Brands use humor to interact with other brands on social media. Previous research has concluded that brands should favor affiliative humor, while aggressive humor should only be considered under special circumstances. However, this recommendation contradicts real-world evidence of brands achieving significant success through aggressive humor. Drawing on vicarious superiority theory, this article investigates the effects of aggressive humor, introducing the mediating role of feelings of superiority and the moderating roles of self-categorization with the perpetrating brand (SCP) and the targeted brand (SCT). Studies 1A and 1B show that a perpetrating brand elicits equally positive brand attitudes when employing aggressive or affiliative humor toward a targeted brand if customers possess SCP. Study 2 investigates the consequences of aggressive humor for the targeted brand, resulting in two conclusions. First, unlike affiliative humor, aggressive humor prevents customers with SCP from transferring their positive attitudes toward the perpetrating brand to the targeted brand. Second, for customers with SCT, a perpetrating brand’s use of aggressive humor encourages less positive attitudes toward the targeted brand. Study 3 explains that aggressive humor makes customers with SCT feel less superior after being exposed to aggressive humor, which they cope with by changing their attitudes toward the targeted brand.

Mental health conditions represent one of the most pressing health challenges of the twenty-first century. Given the global prevalence, high cost, and profound consequences of mental disorders, the marketplace has become a critical arena for advancing mental health awareness and destigmatization. This paper examines how brands promote mental health awareness and seek to mitigate mental health stigma through advertising. Inspired by transformative advertising research, we use institutional theory as our enabling lens and examine brand advertising as institutional work. Using a grounded theory-inspired Gioia method, we analyze 176 advertisements on mental health awareness. The findings reveal three forms of institutional work—advocating, educating, and motivating—that are utilized by brands and suggest mechanisms through which they can lead to destigmatization of mental health issues. By focusing on the institutional role of advertising in raising awareness and facilitating destigmatization of mental health issues, this study contributes to transformative advertising research. It extends strategies that promote mental health awareness and reduce mental health stigma. By considering advertising as institutional work, the study highlights connections amongst macro–meso–micro levels of analyses in destigmatization processes.

Worldwide consumption of ultra-processed foods and beverages continues to rapidly increase, and many believe that this trend has significantly contributed to the rise in obesity rates. Excessive consumption of food and beverage products with added sugar is of particular concern. Drawing from expectation theory, the health belief model, and the protection motivation framework, this research examines consumer responses to warning labels that identify some of the negative health outcomes associated with the consumption of added sugar. Three between-subjects studies conducted on-line produced three key findings. First, results show that responses to a sugar health warning label are stronger when the warning applies to fruity drinks as opposed to soft drinks. Second, contrary to findings within the tobacco literature, these results show that disgust, rather than fear, is the emotion most associated with the effect of health warnings on purchase intention. Third, results show that the effect of a sugar health warning label is attenuated when consumers have self-exempting beliefs regarding the potential negative effects of sugar sweetened beverage consumption. Results offer insight for manufacturers, retailers, marketing researchers, and public policy makers about the potential effects of sugar health warning labels on consumers’ sugar-sweetened beverage beliefs, feelings, and purchase intentions.

The law shapes advertising. It informs businesses and advertising research about the boundaries of exploring with ads. The European Union (EU) has actively restricted surveillance-based digital advertising in the past two decades. In addition to traditional consumer protection and competition law rules, the EU has introduced the General Data Protection Regulation (GDPR), the Digital Services Act (DSA), the Digital Markets Act (DMA), and the Artificial Intelligence Act (AI Act), all of which offer enhanced protections for consumers in the digital markets. The EU Commission is now considering introducing the Digital Fairness Act (DFA) and, potentially, a digital advertising-specific regulation to ensure surveillance advertising practices do not exploit consumers and are otherwise fair. While the EU draws the lines on consumer autonomy, there is limited conceptual clarity on when digital advertising practices precisely cross those lines. At this crucial time for digital advertising, advertising ethics and evidence-based advertising research must join the policymakers to clarify acceptable digital advertising practices. This paper informs advertising research about the legal developments in the EU to call for creating coherence across disciplines. It contributes to such coherence by offering a working theoretical framework for understanding online exploitation in the context of surveillance advertising.

Users are playing an increasingly active role in receiving advertising information, making it essential to understand their assessment of algorithm from folk theory perspectives. In response to this need, we introduce the concept of “misestimation” to describe the gap between users’ perceptions of algorithmic systems and their actual operations. Focusing on the misestimation of targeting on Weibo, this research specifically focusing on female users’ overestimation/underestimation and the factors that contribute to these misestimations. Employing a mixed-method approach, the study conducted surveys and in-depth interviews with 25 participants. By identifying differences between overestimation and underestimation groups, the study developed a theoretical model of the themes influencing these perceptions: algorithm attitude, algorithm literacy, perceived surveillance, gender bias, and motivation to use platform. Methodologically, this study propose a mix-method to operationalize misestimation, which makes folk theory truly operationalizable. Theoretically, this article contributes a key concept for understanding human–machine alignment. Additionally, the findings advance the literature on uses and gratifications theory, algorithm attitude, and perceived surveillance theory by expanding them to human–machine alignment. By adjusting these influencing factors, this research offers suggestions to advertisers on Chinese platforms for how to improve AI service, in navigating personalized advertising content without falling into information cocoons or fostering distrust.

Organizations use sponsorships to inform consumers about their quality and positive intentions. Prior research has explained how these sponsorships signal quality to reduce selection challenges and prosocial intentions to reduce moral hazard concerns. Yet, previous meta-analyses do not assess and compare the relationships that sponsorship signaling has with consumer responses across samples of treatments (i.e., using sponsorships vs. not using sponsorships) that convey primarily quality or intention content. Thus, our meta-analysis focused on how sponsorship treatments relate to consumer responses according to samples conveying generalized content (quality and intention content combined) and distinct quality or intention content. The results suggest that sponsorship treatments conveying generalized content positively related to consumers’ cognitive, affective, and behavioral outcomes. They also suggest that signaling quality content has more positive relationships with consumers’ cognitive and affective outcomes than signaling intention content, and that the relationships quality and intention signaling content have with consumers’ affective responses are moderated by different conditions. Theoretically, quality and intention signaling processes appear to operate in distinct ways. Managerial takeaways are that sponsorships can positively relate to consumer outcomes, these relationships can be accentuated or diminished under various moderating conditions, and sponsorships for cause marketing in particular could require clearer and more credible messaging.

Computational methods such as those used in computational advertising research are often considered at odds with experimental methods. This divergent view of these two paths has frequently led to researchers examining these methodologies in isolation. In this article, we take the view that each path has value and that the strengths of one can inform the other. To this end, we provide a guide to conducting research that focuses on four strengths inherent to experimental research—the principle of control, random assignment of treatments, the exploration of underlying mechanisms, and transparency—that can provide lessons to those using computational advertising data. Similarly, we focus on three strengths inherent to computational advertising research—the collection of behavioral data, the realistic nature in which the data are obtained, and the ability to study dynamic changes over time—that can provide lessons on how to enhance experimental research. In doing so, we assist researchers in strengthening their study designs regardless of their preferred methodology.

The advertising ecosystem has been progressively reshaped by computational advertising models facilitated and controlled by digital media platforms. Meta and Alphabet, two of the dominant players in this ecosystem, have contributed significantly to industry disintermediation via the design and market dominance of their advertising systems, which in turn are symbiotic with their own business models, and feed on large amounts of consumer data generated by everyday platform use. The operations and impacts of these systems remain opaque and difficult to observe. Responding to this challenge, we present a methodological framework integrating cultural, legal, media, and computational perspectives to enable the observation of computational advertising. This methodology comprises three novel computational methods: direct data gathering from ad transparency libraries; automated data donation via participatory citizen science browser plug-ins; and a privacy-aware mobile screen capture app. We describe how our suite of computational approaches enables scalable and participant-situated research, overcoming limitations posed by restricted platform access to computational advertising data. Ethical considerations and practical implications are discussed alongside case study findings, further demonstrating the potential for these methods to inform regulatory debates and scholarly advancements in the field.