies rarely usetwo GIFs in the same message, future research could manipulate GIF level through the duration of the animation rather thanthe number of GIFs. Overall, it is possible that certain characteristics of animations hurt the observed enrichment effects,which opens future research avenues.

On the other hand, pictographs in online communication are quite standardized and firms cannot normally create theirown proprietary pictographs. Still, managers have choices regarding the types of pictographs they select and their placementwithin the text as well as within individual sentences. In our studies, the emojis either mirrored the text message or addedsome meaning to it. Varying results may, however, be obtained if emojis replace text instead of merely reinforcing or com-plementing it (McShane et al., 2021). We expect the effects of such replacement pictographs to be even more disruptive (im-plying even stronger clutter perceptions) because the textual message is then no longer understandable without the emojis.

Finally, animations and pictographs may be differentially powerful, depending on their salience in an email. Many emailclients provide a preview of the first line(s) of an email in the user’s mailbox. Future research could elaborate on whether theinclusion of visual design elements in the first line(s) helps raise open rates. Additionally, our studies manipulated visualdesign elements in the email body. The subject line is, however, a critical anchor in which pictographs can also be used.Our field study included one emoji in the subject line (across all conditions) to increase open rates. Future studies could testwhether this inclusion amplifies or reduces the offsetting effects of combining multiple design elements.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could haveappeared to influence the work reported in this paper.

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Acknowledgments

We thank the anonymous company for the opportunity to run the field experiment and for insightful comments. We aregrateful to Jerry Grimes for help with data collection for the eye-tracking study and acknowledge helpful comments fromIvan Guitart and the marketing department at Grenoble École de Management. Finally, we thank the review team for sug-gestions on previous versions of this article. This research did not receive any specific grant from funding agencies in thepublic, commercial, or not-for-profit sectors. This paper is part of the first author’s dissertation.

Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijresmar.2021.06.008.

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