All posts by Kathryn Ambroze

How to Keep People in the Mood: The Art of an Efficient Ad Campaign

Over the past decade alone, the mediums in which advertisements are viewed have grown rapidly. From television ads and billboards to social media and app advertisements, the various options of exposure have exploded. Yet, with all this potential, ad runs must beg the question of campaign fatigue or wearout. The concept of wearout, or the decline of a response to an ad, is challenging to study since ad exposure cannot exist in a vacuum. Technology provides numerous instantaneous luxuries and distractions making it more challenging to capture the attention of the consumer. As the window of attention shrinks, innovative methods of exposure to products are becoming more important. Gaining consumer insight into the values, disadvantages and priorities of an ad campaign offers a strong foundation in discovering the formula to an effective ad.

Time to Make a Choice

Convenience is a huge factor as to why people are choosing to use on-demand services as opposed to traditional television. One appeal of streaming media is the limited number of ads, forcing ad agencies to seek out ways to create more engaging content. Personalizing ads is commonly seen on Hulu, where viewers can choose which ad to watch. This method is known as advertisement choice, where the consumer is given more agency via selective exposure (Nettelhorst, Jetter, Brannon & Entring, 2017). Having choice creates more favorable attitudes (Schlosser & Shavitt, 2009) and desirability (Ackerman & Gross, 2006).

Research has consistently shown women to be more interested, informed and impacted by ads compared to men. Why?  Centering an individual’s attention on an ad is a huge element in making an impact, and women are more likely to remain more focused on the ad. Similarly, the ability to choose ads has a stronger impact on women (Nettelhorst, Jeter & Brannon, 2014). More ad options create a cognitive reaction to the messaging, focusing attention. However, there can be a point of oversaturation too, known as choice overload (Nettelhorst et al, 2017). Too many options can lead an individual to feel overwhelmed (choice paralysis) and dissatisfied. Cognitive and behavioral outcomes are influenced by many variables, making it hard to predict the impact of personal choice. However, we do know for sure that the ad message is lost unless the consumer is attentive and engaged.

Campaign Wearouts

Campaign wearout occurs when the effectiveness of an ad starts to wane over time. Effectiveness can be measured in several different ways, including sales, purchase intent, consumer awareness and brand/product recall. Individual behavior, such as online browsing behaviors, website cookies or television channel changes, can help profile consumers and segments (Chae, Bruno & Feinberg, 2019) to provide a better understanding of target audiences. The ultimate goal of any campaign is to create and increase familiarity with a brand, leading to purchase or some sort of action. Market researchers for campaigns try to uncover what design and approach will best benefit a return on investment of ads and other components of a campaign for marketers. Many companies favor repeating campaigns because of the cost benefits and increasing consumer views. However, negative associations and inattention can be byproducts of overplaying an ad (Calder & Sternthal, 1980).

Campaign reach, or the number of views, can depend on the platforms (mobile, cable tv, streaming services, etc.) for the ads. Viewership can dramatically fluctuate between platforms— up to 50 times based on online advertising versus traditional channels (Chae, Bruno & Feinberg, 2019). For example, ads on TV are often connected to online searches to predict market performance. Joo, Wilbur and Zhu (2016) found that consumers tend to search brand related words (such as “Geico”) rather than a generic word (such as “car insurance”) when initial exposure is from a TV ad. Retargeting is when a consumer is nudged towards a product through online ads, such as banners, after the consumer already demonstrated interest in the product, but do not purchase it. Additionally, retargeting displaces or blocks competitor ads from consumers (Sahni, Narayanan, & Kalyanam, 2019). The use of online retargeting has become a more popular tactic to increase user engagement and lure consumers back to the product.

Crossovers, or when campaign ads are featured on multiple platforms, are becoming more frequently implemented as marketing networks become more interconnected. Online exposure and traditional channels utilize similar attempts to keep content engaging. Interchangeable variables of an ad, such as the format or phrasing, are paired with an underlining consistent component. Yet, extreme personalization of styles and plots can each separately influence how a consumer will respond (Chang, 2009), furthering the notion that fatigue for ad exposure is a truly individualized experience (Chae, Bruno & Feinberg, 2019). So, if repetition and customization tend to have varying effects, what’s the point of investing in ads at all?

Current research on campaign wearout is full of contradictions. Along with the need for clarification about what qualifies as campaign wearout, identifying wearout may depend on the format or field setting. Consumer research labs analyze the individual response, while empirical market-level studies consider a macro view. Furthermore, most of the research analyzing ad fatigue focuses on the behavioral components (such as market success), while empirical evidence (showing direct cause and effect) regarding campaign wearout is scarce. When reviewing a more macro level sample, insignificant changes were found in various studies testing themes, format models, and exposure levels (Chae, Bruno & Feinberg, 2019). While there are benefits and limitations to both styles of research, noting various perspectives on campaign wearout helps to develop a comprehensive, informed understanding of the concept’s complexity. More research is needed to better understand and predict campaign fatigue.  

A Quick Trip Down ~Memory~ Lane

Advertisements can be categorized into different subsets based on how a company executes a segment. For example, an ad is generally considered either an argumentative or narrative ad. Narrative ads include stories or experiences of a relatable character, while an argumentative ad focuses on justifying the reasons for a claim (Chang, 2009). A narrative ad typically includes a plot to entice viewers to remain engaged. Yet, more complicated ads can lead to consumer confusion and misunderstanding. Within the realm of narrative ads, the extensive plots lead to less favorable ad attitudes when compared to more consistent plot strategies (Chang, 2009). To easily capture the consumer’s attention and understanding, keep it simple.      

Most research that has been conducted on campaign fatigue centers on the immediate reactions to varying levels of exposure. Increasing exposure (viewers) allows for more opportunities for consumers to get the message (Schmidt & Eisend, 2015). However, more views do not directly cause a consumer to run out and purchase the product. Rather than focusing on the direct impact, the long-term effects must also be valued. Just look at how back-to-school ads often start only a few weeks into the summer and holiday items are quick to be pushed into the ad circuit. Expanding the duration of seasonal shopping, such as holiday items in August, can have surprising benefits for participating brands.    

Kronrod and Huber (2018) found long-term benefits of a high initial frequency ad that promotes a product lacking inherent need, such as makeup or headphones. The study concluded that although there are immediate negative effects to high frequency ads; ultimately, the fatigue itself wears off and positive familiarity of the brand persists when considering purchases. Furthermore, measuring the effects of ad repetition may not be appropriate during the campaign, since this study supports that opinions (and buying decisions) change over time. Familiarity and fluency are key features of durable ad messaging.  

So, where do we go from here?

Plan and reflect on the product, your goals and the demographic you want to target for your campaign. Prioritize among expenditure, exposure or engagement to help you shape the components you are willing to optimize or sacrifice. Determining your company’s position may differ depending on the type of brand or product involved, since budgets and objectives vary among companies. Knowing the key focus will also provide some leeway in experimentations. New concepts are constantly being tested to determine alternative ways to share content. Some weigh engagement based on desirability, while others focus on more data-driven responses to build interactions. Defining your company’s position on engagement will guide you to decide what type of research approach you wish to employ.  

Testing ads on Facebook is an easy and cheap way to connect consumers to content; however, exposure may not equate to engagement. Grabbing consumers attention on a site that is saturated with information can be challenging. Pre-testing exploratory research can cater to developing attractive content. Similarly, pre-testing can ensure that the correct platform is being used to target specific demographics. For example, companies are shifting their emphasis from traditional consumer markets to internet-based commercial activity to gain insight into effective marketing channels. Confirming that your target demographic interacts with those channels provides security that the content is viewed. Similarly, testing the ads to best fit the format of the varying mediums (ex. a Youtube ad vs a banner) can help determine where attention is being drawn. Merging the information understood about ad content with innovative platforms will help determine a campaign approach most beneficial for the company needs.   

Long-term familiarity is a consistent objective in order to draw consumers into a product. The target consumer, amount of exposure and cost must be deliberately chosen to minimize surprise during a campaign ad run. Chae, Bruno and Feinberg (2019) summarize the most crucial components of a harmonious ad campaign by sharing that “…it is vital to understand the relative effectiveness across users, within-user over repetition and spacing of exposures, and the channels to reach those users.” By hitting these key elements, and considering the emotional implications of an ad, there can be a stronger indicator of success not necessarily during the airing of an ad, but where it counts the most—during checkout.

For more information on how HCD can help you uncover valuable insights into your brand, product, messaging, please reach out to Allison Gutkowski (


Ackerman, D. S., & Gross, B. L. (2006). How many choices are good? Measurement of the effects of course choice on perceptions of a marketing option. Journal of Marketing Education28(1), 69-80.

Calder, B. J., & Sternthal, B. (1980). Television commercial wearout: An information processing view. Journal of Marketing Research17(2), 173-186.

Chae, I., Bruno, H. A., & Feinberg, F. M. (2019). Wearout or Weariness? Measuring Potential Negative Consequences of Online Ad Volume and Placement on Website Visits. Journal of Marketing Research56(1), 57-75.

Chang, C. (2009). Repetition variation strategies for narrative advertising. Journal of advertising38(3), 51-66.

Joo, M., Wilbur, K. C., & Zhu, Y. (2016). Effects of TV advertising on keyword search. International Journal of Research in Marketing33(3), 508-523.

Kronrod, A., & Huber, J. (2019). Ad wearout wearout: How time can reverse the negative effect of frequent advertising repetition on brand preference. International Journal of Research in Marketing36(2), 306-324.

Nettelhorst, S. C., Jeter, W. K., & Brannon, L. A. (2014). Be careful what you wish for: The impact of advertisement choice on viewers’ expectations. Computers in Human Behavior41, 313-318.

Nettelhorst, S. C., Jeter, W. K., Brannon, L. A., & Entringer, A. (2017). Can there be too much of a good thing? The effect of option number on cognitive effort toward online advertisements. Computers in Human Behavior75, 320-328.

Sahni, N. S., Narayanan, S., & Kalyanam, K. (2019). An experimental investigation of the effects of retargeted advertising: The role of frequency and timing. Journal of Marketing Research56(3), 401-418.

Schlosser, A. E., & Shavitt, S. (2009). The effect of perceived message choice on persuasion. Journal of Consumer Psychology19(3), 290-301. Schmidt, S., & Eisend, M. (2015). Advertising repetition: A metaanalysis on effective frequency in advertising. Journal of Advertising, 44(4), 415–428

The Impact of Logo Colors

As seen in INsights magazine…

A business logo has the potential to encapsulate messages a company hopes to portray. While there are many components of a logo to consider—shape, size, space, etc., color has an interesting impact on the consumer response. The logo color is an essential element that influences graphic design (Henderson et al., 2004). The perception of color is subjective to the viewer and has individualized associations that incites intrinsic emotions. Interestingly, trends have emerged from psychology and marketing to demonstrate how color is a tactful way to communicate the brand message through visual perception. Color develops the identity of the brand through a foundational company component: the logo. Using neuroscientific and psychological tools, the optimal color can be selected for a logo to communicate a brand story and influence consumers behavior.

Logos are used to identify a brand via symbols or text. The overall experience of the brand should be represented within the logo (Fajardo, Zhang, & Tsiros, 2016). Specifically, the theme color is the most predominately used color in the brand design. This element shapes consumer perception while trickling into other areas of management to make lasting impressions. Store, smartphone application, and website designs are intentionally consistent with the color chosen. Netflix, for example, has a red theme color. The dramatic red color on a black background of the Netflix logo works to induce a cinematic feel (“Brand Assets”, 2019). The red theme is intermingled within the platform design which parallels Netflix’s red logo. 

Psychological tests with emotional batteries and scales can examine the emotional responses of consumers to ensure congruency between theme colors and company or brand persona, or as we at HCD Research like to say, Brand Harmony. Timed reactions to visuals, colors, shapes, sounds and/or concepts during an implicit association test allows companies to understand their consumers at a deeper level. The consumer’s emotional reaction to the logo indicates product perception and association strength. HCD uses psychological tests that provide information to keep the company both relevant and aware and ensure harmony between brand perceptions and actual experiences. 

Colors consist of hues, lightness and saturation that influence human behavior (Su, Cui, & Walsh, 2019). The associative learning theory suggests that connections among brand elements are reinforced by frequent combinations via social learning. For example, eco-friendly colors chosen for a company logo are viewed as more ethical compared to a company that has a less eco-friendly color. The color assumption reigns true even when companies are not outwardly ethical (Sundar & Kellaris, 2017). If the company experiences a disconnect between the brand expectation compared to its reality, the result can lead to negative implications. The eco-friendly color associations can have unanticipated effects, such as inflated price perception. The positive and negative inferences associated with how the color impacts the brand is crucial, especially since color cues have strong implications for the brand perception.  

Aesthetic components of a logo, such as color, are also a factor in trustworthiness. Gaining consumer trust is crucial for long-term brand loyalty and brand equity. Research about red or blue logos draws interesting findings, suggesting that blue promotes relaxation, tranquility, improved mental health and higher levels of trust. Contrastingly, red is associated with danger which leads to avoidance (Su, Cui, & Walsh, 2019). Physio-physiological responses are noted with colors as well, showing that exposure to red increases arousal (Sundar & Kellaris, 2017). The use of skin conductance, heart rate variability and facial electromyography are just some of the validated measures that can shed light into physiological responses to logo colors. The listed biometrics have versatility in their research application and can help differentiate small changes in visual stimuli to create valuable results. 

The use of psychological tests or psychophysiological tools in market research creates an approach that when integrated with traditional tools, such as MaxDiff in HCD’s MaxImplicit, can identify innovation opportunities to stand out to consumers by identifying needs and perceptions. Factors, such as brand image, target audiences and overall goals for the emotions experience are part of how colors should be selected. If there is a company that has a product with toxic chemicals, a color that elicits a peacefulness is not ideal. Using the color red can help provoke a sense of urgency and caution. The cues of a color can facilitate the company’s communication strategy; however, the best color to use is subjective based on the type of message attempting to be conveyed. 

Another factor to evaluate when considering logo colors is the target audience. What demographic is the company trying to reach? The way a logo color is interpreted by the consumer is affected by the culture in which they are socialized. Nationalities and cultures may discriminate certain colors differently (Huang, Lin, & Chiang, 2008). White, for example, can have two meanings depending on the country and context. Traditionally, white is worn in China as a symbolic gesture of sorrow at a funeral, while wedding dresses worn in America symbolize purity. Company logos can trigger associations in memory through color (Chung & Kinsey, 2019). The company must plan how to best use the color associations within the target demographic to help trigger brand-name memory, and thus reinforce nonverbal communication.    

Visual retention is a key factor in recalling elements, such as shapes, words, and patterns, to associate a logo with a brand. HCD employs eye tracking in research to gain a quantitative understanding of consumers’ gaze behavior. The logo has a lot of potential to impact the consumer, so it is crucial that the visual catches the attention of the consumer and keeps the consumer engaged. Eye tracking reveals where visual attention is focused, sharing if this form of communication is even seen. Observable attributes are used to help consumers infer information, especially when there is no prior knowledge of the company. Consumers are more likely to remember naturalistic context colors (such as a yellow lemon as opposed to a blue lemon). By creating a color-context familiarity within the logo, there is an opportunity to communicate a message that is more likely to be retained by the consumer. 

Creating a memorable logo helps to differentiate among competitors. Color helps consumers remember an image, making a longer impression when compared to black and white pictures (Brédart, Cornet, & Rakic, 2014). Bright colors elicit positive emotions, whereas dark colors provoke the opposite response. These associations carry over into logo research, where bright colors receive preference over dark colors by consumers (Chung & Kinsey, 2019). Color preference also affects logo recall and recognition through its ability to attract visual attention (Huang, Lin, & Chiang, 2008). 

Color is a contributing component to how a brand logo will appear visually appropriate, attractive and effective. The selection process is a major part of the brand, since the logo is a key vehicle in expressing visual communication. Influential logos also provide consistency and continuity of a company, thus giving meaningful contributions to the company’s identity. Advanced tools, such as neuroscientific measures or psychological tools, could be employed to help make a more informed business decision about this cornerstone for a brand.

HCD is a marketing and consumer sciences company that provides expert recommendations by employing traditional and applied consumer neuroscience to optimize the design of products, experiences and communications. We are “methodologically agnostic” and approach each client inquiry as a unique market research challenge. Our customized solutions employ the most appropriate research tools based on the specific objectives. These tools can include traditional research, psychophysiological measures, psychological testing, or a synergistic combination of these methods.


Brand Assets. (2019). Retrieved November 6, 2019, from

Brédart, S., Cornet, A., & Rakic, J. M. (2014). Recognition memory for colored and black-and-white scenes in normal and color deficient observers (dichromats). PloS one9(5), e98757. 

Chung, A., & Kinsey, D. F. (2019). An Examination of Consumers’ Subjective Views that Affect the Favorability of Organizational Logos: An Exploratory Study Using Q Methodology. Corporate Reputation Review, 1-12.

Fajardo, T. M., Zhang, J., & Tsiros, M. (2016). The contingent nature of the symbolic associations of visual design elements: The cases of brand logo frames. Journal of Consumer Research, 43(4), 549–566. doi:10.1093/jcr/ucw048

Figure 1. Examples of logo colors of low (red) and high (green) eco-friendly colors. Adapted from “How Logo Colors Influence Shoppers’ Judgements of retailer Ethicality: The Mediating Role of Perceived Eco-Friendliness,” by A. Sundary & J.J. Kellaris, 2017, Journal of Business Ethics, 146 (3) 685-701.   

Henderson, P. W., Giese, J. L., & Cote, J. A. (2004). Impression management using typeface design. Journal of marketing68(4), 60-72.

Huang, K. C., Lin, C. C., & Chiang, S. Y. (2008). Color preference and familiarity in performance on brand logo recall. Perceptual and motor skills107(2), 587-596.

Su, L., Cui, A. P., & Walsh, M. F. (2019). Trustworthy Blue or Untrustworthy Red: The Influence of Colors on Trust. Journal of Marketing Theory and Practice27(3), 269-281.

Sundar, A., & Kellaris, J. J. (2017). How logo colors influence shoppers’ judgments of retailer ethicality: The mediating role of perceived eco-friendliness. Journal of Business Ethics146(3), 685-701.

Fame, Fortune or Failure: The Life of Celebrity Endorsements

Athletes, comedians, singers, actors, and more recently social media influencers are often strategically paired with certain brands in an effort to grow both the reach and image of the brand. Celebrity endorsements are in all forms of media consumption. The term “celebrity” implies that the individual has experienced some level of distinguishable accomplishments within a disciple (Osei-Frimpong, Donkor & Owusu-Frimpong, 2019). Companies utilize celebrities for their pre-established public recognition to help promote a product, service, good or brand. The allure engrained in the celebrity status plays on the consumer fantasy, thus enticing purchase decisions. Components such as activeness, likeability and trustworthiness are valued to determine if the persona meets the expectations of the viewers and the company. The celebrity chosen should represent the values of the product, while also having his/her personal charisma transfer a new component of excitement to the brand.

Overall the objective of celebrity endorsements is simple: to sell more products.  The celebrity is encouraged to inform the public, while also influencing ad effectiveness, brand recognition, brand recall, purchase intention and buying behavior (Osei-Frimpong, Donkor & Owusu-Frimpong, 2019). Yet, celebrity lives are explored and exploited in ways that often lead to undesirable scandals or negative press. Adverse situations can transfer to companies associated with the celebrity, creating undesirable effects (Runyan, White, Goddard & Wilbur 2009). Before a company commits to a marketing campaign with a celebrity, it is imperative to ensure alignment with their short- and long-term brand goals.   

Celebrity Endorsements: The Origin Story  

Figure 1: Queen Victoria with her daughter, Princess Beatrice, being featured in a Cadbury’s Cocoa print ad from the 1890s (ALAMY, 2010).

The use of celebrities in advertisements dates as far back as the late nineteenth century with Queen Victoria and Cadbury’s Cocoa. Endorsements continued to emerge with the growth of cinema, radio and television. For some time, it was considered taboo to associate with a brand, essentially considered a sell-out when an actor would be a “brand presenter.” That stigma evolved over time as the industry grew and commercials became more common and celebrity endorsement became accepted (Erdogan, 1999). 

What is a “celebrity?”

Endorsers can be broken down into three general categories: typical, celebrity and expert. A typical endorser is a noncelebrity spokesperson, while an expert has comprehensive knowledge about a specific topic (Kusumasondjaja & Tjiptono 2019). When the term celebrity is thrown around, a main criterion includes having some type of fanbase. More recently, with the rise of social media, influencers or “micro-celebrities” have joined the ranks as social media experts. Outlets, such as Instagram or Snapchat, have made becoming a brand ambassador or a product reviewer a full-time job for everyday people. This niche pushes the boundaries between celebrity, expert and average— and marketers have taken notice.    

Consumers use social media as a consistent part of their daily routine. In 2018, Instagram users spent 257 minutes monthly on the app (Kusumasondjaja & Tjiptono, 2019). Marketers use social media to inform, influence, and target consumers of a brand or product, while also maintaining customer relationships, developing customer-based brand equity, and monitoring customer perceptions. Sharing images that are mobile-friendly has proven to be an effective, persuasive tactic. Social media further perpetuates learned associations from endorsers and products via repeated exposure (Erdogan, 1999; Kusumasondjaja & Tjiptono 2019).

Self-branding, creating branding qualities around an individual, has had a major influx on platforms such as Instagram, Facebook and Youtube. As public figures, individuals can build a following that target specific demographics and popular topics such as fitness, beauty or food (Khamis, Ang & Welling, 2016). Marketers can easily determine if the influencer’s target demographic aligns with the product based on the content shared to the millions of followers for promotion (Kusumasondjaja & Tjiptono 2019; Schouten, Janssen & Verspaget, 2019).

Since influencers are willing to expose so much of their daily life with the public, often focusing in a specific domain, consumers will rely on their judgement. Although companies are seeking a universal goal among endorsers to push their company-sponsored content, some research suggests that there are higher levels of consumer trustworthiness in influencer promotions than celebrity endorsements (Schouten, Janssen & Verspaget, 2019). Yet, by influencers assuming the role of an “ordinary” person to appear relatable, the perception of expertise may diminish.

Trust and motivation are some of the key components to successful endorsements. Transparency in terms of sponsored content on social media has been at the forefront of the United States Federal Trade Commission (USFTC). To give more protection to the consumers, endorsers are required to clearly disclose any monetary incentives from a company when reviewing a product on a social media platform. Captions often include “#ad, #sponsored, or #promotion” as a means of disclaiming any confusion about their relationship with a company. Celebrities are often not as explicit when sharing information or reviews about a partnership, resulting in complaints with the USFTC (Lookadoo & Wong, 2019). The lack of consistency in revealing sponsored content among celebrities may contribute to consumers placing greater trust in influencers, but ultimately the endorser must be aspiring to effectively drive a purchase. More so than influencers, celebrities are perceived by consumers as motivating leaders (Osei-Frimpong, Donkor & Owusu-Frimpong, 2019).It is the celebrities who have social influence based on conforming expectations and providing information about a product. Through the normative and informational means of persuasion, celebrities can really help develop purchasing patterns.

How well are endorsements working—is it worth it?

Some companies, such as Fabletics by Kate Hudson, have dedicated a sizable investment in promoters ranging from celebrities to influencers. By saturating the target demographic with similar representatives to spread the word about the brand, community, message and deals, the company hopes to generate repeat purchases and loyal consumers. Fabletics even launched a promoter program in the beginning of 2019, including metrics such as likes, comments and social reach to assess promoter success. And for Fabletics, the promoter program appears to be successful! Fabletics boasts on its website that it currently has over 1,500,000 VIP members, with 12,800,000 orders shipped. The company program works to create long-term, candid relationships to further engagement and future purchases. Kate Hudson’s company is honing in on each type of endorser with the expectation of return on investment from the power of the #recommendation.

Don’t let the fame get to your head…

Other research using methods like eye-tracking is being used to assess the effectiveness or even harm that endorsers can do to a product or brand (Feliz & Borges, 2014). Based on the level of fame recognition, the use of a celebrity may distract attention from the product or it may promote more positive associations towards purchase intent (Osei-Frimpong, Donkor & Owusu-Frimpong, 2019). So, where do we draw the line between attractiveness and distractedness? As with most things, the answer is subjective.

One repeatedly understood trend among consumers is the value placed in trustworthiness, likability, persuasiveness, believability, and a move towards honesty and accountability (Feliz & Borges, 2014). Consumers understand that a foundational aspect in being an endorser is communication. The importance of clarity is further recognized by influencers who must disclaim any affiliations or incentives upfront to dispel any concern for bias. Furthermore, influencers must actively work to establish foundational similarities and evoke wishful identification with their audience to compel consumers to engage in their content (Schouten, Janssen & Verspaget, 2019). Unlike noncelebrities, consumers inherently consider celebrities to be more credible. Even if the product lacks any legitimacy, consumers are usually drawn to the attractiveness and familiarity of the celebrity (Osei-Frimpong, Donkor & Owusu-Frimpong, 2019). By initially capturing interest, a celebrity is granted a window of opportunity to instill the product message and spotlight characteristics that are attractive to consumers. When the consumer creates associations and becomes acquainted with the endorser, trust is built. To ensure the effectiveness of an endorser, pre-testing is a useful tactic to project how a certain individual-product combination will play out.

Is there really “no such thing as bad publicity?”

Consumers, as normal human beings, need to learn quickly in today’s fast-moving, volatile world. Confirmation bias, our tendency to assume new information supports past experiences or beliefs, adds an important element when considering endorsement deals. Consumers invest in not only the function of the product, but also the meaning attached to it. Similarly, consumer associations of a celebrity endorser can transfer to the product (Runyan, White, Goddard & Wilbur 2009). For example, if a celebrity is known for breaking the law or indulging in inappropriate behavior, consumers are more likely to believe slander in the tabloids. Even if the scandal is later proven to be untrue, the celebrity image can be tarnished by conspiracies, thus negatively influencing the company via association. While celebrity endorsements are more often successful, there is a high risk that investing in an unpredictable celebrity could have negative effects.

Sidenote fun fact: Pepsi and Beyonce signed a celebrity endorsement deal for $50 million in 2012. That’s a lot of honey for Queen B and her Beyhive.      

Pepsi has had its fair share of flops or controversies when it comes to celebrity endorsements, and yet continues to utilize this tactic. Michael Jackson, Madonna, and most recently Kendall Jenner, have all had negative scandals associated with ads for Pepsi, yet the company continues to spend on celebrity initiatives (Erdogan, 1999). A company must remember that it is not only the celebrity that gets scrutinized, but it can also be the content of the ad itself. The “Live Now” campaign with Kendall Jenner drinking a Pepsi hoped to captivate the young target demographic by having a unifying message. In this situation, the ad involves a protest with young demonstrators that Kendall Jenner joins and gives a Pepsi to an officer. The problem was that Pepsi overlooked the potential of polarizing the target audience. And in today’s fast-paced social media, many on both sides were quick to rip the ad and company apart, thus ultimately undermining the “unity” marketing goal (Taylor, 2017).

So, what were the consequences? Ironically, 44% of US consumers felt more favorably towards Pepsi, with only 25% having a more negative view, and 31% having an unchanged perspective showing that the small vocal group dominating social media platforms did not accurately represent consumers (Taylor, 2017). The Kendall Jenner Pepsi incident demonstrates the importance of strategizing image polishing to prevent public controversy during the advertisement run, but also that the repercussions may not be all that detrimental in the long run.

Another common objective of celebrity endorsement involves brand repositioning or accessing and transferring the celebrity capital (public image and level of recognition) to a brand. Anticipating the stage life-cycle of the celebrity, his/her current position in it and the likelihood of the cycle to continue needs to be considered (Erdogan, 1999). Kendall Jenner fit the mold of the target demographic of Pepsi’s youthful and exciting image, and as part of the Kardashian empire, Kendall Jenner is well known among young adults and adolescents. The ad just failed to account for the environment to which it was releasing the ad and all its interpreted messaging.

So, what can or should a company do first?

In order to ensure the celebrity chosen is a good fit, pre-testing methods can be utilized to gauge the target audience in order to better prepare for a campaign launch. Implicit testing, MaxDiff and certain types of biometric tools, such as galvanic skin response (GSR), have capabilities useful as diagnostic tools to better understand if a certain public figure will help or hinder an ad. Arousal has been tested as a strong indicator of web motivational power, while pleasure has been shown to suggest acceptance and further learning (Kusumasondjaja & Tjiptono 2019). Learning about cognitive and affective states helps to uncover what captivates the audience. As mentioned previously, using pre-testing for celebrity campaigns can minimize risk by revealing the influential components and preventable problematic situations. Negative publicity does not significantly influence the associations between celebrity endorsers and consumer purchase intention according to Osei-Frimpong, Donkor, and Owusu-Frimpong (2019); consumers simply put value in other factors when making a buying decision. To reap all the benefits of the investment of a celebrity endorser, it would only make sense to take precautionary steps to ensure high rates of success.

In short, celebrity endorsements are an impactful marketing strategy, as seen in the reoccurrence of this marketing strategy in all forms of media consumption (Kusumasondjaja & Tjiptono 2019). Celebrity endorsements include benefits such as developing a brand personality, relaying a message and grabbing the attention of the viewer (Runyan, White, Goddard & Wilbur 2009); however, consumer evaluation is fickle. To minimize the risk of negative perception, marketers must evaluate the type of impact a celebrity will have on their advertising process.  

Be Cognizant, Not Cocky

Celebrity endorsements, over any other kind, have been proven to be most influential (Kusumasondjaja & Tjiptono 2019). With this powerful impact comes added responsibility. Social and cultural dimensions will have a stronger effect when utilizing celebrity endorsements. Be aware. It is important to be sensitive to the climate of whatever topics are being addressed in the ad campaign. Consider all perspectives and be flexible when listening to critiques. Target demographics resonate with campaigns that are relatable. The industry is molding to emphasize effective communication. Marketing must embrace the new mediums of consumption to perpetuate engagement in order to take advantage of the new data potential. Liking, in both the traditional and now technological sense, is an extremely valuable metric. Presumably, people tend to buy what they like. Implementing a focus on consumers immediate reaction to the ad in conjunction with the traditional measures of brand attitudes, intentions and sales will provide stronger insight into the endorser’s influence (Feliz & Borges, 2014). By marrying strategic traditional tools with newfound innovations, an environment can be established for the right celebrity to enhance any message.


ALAMY. (2010, January 19). The best Cadbury advertisements over the years. Retrieved from

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Felix, R., & Borges, A. (2014). Celebrity endorser attractiveness, visual attention, and implications for ad attitudes and brand evaluations: A replication and extension. Journal of Brand Management21(7-8), 579-593.

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Brainstorming: The Evolution of Thought Theories

Various types of mental frameworks have been developed in attempts to conceptualize the mind and the way we, as humans, think. Classifying the mind, perception and rationality is intended to aid in the explanation of consumer decision-making. This attempt to clarify conscious versus subconscious behavior, motivation and anticipation has inspired numerous researchers to form creative ideas about how the mind works. While applications of decision-making research are infiltrating new fields constantly, the foundational philosophies have supported it for decades. Deliberate and intuitive thinking are approached in various ways. Below is a highlight reel of important theorists who impacted the trajectory of classified thinking. Many theorists are not included in this condensed summary; however, it is important to note at least some of the vast research dedicated to the mysterious theories of the mind. 

The Head and the Heart:

It seems fitting to start with one of the most famous founding fathers of psychoanalysis, Freud. Sigmund Freud introduced a tripartite model of the psyche: id, ego and superego (Freud, 1923). The id is defined as the unconscious, instinctual and primitive aspect of the mind. The superego involves moral consciousness instilled from a parent or authoritative figure during development. The ego mediates between the id and superego as a conscious force through reason. Freud was one of the few psychoanalysts who introduced the idea that behavior is a result of both implicit and explicit factors. Freud’s attempt at explaining the mental life set a foundation for many theorists to follow. Researchers such as Nisbett and Wilson (1944), Stanovich (2004), Wason and Evans (1975), and Wilson and Dunn (2004) all agree with the idea that unconscious processes control behavior without our awareness. Other psychologists, such as Bargh and Ferguson (2000), further believe that the subconscious can even act deliberately. Discussions about thinking can easily turn to a question of ethics, which it often does. Conversations about free will and predetermined destiny are very much a part of the dialogue. It is the relationship and influence of implicit and explicit responses, or dual-processing thinking, that sparks such commentaries and questions that remain consistently at the forefront of our curiosity. 

Each dual-processing theory involves both rule-based processing and associative processing. Rule-based processing is conscious, explicit, motivated behavior, while associative processing is a learned behavior that occurs automatically. Theories springboard from rule-based and associative processing into unique interpretations about how each component, either independently or simultaneously, influence behavior (Smith & DeCoster, 2000).

Kahneman and Frederick (2002) discuss how implicit and explicit modes of thinking operate in conjunction with one another within their literature review. The discussion analyzes how terms have varied over time with ultimately the same meaning behind the new phrases (Kahneman & Frederick, 2002). There are so many synonyms associated with dual systems of thinking (see Table 1a and 1b) that information becomes unclear. Although it can be challenging at times, it is important to not be bogged down by the synonyms for rule-based processing and associative processing. Ultimately, the vast literature is trying to find the right terms to be able to convey an abstract concept into an educational message. The public became much more familiar with the dual system thinking through Daniel Kahneman’s novel, Thinking Fast and Slow which helped unify the terminology. System 1 is defined as the unconscious or instinctive reaction, while System 2 is considered a slower, more rational or deliberative way to comprehend ideas or stimuli (Kahneman, 2011). An honest attempt to keep thinking about thinking simple.

Tables 1a and 1b: Taken from Evans (2008), lists various dual-processing synonyms commonly used within the literature (1a). 1b includes attributes of dual systems of thinking, categorized into clusters.

The irony in problem-solving how we, as humans, problem-solve pushes research to further the conversation and provoke challenging discussions. Petty and Cacioppo (1986) developed a dual-processing theory model to explain how a resulting attitude varies based on the way information is processed. The effectiveness of a message and its various apparatuses to persuade one towards a certain behavior is not only the focus of Petty and Cacioppo’s work, but the overall objective of marketing. Many researchers, including Petty and Cacioppo, worked to understand how different approaches warrant various responses. Fazio (1986) also contributes to this dialog by proposing that attitudes drive behavior via associations given to objects, except when there is deliberate motivation to bring an awareness to one’s attitude.

Attitudes and biases that effect how consumers act are often connected to dual-processing theories. Other runoffs of dual processing include the study of implicit or explicit bias in stereotypes by Devine (1989). By studying the relationship between stereotypes and prejudices, Devine (1989) concludes that implicit responses may be independent of personal explicit impressions. A lot of social science work involves observing how we as humans react through our conscious bias.  

While these contributions are helpful, any dual-processing theory is subject to critics and rebuttals which help to inspire innovative ideas. Evans (2008) summarizes not only the different labels and attributes of numerous dual-process theories in literature, but also reminds readers that there is no strong evidence to support two thinking styles—it is just a way to empirically classify abstract ideas. Evans also argues that assuming every single component of thinking fits under one of two umbrellas may be faulty (Evans & Stanovich, 2013). Keeping a fresh perspective can be challenging since it is easy to get confused by the complexities of this type of research; however, Freud did not solely inspire the mass contributions to the formation of process thinking. As we continue to skim the surface of thought theories, there are a few other notable philosophers to address.

Responses: Automatic or Conditioned

Shifting the perspective to the origins of stimulus-response relationships brings us to Ivan Pavlov. Pavlov is known for his classical conditioning experiments where dogs were given food by experimenters in white coats. The dogs eventually developed an association between white coats and food, thus, were excited to see an experimenter in anticipation of the food (Seligman, Railton, Baumeister, & Sripada, 2013). Classical condition experiments have been replicated and applied to various environments and subjects, as you can see in Figure 1. The free learning model inspired one of the two components utilized in Pezzulo and Rigoli’s 2011 decision-science study. Pezzulo and Rigoli (2011) analyzed conditioned participants responses to a stimulus with the addition of having the participants consider the implications of the action. Three simulations included various choices that led to different motivating implications. Think about if a friend offers you food when you are famished compared to when you just ate a meal. Depending on your level of hunger, your response may vary. Moreover, Pezzulo and Rigoli’s work also intertwines the analysis of positive and negative projections (aka anticipation or dread). The study concluded that considering the overall objective rather than one’s current state leads to a higher reward since the future internal states are given values, thus influencing a decision. So, the anticipation could be the difference between that glorious first bite of a meal while your stomach is growling or feeling as if you are about to roll out the door from overeating. How you predict the food will make you feel will play a part in deciding if you will accept or decline your friend’s offer.

Figure 1: To provide another example of classical conditioning, (1) demonstrates a dog drooling in the presence of food and (2) not drooling at the sound of a bell. However, by ringing the bell when food arrives (3), the dog creates an association where hearing the bell=in the presence of food= drooling. Therefore, if the dog hears the bell and assumes food is coming, the dog will start to salivate—even if there is no food (4). 

Rescorla-Wagner’s model also branches off the work of Pavlovian conditioning in a different direction by analyzing motivation and reward based on anticipation of an unconditioned stimulus. According to Rescorla and Wagner, if something exceeds your expectations, the causal connection is strengthened. Contrastingly, if you are anticipating something and are let down, the connection is weakened, and in turn, you will try to avoid it in the future (Siegel & Allan, 1996). By anticipating an exhilarating feeling, chances of being disappointed increase (van Dijk, Zeelenberg, & van Der Pligt, 2003). Having this type of human response could be why so many critics were not impressed with the last Game of Thrones season. The hype from the previous episodes caused viewers to have extremely high standards that were not met, thus, causing people to react negatively. The model Siegel and Allan (1996) propose expands to various topics of human judgement, learning, perception and regulation. Reasoning is also discussed in this model to determine why a decision is made. It concludes that humans use transitive inference abilities when making decisions. So, when you are presented a situation repeatedly, you will be better equipped to determine how to respond. Critics are eagerly looking for loopholes (such as an unintended Starbucks cup cameo in a world of dragons), since the final season did not match the attention to detail as previous seasons. According to Siegel and Allan (1996), the Rescorla-Wagner model is a large part of the groundwork of basic learning processes research. We can thank the world in Winterfell for reminding us the cliché-but-true quote, “no expectations, no disappointments.”

Whether watching Game of Thrones or learning more about biometrics, advances in technology pushes content in research and entertainment. Kable and Glimcher (2007) examined aspects of decision-making science with the use of an fMRI to pinpoint certain brain regions that may be associated with reward. Their findings indicate that the ventral striatum, medial prefrontal cortex and posterior cingulate cortex all experience an increase in activity when a participant receives or expects a reward (Kable & Glimcher, 2007). While these findings are under certain assumptions due to the nature of fMRI, the research implies that the brain reacts to rewards when goal-oriented choices are decided. When deciding between choices, whether it be a second helping of mashed potatoes or marrying someone, it involves reflection on information and determining what response will have the most advantageous outcome. The expectation or delivery of a reward is a part of how we continuously strive for the best option. 

“Act as if what you do makes a difference. It does.”

– William James

Motivation to pursue anything, especially in finalizing a decision, is a major aspect in what drives our behavior. Strack and Deutsch (2004) reviewed literature on motivation with an overall takeaway that suggests social behavior includes impulsive actions and reflective behavior. From their findings, these responses have underlying adaptive mechanisms to either avoid or approach a specific behavior. These reoccurring social trends can be considered either motivating or demoting, thus, encouraging or deflecting a certain action. Purchasing another box of thin-mints when you walk out of a grocery store may be an impulse action, but it may also be influenced by the charming scouts who need to sell a few more boxes before reaching their goal. The environment, context and descriptors of the situation have just enough impact to convince you to tack on an additional goodie, since it is for a good cause after all. 

While the research that focuses on motivation, avoidance and attraction are all intertwined in explicit and implicit thinking, there is one more component to address in the way we critically assess the mind. Thinking as a design for doing is introduced by the American philosopher and psychologist William James. James believed that there is a difference between expectations based on probability and fantasies that are part of a thought stream when having a futuristic thought. Subscribing to the impression that doing will only influence the future has been coined as “prospection” by Gilbert and Wilson (2007) and Buckner and Carroll (2007). 

Figure 2: (From left to right) William James, Ivan Pavlov and Sigmund Freud.  From “Who Are Some of the More Famous Psychologists?” by K. Cherry, 2017, Public domain image.  

Future thinking, just as most of these topics, are applied in decision making, action planning and emotional regulation. Superficial thought, inner speech, vague images, vivid and consuming scenarios can all be considered forms of future thinking (Neroni, Gamboz & Brandimonte, 2014). Forms of future thinking can also vary from immediate to distant. Research has demonstrated that temporal aspects of future thinking, as well as viewing it as an expectation or a fantasy, influences the type of thoughts that occur. For example, thoughts focusing on the near-future tend to involve concrete details, anticipating how something is going to be accomplished and revolve around errands. Thoughts in the distant future seem more goal-oriented and rooted in relationships (D’Argembeau, Renaud & Van der Linden, 2011). Furthermore, having a positive expectation of these ideals increases motivation since there is an association of past successes tied to the thought, while a positive fantasy decreases that sense of motivation (Oettingen & Mayer, 2002). Depending on if you perceive your thought as a fantasy or an expectation, this makes sense. If you just expect yourself to cut all carbs and sugars and envision yourself with a six-pack, it can be demotivating by feeling overwhelmed by an idealistic goal and drastic demands. Now, imagine that you plan to make it to the gym this week because you are aware of the negative health risks of not being physically fit. There is both reflection on past experiences about learning that going to the gym pays off and recognition that you want to improve your current situation. It is a short-term objective with real concerns to keep motivation high.

Stachenfeld, Botvinick, and Gershman (2017) investigate the way information is stored in our brain to predict the future. Essentially the phrase “practice makes perfect” is how we work to reach most goals. There is a repetitive cycle of trying over until the most efficient way is conditioned into our behavior. Prediction also influences motivation, especially when the associations relate to awards (Schultz, Dayan & Montague, 1997). For example, if there are three steps necessary to play a game (identifying the game via packaging, setting up the game, and then playing the game), this research supports the notion that exposure to the packaging and setting it up to play the game will motivate you prior to playing. Simply put, the package may become motivating.

Animals allocate resources or evaluate environments to further insure the likelihood of a prediction. For example, by burrowing acorns in the dirt, gray squirrels can revisit them in the future as a food source.  Noting landmarks in the surrounding area gives the gray squirrel an advantage in retrieving the acorns when food is scarce. Dayan suggests that truncation—the strategy to proactively and continuously search for information—help animals adapt to situations well (Seligman, Railton, Baumeister & Sripada, 2013). This supports the notion that decisions are based on trying to improve the current moment rather than planning the end results in the beginning (Seligman, Railton, Baumeister & Sripada 2013). Sloman and Lagnado (2015) conducted research on how your focus determines the information you know, which in turn drives your reaction. Your mind anticipates likely outcomes and rationalizes through what to expect. By creating several continuous predictions, you can complete a mental simulation through associations (Sloman & Lagnado, 2015). Each of these efforts are utilized to better our ability to respond to a situation. 

Future Memories and Other Ironies

Future-oriented cognitions require forms of memory to represent what may happen in the future. Personal experiences or general knowledge all influence prospective thinking. Szpunar, Spreng, and Schacter (2014) developed a framework in which episodic (or experiential) and semantic (or factual) forms of memory can be applied to the four modes of future thinking: simulation, prediction, intention and planning. Each mode uses parts of episodic or semantic memory differently. For example, episodic intention focuses on setting a goal to do something in the future, while a semantic intention is the mental act of setting a general or abstract objective (Szpunar, Spreng, & Schacter 2014). It would be the difference between intending to buy a new pair of shoes versus learning a new language. Both components work together as a continuous dimension rather than a variable in which a memory can be categorized.

Figure 3: The time course of the three different system processes. It is important to note that each system is based on associations, therefore, are linked to memories even if the process focuses on present or future thinking.

The work of prospective thinking must involve discussions of memory since that is how associations are developed. Neroni, Gamboz and Brandimonte (2014) discuss if episodic future thinking can help carry out an intended activity in the future. Developing not only a future objective but also creating a plan about how to implement it was shown to improve remembering when compared to either spatial mapping or no preparation. Following the trend of Kahneman’s System 1 (implicit) and System 2 (explicit) thinking, System 3 has emerged to categorize future thinking. With the use of prospection, System 3 involves considering a hypothetical situation to determine not only how someone would react, but also to determine what would be considered the ideal. With the use of imagination and exploring various potential outcomes, future thinking gives us the agency to create and innovate ideas or plans. The endless possibilities of the future allow us to create whatever we want, since nothing is certain.

Even in the midst of writing this blog, new research has been released investigating how humans gather information to decrease uncertainty in social situations. FredmanHall and Shenhav (2019) explain how three interrelated mechanisms—automatic inferencing, controlled inferencing, and social learning— work together to alleviate the adverse feeling of being uncertain. Automatic inferencing involves predicting behavior based on both social norms of an environment and appearances. It is during automatic inferencing where inaccurate and systematical bias can impair how we anticipate an interaction. Then controlled inferencing is initiated to update the initial impression with information gathered from the interaction, as well as using prospective thinking to develop some sense of sympathy by envisioning what it would be like to be the other person. The final mechanism accounted for is social learning, where the information from both the past and current encounter are considered to inevitably determine how to approach a situation and diminish levels of uncertainty with the newly created perspective (FredmanHall & Shenhav, 2019). This research provides insight into how uncertainty motivates social behaviors claiming we have these mechanisms in place to “minimize the aversive affect associated with being uncertain about our own future states.”

Past, present and future thinking are not as much a linear timeline as they are intertwined processes. There are patterns that begin to emerge from selected thought theories mentioned. Taking these trends into consideration, we can accumulate a better approach for ideas that may better conceptualize how to discuss, categorize and implement mechanisms of thinking into research. Through the lens of implicit, explicit and prospective thinking, we can unpack components of memory, motivation and anticipation to really learn about how humans choose to approach the world around us.


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Back to the Future: System 3

Our whole lives are spent anticipating what is yet to come. The time course of decision-making varies depending on the outcomes of both action and inaction. Based on the posed inquiry, you may have limits to how long you can consider your options to reaction. Deciding if you take the next exit on the highway, change the color of a brand logo, or dodge the football heading towards your face all require various amounts of influence from different styles of thinking. Past, present and future systems of thinking are strongly integrated. The way we prepare for the future is a large part of how we conduct ourselves in the present moment, and yet, both are influenced by past experiences. Through anticipation and imagination, the intentions and thoughts towards the future can prove themselves to be useful for leading action. 

Systems of thinking are utilized in literature to categorize abstract information such as thought processes. The research of Epstein (1994) states, “…there are two independent systems for processing information, experiential and rational, and that experiential relative to rational processing is increased when emotional consequences are increased.” Epstein’s approach at dual cognitive processing is expanded upon by Daniel Kahneman’s Thinking Fast and Slow. The book states that System 1 is unconscious, instinctive, and fast reactions. Thought of as a reflex, System 1 triggers an automated thinking process. Since it is considered nearly an instant decision, System 1 can have systematic errors for everyday decisions. There is little to no attention required for System 1, thus, making it possible to quickly decide the multiple decisions necessary to get through the day (Kahneman, 2011). System 1 thoughts can vary from running from the sounds of danger to preferring a brighter color bottle of shampoo. Small impressions enable us to create shortcuts, therefore, quickening our choices without deliberate thought. System 1’s thought process creates a continuous flow of interpretations. We can engage with the world around us while processing the vast amount of information experienced at any given moment because of System 1 thinking.

Figure 1: A general overview of System 1 and System 2.

 Unlike System 1, System 2 is described as the slower, more rational and deliberative notion of attention. System 2 requires our focus because it is a part of how we consciously do work, build relationships, and develop ideas. The self-aware process is critical and logical by constructing thoughts in a series of steps (Kahneman, 2011). If our attention on the stimulus is wavering, it is reflected in the performance. Fully engaging with information, such as providing feedback, is part of System 2 thinking. Challenging questions during an interview, bracing for the starter gun in a race or trying to solve a murder mystery on Netflix are a part of the System 2 thinking process.     

While these two concepts cover a large portion of thought processes, there has been an argument for an additional type of thinking involved in decision-making. System 3 considers the use of imagination by using past experiences to anticipate the future. Being able to create a sensory experience in your mind by conjuring up different scenarios is a unique progression that assists choosing what action to ultimately choose. Both System 1 and System 2 styles of thinking are utilized in the process by acting on associations and focusing on past experiences. With those contributions from the other systems, System 3 creates predictions in the mind and then prioritizes what is the best likely outcome of a potential decision. System 3 thinking is combined with the other modes of thinking by allowing consumers to deliberate on theoretical outcomes, and thus, experience an emotional preview that could be elicited by future-facing possibilities. Consider how you would feel if Google started to charge you each time you searched a new topic. Would you be confused, frustrated, appalled or a combination of these emotions? Do you think you would start using Bing in protest? These theoretical situations provide an idea of how you would feel and react to a potential future change. It is within the practical strategy of System 3 that provides an opportunity to understand what motivates consumers to make choices.

Figure 2: A visual representation of the three systems primary time courses. The systems overlap since the root of processing is associations. Therefore, retrospective thoughts can be applied to future thinking.

A major component of System 3 is the notion that prospective thinking requires generating, exploring, and evaluating past experiences to create multiple alternatives for the present. Developing mental simulations about how to progress in alternative ways create more advantageous decisions (Baumeister, Maranges, & Sjåstad, 2018). Prospection includes four aspects of simulation: navigational, social, intellectual, and memorial (Seligman, Railton, Baumeister, & Sripada, 2013). The ability to map out and envision different routes to get from one place to another is the navigational simulation. Social simulations, or episodic future thinking, include imagining hypothetical events that may happen (Szpunar, 2010). Additionally, proposing an idea in front of a board or to a close friend could be an example of a social simulation. Anticipating reactions, questions or consequences to specific conversations involves considering both the reaction of your audience and your response and is a valuable aspect of social simulation that allows you to prepare mentally for an interaction. The third type of prospective thinking involves critically thinking about how to explain or expand on an idea. Intellectual simulation would be the act of planning to conduct additional research about a topic or simply preparing how you will approach the topic with others. Finally, the fourth sector of prospective thinking involves recalling a situation and developing alternative approaches. Being grateful for wearing a helmet before a bike accident or wishing you turned sooner to avoid the pothole are both examples of this type of thinking. Remembering past decisions and considering hypothetical events that could have happened is known as memorial simulation (Seligman et al, 2013) or episodic counterfactual thinking (De Brigard & Parikh, 2019).

Say you are debating buying a new shirt on Amazon. As you scroll through different styles and colors, you click quickly on one of the top recommended choices [System 1]. Then, maybe you peek at the price and start to rationalize the purchase [System 2] by creating potential outfits or envisioning what the shirt will look like on you [System 3]. While developing these mental stimulations, you elicit a certain type of emotional response about how you would feel in it. During this very common scenario, each system thought-processes has some type of influence about not only if you decide to add the shirt to your cart but also if you are ultimately going to buy it. 

Since System 3 has a major anticipatory component, it useful when attempting to launch a new concept, product, or communications approach. It also may be helpful when first starting to build a brand to see what best resonates with a target audience. The analysis behind System 3 has a lot of potential to include an implication map to visualize how consumers are influenced or graphs to demonstrate different association strengths the consumer has with various attributes. Both qualitative and quantitative analysis can be applied to System 3 research. Play-based or projective testimonies share empirical responses of the consumer, while numerical data, such as time-pressured tasks or rating scales, provide statistical findings. By combining the information from both components of data, the researchers can suggest what is mentally rewarding.

How are market research studies with System 3 conducted?

While System 3 research is still being established, there are some initial attempts at intertwining this concept into a testing setting. One method that has emerged from the development of System 3 includes having a moderator lead a participant with the use of projective techniques. Simultaneously, there are individualized tactile tasks for participants to have the freedom to create, while sharing stories and metaphors to explain responses to deliberately chosen questions.

In the following example, the research topic is about Disney World. The study begins with a series of thought-prompts, asking participants to consider the current state of the industry. The questions are intended to stimulate participants’ imaginations by prodding sensory experiences about envisioning what they hear or see when they think about Disney World. The questions shift to forward thinking by asking what they expect may happen. The transition from present situations to upcoming predictions is to help them carry over current associations to their opinions about the potential future product. Being asked to consider how the company may plan forces them to consider various stakeholders’ perspectives on Disney World.

Figure 3: An example of an implication map from System 3 survey responses. Lighter blue are what participants tend to like, while darker blue is not as popular. Larger nodes are predicted to be influential, while the smaller nodes are less likely to be important. Note this is a hypothetical example not based off actual responses.

Following the series of questions is a basic, timed, check all that apply (CATA) question about what they feel will have the biggest influence on Disney World in the future. While CATA is not able to determine intensity, using this measure can be helpful if the participants find it difficult to verbalize perceptions. An open-ended prompt then asks the participants to name an additional factor not previously listed that may be influential. This encourages the consumers to use future-facing thinking to create personal contributions that could make an impact on the company. Finally, to get a better understanding of participants’ priorities among the CATA options, additional conjoint analysis questions are included to evaluate how each option would rank between each other. The questions emphasize potential scenarios based on the responses from CATA. It could say something such as, “If Disney Springs becomes extremely valuable, which of the following trends will be boosted as a result?” The responses are recorded and utilized to create a visual diagram expressing predictions (based on size) as well as fondness (based on color). The diagram is referred to as an implication map and is intended to provide a visual representation of the responses obtained during testing.   

Figure 4: Two types of tests used to gain an understanding of participants’ characteristics and emotional function: (left) the inkblots used during the Rorschach test and (right) building blocks methodology.

Another strategy to uncover insights about consumer anticipation or aspirations includes playing with building blocks (yup, think K’nex playtime). This System 3 methodology subscribes to the same concept of the Rorschach test. Both tests use different mediums to interpret characteristics and emotional function of the participant: Rorschach through inkblots, while System 3 is via building blocks. Essentially, a moderator poses to a focus group sequential questions that become more abstract as the conversation continues. Participants respond to these prompts both tangibly, by building block models, and verbally, through metaphor explanations or storytelling. For example, let’s say that the moderator is trying to learn about the best type of work infrastructure. Building a green box can symbolize nature because this participant values time outdoors. Furthermore, the participant shares experiences where nature has inspired great ideas or motivated the participant to think outside-the-box. By having a conversation during an activity, this method transforms thoughts into structures by remembering the past to imagine future outcomes. Intangible questions about feelings are intentionally asked to initiate metaphorical thinking.  Symbols are intended to be expressed through tangible objects, hence, a green box representing nature. The conversations that occur among the group inevitably elicit themes. Within a consumer space, it allows companies to gain a unique perspective on what participants view as futuristic proactiveness. 

There are some limitations with this version of System 3 analysis. Since participants are using small building blocks to create structures, there must be a certain level of finger dexterity. Best responses tend to be from participants under the age of 60 years old. While limiting a population sample is never ideal, this is not the only component to be cautious of with this methodology. This System 3 approach is extremely interactive. It relies heavily on the use of a moderator to provide prompts, create a group dynamic and maintain a relaxed atmosphere. Also, due to the high reliance on verbal communication, this approach works best with participants that are more extraverted. It may seem silly to point out, but introverts are consumers too! Limits based on personality-types of participants leave room for inaccurate depictions of what the general consumers are interested in.     

For those extraverts who are open to sharing, there are still some questionable influences due to the reliance of a trained moderator. Subliminal influences can affect how a participant responds. Something as small as vocal intonations or body language can potentially alter a participant’s experience. Yet, a huge component of this System 3 approach is collaborative. Models created by different participants are intentionally meant to build upon each other through the group dynamic and conversation. Research where parent-child partners or male versus female groups have utilized this qualitative research approach has shown this method can cause greater influences on how an individual would respond to a prompt.

The behavioral research process of analyzing prospective thinking relies on a lot of metaphors and symbols to explain what a model means. These narratives and conversations eventually lead to themes of the session. The type of personalities within each group influences the outcome of each research session. Another aspect of the sample that is skewed involves the personality types that qualify for this type of research. Participants are screened to be more susceptible to being verbally expressive. By only analyzing a certain type of personality-type, the findings being measured can be disputed. It can be challenging to overcome these types of concerns, especially if a small sample size is acceptable in this approach. 

The two different approaches mentioned in this article are not the first—nor the last— to utilize System 3 thinking. These methodologies are meant to gain a deeper understanding of how a new product experience would appeal to consumers. These approaches focus on gaining insight into company developments such as trends, team strategies, and product uses. The notion of future thinking allows the consumer to imagine multiple possibilities and gives them agency to envision ideals for businesses to use for future developments.

When would System 3 be beneficial?  

While System 3 is a newer concept and has plenty of components to revise, the use of future thinking in general can be extremely helpful. In everyday life, we engage in “what ifs” to learn from the past to better our future (Smallman & Summerville, 2018). There are also times where we recall experiences of great success with relief to appreciate that a situation had a positive outcome, thus, reinforcing if a similar situation is to occur again, we will be prepared. Fear and anxiety are often caused by future unknowns (Brown, Macleod, Tata, & Goddard, 2002). By formulating calls to action through future thinking, we exercise a sense of control.    

System 3 is a recent development within decision-making research, which means that there are plenty of kinks that need to be worked out. The most glaringly noticeable concern regarding how System 3 works is the inconsistency in its approach. As touched on earlier, some applications insist on including a moderator that prompts participants to discuss the stimulus. Associations, by nature, have bias interconnected with it. By having a moderator, with his or her own bias, involved in the projection technique leads to some concern. No one can control the implicit bias interspersed within the intonation of the way a script is presented.  Although the moderator can consciously (*shout-out to System 2*) attempt to remain consistent among all the trials, there is no way to guarantee each probe will remain the same. This could impact the participant experience, and thus, influence the answers provided in the study. However, there are always exceptions. In situations that involve a participant who struggles to articulate his or her responses, having a moderator help understand what the participant is struggling to express can be useful. Other methodologies use online questionnaires in which the questioning implies future-thinking; yet, there is no standardized language for prospection. With the novelty of prospection research, it raises the question about the definite claims capable of being made from this type of research. This is not a suggestion to halt the studies, but rather to analyze the results like a pilot study— the information reported may be accurate, but generalizations should not be made.

The “System” labels are theoretical constructs designed to help create a language to discuss the functions and processes of the brain. While this concept is not by any means new, developing a type of diction that is universally accepted will help advancement of how the field is able to address behavioral and market research. System 3 is still very much being refined and fine-tuned to find its space within the field. Moving forward with both System 3 and other labels that emerge, it is important to be simultaneously critical and open-minded to these progressions. System 3 thinking does not solidify the future but actively works to shape alternative possibilities. An appreciation for maybes and what-ifs further the development of any critically thought-out idea. Harnessing the importance of what a consumer believes may or may not happen guides us to better steer in the direction of a successful desired outcome.   


Baumeister, R. F., Maranges, H. M., & Sjåstad, H. (2018). Consciousness of the future as a matrix of maybe: Pragmatic prospection and the simulation of alternative possibilities. Psychology of Consciousness: Theory, Research, and Practice5(3), 223.

Brown, G. P., Macleod, A. K., Tata, P., & Goddard, L. (2002). Worry and the simulation of future outcomes. Anxiety, Stress & Coping15(1), 1-17.

De Brigard, F., & Parikh, N. (2019). Episodic Counterfactual Thinking. Current Directions in Psychological Science28(1), 59-66.

Epstein, S. (1994). Integration of the cognitive and the psychodynamic unconscious. American psychologist49(8), 709.

Kahneman, D. (2011). Thinking, fast and slow. Macmillan.

Seligman, M. E., Railton, P., Baumeister, R. F., & Sripada, C. (2013). Navigating into the future or driven by the past. Perspectives on Psychological Science8(2), 119-141.

Smallman, R., & Summerville, A. (2018). Counterfactual thought in reasoning and performance. Social and Personality Psychology Compass12(4), e12376.

Szpunar, K. K. (2010). Episodic future thought: An emerging concept. Perspectives on Psychological Science5(2), 142-162.

The Underlying Red Flags of Usability Testing

Usability testing has emerged in the market research world as a promising method to measure how consumers interact with a product, service or system. The goal of usability testing is to assess concepts such as understandability, learnability, operability to attractiveness or compliance (Rusu et al., 2015) in the consumer experience. Generally, when conducting usability testing, consumers complete a task while being observed by a researcher who notes if issues are encountered. At the core of usability testing, the goal is to minimize or eliminate distraction and find ways to optimize the information for the consumer. Usability testing also aims to discover ways to re-evaluate how to maximize product effectiveness and efficiency, in addition to improving consumer satisfaction (Rusu, C., Rusu, V., Roncagliolo, S., & González, C, 2015). These recommendations on how to improve performance issues are then utilized to further product development.  

Staying competitive in any market is a challenge. Those striving to be “the best” must be able to outline what and how to beat the competition in whatever category is prioritized at the top. A prime example of this can be seen in the transportation industry. The growth of mobile apps has influenced companies like Uber, taxis and Lyft. To remain competitive, Lyft invested in usability testing for its mobile app. The results suggested that the app needed to be revamped. The company redesigned the interface to fit the consumers’ wants and needs through simplifying maps and driver information (Chen, 2016). By continually striving to improve and remain up-to-date, Lyft remains competitive in the field.

Having an unbiased consumer complete tasks or interact with an item allows the researcher observing the participant to gain a better understanding of enjoyment, satisfaction, confusion or criticism in real time throughout the entire test. Companies like Lyft utilize usability testing to advance in the market without exploiting funds or time. Additionally, feedback from the target audience directly minimizes the risk of a product failing, thus, improving sales and helping the company better position itself within the market (Hawley, 2010).

So, if usability testing works, why isn’t everyone using it? It is a good question with a couple of red-flags among the answers…  

The first red flag of usability testing lies within the typical sales pitch about why it is so great: it’s overpromising by guaranteeing more concrete solutions than it can provide. A big part of the usability testing appeal is the claim to uncover a lot of answers. Having such broad and lofty goals creates vague conclusions that are challenging to apply to real-world situations (from deliriously opening a bottle of medicine from bed during a sick day to online shopping at midnight for the perfect date outfit). The oversimplified gray areas in usability testing can create confusion. Tractinsky (2017) questions if usability contributes to satisfaction, or if satisfaction contributes to usability. Odd coexisting variables, such as satisfaction and usability, are feasible due to the fluidity of the current definitions. The lack of consistency makes it possible for current researchers to structure usability testing to best satisfy the goals of a project. What usability is, and the way it should be tested/measured, should remain consistent, regardless of the type of design activity being applied.

Usability testing umbrellas many different trial methods, so much so that there is no standard approach. Having generic guidelines can lead to various interpretations of overarching measures to create note-worthy conclusions. Whatever mode of usability testing is applied to a study should neither be determined by findings, nor should it be altered to fit the framework of a research question (Arnowitz, Dykstra-Erickson, Chi, Cheng, & Sampson, 2005). Let’s say that you wanted to test a new type of scissors. Comparative usability, where multiple scissors are compared to one another, verse explorative usability, an approach to testing content and functionality of a single pair of scissors, are vastly different approaches to determining how the new scissors model resonates with a consumer. This concern could be rectified with a consistent, standardized methodology (i.e. questionnaire) or definition. If you want to run a scissors study with usability testing, a clear step-by-step approach to get valid results should be meticulously determined.  Essentially, walk, don’t run with scissors.  

Usability testing delays research progression just as interactive product design is hindered by debatable measures (Veral & Marcias, 2019). A system must be developed to help build a solid foundation of preliminary research for future research to progress from the initial findings. Such ambiguity makes it near impossible to compare studies since there is such a wide difference in approaches. If the objective is to create a reputable usability test, then it should have a consistent framework with rigorous definitions and strong evidence to reach sound conclusions.

Part of the general problem involves recognizing that any type of usability testing is a construct, not a real-world phenomenon. Maybe your dog is barking, your roommate is on the phone or the neighbor’s car alarm is going off for the third time today. Whatever the situation may be, a controlled environment will never duplicate a real-life scenario. This is especially true because environments are constantly changing. There are constraints to all research in this regard, but that must be acknowledged prior to approaching how an experimental measure will be conducted. Similarly, usability seeks to gain access to the user experience; however, that is not entirely possible since the participant always has an awareness that he or she is a part of a study. The non-naturalistic methodology (aka being told to complete a task) will not reflect the consumer’s real experience.

There are various quantitative metrics used to validate design concepts in usability testing: time spent on a task, success and failure rates, number of clicks needed to complete a task, etc. These measures cannot guarantee that the result is because of the product, system or service. For example, there is no guarantee that the person is focusing on the task rather than a meal from two days ago, and therefore could affect time spent using the item. For a stimulus to become important for a user, there must be some type of motivation to take part in the task (Hertzum, 2016). Understanding these initial mechanisms will develop a stronger methodology to analyze usability results.

The lack of a stable procedure in usability testing can lead to components of the research being problematic. One controversial point within usability research is sample size. It is common practice in usability testing to have 5-7 participants in a sample size. This range of participants is heavily debated; however, a sample size of 5 is utilized in practice frequently because it is budget-friendly (Lindgaard, G., & Chattratichart, J, 2007). While the sample size for all research remains to be subjective, it should be noted that having only a few participants provides a limited amount of feedback.

There are other common research methods that are frequently used and should be questioned for their effectiveness and efficiency. For example, one usability approach involves what is called “thinking aloud” where the participant speaks during a task while an evaluator observes behavior and listens. This method can be divided into two segments: classic and relaxed thinking aloud. Both force the participant to evaluate his or her experience in the explicit, which may or may not be what is verbally expressed. The consumer can say anything that comes to mind, which may be unnatural and forced, especially for prolonged testing periods (Hertzum, 2016). It may also be distracting for the consumer to complete the task while simultaneously explaining each step verbally, creating a time lag for task completion. With such obvious confounds, why still use this as a method? It may be thatthere hasn’t been another measure developed to extract consumer opinions that supersedes the thinking aloud method.

Qualitative data in usability testing includes observing body language, hand movement, facial expressions and facial changes like squinting. A lot of these metrics are left up to interpretation of the observer, who comes with his or her own set of bias. Yes, you can have a recording of the research session; however, a video recording only provides one angle of the respondent and still does not give insight into what the consumer is considering. Think about the subjectivity in hand motions. A gesture’s intention can vary culturally and socially. Hand gestures are used to communicate, but it’s very challenging to know exactly what something like holding your head up with your hands could mean.   

Certain components of usability testing have evolved substantially over the last few years. As interaction paradigms, technology, and software development rapidly increased, the potential avenues of usability testing have followed suit. Several studies have been conducted to refine components of usability testing such as the questionnaire and how to evaluate responses to make them more standardized. Advancements included simplifying ways to use number scales rather than descriptive words when describing a task or developing systematic ways to compare response charts automatically (Huisman & Hout, 2010; Merčun, 2014; Adikari, S., McDonald, C., & Campbell, J., 2016; Berkman, M. I., & Karahoca, D., 2016). This progress should not be undermined and suggests that professionals are aware of the strides necessary to appropriately practice usability testing. 

Usability testing, like anything in life, will have unexpected complications. Each interface upholds its own unique challenges, thus pushing the focus to develop more user-friendly goods or services (Krug, 2000). For example, web design testing can appear simple at first. However, most companies need the web design to now be compatible with not only a desktop, but also a mobile app, a tablet, and a smart phone. These different interfaces are dramatically different from a visualization perspective as well as for human-technology interaction. Something as simple as scrolling capabilities can have a huge influence on what may or may not be strong areas of interest on the stimuli. Learning to account for these unforeseen difficulties during research will help improve usability testing.

Changing trends of familiarity can also influence results within usability testing. Target audiences are another vital component of any study. Among the general population, 77% of Americans have smartphones, with younger adults being more likely to use the mobile device as their primary connection to the internet (Pew Research Center, 2018). Using a mouse or a touchscreen can make a huge impact on how someone interacts based on his or her performance with technology. Finding measures to scale hidden issues, such as the target audience’s technological capabilities, aids the battles that usability testing is facing.

Considering all these red-flags, it makes sense why usability testing isn’t the magic one-size-fits-all solution. We live in a world that requires an extremely fast turnaround in technology. Some companies bypass usability testing completely and just release to the public as much content as possible to see what sticks. Other companies, such as Lyft, want to conduct studies with the fastest turnaround possible. The last decade alone has seen extensive growth in usability because of the emergence of smartphone advancements, AI, website design, etc. It is a challenge to meet the needs of such a rapidly growing market.  

Currently, usability testing can be applied in any field (alluding to the potential that it may be spread too thin to be effective). Usability testing tries to gain a grasp of how consistently a user interacts with an interface, or memorability, and the ability to understand how to use the app, or learnability. To further understand concepts like memorability and learnability, research tools such as eye-tracking have been integrated into usability studies to help determine parameters such as visual attention (Frith, 2019). Any item being tested includes variables which influence a participant’s ability to perform on a specific task. Color, texture, font, font size, images and format are just some of the many components that contribute to the overall picture of how a stimulus is presented. There is a potential for usability testing to provide truly powerful tools for optimizing products. However, it needs to define and progress with the competitive space in which its being utilized.   

Consumers have quirks that influence the interactions between a consumer and software, products or a service. Usability professionals must challenge themselves to recognize the disconnect among testing methodologies and objectives. Defined terms will help to develop a standard methodology, and ultimately, promote a stronger research.

At HCD, we advocate for always using the right tool for the right question. Our motto is “Prove it.” For usability testing, our goal is to make sure that the methodology we use is appropriate for the experience, and that the results are meaningful and actionable. This list of red flags highlights the need for this sort of approach. As a blanket field of research, usability testing can be useful, but fraught with misuse. But if you make sure to use the right tool for the right questions, you may just be able to ensure product success. Please contact Cara Silvestri ( regarding any additional information about how we can help you better your product, service, or system.


Adikari, S., McDonald, C., & Campbell, J. (2016). Quantitative Analysis of Desirability in User Experience. arXiv preprint arXiv:1606.03544.

Arnowitz, J., Dykstra-Erickson, E., Chi, T., Cheng, K., & Sampson, F. (2005). Usability as science. interactions12(2), 7-8.

Berkman, M. I., & Karahoca, D. (2016). Re-assessing the usability metric for user experience (UMUX) scale. Journal of Usability Studies11(3), 89-109.

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Frith, K. H. (2019). User Experience Design: The Critical First Step for App Development. Nursing education perspectives40(1), 65-66.

Krug, S. (2000). Don’t make me think!: a common sense approach to Web usability. Pearson Education India.

Lindgaard, G., & Chattratichart, J. (2007, April). Usability testing: what have we overlooked? In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 1415-1424). ACM.

Hawley, M. (2010). Rapid Desirability Testing: A Case Study. Accessed online15(04), 2010.

Hertzum, M. (2016). Usability testing: too early? too much talking? too many problems?. Journal of Usability Studies11(3), 83-88.

Huisman, G., & Van Hout, M. (2010). The development of a graphical emotion measurement instrument using caricatured expressions: the LEMtool. In Emotion in HCI–Designing for People. Proceedings of the 2008 International Workshop (pp. 5-8).

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Tractinsky, N. (2017). The Usability Construct: A Dead End? Human–Computer Interaction, 33(2), 131–177.

Veral, R., & Macías, J. A. (2019). Supporting user-perceived usability benchmarking through a developed quantitative metric. International Journal of Human-Computer Studies122, 184-195.