The study, published in Frontiers in Communication by Areej Albawardi of Imam Abdulrahman Bin Faisal University and Alyaa Olwi of King Abdulaziz University, is the first to trace the full remediation chain — original advertisement to AI-generated text to AI-generated image — through the lens of multimodal discourse analysis. The researchers selected six advertisements from Almarai, one of the largest dairy companies in Saudi Arabia and among the most valuable companies in the Middle East, recently posted on the company’s official Instagram page with more than ten thousand views each. The ads were chosen precisely because they carry a rich variety of Saudi cultural and social markers: traditional dress, family configurations, gender and age representation, and distinctly Saudi settings.

The experimental design was deceptively simple but technically revealing. Seven AI models — ChatGPT, Gemini, Meta AI, Grok, Qwen, Leonardo AI and Recraft V3 — were each given the same instruction, “turn it into a text prompt,” producing forty-two textual descriptions. Then, using fresh accounts so the models would not rely on stored memory or context, the researchers fed those generated prompts back with the instruction “create an image.” Because Google and Leonardo AI each operate two image-generation submodels — Google Imagen 4 and Gemini 2.5 Flash (Nano Banana), and Leonardo’s Phoenix 1.0 and Lucid Origin — the image stage involved nine generators, yielding fifty-four images. Data collection took place between October 1 and November 1, 2025, using models ranked on Artificial Analysis’ Text-to-Image Leaderboard for accessibility and performance.

To ensure the qualitative judgments had a reliable empirical basis, the two authors independently coded each output against a shared framework, recording whether each model, advertisement and semiotic feature fulfilled a criterion. The coding produced 906 paired decisions — 420 for the image-to-text stage and 486 for the text-to-image stage. Observed agreement reached 91.94 percent, and Cohen’s kappa, which corrects for chance agreement, was 0.8319 — within the “almost perfect” band of the Landis and Koch categories. Alternative coefficients, including Krippendorff’s alpha, Scott’s pi and Gwet’s AC1, all exceeded 0.82, confirming that the coding framework was applied consistently in both directions of transformation.

The analytical framework drew on social semiotic technology, an extension of multimodal discourse analysis rooted in Halliday’s socio-semiotic approach and Kress and Van Leeuwen’s grammar of visual design. Rather than treating AI as a neutral tool, the researchers viewed the models as semiotic technologies that actively shape meaning — echoing Lucy Suchman’s observation that AI works as a “disclosing agent for assumptions about the human.” The analysis identified recurring semiotic resources across the data: color, setting and location, people, action, cultural artifacts, language, intertextuality, logos, and modality, the degree of realism an image communicates through lighting, saturation and filters.

The results split cleanly along a technical fault line. Features that are visually salient and semantically explicit — color, people, action, setting — were transferred with high fidelity. Eight of the nine image generators produced correct colors, and most models accurately rendered the number, gender and activities of the people described in the prompts. Action verbs such as “placing,” “smiling” and “handing,” enriched with adverbs like “gently,” translated reliably into the final images. But features that are implicit, layered or linguistically encoded collapsed in transit. Camera angle and gaze direction were rarely described in the prompts, so generated images showed people looking the wrong way. Screen layering — the advertisement’s graphic plane superimposed on the photographic scene — was misread by several models, which rendered the Almarai sign as if it were physically hanging inside a supermarket rather than added as a logo.

Cultural representation proved the most consequential failure. The researchers found that AI models operate on concentric geographic circles — Muslim, Arab, Middle Eastern, Gulf, Saudi — and dress is represented or misrepresented according to the word chosen. Traditional Saudi clothing in the original advertisements was frequently replaced with Gulf, Palestinian or Pakistani garments. One model described “checkered keffiyehs” as “traditional Saudi Arabian attire,” although that specific garment belongs to Palestinian culture — a contradiction the authors link to what Zoe Hurley has called “image hallucination,” a distortion of reality in which cultural norms are misrepresented. In one generated image, a woman wears a red-and-white shemagh, a headpiece normally worn by men. Race was inferred from dress alone, so when the prompts lacked explicit racial description, models substituted Asian features for Saudi ones.

Language was an equally stark marker of the technology’s limits. Arabic script — the brand name in the logo and the Arabic slogan — was the lowest-scoring feature across all models. Most generators either removed the Arabic text entirely or filled the linguistic void with meaningless gibberish. Only ChatGPT correctly included Arabic textual elements in nearly all of its generated images, and only Qwen3-Max provided an accurate translation of the Arabic text while identifying the language as Arabic. Logos fared little better: none of the nine image generators reproduced the Almarai logo correctly, and several omitted it even when the prompt described it. Notably, Qwen3-Max produced the best textual prompts — averaging 159 to 303 words, rich in cultural and intertextual detail — yet failed to generate accurate images, while ChatGPT, whose prompts lacked detail, produced the best images, possibly because the model stores the input image in its database or draws on additional internet input.

The study also introduces a new conceptual tool: “text hallucination,” derived from Hurley’s taxonomy of image hallucination. Where image hallucination distorts visual reality, text hallucination describes inaccurate textual descriptions of objects or actions in uploaded images — as when one model described a man “holding a hard yellow hat” that does not exist in the original advertisement, which then materialized in the generated image. The authors identify three types: textual, cultural-perspective, and spatial, the last involving misplaced objects and anatomically distorted faces. Hallucinations appeared in the majority of models even when the textual prompts contained no misinformation, suggesting that the image generators themselves add, alter or misrepresent visual content. Only ChatGPT and Google Imagen produced images without any hallucinations.

The implications reach beyond the laboratory. Generative AI is projected to become a 1.3 trillion dollar market within a decade, and advertising agencies are already deploying AI-generated imagery in campaigns for brands like Casablanca, Valentino and Moncler. Advertisements do not merely communicate marketing messages; they create cultural experiences that carry social and ideological perspectives. In a market like Saudi Arabia, where companies deliberately ensure that advertising adheres to local traditions and represents culture accurately, the technology’s inability to render Arabic script, correct logos and authentic cultural dress is not a cosmetic flaw but a functional barrier. The authors argue that their findings carry weight for theories of multimodality and culture, and practical weight for AI developers, who must pay closer attention to scripts, languages and cultural specificity. As the models continue to evolve, the study offers a benchmark: fluency in color and composition is no longer enough. The next frontier for generative AI is cultural precision — the ability to see, and to show, the difference between an Arab advertisement and a Saudi one.

Subject of Research: AI and multimodal communication

Article Title: Cultural and multimodal realization in various AI models: text to image, image to text

Article References: Albawardi, A., & Olwi, A. (2026). Cultural and multimodal realization in various AI models: text to image, image to text. Frontiers in Communication, 11, Article 1959385. https://doi.org/10.3389/fcomm.2026.1959385

Image Credits: AI Generated

DOI: 10.3389/fcomm.2026.1959385

Keywords: generative AI, multimodality, advertising, cultural representation, text-to-image, image-to-text, hallucination, semiotics, Cultural, multimodal, realization, various

The research team followed PRISMA guidelines, searching Scopus and Web of Science for peer-reviewed studies published between 2016 and 2026. After screening 122 initial records and removing duplicates, ineligible papers, and studies with high risk of bias—assessed using the QuADS tool across 13 methodological criteria—66 studies remained. The geographic distribution was striking: China dominated with 26 studies, followed by Taiwan, South Korea, India, and Indonesia, reflecting the rapid growth of digital commerce and brand competition in Asian markets. Spain led European contributions, while the United States, Brazil, and Peru appeared in smaller numbers.

To make sense of the sprawling evidence, the researchers organized emotional design strategies using Patrick Jordan’s four dimensions of pleasure: physio-pleasure, psycho-pleasure, socio-pleasure, and ideo-pleasure. Physio-pleasure strategies target the senses directly—color, shape, texture, brightness, symmetry, and multisensory cues like music and scent that trigger immediate attraction. Psycho-pleasure strategies focus on usability, ergonomics, functionality, and the sense of control a product provides during interaction. Socio-pleasure taps into belonging, social connection, and the use of celebrities or familiar figures as reference points. Ideo-pleasure operates at the deepest level, linking products to identity, values, cultural meaning, and sustainability.

The review found that these strategies are not isolated tools but layered mechanisms. Visual and aesthetic appeal generates rapid visceral responses—snap judgments about whether a product is likable before any rational evaluation occurs. Usability and ergonomic comfort then shape how satisfying the experience of use feels, reinforcing positive impressions over time. Meanwhile, storytelling, cultural narratives, and personalization help construct meaning, allowing products to become vehicles for self-expression and social signaling rather than mere objects of utility.

Emotions themselves proved far more varied than simple happiness. The researchers catalogued positive emotions like joy, pleasure, trust, and affection; negative emotions including fear, guilt, shame, and disgust; and complex relational emotions such as nostalgia, attachment, brand passion, and identification. Negative emotions, the review emphasizes, are not always counterproductive. In contexts like health or personal care advertising, fear and guilt can function as persuasive mechanisms that motivate preventive or corrective behavior. The key insight is that emotional effectiveness depends on alignment between the emotion activated and the message objective—not simply on making people feel good.

When it comes to consumer behavior, the evidence points to consistent and significant effects. Emotional design strategies are associated with higher purchase intention, repurchase behavior, satisfaction, loyalty, brand attachment, and positive word of mouth. Visual attractiveness, perceived ease of use, interactive functionality, packaging design, personalization, and multisensory elements all correlate with greater interest, memorability, visual attention, and perceived quality. Positive emotions like happiness and satisfaction reduce indecision and accelerate the transition from stimulus perception to purchase intention, while emotional connection with a brand reinforces willingness to pay premium prices and recommend products to others.

Crucially, the review shows that emotional design extends beyond immediate transactions into long-term consumer-brand relationships. A positive emotional experience strengthens trust, improves brand image, and consolidates sustained bonds—reflected in repeat purchases, loyalty, and advocacy. In specific contexts such as sustainable, cultural, or experiential products, emotional design also promotes symbolic identification, enhancing perceptions of authenticity, belonging, status, and environmental awareness. This relational dimension suggests that emotional design is not merely a tactic for driving a single sale but a strategic tool for building durable brand equity.

However, the review also flags risks. Negative emotions elicited by poorly executed design can lead to avoidance, rejection, or lasting unfavorable impressions. Emotional advertising may also induce impulsive or less rational purchasing, particularly when it intensifies affective attachment or symbolic appeal. The authors note that the impact of emotional design is not uniformly positive and depends heavily on context, execution, and the consumer’s individual and cultural background.

On the measurement side, the review reveals a field in methodological transition. Traditional self-report techniques—questionnaires, surveys, Likert scales, and semantic differential scales—remain the most common tools for capturing perceptions, attitudes, and purchase intention. But psychophysiological methods are increasingly prominent: electroencephalography (EEG), galvanic skin response (GSR), electromyography (EMG), eye-tracking, and facial recognition allow researchers to record automatic reactions like attention levels, emotional arousal, and brain activity in response to visual stimuli. Mixed approaches combining interviews, focus groups, and participant observation add depth, while frameworks like the Kano model and Kansei Engineering help translate affective perceptions into concrete design parameters.

The most striking trend is the rise of data-driven and machine learning techniques. Text mining, sentiment analysis of product reviews, affective lexicons, and neural network models now enable researchers to process large volumes of digital data, identifying emotional patterns and behavioral trends in real-world consumption contexts. This shift toward computational methods reflects the growing scale and complexity of consumer data available in e-commerce and social media environments.

The authors acknowledge limitations: the high methodological and thematic diversity of the reviewed evidence makes direct comparisons difficult, and the heavy concentration of studies in China and other Asian countries means findings may not generalize across cultures with different norms around emotional expression. They call for future research integrating subjective, physiological, and digital measures in unified designs, comparative cross-cultural analyses, and deeper exploration of negative and complex emotions like fear, guilt, nostalgia, and attachment across the full consumer decision journey—from pre-purchase information search through post-purchase behaviors like brand switching and advocacy.

What emerges from this synthesis is a picture of emotional design as a multidimensional discipline operating simultaneously at the sensory, experiential, social, and symbolic levels. It is no longer sufficient to make products that work; brands must engineer objects and messages that feel, mean, and connect. As the review makes clear, the gap between what designers intend and what consumers actually feel remains a persistent challenge—but the growing toolkit of measurement methods, from EEG caps to sentiment analysis algorithms, is steadily narrowing it.

Subject of Research: Emotional design strategies in advertising and their influence on consumer behavior

Article Title: Emotional design and advertising: a systematic review of the literature on consumer behavior

Article References: Viñan-Carrasco, L. M., Castro-Ortiz, W. X., Rosas-Chávez, P. X., Zambrano-Pontón, R. G., & Dillon-Abarca, S. D. (2026). Emotional design and advertising: a systematic review of the literature on consumer behavior. Frontiers in Communication, 11, Article 1893894. https://doi.org/10.3389/fcomm.2026.1893894

Image Credits: AI Generated

DOI: 10.3389/fcomm.2026.1893894

Keywords: emotional design, advertising, consumer behavior, systematic review, purchase intention, brand loyalty, multisensory stimuli, Kansei engineering, EEG, eye-tracking, sentiment analysis, sustainability

The researchers anchor their analysis in a striking natural experiment: the Coca-Cola Company, one of the world’s most recognizable advertisers, has produced two high-profile AI initiatives that met dramatically different fates. The first, the 2023 Create Real Magic platform, invited digital artists and fans to generate their own artwork using the company’s iconic logo through AI image generation. The platform was broadly embraced and enjoyed by consumers. The second, the company’s 2025 AI-generated “Holidays Are Coming” holiday commercial, was met with ridicule on social media and widespread customer criticism, with Forbes reporting on the backlash that erupted when the ad was released.

The difference between these two outcomes, Choi and Magee argue, lies in whether consumers can easily understand why the advertiser used AI in the first place. Create Real Magic offered something that would be difficult, if not impossible, to deliver without the technology: thousands of unique, personalized art pieces created by individual fans around the world. Producing that volume of bespoke creative work through human designers alone would have been impractical. In that context, AI was not a substitute for human creativity but an enabler of an experience that could not otherwise exist. Consumers, the researchers said, could readily grasp the purpose of the technology, and that transparency of purpose defused suspicion.

By contrast, the 2025 holiday commercial used AI to produce animation that human designers could plausibly have created themselves. When AI is deployed unnecessarily, the study finds, consumers can feel upset or unnerved, and in the case of the holiday ad those feelings spilled over into public ridicule. One YouTube commenter delivered a particularly cutting verdict, calling the spot “the most profitable commercial in Pepsi’s history” — a joke that cut to the heart of the problem, framing the ad as a self-inflicted wound. The researchers point to this reaction as evidence that audiences are not simply hostile to AI; they are hostile to AI that appears to replace human effort without offering anything new in return.

Part of the negative response, Magee explained, stems from a well-documented psychological phenomenon known as the uncanny valley effect, in which a machine or depiction that is almost, but not quite, human induces feelings of unease in observers. “We’re basically programmed to look at everything, whether humans or objects, and make snap judgments about them,” Magee said. “We make these quick decisions, but then our conscious mind reflects and thinks, ‘Machines do not feel, but this machine is telling me that it does.'” According to Magee, that is where the conflict originates: when something is almost human but not quite, the brain’s rapid social-cognitive machinery and its reflective reasoning pull in opposite directions, producing the discomfort that audiences experience as eeriness or distrust.

The uncanny valley becomes especially hazardous, the researchers said, when advertisers use AI to try to engender genuine emotion. A generated depiction of a hurt or hungry child, for example, can leave consumers feeling tricked, because the emotional appeal is being manufactured by a system that has no stake in the suffering it portrays. Magee emphasized that marketers need to be very careful to remind people that they are an organization of people, not just a brand and not just a company. “It’s easy to think of an organization as something impersonal, and to counteract that, you need to underline the human aspect,” he said. “For any organization, whether it’s for-profit or nonprofit, the problem comes when something they’ve created feels artificial or contrived. AI can be another instantiation of that disconnect.”

Even when the uncanny valley effect is not in play, the study identifies a second, more strategic pitfall: consumers can still perceive that marketers have taken a “shortcut” by using AI, substituting a cheaper production method for genuine creative effort. Choi describes the remedy as what the researchers call the AI-ad theme match. “A strong match is when AI is an important part of how the campaign works, like if the intention is to give each consumer a personal experience that would otherwise be impossible,” he said. “In that case, AI is not just a cheaper production tool; it’s an important part of the creative idea.” The distinction is subtle but consequential: when the technology is woven into the conceptual core of a campaign, its use feels purposeful; when it is bolted onto a conventional ad, it reads as cost-cutting at the audience’s expense.

This framework leads to practical guidance that Choi believes many marketers are currently ignoring. “Nowadays, many marketers are just saying, ‘Hey, why don’t we use AI?’ but I don’t recommend that,” he said. “Marketers should start with the campaign idea first, and ask ‘Is AI necessary?’ It’s a simple question. If the answer is yes, the reason why should be clearly understood.” In other words, the creative brief should precede the technology decision, not follow it. If AI is essential to delivering the campaign’s promise — personalization at scale, interactivity, or capabilities beyond human production — then its presence is justified and legible to consumers. If it is not, the study suggests, advertisers risk paying a reputational price for a tool that added nothing the audience values.

The researchers also caution against assuming that today’s consumer sensitivities are permanent. Magee noted that the extent to which people feel negatively about AI in advertising may shift as the technology becomes more widespread and familiar. “Right now, because AI is new, it’s better to err on the side of being careful,” he said. “But norms change. You don’t see as many people thinking about CGI in movies anymore. No one gets upset about it like they used to.” The trajectory of computer-generated imagery in film offers a useful analogy: a technique that once provoked complaints about authenticity gradually became an invisible, accepted part of visual storytelling as audiences learned its conventions and creators learned its limits. “Over time, the norms will probably change, and we’ll accept AI just like we accepted CGI, animation and other new technologies,” Magee said.

For an advertising industry racing to incorporate generative tools into everything from holiday spots to interactive platforms, the study’s central message is a tempering one. The technology’s power to create realistic depictions of just about anything does not mean advertisers should lean on it reflexively. Consumer reactions, the research suggests, hinge on a judgment audiences make quickly and intuitively: whether the machine was used to give them something genuinely new, or simply to do cheaply what a person could have done better. Advertisers who can answer that question convincingly — by matching AI to the intent and theme of the campaign — are far more likely to land on the side of Create Real Magic than on the side of a holiday commercial that handed its rival an accidental victory lap.

Subject of Research: Consumer reactions to AI-generated advertising and the match between AI use and ad campaign themes

Article Title: Study explores why some AI ads fail while others succeed

Article References: Study explores why some AI ads fail while others succeed. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: artificial intelligence, advertising, consumer behavior, uncanny valley, Coca-Cola, marketing, Journal of Interactive Advertising, AI-generated ads, integrated marketing communications, CGI, brand backlash, University of Mississippi

The study employed a multimethod research design that combined large-scale television advertising data with a controlled field experiment involving social media advertisements for a nonprofit organization. This dual approach allowed the researchers to examine micro ad effectiveness both in the high-stakes environment of broadcast television, where a single thirty-second slot during popular programming can cost enormous sums, and in the digital ecosystems where users scroll rapidly through feeds and decide within moments whether to engage with content. By triangulating across these settings, the team sought to establish that the advantage of extreme brevity is not an artifact of any single platform but a robust pattern rooted in how modern consumers process advertising messages. The consistency of results across television and social media lends considerable weight to the central conclusion that shorter ads, when crafted well, outperform their longer counterparts.

The television data produced striking numbers. Micro ads of ten seconds or less increased website visits within five minutes of airing by ten to forty percent relative to longer advertisements. Perhaps more remarkably, the ultra-short spots outperformed even the industry-standard thirty-second commercial by thirteen to twenty-eight percent. The five-minute window is a critical measurement choice, because it captures the immediate behavioral response that advertisers most prize: a viewer seeing an ad and acting on it almost instantly, typically by searching for the brand online or visiting its website. In an era when second screens, smartphones, and tablets sit beside nearly every television viewer, the path from broadcast exposure to online action has never been shorter, and the research suggests that micro ads are uniquely suited to triggering that rapid handoff from passive viewing to active engagement.

Social media results reinforced the pattern. Shorter micro ads generated more clicks and greater community engagement, including likes and shares, even when they conveyed exactly the same information as their longer versions. This detail matters because it rules out one of the most obvious alternative explanations: that micro ads win simply because advertisers compress more essential content into them. The experiment showed that identical informational content performs differently depending on its duration, which points to the psychological experience of the viewer rather than the message itself as the decisive factor. On platforms where users swipe through content at high speed and abandon anything that demands sustained attention, the ability of a ten-second spot to deliver its payload before impatience sets in appears to translate directly into measurable engagement metrics that brands monitor closely.

The researchers identified impatience as the key psychological mechanism behind the phenomenon. Modern media users, they argue, prefer to reach conclusions quickly and grow bored or lose concentration when confronted with longer advertisements. Short ads that communicate their key message rapidly reduce the cognitive burden on viewers and are more likely to prompt immediate actions such as online searches or website visits. This framing connects the study to a broader body of research on attention and cognitive load, suggesting that the effectiveness of an advertisement depends not only on what it says but on how much patience it demands from its audience. In a media environment where consumers exercise near-total control over what they watch, content that respects their time earns their engagement, while content that overstays its welcome risks being skipped, muted, or mentally tuned out entirely.

One of the most revealing findings came from an experimental manipulation designed to test the impatience hypothesis directly. When viewers in the field experiment were first nudged to think about the future, a prompt known to encourage patience, the advantage of micro ads disappeared. This result provides strong causal support for the mechanism: it is not merely that short ads happen to perform better, but that their performance advantage depends on the impatient mindset that characterizes ordinary media consumption. When that mindset is temporarily suspended, longer ads lose their handicap. For advertising researchers, this kind of moderation analysis is valuable because it moves the finding from correlation toward causal explanation, demonstrating that the psychological state of the audience, not just the length of the ad, governs which format wins.

The study also offers practical guidance on creative execution. Micro ads that revealed the brand name early rather than withholding it were especially effective at driving website traffic, as were ads that used language building anticipation. Both choices run counter to some traditional advertising wisdom, in which brands often save their identity for a climactic reveal and build suspense across the full length of a spot. In the micro ad format, there is no time for such narrative arcs. Viewers need to know immediately who is speaking to them and why they should care, and language that creates a sense of anticipation appears to channel the viewer’s residual curiosity into action, such as clicking through or searching for more information, rather than letting it dissipate once the ad ends.

Beyond its academic contributions, the research carries significant economic implications for advertisers. Micro ads are less expensive to produce than full-length commercials, requiring fewer shooting days, smaller crews, and simpler post-production. They can also substantially reduce television advertising costs, since shorter slots command lower prices than standard thirty-second placements. Taken together, these savings mean that companies can attract considerably more consumers with the same advertising budget, either by purchasing more airtime, by reaching audiences across more platforms, or by freeing resources for other marketing activities. For small businesses and nonprofit organizations with limited budgets, the finding is particularly encouraging: the study’s field experiment involved a nonprofit’s social media advertising, demonstrating that the micro ad advantage extends beyond deep-pocketed national brands to organizations for whom every advertising dollar must work as hard as possible.

The publication of the study in the Journal of Marketing, one of the leading academic journals in the field, positions it as an important milestone for both scholars and practitioners as marketing strategies shift toward short-form content. The rise of platforms built around brief vertical videos, the proliferation of skippable ads, and the growing sophistication of programmatic buying have all pushed the industry toward shorter formats, but rigorous evidence about where the optimal duration lies has been comparatively scarce. By quantifying the engagement and traffic benefits of ten-second advertising and by identifying the psychological mechanism that produces those benefits, the SKKU-led team has given marketers an evidence-based foundation for decisions that were previously guided largely by intuition and convention.

The broader lesson of the research may extend well beyond advertising itself. It suggests that in the short-form era, the scarce resource is not information but the willingness of audiences to wait for it. Creators of any kind of persuasive content, from public health campaigns to political messaging to educational outreach, face the same impatient viewer that the researchers documented. Messages that front-load their identity, respect the audience’s time, and build anticipation in compact language are rewarded with immediate action, while those that delay their point are penalized. As the researchers’ patience experiment showed, the preference for speed is a state of mind that shapes how all media is consumed. Brands that internalize this insight, and that treat the ten-second advertisement not as a diminished version of a longer one but as a distinct craft with its own rules, stand to gain a measurable advantage in capturing the fleeting attention of the modern consumer.

Subject of Research: The effectiveness of ultra-short micro advertisements in driving immediate consumer engagement on television and social media

Article Title: SKKU research team demonstrates the powerful engagement and customer-driving effects of ‘micro ads’ in the short-form era

Article References: SKKU research team demonstrates the powerful engagement and customer-driving effects of ‘micro ads’ in the short-form era. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: micro ads, advertising, consumer behavior, short-form content, television advertising, social media marketing, attention span, impatience, Journal of Marketing, website traffic, marketing strategy, consumer engagement