essay on advertising research

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2021 Theses Doctoral

Essays on Digital Advertising

Gritckevich, Aleksandr

Digital advertising has seen dramatic growth over the last decade. Total digital ad spending in the US has increased 6 times between 2010 and 2020, from $26 billion to $152 billion(eMarketer). This impressive development has in turn sparked a huge stream of literature studying all the different aspects of advertising in the digital media. My dissertation contributes to this literature via two essays. In the first essay, I consider a very important topic of ad blocking, that in the recent years has become a significant threat to advertising supported content. With a specific focus on consumer and total welfare, I show the detrimental role of the adblockers’ current revenue model in decreasing content quality, consumer surplus and total welfare. In the second essay, I study demand learning in digital advertising markets, where firms learn over time how their advertising campaigns impact consumer demand by using their advertising campaign outcomes in earlier periods. By developing an analytic model, I demonstrate in several scenarios, such as monopoly and competition, that learning has an ambiguous effect on the key market parameters and, in particular, on the equilibrium advertising and quantities.

Geographic Areas

• United States
• Internet advertising
• Internet marketing
• Consumption (Economics)

More About This Work

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Research in marketing strategy

• Review Paper
• Published: 18 August 2018
• Volume 47 , pages 4–29, ( 2019 )

Cite this article

• Neil A. Morgan 1 ,
• Kimberly A. Whitler 2 ,
• Hui Feng 3 &
• Simos Chari 4  

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Marketing strategy is a construct that lies at the conceptual heart of the field of strategic marketing and is central to the practice of marketing. It is also the area within which many of the most pressing current challenges identified by marketers and CMOs arise. We develop a new conceptualization of the domain and sub-domains of marketing strategy and use this lens to assess the current state of marketing strategy research by examining the papers in the six most influential marketing journals over the period 1999 through 2017. We uncover important challenges to marketing strategy research—not least the increasingly limited number and focus of studies, and the declining use of both theory and primary research designs. However, we also uncover numerous opportunities for developing important and highly relevant new marketing strategy knowledge—the number and importance of unanswered marketing strategy questions and opportunities to impact practice has arguably never been greater. To guide such research, we develop a new research agenda that provides opportunities for researchers to develop new theory, establish clear relevance, and contribute to improving practice.

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We follow Varadarjan’s (2010) distinction, using “strategic marketing” as the term describing the general field of study and “marketing strategy” as the construct that is central in the field of strategic marketing—just as analogically “strategic management” is a field of study in which “corporate strategy” is a central construct.

Following the strategic management literature (e.g., Mintzberg 1994 ; Pascale 1984 ), marketing strategy has also been viewed from an “emergent” strategy perspective (e.g. Hutt et al. 1988 ; Menon et al. 1999 ). Conceptually this is captured as realized (but not pre-planned) tactics and actions in Figure 1 .

These may be at the product/brand, SBU, or firm level.

These strategic marketing but “non-strategy” coding areas are not mutually exclusive. For example, many papers in this non-strategy category cover both inputs/outputs and environment (e.g., Kumar et al. 2016 ; Lee et al. 2014 ; Palmatier et al. 2013 ; Zhou et al. 2005 ), or specific tactics, input/output, and environment (e.g., Bharadwaj et al. 2011 ; Palmatier et al. 2007 ; Rubera and Kirca 2012 ).

The relative drop in marketing strategy studies published in JM may be a function of the recent growth of interest in the shareholder perspective (Katsikeas et al. 2016 ) and studies linking marketing-related resources and capabilities directly with stock market performance indicators. Such studies typically treat marketing strategy as an unobserved intervening construct.

Since this concerns integrated marketing program design and execution, marketing mix studies contribute to knowledge of strategy implementation–content when all four major marketing program areas are either directly modeled or are controlled for in studies focusing on one or more specific marketing program components.

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Kelley School of Business, Indiana University, 1309 E. Tenth St., Bloomington, IN, 47405-1701, USA

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Morgan, N.A., Whitler, K.A., Feng, H. et al. Research in marketing strategy. J. of the Acad. Mark. Sci. 47 , 4–29 (2019). https://doi.org/10.1007/s11747-018-0598-1

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Received : 14 January 2018

Accepted : 20 July 2018

Published : 18 August 2018

Issue Date : 15 January 2019

DOI : https://doi.org/10.1007/s11747-018-0598-1

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A growing share of consumer-goods media spending is shifting away from traditional advertising media (television, radio, print, and outdoor).

Traditional media vs. new media.

Traditional media are also suffering from a long-term trend toward promotional expenditures which consume a larger share of the marketing budget. Many consumer-goods companies are spending less on advertising in total, as their executives strive to please Wall Street with improved short-term profits. The hope that one’s ads might “go viral” and accomplish advertising miracles on a low budget adds to the upheaval and distracts executives from the hard work of building effective advertising campaigns.

This decline of traditional media advertising in many consumer-goods companies is creating an opportunity for companies that appreciate the power of traditional advertising.

Social media, online, search, and mobile advertising all grab the headlines and attract marketing executives like moths to a flame, but we should not be dazzled or awed by the new media. They have their advantages and can be a part of the media mix, but they are probably still a distant “second fiddle” to traditional media. Indeed, we would argue that mastery of traditional media—especially television, radio, and outdoor advertising—is a far wiser strategy than aggressive pursuit of new media.

Media Choices

Strategic investment in media advertising is a key factor in corporate success. Think of media advertising as the long-range artillery of marketing; your goal is long-term bombardment to take possession of the consumer’s awareness and perceptions. A brand might have to take financial losses for a year or two while it builds awareness, tells its story, and crafts a brand image. This is the price of marketing success. Where to place strategic media spending is a daunting task in a world of infinite media choices. Here are some guidelines to help you think through the best options.

Television Advertising

Not only does the term “television advertising” refer to a commercial with color, motion, and sound (like those you see on television), it also refers to those same types of “commercials” you might see on a website, on various social media platforms, on YouTube, or on Facebook.

Thusly defined, television advertising is still the gold standard and the most effective of all media for consumer products. Television commercials have the greatest impact and tend to move awareness numbers up swiftly—with sufficient media weight. Generally, the equivalent of 100 GRPs (Gross Rating Points) per week is the lower limit of spending if you hope to see measurable increases in advertising awareness. Also, television commercials (like all advertising) will be more effective if a higher share are tested among consumers before airing.

Radio Advertising

Radio commercials can be as effective as television commercials, based on sales return per dollar spent on media. However, radio commercials seldom achieve their true potential because they tend to be inferior to television commercials in content and production quality. Typically, radio production budgets are much less than television budgets, and radio commercials are rarely tested among consumers. If you plan to use radio, pretest the commercials to make sure they work.

Print Advertising

Print advertising tends to work more slowly than television or radio. Therefore, an especially long period of time (or an especially heavy media schedule) is required to fully evaluate the total effects of print advertising. Print is an important arrow in the media quiver, however, because a share of the population tends to be heavy readers. You won’t reach this demographic with television, radio commercials, YouTube, or sponsorships of tractor races.

Outdoor Advertising

Outdoor advertising (also called out-of-home, or OOH) is especially effective as an advertising medium, if used properly. First, the message must be (a) on strategy, and (b) expressed in few words (the fewer words, the better). Second, traditional outdoor advertising (i.e., a static print ad, in effect) is more effective, in most instances, than the digital or electronic billboards, where a series of ads are shown briefly (changing ads every 8 seconds or so).

Outdoor advertising is great at extending or reinforcing the key theme of a television or radio campaign. If some of the visual elements and the key themes of a television campaign can be condensed and shown via outdoor advertising, the awareness-build of television can be accelerated. Outdoor advertising can also add a visual element to a radio campaign (e.g., show the retail package), and it can help boost the awareness-build of a print-advertising campaign.

Social Media Advertising

Social media continues to grow in importance and reach; its ultimate value as an advertising media remains to be seen. Ads and commercials intended for online delivery or social media distribution operate by the same rules as all other advertising. Television-testing techniques, for example, can be applied to commercials that look like television commercials, regardless of where those commercials are aired. Static banner ads are similar to print ads and can be evaluated by those metrics. What’s important is that social media ads go through the same research processes as other commercials do.

Advertising is primarily a strategic weapon, as previously noted. It’s the heavy, long-range artillery. Its total effects must be evaluated in the context of years, not weeks or months. Advertising cannot compete with sales-promotion and direct-marketing activities in generating short-term sales effects. But in the long term, the cumulative force of strategically sound media advertising can achieve results that cannot be equaled by sales promotion or other marketing activities.

Advertising Effectiveness

Advertising for new products tends to be more effective than advertising for established products (it’s the “news value” of the new product). In other words, it’s easier to create effective advertising for new products than it is for established products.

Given the greater effectiveness of new-product advertising, one of the most common marketing mistakes is failure to take advantage of this inherent benefit (i.e., the failure to fully exploit the new product advertising advantage).

Perhaps up to half of all advertising for established products is not effective, or is only minimally effective, based on Decision Analyst’s research. It could be said that no other industry has a failure rate as high as that of the advertising industry (with the possible exception of the promotion industries, direct-marketing industries, telemarketing industries, and other alternatives to traditional advertising).

The persistently high advertising failure rate results primarily from the lack of an accurate feedback mechanism—a lack of testing and evaluation. If an advertising agency doesn’t know when its advertising is bad or why it’s bad, how can the agency possibly improve its advertising? Marketing research can provide this feedback, but it’s often too expensive for the typical advertisement or commercial budget.

Among commercials that are effective, the degree of sales effectiveness can vary greatly from one commercial to the next. One commercial might be several times more effective than another. This indicates that the quality of advertising tends to be more important than the quantity of advertising. Nevertheless, the quantity of advertising (i.e., the media weight) must achieve a threshold level for the advertising to have any positive effects.

Limited online surveys or telephone tracking research (even with modest budgets) can monitor the cumulative effects of advertising on consumer awareness, brand image, and consumer attitudes. This is one of the simplest and most effective ways to make sure that your advertising is doing its job.

Recall of specific messages from advertising is not a very good indicator of advertising effectiveness, and some very effective commercials produce little measurable message recall. Message recall is a positive factor, but its importance should not be overstated. Brand registration , however, is always important (as opposed to message or element recall).

Brand Registration

If consumers don’t remember the brand name, the effectiveness of the advertising is correspondingly reduced. Failure to register the brand name is one of the most common advertising mistakes. The next time you review your advertising, just make sure that the brand name is clearly stated and clearly shown in the commercial. If your brand name is not easy to remember, then more emphasis must be placed on that brand name in commercials.

Advertising-Effectiveness Measures

Ultimate truth is elusive. Advertising effectiveness cannot be determined by any one measure, such as persuasion or recall. Recall is a good measure for some commercials, but not for others. Persuasion scores don’t work very well for brands with high market shares, and they cannot be relied upon for brands in poorly defined product categories.

Purchase intent works reasonably well for new products, but not so well for established products. A large number of important variables must be examined in concert to judge the potential effectiveness of advertising.

Advertising that offends the viewer, or is in poor taste, is almost always ineffective. The only exception to this rule is the commercial that presents a lot of relevant news, where the message is so important that how it is said doesn’t matter much. If viewers like a commercial, its chances of being effective are improved. Likeability, however, is not sufficient in and of itself to ensure advertising success.

From the marketer’s perspective, what are the “secrets” to achieving every company’s goal—advertising that really works? There is no simple “success” formula, unfortunately, but here are some guidelines:

• Advertising works in the arms of sound strategy. What role is advertising to play in the brand’s marketing plan? What messages must the advertising communicate? What images should the advertising project? These are strategy issues, and they bring us to this conclusion: without sound strategy, the chances of advertising success are very low. Several research techniques are available to identify and resolve strategy issues before creative development begins.  
• Homework and hard work are more likely to yield effective advertising than creative brilliance and flashes of creative genius. Great advertising evolves from research feedback, tinkering, and tweaking. Pretesting each commercial is a laboratory experiment, an opportunity to learn how to re-edit current creative and how to make the next commercial even better.  
• Big egos (creative egos, client egos, research egos, and agency egos) are barriers to the creation of effective advertising, because big egos tend to substitute wishes and emotions for thinking, reasoning, and objectivity. If your agency (or your client) is unwilling to make adjustments to improve the creative product—based upon objective consumer feedback—then you have the wrong agency (or the wrong client).  

Advertising Testing

Test your advertising. Show it to members of your target audience and see how they react. No one (not the client, the agency, or the researcher) is smart enough to know how consumers will perceive and react to a given commercial. If you can’t afford one of the advertising-testing services, test it yourself. Show the new commercial and a couple of old ones, and ask some consumers which one would most influence their interest in buying the brand. If you can’t afford that, then ask your spouse what he/she thinks of your advertising. (The latter method is surprisingly accurate, but beware it can lead to a messy divorce.)

Once you have chosen an advertising-testing system, stick with it so that you (the agency, the creatives, the brand managers, and the researchers) all learn how to use the system and how to interpret the test results for your product category and your brand.

“Sticking with” and learning a testing system is more important than which system you select. No testing system is perfect. No testing system can be used blindly. A large dose of intelligent human judgment must always be incorporated into the advertising-evaluation process.

If budgets permit, test at the rough, as well as the finished, stages of creative development. Once you’ve spent $900,000 producing a finished commercial, you might not be very open to research that questions the effectiveness of that commercial. Testing at the rough stage can help you refine the creative before spending the big dollars on production. The more early-stage executions you evaluate, the greater the probability that the winning execution will be effective.

Testing at the finished stage can help guide final editing or re-editing of commercials, can help determine how much weight should be put behind the creative, and can provide understanding to help guide campaign evolution and the creation of subsequent commercials.

Final Thoughts

Be sure your advertising puts enough emphasis upon the brand name so that the audience will remember it. Don’t forget to give buyers some positive information about your product (i.e., a reason to purchase), and, last but not least, don’t forget to pretest your advertising. Good luck!

About the Author

Jerry W. Thomas ( [email protected] ) is President/CEO of Dallas-Fort Worth based Decision Analyst. He may be reached at 1-800-262-5974 or 1-817-640-6166 .  

Copyright © 2022 by Decision Analyst. This article may not be copied, published, or used in any way without written permission of Decision Analyst.

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The Emotional Effectiveness of Advertisement

Associated data.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Based on cognitive–emotional neuroscience, the effectiveness of advertisement is measured in terms of individuals’ unconscious emotional responses. Using AFFDEX to record and analyze facial expressions, a combination of indicators that track both basic emotions and individual involvement is used to quantitatively determine if a spot causes high levels of ad liking in terms of attention, engagement, valence, and joy. We use as a test case a real campaign, in which a spot composed of 31 scenes (images, text, and the brand logo) is shown to subjects divided into five groups in terms of age and gender. The target group of mature women shows statistically more positive emotions and involvement than the rest of the groups, demonstrating the emotional effectiveness of the spot. Each other experimental groups show specific negative emotions as a function of their age and for certain blocks of scenes.

Introduction

Measuring the effectiveness of an advertising campaign is a major challenge for most companies, marketing professionals, and scientists in the twenty-first century ( McCarthy and McDaniel, 2000 ). Starting back in the 1960s, a first conceptual model was developed to define the effectiveness of advertising. The “Hierarchy of Effects” model ( Lavidge and Steiner, 1961 ) articulated customer response to spots in three stages: (1) cognitive, characterized by consciousness and information gathering, (2) affective, where liking for the spot and preferences for the product are set, and (3) behavior, where propensity or actual buy takes place. Following this model, the measurement of the impact of the consumer’s response to advertising campaigns was carried out through studies primarily aimed at the conscious cognitive processes of consumers (self-reporting, surveys, focus groups, etc.; Lewinski et al., 2014 ). These approaches generally failed to provide clear findings because they were not able to observe what happens in the consumer’s brain while taking a decision, they did not understand the role that emotions play both in understanding the message and in decision making, and they did not fully capture the way in which consumers process and understand cognitive responses to messages in advertising ( Morin, 2011 ; Bercea, 2012 ; Cherubino et al., 2019 ). Moreover, in most cases, these techniques generated strong biases, such as social desirability ( Benstead, 2013 ).

A new discipline, called consumer neuroscience, was born to try to resolve these voids of the previous model, going beyond emphasizing the conscious cognitive processes. Consumer neuroscience based “its objective in better understanding the consumer, through their unconscious processes. Explaining consumer preferences, motivations and expectations, predicting their behavior, and explaining successes or failures of advertising messages” ( Bercea, 2012 ).

Consumer neuroscience was developed to penetrate in the consumers’ brain, and one of its focuses is on measuring effectiveness of advertising more precisely. The focus was shifted from the cognitive processes (stage 1 of the “Hierarchy of Effects” model), which were no longer considered to be the main drivers of consumer behavior, toward emotions and sentiments (primarily stage 2), which incorporated perceptions, experience, and recall ( Halls, 2002 ). More precisely, marketing professionals and researchers, when measuring emotional responses following consumer neuroscience principles, were able to evaluate the unconscious assessment of the respondent ( Poels and Dewitte, 2006 ; Lewinski et al., 2014 ; Varan et al., 2015 ), thereby providing a greater understanding of the effects of emotion on memory ( Vecchiato et al., 2013 ).

Since the turn of the century, emotions have therefore been proposed to be a good predictor of advertising effectiveness ( Poels and Dewitte, 2006 ) with a known important impact also in the cognitive process ( Hamelin et al., 2017 ). Moreover, emotions have demonstrated to be necessary for the human function because they are strongly correlated with attention, decision-making, and memory ( Le Blanc et al., 2014 ). Emotions also had an impact on the allocation of resources to the visual system and that more attention is placed on negative than on neutral stimuli ( Öhman and Mineka, 2001 ; Algom et al., 2004 ; Estes and Verges, 2008 ).

Emotions have also been shown to impact highly on an individual’s response to receiving a message ( Mai and Schoeller, 2009 ; Lewinski et al., 2014 ). Likewise, providing an emotional message in publicity increases the audience’s attention to the advertisement, and the product enhances the product’s appeal and generates a higher level of brand recall. Indeed, advertisements with emotional content are more likely to be remembered than those conveying news ( Page et al., 1990 ).

Therefore, one necessary approach in this day and age to quantify the effectiveness of advertisements is to resort to emotions and emotional responses in the quest for properly measuring “ad liking and purchase intent” ( McDuff et al., 2015 ).

To successfully achieve this quantitative challenging task, the pillars of consumer neuroscience are cognitive–emotional neuroscience, neuroimaging technologies, and biometric measurements, which together allow for obtaining objective data about emotions after observing and studying in real time what happens inside the consumer’s brain. The available tools permit the analysis of emotional activity without cognitive biases, providing instantaneous and continuous data that can be broken down into small pieces of study.

Accordingly, both advertising and marketing companies look for new or improved models, methodologies, indicators, tools, and techniques that can evaluate and predict consumer behavior based on unconscious emotional responses, making it difficult for customers to hide their true response. Some of these tools focuses on recording in a real environment or using virtual reality (VR) the metabolic activity [functional magnetic resonance imaging (fMRI), positron emission tomography], others on recording electrical activity in the brain [electroencephalography (EEG), magnetoencephalography, transcranial magnetic stimulation, steady-state topography], and still others without recording brain activity [eye tracking (ET), galvanic skin response (GSR), electromyography (EMG) or facial expression recognition].

These neuroscience tools are becoming popular for quantifying the emotional effectiveness advertising, especially (1) ET ( Wedel and Pieters, 2008 ; Ramsøy et al., 2012 ; Lewinski et al., 2014 ; De Oliveira et al., 2015 ), (2) analysis of facial microexpression ( Teixeira et al., 2012 ; Lewinski et al., 2014 ; Wedel and Pieters, 2014 ; Taggart et al., 2016 ), (3) fMRI ( Bakalash and Riemer, 2013 ; Venkatraman et al., 2015 ; Couwenberg et al., 2017 ), and (4) VR ( Bigné et al., 2016 ).

As for the analysis of facial microexpressions, different software was used to assess the effectiveness of advertising using the Facial Action Coding System, for example, FaceReader-FEBE system ( Lewinski et al., 2014 ), GfK-EMO Scan software ( Hamelin et al., 2017 ), and FACET and AFFDEX ( Stöckli et al., 2018 ).

In all these studies, the measurement of emotions was mainly focused on understanding the seven basic emotions proposed by Ekman (1972) : two positive (joy and surprise) and five negative (anger, contempt, disgust, fear, and sadness). In some cases, valence was also analyzed, directly from facial expressions or coupled with questionnaires ( Timme and Brand, 2020 ).

It is important that all the available information is used to enrich the studies. On that regard, AFFDEX is a state-of-the-art software that, after recording people’s faces in front of stimuli, provides not only seven indicators about the likelihood on the emotional response being present in terms of the Ekman’s basic emotions, but also three additional indicators about the emotional involvement of the individual, namely, attention (focus of the participants during the experiment based on head position), engagement (the emotional responsiveness that the stimuli trigger), and valence (the positive or negative nature of their experimental experience; iMotions, 2020 ). All of the seven emotional indicators are scored in a normalized scale indicating the proportional likelihood of detecting the emotion. Attention and engagement have a range from 0 to 100, whereas the range of valence is from −100 to 100, providing an indication of positive, neutral, or negative experience. The initial thresholds are usually arbitrarily set at ±50 ( iMotions, 2020 ).

Its applications in the last few years in diverse fields are numerous, for example:

• geriatric care ( Taggart et al., 2016 ),
• forensics ( Lei et al., 2017 ; Kielt et al., 2018 ),
• pain studies ( Xu and Sa, 2020 ),
• sport ( Timme and Brand, 2020 ),
• the influence of negative emotions on driving ( Braun et al., 2019 ), and
• consumer satisfaction from tourism ( González-Rodríguez et al., 2020 ).

To our knowledge, AFFDEX has not been yet applied in marketing to its full potential. One key novelty of this study is the integration of the three involvement measures together with the analysis of the seven basic emotions to develop a framework to quantify emotional effectiveness of commercial advertising ( Figure 1 ). We believe that the joint use of these complementary measures provides new insights into the emotional response that the advertisement provokes to fully measure the effectiveness of a given spot. Moreover, as will be demonstrated in this article, the differences among scenes of the spots or among the groups of viewers of the advertisement might be analyzed in detail.

Framework to quantify emotional effectiveness by target group and/or blocks of scenes.

The theoretical objective of this research is therefore to shed new light on the quantification of the emotional effectiveness of advertising among different groups based on the measurement and joint specification of emotions and emotional involvement using the analysis of facial expressions provided by AFFDEX and its 10 indicators.

Accordingly, we propose within this framework a set of three hypotheses to measure the emotional effectiveness of advertisement. The first hypothesis is stated as:

• the average level of attention is high (≥50 in AFFDEX),
• the average level of engagement is high (≥50 in AFFDEX),
• the average level of valence is positive (≥0 in AFFDEX), and
• the predominant emotion is joy and its average level is high (≥50 in AFFDEX).

Indeed, one of the objectives in advertising communication is to achieve high levels of attention and engagement because emotions are highly correlated with decision-making and memory ( Le Blanc et al., 2014 ). Thus, high levels of engagement to an advertisement influence long-term memory and increase consumer purchase ( Loewenstein, 1966 ; Kiehl et al., 2001 ; Öhman and Mineka, 2001 ; Algom et al., 2004 ; Estes and Verges, 2008 ; Milosavlejevic and Cerf, 2008 ; Ramsøy et al., 2012 ; Le Blanc et al., 2014 ).

We propose valence to understand the general emotional experience. The analysis of valence allows us to understand the quality, positive or negative, of the emotions. A positive valence in commercial advertising reflects approaching behavior, whereas a negative valence is a sign of distancing behavior ( Timme and Brand, 2020 ). Similarly, in order to demonstrate ad liking, out of the seven emotions, the predominant one must be joy ( Tomkovick et al., 2001 ; Lewinski et al., 2014 ; Shehu et al., 2016 ).

A second hypothesis relates to the sequence of scenes or maybe block of scenes of the advertisement and the aim to measure if the emotions are stable throughout the whole length of the spot or some scenes trigger certain emotions ( Dimberg et al., 2011 ).

• Hypothesis 2: An advertisement is emotionally effective across scenes whenever the indicators for some scenes are higher on average than the indicators for the rest of the scenes in the spot.

We propose Hypothesis 2 after realizing that spots are usually broken into scenes to compare among spots that differ only in one scene or to focus on the scene of a single spot that triggers the emotions that are sought ( Lienhart et al., 1997 ; Teixeira et al., 2012 , 2014 ; Braun et al., 2013 ; Vecchiato et al., 2014 ; Yang et al., 2015 ). In fact, if Hypothesis 2 is accepted because of differences between scenes, a deeper analysis could and should be carried out to determine why some scenes are more emotional than others. If Hypothesis 2 is rejected, the conclusion might be that the scenes provoke stable emotions throughout the length of the spot.

Finally, an effective advertisement is also one that reaches its target audience and positively influences the emotional attitude and responses of the consumers ( Meyers-Levy and Malaviya, 1999 ; Lee et al., 2015 ). The experimental objective of many studies is to evaluate the effectiveness of the advertisement in terms of ad liking for the different groups of participants, differentiating between the target group and the rest of participants ( Kotler et al., 2000 ; Ansari and Riasi, 2016 ; Gountas et al., 2019 ). The third hypothesis might accordingly be stated as follows:

• Hypothesis 3: An advertisement is emotionally effective for the target group whenever the indicators for this group are higher on average than the indicators for the rest of participants.

As a test case to validate the framework, this study aims to quantify the effectiveness of advertisement using a 91-s commercial spot composed by 31 scenes made by Scotch-Brite and its line of scouring pads to celebrate the sixtieth anniversary of the brand’s presence in Spain. The spot shows scenes along the lifetime of women while the study focuses on mature women and moms as the target group to compensate their lifelong efforts on raising children and creating family ties while buying and using its products and developing the brand’s name.

The article is structured as follows. After setting the theoretical background and the framework to measure effectiveness of advertisement in this introduction, “Materials and Methods” section explains the method of analysis based on facial expressions as well as the experimental setting, including the spot and its scenes and the grouping of subjects. “Results” section shows the results by scene and gender and statistically test the hypothesis. “Discussion” section is used to discuss the possibilities of emotions being the tool for marketing in the future.

Materials and Methods

Affdex, the analysis of facial expressions and emotional reactions.

Facial expressions are a gateway to the human mind, emotion, and identity. They are a way of relating to others, of sharing understanding and compassion. They are also a way of expressing joy, pain, sorrow, remorse, and lack of understanding ( Taggart et al., 2016 ). These characteristics can be crucial while capturing the key features of a stimulus in the form of a video or image frame. Individual facial recording while watching the computer screen is compared with a biometric database that represents “true” emotional faces, while looking for similarities or even a possible match. Therefore, facial recognition is used to measure and analyze the emotional reactions of the subjects to a given stimulus.

To carry out the emotional measurements in this study, a software platform for biometric measurements research called iMotions was used ( iMotions, 2020 ). This company indicates that its software can combine “eye tracking, facial expression analysis, EEG, GSR, EMG, ECG, and surveys” ( Taggart et al., 2016 ). The platform is used for various types of academic and business research. Version 7.0 was used in this research.

The software records several raw indicators per frame based on biometric measurements or action units while an experimental subject is watching a stimulus on the computer screen: 34 core facial landmarks (jaw, brows, nose…), interocular distance, and head position (yaw, pitch, and roll).

The recorded values for the raw indicators are then transformed by the software underlying models into Ekman’s seven basic emotions. An indicator for each emotion is provided based on the probability of appearance of the emotion, so the range of values for each of them is from 0 to 100. A value of 50 is proposed by AFFDEX as an initial threshold to determine if an emotional response has been detected.

Three involvement indicators are also calculated after combining the raw values. Attention is calculated from the head position and gives an indication of the focus of the individual. Attention ranges from 0 to 100, although is not a probability. Engagement or the level of responsiveness has also a range from 0 to 100. Finally, the range of valence is from −100 to 100, providing an indication of positive, neutral, or negative experience. The initial thresholds are usually arbitrarily set at ±50.

The Stimulus: The Spot

The stimulus was a spot that belonged to a campaign to mark the sixtieth anniversary of the Scotch-Brite brand’s presence in Spain. The video presentation of the spot lasted 91 s and was broadcast on social networks 1 .

The content of the video describes the accompanying role that a mother plays throughout the life of a child, from birth to adulthood. The video was split into 31 scenes by the advertising company ( Table 1 ). There are 22 real images with family connotations, six frames with text, a black scene, the logo of the sixtieth anniversary, and the campaign’s hashtag.

Description of the scenes.

The Experiment

Recruitment to watch the spot was done through a snowball process. Snowball is traditionally used whenever the theme of study is relatively new ( Strydom and Delport, 2005 ) and for which it is difficult to find participants ( Babbie, 2008 ). This type of sampling is particularly used to influence in the buying process of both consumers and nonconsumers ( Rozalia 2007 , Venter et al., 2011 ).

The snowball process helps complete the sample based on a specified set of criteria ( Henning et al., 2004 ), once the first subjects are selected ( McDaniel and Gates, 2007 ). For this research, the target group had to be habitual consumers and users of scoring pads and older than 18 years. The first requisite, that of consumers and users, drove the sample toward women, because they are primarily those that do the housework. Seventy-eight percent of European women (84.5% in Spain) perform house cleaning (which includes dishwashing), whereas only 33.7% (41.9% in Spain) of the men do ( EIGE, 2018 ).

The recruiting process started with a first sample provided by the company that produced the spot, both consumers and nonconsumers. The first contact was made by telephone after randomly selecting the potential participants, and if available, they were scheduled to go to the research site and participate by watching the video. These selected subjects were also asked to provide contacts to guarantee a continuous chain of sampling ( Strydom and Delport, 2005 ).

The subjects were divided into two large groups: that of product consumers or the target group (mature women between the ages of 50 and 65 years) and that of nonconsumers. To further divide the nonconsumers, while keeping the gender perspective, the decision was to divide the women in two groups by age (young and middle aged) and assign men to a third group. Four groups were therefore available at the initial stages of sampling: three for women and one for men.

However, while interviewing the initial set of participants, we identified some rejection or repulse to the theme under study, that of washing dishes with scouring pads. As a consequence, it made sense to include women with a strong feminist sensitivity as a separate fifth group. This additional “feminist” group was identified by asking women the following question:

“As for the social movements that claim to incorporate the gender perspective in the different instances of society, up to what level do you identify with this type of proposal?”

The answer had to be provided using a 10-point Likert scale, ranging from 1 (nothing identified) to 10 (fully identified). Those women regardless of age who responded with scores between 8 and 10 were included in this special group.

As a result of the snowball process, 100 people participated in the experiment (80 women and 20 men, between 18 and 65 years old). The five groups with 20 people each were then defined as follows:

• group 1: young women, aged 18–29 years
• group 2: middle-aged women, aged 30–49 years
• group 3: mature women, aged 50–65 years
• group 4: women with a strong feminist sensitivity, aged 18–65 years
• group 5: men, aged 35–65 years

The experiment was carried out at the Brain Research Lab of the Universidad Rey Juan Carlos, Madrid, Spain, between November 10, 2018, and December 10, 2018. The room was kept at a constant temperature of 22°C throughout the experimentation phase. The room was isolated from the outside by means of a soundproofing system. We also used the same indirect lighting system for all the participants, so the emotional comparison across groups of subjects was robust.

The participants were scheduled in 10-min intervals and viewed the video on their own. The subjects entered the room, where they sat in front of a 14-inch monitor on which the spot would be projected, at a distance of 50 cm from the screen. On top the screen, there was a Logitech HD recording camera. iMotions was then calibrated to ensure that the facial microexpression detection mask captured the entire face of the subjects. Once an adjustment of 96% was achieved by iMotions, the spot was viewed. They did not know a priori what the spot was about.

The participants received a compensation of 20 euros for their collaboration. All subjects signed a priori a consent form that considers all aspects of data protection. This consent ensures the nonidentification of the participants, nor the dissemination of individualized data of a personal nature.

Statistical Methods

We developed a database with the 10 indicators provided by AFFDEX by scene and subject. Because 100 individuals watched the 31 scenes of spot, there were 3,100 registers in the database, each with 10 columns, one per indicator. Each indicator ranges between 0 and 100, indicating the likelihood of the emotion being present (0 = absent, 100 = present). Therefore, the higher the values that are recorded, the higher the emotional levels that are shown. Two additional columns were added to identify each register with the levels of the two control variables: scene (1–31) and group (1–5).

A descriptive analysis was first carried out to obtain an overall idea of the emotional responsiveness to the spot. Besides providing plots and summary statistics of the whole set of records, the extreme values, defined as those above the 90 th percentile and below the 10 th percentile, were highlighted in colors (green for high, red for low).

An inferential analysis was then undertaken to compare the average values of the different levels of the control variables. The general linear analysis of variance (ANOVA) model was used to capture the differences among levels of one variable at a time, as well as both variables together. A series of F tests (one per each of the 10 emotional indicators) were used to statistically reject (or not) the null hypothesis of equality of means across levels. A significance value of 5% was used as the threshold for rejection. The corresponding values of p were calculated, highlighting those that are lower than 5%, to demonstrate which variables significantly influence on the emotional responses by group and/or scene.

Concerning which levels of the variable are significantly different than the rest, we performed a series of t -tests, maintaining the significance level at 5% The values of p are provided, highlighting those that are lower than 5%, to demonstrate which levels of the variables significantly influence on the emotions. A “+” sign was used to demonstrate higher values than average, and a “−” to depict lower values than average.

Overall Results

The results for the three involvement indicators and the seven basic emotions are shown in Table 2 and depicted in Figure 2 . Each observation in the plot is the average measurement by individual and scene, for a total of 3,100 samples (31 scenes and 100 subjects) in each of the 10 plots. The lower and upper sides of the boxes correspond to the first and third quartiles, with the dots indicating the values outside the box. For illustrative purposes, and following AFFDEX initial configuration, thresholds are shown at ±50. Nevertheless, the research is based on the whole set of values.

Descriptive statistics for the whole sample by emotional response.

Descriptive plot for the whole sample by emotional response.

Only four of the indicators show reads consistently outside the thresholds and almost reaching the maximum value of 100: attention, engagement, valence, and joy. None of the five negative emotions reach 100, with only a few combinations of scene and individual above 50 for anger (three reads, 0.1%) and contempt (five reads, 0.02%). Moreover, three of the negative emotions (disgust, fear, and sadness) do not even reach the initial threshold.

Starting with attention, all the values are above 75 except for only a single value at 61.22. The lower quartile is above 96. Therefore, attention during the spot is kept across the individuals. High mean values (≥50) for this indicator show involvement and therefore emotional effectiveness, corroborating Hypothesis 1a.

Engagement shows most of the values under the threshold, but 309 of the 3,100 (10%) are above the limit. Whereas the third quartile is just at 9.27 and the average at 12.34. Because these values are not high (<50), Hypothesis 1b is not fully supported for the entire sample, but it looks worthy to investigate emotional reactions further by segregating by scene and group.

The same happens for valence, which shows more values above the positive threshold (168, 5.4%, maximum at 99.94) than below −50 (12, 0.4%, minimum at −76.24). Hypothesis 1c is not fully supported either for the entire sample, because not a significant amount of the samples is positive.

The predominant emotion is joy, with the percentage of values above 50 (probability of the emotion being present) being 4.7% (147 observations). Once again, from a descriptive perspective, the percentage is low enough to not accept Hypothesis 1d for the entire sample.

The overall descriptive analysis implies that the positive emotions are present at times, whereas the negative ones are almost never shown. Although the hypotheses are not fully accepted for the whole sample, the aim behind the spot is that the positive emotions are shown primarily by the target group, triggered by the sequence of images and text. Let us proceed therefore with the inferential analysis by scene and gender and age groups in order to quantify emotional effectiveness.

Results by Scene

Figure 3 shows the results averaged across individuals for each of the 31 scenes. For each of the 10 emotional indicators, the three scenes (first decile or top 10%) with the highest values are highlighted in green, and the three scenes with the lowest values in red.

Inferential analysis per scene.

The main green zone for positive emotions and involvement indicators goes from scene 9 to scene 15. Attention provides the two highest values in this block of scenes, with engagement, valence, and joy providing all of their top three scores. Fear and sadness also give maxima in this block. The text scenes are “She taught us how to grow” and “She gave us the most sincere love,” both related to the important role of moms. It must be remembered at this point that mature women are the target of the spot.

The negative emotions are higher at the beginning (scenes 1–4, anger and contempt) and at the end of the spot (29–31, disgust). It is eye catching that the scene with the company logo, scene 30, shows a peak of disgust, not showing any high values for the rest of the indicators except for surprise. The lower peaks of involvement (in red) are found between scenes 16 and 22, whereas joy is low between scenes 5 and 8.

To statistically test if these visual differences are significant per indicator, a series of one-way ANOVA test are performed. The null hypothesis is that the average value for each scene is the same, and the alternative hypothesis is that not all the scene averages are equal. The bottom rows of Figure 2 include the values of F and the p of each test ( p < 0.05 for rejection). Only anger (three individual values above 50) and fear (0 observations over 50), both negative emotions with very low averages, show differences across scenes. Their peaks surprisingly occur while positive emotions flourish, whereas their valleys are found at the end of the spot.

The summary is that Hypothesis 2 is not corroborated for scenes because no significant differences are found across them. Therefore, no clear emotional effectiveness by scene is shown. It is worth highlighting that the three involvement indicators and joy (relating all four to Hypothesis 1) are stable throughout.

To deeper analyze the spot, we continue the study in terms of block of scenes and not just single scenes. After the results of the descriptive analysis based on top and bottom deciles, we have broken the scenes into five blocks, which have been named according to a common theme:

• block 1: birth (1–4),
• block 2: first cares (5–8),
• block 3: teaching growth and love (9–15),
• block 4: sharing the best moments (16–22),
• block 5: reunion of three generations (23–28), and
• block 6: logo (29–31).

Figure 4 includes the statistical analysis by block. Differences are found on average emotional involvement across blocks of scenes in engagement and valence, with block 3 related to growth and love obtaining the highest results for involvement and positive emotions. Correspondingly, block 3 causes joy, although it is also significantly different in fear.

Inferential analysis per group.

Block 1, related to birth, is significantly high in negative emotions of contempt and sadness. Block 2 shows low values for positive emotions, block 4 for engagement, and block 5 for attention and contempt. Finally, and although the indicator is not significant, block 6 shows a peak of contempt.

Therefore, Hypothesis 2 is corroborated for blocks, providing an indication that the advertisement provokes emotions unevenly along the duration of the commercial. The spot is emotionally effective for blocks of scenes, in this case, generating higher positive involvement and joy for block 3: teaching growth and love.

Results by Group

The analysis of the groups is critical also to determine ad liking, specifically for the target group. Figure 5 has the same format as Figures 3 , ​ ,4 4 but, instead of segregating by scene, the averages are calculated for each of the five groups in which the individuals were pooled together. The maximum values per indicator are highlighted in green, and the minimum values in red.

Inferential analysis per block.

Indeed, all groups behave differently, as demonstrated by the one-way ANOVA shown at the bottom row of Figure 5 . Differences across groups in terms of average emotional response are found significant for each and every one of the 10 indicators. All the values of p are 0, indicating that the null hypothesis of equality of averages across groups is rejected in favor of the alternative hypothesis of differences in averages across groups.

Moreover, group 3 of mature women gets the top values in engagement, valence, joy, and surprise, and the middle one in attention (although at a very high 96.77). Therefore, for this group, which is the target group, all four indicators included in Hypothesis 1 are higher than those of the nontarget groups.

Compared to the average behavior, each group shows peaks of negative emotions, different in each case. Group 1 of young women demonstrates negative valence and disgust. Group 2 of middle-aged women shows attention but no emotions other than some fear. Group 3 of mature women exhibit sadness. Group 4 lacks attention while showing some contempt. Group 5 demonstrates some anger.

The summary of this section is consequently that Hypothesis 3 is corroborated. The spot is effective because involvement and joy are higher for the target group, in this case, the mature women.

Results by Group and Block of Scenes

We finalize the analysis by performing a two-way ANOVA to jointly study scenes (or blocks) and groups. As expected, following the rationale and conclusions of the previous sections, group is significant across indicators ( p = 0), and scene is nonsignificant in all the cases ( p ≥ 0.277), so it looks more appropriate to direct the efforts toward studying the relationship between groups and blocks of scenes.

Figure 6 shows the two-way ANOVA by group and block of scenes that helps summarize the whole study. The first part of the table shows the analysis of significance for the variable “group,” the second for the variable “block,” and the third for their interaction “group × block.” The first row of each section includes the F values, and the second the values of p for each indicator. For the variable group, there are significant differences on average for each of the 10 indicators ( p < 0.05, highlighted in green). For block, there are differences for engagement, valence, and joy, and also for fear and sadness. For the interaction, differences are found for joy, contempt, and fear.

Inferential analysis per group and block.

It is worth mentioning at this point that, except for two values of p that are close to 1, the rest of the values of p are below 0.25, a threshold that is commonly used for exploratory purposes. Accordingly, we have analyzed all the indicators while highlighting the values for group, block, or the interaction that are responsible for determining the significant differences. These differences are depicted with a “+” (if the value is significantly higher than average) or a “−” sign (if significantly lower) in the ANOVA table. For example, for group 2, the “+” sign in the attention column indicates that this group has paid significantly more attention than the average attention for all the groups. The “−” sign in the Engagement column indicates that group 2 was significantly less engaged than the average.

For groups, and concerning positive emotions, group 3 of mature women is the only group above average, the exception being attention, in which group 2 of middle-aged women and group 5 of men excel. For negative emotions, group 1 of young women is above average in disgust, group 2 of middle-aged women in fear, group 3 of mature women in anger and sadness, group 4 of feminists in contempt and group 5 of men in anger, corroborating the importance of the “group” variable, as anticipated by the one-way study.

For blocks, block 1, related to birth, causes higher responses in negative emotions (anger, contempt, and surprise); block 3, with the focus on the mom, shows higher responses in engagement, valence, joy, and sadness; and block 6, the logo and the hashtag, provokes disgust.

For the group × block interaction, we list those specific interactions that are significantly “higher” than average and those that are significantly “lower,” the latter within parenthesis. For example, and with respect to attention, two combinations are higher than average: group 3 × block 5 (the interaction between mature women and the reunion of generations) and group 4 × block 2 (feminists and first cares). Two others are lower than average: group 3 × block 2 (mature women and first cares) and group 4 and block 5 (feminists and reunion of three generations).

Assessment of the Spot

The results indicate that the spot is indeed emotionally effective because it generates involvement and positive emotions (Hypothesis 1) on the target group, indicators that are statistically higher than the average for the rest of the groups (Hypothesis 3). Mature women like the spot especially whenever the role of the mother is stressed, even showing some sadness, probably due to remembrance of old times.

The rejection of Hypothesis 2 in block of scenes indicates that ad liking might be improved especially for the nontarget group, addressing the scenes or blocks of scenes that cause negative emotions. By looking at the interactions that generate “higher” averages for the negative emotions, spot designers might understand better the “brain” of the participants and the groups they represent. It is eye-striking that each group is characterized by one negative emotion, which is usually triggered by a few blocks of the spot.

Young females (group 1) show disgust, the opposite of ad liking. This emotion is caused by blocks B4 (sharing best moments) and B6 (logo). The explanation might be that these youngsters do not like the best moments with mom or in family. It is worth to remember that they probably have not had relation with the advertised brand.

Middle-age females (group 2) show fear, as well as attention. They like the ad, but emotions are caused by block B4 (sharing best moments). The interpretation is that they are feeling mixed emotions about their life experiences.

Feminists (group 4) show contempt throughout the spot. All interactions between blocks of scenes and disgust are significantly high, except for block B4, which provokes low surprise. Feminist might not like family traditions or pregnancy periods.

Men (group 5) show anger, as well as attention. After showing anger during the first block, their attention level is high, indicating ad liking but without showing positive emotions. It is like they are watching from the outside and not showing extraordinary emotional responses.

The targeted mature women (group 3) show anger and sadness, although not particularly caused by any block of scenes. These two emotions might not be “negative” for this spot in particular as they must be related to reminiscences of the past. In fact, those negative emotions are justified in the literature as an empathy mediator ( Eisenberg et al., 1994 ; Sonnby-Borgström, 2002 ; Dimberg et al., 2011 ; Johnson et al., 2014 ; Richaud and Mesurado, 2016 ; Rawal and Saavedra, 2017 ). Anyhow, group 3 however shows above-average involvement and joy.

After this analysis by groups, we can conclude that emotions as expected are a good predictor of ad liking, which is the key measure of emotional effectiveness of advertisement. The combinations of positive and negative emotions that have been found in each of the groups clearly define the groups, relating them to their stage in life. As a conclusion, marketing professionals therefore have tools to measure emotional effectiveness of advertising before and during campaigns.

One necessary approach in this day and age to quantify the effectiveness of advertisements is to resort to emotions and emotional responses in the quest for properly measuring “ad liking and purchase intent” ( McDuff et al., 2015 ). We have proposed a framework composed of a set of three hypotheses to determine the emotional effectiveness of spots and blocks of scenes primarily focusing on ad liking. The novelty was to merge emotional responses, covering the seven basic emotions of Ekman, with involvement, including attention engagement and valence. Under this emotional perspective, the spot must cause involvement and joy in high, constant levels throughout the scenes for the target group.

We have tested the framework with a commercial spot that strived for strong positive emotional reactions among the target group. The combined use of involvement and emotional indicators has proven to be more than satisfactory. There is a direct relationship between blocks and experimental groups, indicating that the emotional responses of the different groups vary with the composition of the groups. On that regard, if the spot was supposed to be attractive to several groups, the effectiveness is lost outside the target group ( Kotler et al., 2000 ). The target group is emotionally more attracted to the spot. The results after analyzing a spot of scouring pads help validate the framework and determine that unconscious emotional responses are liable to be used to quantify effectiveness of advertisement. Therefore, the proposed framework should and must be used also before a spot is marketed to increase its emotional effectiveness.

Several limitations or words of caution might be mentioned at this point. The first one relates to the application of the proposed framework to any sort of spots. The framework looks to be readily usable for advertisements whose aim is to provoke positive emotions throughout the length of the spot. That is usually the case of commercial spots, although some also try to provoke peaks of emotions at certain (blocks of) scenes of the spot. The framework might be used to test these “peaky” commercials, because the null hypothesis of Hypothesis 2 is stability, and therefore the alternative hypothesis is lack of stability. In fact, for the test case that was used for this research, an analysis by scene and by block provided different results. No differences were found among scenes, but differences were found among blocks. We argue that the important feature of the framework is to be able to highlight differences, whether that is a proof of effectiveness or lack of it.

The spots might also look for negative emotions ( Rivera et al., 2000 ). Adding (or substituting) emotions to Hypothesis 1 is straightforward. The methods of analysis of emotions other than joy are the same. In fact, we have analyzed in this research all of the seven Ekman’s emotions, although we have focused primarily on joy because, to demonstrate ad liking, out of the seven emotions, the predominant one must be joy ( Tomkovick et al., 2001 ; Lewinski et al., 2014 ; Shehu et al., 2016 ). We, however, acknowledge that there are certain situations in which responses based on negative emotions are sought for example, for fear ( Basil et al., 2008 ).

There are also several directions for future improvement and research. To further quantity purchase intent and recall, questionnaires might be added to spot viewing. Moreover, the questionnaires might be shown to the participants on the screen, so emotions might be measured both while watching the spot and while filling the questionnaire. An additional step is to measure empathy, which in neuroscience is usually quantified with fMRI and EEG ( Mouras et al., 2008 ; Neumann and Westbury, 2011 ; Touchette and Lee, 2016 ), while measuring the reaction of mirror neurons to the stimuli; this approach, however, could make the research unaffordable for a reasonable sample size.

Continuing with technology, while the framework is based on neuroscience techniques and tools, namely, those provided by the software platform AFFDEX, other software that quantify the proposed indicators included in the hypotheses might be also used. The state-of-the-art platform AFFDEX translates microfacial expressions at the same time into attention, engagement, valence, and the seven basic emotions of Ekman. Many steps forward are, however, necessary to be able to improve the study of the consumers’ brain using these tools. We have only used the analysis of facial microexpressions, but some other techniques might be simultaneously used, for example, eye tracking or EEG. The statistical analysis of this type of combined output will shed new light on how the emotions are trigged, in lieu of a more thorough analysis of emotional effectiveness of advertising.

To conclude, we have been able to demonstrate that unconscious emotional responses might be used to understand more about the consumers’ brain. In the era of big data and the internet of things, the more indicators are present, the better the analysis might be. Consistently measuring emotions based on the principles of consumer neuroscience might be the key to understanding consumer behavior and effectiveness of advertisement in the upcoming years.

Data Availability Statement

Ethics statement.

The studies involving human participants were reviewed and approved by Ethics Committee – 3M Spain SL. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

FO and DS designed the study. DS acquired the data. FO prepared and analyzed the data. FO and DS drafted the manuscript. All authors revised and approved the final version of the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We acknowledge the research participants. We also acknowledge the rest of members of the Brain Research Lab and the Behavioural Neuroeconomics Research Group. We also acknowledge the referees for their invaluable comments.

Funding. This work was partially supported by a grant from 3M España S.L.

1 The video is available at https://www.youtube.com/watch?v=yaLqgWmoh2Y

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Unlocking insights into marketing to mature consumers: A new research synthesis

by Queen Mary, University of London

Research from Queen Mary University of London academic Dr. Mina Tajvidi, delves into marketing communications targeted at mature consumers aged 50 and above, addressing definitional inconsistencies and reviewing research published since 1972.

In an era marked by the increasing significance of an aging population, understanding and effectively targeting mature consumers has become paramount for marketers worldwide. A research paper titled "What we do know and don't know about marketing communications on mature consumers" sheds light on this crucial demographic, resolving long-standing definitional inconsistencies and providing a roadmap for future research and practice in marketing communications.

Authored by a team of esteemed scholars, this paper offers a comprehensive synthesis of existing research on marketing to mature consumers, encompassing individuals aged 50 and above. Drawing from a meticulous analysis of 106 papers published in premier marketing journals since 1972, the study identifies key themes and unveils critical insights into this demographic.

The findings of the paper underscore three primary research themes: the segmentation of mature consumers, their attitudes and behaviors, and effective marketing strategies tailored to this demographic. Furthermore, the research outlines a series of compelling future research directions, urging scholars and practitioners to delve deeper into understanding the complexities of mature consumer behavior and preferences.

One of the standout contributions of this paper is its proposal for an expanded definition of mature consumers, transcending mere chronological age to encompass biological, psychological, and social dimensions, as well as life events and circumstances. This holistic approach promises to revolutionize how marketers conceptualize and engage with this diverse demographic.

From a practical standpoint, the research emphasizes the importance of personalized marketing approaches for mature consumers, recognizing their unique information processing mechanisms and the varying impact of marketing mix elements on their behavior. It advocates for the adoption of alternative methodologies to fully capture the nuances of this market segment.

Co-author of the paper , Tajvidi, codirector of MSc Marketing program, said, "Our research underscores the pressing need for marketers to move beyond simplistic age-based stereotypes and embrace a more nuanced understanding of mature consumers.

"By doing so, businesses can unlock untapped opportunities and forge deeper connections with this increasingly influential demographic. As businesses navigate the evolving landscape of marketing communications, this research serves as a beacon, illuminating the path forward towards more effective and inclusive strategies for engaging mature consumers."

The findings are published in the European Journal of Marketing .

Provided by Queen Mary, University of London

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The Creativity Decline: Evidence from US Patents

Working Paper 2024-008A by Aakash Kalyani

Economists have long struggled to understand why aggregate productivity growth has dropped in recent decades while the number of new patents filed has steadily increased. I offer an explanation for this puzzling divergence: the creativity embodied in US patents has dropped dramatically over time. To separate creative from derivative patents, I develop a novel, text-based measure of patent creativity: the share of technical terminology that did not appear in previous patents. I show that only creative and not derivative patents are associated with significant improvements in firm level productivity. Using the measure, I show that inventors on average file creative patents upon entry, and file derivative patents with more experience. I embed this life-cycle of creativity in a growth model with endogenous creation and imitation of technologies. In this model, falling population growth explains 27% of the observed decline in patent creativity, 30% of the slowdown in productivity growth, and 64% of the increase in patenting.

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Computer Science > Computation and Language

Title: realm: reference resolution as language modeling.

Abstract: Reference resolution is an important problem, one that is essential to understand and successfully handle context of different kinds. This context includes both previous turns and context that pertains to non-conversational entities, such as entities on the user's screen or those running in the background. While LLMs have been shown to be extremely powerful for a variety of tasks, their use in reference resolution, particularly for non-conversational entities, remains underutilized. This paper demonstrates how LLMs can be used to create an extremely effective system to resolve references of various types, by showing how reference resolution can be converted into a language modeling problem, despite involving forms of entities like those on screen that are not traditionally conducive to being reduced to a text-only modality. We demonstrate large improvements over an existing system with similar functionality across different types of references, with our smallest model obtaining absolute gains of over 5% for on-screen references. We also benchmark against GPT-3.5 and GPT-4, with our smallest model achieving performance comparable to that of GPT-4, and our larger models substantially outperforming it.

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• 02 April 2024

Impact factors are outdated, but new research assessments still fail scientists

• Kelly Cobey 0

Kelly Cobey is a scientist at the University of Ottawa Heart Institute, an associate professor in the University of Ottawa School of Epidemiology and Public Health and co-chair of the Declaration on Research Assessment.

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You have full access to this article via your institution.

Last September, while completing a grant application, I faltered at a section labelled ‘summary of progress’. This section, written in a narrative style, was meant to tell reviewers about who I was and why I should be funded. Among other things, it needed to outline any family leave I’d taken; to spell out why my budget was reasonable, given my past funding; and to include any broad ‘activities, contributions and impacts’ that would support the application.

How could I sensibly combine an acknowledgement of two maternity leaves with a description of my engagement with open science and discuss why I was worthy of the funding I’d requested? There was no indication of the criteria reviewers would use to evaluate what I wrote. I was at a loss.

Bring PhD assessment into the twenty-first century

When my application was rejected in January, the reviewers didn’t comment on my narrative summary. Yet they did mention my publication record, part of the conventional academic CV that I was also required to submit. So I’m still none the wiser as to how the summary was judged — or if it was considered at all.

As co-chair of the Declaration On Research Assessment (DORA) — a global initiative that aims to improve how research is evaluated — I firmly believe in using narrative reflections for job applications, promotions and funding. Narratives make space for broad research impacts, from diversity, equity and inclusion efforts to educational outreach, which are hard to include in typical CVs. But I hear stories like mine time and again. The academic community is attempting, in good faith, to move away from narrow assessment metrics such as publications in high-impact journals. But institutes are struggling to create workable narrative assessments, and researchers struggling to write them .

The problem arises because new research assessment systems are not being planned and implemented properly. This must change. Researchers need explicit evaluation criteria that help them to write narratives by spelling out how different aspects of the text will be weighted and judged.

Research communities must be involved in designing these criteria. All too often, researchers tell me about assessment systems being imposed from the top down, with no consultation. This risks these new systems being no better than those they are replacing.

How to boost your research: take a sabbatical in policy

Assessments should be mission-driven and open to change over time. For example, if an institute wants to increase awareness and implementation of open science, its assessments of which researchers should be promoted could reward those who have undertaken relevant training or implemented practices such as data sharing. As open science becomes more mainstream, assessments could reduce the weight given to such practices.

The value of different research outputs will vary between fields, institutes and countries. Funding bodies in Canada, where I work, might favour grants that prioritize Indigenous engagement and perspectives in research — a key focus of diversity, equity and inclusion efforts in the Canadian scientific community. But the same will not apply in all countries.

Organizations must understand that reform can’t be done well on the cheap. They should invest in implementation scientists, who are trained to investigate the factors that stop new initiatives succeeding and find ways to overcome them. These experts can help to get input from the research community, and to bring broad perspectives together into a coherent assessment framework.

Some might argue that it would be better for cash-strapped research organizations to rework existing assessments to suit their needs rather than spend money on experts to develop a new one. Yes, sharing resources and experiences is often useful. But because each research community is unique, copying a template is unlikely to produce a useful assessment. DORA is creating tools to help. One is Reformscape (see go.nature.com/4ab8aky ) — an organized database of mini case studies that highlight progress in research reform, including policies and sample CVs that can be adapted for use in fresh settings. This will allow institutions to build on existing successes.

The postdoc experience is broken. Funders such as the NIH must help to reimagine it

Crucially, implementation scientists are also well placed to audit how a new system is doing, and to make iterative changes. No research evaluation system will work perfectly at first — organizations must commit sustained resources to monitoring and improving it.

The Luxembourg National Research Fund (FNR) shows the value of this iterative approach. In 2021, it began requesting a narrative CV for funding applications, rather than a CV made up of the usual list of affiliations and publications. Since then, it has been studying how well this system works. It has had mostly positive feedback, but researchers in some fields are less satisfied, and there is evidence that institutes aren’t providing all researchers with the guidance they need to complete the narrative CV. In response, the FNR is now investigating how to adapt the CV to better serve its communities.

Each institution has its own work to do, if academia is truly to reform research assessment. Those institutions that drag their feet are sending a message that they are prepared to continue supporting a flawed system that wastes research time and investment.

Nature 628 , 9 (2024)

doi: https://doi.org/10.1038/d41586-024-00899-8

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Competing Interests

K.C. is the co-chair of DORA (Declaration On Research Assessment) — this in an unpaid role.

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• Vehicle Technologies Office
• About the Vehicle Technologies Office
• Technology Areas
• Technology Integration
• Reports and Publications

Office: Vehicle Technologies Office FOA number:  DE-FOA-0003248 Link to apply:  Apply on EERE Exchange FOA Amount: $45,800,000

Today, the Department of Energy (DOE) announced $45.8 million in new funding for projects that will advance research, development, demonstration, and deployment (RDD&D) critical to achieving net-zero greenhouse gas emissions in the transportation sector. The funding will drive innovation in equitable clean transportation and is aligned with strategies detailed in the U.S. National Blueprint for Transportation Decarbonization . 

The funding is through DOE’s Office of Energy Efficiency and Renewable Energy (EERE). Topic areas in the Vehicle Technologies Office (VTO) Fiscal Year (FY) 2024 R&D funding opportunity include:

• Next-generation phosphate-based cathodes.
• Advancing the state of the art for sodium-ion batteries.
• Developing concepts for decreasing greenhouse gas emissions from off-road vehicles such as construction, agriculture, mining, and forestry vehicles.
• Developing and deploying vehicle-to-everything technologies that can lead to meaningful savings at the vehicle and transportation system level.
• Developing high-performance, domestically produced electrical steels (E-steels) for use in electrified powertrains.
• Addressing critical cybersecurity needs for smart and secure electric vehicle charging.

As part of the Biden-Harris Administration’s commitment to ensuring the benefits of a clean transportation system are shared equally, the funding seeks the participation of underserved communities and underrepresented groups. Applicants are required to describe how diversity, equity, and inclusion objectives will be incorporated into their project. 

VTO provides a series of funding opportunity announcement (FOA) information session videos , which help applicants understand VTO’s FOA process and requirements. The recently released, Session 3: Tips for a Strong FOA Application, includes best practices for incorporating Diversity, Equity, Inclusion, and Accessibility in a project.

Learn more about this and other funding opportunities on VTO’s funding webpage . 

Topic Areas

Topic Area 1: Next-Generation Phosphate-Based Cathodes

This topic area targets the development of phosphate-based cathode materials that surpass the performance of state-of-the-art lithium iron phosphate (LFP) cathode materials, which are currently gaining traction as an alternative low-cost solution. The primary objective of this area of interest is to develop high energy density battery cells containing phosphate-based cathodes at the material and cell level.

Topic Area 2: Na-ion Battery Seedling Projects for Electric Vehicle Applications

While shifting to alternative cathode materials like LFP can alleviate the impact of nickel and cobalt, the impact of lithium has not been adequately addressed. One alternative to lithium is sodium (Na). While there is much promise for Na-ion chemistries, key issues still limit their adoption. This objective of this topic area is to advance the state of the art for Na-ion batteries by solving key challenges for the cathode, anode, or electrolyte through the development of 1 Ah full cells utilizing cell chemistries that are significant advancements over current industry state-of-the-art Na-ion technology.

Topic Area 3: Low-GHG Concepts for Off-Road Vehicles

The objective of this topic area is to develop and validate technology concepts capable of significantly decreasing greenhouse gas emissions, energy use, harmful criteria emissions, and total cost of ownership across the entire off-road vehicle sector, including construction, agriculture, mining, forestry, ports, warehouses, etc. Concepts must demonstrate they can meet the unique requirements for off-road vehicles and gain customer acceptance.

Topic Area 4: Saving Energy with Connectivity

Research has shown that vehicle-to-everything (V2X) communications can lead to meaningful energy savings at the vehicle and transportation system level by integrating interoperable vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-pedestrian (V2P) communications. The objective of this topic area is to develop and deploy V2X technologies with a focus on the efficiency and convenience of the mobility ecosystem, while reducing transportation’s environmental impacts. Examples could include but are not limited to eco-driving along connected corridors, transit or freight priority, integrated corridor management, or passenger or freight trip-chaining optimization.

Topic Area 5: Domestically Produced Electrical Steels (E-Steels)

The US transportation sector is in a technology revolution where light-duty vehicles are rapidly transitioning from internal combustion engines to electrified powertrains. Although most of the vehicles are produced in the US, many of the powertrain components rely on imports and foreign supply chains. Of particular interest are traction motors and their components. The objective of this topic are is to develop E-Steels meeting properties including frequency, thickness, ductility, cost, and manufacturability. 

Topic Area 6: Cybersecurity for Smart and Secure Electric Vehicle Charging

This topic area is addressing critical cybersecurity needs to address through two subtopics: 

• Subtopic 6.a: Enabling Wide-scale, Cybersecure EV/EVSE Aggregation for Grid Services :  To support the integration of electric vehicles (EVs) and their charging requirements with the electric grid, both government and the private sector have made significant investments in the development of smart charge management (SCM) systems and technologies for EV charging infrastructure. The objective of this subtopic area is to research, develop, and demonstrate systems, technologies, and tools necessary for the cybersecure aggregation of EVs and charging infrastructure to provide widescale, cybersecure grid services.
• Subtopic 6.b: Tools to Assess EV/EVSE/Charging System Cybersecurity Posture and Compliance with Standards and Protocols for Communications, Controls, and Monitoring :   Testing and evaluation of Electric Vehicle Supply Equipment (EVSE) by DOE national laboratories has clearly indicated a lack of compliance by many vendors with certified and/or regulated EV charging standards and protocols. In addition to creating cybersecurity vulnerabilities, this non-compliance greatly inhibits interoperability, supplier-managed SCM, and right-to-repair. The objective of this subtopic is to research, develop, and validate a suite of tools and associated procedures to comprehensively assess EV/EVSE/charging system compliance with relevant standards and protocols and cybersecurity posture.

Additional Information

• Download the full funding opportunity  on the EERE Exchange website.
• For FOA-specific support, contact  [email protected] . 
• Sign up for the  Office of Energy Efficiency and Renewable Energy (EERE) funding email list  to get notified of new EERE funding opportunities. Also sign up for  VTO’s newsletter to stay current with the latest news.
• Watch the VTO Funding Opportunity Announcement information series webinars.