Hasil untuk "Advertising"

Robert H. Ducoffe

of the overall performance of the industry or an industry category from a consumer point of view. This article is divided into four main sections. In the first, an overview of the literature is presented and selected causes and consequences of advertising value are discussed. In the second section, the focus is on the Web and why advertising in this new medium has the potential to offer consumers greater value. The third section presents results from a consumer intercept survey and the final section raises a number of implications and applications that stem from this research.

D. Vakratsas, T. Ambler

E. Boyland, S. Nolan, B. Kelly et al.

Dildar Ali, Abishek Salaria, Ansh Jasrotia et al.

Recently, out-of-home advertising has become a popular marketing technique, due to its higher return on investment. E-commerce houses approach the influence provider to achieve effective advertising through their tags (advertising content), influence demand, and budgets. The influence provider's goal will be to make proper tag allocations, meet the required influence demand within the budget constraint, and minimize total regret. We formalize this as a combinatorial optimization problem and refer to it as \textsc{Tag-specific Regret Minimization in Outdoor Advertising (TRMOA)}. We show that TRMOA is NP-hard and inapproximable within a constant factor. The regret model we consider is non-monotone and non-submodular, and the simple greedy approach is ineffective. We introduce a fairness-aware greedy round-robin approach that reduces regret with balanced allocation across advertisers. To improve, we also introduce randomized greedy and local search algorithms. We have experimented with all the methodologies using real-world trajectory and billboard datasets to show the effectiveness and efficiency of the solution methodologies.

N. Malhotra, T. Schaller, Ashutosh Patil

Jacob Erickson

Digital commerce thrives on advertising, with many of the largest technology companies relying on it as a significant source of revenue. However, in the context of information-seeking behavior, such as search, advertising may degrade the user experience by lowering search quality, misusing user data for inappropriate personalization, potentially misleading individuals, or even leading them toward harm. These challenges remain significant as conversational search technologies, such as ChatGPT, become widespread. This paper critically examines the future of advertising in conversational search, utilizing several speculative examples to illustrate the potential risks posed to users who seek guidance on sensitive topics. Additionally, it provides an overview of the forms that advertising might take in this space and introduces the "fake friend dilemma," the idea that a conversational agent may exploit unaligned user trust to achieve other objectives. This study presents a provocative discussion on the future of online advertising in the space of conversational search and ends with a call to action.

Pieter Wolters, Frederik Zuiderveen Borgesius

What does the Digital Services Act (DSA) mean for online advertising? We describe and analyse the DSA rules that are most relevant for online advertising and adtech (advertising technology). We also highlight to what extent the DSA's advertising rules add something to the rules in the General Data Protection Regulation (GDPR) and the ePrivacy Directive. The DSA introduces several specific requirements for online advertising. First, the DSA imposes transparency requirements in relation to advertisements. Second, very large online platforms (VLOPs) should develop a publicly available repository with information about the ads they presented. Third, the DSA bans profiling-based advertising (behavioural advertising) if it uses sensitive data or if it targets children. Besides these specific provisions, the general rules of the DSA on illegal content also apply to advertising. Advertisements are a form of information, and thus subject to the general DSA rules. Moreover, we conclude that the DSA applies to some types of ad tech companies. For example, ad networks, companies that connect advertisers to publishers of apps and websites, should be considered platforms. Some ad networks may even qualify as VLOPs. Hence, ad networks must comply with the more general obligations in the DSA. The application of these general rules to advertisements and ad networks can have far-reaching effects that have been underexplored and deserve further research. We also show that certain aspects of the DSA are still unclear. For instance, we encourage the European Commission or regulators to clarify the concepts of 'online platform' and 'recipients' in the context of ad networks and other adtech companies.

Haoyang Feng, Yanjun Dai, Yuan Gao

Although large language models have demonstrated the potential for personalized advertising recommendations in experimental environments, in actual operations, how advertising recommendation systems can be combined with measures such as user privacy protection and data security is still an area worthy of in-depth discussion. To this end, this paper studies the personalized risks and regulatory strategies of large language models in digital advertising. This study first outlines the principles of Large Language Model (LLM), especially the self-attention mechanism based on the Transformer architecture, and how to enable the model to understand and generate natural language text. Then, the BERT (Bidirectional Encoder Representations from Transformers) model and the attention mechanism are combined to construct an algorithmic model for personalized advertising recommendations and user factor risk protection. The specific steps include: data collection and preprocessing, feature selection and construction, using large language models such as BERT for advertising semantic embedding, and ad recommendations based on user portraits. Then, local model training and data encryption are used to ensure the security of user privacy and avoid the leakage of personal data. This paper designs an experiment for personalized advertising recommendation based on a large language model of BERT and verifies it with real user data. The experimental results show that BERT-based advertising push can effectively improve the click-through rate and conversion rate of advertisements. At the same time, through local model training and privacy protection mechanisms, the risk of user privacy leakage can be reduced to a certain extent.

Madiha Touab

Le présent article porte sur le rapport entre le contenu de la publicité sociale et le recours aux éléments de la culture marocaine dans la ville de Safi. Il expose les différentes démarches utilisées contextuellement en vue de modifier les comportements des habitants et les inciter à adopter des conduites plus appropriées en matière socio-sanitaires. Une analyse qualitative et quantitative a été menée afin de faire ressortir les spécificités de l’usage de la culture dans la conception des messages diffusés, leur rétroaction et leur décodage auprès de la population de l’espace examiné.

Aline Sousa, Shawana Gabriela de Jesus Chagas, Luany Gabrielly Santos Oliveira et al.

This study presents a literature review based on the Veterinarian’s Code of Ethics, focusing on Chapter XIII – On Advertising and Scientific Works, with the aim of clarifying what is permitted and prohibited regarding veterinarians’ conduct on social media. The research highlights the growing presence of veterinary professionals in digital environments and identifies frequent ethical violations related to self-promotion and misinformation. By analyzing national regulations and comparing them with international ethical frameworks, the study emphasizes the need for clearer digital guidelines, stronger oversight by professional councils, and greater awareness among veterinarians about the responsible use of social media as an educational and ethical communication tool.

Jorge Álvarez, Juan Carlos Armenteros, Camilo Torrón et al.

Radio advertising remains an integral part of modern marketing strategies, with its appeal and potential for targeted reach undeniably effective. However, the dynamic nature of radio airtime and the rising trend of multiple radio spots necessitates an efficient system for monitoring advertisement broadcasts. This study investigates a novel automated radio advertisement detection technique incorporating advanced speech recognition and text classification algorithms. RadIA's approach surpasses traditional methods by eliminating the need for prior knowledge of the broadcast content. This contribution allows for detecting impromptu and newly introduced advertisements, providing a comprehensive solution for advertisement detection in radio broadcasting. Experimental results show that the resulting model, trained on carefully segmented and tagged text data, achieves an F1-macro score of 87.76 against a theoretical maximum of 89.33. This paper provides insights into the choice of hyperparameters and their impact on the model's performance. This study demonstrates its potential to ensure compliance with advertising broadcast contracts and offer competitive surveillance. This groundbreaking research could fundamentally change how radio advertising is monitored and open new doors for marketing optimization.

Youze Xue, Binghui Chen, Yifeng Geng et al.

Customized generative text-to-image models have the ability to produce images that closely resemble a given subject. However, in the context of generating advertising images for e-commerce scenarios, it is crucial that the generated subject's identity aligns perfectly with the product being advertised. In order to address the need for strictly-ID preserved advertising image generation, we have developed a Control-Net based customized image generation pipeline and have taken earring model advertising as an example. Our approach facilitates a seamless interaction between the earrings and the model's face, while ensuring that the identity of the earrings remains intact. Furthermore, to achieve a diverse and controllable display, we have proposed a multi-branch cross-attention architecture, which allows for control over the scale, pose, and appearance of the model, going beyond the limitations of text prompts. Our method manages to achieve fine-grained control of the generated model's face, resulting in controllable and captivating advertising effects.

Yingtai Xiao, Jian Du, Shikun Zhang et al.

Online advertising is a cornerstone of the Internet ecosystem, with advertising measurement playing a crucial role in optimizing efficiency. Ad measurement entails attributing desired behaviors, such as purchases, to ad exposures across various platforms, necessitating the collection of user activities across these platforms. As this practice faces increasing restrictions due to rising privacy concerns, safeguarding user privacy in this context is imperative. Our work is the first to formulate the real-world challenge of advertising measurement systems with real-time reporting of streaming data in advertising campaigns. We introduce AdsBPC, a novel user-level differential privacy protection scheme for online advertising measurement results. This approach optimizes global noise power and results in a non-identically distributed noise distribution that preserves differential privacy while enhancing measurement accuracy. Through experiments on both real-world advertising campaigns and synthetic datasets, AdsBPC achieves a 33% to 95% increase in accuracy over existing streaming DP mechanisms applied to advertising measurement. This highlights our method's effectiveness in achieving superior accuracy alongside a formal privacy guarantee, thereby advancing the state-of-the-art in privacy-preserving advertising measurement.

Łukiewska Katarzyna

The aim of the research is to determine the impact of innovations and Industry 4.0 solutions on the international competitiveness from the perspectives of representatives of food industry enterprises. The empirical layer used information collected on the basis of a survey using the CATI method conducted on a representative sample of representatives of food industry enterprises. Descriptive statistics, the Kruskal-Wallis test, Mann-Whitney test, multiple comparison test and box-plot plots were used to analyse the data. The study confirmed that implementing certain innovations and solutions, both intangible and tangible, is important for maintaining and improving competitiveness on the international market. This applies particularly innovative, modern ways of reaching the customer, developing innovative products, the use of IT systems and the use of innovative methods in advertising and promotion. The conclusions present direct implications for managers of food enterprises who formulate competitive strategies.

Till Speicher, Muhammad Ali, Giridhari Venkatadri et al.

Lisette de Vries, S. Gensler, P. Leeflang

Motahhare Eslami, S. Kumaran, Christian Sandvig et al.

L. Hudders, P. De Pauw, Veroline Cauberghe et al.

Jörg L. Spenkuch, D. Toniatti

Muhammad Ali, Angelica Goetzen, Alan Mislove et al.

Targeted advertising remains an important part of the free web browsing experience, where advertisers' targeting and personalization algorithms together find the most relevant audience for millions of ads every day. However, given the wide use of advertising, this also enables using ads as a vehicle for problematic content, such as scams or clickbait. Recent work that explores people's sentiments toward online ads, and the impacts of these ads on people's online experiences, has found evidence that online ads can indeed be problematic. Further, there is the potential for personalization to aid the delivery of such ads, even when the advertiser targets with low specificity. In this paper, we study Facebook -- one of the internet's largest ad platforms -- and investigate key gaps in our understanding of problematic online advertising: (a) What categories of ads do people find problematic? (b) Are there disparities in the distribution of problematic ads to viewers? and if so, (c) Who is responsible -- advertisers or advertising platforms? To answer these questions, we empirically measure a diverse sample of user experiences with Facebook ads via a 3-month longitudinal panel. We categorize over 32,000 ads collected from this panel ($n=132$); and survey participants' sentiments toward their own ads to identify four categories of problematic ads. Statistically modeling the distribution of problematic ads across demographics, we find that older people and minority groups are especially likely to be shown such ads. Further, given that 22% of problematic ads had no specific targeting from advertisers, we infer that ad delivery algorithms (advertising platforms themselves) played a significant role in the biased distribution of these ads.