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Marketing by Design: The Influence of Perceptual Structure on Brand Performance
Journal: Journal of Marketing Manuscript ID JM.21.0068.R3 Manuscript Type: Revised Submission
Research Topics: Aesthetics/Sensory, Consumer Cognitive Psychology, Consumer Goals, Consumer Social Psychology , Product Design
Methods: Field Experiments, Lab Experiments, Mediation Models, Survey Research
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FELIPE M. AFFONSO CHRIS JANISZEWSKI
Felipe M. Affonso ([email protected]) is Assistant Professor of
Marketing, Spears School of Business, Oklahoma State University, Stillwater, OK 74078. Chris Janiszewski ([email protected]) is the Russell Berrie Eminent Scholar Chair and Professor of Marketing, Warrington College of Business Administration, University of Florida, Gainesville, FL 32601. Supplementary materials are included in the Web Appendix accompanying the online version of this article.
The authors thank the Editor and the JM review team for their thoughtful comments and guidance throughout the review process. They are also grateful to Juliano Laran and Aner Sela for their helpful comments. This article is based on the first author’s doctoral dissertation.
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Abstract
Visual marketing communications consist of two components: (1) semantic content (e.g., headings, images, copy) that communicates a brand’s positioning, benefits, and personality and (2) visual design (e.g., font selection, image size, the organization of the content) that encourages inferences about brand claims. We investigate how visual design can be used to encourage inferences that support brand claims and improve brand performance. We find that brands with a utilitarian positioning perform better when the visual design of their marketing communications encourages structured perceptions, whereas brands with a hedonic positioning perform better when the visual design of their marketing communications encourages unstructured perceptions.
In both cases, (un)structured perceptions encourage inferences that reinforce brand claims and, consequently, improve brand performance. This research offers actionable insights into how marketing communication specialists can coordinate logo design, product design, package design, visual merchandising, and retail environments to reinforce brand claims.
Keywords: visual design, marketing communications, logos, package design, branding, retail architecture
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Visual marketing communication is a critical part of any integrated marketing
communication strategy (Andrews and Shimp 2018). Visual marketing communication concerns the use of logos, packaging, advertising, websites, social media broadcasts, point-of-purchase displays, visual merchandising, and retail architecture to make and reinforce brand claims (Andrews and Shimp 2018). Visual marketing communications allow marketers to inform consumers about brand positioning, brand benefits, brand personality, consumption practices, and product usage contexts.
Visual marketing communications consist of two components: (1) semantic content (e.g., headings, images, and copy on a website) that communicates a brand’s positioning, benefits, and personality and (2) visual design (e.g., font selection, image size, the layout and organization of the content) that encourages inferences about brand claims. Although there has been a
considerable amount of research on how the semantic content of a marketing communication influences brand meaning, persuasion, and product choice, there is much less research on how visual design influences similar outcomes. The research that does exist investigates the influence of specific visual design manipulations (e.g., logo font, merchandise display arrangement, visual borders) on cognitions and/or behaviors (Bajaj and Bond 2018; Chae, Lu, and Zhu 2013;
Cutright 2012; Kahn and Wansink 2004; Luffarelli, Stamatogiannakis, and Yang 2019; Sevilla and Townsend 2016).
This research investigates a perceptual construct that is sensitive to visual design:
perceptual structure. A structured perception is cohesive (i.e., the elements are perceived as interrelated), homogeneous (i.e., the common features of elements are fundamental to the perception), predictable (i.e., the repetition of elements makes it possible to use one part of the
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to vacillation between competing perceptions), and systematic (i.e., the elements of the
perception can be effectively segregated or categorized should there be a motivation to do so).
For instance, Intel uses a visual design strategy that encourages structured perceptions (see https://tinyurl.com/3k7z45tj). Intel’s logo was purposefully “crafted with an underlying
geometry”, and “has a refined symmetry, balance, and proportion” (Intel 2022). Similar design objectives guide Intel’s advertising, website design, billboards, and social media sites.
An unstructured perception is incohesive (i.e., the elements are perceived as unrelated or contradictory), heterogeneous (i.e., the elements lack commonality in their features),
unpredictable (i.e., the lack of repeated elements makes it difficult to use one part of the image to anticipate other parts of the whole), unstable (i.e., there is vacillation between competing
perceptions), and unsystematic (i.e., the elements of the perception are difficult to segregate or categorize). For instance, Pepsi uses a visual design strategy that encourages unstructured perceptions (see https://tinyurl.com/3k7z45tj). Pepsi’s logo has visual elements with dissimilar and asymmetric shapes, sizes, and colors. Similar design objectives guide Pepsi’s package design, merchandising, advertising, and related visual communications.
We find that brands with a utilitarian positioning perform better when the visual design of their marketing communications supports structured perceptions, whereas brands with a hedonic positioning perform better when the visual design of their marketing communications supports unstructured perceptions. This occurs because design principles that generate structured perceptions (e.g., similarity, symmetry, regularity) can be used to make inferences about
objective, actionable, and stable outcomes, such as utilitarian benefits (e.g., reliability, efficacy).
For example, Intel’s visual marketing communications encourage the structured perceptions that
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unstructured perceptions (e.g., low similarity, asymmetry, irregularity) can be used to make inferences about subjective, experiential, and dynamic outcomes, such as hedonic benefits (e.g., fun, excitement). For example, Pepsi’s visual marketing communications encourage the
unstructured perceptions that reinforce Pepsi’s claims of being dynamic, exciting, and fun. We illustrate this idea in Figure 1, where (un)structured perceptions influence inferences about a brand’s ability to deliver promised benefits which, in turn, influences brand performance.
This research makes four contributions. First, we propose and provide evidence for a novel construct: perceptual structure. Second, we demonstrate how perceptual structure
influences consumer inferences and/or behaviors. Third, we document how practitioners can use visual design to encourage (un)structured perceptions of marketing stimuli including logos, product packages, websites, social media communications, and retail environments. Finally, we illustrate how perceptual structure can be used as an efficient marketing communication tool.
Perceptual structure can encourage consumer inferences at the point of purchase, hence is a relatively costless way to support brand claims.
-- Insert Figure 1 about here -- GESTALT THEORY
Historically, there have been two competing approaches to perception: a bottom-up reductionist approach and a top-down holistic approach. The reductionist approach assumes people initially perceive specific features in a visual display and then integrate the specific features into a perception (e.g., Feature-Integration Theory; Treisman and Gelade 1980). In contrast, the holistic approach assumes people perceive the most coherent, parsimonious
organization of the available information (i.e., there is an emergent process that encompasses as
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perception is an act of trying to use the available information to generate a meaningful
interpretation. A perception “consists of more or less definitively structured wholes and whole processes with their whole properties and laws” (Wertheimer 1924, p. 14).
Gestalt theory has sought to understand (1) why one perception is subjectively experienced while alternative perceptions are not (e.g., why one sees 10 people as “a group”
rather than as “10 individuals”) and (2) why a perception is stable or not (e.g., why one continues to see “a group” instead of vacillating between seeing “a group” and “individuals”) (Koffka 1935; Wagemans et al. 2012a; Wertheimer 1924). A Gestalt theorist assumes a visual perception emerges and is sustained owing to the relationships among the elements in a visual display (i.e., principles of visual organization). Principles of visual organization (e.g., proximity, similarity, symmetry) encourage the simplest – a perceptual process that encourages the “grouping” of the elements in a display – and most encompassing – a perceptual process that allows grouped elements to be more than the sum of the elements – organization of the information (Palmer 2002; Wagemans et al. 2012b). Importantly, Gestalt theorists do not view the principles of visual organization as causal. Instead, organizational principles are stimulus characteristics (an
objective state of the environment) that perceptual processes (an internal process) are sensitive to. Hence, visual organizational principles can be directly manipulated to encourage structured or unstructured perceptions. Appendix H provides 30 pages of real-world examples of visual
marketing communications with different levels of perceptual structure.
THE PROPERTY OF STRUCTURE
Perceptual structure is a hypothesized property of a perception that is based on the Gestalt theory assumption that perceptions can vary in their degree of gestaltet (i.e., structure, design)
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suggest structured perceptions have five characteristics: cohesive, homogeneous, predictable, stable, and systematic. Cohesive perceptions contain related or joined parts, whereas incohesive perceptions contain unrelated or contradictory parts (Milne and Szczerbinski 2009).
Homogeneous perceptions contain elements that have common features (e.g., color, forms, lines, textures, contour, borders, shape, objects), whereas heterogeneous perceptions have elements with features that lack commonality (Pomerantz and Portillo 2011). Predictable perceptions contain repeated parts such that one part of the perception can be used to anticipate other parts of the perception. Stable perceptions consist of a single enduring perception, as opposed to
vacillation between competing perceptions (Attneave 1971; Schwartz et al. 2012). Systematic perceptions have constituent parts that can be effectively segregated or categorized into manageable groups in a two-dimensional space (Palmer 1977). Each of these characteristics captures what a person experiences when they have a structured perception, though all
characteristics may not be present in any single perception. Still, we expect measures of these characteristics to represent a unidimensional scale we call perceptual structure.
Perceptual structure is anticipated to influence judgments in a manner similar to another property of a perception – perceptual fluency. Perceptual fluency is a feeling of ease that can accompany the processing of perceptual information (Schwarz et al. 2021; Shapiro 1999).
Perceptual fluency occurs when an image is previously exposed, has high contrast, or is salient relative to surrounding information so that its perception feels relatively effortless (Whittlesea and Williams 2000). Perceptual fluency influences judgments about the familiarity, recognition, liking, acceptability, risk, and truth of the content of a perception (Schwarz et al. 2021; Shapiro 1999). Perceptual fluency effects depend on context-dependent non-conscious attributions
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requires a judgment about liking, and prior experience has created an association between processing fluency and liking, then a person can attribute fluency with a stimulus to liking.
Importantly, attributions about fluency are only made if the fluency is diagnostic for the judgment. For example, fluent negative names are more likely to be judged as belonging to a criminal, but not to a senator (Klinger and Greenwald 1994). Similarly, fluent positive names are more likely to be judged a belonging to senator, but not to a criminal. Thus, nonconscious
attributions about fluency depend on an alignment between the semantic content of the
perception and the nature of the judgment to be made (Bornstein and D’Agostino 1994; Klinger and Greenwald 1994).
We propose that perceptual structure can also influence judgments, but that the scope of this influence is not as far-reaching as that of perceptual fluency. Perceptual structure should inform attributions about metacognitive confidence. Metacognitive confidence refers to the certainty one has about a thought or a belief (Petty et al. 2007). When metacognitive confidence is relatively low (i.e., there is ambiguity about the belief), people seek additional information that can improve confidence (Petty et al. 2007). Perceptual structure is one type of evidence that can alter confidence in a belief about a product claim. A structured perception is more cohesive, homogeneous, predictable, stable, and systematic. These characteristics should align with product-claim beliefs that are more objective, actionable, and stable over time. An unstructured perception is incohesive, heterogeneous, unpredictable, unstable, and unsystematic. These characteristics should align with product-claim beliefs that are more subjective, experiential, and dynamic over time. In each case, alignment of perceptual structure and the type of belief should increase confidence in the belief. In the next section, we provide more detail on the types of
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UTILITARIAN AND HEDONIC PRODUCT BENEFITS
Two broad classes of consumption goals are utilitarian goals and hedonic goals (Babin and Darden 1994). Utilitarian goals focus on the functional and instrumental objectives of consumption, whereas hedonic goals focus on the enjoyment and pleasure of consumption.
Businesses appeal to consumers with utilitarian (hedonic) consumption goals by offering products and services that promise utilitarian (hedonic) benefits. Utilitarian benefits tend to be prominent in product categories like automotive services, health care, and household appliances.
Hedonic benefits tend to be prominent in product categories like apparel, beauty, and entertainment.
Of course, many product categories have both utilitarian and hedonic benefits, as exemplified by the research in consumer behavior (see table 1). Nonetheless, although
consumption can produce both utilitarian and hedonic value (Babin et al. 1994; Hirschman and Holbrook 1982; Voss, Spangenberg, and Grohmann 2003), the weight placed on utilitarian or hedonic benefits should depend on whether the brand is positioned to provide primarily utilitarian or hedonic benefits (Botti and McGill 2011; Khan et al. 2005).
-- Insert Table 1 about here -- Inferring Utilitarian Value from Structured Perceptions
When a brand promises to provide utilitarian benefits, a consumer can make an
assessment about the extent to which the utilitarian benefit will be present (i.e., assess confidence in the belief the brand delivers the benefit). Although there is no direct evidence that the
structure of a perception can be used to make inferences about utilitarian benefits, there is evidence that manipulations of structured perceptions influence objective, actionable, and stable
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benefit causality (Chae et al. 2013), ads with repeated products (i.e., similarity) encourage inferences of product efficacy (VanBergen et al. 2020), brand logos with symmetry encourage inferences of functionality (Bettels and Wiedmann 2019), stimuli with a common region encourage inferences about control (Cutright 2012), and completeness in the typeface of logos encourages inferences of trustworthiness (Hagtvedt 2011). Causality, product efficacy, product functionality, control, and trustworthiness are beliefs one might have about products that offer utilitarian benefits. Thus, we assume that this class of manipulations encourages a structured perception and hypothesize:
H1: (a) When a brand promises to provide utilitarian benefits, structured perceptions (relative to unstructured perceptions) positively affect product interest, product evaluation, and brand equity.
(b) This effect is mediated by an increase in perceived utilitarian value.
Inferring Hedonic Value from Unstructured Perceptions
There is no direct evidence that unstructured perceptions can be used to make inferences about hedonic benefits. Yet, there is evidence that the manipulations we expect to result in unstructured perceptions influence subjective, experiential, and dynamic outcomes. For example, low proximity between information encourages inferences of prestige (Sevilla and Townsend 2016), low similarity in a product display encourages inferences of variety (Kahn and Wansink 2004), asymmetric logos encourage inferences of excitement (Luffarelli et al. 2019), lack of a logo frame (i.e., common region) encourages inferences of about being unconfined (i.e., freedom) (Fajardo et al. 2016), and a lack of completeness encourages inferences of
innovativeness (Hagtvedt 2011). Prestige, variety, excitement, freedom, and innovativeness are
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assume that this class of manipulations encourages an unstructured perception and hypothesize:
H2: (a) When a brand promises to provide hedonic benefits, unstructured perceptions (relative to structured perceptions) positively affect product interest, product evaluation, and brand equity.
(b) This effect is mediated by an increase in perceived hedonic value.
Six studies1 were used to investigate the hypotheses. Study 1 is a large-scale field experiment that uses Facebook Ads to demonstrate that products with structured (unstructured) perceptions generate higher click-through rates when the product offers utilitarian (hedonic) benefits. Study 2 shows, across several different organizational principles, that products with structured (unstructured) perceptions are preferred when a buyer seeks utilitarian (hedonic) benefits. Study 3 uses industry brand equity data to show that structured (unstructured)
perceptions of brand logos are associated with more brand value for brands offering utilitarian (hedonic) benefits. Study 4 demonstrates the process by mediation -- a structured (unstructured) perception of the environment increases perceived utilitarian (hedonic) value, leading to higher evaluations when the product is positioned as utilitarian (hedonic). Studies 5a and 5b
demonstrate the process by moderation -- when diagnostic information about benefit performance is accessible (i.e., information on actual utilitarian/hedonic performance is available), so that inferences about benefits is unnecessary, consumers do not use a structured (unstructured) perception as a cue to infer utilitarian (hedonic) value. In all experimental studies,
1The web appendix reports all stimuli, materials, additional methodological details, additional analyses (if any), and data exclusions (if any) for all experiments. We provide the link for preregistrations in the text. The raw data and code for all studies (except for study 3, which involves proprietary data) are available at the Open Science Framework
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operationalizations that can be used to manipulate the perceptions of structure, which is our key independent variable.
STUDY 1: LARGE-SCALE FIELD EXPERIMENT USING FACEBOOK ADS In Study 1, we provide initial evidence for H1a and H2a. We conducted a field experiment using Facebook Ads using ads for perfumes. We chose perfumes because this product category allows us to manipulate perceptual structure and product positioning while keeping other product features constant, similar to prior research (Bajaj and Bond 2018).
Method
Design. The design was a 2 (perceptual structure: structured vs. unstructured) x 2 (product positioning: utilitarian vs. hedonic) x 2 (product replicate) between-subjects design.
Stimuli. We created a page for a fictitious fragrance store named PerfumeGallery.com and launched eight advertisements for the organization on Facebook. We used eight
advertisements (two replicates for each perceptual structure x product positioning combination) to assess the robustness of our findings across different designs. All advertisements were reviewed and approved by Facebook, and the procedure of this and our other studies were approved by an Institutional Review Board.
Each advertisement consisted of a brand name (Horizon – 3.4oz Eau de Parfum), positioning claim, and package image. The utilitarian positioning claim was: “Long-lasting.
Great for work and everyday occasions. Click to learn more.” The hedonic positioning claim was: “Delightful. Great for special and fun occasions. Click to learn more.” The (un)structured perception was manipulated by using a different set of design principles for each replicate. In the first replicate, we manipulated the symmetry, balance, organization, and regularity of several
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balance, organization, and regularity of a forest landscape. For the exact stimuli used in this and all other studies, see the Web Appendix.
Pretests. We assessed whether the replicates (N = 100 per replicate) generated perceptions with different levels of structure. Five items measured the characteristics of perceptual structure: cohesiveness, homogeneity, predictability, stability, and systematic.
Cohesiveness (i.e., the elements are perceived as interrelated) was measured with 1 =
“incohesive” and 9 = “cohesive.” Homogeneity (i.e., the common features of elements are
fundamental to the perception) was measured with 1 = “heterogeneous” and 9 = “homogeneous.”
Predictability (i.e., the repetition of elements makes it possible to use one part of the image to anticipate other parts or the whole) was measured with 1 = “unpredictable” and 9 =
“predictable”. Stability (i.e., one perception is dominant, as opposed to vacillating between competing perceptions) was measured with 1 = “unstable” and 9 = “stable”. Systematicity (i.e., the elements of the perception can be effectively segregated or categorized should there be a motivation to do so) was measured with 1 = “unsystematic” and 9 = “systematic”. The definition of each measure was explained before soliciting the participant’s rating (see Web Appendix A).
For the first replicate, the perceptual structure (α = .78) was greater for the structured stimulus (M = 6.52, SD = 1.42) than the unstructured stimulus (M = 5.55, SD = 1.64; (F(1, 98) = 10.01, p = .002). For the second replicate, the perceptual structure (α = .88) was greater for the structured stimulus (M = 7.06, SD = 1.61) than the unstructured stimulus (M = 6.05, SD = 1.74;
(F(1, 98) = 9.21, p = .003).
Procedure. The study targeted U.S. Facebook users who have an interest in fragrances.
The potential reach (i.e., the size of the audience that was eligible to see the ads) was 58 million
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well as the sponsoring page, which was purportedly a fragrance e-commerce website),
consumers were unfamiliar with the brand at the time of the experiment. We set up the ads to be optimized for clicks (i.e., the ads were delivered to the people most likely to click on them). The daily budget for each of the eight ads was $20. This determined the daily estimated reach and the opportunity for clicks on each of the ads.
The dependent variable was advertising click-through rates (CTRs), a function of impressions and clicks. CTRs can determine advertising effectiveness and are associated with real product interest. We anticipated that perfumes with utilitarian (hedonic) positioning would generate more CTRs when the perception was structured (unstructured).
Results
Click-Through Rate. The eight ads generated a total of 82,413 impressions, 2,503 of which were clicked on (2.23%). We used these data to conduct a 2 (perceptual structure) x 2 (product positioning) x 2 (product replicate) Poisson log-linear regression based on the number of times an ad had been served and whether or not the ad has been clicked (Click Through Rate:
CTR) as the binary choice (1 = yes, 0 = no). First, there was no three-way interaction (χ2(1) = .116, p = .733). Thus, we collapsed across replicates for the subsequent analyses (see Web Appendix A for the analyses split by replicate).
We then ran a 2 (perceptual structure) x 2 (product positioning) Poisson log-linear model, which revealed the predicted interaction (β = .56, χ2(1) = 34.73, p < .001). As predicted, a
structured perception increased CTR when the product was positioned as utilitarian (CTRstructured
= 2.53% vs. CTRunstructured = 1.82%; χ2(1) = 24.82, p < .001), but decreased CTR when the product was positioned as hedonic (CTRstructured = 2.03% vs. CTRunstructured = 2.54%; χ2(1) =
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positioning (χ2(1) = 1.48, p = .223). We present additional metrics (e.g., cost-per-click), on which we find a similar pattern of results, in Web Appendix A.
Discussion
Study 1 provides initial support for the notion that a structured perception of a product increases product interest when the product is positioned as utilitarian, whereas an unstructured perception of a product increases product interest when the product is positioned as hedonic (H1a and H2a). In Study 2, we will investigate consumer choices and show that our effects are robust across different manipulations of a (un)structured perception of a product.
STUDY 2: ROBUSTNESS ACROSS DIFFERENT VISUAL MARKETING COMMUNICATIONS AND MANIPULATIONS OF STRUCTURE
Study 2 (preregistration:https://aspredicted.org/D3G_8PT) sought to demonstrate that hypotheses 1 and 2 are robust across different manipulations of (un)structured perceptions and different types of visual marketing communications. Perceptual structure was manipulated using different combinations of visual design principles in a single stimulus because visual design principles typically co-occur in natural stimuli.
Method
Participants and Design. Two hundred MTurk workers (58.3% male, Mage = 40.30) were randomly assigned to two conditions (shopping goal: utilitarian vs. hedonic). Participants made choices between brands with visual marketing communications that generated structured versus unstructured perceptions.
Stimuli. The stimuli were 10 sets of options designed to generate structured versus unstructured perceptions (see Web Appendix B). A first pretest (N = 100; see table in Web
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cohesive, heterogenous – homogeneous, unpredictable – predictable, unstable – stable,
unsystematic – systematic) (F-values ranging from 5.69 to 31.64, p-values from .019 to < .001).
A second pretest (N = 270; Web Appendix B) ensured that the options were equally preferred in the absence of a utilitarian or hedonic shopping goal. We also show in Web Appendix B that the manipulation of perceptual structure is robust across different types of design manipulations.
Procedure. We told participants we were investigating consumers’ personal preferences and opinions, and that they would read different scenarios and make choices. For instance, in the spa replicate, participants who wanted a spa for a utilitarian (hedonic) goal were told: “Imagine you are looking for a spa where you can get a massage that provides immediate pain relief and reduces body fatigue [is enjoyable and relaxes your body]. Which spa would you choose?”
Participants chose between a spa logo designed to generate an unstructured perception and a competing spa logo designed to generate a structured perception.
Each participant made 10 choices, resulting in a total of 2000 choices in the experiment.
The shopping goal was counterbalanced so that the utilitarian (hedonic) goal was salient for the spa, grocery store, sunglasses, headphones, and artisan soaps (perfume, coffee, gift shop, spa store, and restaurant) replicates for one-half of the participants. The replicate presentation order was randomized. The counterbalancing conditions ensured that there would be an equal number of choices for each possible combination of replicates and shopping goals.
We coded participants’ choices as 1 if they were consistent with our hypotheses, i.e., a choice of an option generating a structured (unstructured) perception when considering a utilitarian (hedonic) benefit. For each participant, we created an index of hypothesis-consistent choices (varying from 0 to 10). If our hypotheses are correct, the index should be significantly
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our effects are robust across different visual design manipulations of structure and different types of visual communications, then there should be no effects of the counterbalancing condition or the product replicate on the index of hypothesis-consistent choices. Finally, for each replicate, the choice shares of the options generating a structured (unstructured) perception should be higher when consumers are considering the options to satisfy a utilitarian (hedonic) benefit.
Results
Hypothesis-Consistent Choices. Table 2 shows the choice shares by replicate. A 2 (counterbalancing condition) x 10 (replicate) mixed ANOVA revealed no main effects or interactions (all F’s < 1). A t-test that aggregated the choices for each participant showed the hypothesis-consistent choice proportion was above .50 (M = .631, SD = .168, t(199) = 11.00, p <
.001, Cohen’s d = .63). A z-test that assumed each participant’s choices were independent showed the hypothesis-consistent choice proportion was above .50 (M = .631, z = 11.67, p <
.001, Cohen’s d = .56). These results suggest that consumers are more likely to choose an option with a structured (unstructured) perception when they seek a utilitarian (hedonic) benefit.
-- Insert Table 2 about here -- Discussion
Study 2 provides support for the notion that consumers are more likely to choose options perceived as structured (unstructured) when they seek utilitarian (hedonic) benefits. The effects emerged across a variety of marketing contexts (i.e., logos, typeface design, product packaging, product package imagery, retail store displays, and restaurant environments) and manipulations of perceptual structure (i.e., proximity, similarity, symmetry, common region, balance,
regularity, geometric), suggesting that the influence of structured (unstructured) perceptions is a
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rather than an inference-making, mechanism. It is possible that participants looked at each pair of stimuli prior to reading the shopping goal, spontaneously generated associations to the stimuli, and subsequently matched the stimuli to the shopping goal. We address this alternative
explanation in studies 4, 5a, and 5b.
STUDY 3: (UN)STRUCTURED PERCEPTIONS OF BRAND LOGOS AND BRAND FINANCIAL VALUATION
In study 3, we demonstrate that a structured (unstructured) perception of a brand logo, and its influence on inferences about product performance, can influence brand equity.
Specifically, we show that when a brand is strongly associated with utilitarian (hedonic) considerations, a structured (unstructured) perception of the brand logo is associated with an increase in customer-based brand equity.
The data used in this study were collected from five sources: “BrandZ Top 100 Most Valuable U.S. Brands 2020” by Kantar Millward Brown, “Brand Finance US 500 2020” by Brand Finance, the Brand Asset Valuator (BAV) customer-based brand equity tracker by VMLY&R, and two surveys of U.S. consumers that we conducted.
Data Description
First Dependent Variable: Brand Valuation. Kantar Millward Brown (BrandZ) and Brand Finance, two leading marketing consultancy firms, publish annual rankings of the top 100 (top 500 for Brand Finance) most valuable brands. While each firm uses a unique methodology, both estimate brand equity in billions of U.S. dollars. We analyzed the top 100 BrandZ and Brand Finance brands. For conciseness, we report the analysis of the BrandZ top 100 brands here and the analysis of the Brand Finance top 100 brands in Web Appendix C.
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dollar valuations are known to be sensitive to many financial factors, we sought a more consumer-centric way to analyze the “top 100 brands”. VMLY&R’s Brand Asset Valuator (BAV) is based on four brand “pillars”: perceived brand differentiation, relevance, esteem, and knowledge. Collectively, these four pillars form the Brand Asset Index.2 The BAIndex is calculated using responses from a representative sample of 12,412 U.S. consumers. The Brand Asset Index (henceforth BAIndex) is an important predictor of the financial valuation (e.g., BrandZ, Brand Finance) and the performance of brands (Datta, Ailawadi, and Van Heerde 2017;
Mizik 2014; Mizik and Jacobson 2008). We extracted the BAIndex for 121 of the 128 brands3 listed in the top 100 BrandZ or top 100 Brand Finance rankings. Higher Brand Asset Index scores (M = 7.96, SD = 1.79) reflect higher levels of consumer-based brand equity.
Independent Variable: (Un)structured Perception of a Brand Logo. We measured the degree to which the perception of a brand logo is structured for the 128 unique brands in the BrandZ and Brand Finance top 100 rankings. The sample was 1300 U.S. Prolific workers (32.5%
male, Mage = 35.29). The sample size was determined by planning for each participant to rate 5 random brands (out of the 128) and for each brand to receive a minimum of 50 ratings ([128/5] x 50 = 1280 participants). For each of the brands, participants saw the logo “The [brand] logo is below. Please look carefully at the logo image and consider its visual characteristics and elements (e.g., colors, shapes, forms, lines, textures, objects). How would you evaluate the logo?” and were asked to evaluate the logo using the five indicators of a structured perception:
2 Mizik (2014) and Mizik and Jacobson (2008) provide detailed descriptions of the BAV model.
3 There were 128 unique brands in the two top-100 lists, with 72 brands simultaneously present in both rankings. Seven out of the 128 brands are not tracked by VMLY&R, and therefore were not available in the data set: Anthem, Century Link, Cognizant, HCA, Hewlett-Packard
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“homogeneous”), predictability (1 = “unpredictable” and 9 = “predictable”), stability (1 =
“unstable” and 9 = “stable”), and systematic characteristic (1 = “unsystematic” and 9 =
“systematic”). The definition of each item was explained to participants (see Web Appendix C).
Moderator: Brand Hedonic-Utilitarian Benefit. We measured the perceived hedonic- utilitarian benefit for the 128 unique brands using 1301 U.S. consumers via MTurk (54.3% male, Mage = 39.24). For each of the brands, participants indicated how utilitarian versus hedonic each brand was: “Now, please evaluate the brand on the following dimensions. When evaluating, consider the products/services offered by this brand” with 1 = “utilitarian (satisfies useful needs)” and 9 = “hedonic (provides pleasure)”; 1 = “functional (performs practical functions)”
and 9 = “experiential (provides experiences and indulgences)”; 1 = “instrumental (provides material benefits)” and 9 = “transformational (transforms the consumption experience)” as scale items. In this and in subsequent studies, we reversed scored these items such that higher scores indicate brands more associated with utilitarian rather than hedonic benefits. This survey also measured the organizational principles of symmetry, balance, regularity, and geometry. These measures yielded results consistent with those of the structure measure (see Web Appendix C for additional analyses). We collapsed the structure measure (α = .98) and the hedonic-utilitarian measure (α = .98) at the brand level, such that each brand would have a structure score and a hedonic-utilitarian score, both ranging from 1 to 9. All logo images are on the OSF website.
Control Variables. VMLY&R encourages researchers to use control variables in analyses of the Brand Asset Index.4 These control variables are strong predictors of brand equity; hence
4 Of the 121 brands for which the BAIndex is available, control variable data for thirteen brands (3M, Accenture, Boeing, Cisco, Deloitte, ExxonMobil, General Electric, John Deere, Lockheed
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brand equity. The control variables were (1) brand usage (percentage of respondents who used the brand at least occasionally and plan to do so in the future), (2) brand preference (percentage of participants who consider the brand as their preferred within a category), and (3)
recommendation behavior (percentage of participants who would recommend the brand).
Analyses and Results
First Dependent Measure: BrandZ. We performed a perceptual structure of the brand logo (continuous) × brand hedonic-utilitarian benefit score (continuous) regression on brand valuation. Because the distribution of the brand valuation amounts was right-skewed (range =
$6.51B to $334.65B, mean = $39.80B, SD = $65.32B, skewness = $3.32B), we normalized it a using log-transformation. In support of H1a and H2a, we found a significant interaction (ß = .31, t(85) = 3.15, p = .002). To decompose this interaction, we conducted a floodlight analysis
(Johnson and Neyman 1936; Spiller et al. 2013) using PROCESS Model 1 (Hayes 2018). As Figure 2 shows, the floodlight analysis revealed that the Johnson-Neyman (J-N) points (p < .05) on the hedonic-utilitarian index moderator occurred at 3.62 (1.59 SD below the mean) and 5.80 (.29 SD above the mean). These results suggest that for brands associated with utilitarian (hedonic) considerations, structured (unstructured) perceptions of logos are associated with increased financial valuation. Web Appendix C presents additional analyses and robustness tests.
-- Insert Figure 2 about here --
Second Dependent Variable: Brand Asset Index. A perceptual structure of the brand logo (continuous) × brand hedonic-utilitarian index (continuous) regression on customer-based brand
in the main text do not include these brands. Web Appendix C provides a list that details what
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shows, the floodlight analysis revealed that the J-N points (p < .05) on the hedonic-utilitarian index moderator occurred at 4.66 (.69 SD below the mean) and 6.19 (.63 SD above the mean).
Web Appendix C presents a series of robustness tests involving the BAIndex.
-- Insert Figure 3 about here -- Discussion
Study 3 showed that brands offering utilitarian (hedonic) benefits have greater brand equity when the perception of the brand logo is more (less) structured, supporting H1a (H2a).
These effects were observed for the top 100 brands in the marketplace. These effects are likely a consequence of two phenomena. First, consumers often use visual marketing communications (e.g., brand logos) to make inferences about the performance of a brand, especially when they lack experience with a brand. It may be that people are more likely to try, and subsequently adopt, a brand when the structure of the logo perception aligns with brand claims. Second, major brands tend to have visual design consistency across visual marketing communications (e.g., logos, product design, product packaging, visual merchandising, retail architecture). To the extent the structure in the structure of the logo perception is similar to the structure of other visual marketing perceptions, perceptions of all visual communications should influence brand inferences and brand equity. That is, if logo perceptions can be treated as an indicator of visual marketing communications perceptions, then logo perceptions can explain differences in brand equity that depend on a variety of marketing communications.
We acknowledge that Study 3 has limitations. First, the results consist of only 128 top- ranked brands. In addition, we were not able to directly control for many brand-level variables, such as advertising expenditure or market share (i.e., it could be that highly structured
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these limitations, the results of this study are consistent with the results of studies 1 and 2.
STUDY 4: PROCESS-BY-MEDIATION EVIDENCE WITH RETAIL ENVIRONMENTS THAT GENERATE (UN)STRUCTURED PERCEPTIONS
Thus far, we have shown that structured perceptions boost interest, choice, and brand financial valuation when the product is positioned as utilitarian, whereas the opposite occurs when the product is positioned as hedonic. We contend this happens because consumers use structured (unstructured) perceptions as a cue to infer utilitarian (hedonic) value when the brand promises utilitarian (hedonic) benefits (H1a and H2a). In Study 4 (preregistration:
https://aspredicted.org/CLG_KMC), we investigate the hypothesized mediating processes:
inferences about utilitarian (H1b) and hedonic (H2b) value.
In addition, the study tests an alternative explanation for the previously observed results:
conceptual fluency. Conceptual fluency refers to the ease with which customers can process and understand information (e.g., brand meaning, product claims) (Shapiro 1999). Previous research has shown that matching anticipated product needs with delivered product benefits increases conceptual fluency and, consequently, encourages positive responses (Lee and Labroo 2004).
One could argue that when structured (unstructured) perceptions of visual marketing communications are paired with utilitarian (hedonic) positioning, conceptual fluency is
experienced. If this explanation holds, then we should observe a perceptual structure by product positioning interaction on the conceptual fluency dependent measure, as well as a mediating influence of conceptual fluency.
Method
Participants and Design. Eight hundred Prolific workers (58.3% male, Mage = 27.60)
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positioning: utilitarian vs. hedonic) x 2 (product replicate) between-subjects design.
Stimuli. Perceptual structure was manipulated using a symmetric, balanced, and regular (asymmetric, unbalanced, and irregular) image of a library interior. The operationalization varied by product replicate. In the first replicate, the structured (unstructured) perception was
manipulated by showing a perfectly mirrored library environment (a library environment with curved shelves and no specific pattern) (see Web Appendix D). In the second replicate, the structured (unstructured) perception was manipulated using pictures of the same library space taken from two different angles (see Web Appendix D).
A pretest (N = 101 for replicate 1 and N = 99 for replicate 2) confirmed the 5-item
structure scale differentiated the first (α = .77; Mstructured = 6.69, SDstructured = 1.46 vs. Munstructured = 5.90, SDunstructured = 1.47; F(1, 99) = 7.38, p = .008) and second (α = .85; Mstructured = 7.75,
SDstructured = 1.31 vs. Munstructured = 5.96, SDunstructured = 1.73; F(1, 97) = 33.88, p < .001) replicates.
Procedure. Participants were told the study investigated consumers’ personal preferences. Participants saw a picture and read a description of “The Spectrum Library.”
Product positioning was manipulated by highlighting the utilitarian or hedonic benefits of the library. Participants in the utilitarian positioning condition read a description of the library highlighting a quiet, safe, and connected environment for personal productivity with a wide selection of opportunities to learn, search, and inquire. In addition, participants also read the library provided access to several research resources, databases, career services, and individual study rooms. Participants in the hedonic positioning condition read a description of the library highlighting a stimulating, pleasurable, and comfortable environment for personal growth with a wide selection of opportunities to learn, discover, and have fun. In addition, participants also
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socializing, cafes, and modern cinema/media rooms.
Immediately after seeing the product picture and description, participants responded to the dependent measure: “Please provide your overall evaluation for The Spectrum Library” (1 =
“very bad” and 9 = “very good”; 1 = “very negative” and 9 = “very positive”; 1 = “very unfavorable” and 9 = “very favorable”) (α = .95). Next, the mediators were measured. The inferences about utilitarian value measure was: “How effective do you think The Spectrum Library is at offering…” (“an effective learning environment?”, “access to a large amount of information?”, “professional development opportunities?”, and “a safe environment?”; all with 1
= “not effective at all” and 9 = “very effective”) (α = .91). The inferences about hedonic value measure was: “How effective do you think The Spectrum Library is at offering…” (“interesting experiences?”, “exciting experiences?”, “unexpected experiences?”, and “enjoyable
experiences?” all with 1 = “not effective at all” and 9 = “very effective”) (α = .87). The measurement of mediators was counterbalanced.
Next, to measure conceptual fluency, we used: “The description and picture of the library was: (1 = “difficult to process” and 9 = “easy to process”; 1 = “difficult to understand” and “9 =
“easy to understand”; 1 = “required a lot of effort to understand” and 9 = “required no effort to understand”; 1 = “very complex” and 9 = “very simple”) (α = .88).
Then, we asked: “How aesthetically pleasing is The Spectrum Library?” with 1 = “not at all” and 9 = “very much”. To confirm the positioning manipulated perceived benefits, we asked about product positioning: “We want you to evaluate The Spectrum Library in terms of what you think it is most consistent at relative to what it claims to offer. This question is not about how well The Spectrum Library can offer what it claims to offer, but about what The Spectrum
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(provides pleasure)”; 1 = “functional (performs practical functions)”, and 9 = “experiential (provides experiences and indulgences)”; 1 = “instrumental (provides material benefits)”, and 9
= “transformational (transforms the consumption experience)” (α = .79) (reverse scored).
Results
There were no three-way interactions between perceptual structure, product positioning, and replicate on any of the dependent measures (all F’s NS), so we collapsed across replicates.
Manipulation Check – Positioning. As expected, participants in the utilitarian positioning condition (M = 5.29, SD = 2.03) perceived the library to be more utilitarian than participants in the hedonic positioning condition (M = 4.21, SD = 1.77; F(1, 796) = 63.93, p < .001). There was no main effect of perceptual structure (F(1, 796) = .174, p = .677) or an interaction of perceptual structure and positioning (F(1, 796) = 2.52, p = .113).
Evaluation. A two-way ANOVA on product evaluation revealed the predicted interaction (F(1, 796) = 14.09, p < .001, ω2p = .017; see Figure 4). As expected, structure increased
evaluations when the library was positioned as utilitarian (Mstructured = 8.25, SDstructured = 1.15 vs.
Munstructured = 7.79, SDunstructured = 2.05; F(1, 796) = 8.47, p = .004, Cohen’s d = .28), but
decreased evaluations when the library was positioned as hedonic (Mstructured = 7.75, SDstructured = 1.86 vs. Munstructured = 8.14, SDunstructured = 1.01; F(1, 796) = 5.75, p = .017, Cohen’s d = .25).
-- Insert Figure 4 about here --
Mediation by Inferences about Utilitarian and Hedonic Value. To confirm that the dependent variable (evaluation) and the mediators (hedonic and utilitarian value) were distinct constructs, we tested for discriminant validity. First, the average variance extracted (AVE) for evaluation and utilitarian value exceeded their squared correlation (AVE evaluation = .85; AVE
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their squared correlation (AVE evaluation = .85; AVE hedonic value = .605; squared correlation
= .549), and for utilitarian value and hedonic value exceeded their squared correlation (AVE utilitarian value = .698; AVE hedonic value = .605; squared correlation = .504) (Fornell and Larcker 1981). Second, the 95% confidence interval (CI) around the correlation between the two factors excluded 1 for evaluation and utilitarian value (CI = [.78; .84]), evaluation and hedonic value (CI = [.70; .78]), and utilitarian and hedonic value (CI = [.6
