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Numerical information can be communicated using different number formats, such as digits ("5") or number words ("five"). For example, a battery product may claim to last for "5 hours" or "five hours." And while these two formats are used interchangeably in the marketplace, it is not clear how they influence consumer judgments and behavior. Via six experimental studies, two online ad campaigns, and one large secondary dataset analysis, we find that digits, compared to number words, positively affect consumer behavior. We refer to this phenomenon as the number format effect. We further show that the number format effect occurs because consumers feel that digits (vs. number words) are the right way to present numerical information: digits lead to a sense of feeling right that then affects consumer behavior. Finally, we show that the number format effect is amplified when credibility of the source of information is low, and attenuated when source credibility is high. The current research advances knowledge of how numerical information influences consumer judgments and behavior and carries important implications for marketers and policymakers as they communicate numerical information to consumers. [ABSTRACT FROM AUTHOR]

With the growing prevalence of digital platforms for online shopping, advertising, and marketing activities in general, it is imperative to better understand how designs of virtual elements on digital interfaces influence click behavior. Websites and online advertisements contain virtual elements such as call-to-action buttons, images, and logos. This research examines how curved versus sharp angled shapes of virtual elements in online ads and on websites influence click-through rates (CTRs). The findings of a series of studies, including three field experiments and an eye tracking study, show that website and online ad elements in curved (vs. sharp angled) shapes generate higher CTRs. Process evidence suggests that curved (vs. sharp angled) digital elements enhance visual appeal, leading to approach motivation and greater CTR. In terms of practical implications, the findings of this research have strong relevance for designing online ads and website interfaces and for digital marketing strategies. Specifically, digital marketers desiring higher click rates would benefit from having more curved (than sharp angled) virtual elements on websites and in online ads. [ABSTRACT FROM AUTHOR]

Although in-stream video advertising is common, its effects on advertisement (ad) information encoding remain unclear. We investigated the effects of in-stream video advertising by comparing two groups: those watching mid-roll (between the program) ads and those watching pre- and post-roll (before and after the program, respectively) ads. To elucidate how advertising content is encoded in the context of in-stream video advertising, we integrated two theoretical frameworks: the negative emotion–memory model (NEMM) and the limited capacity model of motivated–mediated message processing (LC4MP). We used electroencephalography (EEG) to assess negative emotions and bottom-up attention during advertisement viewing. The findings indicate that the first mid-roll ad induced negative emotions, but these feelings were attenuated during subsequent mid-rolls. In addition, negative emotions induced by mid-roll ads attenuated the role of bottom-up attention in the information encoding process. However, the pre- and post-roll ads were not accompanied by negative emotions; thus, bottom-up attention played a major role in the information encoding of these ads. The results also suggest that despite the negative emotions experienced during mid-rolls, such transient negative reactions did not affect purchase intention for the advertised products. [ABSTRACT FROM AUTHOR]

As mobile devices have become a necessity in our daily lives, mobile advertising is also prevalent. Accordingly, it is critical for practitioners to understand how consumers visually attend to mobile advertisements. One popular way of doing so is via eye-tracking methodology. However, scant eye-tracking research exists in mobile settings due to technical challenges, e.g., cumbersome data annotation. To tackle these challenges, the authors propose an object-detection machine learning (ML) algorithm—You Only Look Once (YOLO) v3—to analyze eye-tracking videos automatically. Moreover, we extend the original YOLO v3 model by developing a novel algorithm to optimize the analysis of eye-tracking data collected from mobile devices. Through a lab experiment, we investigate how two types of ad elements (i.e., textual vs. pictorial) and shopping devices (i.e., mobile vs. PC) affect consumers' visual attention. Our findings suggest that (1) textual ad elements receive more attention than pictorial ones, and such differences are more pronounced in ads on mobile devices than those on PCs; and (2) mobile ads receive less attention than PC ads. Our findings provide managerial insights into developing effective digital advertising strategies to improve consumers' visual attention in online and mobile advertisements. [ABSTRACT FROM AUTHOR]

In realizing that consumers regularly straddle the work–life interface, some companies position their products according to their ability to address work and life needs together, then communicate this offering to consumers. Whether using a work–life positioning strategy is effective remains unclear , however. If this strategy signals work–life enrichment, it should increase consumers' interest, but only if the product demands few resources from consumers. If the product instead demands substantial resources, a work–life positioning might inadvertently trigger perceptions of work–life conflict and lower consumers' interest. To test these predictions, the authors partnered with three businesses to advertise their products, which impose varying resource demands, on social media using content that highlights the work–life interface or not. Analyses of ad click data support the predictions: Work–life ads are less effective than single-domain (work or life) ads if the advertising involves resource-demanding products, but they are more effective if it pertains to resource-undemanding products. Furthermore, the effects are stronger among consumer segments that experience more work–life conflict in general. With this initial application of work–life theory to a marketing context, this article offers relevant insights for both research and practice. [ABSTRACT FROM AUTHOR]

Although in-feed advertising is popular on mainstream platforms, academic research on it is limited. Platforms typically deliver organic content through two methods: subscription by users or recommendation by artificial intelligence. However, little is known about the ad performance between these two channels. This research examines how the performance of in-feed ads, in terms of click-through rates and conversion rates, differs between subscription and recommendation channels and whether these effects are mediated by ad intrusiveness and moderated by ad attributes. Two ad attributes are investigated: ad appeal (informational vs. emotional) and ad link (direct vs. indirect). Study 1 finds that the recommendation channel generates higher click-through rates but lower conversion rates than the subscription channel, and these effects are amplified by informational ad appeal and direct ad links. Study 2 explores channel differences, revealing that the recommendation channel yields less source credibility and content control, reducing consumer engagement with organic content. Studies 3 and 4 validate the mediating role of ad intrusiveness and rule out ad recognition as an alternative explanation. Study 5 uses eye-tracking technology to show that the recommendation channel has lower content engagement, lower ad intrusiveness, and greater ad interest. [ABSTRACT FROM AUTHOR]

The European Union's General Data Protection Regulation (GDPR), with its explicit consent requirement, may restrict the use of personal data and shake the foundations of online advertising. The ad industry has predicted drastic loss of revenue from GDPR compliance and has been seeking alternative ways of targeting. Taking advantage of an event created by an ad publisher's request for explicit consent from users with European Union IP addresses, the authors find that for a publisher that uses a pay-per-click model, has the capacity to leverage both user behavior and web page content information for advertising, and observes high consent rates, GDPR compliance leads to modest negative effects on ad performance, bid prices, and ad revenue. The changes in ad metrics can be explained by temporal variations in consent rates. The impact is most pronounced for travel and financial services advertisers and least pronounced for retail and consumer packaged goods advertisers. The authors further find that web page context can compensate for the loss of access to users' personal data, as the GDPR's negative impact is less pronounced when ads are posted on web pages presenting relevant content. The results suggest that publishers and advertisers should leverage targeting based on web page content after the GDPR's rollout. [ABSTRACT FROM AUTHOR]

Using an experimental tool that tracks viewers' real-time ad-skipping behavior, the current research tested when and why a highly arousing emotional appeal ad that induces a set of complex discrete emotions can reduce the ad-skipping rate on social media platforms such as YouTube. Across three experiments, we showed the following results. First, the ad-skipping rate of emotional appeal ads was lower among consumers who had the goal of watching emotional (versus informational) videos. Second, ad-elicited empathy mediated this effect. Third, the effects of the emotional appeal ad on ad-skipping behaviors were contingent upon consumers' predisposition to approach emotional experiences. Among consumers who were seeking emotional experiences, higher levels of empathy resulted in lower ad-skipping rates and longer ad-viewing duration when the emotional appeal of the ad matched with the emotional goal of video watching; in contrast, among consumers who were not seeking emotional experiences, the opposite effect was found. [ABSTRACT FROM AUTHOR]

This study explored factors influencing advertising avoidance in the context of brand webtoons (i.e., a digital cartoon format of stories on online platforms) originated in South Korea. We conducted a survey (n = 242) to investigate the relationship between webtoon familiarity, empathy, brand experience (i.e., intellectual experience, affective experience), persuasion knowledge (PK), and ad avoidance. Considering brand experiences as key mediating variables, hierarchical relationships are investigated. The findings showed that webtoon familiarity influenced empathy of brand webtoons which in turn leads to intellectual and affective experiences. Intellectual experiences increased persuasion knowledge, and affective experiences decreased persuasion knowledge. Affective experiences are also found to decrease ad avoidance. Finally, persuasion knowledge increased ad avoidance. These results suggested specific academic implications of covert marketing, and managerial implications are also discussed. [ABSTRACT FROM AUTHOR]

Search platforms are contemporary digital services that create value on multi-sided markets through providing services to each side of their platforms—indexing contents for all website operators on the internet, placing pages and tracking performance for paid advertisers or sellers, and searching market offerings to match inquiries for web users. Search platforms are interested in developing strategies at the sponsored ad set level to optimize consumer clicks. Drawing literature supports from hospitality, search engine advertising, and product assortment areas, our study examines the relationships between search platforms' paid ad strategies (in paid ad set size and set attractiveness) and consumer's single or multiple clicks on the sponsored ad section. We empirically test the relationships in a multinomial logit modeling approach using a large-scale disaggregated, hotel-search dataset collected from a leading Chinese search engine. Our results suggest a positive but diminishing impact of paid ad set size on a consumer's set selection and multiple click-through behavior. Our study also spotlights the distinct and often divergent moderating effects of two types of ad attractiveness, preference-based and social-based, on the relationship between paid ad set size and consumer responses. We discuss the theoretical and managerial implications of this research to search platforms and advertisers in hospitality alike. [ABSTRACT FROM AUTHOR]

This paper aims to provide solutions to the dynamic investment strategies of online advertising for multi-oligopoly enterprises. Specifically, by considering the spillover effect of online advertising, the investment cost function incorporating the characteristics of online advertising is constructed and then combined with external interference factors, and the dynamic investment decision-making model of online advertising of three oligarchic competitive enterprises is constructed. Subsequently, using the Hamilton–Jacobi–Bellman function, the Nash equilibrium solutions of the online advertising amount and profits are attained in symmetrical, semisymmetric, and asymmetric cases. We then calculated and empirically analysed the share of the market. Finally, the model is extended to n-dimensional space. Our study suggests that (1) investment in fixed-location online advertising is inversely proportional to the spillover effect, while the amount of pay-per-click online advertising investment is directly proportional to the spillover effect. (2) In the semisymmetric case, enterprises with a low initial share are easily affected by the spillover effect, while in the semisymmetric and asymmetric cases, dominant enterprises are more easily affected by the spillover effect. (3) The amount of investment in online advertising is inversely proportional to external interference factors. (4) When there are more than three enterprises in the market, the profit is negative, indicating that new enterprise should be cautious about entering the industry. [ABSTRACT FROM AUTHOR]

Advertising is a crucial revenue source for e-commerce platforms and a vital online marketing tool for their sellers. In this paper, we explore dynamic ad allocation with limited slots upon each customer's arrival for an e-commerce platform, where customers follow a choice model when clicking the ads. Motivated by the recent advocacy for the algorithmic fairness of online ad delivery, we adjust the value from advertising by a general fairness metric evaluated with the click-throughs of different ads and customer types. The original online ad-allocation problem is intractable, so we propose a novel stochastic program framework (called two-stage target-debt) that first decides the click-through targets and then devises an ad-allocation policy to satisfy these targets in the second stage. We show the asymptotic equivalence between the original problem, the relaxed click-through target optimization, and the fluid-approximation (Fluid) convex program. We also design a debt-weighted offer-set algorithm and demonstrate that, as long as the problem size scales to infinity, this algorithm is (asymptotically) optimal under the optimal first-stage click-through target. Compared with the Fluid heuristic and its resolving variants, our approach has better scalability and can deplete the ad budgets more smoothly throughout the horizon, which is highly desirable for the online advertising business in practice. Finally, our proposed model and algorithm help substantially improve the fairness of ad allocation for an online e-commerce platform without significantly compromising efficiency. This paper was accepted by Jeannette Song, operations management. Funding: Y. Rong is supported by the National Natural Science Foundation of China [Grants 72025201, 72331006, and 72221001]. R. Zhang is grateful for the financial support from the Hong Kong Research Grants Council General Research Fund [Grants 14502722 and 14504123] and the National Natural Science Foundation of China [Grants 72293560 and 72293565]. H. Zheng is supported by the National Natural Science Foundation of China [Grants 72231003, 72325003, and 72221001]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.04091. [ABSTRACT FROM AUTHOR]

The Tor network is known for its opaque characteristics and involvement in illicit activities, motivating to shed light on the exposure, lifetime, and functionalities of onion services. This study focuses on the appearance of Tor links in online advertising and monitors the connectivity status and protocols of the collected onion domains through the Tor network over 105 days. Out of 54,602 onion addresses gathered, it was found that 38% of Tor links were advertised only once, 43% between two and five times, and 19% more than five times. Furthermore, 50% of the addresses were exclusively advertised on the surface web, 6% on the dark web, and 44% on both portions. The temporal analysis revealed that 67% of the addresses were predominantly active, 7% were intermittent, and 26% were mostly inactive. The study examined fifteen protocols used by onion services, concluding that 94% employed a single protocol, while 6% utilized between two and eight protocols. Among active sites, HTTP was present in 99.75% of cases, followed by SSH (4.95%) and HTTPS (0.64%). Additionally, onion services without web services often deploy cryptocurrency or instant messaging servers. This study offers a comprehensive and current understanding of the dark web, surpassing previous research in its scope. [ABSTRACT FROM AUTHOR]

The use of social media advertising allows for a significant increase in reach, the ability to target audiences with precision, direct interaction, and enhanced capabilities for measurement and data analysis. Ads on LinkedIn are designed to reach a relevant audience with an interest in knowledge, academia, job opportunities, and collaborations. This research analyses the advertising actions carried out on LinkedIn by Spanish universities with the largest number of students in Spain. The findings of this study provide insights into the innovations and trends that the main educational brands are adopting in online advertising. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Live e‐commerce is one of the important forms of digital transformation of supply chain, which has become increasingly popular in recent years as a powerful tool for internet advertising, interaction, and merchandising. We propose two pricing models to examine the strategies of information disclosure and pricing within the live‐streaming supply chain. One model revolves around the agency framework, wherein the manufacturer determines the price while the streamer decides the level of information disclosure. The other model pertains to a self‐implementing framework, wherein the manufacturer determines both the level of information disclosure and the price. To evaluate the robustness of our findings, we extend our models to encompass three significant scenarios: (i) the platform involvement model, where a live‐streaming support platform becomes part of the supply chain; (ii) the fixed price model, which externalizes the manufacturer's pricing decision; and (iii) the dual‐channel operations model. Our analysis reveals that the cost associated with information disclosure impacts the streamer's profits in a nonlinear manner within the agency model. Interestingly, expanding the revenue‐sharing parameter does not invariably favor the manufacturer. Moreover, we observe that none of the live‐streaming sales models exhibits a clear advantage over the others. Additionally, we demonstrate that the platform's involvement limits the manufacturer's ability to utilize the agency model for live‐streaming sales. In situations with fixed pricing, the manufacturer is advised to disclose more production information during live‐streaming sales. [ABSTRACT FROM AUTHOR]

Copyright of Wirtschaft und Wettbewerb is the property of Fachmedien Otto Schmidt KG and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study investigates the virtual semiotic landscape of a South African online advertisement campaign, Vagina Varsity, by sanitary towel company Libresse®. The article extends notions of linguistic landscape studies and virtual semiotic landscape studies to bring attention to the various semiotics used in online spaces for communication and meaning-making. Using a social semiotic approach to multimodality as a theoretical framework, the investigation found that the campaign addressed the topic of menstruation in a manner that assuaged cultural sensitivities towards this topic. The campaign advertised sanitary products by drawing on semiotics usually related to a teaching genre, as well as on popular cultural references. The affordances of the internet allowed for the creation of spaces where the female anatomy and related matters were presented, constructing a conducive atmosphere for young women to share, discuss and motivate others within that space. [ABSTRACT FROM AUTHOR]

Introduction: As the pharmaceutical advertising landscape evolves with digital advancements, this study examines public awareness and perceptions of medication advertisements in Saudi Arabia. It focuses on the effects of regulatory frameworks and evaluates how they influence public understanding and attitudes toward these advertisements. Method: A cross-sectional study was conducted using an electronic survey in Saudi Arabia in December 2023. The survey was distributed on social media platforms and reached a diverse sample of 440 participants. It covered public perception and attitudes toward drug advertisements, knowledge of regulatory laws, and preferences regarding advertising mediums. Results: Out of the 440 participants in the study, who were primarily employees with bachelor's degrees, there was a clear awareness of drug advertisements. The average age of the group was 33 years, and a significant portion (71.1%) held a bachelor's degree, with 51.1% being employed. The findings revealed that 25.5% of participants frequently noticed drug ads, while 22.7% saw them very often. Although many found the ads informative, there were significant concerns about unrealistic expectations and the risk of overmedication; 89.8% believed the ads set unrealistic expectations about the effectiveness of medications. Additionally, 60.7% thought that celebrity endorsements might mislead the audience, and 91.1% felt that ads should provide more detailed information about potential risks and side effects. Regarding preferred advertising platforms, mobile apps and websites were favored (47%), followed closely by social media (46.4%). A striking 93.2% of participants believed that drug ads on social media should be subject to stricter regulations, and 96.4% wanted more proactive monitoring of online advertising. Many also reported using other sources, such as medical review sites, to verify medication information. Conclusions: Pharmaceutical advertising in Saudi Arabia must balance ethical transparency with educational value. The influence of digital platforms underscores the necessity for stricter regulation and accurate information dissemination. A collaborative approach is essential to align advertising practices with public health interests and regulatory standards. [ABSTRACT FROM AUTHOR]

Advertising to a consumer provides potentially useful information to the consumer and moves them along the purchase journey, and tracking the consumer's online activities enables an advertiser to infer the consumer's purchase journey state and target repeat ads accordingly. However, many consumers dislike being tracked, and, furthermore, repeat advertising may lead to ad wearout. The authors develop a model with consumers, an advertiser, and an ad network to investigate, under the preceding considerations, the impact of regulations that endow consumers with the choice to opt in to or out of online tracking. The authors find that, if ad effectiveness is intermediate, opting in to tracking decreases ad repetition; otherwise, opting in increases ad repetition. To make an opt-in decision, a consumer weighs the cost of ad wearout from repeat ads against the benefit of obtaining potentially relevant product information from them, and the consumer opts in to tracking if either ad effectiveness is intermediate or sensitivity to ad wearout is low. This opt-in pattern creates counterintuitive implications; for instance, higher ad effectiveness, even though it implies higher ad valuation for the advertiser, may reduce repeat ads and the ad network's profit. Under regulation that requires consumer consent for tracking, the results shed light on when and why consumers give such consent, and provide useful insights for practitioners and policy makers. [ABSTRACT FROM AUTHOR]

The aim of this research is to determine how football stars influence the attention paid to online poker advertising and whether a corresponding nationality might influence the perception of audiences between 18 and 24 years of age, which is the adult age group most vulnerable to this type of game. Using an eye tracking technique, a sample of 60 Portuguese young people who are amateur online poker players has been studied. The results suggest that affinity with a sports star increases the level of attention paid to the image of the star. Otherwise, the poker hand registers the greatest amount of attention. Sports stars who are currently playing attract more attention than those who are retired, and smiles make it possible to attract even more attention from the audience. These stars facilitate advertising brands in associating online gaming with a form of leisure. [ABSTRACT FROM AUTHOR]

Introduction: It is broadly recognised that chemsex is more prevalent among men who have sex with men, but little is known about chemsex in the context of commercial sexual encounters between men. This study investigates sex worker advertising characteristics and their advertised willingness to engage in chemsex with clients. Methods: Data were web scraped from the profiles of US‐based male sex workers (N = 3773) advertising services on an internet advertising platform in February 2021. This study describes the association between chemsex advertising and advertised age, race/ethnicity, sexual orientation, encounter type and COVID‐19 acknowledgement. Results: 28.5% of sex workers (n = 1077) advertised chemsex, 64.7% of whom were 25–34 years‐old (n = 697). The odds of chemsex advertising increased between ages 21–24 (aOR = 1.20, 95% CI 1.09–1.32) and declined among sex workers over 35 years‐old (aOR = 0.97, 95% CI 0.95–1.00). Sex workers advertising as bisexual were more likely to advertise chemsex than those identifying as gay (aOR = 1.38, 95% CI 1.18–1.63). Sex workers acknowledging COVID‐19 were less likely to advertise chemsex compared to those who did not (aOR = 0.65, 95% CI 0.48–0.89). Encounter type was associated with chemsex advertising among sex workers in this sample; sex workers not offering the "boyfriend experience" were more than 50% less likely to advertise chemsex than those who did offer the boyfriend experience (aOR = 0.47, 95% CI 0.36–0.61). Discussion and Conclusions: Chemsex advertising in this population is likely influenced by multiple sociodemographic and occupational characteristics. Identifying sex workers likely to engage in chemsex based on advertising data could inform targeted education and harm‐reduction campaigns in this population. [ABSTRACT FROM AUTHOR]

The study makes the case that adding social media campaign tactics to the requirements for local office holders will encourage more responsible, knowledgeable, and involved grassroots politics. This study explores the qualification standards for local candidates and how these standards can be redefined in the context of social media campaigns. As digital channels have a greater influence on voter involvement and perception, identifying the criteria that resonate with people becomes critical. The study highlights important characteristics— such as ethics, community involvement, and digital literacy— that distinguish successful local candidates. The study presents a methodology for optimizing campaign messaging and outreach by studying the candidates' successful social media techniques. Finally, the findings aim to help future politicians use social media to increase democratic engagement and develop meaningful ties with voters. This study emphasizes the essential interplay of candidate qualities and internet campaigning in determining electoral results. [ABSTRACT FROM AUTHOR]

Online targeted advertising leverages an information asymmetry between the advertiser and the recipient. Policymakers in the European Union and the United States aim to decrease this asymmetry by requiring information transparency information alongside political advertisements, in the hope of activating citizens' persuasion knowledge. However, the proposed regulations all present different directions with regard to the required content of transparency information. Consequently, not all proposed interventions will be (equally) effective. Moreover, there is a chance that transparent information has additional consequences, such as increasing privacy concerns or decreasing advertising effectiveness. Using an online experiment (N = 1331), this study addresses these challenges and finds that two regulatory interventions (DSA and HAA) increase persuasion knowledge, while the chance of raising privacy concerns or lowering advertisement effectiveness is present but slim. Results suggest transparency information interventions have some promise, but at the same time underline the limitations of user-facing transparency interventions. [ABSTRACT FROM AUTHOR]

Copyright of Medkom: Jurnal Media dan Komunikasi is the property of Universitas Airlangga and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Advertising was once touted as a 'big boys' marketing tool, but the advent of the Internet has led to the emergence of online advertising, which has benefitted marketing organizations of all sizes, especially small and medium enterprises (SMEs), for 20 years since the new millennium. However, the emergence and proliferation of digital natives as a new generation of consumers necessitate further research to ensure the continued attractiveness, effectiveness, and relevance of online advertising. Furthermore, there is a need to consolidate the multifaceted nature of technology and media in online advertising and the corresponding behavior enacted by consumers, particularly digital natives, today. In this regard, this paper endeavors to examine the perceptions and reactions of digital natives toward online advertising using the theory of planned behavior, technology acceptance model, and uses and gratifications theory as an integrated theoretical lens to guide this examination. Using covariance structural equation modeling on a sample of 380 digital natives, this paper finds that the ease of use, usefulness, entertainment gratification, credibility, design, and personalization of online advertisement positively influence the attitude of digital natives toward online advertisement. However, assurance from online advertisement did not produce any significant effect, a unique finding that exemplifies the confidence that digital natives exhibit in digital environments as a self-assured generation. Finally, this paper finds that the attitude and subjective norms among digital natives toward online advertisement and the security and responsiveness of the website of the online advertised product positively influence the intention of digital natives to purchase the online advertised product. The implications of these findings for theory and practice as well as its limitations and future research directions conclude the paper. [ABSTRACT FROM AUTHOR]

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background Methods Results Conclusion Recruiting older adults into clinical trials can be particularly challenging. Our objective was to determine if targeted web‐based advertising is an effective recruitment strategy.We compared the recruitment rates of traditional and targeted web‐based methods for three representative clinical trials involving older adults. All studies utilized traditional recruitment methods initially, but shifted toward primarily targeted web‐based advertising after experiencing slow recruitment rates.We found that web‐based advertising reached more individuals compared to traditional methods. Compared to traditional methods, web‐based methods also had at least twice the rate of expressed interest, completion of telephone and in‐person screening, eligibility, and enrollment. Additionally, the proportion of individuals excluded after the telephone screening did not differ according to whether targeted web‐based advertising (STAMINA: 51%; Berries and Steps: 62%; ISTIM: 20%) or traditional methods (STAMINA: 48%; Berries and Steps: 69%; ISTIM: 23%) were used within each study. Those recruited using web‐based advertisements tended to be younger compared to traditional methods, but were similar in racial distribution and education.Targeted web‐based advertisements may be more effective in recruiting older adults for clinical trials at a faster rate than traditional recruitment methods, but need further evaluation of compatible study designs, potential population bias, and cost‐effectiveness. [ABSTRACT FROM AUTHOR]

Recent innovations in online advertising facilitate the use of a wide variety of data sources to build micro-segments of consumers, and delegate the manufacture of audience segments to machine learning algorithms. Both techniques promise to replace demographic targeting, as part of a post-demographic turn driven by big data technologies. This article empirically investigates this transformation in online advertising. We show that targeting categories are assessed along three criteria: efficiency, communicability, and explainability. The relative importance of these objectives helps explain the lasting role of demographic categories, the development of audience segments specific to each advertiser, and the difficulty in generalizing interest categories associated with big data. These results underline the importance of studying the impact of advanced big data and AI technologies in their organizational and professional contexts of appropriation, and of paying attention to the permanence of the categorizations that make the social world intelligible. [ABSTRACT FROM AUTHOR]

This study aims to analyse the influence of perceived advertising values which include informativeness, entertainment, and irritation on purchase intention through attitudes towards Instagram advertising. This study was conducted on Generation Z who followed Emina's Instagram account in Malang City. Using a quantitative approach, data was obtained through a questionnaire using the purposive sampling method and analyzed using the PLS-SEM model. The results showed that informativeness and entertainment had a positive influence on purchase intention, while irritation had no significant effect. In addition, attitudes towards Instagram advertising have been proven to mediate the relationship between the three elements of perceived advertising values and purchase intention. These findings reinforce the importance of creating informative and entertaining Instagram ads, while minimizing irritating elements to increase positive attitudes towards ads and consumer purchase intent. This research makes a theoretical contribution by supporting the theory of Planned Behavior and offers practical insights for marketers in designing advertising strategies on social media. [ABSTRACT FROM AUTHOR]

Click-through rate (CTR) prediction aims to estimate the likelihood that a user will interact with an item. It has gained significant attention in areas such as online advertising and e-commerce. Existing studies have verified that feature interactions play a crucial role in CTR prediction, highlighting the need for efficient modeling of these interactions. However, most existing approaches in CTR prediction tend to overlook specific feature characteristics, relying instead on deep neural networks or advanced attention mechanisms to learn meaningful feature interactions. In real-world scenarios, features can be categorized into groups based on prior information, which motivates the explicit consideration of interactions between groups of features. For example, the unique context of an item often has a substantial correlation with a particular user, and a specific item often has a strong relationship with a particular user demographic. An efficient model, therefore, requires an appropriate inductive bias to learn these relationships. To address this issue, we present a Context-aware Cross Feature Attentive Network (CCFAN) that explicitly considers the relationship or association between items and users. We categorize input variables into four groups: user, item, user context, and item context, which allows learning significant interactions between (user)-(item context) and (item)-(user context) in an explicit way. These interactions are learned using a multi-head self-attention network that includes modules for user-item interaction and cross-feature interaction. To demonstrate the effectiveness of CCFAN, we conduct experiments on two public benchmark datasets, MovieLens1M and Frappe, and one real-world dataset from an educational service provider, WJTB. The experimental results show that CCFAN not only outperforms previous state-of-the-art CTR methods but also offers a high degree of explainability. [ABSTRACT FROM AUTHOR]

Online video platforms face the challenge of balancing the needs of their users with those of their advertisers. Although users typically prefer to have less intrusive ads, advertisers aim to effectively catch user attention. This paper investigates how the provision of ad choice affects the effectiveness of video advertising. We argue that allowing users to choose an ad to view may trigger a "conjecture-formation-and-confirmation" process that motivates users to pay more attention to the selected ad. Two online experiments and four laboratory experiments are conducted to test the theorized underlying mechanism of the ad choice effect. Study 1 finds when users are unfamiliar (versus familiar) with the content of ad options (i.e., they need to make conjectures about ad content), ad choice is more likely to increase user attention to the chosen ad. Study 2 and Study 3 show that the impact of ad choice on user attention is more likely to be positive when users are enabled to make conjectures about ad content, such as when choice options provide more relevant information about ad content. Study 4a and Study 4b provide more direct support for the underlying mechanism by showing that the ad choice effect is attenuated when users cannot form conjectures about ad content at the choice stage. Study 5 further demonstrates that the positive effect of ad choice is robust across different ad settings. Taken together, these studies show ad choice is more likely to boost the effectiveness of video advertising when the "conjecture-formation-and-confirmation" process is triggered. This paper was accepted by Kartik Hosanagar, information systems. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71702126, 72022008 and 72372112], the University Research Committee of the University of Hong Kong [Grant 201905159007], HKU Education Consulting (Shenzhen) [Theme-Based Research Fund SZRI2023-TBRF-02], and the Research Grant Council Hong Kong [Project 17502921]. Supplemental Material: The data and online appendices are available at https://doi.org/10.1287/mnsc.2019.03291. [ABSTRACT FROM AUTHOR]

The increasing diversity in advertising options warrants more research on user engagement with advertisements across different digital platforms. Drawing on the elaboration likelihood model and consumer skepticism theories, we examine the central role of the three-dimensional construct of trust—cognitive, emotional, and behavioral—in overcoming advertisement skepticism (AS) and improving advertisement engagement (AE). Using survey data from a large sample in the United States (N = 841), we compare the effect of skepticism on engagement as mediated by trust, on social (Facebook) versus search (Google) platforms. In a multistage analysis, initial results indicated that trust is a significant mediator for both Facebook and Google. Subsequent testing revealed that for Facebook, the behavioral dimension of trust fully mediated the skepticism and engagement link, whereas for Google, all three dimensions of trust only partially mediated the skepticism and engagement link. This suggests that on social media platforms, behavioral trust is crucial for overcoming skepticism to achieve better engagement. Furthermore, when comparing all pathways in the model, we found that on search platforms, cognitive trust was significantly more influential in overcoming skepticism for better engagement. [ABSTRACT FROM AUTHOR]

Introduction: Despite an evolving e-cigarette environment, few studies have looked at adolescent exposure to e-cigarette advertising over time and its associations with curiosity about and susceptibility to using e-cigarettes. We examined e-cigarette advertising exposure and its associations with curiosity and susceptibility across multiple years among adolescents who have never used e-cigarettes. Methods: We obtained data from the National Youth Tobacco Surveys (NYTSs), 2014-2020 (N = 97,496). The NYTS identified e-cigarette advertising exposure from four channels: Internet, newspapers and magazines, convenience stores, and TV. Logistic regressions explored e-cigarette advertising exposure over time and the associations between exposure from the four channels and both curiosity and susceptibility to using e-cigarettes. Results: Youth exposure to e-cigarette advertising on the Internet and in convenience stores formed an increase-decrease-increase pattern from 2014 to 2020, whereas exposure in newspapers and magazines and on TV generally decreased over this period. Exposure on the Internet and in convenience stores was consistently associated with curiosity and susceptibility; but exposure in newspapers and magazines and on TV was sporadically associated with the outcomes. Conclusions: Despite a changing e-cigarette marketplace, youth were consistently exposed to e-cigarette advertising, especially on the Internet and in convenience stores. This pattern is worrisome, as it may increase youth curiosity and susceptibility to using e-cigarettes. Comprehensive tobacco prevention efforts to prevent e-cigarette use in adolescents should continue to restrict e-cigarette advertising and marketing, thereby reducing exposure and discouraging e-cigarette use. Regular efforts should also be made to educate adolescents about the risks of using e-cigarettes to counteract the impact of high e-cigarette advertising exposure. [ABSTRACT FROM AUTHOR]

This article demonstrates how mediatization facilitates the (re)production of mock language. Through an examination of Chinese netizens' reactions to a series of viral internet commercials that feature three Hong Kong actors speaking nonstandard Mandarin, it uncovers the processes whereby Gangpu (Hong Kong Mandarin) has become increasingly perceived in China as funny. The vast scale of uptake formulations enabled by mediatization has made it possible for Chinese netizens to engage in a collaborative effort not only in highlighting certain features of Gangpu and certain elements of the commercials but also in presenting them in ways that evoke specific meanings and interpretations. Ultimately, it is through the parodic revoicing of Hong Kong celebrities speaking nonstandard Mandarin that this non-native variety has come to be keyed as humorous. This study shows that we gain a better understanding of how mock practices reinforce and build on each other by tracing their uptake and circulation. (Mediatization, mock language, parody, listening subject, Mandarin Chinese, Hong Kong, China) [ABSTRACT FROM AUTHOR]

Sourcing algorithms are technologies used in online platforms to identify, screen, and inform potential applicants about job openings. The popularity of such technologies is rapidly increasing due to their pervasiveness in online advertising and beliefs that sourcing algorithms can decrease time to hire while improving the quality of new hires. What is little known, however, are their potential risks: sourcing algorithms could (intentionally or unintentionally) encode or exacerbate occupational demographic disparities, thereby hindering organizational diversity and/or decreasing the effectiveness of online hiring practices. Because sourcing algorithms identify and screen potential job applicants before they are made aware of employment opportunities, methods for evaluating discrimination in hiring which focus solely on job applicants (e.g., adverse impact ratio), may fail to detect the effects of discriminatory sourcing algorithms. Thus, we propose an expanded model of the employee hiring process to take into account the role of sourcing algorithms. Based on empirical approximations, we conducted a Monte Carlo simulation study to examine the magnitude and nature of sourcing algorithms' influence on hiring outcomes. Our findings suggest that sourcing algorithms could hinder the diversity of new hires while misleadingly suggesting positive diversity outcomes in personnel selection. We provide practical guidance for the use of sourcing algorithms and call for a systematic re‐examination of how to evaluate selection system fairness in the era of algorithmic recruitment. [ABSTRACT FROM AUTHOR]

Against the backdrop of calls for greater platform transparency, this exploratory article investigates Meta's 'Why Am I Seeing This Ad' (WAIST) feature, which is positioned as a consumer-level explanation of Meta's advertising model. Drawing on our own walkthroughs of Facebook and Instagram and data from the Australian Ad Observatory, we find the feature falls short in two ways. First, the explanations do not always align with how the system and its audience-building tools are sold to and used by advertisers. Second, the feature is focused narrowly on single ads and individual users, doing nothing to generate understanding of the patterns and sequences of targeted advertising in relation to other users or over time. We propose both platform practices and independent research strategies that could help to fill this gap between individual explanations, population-level patterns of targeted online advertising and the societal issues associated with it. [ABSTRACT FROM AUTHOR]

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With the development of machine learning algorithms and the increasing computational resources available, artificial intelligence has achieved great success in many application domains. However, the success of machine learning has also raised concerns about the fairness of the learned models. For instance, the learned models can perpetuate and even exacerbate the potential bias and discrimination in the training data. This issue has become a major obstacle to the deployment of machine learning systems in high‐stakes domains, for example, criminal judgment, medical testing, online advertising, hiring process, and so forth. To mitigate the potential bias exhibited by machine learning models, fairness criteria can be integrated into the training process to ensure fair treatment across all demographics, but it often comes at the expense of model performance. Understanding such tradeoffs, therefore, is crucial to the design of optimal and fair algorithms. My research focuses on characterizing the inherent tradeoff between fairness and accuracy in machine learning, and developing algorithms that can achieve both fairness and optimality. In this article, I will discuss our recent work on designing post‐processing algorithms for fair classification, which can be applied to a wide range of fairness criteria, including statistical parity, equal opportunity, and equalized odds, under both attribute‐aware and attribute‐blind settings, and is particularly suited to large‐scale foundation models where retraining is expensive or even infeasible. I will also discuss the connections between our work and other related research on trustworthy machine learning, including the connections between algorithmic fairness and differential privacy as well as adversarial robustness. [ABSTRACT FROM AUTHOR]

Privacy and antitrust are on a collision course. Large firms with monopoly power or near-monopoly power can build products that ostensibly enhance user privacy while raising rivals' costs and allowing monopolist firms to gain footholds in secondary markets. In this Note, I use the example of Apple's privacy changes, the impact these changes had on digital advertising, and Apple's subsequent expansion of its own digital marketing offerings as a motivating example of how current antitrust doctrine is ill-equipped to handle this new form of monopolist behavior. Privacy is the latest example of an "incommensurability" problem in the final stage of the standard rule of reason analysis. Even if a plaintiff can demonstrate competitive harm from privacy-preserving conduct, courts are constrained in what type of relief they can order. Fortunately, one of the main federal antitrust enforcers--the Federal Trade Commission--is also the source of much of federal privacy law. It has the authority and expertise to promulgate rules pursuant to section 18 of the Federal Trade Commission Act to address anticompetitive privacy and strike the appropriate balance between competition and consumer protection. Much of the Commission's privacy enforcement has reflected the heavily criticized "notice-and-choice" privacy framework. However, the Commission's recent initiation of the Magnusson-Moss rulemaking process signals an opening to not only move beyond this framework, but also to consider both competition and privacy in the formation of any rule regulating technology firms. [ABSTRACT FROM AUTHOR]

The research objectives are to assess the digital marketing capabilities of micro, small, and medium-sized enterprises (MSMEs) in Thailand's service sector, as well as to develop persona profiles for each enterprise size. The survey of 400 digital marketing personnel was conducted alongside comprehensive interviews with 10 marketing executives. The result suggests that MSMEs have moderate digital marketing skills but need to improve their knowledge in creative thinking, online consumer interaction, affiliate marketing, and change management. An executive interview emphasized issues unique to micro-enterprises, like mismatched advertising and inadequate online marketing expertise. Medium- sized enterprises have difficulties achieving online target engagement due to their inadequate SEO capabilities, whereas small enterprises struggle to effectively manage customer inquiries. The study introduces persona cards that reveal distinct competency levels and areas for improvement for each enterprise size. The research provides actionable insights for MSMEs seeking to refine their digital marketing strategies and contributes to the academic literature with understanding. Persona cards serve as a novel tool for visualizing digital marketing competencies and needs within the MSME sector. [ABSTRACT FROM AUTHOR]

This work introduces a novel cue that consumption advisers, like stylists and interior designers, can use to signal expertise: combinatory recommendations. In a combinatory recommendation, a person offers an opinion about compatibility among multiple products intended for joint usage. Across nine studies conducted in the lab and field, the authors find that offering a combinatory recommendation signals greater expertise (Study 1a, Study 2a) and, specifically, greater depth of knowledge (Study 1b), compared with other types of recommendations involving the same number of products. This effect does not depend on the helpfulness of the adviser (Study 2b) but is qualified by features of the recommendation itself (Study 3a) as well as the type of combination recommended (Study 3b). Importantly, the authors find this effect to have important downstream consequences, as the increased perceptions of expertise that follow a combinatory recommendation improve consumers' attitudes both toward products included in the recommendation and toward subsequent recommendations made by the adviser (Study 4, Study 5). The real-world persuasive value of combinatory recommendations is also tested in a field study (Study 6) that explores the effect of combinatory recommendations on click-through rates of Instagram advertisements. [ABSTRACT FROM AUTHOR]

On January 1, 2017, Philadelphia implemented a beverage excise tax. The study's objective was to determine whether beverage advertising expenditures and the number of beverage ads purchased changed in Philadelphia compared to Baltimore because of this tax. Monthly beverage ad expenditures and the number of beverage ads purchased by brand from January 2016 through December 2019 were obtained. Ads were coded as being for taxed or not taxed beverages and analyzed in 2023. The primary outcomes were quarterly taxed beverage ad expenditures and number of ads purchased. A controlled interrupted time series design on segmented linear regression models was used. Models (aggregated and stratified by internet, spot TV, and local radio) compared whether levels and trends in the outcomes changed from pre- to post-tax in Philadelphia compared to Baltimore. There were no significant differences in taxed beverage advertising expenditures between Philadelphia and Baltimore for trends pretax, at implementation, or post-tax. There were 0.13 (95% CI: −0.25, −0.003) fewer quarterly taxed beverage ads purchased per 100 households in Philadelphia versus Baltimore at baseline. Among internet advertising, there were 0.42 (95% CI: −0.77, −0.06) fewer quarterly taxed beverage ads purchased per 100 households in Philadelphia versus Baltimore immediately post-tax. For spot TV ads, the percentage of taxed beverages ads purchased per quarter was greater at baseline in Philadelphia by 28.0 percentage points (95% CI: 1.9, 54.1). This study found little evidence of changes in mass media advertising on the examined platforms between 2016 and 2019 due to the Philadelphia beverage tax. [ABSTRACT FROM AUTHOR]

Sequential recommendation frames the recommendation task as a next‐item prediction problem, where the model is trained to predict the next item given a user behavior sequence. While recent research has made significant progress in developing advanced models for this task, there exists a notable gap in the exploration of subsequences and the predictability inherent in user behavior sequences. This oversight can lead models to recall inconsequential sequential patterns, adversely affecting recommendation quality. In this paper, we introduce a novel approach to augmenting sequential recommendation by integrating predictability awareness into subsequence modeling. Our method begins by discerning the predictability of target items; those easily predicted often align with the preceding subsequence, while those that are hard to predict typically indicate transitions to other subsequences. Leveraging this predictability information, we enhance the discovery of meaningful subsequences within individual user behavior sequences. Evaluation of four benchmark data sets using various state‐of‐the‐art sequential models illustrates the efficacy of our approach in enhancing recommendation performance. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]