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research paper on advert

  • 20 Jun 2023
  • Cold Call Podcast

Elon Musk’s Twitter Takeover: Lessons in Strategic Change

In late October 2022, Elon Musk officially took Twitter private and became the company’s majority shareholder, finally ending a months-long acquisition saga. He appointed himself CEO and brought in his own team to clean house. Musk needed to take decisive steps to succeed against the major opposition to his leadership from both inside and outside the company. Twitter employees circulated an open letter protesting expected layoffs, advertising agencies advised their clients to pause spending on Twitter, and EU officials considered a broader Twitter ban. What short-term actions should Musk take to stabilize the situation, and how should he approach long-term strategy to turn around Twitter? Harvard Business School assistant professor Andy Wu and co-author Goran Calic, associate professor at McMaster University’s DeGroote School of Business, discuss Twitter as a microcosm for the future of media and information in their case, “Twitter Turnaround and Elon Musk.”

research paper on advert

  • 06 Jan 2021
  • Working Paper Summaries

Aggregate Advertising Expenditure in the US Economy: What's Up? Is It Real?

We analyze total United States advertising spending from 1960 to 2018. In nominal terms, the elasticity of annual advertising outlays with respect to gross domestic product appears to have increased substantially beginning in the late 1990s, roughly coinciding with the dramatic growth of internet-based advertising.

  • 15 Sep 2020

Time and the Value of Data

This paper studies the impact of time-dependency and data perishability on a dataset's effectiveness in creating value for a business, and shows the value of data in the search engine and advertisement businesses perishes quickly.

research paper on advert

  • 19 May 2020
  • Research & Ideas

Why Privacy Protection Notices Turn Off Shoppers

It seems counterintuitive, but website privacy protection notices appear to discourage shoppers from buying, according to Leslie John. Open for comment; 0 Comments.

  • 02 Mar 2020
  • What Do You Think?

Are Candor, Humility, and Trust Making a Comeback?

SUMMING UP: Have core leadership values been declining in recent years? If so, how do we get them back? James Heskett's readers provide answers. Open for comment; 0 Comments.

research paper on advert

  • 06 Aug 2019

Super Bowl Ads Sell Products, but Do They Sell Brands?

Super Bowl advertising is increasingly about using storytelling to sell corporate brands rather than products. Shelle Santana discusses why stories win (or fumble) on game day. Open for comment; 0 Comments.

research paper on advert

  • 27 Jul 2019

Does Facebook's Business Model Threaten Our Elections?

America's 2016 presidential election was the target of voter manipulation via social media, particularly on Facebook. George Riedel thinks history is about to repeat itself. Open for comment; 0 Comments.

research paper on advert

  • 10 Oct 2018

The Legacy of Boaty McBoatface: Beware of Customers Who Vote

Companies that encourage consumers to vote online should be forewarned—they may expect more than you promise, according to research by Michael Norton, Leslie John, and colleagues. Open for comment; 0 Comments.

  • 27 Sep 2018

Large-Scale Demand Estimation with Search Data

Online retailers face the challenge of leveraging the rich data they collect on their websites to uncover insights about consumer behavior. This study proposes a practical and tractable model of economic behavior that can reveal helpful patterns of cross-product substitution. The model can be used to simulate optimal prices.

research paper on advert

  • 18 Jun 2018

Warning: Scary Warning Labels Work!

If you want to convince consumers to stay away from unhealthy diet choices, don't be subtle about possible consequences, says Leslie John. These graphically graphic warning labels seem to do the trick. Open for comment; 0 Comments.

research paper on advert

  • 18 Sep 2017

'Likes' Lead to Nothing—and Other Hard-Learned Lessons of Social Media Marketing

A decade-and-a-half after the dawn of social media marketing, brands are still learning what works and what doesn't with consumers. Open for comment; 0 Comments.

research paper on advert

  • 26 Jul 2017

The Revolution in Advertising: From Don Draper to Big Data

The Mad Men of advertising are being replaced by data scientists and analysts. In this podcast, marketing professor John Deighton and advertising legend Sir Martin Sorrell discuss the positives and negatives of digital marketing. Open for comment; 0 Comments.

  • 13 Mar 2017

Hiding Products From Customers May Ultimately Boost Sales

Is it smart for retailers to display their wares to customers a few at a time or all at once? The answer depends largely on the product category, according to research by Kris Johnson Ferreira and Joel Goh. Open for comment; 0 Comments.

  • 06 Mar 2017

Why Comparing Apples to Apples Online Leads To More Fruitful Sales

The items displayed next to a product in online marketing displays may determine whether customers buy that product, according to a new study by Uma R. Karmarkar. Open for comment; 0 Comments.

  • 13 Feb 2017

Paid Search Ads Pay Off for Lesser-Known Restaurants

Researchers Michael Luca and Weijia Dai wanted to know if paid search ads pay off for small businesses such as restaurants. The answer: Yes, but not for long. Open for comment; 0 Comments.

research paper on advert

  • 08 Dec 2016

How Wayfair Built a Furniture Brand from Scratch

What was once a collection of 240 home furnishing sites is now a single, successful brand, Wayfair.com. How that brand developed over time and the challenges and opportunities presented by search engine marketing are discussed by Thales Teixeira. Open for comment; 0 Comments.

  • 04 May 2016

What Does Boaty McBoatface Tell Us About Brand Control on the Internet?

SUMMING UP. Boaty McBoatface may have been shot down as the social-media sourced name of a research vessel, but James Heskett's readers are up to their hip-boots in opinions on the matter. Open for comment; 0 Comments.

  • 02 May 2016

Why People Don’t Vote--and How a Good Ground Game Helps

Recent research by Vincent Pons shows that campaigners knocking on the doors of potential voters not only improves overall turnout but helps individual candidates win more of those votes. Open for comment; 0 Comments.

  • 21 Mar 2016

Can Customer Reviews Be 'Managed?'

Consumers increasingly rely on peer reviews on TripAdvisor and other sites to make purchase decisions, so it makes sense that companies have a stake in wanting to shape those opinions. But can they? Thales Teixeira says a good product trumps all. Open for comment; 0 Comments.

  • 28 Oct 2015

A Dedication to Creation: India's Ad Man Ranjan Kapur

How do you build a brand amid the uncertainties and opportunities of a developing market? Harvard Business School Professor Sunil Gupta shares lessons learned from Ranjan Kapur, an iconic figure in the Indian advertising industry. Open for comment; 0 Comments.

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Exploring global trends and future directions in advertising research: A focus on consumer behavior

  • Published: 03 June 2023

Cite this article

  • Ahmed H. Alsharif 1 ,
  • Nor Zafir Md Salleh 1 ,
  • Mahmaod Alrawad 2 , 3 &
  • Abdalwali Lutfi 4  

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This study aims to select the physiological and neurophysiological studies utilized in advertising and to address the fragmented comprehension of consumers' mental responses to advertising held by marketers and advertisers. To fill the gap, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was employed to select relevant articles, and bibliometric analysis was conducted to determine global trends and advancements in advertising and neuromarketing. The study selected and analyzed forty-one papers from the Web of Science (WoS) database from 2009–2020. The results indicated that Spain, particularly the Complutense University of Madrid, was the most productive country and institution, respectively, with 11 and 3 articles. The journal Frontiers in Psychology was the most prolific, with eight articles. The article "Neuromarketing: The New Science of Consumer Behavior" had the most citations (152 T.Cs). Additionally, the researchers discovered that the inferior frontal and middle temporal gyri were associated with pleasant and unpleasant emotions, respectively, while the right superior temporal and right middle frontal gyrus was connected to high and low arousal. Furthermore, the right prefrontal cortex (PFC) and left PFC were linked to withdrawal and approach behaviors. In terms of the reward system, the ventral striatum played a critical role, while the orbitofrontal cortex and ventromedial PFC were connected to perception. As far as we know, this is the first paper that focused on the global academic trends and developments of neurophysiological and physiological instruments used in advertising in the new millennium, emphasizing the significance of intrinsic and extrinsic emotional processes, endogenous and exogenous attentional processes, memory, reward, motivational attitude, and perception in advertising campaigns.

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Introduction

Qualitative methods have been used in marketing research for a long time to measure the consumer’s attitudes and behaviors toward advertising campaigns, which is actually measuring consumers' awareness behavior such as attitudes and perceptions (Carrington et al., 2014 ). Therefore, advertisers and marketers resorted to the use of neuroscientific methods or techniques such as functional magnetic resonance imaging (fMRI) in the marketing field in general and advertising in specific to study, measure and understand the unconscious/subconscious responses of customers to stimuli, which largely contribute in the decision-making process (Harris et al., 2018 ; Plassmann et al., 2012 ). Using neuroscientific methods to better understand the concealed behavior of customers toward external stimuli such as marketing and environmental in the last 20 years has led to an emerging mixed field, so-called “Neuromarketing”, which used neuroscientific and self-report methods to get more accurate findings about conscious and unconscious responses of the customer to advertising (Alsharif et al., 2021a ). According to the literature, in 2022, professor Smidts ( 2002 ) coined the NM (NM) term. According to Javor et al. ( 2013 ), NM is placed on the borderline of neuroscience, marketing, and psychology, which has been spread by the USA company named "Bright House Company" (Fortunato et al., 2014 ), when this company established the first neuroscience department for marketing research.

Contemporarily, NM is one of the most important fields for studying customers' neural and physiological responses, such as inner and extrinsic responses toward marketing stimuli and advertising. In addition to the aforementioned, some researchers and scholars considered NM an embryonic field that needs more improvements to overcome the artifacts in some techniques (Alsharif et al., 2021b ). Bočková et al. ( 2021 ) mentioned that NM is in an improving process because of technological advancement in communication and medical fields recently. The technology has been utilized by the marketing and advertising leader to enhance marketing and advertising success by managing and reducing task conflicts, as stated by To et al. ( 2021 ). Isabella et al. ( 2015 ) have categorized neuromarketing instruments into two groups: (1) neurophysiological tools, including EEG, MEG, fMRI, PET, and TMS, and (2) physiological tools, such as GSR, ET, ECG, and EMG. As mentioned by Ahmed et al. ( 2022c ); Izhikevich ( 2003 ), neurophysiological instruments capture the cognitive and emotional reactions toward advertising, including arousal, pleasure, engagement, approach, and withdrawal. Meanwhile, physiological tools like eye-tracking (ET), according to (Ahmed et al., 2020 ; Dimpfel, 2015 ), monitor physiological responses such as visual fixation, pupil dilation, eye movements, heartbeat, perspiration, and excitement at the point of purchase. This enables the acquisition of dependable and useful information concerning preferences, such as whether a product is liked or not liked.

According to the literature, the first official publication in NM was done in 2004 by McClure et al. ( 2004 ), which contributed to shifting the NM studies from a pure study to a practical one. NM research is highly significant for the academic and industrial world to overcome the limitations of traditional methods, such as consumer social bias (e.g., consumer choices can be affected by others) (Alsharif et al., 2022 ; Fortunato et al., 2014 ). The COVID-19 pandemic has created a lot of concerns globally in markets, businesses, and establishments’ activities (Aki et al., 2020 ). However, understanding the global trends in advertising research within the NM field (e.g., the most prolific countries/academic institutions, the most-cited articles, the most productive journals, authors, and so forth) is still unclear in academic studies. Thus, we aim to provide a comprehensive overview of the top and new approaches in the field, recent methods, and other relevant aspects that would be more interesting and beneficial to scholars. Our key contribution is to provide a broader perspective that goes beyond just publication and citation data, and we hope that our article will be useful to researchers and practitioners in their research. In addition, The main difference in the current paper is that the current paper focused on empirical that used neurophysiological such as fMRI, EEG, fNIRS and physiological tools such as ET, ECG, GSR/EDA, and EMG to study the mental responses of consumers behavior (e.g., inner and extrinsic emotional responds, perceptions, motivational of customers attitudes, reward system, endogenous and exogenous attentional processes, and memory) toward advertising research within NM. The present study endeavors to achieve a precise and succinct conclusion by conducting an in-depth analysis of the extracted articles. The primary contributions of this research are outlined as follows:

Provide the latest update on the global trends in advertising research within the NM field, such as the most prolific countries/academic institutions, the most-cited articles, the most productive journals and authors, etc.

Provides a comprehensive assessment of the up-to-date advertising studies that have used neurophysiological and physiological techniques to investigate the consumers’ behavior, such as inner and extrinsic emotional responses, motivational attitudes, perceptions, reward, memory, and endogenous and exogenous attentional processes toward advertising.

Provides a comprehensive overview of studies that used neurophysiological and physiological tools between 2009 and 2020.

In summary, this study offers a thorough examination of neuromarketing and its present research objectives. The second section outlines the data collection materials and methodologies, while the third section presents the bibliometric and content analysis of the articles selected for this study. The fourth section discusses the limitations and challenges of applying neuromarketing. Findings are discussed in section five, and the study concludes in section six.

This study is designed to identify original articles on advertising in the field of neuromarketing by searching the Web of Science (WoS) database, thereby addressing a gap in the existing literature. WoS was selected over Scopus due to its cleaner data, which helps to minimize duplication, and because it includes publications from top-tier journals (Strozzi et al., 2017 ). In the first step of our research, we followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol developed by Moher et al. ( 2015 ) to identify empirical articles in advertising research that used neurophysiological and physiological tools to investigate consumers' behavior in the context of neuromarketing (as shown in Fig.  2 ). For the second step, we conducted a bibliometric analysis to identify global trends and advancements in advertising research within the field of neuromarketing, including the most productive countries and academic institutions, the number of publications and citations, and the most prolific authors in the field (as recommended by Ahmed et al. ( 2022a ), (Ahmed et al., 2022b ); Ahmed et al. ( 2021 ); Pilelienė et al. ( 2022 )). To conduct this analysis, we followed the guidelines Block and Fisch ( 2020 ) set forth to ensure that our analysis was impactful and accurate, and used VOSviewer software to visualize our findings. VOSviewer is a widely-used tool for bibliometric research and has been employed in previous studies (see Abbas et al. ( 2022 ); Ali et al., ( 2021a , 2021b ); Alsharif et al. ( 2020 ); Alsharif et al. ( 2021c )). Figure  1 provides an overview of the analytical structure of our study, including the methods used and the organization and structure of the study itself.

figure 1

Analytical structure of the current paper

These processes will give us a deep insight into advertising advancement by identifying and analyzing the general and specific domains. Additionally, it would give us a comprehensive understanding of the most common NM tools used in advertising research, the most productive academic institutions, and the top productive authors to be considered when conducting further research in advertising research. Therefore, the findings provide a guide for scholars who are interested in the advertising and NM field.

Relevant documents were extracted from the WoS by using the following query applied to the title, abstract, and keywords: (("neuromarketing" OR "consumer neuroscience") AND ("adverti*")) to extract the relevant articles related to this study and fill the gap. This paper has focused on the papers that used neurophysiological and physiological tools in advertising research between 2009 and 2020; therefore, the total number of publications was 125 documents from 2009 to 2020. The study focused on original journal articles, which are subject to a more thorough review process than conferences and book chapters. This helps to increase the credibility of research published in journals (Saha et al., 2020 ).

The researchers selected 41 articles from the WoS database and followed the PRISMA protocol, which involves four steps for selecting relevant articles. These steps include identification through database searching, screening of publications, assessing eligibility, and selecting relevant articles. The included articles had to meet the specific characteristics outlined in Fig.  2 :

Articles published in advertising research within the NM context from 2009 to 2020 were included.

Articles that used neurophysiological and physiological tools in advertising research were included.

Articles published in the non-English language were excluded.

Publications such as book chapters, conferences, and so forth were excluded.

figure 2

PRISMA flow chart for selecting publications for the current study

Table 1 provides an overview of the chosen articles in advertising research. By analyzing these articles, we were able to identify three key dimensions in the field of neuromarketing: (i) studies on advertising in the context of neuromarketing; (ii) the use of neurophysiological and physiological techniques in advertising; and (iii) consumers' unconscious and subconscious reactions to advertising. Through our review of these papers, we hope to gain a deeper understanding of the subject matter and meet the goals of this review article.

Descriptive analysis

We conducted a descriptive analysis of forty-one articles in the advertising field and utilized neuromarketing instruments to determine the overall trend in advertising, including annual and cumulative publications of countries, academic institutions, and journal outlets.

Growth of the publication

Forty-one articles in journals belonging to the WoS database related to advertising have been published, which have used NM tools. As we can see there is a fluctuation in the number of publications in advertising and NM research from 2009 to 2020. In 2020, it was the highest number of annual publications with thirteen articles, as depicted in Fig.  3 .

figure 3

The annual and cumulative publications in advertising and NM

Journal outlets

The results indicate that six countries were represented by eight publishers, who published a minimum of two articles in advertising and NM. Table 2 lists the publishers and their respective number of publications. Frontiers Media Sa and MDPI, both based in Switzerland, were the top publishers in NM and advertising with fourteen articles, which accounted for 34% of the total articles. Grupo Comunicar and University Complutense Madrid, based in Spain, followed with six articles. While Hindawi Ltd had only published two articles in advertising and NM, their article by Vecchiato et al. ( 2011 ) had the most citations with fifty-seven citations. Stallen et al. ( 2010 ) from the Netherlands had the second most cited article with thirty-eight citations, and they also published two articles.

Bibliometrics analysis

Productive countries and academic institutions.

A total of 41 papers from the WoS database were analyzed, and the results are summarized in Table 3 . The findings reveal that Spain, Italy, and the USA are the primary contributors to advertising research in the context of NM, accounting for over 60% of the total publications. This suggests that these countries play a crucial role in advancing studies in advertising research within the NM context. Specifically, Spain had the highest number of publications, with eleven papers (approximately 26.83% of total papers), followed by Italy, with almost eight documents (19.51% of total documents). The USA ranked third with seven documents (almost 17% of total documents), while Australia and England tied for fourth place with four documents each. The Netherlands had three documents, ranking fifth. Finally, China, Lithuania, and Germany, with two documents each.

Table 4 presents a group of academic institutions that have contributed significantly to advertising research in NM, publishing at least two papers. Sapienza University Rome, Complutense University of Madrid, and Universidad Rey Juan Carlos are the most prolific institutions. They have produced nine documents with three publications from each institution. Following them are Brainsigns SRL, Catholic University of the Sacred Heart, Erasmus University Rotterdam, Vytautas Magnus University, Swinburne University of Technology, and the University of Salamanca, having contributed two documents each, making twelve publications among them.

Prolific authors

We have identified the top prolific authors in the NM context who have contributed at least two advertising research papers. These authors belong to four countries, Italy, Spain, Lithuania, and Australia. Table 5 presents the list of these authors who collaborated extensively and published 26 documents. Notably, each author has published an equal number of two papers, and hence, we have grouped them into four clusters, as shown in Fig.  4 . Cluster 1, the most collaborative cluster, comprises six authors from Sapienza University Rome (Italy), namely Babiloni, Fabio; Cherubino, Patrizia; Carato, Myriam; Rossi, Dario; Modica, Enrica; and Cartocci, Giulia. Cluster 2 includes four authors from Universidad Rey Juan Carlos (Spain), namely Goya-Esteban, Rebeca; Banos-Gonzalez, Miguel; Baraybar-Fernandez, Antonio, Barquero-Perez, Oscar. Cluster 3 comprises two authors, Pileliene, Lina, and Grigaliunaite, Viktorija, from Vytautas Magnus University (Lithuania). Finally, Ciorciari, Joseph from Swinburne University of Technology (Australia) belongs to cluster 4.

figure 4

The network map of leading authors (minimum contribution of two documents)

Leading journal

In this section, we have identified eight highly productive journals that have published at least two papers in advertising research within the NM context. These journals are presented in Table 6 , and Frontiers in Psychology emerged as the most prolific journal, publishing eight articles. Comunicar and Frontiers in Neuroscience followed closely behind, with four articles each. The remaining journals, including Computational Intelligence and Neuroscience, Journal of Economic Psychology, Scientific Annals of Economics and Business, Behavioral Sciences, Neuropsychological Trends, and Vivat Academia, contributed two papers each. Additionally, the number of citations a journal receives is an indicator of its article's quality and popularity, while the publication number reflects its productivity. To evaluate the leading journals, we computed their average citation per item (ACI) using total citations (TC) and total publications (TP) from the WoS database. Table 6 indicates that the Journal of Economic Psychology and Computational Intelligence and Neuroscience have the highest ACI, despite only publishing two papers on advertising and NM, with 32 and 31 citations, respectively. These findings suggest that many publications do not necessarily imply a high number of citations.

Keywords analysis

The bibliometric analysis involves representing the frequency of keywords in papers numerically (Wang & Chai, 2018 ), to assess their relevance and coherence with the papers' content (Comerio & Strozzi, 2019 ). Additionally, the correlation between pairs of keywords is expressed numerically as link strength, where a higher numerical value indicates a stronger link based on the number of times both keywords appear in the same paper (Ravikumar et al., 2015 ). The total number of links signifies the overall number of appearances of the two keywords in the same article. In this study, we conducted a keyword co-occurrence analysis on 56 keywords from 41 articles in 23 journals, with a minimum requirement of one source document. Synonymous keywords were also analyzed before inclusion, such as "neuromarketing" and "consumer neuroscience." To appear on the bibliometric map between two keywords that occur together in the same paper, a minimum of two occurrences of a keyword was required in VOSviewer.

According to Comerio and Strozzi ( 2019 ), keyword co-occurrence analysis is a crucial technique for understanding the content of articles and evaluating current research trends in a specific topic, such as advertising in neuromarketing. This technique is useful in identifying research directions and assessing hot themes. Ahmed et al. ( 2020 ) also noted that the analysis could reveal current academic documents in advertising and neuromarketing trends. The keyword co-occurrence map, as shown in Fig.  5 , indicates that neuromarketing research mainly focuses on marketing practices like advertising (12 occurrences, 63 total link strength), which means that advertising is mentioned 12 times and is linked to the NM theme 63 times. Brand (6 occurrences, 42 total link strength) is the second most frequent keyword, followed by brain processes such as attention, emotion, and memory. Finally, neuromarketing and advertising are linked to neuroscience tools such as EEG and fMRI, indicating that NM research aims to explore the consumer's brain responses to marketing stimuli such as advertising and brand.

figure 5

All keywords co-occurrence (with min. two occurrences)

We hypothesized a strong association between neuromarketing/consumer neuroscience and neurophysiological tools such as EEG and fMRI. We also expected a robust relationship between unconscious and subconscious responses like "attention," "emotion," and "memory," and marketing stimuli such as "brand," "advertising," and "advertising effectiveness." As an illustration, "advertising" emerged as the most prominent theme with twenty-seven frequencies and hundred-twenty-seven total link strength (TLS), followed by "attention" with eight frequencies and fifty-one TLS, and "emotion" with six frequencies and twenty-seven TLS. Notably, "EEG" exhibited a high connection with neuromarketing and advertising research. Table 7 summarizes the most frequent keywords with a minimum of five occurrences, and the most common keyword is "NM.".

Citation analysis

In this section, we employed citation analysis to identify the most popular articles in the area of advertising and NM. Citation analysis is a method that counts the number of times other scholars refer to a paper. It is an effective way to determine the most popular articles in a given field (Kumar et al., 2019 ). We scrutinized and evaluated the citations of forty-one papers. The outcomes are presented in Table 8 , which summarizes the most-referenced articles in the field of advertising and NM with a minimum of ten citations. Our results indicate that the most-cited article in this area is "Neuromarketing: The New Science of Consumer Behavior" authored by Morin ( 2011 ) and published in the journal Society, with a total of hundred-fifty-two citations. The second most-cited paper is "On the Use of EEG or MEG Brain Imaging Tools in Neuromarketing Research" authored by Vecchiato et al. ( 2011 ) and published in the journal Computational Intelligence and Neuroscience, with 57 citations as of the end of 2020. Finally, the least cited paper on the list is "Using Support Vector Machine on EEG for Advertisement Impact Assessment" published by Wei et al. ( 2018 ) in the journal Frontiers in Neuroscience, with a total of ten citations.

Content analysis of selected papers

Inner and extrinsic emotional processes.

According to Dolcos et al. ( 2019 ); Eijlers et al. ( 2020 ), feelings are an extrinsic emotional status that is a conscious response of customers, which can be translated to pleasant/unpleasant responses toward advertising. Feelings are considered an important aspect of understanding and interpreting the physiological response to advertising campaigns (Eijlers et al., 2020 ; Siddharthan et al., 2018 ). Morris et al. ( 2009 ); Pham et al. ( 2013 ) mentioned that feelings stimulated by advertising refer to the response of customers toward that advertisement, which can be considered as the enormous index of customers' responses to the advertisement (Ahmed et al., 2023b ). According to Dolcos et al. ( 2019 ); Ramsoy ( 2014 ), emotions are an inner emotional status, which is an unconscious/subconscious response of customers. These responses are linked to the involuntary nervous system, for example, increased heartrate in some cases as fear, anger, happiness, and so forth, which plays a vital role in the decision-making process, learning, and solving problems (Gordon, 2006 ). In addition to the aforementioned, changes in the involuntary nervous system result in changes in facial muscles, such as zygomatic and corrugator muscles, which can provide important information about unspoken decisions, customers' inner and extrinsic emotional status toward advertising (Winkielman et al., 2008 ). Thus, inner and extrinsic emotional responses have grabbed advertisers and marketers researchers to employ both in advertising campaigns to grab customers’ attention and influence their decision (Alsharif et al., 2021a ).

According to Barrett and Satpute ( 2013 ), emotions are produced from a set of neural activities, which execute basic functions psychological such as perception and memory. Emotion has several definitions based on the following way of the scholar. For example, Damasio and Carvalho ( 2013 ); Damasio ( 1999 ) defined emotion as changes in a customer's or individual's neural and physiological responses according to previous experiences. At the same time, LeDoux and Brown ( 2017 ) defined it as the relationship between the customer/individual and the surrounding environment, including physiological and behavioral factors. Accordingly, emotions' role in decision-making has been explored and interpreted using neurological and cognitive frameworks such as somatic sign theory (Damasio, 2012 ; Reimann & Bechara, 2010 ). Valence measures from positive, i.e., a pleasure, to negative, i.e., displeasure; at the same time, arousal measures from high, i.e., a surprise, to low, i.e., calmness, as depicted in Fig.  6 (Lang et al., 1997 ; Posner et al., 2005 ; Russell & Barrett, 1999 ).

figure 6

Dimensions model of emotions (Posner et al., 2005 )

(Sundar & Kalyanaraman, 2004 ) noted that several studies have utilized self-report and physiological methods to map and measure customers' emotional responses to ads. For instance, Lajante et al. ( 2020 ) used both EMG and self-report to assess customers' positive or negative reactions to ads. They found that these reactions positively affected customers' attitudes toward the ads. Baraybar-Fernández et al. ( 2017 ) employed ECG, EDA/GSR, and questionnaires to evaluate the impact of visual and audio messages in ads on participants. They discovered that sad messages had a significant influence on the participants. Barquero-Pérez et al. ( 2020 ) used ECG, EDA, and questionnaires to investigate the emotional responses to different kinds of ads and found that each type of ad elicited a unique emotion, including surprise. Guixeres et al. ( 2017 ) conducted brain response, ECG, and eye-tracking studies and found a strong correlation between ad effectiveness and the number of views on YouTube. According to Herrador et al. ( 2020 ), the EDA experiment showed that both male and female groups experienced strong initial activation. Still, there was reduced activation during the male group's most critical part of the video material. Finally, Venkatraman et al. ( 2015 ) discovered that activity in the ventral striatum could predict the response to advertising.

Neurophysiological instruments like fMRI and EEG have been extensively utilized in advertising research by various researchers research (Banos-González et al., 2020 ; Boscolo et al., 2020 ; Crespo-Pereira et al., 2017 ; Eijlers et al., 2020 ; Guixeres et al., 2017 ; Silberstein & Nield, 2012 ). For instance, the EEG analysis by Vecchiato et al. ( 2010 ); Vecchiato et al. ( 2012 ) indicated that the activity in the right frontal alpha is related to positive/liked ads, whereas the left frontal alpha is correlated with negative/disliked ads. Eijlers et al. ( 2020 ) used EEG to investigate and found that arousal is positively associated with successful ads among a large population, but consumer attitudes have a negative association. Morris et al. ( 2009 ); Shen and Morris ( 2016 ) employed fMRI to determine the affective responses of individuals towards advertising and found that the inferior frontal and middle temporal gyri were activated in response to positive and negative stimuli, respectively. Furthermore, they found that the right superior temporal and right middle frontal gyrus were activated in response to low and high arousal. Leanza ( 2017 ) EEG study found that some emotional aspects of the Virtual Reality (VR) experience significantly impacted consumer preferences.

Endogenous and exogenous attentional processes

Attention is described as "the tendency of humans to seek, accept, and absorb messages that match their interests, beliefs, values, expectations, and ideas while ignoring those that are incompatible with this system"(Hovland et al., 1949 ). Additionally described as selective perception (Wu et al., 2019 ). Selective perception is characterized by filtering away irrelevant information and focusing on essential information (e.g., different aspects of stimulus or different stimuli) (Dayan et al., 2000 ). Daily, customers are exposed to about 10 million pieces of visual information (such as advertisements, pictures, music, video, and color) through their senses (such as their eyes, ears, and skin). The majority of incoming data passes undetected, although consumers may digest about 40 bits of input data every second (Cherubino et al., 2019 ; Scheier & Held, 2006). That leads us to conclude that attention substantially impacts how consumers represent, interpret, and process information and, therefore, how they choose to prioritize information (Ahmed et al., 2020 ). Attentional and emotional processes are intertwined, and emotion is seen as a trustworthy and successful means of attracting customers' attention (Genco et al., 2013 ; Matthews & Wells, 1999 ). For instance, emotional stimuli are associated with the activation of the amygdala (AMY) and cingulate cortex (CC) in the brain (Montazeribarforoushi et al., 2017 ).

Attention is a fundamental brain activity that plays a vital role in assessing the efficiency of advertising campaigns; hence, it indicates consumer behavior and advertising effectiveness (Hamelin et al., 2021 ). The bulk of researchers have identified two systems to evaluate attention to advertising: (i) the exogenous attentional system and (ii) the endogenous attentional system (Kandel, 2009 ; Knudsen, 2007 ; Venkatraman et al., 2015 ). The exogenous attentional system (visual saliency/exogenous/involuntary) is triggered by external stimuli such as color, discount, voice, promotion, faces, text, novelty, brightness, etc., leading to the automatic processing of the information contained in external stimuli. Top-Down (goal-driven/endogenous/voluntary) attention, this other sort of attentional system is launched by internal and external objectives and expectations; hence, it is necessary to concentrate all of your mental energy on the goal you are seeking to accomplish, thereby filtering aims to reach your goals (Knudsen, 2007 ; Plassmann et al., 2012 ; Van Zoest et al., 2004 ).

Due to this, the underlying brain processes of attention and visual processing have a strong interest in advertising. In addition, the anterior cingulate cortex (ACC) is strongly associated with top-down and bottom-up attentional processes (Crottaz-Herbette & Menon, 2006 ; Meneguzzo et al., 2014 ). For example, Smith and Gevins ( 2004 ) revealed that the occipital lobe (OL) is connected with the processes of paying attention to television ads. Recent fMRI examinations of Casado-Aranda et al. ( 2018 ) discovered that advertisement and gender voice (male, female) stimulate attentiveness-related brain areas. Ananos ( 2015 ) Examined the attention level and processing of information in advertising (content recognition) between groups of old and young individuals using EEG. According to their results, the attention levels of both age groups are the same, but the recognition level of young people is greater than that of the old. Guixeres et al. ( 2017 ) Using neural networks and neuroscience-based measures, we have undertaken an experiment to determine the correlation between ad efficacy (e.g., remember ad) and YouTube channel views (e.g., brain response, ECG, and ET). Their results indicate a significant correlation between neuroscience measurements, self-reported ad efficacy (e.g., ad recall), and YouTube views. Cuesta-Cambra et al. ( 2017 ) examine how information is processed and learned, as well as visual attention. Their findings indicated that the visual activity of men differs from that of women, but that this difference does not affect subsequent recall, where recall depends on the emotional value and simplicity of advertisements, while complex advertisements require more visual fixation and are therefore difficult to remember. EEG also demonstrated the significance of the fun component of memory and low involvement processing. Treleaven-Hassard et al. ( 2010 ) evaluated the involvement of consumers with interactive and non-interactive television advertisements for a certain brand. The results demonstrated that companies associated with interactive advertisements get higher automatic attention. Boscolo et al. ( 2020 ) used EEG and questionnaires; an experiment was undertaken to evaluate variations in the visual attention paid by males and females to print advertisements. Their results demonstrated a difference in visual attention between males and females but no difference between males and females.

According to Simson ( 2010 ), the marketing mix ingredients may be altered to impact the perceived value of a product, as shown by research on the formation of value perceptions. But research on how attention systems influence customers' perceptions and behaviors have been restricted to consumer report and behavioral studies, which rely on a rational report and are insufficient to describe attention processes. Two attentional systems affect consumers' perceptions (e.g., endogenous and exogenous attentional systems) (Ramsoy, 2014 ). Consumer perception is the first stage in engaging with marketing stimulus or any other environmental stimulation (Rezaee & Farahian, 2015 ). Hogg et al. (2006) The process of selecting, organizing, and interpreting marketing stimuli is termed perception. Therefore, people add meaning and interpret information in a specific manner, resulting in perceptions as the person's discoveries for each individual. As shown by Belch and Belch ( 2007 ), perception processing relies heavily on internal processes, including previous knowledge (experiences), present objectives, beliefs, expectations, wants, and emotions, as well as exterior inputs like color, direction, intensity, and movement (Ramsoy, 2014 ). Although this explains how consumer perceptions are created, the section about the explanation of sensations and the internal and unique assignment of meaning to sensations remains hidden and unexplained in depth in the present literature on consumer behavior. However, it is widely thought that the unconscious drives this process.

Cartocci et al. ( 2017 ); Modica et al. ( 2018 ) performed an experiment to determine the accuracy of EEG, GSR, and ECG measurements of the cerebral and emotional perception of social advertising campaigns (i.e., antismoking). According to the approach-withdrawal index, the anti-smoking campaign that used a symbolic communication style had the greatest approach scores. While images with a "fear-inducing appeal" and a narrative style had the highest and lowest effort value indices, respectively, those with a "fear-inducing appeal" had the highest effort value index. The fMRI investigation of Falk et al. ( 2012 ) To forecast the population-wide (non-sample) efficacy of stop-smoking advertisements. The results demonstrated that activity in a previous mPFC predicted the performance of numerous real-world advertising initiatives. Plassmann et al. ( 2008 ) Using the fMRI device, research was conducted on the sense of pleasantness in the taste of wines. Their results revealed a larger activity in the brain's medial OFC (mOFC) areas, responsible for perceived pleasure when individuals felt they were drinking costly wine. This suggested that the correlation between the pleasantness report and perceived product value and price was stronger than the correlation with flavor itself. Neuroscientists have discovered that the OFC and ventromedial prefrontal cortex (vmPFC) are engaged in decision-making via product perceived value (Daw et al., 2006 ). Nuñez-Gomez et al. ( 2020 ) conducted an EEG experiment to test how two groups perceive advertising materials (e.g., a healthy group and a group with Asperger syndrome). The results indicate that the two groups perceptions of emotion and attention characteristics vary significantly. Gong et al. ( 2018 ) Using EEG/ERP, we conducted an experiment to determine the effect of sales promotion (e.g., gift-giving, discount) on customer perception and purchasing choices. The data indicate that discount promotions influence purchasing choices more than gift-giving promotions.

Motivational Attitude

Emotional and motivational processes complement one another. Lang and Bradley ( 2008 ). Chiew and Braver ( 2011 ); Pessoa ( 2013 ), motivating processes were discovered to have a significant impact on customers' cognition and behavior. The positive motivating cues, for instance, will encourage people to accomplish their objectives (e.g., get or predict a reward for performing a task correctly) (Chiew & Braver, 2016 ). The negative motivating stimuli may lead to distraction, whilst positive motivational stimuli can lead to focus. (Anderson et al., 2013 ). Pessoa ( 2013 ); Raymond ( 2009 ) argued that motivational processes are a compass of consumers' attitudes toward external stimuli in order to interact with the environment and attain objectives. Higgins ( 1998 ) proposed two dimensions for measuring motivational processes: withdrawal and approach attitudes. Researchers and practitioners examined the neurological responses of motivational processes to better comprehend customer reactions to commercials and goods.(Vecchiato et al., 2010 ). For instance, Cherubino et al. ( 2015 ); Davidson ( 2004 ) EEG was used to study the link between the prefrontal cortex and motivational characteristics. The results demonstrated that the PFC is associated with motivational aspects, with the right PFC correlating with withdrawal attitude and the left PFC with approach attitude. The EEG examination of Pozharliev et al. ( 2015 ); Zhang et al. ( 2019 ) documented brain reactions to luxury items (motivations). Findings revealed that social incentives play a crucial role in encouraging the purchase of luxury items to achieve social aspirations (at least one goal). The EEG investigation of Bosshard et al. ( 2016 ) In the right parietal cortices, liked brands display greater motivational features and activity signals than disliked ones. Therefore, a high correlation exists between PFC activity and motivational characteristics in response to marketing stimuli such as ads (Davidson et al., 1990 ). In order to orient the marketing mix, marketing academics, and practitioners must concentrate on the motivating processes of customers (e.g., target appropriate audiences and increase the effectiveness of ads and products) (Bahrabad & Farrokhian, 2017 ). According to past studies, NM study has evaluated television advertisements using an approach-withdrawal attitude (Di Flumeri et al., 2016 ). Therefore, approach/withdrawal motivational attitudes are critical to marketing and advertising research.

Reward Processing

According to the findings, scientists and practitioners must examine and understand the brain responses involved in processing rewards such as money, food, and social activities (Case & Olino, 2020 ); (Berridge, 1996 ; Knutson et al., 2001 ; Lehner et al., 2017 ). Because the positive incentive, such as monetary gain, food, or other rewards, improves precision and cognitive task performance (Anderson, 2016 ; Gilbert & Fiez, 2004 ; Krawczyk et al., 2007 ) by the modification of the initial attentional process. Anderson et al. ( 2013 ) It has been established that visual characteristics (e.g., product design) that are associated with reward will immediately capture the consumer's attention since they are prioritized. For instance, the design/preference of a product or brand may enhance activity in regions involved in reward processing, resulting in increased activation in areas of motives that may influence customers' purchasing choices (Cherubino et al., 2019 ). Numerous research focused on people's reactions to monetary rewards by analyzing their approach/avoidance attitude (Case & Olino, 2020 ; Knutson et al., 2001 ). For instance, Bechara et al. ( 1994 ) used GSR, an experiment titled "Iowa Gambling Task" was conducted to examine the effect of reward on decision-making. Participants were separated into two groups: the healthy group and the group with vmPFC lesions. The results indicated that healthy individuals were more perspiring, indicating that they experienced a negative emotional response when picking up cards from a losing deck, but the lesion group picked up cards regardless of whether they were winners or losers. Consequently, rewards significantly impact decision-making (Bechara & Damasio, 2005 ; Case & Olino, 2020 ; Maia & McClelland, 2004 ).

Numerous researchers have proven the significance of striatal activity in reward processing, with striatal components such as the caudate nucleus, nucleus accumbens (NAcc), and putamen playing a vital role in reward anticipation and appraisal (Knutson & Wimmer, 2007 ; Lehner et al., 2017 ; Padmala & Pessoa, 2011 ). For example, Galvan ( 2010 ); Geier et al. ( 2010 ) Investigated the link between reward processing and the striatum via experimentation. Their results demonstrated that the ventral striatum (VS) is crucial in reward prediction. Padmanabhan et al. ( 2011 ) examined the relationship between the reward system and attention processes. Their research demonstrated that rewards promote cognitive control. Prior neurophysiological research has shown that rewards engage the ventral medial prefrontal cortex (vmPFC) and ventral striatum (Davey et al., 2010 ; Izuma et al., 2008 ; Lieberman, 2013 ). The ventral striatum has previously been mentioned in relation to the reward system (Fliessbach et al., 2007 ). Consequently, the results imply that neurodevelopmental changes in striatum systems may lead to alterations in the manner in which reward influences attentional processes (Dolcos et al., 2019 ).

Memory is described as a continuous, brain-based learning process with input and output functions (Endo & Roque, 2017 ; Myers & DeWall, 2021 ). The input function encodes information while the output function retrieves it, which is important for advertising research (Atkinson & Shiffrin, 1968 ; Genco et al., 2013 ). Recall and recognition of advertising information are examples of the retrieval function (Venkatraman et al., 2015 ). Atkinson and Shiffrin ( 1968 ); Myers and DeWall ( 2021 ) proposed a three-step model of memory, known as the multistore model, which includes sensory memory, short-term memory (STM), and long-term memory (LTM) (McLeod, 2017 ). As McGaugh ( 2000 ) demonstrated, memory-related brain processes can positively impact customer behavior, particularly regarding advertising recall and recognition. Research has shown that memory-related brain processes positively impact customer behavior, such as in advertising recall and recognition (Genco et al., 2013 ; Plassmann et al., 2012 ). Memory and emotion are intricately intertwined. For instance, past research has shown that emotional experiences are often recalled more than neutral ones, particularly if they match the events occurring at the time (Bradley et al., 1992 ).

According to extensive research, the hippocampus (HC) situated in the temporal lobe (TL) plays a crucial role in forming and processing memories (McGaugh, 2000 ). Additionally, HC activation has a strong association with long-term memory (LTM) and short-term memory (STM), which significantly influences customers' purchasing choices (Murty & Adcock, 2014 ; Wittmann et al., 2005 ). The AMY, located near the HC, is also critical for the memory system (McGaugh, 2000 ). Research has shown that stronger activity in the left prefrontal areas is connected with advertising efficacy and is deemed a predictor of advertising success (Silberstein & Nield, 2008 ) (Rossiter et al., 2001 ). Astolfi et al. ( 2009 ); Fallani et al. ( 2008 ) have utilized the EEG to assess the brain areas activated by effective memory encoding of television advertisements. They discovered increased activity in the cortical areas. Morey ( 2017 ) The effect of advertising messages on recognition memory was explored. The results demonstrated that gamma band activity has a direct influence on memory. The fMRI investigation of Bakalash and Riemer ( 2013 ); Seelig et al. ( 2014 ) tested the brain areas responsible for memory advertising. Stronger activity in the amygdala (AMY) and frontotemporal areas was related to memorable versus forgettable advertisements. Tests have been conducted to examine the relationship between ad content and the activity of frontal areas and memory, indicating that ad content boosted activity in frontal areas and memory input function (Langleben et al., 2009 ). Additionally, systematic fixations on the brand and graphical aspects of printed advertisements enhance brand memory, while text fixations have little impact on later recall Pieters and Wedel ( 2004 ).

To improve advertising research, it is important to focus on mental processes such as emotion, attention, memory, reward processing, motivation, and perception.

Difficulties and constraints of NM application

Data interpretation, time-consuming, and sample size.

According to the literature, NM deployment around the globe faces a number of challenges, including data interpretation and time consumption. For example, according to Ariely and Berns ( 2010b ); Banos-González et al. ( 2020 ); Cherubino et al. ( 2019 ); Gang et al. ( 2012 ), the extracted data from the NM experiment using fMRI or EEG is more difficult to understand than eye tracking data, which is one of the difficulties addressed by NM researchers. NM employs neurophysiological and physiological technologies that are exclusive to the medical industry, hospitals, and a few institutions. Consequently, NM trials often include small populations. According to Banos-González et al. ( 2020 ); Bercea ( 2012 ); Berns and Moore ( 2012 ); Dierichsweiler ( 2014 ); Gang et al. ( 2012 ); Hensel et al. ( 2017b ); Isa et al. ( 2019 ); Plassmann et al. ( 2015 ); Stanton et al. ( 2017 ); Wolf and Ueda ( 2021 ), Small sample sizes in NM and consumer behavior research are seen as one of the obstacles to generalizing experimental results. According to Eser et al. ( 2011 ), it is difficult to recruit subjects for experiments due to the unfavorable reputation associated with legal and ethical difficulties. In addition, employing neurophysiological techniques such as but not limited to the fMRI is time-consuming, with each participant requiring between 30 and 60 min for a single experiment. Banos-González et al. ( 2020 ); Dierichsweiler ( 2014 ); Schiessl et al. ( 2003 ); Turna and Babus ( 2021 ) mentioned that the complexity of the data, which necessitates time-consuming analysis, as well as the requirement for adequate time to design the experiment and recruit individuals to execute an experiment, are cited as one of the NM's most time-intensive obstacles.

Cost of NM approaches and research

According to the available literature, NM trials use pricey equipment. For instance, the fMRI device cost more than $1.5 million US. As endorsed by Ahmed et al. ( 2023a ); Ariely and Berns ( 2010a , 2010b ); Bercea ( 2012 ); Chandwaskar ( 2019 ); Dierichsweiler ( 2014 ); Gang et al. ( 2012 ); Mansor and Isa ( 2018 ); Sebastian ( 2014 ); Turna and Babus ( 2021 ), the expense of NM techniques like as fMRI (estimated at $1.5 million) is one of the most significant hurdles and limits encountered by neuro-marketers and researchers interested in conducting trials. Additionally, NM research is costly. As shown by Ariely and Berns ( 2010a , 2010b ); Bercea ( 2012 ); Hensel et al. ( 2017a ); Isa et al. ( 2019 ); Plassmann et al. ( 2015 ); Turna and Babus ( 2021 ) The high expense of performing NM trials in the business area to examine consumer behavior, such as emotions and decision-making, is one of the most significant obstacles and limitations to the expansion of NM research.

Neuromarketing specialists

Neurophysiological and physiological instruments need the employment of specialists with a medical or physiological background. As described by Dierichsweiler ( 2014 ); Hammou et al. ( 2013 ), NM research utilizes software and advanced technology. Experiments thus need a high degree of technical understanding of how to utilize NM tools, how to conduct experiments, and how to analyze the data/findings, which we lack (Ahmed et al., 2023a ; Banos-González et al., 2020 ).

Ethical concerns

In the last decade, there has been a rapid increase in interest in the phrase "NM."(Ariely and Berns ( 2010a , 2010b ); Du Plessis, 2011 ). NM is a relatively young branch of study concerned with cognitive and emotional neuroscience as well as tactics for influencing consumer behavior. This prompted society and academic sectors (e.g., researchers, journalists, and press) to explore the ethical implications (e.g., privacy, autonomy, secrecy) of utilizing these new approaches to control/influence consumer behavior (Martineau & Racine, 2019 ; Murphy et al., 2008 ; Singer, 2004 ; Thompson, 2005 ; Ulman et al., 2015 ). For instance, when the press and media have reported on the possible dangers of employing NM methods to locate a "purchase button" in the brains of persons (Blakeslee, 2004 ; Isa et al., 2019 ; Stanton et al., 2017 ; Thompson, 2003 ) to analyze their thoughts, memory, attention, and emotions in order to influence their purchase choices, in addition to ads and marketers manipulating their brains (Racine et al., 2010 ). The primary objective of NM is to locate a "purchase button" in the human brain that may be targeted and activated by commercial advertising in the future (Spence, 2020 ). Undoubtedly, some folks are concerned about the influence of NM. Thus, NM's potency has prompted several nations (such as France) to adopt specific measures against the unauthorized use of brain-imaging methods (Nemorin & Gandy-Jr, 2017 ; Oullier, 2012 ; Ulman et al., 2015 ). For instance, the French parliament updated its 2004 bioethics regulations to read: "brain-imaging methods may only be employed for medical or scientific research or in the framework of judicial expertise"(Spence, 2020 ).

In fact, NM is used to produce more appealing goods and advertisements, but not to control people's thoughts.(Stanton et al., 2017 ). According to Ariely and Berns ( 2010a , 2010b ), The use of NM methods in harmful advertising campaigns (e.g., cigarettes, alcohol, etc.) to promote profit rather than the well-being of customers led to a rise in concerns, and therefore, the discussion of the possible ethical difficulties of NM. The Malaysian Communication and Multimedia Commission has prohibited these sorts of advertisements (MCMC) (Isa et al., 2019 ). Therefore, ethical concerns should be carefully explored (Pop et al., 2014 ). Thus, businesses must comply with government regulations and ethical standards (Arlauskaitė et al., 2013 ). In this context, several scientists and academics have identified the following ethical considerations for corporations and researchers: (i) privacy and secrecy; and (ii) independence(Isa et al., 2019 ).

Confidentiality and privacy

They are regarded as one of the most critical challenges, including maintaining participant data's confidentiality and anonymity. Due to the fact that neurophysiological procedures might reveal sensitive information that, if disclosed to the general public or marketing agencies, could be abused and breach ethical standards, these techniques are not permitted. Consequently, NM approaches may threaten people's privacy if this technology can efficiently and precisely interact with the consumer's brain (Murphy et al., 2008 ). Others, however, have stated that these concerns are probably unwarranted since current imaging technology does not provide exact forecasts of consumer choices (Brammer, 2004 ; Neurology, 2004 ). Therefore, the ethical dimension poses the greatest obstacle to the application of NM techniques. The protection of experiment participants is one of the top ethical concerns in NM research, as identifying the neural correlates of emotional and cognitive processes of interest for advertising can produce crucial information about consumer behavior (i.e., decision-making) and, thus, may compromise the participants' privacy (Hubert & Kenning, 2008 ; Javor et al., 2013 ; Murphy et al., 2008 ; Ulman et al., 2015 ), the risks associated with data confidentiality (Flores et al., 2014 ).

It is people's right that must make choices without external influence. However, some opponents have argued that the neuro marketer may employ NM tactics to influence customer decisions and disregard their autonomy in selecting items or commercials (Dierichsweiler, 2014 ). For instance, it conducted an experiment to explore the influence of the frequent presentation (without their conscious knowledge of the brand) of a Dasani water bottle on customer decisions, in which participants were instructed to pick one bottle from four distinct brands. The majority of participants selected the Dasani water bottle above others, indicating that regular exposure to a product, brand, or commercial may influence the consumer's brain and decision-making (Stanton et al., 2017 ). According to detractors, this is improper and immoral since it demonstrates that consumer autonomy has been undermined by repeated exposure to a certain product (Isa et al., 2019 ). However, the critics' argument that repeated exposure to a particular product, marketing, or brand plays a major role in affecting decision-making is not entirely accurate since the human brain is not that straightforward. It may be accurate to say that frequent exposure will lead to the prioritization of this product at the point of purchase but not necessarily to a purchase decision. Furthermore, decision-making processes in the brain are not easily measured or predicted because they are interconnected with numerous brain processes (e.g., emotional and cognitive processes). It has openly challenged what they define as a rush to embrace neurophysiology and explain all human brain activities, prompting him to invent the word "neuromania" to represent all of these concerning elements (Cherubino et al., 2019 ).

Based on an evaluation of relevant literature, the number of publications both yearly and cumulatively, has risen since 2004. The PRISMA framework recommended by Moher et al. ( 2015 ) was used to determine the appropriate papers for this study, as mental processes must be considered in advertising research. Among all publications, more than half were produced by three nations: Spain, Italy, and the United States. Spain was the most prolific, with eleven papers and 96 citations by the end of 2020, while Italy only released eight papers. The study employed bibliometric analysis to identify global trends in the subject area of interest. The most prolific journals with at least two publications each were located in six nations, including Switzerland, Spain, England, Poland, Netherland, and Italy. Frontiers in Psychology, with eight papers, was the most productive journal in NM and advertising, followed by Comunicar and Frontiers in Neuroscience, each with four papers. Additionally, the most-cited paper was "Neuromarketing: The New Science of Consumer Behavior," published by Morin ( 2011 ) in the journal Society, with over 152 citations.

According to this study, inner and extrinsic emotional responses, endogenous and exogenous attentional processes, memory, perception, motivational attitudes, and reward processing are the most important brain functions to be addressed in advertising research. The authors revealed that the right dlPFC plays a vital role in the motivational attitudes of customers, which in turn influences customer behavior to approach or avoid the advertisement, product, or even brand. At the same time, the gyrus regions have a key role in emotional valence and arousal responses such as pleasure/displeasure and high arousal/low arousal, wherein the inferior frontal and middle temporal gyri are related to pleasant and unpleasant responses. At the same time, the right superior temporal gyrus and middle frontal gyrus are associated with the intensity of arousal, i.e., high and low. In terms of memory and attention, the occipital lobe (OL) is important for both endogenous and exogenous attention processes, whereas the hippocampus (HC) plays a critical role in memory processes. Other brain regions, such as the amygdala and frontotemporal regions, are associated with remembering/un-remembering information. The ventral striatum (VS) in the basal ganglia plays a central role in reward processing, with components like the putamen, caudate nucleus, and nucleus accumbens (NAcc) involved in assessing consumer expectations versus actual reward. The ventral tegmental area is also considered part of the reward system, transmitting dopamine to other brain regions to influence goal-seeking behavior. The anterior cerebral hemispheres are important for withdrawal/approach motivation, with activity in the right prefrontal cortex (PFC) associated with withdrawal behavior and activity in the left PFC linked to approach behavior (Cherubino et al., 2015 ; Davidson, 2004 ). The orbitofrontal cortex (OFC) and ventromedial prefrontal cortex (vmPFC) are crucial in perceiving reality and perceived value (Daw et al., 2006 ).

Implications for theory and practice of the study findings

Theoretically, neuroscientific tools and methods allow the measurement of brain and body activity signals that indicate consumers' reactions to advertising campaigns. These signals can include emotions, attention, memory, perception, reward processing, and motivation. For example, neurophysiological instruments such as fMRI, EEG, and fNIRS can record the neural signals of mental responses, such as advertising recall and recognition, while physiological instruments like ET, GSR, EMG, and ECG can provide valuable information on physiological responses like pupil dilation, heart rate, and so forth. By identifying the strengths and weaknesses of advertising campaigns before implementation, advertisers can develop more effective campaigns and rectify negative aspects that generate a negative attitude towards advertising. Furthermore, researchers can use the neural and physiological correlates of emotions, attention, memory, reward processing, motivation, and perception to predict consumer behavior following advertising campaigns, such as in response to presenter features like celebrities, gender voice, commercial appeal, social efforts like anti-smoking campaigns, and public health. Practically, this study can provide insight into how advertising works in customers' minds, which can be applied to develop appealing advertising in various sectors, including political, social, and commercial.

General Conclusion

Neuromarketing is a field with enormous potential to address commercial issues such as advertising effectiveness and budget waste and to develop more impactful advertising campaigns in the social, political, and public health domains to increase public awareness. With intense competition in the advertising industry, each agency seeks to discover the most advantageous strategies to outperform rivals and become the consumer's top choice. Therefore, advertisers and marketers have employed neuroscientific methods and techniques to explore, analyze, explain, and predict consumers' mental and physiological responses to marketing stimuli, especially advertising. They can increase advertising effectiveness by identifying the most important mental and physiological processes involved in advertising research (as described above in Sect. 3.3). The majority of advertising research studies have focused on these primary mental processes.

The results indicated that neuroscientific approaches and procedures are crucial for capturing and recording consumers' mental and physiological reactions to marketing stimuli, including but not limited to advertising research. For instance, neurophysiological instruments allow for measuring and recording the brain's activity signals, while physiological tools may record bodily reactions such as eye movements, perspiration levels, and fixation. We think this study gives a complete review of the current and most important neuroscientific approaches used in advertising research and the most important mental processes to be addressed in advertising research. Furthermore, we think that this study will assist researchers in identifying the appropriate mental processes for obtaining accurate and high-quality outcomes.

Limitations and future directions

The aim of this study was to reduce errors in methodology, but there are constraints that offer opportunities for further academic research. The study focused exclusively on empirical papers in advertising within the neuromarketing context, published in English, and using physiological and neurophysiological instruments, ignoring non-English books, conference papers, proceeding books, and chapter books, resulting in some bias. To overcome these obstacles in future studies, including the high cost of instruments and research, inadequate facilities in Business Schools, time consumption in data interpretation, experiment design, and participant recruitment, as well as increased funding in NM research and instruments (Ahmed et al., 2023a ). The authors suggest investigating the effect of ads on consumer persuasion, attractiveness, engagement, and enthusiasm, as well as the contributions of NM research to other areas, such as social sciences, public health, politics, and stocks. Additionally, researchers from developing countries are invited to publish in this field. To obtain accurate results, researchers and practitioners must use the appropriate instruments for their research.

Data Availability

Not applicable.

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The authors would like to thank Universiti Teknologi Malaysia, Azman Hashim International Business School; King Faisal University, College of Business Administration; Al-Hussein Bin Talal University, College of Business Administration and Economics; King Faisal University, for supporting this study.

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Alsharif, A.H., Salleh, N.Z.M., Alrawad, M. et al. Exploring global trends and future directions in advertising research: A focus on consumer behavior. Curr Psychol (2023). https://doi.org/10.1007/s12144-023-04812-w

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Advertising Effectiveness

Advertising Effectiveness

research paper on advert

By Peter J. Danaher

The internet has enabled many business developments, but it has turned media allocation and planning on its head. In traditional mass media like television, advertisers can purchase a commercial slot and expect large audiences.

However, many of those reached are not interested in the advertised product or service, so a large percentage of those exposed to advertising do not respond to the message. In digital advertising, websites containing specialized content (e.g., model airplanes) allow advertisers to display their products to loyal and attentive audiences. In the social media space, Facebook delivers ad content to ideal target audiences by examining the web activity of users and their networks. Paid search advertising sends firms customers who are already “in the market” for their products, as indicated by their keyword use.

Over the past 15 years, television channels have grown in number. But the more significant change has been the exponential growth in websites supporting themselves with advertising, not to mention the rapid uptake of paid search advertising.

Advertisers have moved to new digital media outlets not only because of their ability to target customers, but also their lower cost compared to traditional media. Furthermore, digital media allows firms to connect ad exposures and search clicks to downstream sales, a feature Danaher and Dagger (2013) suggest eludes traditional media. Sethuraman, Tellis, and Briesch (2011) show the most convincing way for firms to demonstrate advertising’s effectiveness is by linking the effort to sales. In turn, researchers can use two methods to assess advertising effectiveness: field experiments and econometric models.

Field Experiments

Targeting and retargeting customers who are more likely to respond to offers, an increasingly common practice, makes advertising appear more effective than it is. Lambrecht and Tucker (2013) , in an award-winning Journal of Marketing Research paper, reported a comparison of advertising response between customers exposed to standard banner ads and retargeted banner ads showed the ads displaying products previously viewed were six times more effective at generating sales. However, the consumers receiving retargeted ads had already demonstrated product predilection. The researchers therefore randomly assigned consumers to a treatment group seeing retargeted, product-specific ads and a control seeing generic product category ads. They found the retargeted ads were less effective than the generic ads, as the customers were in different stages of the purchase funnel, and while retargeted ads work well near purchase, they are not effective for the larger group of customers embarking on their search.

The use of field experiments to determine ad effectiveness has subsequently blossomed, with studies using “ghost ads” on Google ( Johnson, Lewis, and Nubbemeyer 2017 ) and Facebook ( Gordon et al 2019) to create randomized control groups. For example, Sahni (2016) used a field experiment to show digital ads for one restaurant increased sales at competing restaurants offering similar cuisine.

In every case, these field experiments have shown that advertising effects are often difficult to detect. For example, the study of Facebook ads by Gordon and colleagues (2019) examined 15 campaigns and found that only eight produced a statistically significant lift in sales.

Econometric Models

The studies by Johnson, Lewis, and Nubbemeyer and Gordon and colleagues also highlight the challenges of designing an experiment to assess digital ad effectiveness. Individual customers use the internet in different ways, and providers deliver digital ads via unique online auction processes. Econometric models therefore provide a versatile approach to gauging advertising effectiveness. And while field experiment studies have been limited to examining one medium at a time, econometric models allow researchers to compare effectiveness across several media.

Researchers can use econometric models to examine time series data, such as weekly or monthly advertising and sales records. Dinner, van Heerde, and Neslin (2014) studied traditional and digital advertising’s effects on in-store and online sales for an upscale clothing retailer across 103 weeks. The retailer made about 85% of its sales in-store, and the researchers examined three media: traditional (i.e., total spend on newspapers, magazines, radio, television, and billboards), online banner advertising, and paid search. They found online display and paid search were more effective than traditional advertising. Although firms might expect digital advertising to influence only online sales, the researchers found it also influenced in-store sales.

Researchers can also use econometric models to examine single-source data linking individual-level ad exposure to sales, the strategy employed by Danaher and Dagger in 2013. They examined 10 media types employed by a large retailer: television, radio, newspaper, magazines, online display ads, paid search, social media, catalogs, direct mail, and email. The researchers found traditional media and paid search effectively generated sales, while online display and social media advertising did not.

Multimedia, Multichannel, and Multibrand Advertising

Danaher and colleagues (2020) also used single-source data but extended it to multiple retailer-brands, two purchase channels, and three media (email, catalogs, and paid search). They collected the data from a North American specialty retailer selling mostly apparel, where 80% of sales were in-store. The parent retailer owned three relatively distinct brands operating independently. They collected customer data in a combined database, giving them information on sales for each retailer-brand over a two-year period.

The researchers found emails and catalogs from one retailer-brand negatively influenced competing retailer-brands in the category. Paid search influenced only the focal retailer-brand. However, competitor catalogs often positively influenced focal retailer-brand sales among omni-channel customers. The researchers also segmented customers by retailer-brand and channel usage, revealing customers shopping across multiple retailer-brands and both purchase channels were the most responsive group to multimedia advertising.

In the contemporary business environment of ever-increasing media channels but static advertising budgets, firms must be able to measure advertising effectiveness. Many businesses have shifted their advertising expenditure toward digital media, but multiple studies show traditional media remain effective.

How do marketing managers decide what is best for their companies? Digital media firms like Google and Facebook offer in-house field experiment methods of examining advertising effectiveness. For multimedia studies, analysts can apply econometric models in any setting where time series or single-source data are available.

Peter Danaher is Professor of Marketing and Econometrics and Department Chair at Monash Business School in Melbourne, Australia. He was recently appointed a co-editor of the Journal of Marketing Research .

Danaher, Peter J. (2021), “Advertising Effectiveness,” Impact at JMR , (January), Available at: https://www.ama.org/2021/01/26/advertising-effectiveness/

Danaher, Peter J., and Tracey S. Dagger (2013), “Comparing the Relative Effectiveness of Advertising Channels: A Case Study of a Multimedia Blitz Campaign,” Journal of Marketing Research , 50(4): 517-534. https://doi.org/10.1509/jmr.12.0241

Danaher, Peter J., Tracey S. Danaher, Michael S. Smith, and Ruben Laoizo-Maya (2020), “Advertising Effectiveness for Multiple Retailer-Brands in a Multimedia and Multichannel Environment,” Journal of Marketing Research , 57(3): 445-467. https://doi.org/10.1177/0022243720910104

Dinner, Isaac, Harald J. van Heerde, and Scott A. Neslin (2014), “Driving Online and Offline Sales: The Cross-channel Effects of Traditional, Online Display, and Paid Search Advertising,” Journal of Marketing Research , 51(5): 527-545. https://doi.org/10.1509/jmr.11.0466

Gordon, Brett R., Florian Zettelmeyer, Neha Bhargava, and Dan Chapsky (2019), “A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook,” Marketing Science , 38(2): 193-225. https://doi.org/10.1287/mksc.2018.1135

Johnson, Garrett A., Randall A. Lewis, and Elmar I. Nubbemeyer (2017), “Ghost Ads: Improving the Economics of Measuring Online Ad Effectiveness,”  Journal of Marketing Research , 54(6): 867-84. https://doi.org/10.1509/jmr.15.0297

Lambrecht, Anja, and Catherine Tucker (2013), “When Does Retargeting Work? Information Specificity in Online Advertising,” Journal of Marketing Research , 50 (October): 561-576. https://doi.org/10.1509/jmr.11.0503

Sahni, Navdeep S. (2016), “Advertising Spillovers: Evidence from Online Field Experiments and Implications for Returns on Advertising,” Journal of Marketing Research , 53(4): 459-78. https://doi.org/10.1509/jmr.14.0274

Sethuraman, Raj, Gerard J Tellis, and Richard A. Briesch (2011), “How Well Does Advertising Work? Generalizations from Meta-Analysis of Brand Advertising Elasticities,” Journal of Marketing Research , 48 (June): 457-471. https://doi.org/10.1509/jmkr.48.3.457

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Young people are exposed to an abundance of advertising for unhealthy products (eg, unhealthy foods, tobacco, alcohol). Because of their developing cognition, children may not be able to understand the intent of advertising. However, advertising restrictions often assume that adolescents have critical reasoning capacity and can resist the effects of advertising. This review seeks to assess whether the evidence supports this assumption.

Ten databases were searched in December 2020. Inclusion criteria were participants aged 6 to 17 years, any advertising exposure, objectively measured understanding or attitudinal outcome, a comparison, control, and between-group comparison. This study included all languages and excluded studies published pre-2010. Two reviewers independently extracted data and assessed study quality.

Thirty-eight articles were included. Meta-analysis of 9 studies with attitudinal outcomes indicated that unhealthy product advertising generated more positive brand or product attitudes compared with neutral or no advertising control in all ages. There were significant effects for digital and nondigital advertising formats. We found greater understanding did not protect against the impact of advertising on brand or product attitudes. Limitations include the inability to meta-analyze the impact of advertising on understanding or the influence of age.

Evidence shows that the attitudes of young people were influenced by advertising. Critical reasoning abilities did not appear to be fully developed during adolescence and not found to be protective against the impact of advertising. Policymakers should ensure regulations to restrict marketing of unhealthy commodities protects adolescents as well as younger children.

Young people are exposed to an abundance of advertising and marketing, primarily for toys and food products (mostly high in fat, salt, and sugar [HFSS]). 1 – 4   Advertising can lead to behavior change through direct and indirect pathways, which leads to harm through unhealthy behaviors. 5   The hierarchy of effects model suggests that advertising creates awareness of and interest in a brand or product, which leads to heightened preference and then to a decision to purchase and consume. 6   Much of the advertising children are exposed to is for potentially harmful products (eg, HFSS food, alcohol) which may increase unhealthy behaviors that are associated with a number of detrimental and harmful effects. 7 , 8   Direct tobacco advertising is banned in most countries, but young people are still exposed to indirect advertising, for example, through viewing tobacco use on television (TV), shown to result in smoking initiation in young people. 9   Electronic cigarettes (e-cigarettes) have also grown in popularity over the last decade, and provaping advertising are prevalent on social media, with emerging evidence of harm. 10 , 11   Research on the impacts of advertising on children over the past decade has focused particularly on HFSS food advertising. 12   Young people are exposed to large amounts of food advertising through various media, which is often child-targeted and mostly for HFSS foods. 13 , 14   Meta-analyses show that food advertising increases acute calorie intake in children. 15 , 16  

There has been a strong policy focus on tightening regulations around food advertising, although restrictions frequently only apply to children up to 12 years of age. 17   There are widespread restrictions to prevent alcohol and tobacco advertising that targets children, since these products are illegal for children to purchase or use, 18   with calls to make these restrictions worldwide to address noncommunicable diseases. 19   Researchers have raised concerns over the ability of children and young people to identify, understand, and apply critical reasoning in response to advertising. Thus, they are more susceptible to the influence of advertising, especially in digital formats (including embedded content on webpages, social media platforms and advergames), making this a policy target and active research area. 20  

There is substantial literature on the understanding of advertising. A prominent framework has been the “Persuasion Knowledge Model,” which proposes that to resist advertising, individuals must first recognize that an advert is trying to sell something (persuasion knowledge). 21   Various aspects of understanding have been identified: recognizing advertising; perception of who pays for advertising and audience targeting; understanding the selling intent of advertising (ie, that advertisers are trying to sell products), persuasive intent (ie, that advertisers are trying to influence behavior via changing attitudes toward products or brands), tactics (ie, specific strategies used), and bias regarding the product (ie, discrepancies between advertised and actual product). 22   Evidence suggests that “advertising literacy” (ie, knowledge and understanding of advertising intent and tactics) does not fully develop during childhood; therefore, children do not possess the necessary cognitive ability to resist advertising. 22 , 23   For this paper, we view critical reasoning as the ability to recognize and understand advertising (advertising literacy) and how it impacts children and young people’s response to advertising. Much of the work around children and advertising, and children’s broader position as consumers, has been informed by Piagetian theory, which presents age-specific stages in children’s development driven by cognitive ability. 24 , 25   This suggests progressive growth in understanding, showing that as children get older, cognitive ability increases along with an increased ability to understand and resist advertising. This understanding was largely developed when TV was the main advertising medium, but the applicability to the digital age of advertising has been questioned (even for older children), as entertainment and advertising content are not clearly distinguished. 26  

Social-cognitive models present the effects of advertising occurring automatically without any information processing, suggesting that understanding alone is insufficient to counteract the potentially harmful effects of advertising. 25   Concerning food, the Food Marketing Defense Model posits that awareness, understanding, ability (including cognitive capacity), and motivation (to resist advertising) are all required to withstand food advertising. 25   Advertising, especially when digitally embedded, is designed to bypass conscious and rational decision-making and instead rely on emotional responses and unconscious processing, thereby inhibiting the ability to resist effectively. 27 , 28  

Reasoning abilities are not fully developed by the age of 16, older than the 12-year threshold used in many regulations; other faculties associated with decision-making also continue to develop into adulthood. 29   It is established that teenagers engage in riskier behavior than both children and adults, attributed in part to changes in reward sensitivity occurring from early adolescence and the later development of self-regulatory competence. 29   In addition, they may be particularly susceptible to the social influence of their peers. 30   This evidence may be relevant to young people’s critical reasoning of advertising, since developmentally, they may not be cognitively equipped to protect themselves from the potentially harmful effects of advertising. Studies indicate that children of all ages have difficulties identifying digital marketing. 26 , 27   Adolescents are particularly vulnerable to digital advertising because of their engagement with digital technology and media, which plays an important role in their social identity development. 17 , 27  

Existing reviews and meta-analyses have shown that children of all ages are impacted by advertising, 13 – 16   but the notion that understanding of advertising and older age are sufficiently protective remains pervasive. This review focused on 2 areas of interest; the ability of young people to recognize and understand advertising and how they respond to advertising in terms of attitudes toward the advertised brand or product (ie, the impact on diet and attitudes). The review aimed to explore whether evidence supports the notion that critical reasoning ability affects behavioral responses and how this may differ across childhood and adolescence. Critical reasoning relates to the former, but response is likely to include broader factors that could impact on what decisions young people make and their subsequent behavior. For example, attitudes to the advertised product or brand and level of motivation to resist the impact of advertising exposure.

We conducted our systematic review using EPPI-Reviewer 4 software. 31   The study was preregistered with PROSPERO (CRD42018116048), and the systematic review is reported in accordance with the Preferred Reporting Items for Systemic Reviews and Meta-analysis checklist. 32  

The search strategy was created in collaboration with an information specialist (C.S.). The search was based on terms for population (children and young people), intervention (eg, marketing, advertising, advergame*), and a measure of “understanding” or “attitudes” (eg, reasoning, psychology, advertising literacy, cognition). Systematic searches of the following databases were conducted: ASSIA (Proquest), Child Development and Adolescent Studies (EBSCO), Cochrane Central Database of Controlled Trials, Medline (OVID), PsycINFO (OVID), Sociological Abstracts (Proquest), Social Policy and Practice (OVID) and SCOPUS, and Web of Science – databases (Social Science Citation Index, Emerging Sources Citation Index). The full search strategy is included in a supplemental file ( Supplemental Tables 2A , 2B , and 3 ). Searches were conducted on November 7, 2018 and updated on December 10, 2020. The search results were imported into Endnote reference manager software and duplicates removed. The remaining articles were imported into EPPI-Reviewer 4 software and duplicate records screened and removed; this software was used to manage the screening.

The focus of the review was initially broad, as the scope of the literature was unknown. Following the initial search, a mapping exercise was undertaken to determine the full-text inclusion criteria. A decision was made to focus on experimental studies with an administered exposure ( Supplemental Fig 4 ) for full details of this initial stage and mapping diagram).

Eligible for inclusion during full text screening were: studies with participants aged 6 to 17 years of age inclusive; intervention criteria of any form of advertising for any product (including HFSS products, tobacco, toys); and outcomes of objectively measured understanding (including recognition or identification of advertising, understanding selling, or persuasive intent) or attitudes (toward brand or product including liking or perceptions). Experimental and intervention studies, including randomized or quasi-randomized studies, were included and required to have an appropriate comparison or control group, including no advert, a neutral advert, or a between group comparison (age, gender, socioeconomic status [SES]) with an advert exposure. Neutral adverts were defined by the studies and included adverts that were not the focus of the study, eg, a toy or nonfood advert for studies with a food advertising exposure and food product outcome. Studies were included from 2010 onwards as these were considered most relevant to contemporary advertising practices. There were no restrictions by geography or language. Exclusion criteria were date (pre-2010), intervention (any exposure that evaluated health promotion prevention programs, charity advertising, creation, and testing of models of cognition, media training or advertising literacy, branding only), outcome measures (any nonunderstanding or attitude measures including dietary intake or purchases), study design (qualitative studies, reviews, and dissemination format [nonpeer reviewed], eg, dissertations, conference abstracts, magazine abstracts). A random sample of studies were double-screened by 2 reviewers (H.C., and J.P.) on title and abstract using EPPI-Reviewer 4 software. All screening queries were reconciled by the reviewers. We used the machine learning capabilities of the EPPI-Reviewer software to assist with the screening because of the anticipated number of records from test searches (over 10 000). We employed an “active learning approach,” where the prioritization of records was frequently refreshed so the most relevant articles were screened first. The algorithm was trained using our screening decisions. Articles screened were plotted against studies included, and this was used to indicate when to stop screening (ie, the rate of inclusion plateaued indicating that there were unlikely to be unscreened relevant articles). A classifier model was then created and applied to all unscreened records, with a score based on relevance (0–100) generated and used to double-check exclusion. For full details on the machine learning approach and updated search methods see Supplemental Table 4 and the following reference. 33   Full-text screening was then independently completed by the same 2 reviewers (H.C. and J.P.) using EPPI-Reviewer 4 software and queries were jointly reconciled.

Descriptive data were extracted by 1 reviewer (J.P.) and checked for accuracy by another reviewer (H.C.). Data from experimental studies for inclusion in meta-analyses were independently extracted by 2 authors (J.P., and H.C.) and any discrepancies resolved by reextraction. Corresponding authors were contacted to provide raw data where necessary; 15 authors were contacted for additional information, and 9 provided additional data and 6 did not (1 was contacted regarding understanding outcomes only).

Risk of bias for the experimental studies was assessed by 2 reviewers (H.C., and J.P.) using Cochrane methods, 34   either RoB 2.0 for randomized trials 35   or ROBINS-I tool for nonrandomized studies. 36   To assess publication bias, funnel plots were created to assess asymmetry using Egger’s test. 37  

For inclusion in meta-analyses for understanding of advertising or brand or product attitudes, studies were required to compare the effect of an unhealthy product (eg, food, alcohol, tobacco) advert exposure to a nonadvert control, or to a control advert (advert for unrelated products).

Studies measuring attitudinal outcomes were required to have mean values with standard deviations. Because of differences in reported outcome measures, which included a variety of different scales (eg, 1–5, 1–3, dichotomous), the DerSimonian-Laird random-effects model was used to allow for synthesis of studies and standardized mean difference (SMD) was used as the outcome for the meta-analyses. All analyses were conducted using Stata 16 (16.1, StataCorp LLC, College Station, TX, USA). 37   Further details of how advert exposure conditions were combined; the outcome measures and scales and criteria for inclusion in the meta-analyses are provided in a supplemental file ( Supplemental Table 5 ).

Two meta-analyses comparing an advert exposure to control or neutral advert were conducted, by attitude type (brand or product) and by advertising format (digital or nondigital). For this review, we define brand attitude as the attitudes toward the advertised brand and product attitude as the attitudes toward the advertised product. Digital advertising formats included advergames, webpages, social media platforms, and influencer marketing, whereas nondigital advertising formats included TV and printed adverts, and product placement on TV or in movie clips. For all studies except 1, 38   a single combined advert exposure group was calculated for each group using Cochrane methods. 34   The exception was a study where 3 separate data points were included with the advert exposure of a specific product matched to the specific product attitudinal outcome measure. 38   We additionally conducted meta-analyses examining the impact of advertising on attitudes by age (children ≤12 years, teenagers >12 years because of legislation cut-offs).

Findings of studies not included in the meta-analysis are reported narratively, presented by outcome (understanding or attitudinal) and by impact of age and advertising features.

The database searches yielded 15 656 papers, resulting in 9325 studies once duplicates were removed. A random subset of 1790 studies were screened on title and abstract to trigger the machine learning from the original search and a further 208 screened on title and abstract from the updated search. Screening on title and abstract ultimately resulted in 272 studies to be screened on full-text and assessed for eligibility. This resulted in 39 studies, from 38 articles, which met the inclusion criteria. Nine of the studies that reported an attitudinal outcome were included in the meta-analyses ( Fig 1 ).

Preferred Reporting Items for Systemic Reviews and Meta-analysis screening flowchart.

Preferred Reporting Items for Systemic Reviews and Meta-analysis screening flowchart.

A summary of the descriptive data are provided in Table 1 , including details on setting (country, study), participants (sample size, age details), design, advertising exposure, outcomes measures, and findings.

Descriptive Summary of the Included Experimental Studies

NS, not stated.

Half of the sample may be reported in both.

Same sample but reporting of different outcomes.

May be the same participants across all 3 studies.

May be the same participants across the 2 studies.

Three out of the 4 schools may be reported in both.

Participant ages ranged from 4 to 18 and were broadly categorized as 12 years and under ( n = 19), 38 , 39 – 52   over 12 years ( n = 7), 53 – 59   or had participants in both age groups ( n = 13). 60 – 75   Most of the studies were conducted in Europe ( n = 16; Austria n = 5, Netherlands n = 4, Belgium n = 3, UK n = 3, Portugal n = 1), followed by the United States ( n = 12), Australia ( n = 6), Chile ( n = 2), and Israel ( n = 1), India ( n = 1), South Korea ( n = 1). Studies were mostly conducted in classroom settings ( n = 21). Advertising exposure was most commonly for food ( n = 29; all included a HFSS product or brand, eg, fast food or sugary cereal; in addition to some non-HFSS products), followed by e-cigarettes ( n = 7) or an assortment of products ( n = 3, including games, banks, and a financial services company). Majority of the advertising exposures were nondigital ( n = 25, including TV adverts, product placement, print advert, TV sponsorship, or movie trailers), compared with digital ( n = 18, including advergames, banner or pop-ups, social media). Outcomes, related to the advertised product, measured either understanding ( n = 10, eg, identification of commercial content, selling intent, persuasive intent, perceived advertising intentions) or attitudinal ( n = 23, eg, product liking, product perceptions, perceived benefits, appeal) or studies that measured both ( n = 13).

Meta-analysis was not possible for understanding measures, owing to the heterogeneity of exposures and outcomes for relevant studies. Many studies had control groups where, because of the nature of the questions, understanding of advertising was not able to be assessed (ie, cannot assess understanding about an advert the group did not see).

Where compared across age groups, understanding of advertising increased significantly with age (8 studies, mostly assessed as some concerns of bias and 1 as low risk of bias), 39 , 40 , 41 , 46 , 47 , 49 , 62 , 74   although no significant effects were found in 4 studies (mostly assessed as having some concerns of bias and one as low risk of bias), 45 , 48 , 66 , 67   and understanding decreased with age (assessed as low risk of bias). 75   Most of these studies were conducted with children under 12 years, so evidence was limited for teenagers. Of 2 studies conducted with teenagers, 1 study assessed as having some concerns of bias directly compared children aged 9, 12, and 15 years and found that advertising recognition significantly increased as age increased 74   ; the other study assessed as low risk of bias found 12 to 14 years olds had significantly higher recognition of sponsored content in a YouTube video compared with 15 to 16 year olds, but there was no significant difference between age groups for understanding persuasive intent. 75  

One study with some concerns of bias reported that persuasion knowledge increased with higher brand integration (in relation to advergames), but persuasion knowledge was very low across all groups and the magnitude of differences modest. 47   In relation to child “involvement” with advertising (ie, engagement with advergame), 1 study with some concerns of bias showed that children more involved with an advergame were less likely to identify commercial content. 48   One study with low risk of bias looked at differences in recognition of commercial content in advergames between a familiar HFSS brand and a fictitious or unbranded pizza game and found that recognition of the familiar brand was significantly greater than the unbranded game. 58   A similar study with some concerns of bias assessed persuasion knowledge between a branded advergame and a noncommercial advergame and found no significant difference. 52   Seven studies of mixed bias assessments (4 with some concerns, 3 low risk) measured different types of understanding; 4 found that awareness of selling intent was higher than persuasive intent in children aged 4 to 12 years (2 were significant 39 , 45   ; 2 did not test significance) 46 , 48 , 62   ; 2 found recognition of advertising in 7 to 16 year olds was greater than understanding persuasive intent 76   or advertising literacy 44   ; finally, 1 found skeptical attitudes toward advertising were greater than recognition of advergames as advertising, because of very low recognition in 7 to 11 year olds (62.5% to 72% vs 48.5%). 49   Four studies with some concerns of bias measured the impact of advertising format and found significantly greater understanding with nondigital advertising (TV) compared with digital advertising (primarily advergames). 46 , 48 , 62   Overall, understanding of the persuasive intent of adverts to impact on attitudes and behaviors was generally low across studies, for example, only 40% in 11 to 12 year olds, 39   and only 1% of 7 to 9 year olds and 12% 10 to 12 year olds. 44  

A meta-analysis comparing all advert exposures to no advert or neutral advert control by attitude type ( Fig 2 ), showed that overall, any advertising exposure significantly increased positive attitudes toward the brand or product, SMD = 0.397 ( P = .001; 95% confidence interval [CI] 0.154–0.639; I 2 = 91.4%). The subgroup meta-analysis by attitude type also showed that the effect of an advertising exposure was significant for both product attitudes (SMD = 0.430 [ P = .014; 95% CI 0.087–0.774; I 2 = 85.7%]) and brand attitudes (SMD = 0.369 [ P = .049; 95% CI 0.001–0.736; I 2 = 94.0%]).

Forest plot showing SMD in brand and product attitudes between any advertising exposure and no advert or neutral advert controls; 95% CIs and study weights are indicated. Overall SMD was generated by a random effects model. (1) Data from cola product placement vs control with cola attitude question; (2) Data from juice product placement vs control with juice attitude question; (3) Data from milk product placement vs control with milk attitude question.

Forest plot showing SMD in brand and product attitudes between any advertising exposure and no advert or neutral advert controls; 95% CIs and study weights are indicated. Overall SMD was generated by a random effects model. (1) Data from cola product placement vs control with cola attitude question; (2) Data from juice product placement vs control with juice attitude question; (3) Data from milk product placement vs control with milk attitude question.

A meta-analysis exploring the effect of advertising by format ( Fig 3 ) showed overall that any advert significantly increased positive attitudes, compared with no advert or neutral control, SMD = 0.36 ( P = .009; 95% CI 0.14–0.58; I 2 = 91.2%). When examined by advertising format, both digital advertising exposure and non-digital advert exposures had a significant positive effect on attitudes, SMD = 0.35 ( P = .005; 95% CI 0.01–0.068; I 2 = 93.2%) and SMD = 0.36 ( P = .005; 95% CI 0.08–0.65; I 2 = 84.5%), respectively. Egger’s regression analysis found no evidence of bias for either meta-analysis, although funnel plots showed some evidence of asymmetry ( Supplemental Figs 5 and 6 ). Trim and fill analysis showed no strong evidence of missing studies for either meta-analysis ( Supplemental Figs 7 and 8 ). Sensitivity analysis was completed running a fixed effect model; none of the findings changed in significance in either direction.

Forest plot showing SMD in brand or product attitudes between digital and non-digital advertising exposure and no advert or neutral advert controls; 95% CIs and study weights are indicated. Overall SMD was generated by a random effects model. Matthes (1) brand attitude outcome; Matthes, (2) product attitude outcome; Royne (1) data from cola product placement versus control with cola attitude question; Royne (2) Data from juice product placement versus control with juice attitude question; Royne (3) data from milk product placement versus control with milk attitude question.

Forest plot showing SMD in brand or product attitudes between digital and non-digital advertising exposure and no advert or neutral advert controls; 95% CIs and study weights are indicated. Overall SMD was generated by a random effects model. Matthes (1) brand attitude outcome; Matthes, (2) product attitude outcome; Royne (1) data from cola product placement versus control with cola attitude question; Royne (2) Data from juice product placement versus control with juice attitude question; Royne (3) data from milk product placement versus control with milk attitude question.

An additional meta-analysis was conducted, which looked at the impact of advertising on attitudes by age ( Supplemental Fig 9 ). Advertising had a positive impact on attitudes compared with the control condition for both age groups (ie, >12 years and ≤12 years). A further meta-analysis was carried out as a sensitivity analysis ( Supplemental Fig 10 ) to explore whether the effect held when the largest effect size was removed and the effect was still seen.

The majority of controlled studies not suitable for meta-analysis supported the above findings, namely that adverts brought about more positive attitudes (7 studies, mixed bias assessments: 3 low, 3 some concerns and 1 high) 41 , 42 , 47 , 48 , 53 , 57 , 59   ; however, 5 studies found no significant differences between groups (mixed bias assessment: 3 low, 2 some concerns). 51 , 57 , 58 , 69 , 73   One study, assessed as having a high risk of bias, explored the impact of e-cigarette adverts designed with low and high youth appeal and found the low youth appeal advert resulted in more positive attitudes than a none-cigarette control advert, but there was no difference between the high youth appeal and control adverts. 56   One study, assessed as low risk of bias, found that the younger group (5–6 years) had significantly more positive product attitudes following exposure to TV advert for HFSS cereal compared with the older group (10–11 years). 50   Another study with some concerns of bias found that brand preference following exposure to product placement decreased significantly with increasing age (9 vs 12 vs 15 years). 74  

Two studies with low risk of bias examined the impact of glamorized e-cigarette advertising on perceptions of cigarette smoking or e-cigarettes, compared with neutral or no advert control. They found the adverts led to occasional cigarette smoking being perceived as less dangerous and harmful 63 , 71   and the use of e-cigarettes by children as being more common. 63   One also found there was no difference in the appeal of e-cigarettes between adverts that glamorized e-cigarettes compared with adverts that associated e-cigarettes with health. 63  

Seven studies measured the interaction between understanding and attitudinal outcomes and reported interactions. Five studies found no interaction, showing that greater understanding of advertising did not limit favorable attitudes toward the advertised product 44 , 47 , 48 , 58 , 65   and 2 found some evidence of an interaction. 62 , 67   Six of these studies were found to have some concern of bias, and the other was assessed to have low risk of bias. 58   This study found, for children aged 13–18 years, recognition of commercial intent had no effect on brand attitude for either an unfamiliar or familiar brand. The age range for children from studies that found no interaction was broader than those that found interactions (5–18 vs 7–14 years). Of the 2 studies that found an interaction between lack of persuasion knowledge and greater attitudinal outcomes, the first had online pop-up adverts, which are heavily embedded, as the advertising exposure, 72   whereas the second only found an interaction among children that understood the snack was unhealthy (the interaction was not observed if children thought the advertised snack was healthy). 67  

For nonrandomized studies, 2 were rated as low and 11 as moderate risk of bias ( Supplemental Fig 11 ). Moderate risk of bias was mostly caused by the domain “bias caused by confounding,” as not enough information was provided or confounding variables were not included in analyses. Of the randomized studies, 10 were rated as low risk of bias, 13 as some concerns, and 3 with high risk of bias ( Supplemental Fig 12 ). The studies with some concerns were mostly because of lack of detail about the randomization process or unreported information about the selection of the reported results. Results were consistent between studies rated as low to high risk of bias. Sensitivity analyses were run excluding studies rated as high risk from the meta-analysis ( Supplemental Fig 14 ). The overall impact of advertising on attitudes remained but product attitude subgroup was no longer significant.

In this systematic review, data suggested that children’s understanding of advertising intent was limited and not nuanced, ie, children could recognize that adverts intended to sell a product but not that these were intended to change their attitudes and behavior. There was limited evidence that understanding increased with age, but more research is needed in this area. Understanding was lower for digital compared with nondigital formats, and lower when children were more involved with the medium (eg, advergames or online advertising). In terms of attitudes, meta-analyses indicated that advertising brought about more positive attitudes to both brands and products compared with controls; this was observed across all age groups. There was no evidence that adverts with high “youth appeal” were more effective, but evidence was limited for these exposures. Findings suggested that greater understanding of advertising is not protective, with evidence that attitudinal outcomes were impacted positively regardless of level of understanding. These findings collectively indicate that advertising impacts children, regardless of age, level of understanding, format, or specific targeting or youth appeal.

Our findings indicate that children and young people of all ages have some difficulties in understanding advertising. This fits with the developmental perspective that young people’s critical reasoning abilities continue developing into late adolescence. 29   We found that greater understanding does not necessarily protect against advertising, consistent with the Food Marketing Defense Model that challenges the focus on understanding to counteract the effects of advertising. The model instead proposes that advertising influences young people without conscious processing and that motivation to resist is also required, which may be lower among young people. 25   We did not include disclosure or media literacy intervention exposures in this review, but our findings suggest that the inclusion of disclosures (eg, declarations stating “this is an advert”) or media literacy training designed to increase understanding or advertising literacy would not necessarily protect children and adolescents from the influence of advertising. 76   This is supported in the literature as 1 experiment found that children who viewed food marketing with a disclosure actually consumed significantly more of a marketed snack than a control group. 76   A study in adolescents found that disclosures did not mitigate persuasion and increased brand memory, despite increasing understanding of persuasive intent. 77   Media literacy programs are a strategy often suggested by the food and beverage industry to increase persuasion knowledge in children, in lieu of improved regulations, such as industry-funded Media Smart (see https://mediasmart.uk.com/ ). 78 , 79  

Our findings that advertising had a positive impact on attitudes are consistent with previous research on food advertising. 12 , 14 , 80 , 81   Further supporting these findings, adverts (TV and advergames) for “unhealthy” unfamiliar food products have been found to elicit positive attitudes in children (aged 7–12 years) to a greater extent with advergames compared with TV advertising. 82   We found effects on attitudes regardless of age, consistent with other studies in different age groups. There is evidence that preschool children exposed to adverts for a range of child-directed foods had positive attitudes about these foods, 83   and that adolescents reported positive attitudes after viewing online adverts for fast food and confectionery. 84  

Comparing digital and nondigital advertising formats, we found no difference in impact on attitudes in subgroup meta-analysis, but narrative synthesis indicated that understanding was lower for digital formats. This is unsurprising since digital advertising is more integrated and, therefore, may be less explicit and more difficult to identify and understand, in addition to greater personalization and targeting. 21 , 25   This is important given the ubiquity of these formats, especially for adolescents, who because of their extensive engagement with digital media with less supervision, may be more susceptible to digital advertising. 85   For adolescents, media plays an important role in their social identity development, as they place more value on the opinions and actions of peers and figure out their perception of how they fit with others. 17 , 18 , 27   Digital marketing, especially on social media, is designed to target these unique developmental vulnerabilities. 86  

The findings from this review support understanding not being fully developed during childhood or adolescence. We also found that advertising influences the attitudes of young people of all ages, suggesting a need to protect older as well as younger children. Our results suggest that understanding does not protect children from the harmful impacts and influence of advertising, as per the Food Marketing Defense Model. 25   Reducing exposure to advertising is therefore likely to be more effective than improving understanding through disclosures or media literacy training. Existing regulations typically only apply to children up to 12 years of age, as they have historically been regarded as more vulnerable to advertising, therefore needing greater protection. 87   Our findings do not support lesser restrictions for advertising to teenagers, as there is no distinct evidence-based threshold for understanding that supports a cut-off of 12 years and suggest that appropriate protection from advertising exposure would benefit all young people. 17  

The limitations of this review include a lack of suitable data or studies to meta-analyze the impact of advertising on understanding or the influence of age. Meta-analysis limitations include the high heterogeneity of studies, despite using a random effects model and standardized mean difference outcome. The machine learning method has limitations, as a large number of articles were excluded without screening on title and abstract. The majority of the included studies were assessed as having some concerns of bias, which needs to be taken into consideration when interpreting the findings, although sensitivity analyses removing studies with high risk of bias and the largest effect size were conducted and not found to impact results. We may not have identified all product placement exposure studies, as this term was not included in our search strategy; however, studies with advert or marketing key words were included. Some of the studies may have been conducted in the same or similar group of participants (Tarabashkina 66 – 68   ; Duke and Farrelly 53 , 54   ; Uribe 69 , 74   ; van Berlo 57 , 58   ; Castonguay 40 , 50   ), but these do not interfere with the meta-analysis as only 1 was included. The time since the searches were completed is a limitation, with original searches completed in October 2018 and then updated in December 2020. This subject area is complex, so the review process is time intensive. Updating the searches would be low yield as the substantive findings of the work remained unchanged following the update searches, and we have no reason to believe the main findings of the paper would be subject to change. The main strength of the paper is that it meets an evidence gap, specifically addressing if children over 12 years of age have critical reasoning capacity and can therefore resist the effects of advertising. We were also able to quantitatively assess the impact of advertising on attitudinal outcomes. The search was carefully planned and executed, with double screening and data extraction. Studies were contemporary, adding to the relevance for current policy. Because of the delay observed in research, we found fewer studies using digital advertisement exposures, which is an area where more primary research is needed. There is also a need for further primary research in teenagers in relation to critical reasoning and advertising, and especially digital formats.

This systematic review and meta-analysis provide evidence that advertising impacts upon the attitudes of children and young people of all ages, regardless of their level of understanding and critical reasoning abilities. These findings may be useful to inform the thinking of policy makers, particularly in terms of restrictions based on age and changing patterns of media consumption.

Ms Packer designed the study, screened the studies, extracted all data, conducted the meta-analysis, completed bias assessment, and codrafted the initial manuscript; Dr Croker conceptualized and designed the study, screened the studies, extracted quantitative data and checked descriptive extraction, and codrafted the initial manuscript; Dr Stansfield assisted with the design of the study and provided expert assistance with the methodology; Dr Russell and Goddings provided input for the design of the study and assisted with methodological queries throughout the process; Drs Viner and Boyland conceptualized the study; and all authors reviewed and revised the manuscript, approved the final manuscript as submitted, and agree to be accountable for all aspects of the work.

FUNDING: This report is independent research commissioned and funded by the National Institute for Health and Care Research Policy Research Programme Grant Number: 174868. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health and Care Research, the Department of Health and Social Care or its arm's length bodies, and other Government Departments.

CONFLICT OF INTEREST DISCLOSURES: The authors have indicated they have no conflicts of interest relevant to this article to disclose.

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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.

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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.

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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.

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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.

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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.

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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.

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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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'all of us' research project diversifies the storehouse of genetic knowledge.

Rob Stein, photographed for NPR, 22 January 2020, in Washington DC.

Results from a DNA sequencer used in the Human Genome Project. National Human Genome Research Institute hide caption

Results from a DNA sequencer used in the Human Genome Project.

A big federal research project aimed at reducing racial disparities in genetic research has unveiled the program's first major trove of results.

"This is a huge deal," says Dr. Joshua Denny , who runs the All of Us program at the National Institutes of Health. "The sheer quantify of genetic data in a really diverse population for the first time creates a powerful foundation for researchers to make discoveries that will be relevant to everyone."

The goal of the $3.1 billion program is to solve a long-standing problem in genetic research: Most of the people who donate their DNA to help find better genetic tests and precision drugs are white.

"Most research has not been representative of our country or the world," Denny says. "Most research has focused on people of European genetic ancestry or would be self-identified as white. And that means there's a real inequity in past research."

For example, researchers "don't understand how drugs work well in certain populations. We don't understand the causes of disease for many people," Denny says. "Our project is to really correct some of those past inequities so we can really understand how we can improve health for everyone."

But the project has also stirred up debate about whether the program is perpetuating misconceptions about the importance of genetics in health and the validity of race as a biological category.

New genetic variations discovered

Ultimately, the project aims to collect detailed health information from more than 1 million people in the U.S., including samples of their DNA.

In a series of papers published in February in the journals Nature , Nature Medicine , and Communications Biology , the program released the genetic sequences from 245,000 volunteers and some analysis of those data.

"What's really exciting about this is that nearly half of those participants are of diverse race or ethnicity," Denny says, adding that researchers found a wealth of genetic diversity.

"We found more than a billion genetic points of variation in those genomes; 275 million variants that we found have never been seen before," Denny says.

"Most of that variation won't have an impact on health. But some of it will. And we will have the power to start uncovering those differences about health that will be relevant really maybe for the first time to all populations," he says, including new genetic variations that play a role in the risk for diabetes .

Researchers Gather Health Data For 'All Of Us'

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Researchers gather health data for 'all of us'.

But one concern is that this kind of research may contribute to a misleading idea that genetics is a major factor — maybe even the most important factor — in health, critics say.

"Any effort to combat inequality and health disparities in society, I think, is a good one," says James Tabery , a bioethicist at the University of Utah. "But when we're talking about health disparities — whether it's black babies at two or more times the risk of infant mortality than white babies, or sky-high rates of diabetes in indigenous communities, higher rates of asthma in Hispanic communities — we know where the causes of those problem are. And those are in our environment, not in our genomes."

Race is a social construct, not a genetic one

Some also worry that instead of helping alleviate racial and ethnic disparities, the project could backfire — by inadvertently reinforcing the false idea that racial differences are based on genetics. In fact, race is a social category, not a biological one.

"If you put forward the idea that different racial groups need their own genetics projects in order to understand their biology you've basically accepted one of the tenants of scientific racism — that races are sufficiently genetically distinct from each other as to be distinct biological entities," says Michael Eisen , a professor of molecular and cell biology at the University of California, Berkeley. "The project itself is, I think, unintentionally but nonetheless really bolstering one of the false tenants of scientific racism."

While Nathaniel Comfort, a medical historian at Johns Hopkins, supports the All of Us program, he also worries it could give misconceptions about genetic differences between races "the cultural authority of science."

Denny disputes those criticisms. He notes the program is collecting detailed non-genetic data too.

"It really is about lifestyle, the environment, and behaviors, as well as genetics," Denny says. "It's about ZIP code and genetic code — and all the factors that go in between."

And while genes don't explain all health problems, genetic variations associated with a person's race can play an important role worth exploring equally, he says.

"Having diverse population is really important because genetic variations do differ by population," Denny says. "If we don't look at everyone, we won't understand how to treat well any individual in front of us."

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More than one-quarter of scholarly articles are not being properly archived and preserved, a study of more than seven million digital publications suggests. The findings, published in the Journal of Librarianship and Scholarly Communication on 24 January 1 , indicate that systems to preserve papers online have failed to keep pace with the growth of research output.

“Our entire epistemology of science and research relies on the chain of footnotes,” explains author Martin Eve, a researcher in literature, technology and publishing at Birkbeck, University of London. “If you can’t verify what someone else has said at some other point, you’re just trusting to blind faith for artefacts that you can no longer read yourself.”

Eve, who is also involved in research and development at digital-infrastructure organization Crossref, checked whether 7,438,037 works labelled with digital object identifiers (DOIs) are held in archives. DOIs — which consist of a string of numbers, letters and symbols — are unique fingerprints used to identify and link to specific publications, such as scholarly articles and official reports. Crossref is the largest DOI registration agency, allocating the identifiers to about 20,000 members, including publishers, museums and other institutions.

The sample of DOIs included in the study was made up of a random selection of up to 1,000 registered to each member organization. Twenty-eight per cent of these works — more than two million articles — did not appear in a major digital archive, despite having an active DOI. Only 58% of the DOIs referenced works that had been stored in at least one archive. The other 14% were excluded from the study because they were published too recently, were not journal articles or did not have an identifiable source.

Preservation challenge

Eve notes that the study has limitations: namely that it tracked only articles with DOIs, and that it did not search every digital repository for articles (he did not check whether items with a DOI were stored in institutional repositories, for example).

Nevertheless, preservation specialists have welcomed the analysis. “It’s been hard to know the real extent of the digital preservation challenge faced by e-journals,” says William Kilbride, managing director of the Digital Preservation Coalition, headquartered in York, UK. The coalition publishes a handbook detailing good preservation practice.

“Many people have the blind assumption that if you have a DOI, it’s there forever,” says Mikael Laakso, who studies scholarly publishing at the Hanken School of Economics in Helsinki. “But that doesn’t mean that the link will always work.” In 2021, Laakso and his colleagues reported 2 that more than 170 open-access journals had disappeared from the Internet between 2000 and 2019.

Kate Wittenberg, managing director of the digital archiving service Portico in New York City, warns that small publishers are at higher risk of failing to preserve articles than are large ones. “It costs money to preserve content,” she says, adding that archiving involves infrastructure, technology and expertise that many smaller organizations do not have access to.

Eve’s study suggests some measures that could improve digital preservation, including stronger requirements at DOI registration agencies and better education and awareness of the issue among publishers and researchers.

“Everybody thinks of the immediate gains they might get from having a paper out somewhere, but we really should be thinking about the long-term sustainability of the research ecosystem,” Eve says. “After you’ve been dead for 100 years, are people going to be able to get access to the things you’ve worked on?”

Nature 627 , 256 (2024)

doi: https://doi.org/10.1038/d41586-024-00616-5

Updates & Corrections

Clarification 05 March 2024 : The headline of this story has been edited to reflect the fact that some of these papers have not entirely disappeared from the Internet. Rather, many papers are still accessible but have not been properly archived.

Eve, M. P. J. Libr. Sch. Commun. 12 , eP16288 (2024).

Article   Google Scholar  

Laakso, M., Matthias, L. & Jahn, N. J. Assoc. Inf. Sci. Technol. 72 , 1099–1112 (2021).

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"We decide when and how to use AI tools in our work." —

Producing more but understanding less: the risks of ai for scientific research, a psychologist and an anthropologist ponder the epistemic risks ai could pose for science..

Jennifer Ouellette - Mar 6, 2024 6:08 pm UTC

3d illustration of brain with wires

Last month, we witnessed the viral sensation of several egregiously bad AI-generated figures published in a peer-reviewed article in Frontiers, a reputable scientific journal. Scientists on social media expressed equal parts shock and ridicule at the images, one of which featured a rat with grotesquely large and bizarre genitals.

As Ars Senior Health Reporter Beth Mole reported , looking closer only revealed more flaws, including the labels "dissilced," "Stemm cells," "iollotte sserotgomar," and "dck." Figure 2 was less graphic but equally mangled, rife with nonsense text and baffling images. Ditto for Figure 3, a collage of small circular images densely annotated with gibberish.

The paper has since been retracted, but that eye-popping rat penis image will remain indelibly imprinted on our collective consciousness. The incident reinforces a growing concern that the increasing use of AI will make published scientific research less trustworthy, even as it increases productivity. While the proliferation of errors is a valid concern, especially in the early days of AI tools like ChatGPT, two researchers argue in a new perspective published in the journal Nature that AI also poses potential long-term epistemic risks to the practice of science.

Molly Crockett is a psychologist at Princeton University who routinely collaborates with researchers from other disciplines in her research into how people learn and make decisions in social situations. Her co-author, Lisa Messeri , is an anthropologist at Yale University whose research focuses on science and technology studies (STS), analyzing the norms and consequences of scientific and technological communities as they forge new fields of knowledge and invention—like AI.

Further Reading

The original impetus for their new paper was a 2019 study published in the Proceedings of the National Academy of Sciences claiming that researchers could use machine learning to predict the replicability of studies based only on an analysis of their texts. Crockett and Messeri co-wrote a letter to the editor disputing that claim, but shortly thereafter, several more studies appeared, claiming that large language models could replace humans in psychological research. The pair realized this was a much bigger issue and decided to work together on an in-depth analysis of how scientists propose to use AI tools throughout the academic pipeline.

They came up with four categories of visions for AI in science. The first is AI as Oracle, in which such tools can help researchers search, evaluate, and summarize the vast scientific literature, as well as generate novel hypotheses. The second is AI as Surrogate, in which AI tools generate surrogate data points, perhaps even replacing human subjects. The third is AI as Quant. In the age of big data, AI tools can overcome the limits of human intellect by analyzing vast and complex datasets. Finally, there is AI as Arbiter, relying on such tools to more efficiently evaluate the scientific merit and replicability of submitted papers, as well as assess funding proposals.

Each category brings undeniable benefits in the form of increased productivity—but also certain risks. Crockett and Messeri particularly caution against three distinct "illusions of understanding" that may arise from over-reliance on AI tools, which can exploit our cognitive limitations. For instance, a scientist may use an AI tool to model a given phenomenon and believe they, therefore, understand that phenomenon more than they actually do (an illusion of explanatory depth). Or a team might think they are exploring all testable hypotheses when they are only really exploring those hypotheses that are testable using AI (an illusion of exploratory breadth). Finally, there is the illusion of objectivity: the belief that AI tools are truly objective and do not have biases or a point of view, unlike humans.

This error-ridden AI-generated image, published in the journal Frontiers, is supposed to show spermatogonial stem cells, isolated, purified, and cultured from rat testes.

The paper's tagline is "producing more while understanding less," and that is the central message the pair hopes to convey. "The goal of scientific knowledge is to understand the world and all of its complexity, diversity, and expansiveness," Messeri told Ars. "Our concern is that even though we might be writing more and more papers, because they are constrained by what AI can and can't do, in the end, we're really only asking questions and producing a lot of papers that are within AI's capabilities."

Neither Crockett nor Messeri are opposed to any use of AI tools by scientists. "It's genuinely useful in my research, and I expect to continue using it in my research," Crockett told Ars. Rather, they take a more agnostic approach. "It's not for me and Molly to say, 'This is what AI ought or ought not to be,'" Messeri said. "Instead, we're making observations of how AI is currently being positioned and then considering the realm of conversation we ought to have about the associated risks."

Ars spoke at length with Crockett and Messeri to learn more.

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Supreme Court Justice Sandra Day O’Connor Papers Open for Research at the Library of Congress

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Supreme Court Justice Sandra Day O’Connor Papers Open for Research at the Library of Congress

A major portion of the papers of Supreme Court Associate Justice Sandra Day O’Connor, consisting of approximately 600 containers, opens for research use March 11, in the Manuscript Division of the Library of Congress. The collection documents the trajectory of O’Connor’s life in politics and law at the state level in Arizona and later nationally, as the Supreme Court’s first woman justice.

Appointed to the court in 1981, O’Connor served until retiring in early 2006. The case files in the collection document O’Connor’s role as the court’s crucial deciding vote and to varying degrees capture the internal workings of her chambers as well as discussions among her eight peers in determining the constitutionality of the nation’s laws. The Sandra Day O’Connor Papers also chronicle O’Connor’s rise in Arizona state politics as a legislator and judge and her ascension to the national stage.

O'Connor donated her papers to the Library of Congress in 1990, and they arrived in installments from 1991 to 2008.

O’Connor’s papers join those of more than three dozen other justices and chief justices of the Supreme Court available for research at the national library, including John Marshall, Thurgood Marshall, Hugo Black, Earl Warren, Harry A. Blackmun, William J. Brennan, Ruth Bader Ginsburg and John Paul Stevens.

While serving more than two decades on the court, O’Connor participated in numerous significant decisions on issues ranging from the First Amendment in Lynch v. Donnelly (1983) and Wallace v. Jaffree (1984) to abortion rights in City of Akron v. Akron Center for Reproductive Health (1982), Planned Parenthood v. Casey (1992) and Webster v. Reproductive Health Services (1989). Considered the swing vote on the court, O’Connor was often at the center of many cases when she did not author a majority opinion or dissent.

At this time, the case files and docket sheets are open to researchers through the October Term 1990. Access to cases heard by the court from the 1991 through 2005 terms remains closed to researchers as long as any justice who participated in the decision of a case continues to serve on the Supreme Court. Other material open to researchers from her tenure as a justice includes correspondence, administrative files relating to her nomination, speeches, and writings by O’Connor. Also open for research are files relating to her political and judicial career in Arizona, book manuscripts and other writings, and selected family papers.

About Justice Sandra Day O’Connor

Sandra Day O’Connor was born in El Paso, Texas in 1930. Growing up in Arizona, O’Connor attended Stanford University for both undergraduate and law school. After law school, she served as the first deputy county attorney in San Mateo, California, assistant attorney general for the state of Arizona, and entered politics as a state legislator, rising to the position of majority leader in the Arizona State Senate. After retiring from the state senate, O’Connor was appointed to Arizona’s Superior and Appellate Courts. President Ronald Reagan appointed her to the Supreme Court in 1981.

How to Conduct Research in the Manuscript Division

Access to the O’Connor Papers will be on a first-come, first-serve basis to researchers in the Manuscript Division Reading Room.

A finding aid provides guidance on the contents of the O’Connor Papers and is available in both HTML and PDF formats.

Patrons can make copies of collection items consistent with current photocopy and scanning procedures – and staff will also encourage researchers to use smartphones to make single-copy reproductions to reduce the handling of originals.

Patrons must follow normal procedures before gaining access to the collection, such as obtaining a reader registration card , a process they can begin online in advance of visiting. Personal belongings must be secured in lockers, and all reading room rules and regulations must be followed. For additional information about using the Manuscript Division’s collections, please visit the division’s website .

The Library of Congress is the world’s largest library, offering access to the creative record of the United States — and extensive materials from around the world — both on-site and online. It is the main research arm of the U.S. Congress and the home of the U.S. Copyright Office. Explore collections, reference services and other programs and plan a visit at loc.gov ; access the official site for U.S. federal legislative information at congress.gov ; and register creative works of authorship at copyright.gov .

Media Contacts: Brett Zongker, [email protected] ; Maria Peña, [email protected]  

PR 24-027 03/11/2024 ISSN 0731-3527

Inequality Within Countries is Falling: Underreporting-Robust Estimates of World Poverty, Inequality and the Global Distribution of Income

Household surveys suffer from persistent and growing underreporting. We propose a novel procedure to adjust reported survey incomes for underreporting by estimating a model of misreporting whose main parameter of interest is the elasticity of regional national accounts income to regional survey income, which is closely related to the elasticity of underreporting with respect to income. We find this elasticity to be substantial but roughly constant over time, implying a large but relatively constant correction to survey-derived inequality estimates. Underreporting of income by the bottom 50% of the world income distribution has become particularly important in recent decades. We reconfirm the findings of the literature that global poverty and inequality have declined dramatically between 1980 and 2019. Finally, we find that within-country inequality is falling on average, and has been largely constant since the 1990s.

We thank Ruchi Avtar and Marie Camara for outstanding research assistance. We thank Leonardo Gasparini and Leopoldo Tornarolli for sharing with us standardized regional survey data for multiple Latin American countries through SEDLAC. We thank Arvind Subramanian for guiding us to the "junked" report of the 2017 Indian NSS. We thank numerous staff members at the Luxembourg Income Study for help using their data. We thank Christoph Lakner for sharing with us code for using the Luxembourg Income Study data. The views expressed in this paper are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York, the Federal Reserve System, or the National Bureau of Economic Research. Any errors or omissions are our own.

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