Hasil untuk "Advertising"

Jennifer L. Harris, J. Bargh, K. Brownell

H. E. Krugman

J. Edell, M. Burke

J. Phelps, R. Lewis, Lynne J. Mobilio et al.

K. Bagwell

Avi Goldfarb, Catherine Tucker

We use data from a large-scale field experiment to explore what influences the effectiveness of online advertising. We find that matching an ad to website content and increasing an ad's obtrusiveness independently increase purchase intent. However, in combination, these two strategies are ineffective. Ads that match both website content and are obtrusive do worse at increasing purchase intent than ads that do only one or the other. This failure appears to be related to privacy concerns: the negative effect of combining targeting with obtrusiveness is strongest for people who refuse to give their income and for categories where privacy matters most. Our results suggest a possible explanation for the growing bifurcation in Internet advertising between highly targeted plain text ads and more visually striking but less targeted ads.

Gergely Nyilasy, Harsha Gangadharbatla, A. Paladino

Yoo-Jung Kim, Jinyoung Han

Yogesh Kumar Dwivedi

Sara Solarova, Matej Mosnar, Matus Tibensky et al.

Adolescents spend an increasing amount of their time in digital environments where their still-developing cognitive capacities leave them unable to recognize or resist commercial persuasion. Article 28(2) of the Digital Service Act (DSA) responds to this vulnerability by prohibiting profiling-based advertising to minors. However, the regulation's narrow definition of "advertisement" excludes current advertising practices including influencer marketing and promotional content that serve functionally equivalent commercial purposes. We provide the first empirical evidence of how this definitional gap operates in practice through an algorithmic audit of TikTok. Our approach deploys sock-puppet accounts simulating a pair of minor and adult users with distinct interest profiles. The content recommended to these users is automatically annotated, enabling systematic statistical analysis across four video categories: containing formal, disclosed, undisclosed and none advertisement; as well as advertisement topical relevance to user's interest. Our findings reveal a stark regulatory paradox. TikTok demonstrates formal compliance with Article 28(2) by shielding minors from profiled formal advertisements, yet both disclosed and undisclosed ads exhibit significant profiling aligned with user interests (5-8 times stronger than for adult formal advertising). The strongest profiling emerges within undisclosed commercial content, where brands/creators fail to label promotional content/paid partnership and the platform neither corrects this omission nor prevents its personalized delivery to minors. We argue that protecting minors requires expanding the regulatory definition of advertisement to encompass brand/influencer marketing and extending the Article 28(2) prohibition accordingly, ensuring that commercial content cannot circumvent protections merely by operating outside formal advertising channels.

D. Lou

M. Dehghani, M. Tumer

Zuowu Zheng, Ze Wang, Fan Yang et al.

Traditional online industrial advertising systems suffer from the limitations of multi-stage cascaded architectures, which often discard high-potential candidates prematurely and distribute decision logic across disconnected modules. While recent generative recommendation approaches provide end-to-end solutions, they fail to address critical advertising requirements of key components for real-world deployment, such as explicit bidding, creative selection, ad allocation, and payment computation. To bridge this gap, we introduce End-to-End Generative Advertising (EGA-V2), the first unified framework that holistically models user interests, point-of-interest (POI) and creative generation, ad allocation, and payment optimization within a single generative model. Our approach employs hierarchical tokenization and multi-token prediction to jointly generate POI recommendations and ad creatives, while a permutation-aware reward model and token-level bidding strategy ensure alignment with both user experiences and advertiser objectives. Additionally, we decouple allocation from payment using a differentiable ex-post regret minimization mechanism, guaranteeing approximate incentive compatibility at the POI level. Through extensive offline evaluations we demonstrate that EGA-V2 significantly outperforms traditional cascaded systems in both performance and practicality. Our results highlight its potential as a pioneering fully generative advertising solution, paving the way for next-generation industrial ad systems.

Hangcheng Zhao, Ron Berman

When online sellers use AI learning algorithms to automatically compete on e-commerce platforms, there is concern that they will learn to coordinate on higher than competitive prices. However, this concern was primarily raised in single-dimension price competition. We investigate whether this prediction holds when sellers make pricing and advertising decisions together, i.e., two-dimensional decisions. We analyze competition in multi-agent reinforcement learning, and use a large-scale dataset from Amazon.com to provide empirical evidence. We show that when consumers have high search costs, learning algorithms can coordinate on prices lower than competitive prices, facilitating a win-win-win for consumers, sellers, and platforms. This occurs because algorithms learn to coordinate on lower advertising bids, which lower advertising costs, leading to lower prices and enlarging demand on the platform. We also show that our results generalize to any learning algorithm that uses exploration of price and advertising bids. Consistent with our predictions, an empirical analysis shows that price levels exhibit a negative interaction between estimated consumer search costs and algorithm usage index. We analyze the platform's strategic response and find that reserve price adjustments will not increase platform profits, but commission adjustments will, while maintaining the beneficial outcomes for both sellers and consumers.

Jae Woo Shin

As digital media companies pursue sustainable revenue, AI-based strategies like personalized advertising and dynamic paywalls have become prevalent. These monetization models involve different forms of consumer data collection, raising distinct privacy concerns. This study investigates how digital news users perceive privacy trade-offs between these two AI-driven models. Based on Communication Privacy Management Theory and Privacy Calculus Theory, we conducted a survey of 336 Korean news consumers. Findings indicate that perceived control and risk significantly affect users’ willingness to disclose data. Moreover, users with different privacy orientations prefer different monetization models. Those favoring dynamic paywalls tend to be more privacy-sensitive and show a higher willingness to pay for personalized, ad-free content. While personalization benefits are broadly acknowledged, the effectiveness of privacy control mechanisms remains limited. These insights highlight the importance of ethical, user-centered AI monetization strategies in journalism and contribute to theoretical discussions around algorithmic personalization and digital news consumption.

Amanda Balmer, Annette Brömdal, Amy B. Mullens et al.

Abstract This exploratory study aimed to identify barriers and facilitators of a nurse-led satellite pilot clinic to improve sexually transmitted infection (STI) testing, treatment, and prevention in an Australian male correctional center. A descriptive qualitative research design was utilized to inform program evaluation. Qualitative data were collected from nurses via semi-structured interviews, and from incarcerated participants who attended the satellite clinic through open-ended satisfaction survey responses. Data were analyzed using deductive thematic analysis guided by the consolidated framework for implementation research (CFIR). Six major themes, aligned with the five CFIR domains were developed: (1) Meeting the needs of incarcerated people (outer setting domain); (2) Adaption based on needs; (implementation process domain); (3) Nursing staff motivation (individuals domain); (4) Incarcerated population’s desire to be healthy (individuals domain); (5) Shared vision (inner setting domain); and (6) Efficacy of clinics due to the satellite method (innovation domain). Barriers and facilitators raised by the two participant groups in this study identified a range of key recommendations: (1) Additional sexual health education; (2) Hiring appropriately skilled clinical nurses; (3) Collaborating with policy makers; (4) Continuing intra-organizational cooperation and adaptability; and (5) Additional days, advertising and modes of delivery. These recommendations highlight the requirement for these clinics to meet the needs of the incarcerated population.

Dvorovenko Olga

Children’s art schools use virtual accounts to promote their activities, teachers’ achievements, curriculum, services, and events. The author studied twelve accounts of children’s art schools located in the Irkutsk Region to evaluate the advertising options that virtual space offers to this category of education establishments. The research covered publications made in VKontakte social network between September 18 and October 18, 2024. It revealed a set of statistical indicators that demonstrated subscribers’ engagement and the most relevant topics, e.g., news, upcoming events, curriculum, local culture, etc. The most popular publications included information about teachers, students’ achievements, and art school projects. Most accounts owed their high engagement rates to diverse content and high subscribers’ activity. The communities of the Tulun children’s art school and children’s music school showed very high levels of engagement. The highest engagement levels belonged to the children’s art and music schools in Tulun. These schools used social nets to promote their education programs and broadcast achievements of theirstudents and teachers. The accounts with high engagement rates became involved in social media recommendation algorithms.

Kaichen Zhang, Zixuan Yuan, Hui Xiong

In recent years, Optimized Cost Per Click (OCPC) and Optimized Cost Per Mille (OCPM) have emerged as the most widely adopted pricing models in the online advertising industry. However, the existing literature has yet to identify the specific conditions under which these models outperform traditional pricing models like Cost Per Click (CPC) and Cost Per Action (CPA). To fill the gap, this paper builds an economic model that compares OCPC with CPC and CPA theoretically, which incorporates out-site scenarios and outside options as two key factors. Our analysis reveals that OCPC can effectively replace CPA by tackling the problem of advertisers strategically manipulating conversion reporting in out-site scenarios where conversions occur outside the advertising platform. Furthermore, OCPC exhibits the potential to surpass CPC in platform payoffs by providing higher advertiser payoffs and consequently attracting more advertisers. However, if advertisers have less competitive outside options and consistently stay in the focal platform, the platform may achieve higher payoffs using CPC. Our findings deliver valuable insights for online advertising platforms in selecting optimal pricing models, and provide recommendations for further enhancing their payoffs. To the best of our knowledge, this is the first study to analyze OCPC from an economic perspective. Moreover, our analysis can be applied to the OCPM model as well.

Fangru Lin, Jie Yuan, Zhiwei Chen et al.

Abstract Film and movie genres play a pivotal role in captivating relevant audiences across interactive multimedia platforms. With a focus on entertainment, streaming providers are increasingly prioritizing the automatic generation of movie genres within cloud-based media services. In service management, the integration of a hybrid convolutional network proves to be instrumental in effectively distinguishing between a diverse array of video genres. This classification process not only facilitates more refined recommendations and content filtering but also enables targeted advertising. Furthermore, given the frequent amalgamation of components from various genres in cinema, there arises a need for social media networks to incorporate real-time video classification mechanisms for accurate genre identification. In this study, we propose a novel architecture leveraging deep learning techniques for the detection and classification of genres in video films. Our approach entails the utilization of a bidirectional long- and short-term memory (BiLSTM) network, augmented with video descriptors extracted from EfficientNet-B7, an ImageNet pre-trained convolutional neural network (CNN) model. By employing BiLSTM, the network acquires robust video representations and proficiently categorizes movies into multiple genres. Evaluation on the LMTD dataset demonstrates the substantial improvement in the performance of the movie genre classifier system achieved by our proposed architecture. Notably, our approach achieves both computational efficiency and precision, outperforming even the most sophisticated models. Experimental results reveal that EfficientNet-BiLSTM achieves a precision rate of 93.5%. Furthermore, our proposed architecture attains state-of-the-art performance, as evidenced by its F1 score of 0.9012.

Diana MOTRENIUC

The aesthetic dimension in ecological advertising plays a crucial role in establishing an emotional connection between the audience and environmental issues, stimulating involvement and commitment to conservation efforts. The aesthetic function embedded in ecological advertising serves as a foundation for raising awareness and mobilizing the public regarding environmental issues. This aesthetic approach fosters deep emotional connections, motivating active involvement in environmental initiatives and significantly contributing to leaving a lasting impression and focusing attention on the ecological message conveyed. The use of visually captivating imagery, including landscapes and vibrant colors, grabs attention and sparks curiosity among viewers. Their distinctive style, together with origindesign elements, elevates them to an artistic level, enriching their impact and resonance. Ecological advertising draws inspiration from diverse aesthetic influences, similar to those found in fine art, with deliberate reinterpretations and realignments to serve its purpose. Adherence to fundamental design principles ensures impactful and memorable ecological advertising campaigns. By presenting aesthetic means, namely disturbing aspects of environmental degradation, ecological ads can evoke a strong emotional response among the audience, prompting them to take action or support initiatives for nature conservation. However, it is essential for such imagery to be used judiciously and accompanied by messages/texts that provide solutions or inspire hope and positive action in the face of environmental issues. We will observe how techniques like unity, harmony, and balance, along with intentional emphasis and contrast, enhance the effectiveness of ecological advertising in conveying its message to the public.