Hasil untuk "Advertising"

Yuki Kobayashi, Koichi Toida

Extended Reality (XR) affords an enhanced sense of bodily presence that supports experiential modes of comprehension and affective engagement which exceed the possibilities of conventional information delivery. Nevertheless, the psychological processes engendered by XR, and the manner in which these processes inform subsequent behavioural intentions, remain only partially delineated. The present study addresses this issue within an applied context by comparing non-immersive 2D viewing advertising with immersive XR experiential advertising. We examined whether XR strengthens internal responses to a product, specifically perceived comprehension and empathy, and whether these responses, in turn, influence the behavioural outcome of purchase intention. A repeated-measures two-way ANOVA demonstrated a significant main effect of advertising modality, with XR yielding higher ratings on all evaluative dimensions. Mediation analysis further indicated that the elevation in purchase intention was mediated by empathy, whereas no significant mediating effect was observed for comprehension within the scope of this study. These findings suggest that immersive XR experiences augment empathic engagement with virtual products, and that this enhanced empathy plays a pivotal role in shaping subsequent behavioural intentions.

Sebastian Heineking, Wilhelm Pertsch, Ines Zelch et al.

Large language models (LLMs) enable a new form of advertising for retrieval-augmented generation (RAG) systems in which organic responses are blended with contextually relevant ads. The prospect of such "generated native ads" has sparked interest in whether they can be detected automatically. Existing datasets, however, do not reflect the diversity of advertising styles discussed in the marketing literature. In this paper, we (1) develop a taxonomy of advertising styles for LLMs, combining the style dimensions of explicitness and type of appeal, (2) simulate that advertisers may attempt to evade detection by changing their advertising style, and (3) evaluate a variety of ad-detection approaches with respect to their robustness under these changes. Expanding previous work on ad detection, we train models that use entity recognition to exactly locate an ad in an LLM response and find them to be both very effective at detecting responses with ads and largely robust to changes in the advertising style. Since ad blocking will be performed on low-resource end-user devices, we include lightweight models like random forests and SVMs in our evaluation. These models, however, are brittle under such changes, highlighting the need for further efficiency-oriented research for a practical approach to blocking of generated ads.

Daiki Mayumi, Hiroki Matsuda, Tetsuya Yokota et al.

This study presents the construction of the first digital twin utilizing non-identifiable television viewing history data. As the media landscape continues to evolve, understanding viewer behavior has become increasingly crucial. By simulating viewing behaviors based on real-time data, our approach enables the virtual reproduction of viewer preferences and behavior patterns, facilitating optimized advertising, content production, and marketing strategies. We propose a method for classifying user viewing tendencies using large-scale, non-identifiable data and develop a simulator based on these classifications. A detailed analysis of the data led to the extraction of tailored features for television viewing and the development of a highly accurate classification model. The weekday and weekend models achieved F1 scores of approximately 0.95, demonstrating their strong predictive capabilities. This study provides valuable insights into digital twin construction for television viewing and opens new avenues for data-driven media strategies.

Sophie C. Boerman, Sanne Kruikemeier, Frederik J. Zuiderveen Borgesius

Advertisers are increasingly monitoring people's online behavior and using the information collected to show people individually targeted advertisements. This phenomenon is called online behavioral advertising (OBA). Although advertisers can benefit from OBA, the practice also raises concerns about privacy. Therefore, OBA has received much attention from advertisers, consumers, policymakers, and scholars. Despite this attention, there is neither a strong definition of OBA nor a clear accumulation of empirical findings. This article defines OBA and provides an overview of the empirical findings by developing a framework that identifies and integrates all factors that can explain consumer responses toward OBA. The framework suggests that the outcomes of OBA are dependent on advertiser-controlled factors (e.g., the level of personalization) and consumer-controlled factors (e.g., knowledge and perceptions about OBA and individual characteristics). The article also overviews the theoretical positioning of OBA by placing the theories that are used to explain consumers' responses to OBA in our framework. Finally, we develop a research agenda and discuss implications for policymakers and advertisers.

Pierpaolo Della Monica, Ivan Visconti, Andrea Vitaletti et al.

Before a fair exchange takes place, there is typically an advertisement phase with the goal of increasing the appeal of possessing a digital asset while keeping it sufficiently hidden. Advertisement phases are implicit in mainstream definitions, and therefore are not explicitly integrated within fair-exchange protocols. In this work we give an explicit definition for such a fair exchange in a setting where parties communicate via broadcast messages only (i.e., no point-to-point connection between seller and buyer is needed). Next, we construct a fair-exchange protocol satisfying our new definition using zk-SNARKs and relying on mainstream decentralized platforms (i.e., a blockchain with smart contracts like Ethereum and a decentralized storage system like IPFS). Experimental results confirm the practical relevance of our decentralized approach, paving the road towards building decentralized marketplaces where users can, even anonymously, and without direct off-chain communications, effectively advertise and exchange their digital assets as part of a system of enhanced NFTs.

Zhen Zhang, Weian Li, Yuhan Wang et al.

Online advertisements are a primary revenue source for e-commerce platforms. Traditional advertising models are store-centric, selecting winning stores through auction mechanisms. Recently, a new approach known as joint advertising has emerged, which presents sponsored bundles combining one store and one brand in ad slots. Unlike traditional models, joint advertising allows platforms to collect payments from both brands and stores. However, each of these two advertising models appeals to distinct user groups, leading to low click-through rates when users encounter an undesirable advertising model. To address this limitation and enhance generality, we propose a novel advertising model called ''Hybrid Advertising''. In this model, each ad slot can be allocated to either an independent store or a bundle. To find the optimal auction mechanisms in hybrid advertising, while ensuring nearly dominant strategy incentive compatibility and individual rationality, we introduce the Hybrid Regret Network (HRegNet), a neural network architecture designed for this purpose. Extensive experiments on both synthetic and real-world data demonstrate that the mechanisms generated by HRegNet significantly improve platform revenue compared to established baseline methods.

Edwin Koyoo

City identity and branding have traditionally been shaped by iconic architecture, including monumental buildings and landmarks. However, recent studies have begun to explore city branding through corporate social responsibility (CSR) initiatives, particularly from a landscape perspective. There is limited research, particularly in the Global South, examining the role of CSR in the enhancement of open green spaces and its contribution to shaping urban landscape identity. This study investigates CSR-driven beautification projects in Kisumu City, Kenya, where corporate-funded initiatives have sought to improve urban landscapes through the landscaping of road islands and roundabouts, alongside broader urban renewal efforts. The article aims to document the spatial attributes of these CSR projects; assess their current status in terms of cleanliness, safety, and maintenance, as well as identify the challenges faced in implementing CSR-driven beautification efforts. The aim of these projects is not only to promote the city’s unique urban identity, but also to enhance the corporate image through landscape branding. A qualitative research methodology is employed, involving field observations, GIS mapping, and photography, complemented by purposive interviews. The findings reveal not only diverse landscape designs, including water features and sculptures, but also highlight significant issues with upkeep, safety concerns from street urchins, and the overall deterioration of some sites. These results are valuable for policymakers in city governments and corporations involved in CSR projects, offering insights into how such initiatives can better contribute to urban landscape identity, city branding, and the sustainability of urban beautification efforts.

Solovyeva Elena, Deorari Rajesh, Pushkarna Gaurav et al.

The average age of the participants in this research, which evaluated the effects of public display advertising in smart cities, was found to be 31.2 years, with a gender distribution that is balanced. When compared to a prior review, exposure and memory rates showed a 5% improvement in recall rates and a 12% increase in exposure length, suggesting increased advertising effectiveness and reach. Purchase intent increased by 11.8% and interaction levels improved by 10%, according to consumer engagement ratings. In addition, post-exposure attitudes demonstrated a 2.7% improvement in relevance and a 5.4% rise in likeability, highlighting a favorable opinion of public display advertising. These results contribute to the disciplines of urban informatics and advertising effectiveness by providing insightful information on the changing role of public display advertising in the setting of smart cities.

Hajar Ebrahimi, Fahimeh Babalhavaeji, Dariush Matlabi et al.

Objective: This study aims to investigate the factors influencing professional publishing and propose a model for professional book publishing in Iran. Method: The research was conducted using a survey method with a researcher-developed questionnaire. The statistical population consisted of professional publishing managers in the country, totaling 581 publishers, from which 231 were selected as a sample based on Cochran's formula. Ultimately, 211 questionnaires were completed and analyzed. Data processing was carried out using SPSS software, employing exploratory the factor analysis and multiple regression test. Findings: Based on exploratory factor analysis, nine factors have been identified as influential in professional publishing: the economics of publishing, the supply and display of publishing products, government support and backing, adherence to copyright, publishing evaluation and auditing, advertising, marketing and branding, publishing management, and the creation of publishing content. Additionally, five factors have been recognized as dimensions of professional publishing, which include technical elements, cultural and literary circles and centers, authors and audiences, electronic systems, and distribution and marketing elements. Ultimately, in the regression model, five independent variables were included in the equation due to their significance level being below .05. Conclusion: The findings of this research contribute to enhancing the awareness and understanding of audiences regarding publishing processes. They also assist publishers and industry managers in recognizing successful trends and existing challenges within the field, as well as in formulating supportive policies and strategies for publishing by relevant authorities.

Neo Ligaraba, Tinashe Chuchu, Brighton Nyagadza

The paper examines the influence of opt-in e-mail marketing on consumer behaviour. The study attempts to extend the Stimuli–Organism–Response (S–O–R) theory that has been broadly explored in consumer research. Following a critical review of the literature organisation approach, a hypothetical model has been proposed for this study, based on identified factors, such as, informational value, entertainment-based message content, layout, visual appeal, attitude toward e-mail advertising and intention towards the sender in the context of opt-in email marketing. Data were collected in South Africa through an online survey of 436 opt-in e-mail marketing subscribers. Structural equation modelling (SEM) was employed to measure the proposed hypotheses of the study. The research results suggest that even during a pandemic, e-mail marketers could employ certain features in promotional and informational e-mail marketing communication, particularly informational value, entertainment-based message content, layout, visual appeal, as a means to design their e-mail marketing messages and plan e-mail advertising campaigns. The findings of the study are intended to advance the e-mail marketing knowledge base to help marketers during a pandemic, such as COVID-19. The paper provides marketers with relevant insights on how to effectively engage with e-mail subscribers.

Shipra Agrawal, Yiding Feng, Wei Tang

We consider a novel pricing and advertising framework, where a seller not only sets product price but also designs flexible 'advertising schemes' to influence customers' valuation of the product. We impose no structural restriction on the seller's feasible advertising strategies and allow her to advertise the product by disclosing or concealing any information. Following the literature in information design, this fully flexible advertising can be modeled as the seller being able to choose any information policy that signals the product quality/characteristic to the customers. Customers observe the advertising signal and infer a Bayesian belief over the products. We aim to investigate two questions in this work: (1) What is the value of advertising? To what extent can advertising enhance a seller's revenue? (2) Without any apriori knowledge of the customers' demand function, how can a seller adaptively learn and optimize both pricing and advertising strategies using past purchase responses? To study the first question, we introduce and study the value of advertising - a revenue gap between using advertising vs not advertising, and we provide a crisp tight characterization for this notion for a broad family of problems. For the second question, we study the seller's dynamic pricing and advertising problem with demand uncertainty. Our main result for this question is a computationally efficient online algorithm that achieves an optimal $O(T^{2/3}(m\log T)^{1/3})$ regret rate when the valuation function is linear in the product quality. Here $m$ is the cardinality of the discrete product quality domain and $T$ is the time horizon. This result requires some mild regularity assumptions on the valuation function, but no Lipschitz or smoothness assumption on the customers' demand function. We also obtain several improved results for the widely considered special case of additive valuations.

Carl F. Mela, Jason M. T. Roos, Tulio Sousa

Direct buy advertisers procure advertising inventory at fixed rates from publishers and ad networks. Such advertisers face the complex task of choosing ads amongst myriad new publisher sites. We offer evidence that advertisers do not excel at making these choices. Instead, they try many sites before settling on a favored set, consistent with advertiser learning. We subsequently model advertiser demand for publisher inventory wherein advertisers learn about advertising efficacy across publishers' sites. Results suggest that advertisers spend considerable resources advertising on sites they eventually abandon -- in part because their prior beliefs about advertising efficacy on those sites are too optimistic. The median advertiser's expected CTR at a new site is 0.177\%, four times higher than the true median CTR of 0.045\%. We consider how an ad network's pooling of advertiser information remediates this problem. As ads with similar visual elements garner similar CTRs, the network's pooling of information enables advertisers to better predict ad performance at new sites. Counterfactual analyses indicate that gains from pooling advertiser information are substantial: over six months, we estimate a median advertiser welfare gain of \$3,621 (an 18.3\% increase) and a median revenue gain of \$13,558 (a 77.7\% increase) among the 20 largest publishers.

A. V. Veretyokhin

The digital advertising development features and prospects in the world and in the Russian Federation in particular have been considered. An approach to defining the advertising concept essence in the digital field has been presented. A summary of the scientific research results has made it possible to identify the industry’s growth constraints and its main development drivers, as well as define the importance and distinguishing features of modern advertising campaigns in the digital space. Based on analysis of current data from relevant organisations for the digital advertising market, an increase in growth rates has been identified overall by region and country, as well as for individual advertising segments.

Yan Long, Hongshan Zhao

Game theory has become an important tool to study the competition between oligopolistic enterprises. After combing the existing literature, it is found that there is no research combining two-stage game and nonlinear dynamics to analyze the competition between enterprises for advertising. Therefore, this paper establishes a two-stage game model to discuss the effect of the degree of firms’ advertising input on their profits. And the complexity of the system is analyzed using nonlinear dynamics. This paper analyzes and studies the dynamic game for two types of application network models: data transmission model and transportation network model. Under the time-gap ALOHA protocol, the noncooperative behavior of the insiders in the dynamic data transmission stochastic game is examined as well as the cooperative behavior. In this paper, the existence of Nash equilibrium and its solution algorithm are proved in the noncooperative case, and the “subgame consistency” of the cooperative solution (Shapley value) is discussed in the cooperative case, and the cooperative solution satisfying the subgame consistency is obtained by constructing the “allocation compensation procedure.” The cooperative solution is obtained by constructing the “allocation compensation procedure” to satisfy the subgame consistency. In this paper, we propose to classify the packets transmitted by the source nodes, and by changing the strategy of the source nodes at the states with different kinds of packets, we find that the equilibrium payment of the insider increases in the noncooperative game with the addition of the “wait” strategy. In the transportation dynamic network model, the problem of passenger flow distribution and the selection of service parameters of transportation companies are also studied, and a two-stage game theoretical model is proposed to solve the equilibrium price and optimal parameters under Wardrop’s criterion.

Miral-Sabry AlAshry

The purpose of this study is to investigate the effectiveness of the Egyptian Personal Data Protection Law No. 151 for 2020, as well as its implications for journalistic practice. More specifically, the focal point of this study was to explore how Egyptian journalists interpret the law and its implication for press freedom in Egypt. The underpinning theoretical framework was informed by the Authoritarian school of thought. Questionnaires were distributed to 199 journalists from both independent and semi-governmental representing thirteen official newspapers of Egypt, while in-depth interviews were done with (3) Editors, (4) journalists, and (3) human rights lawyers. The finding of the study indicated that the government placed restrictions on journalists by using Data Protection Law relating to the media. That law is negatively impacting journalists and media houses. It was clear from the findings that the journalists see the law as an obstacle to media independence, as it allows the government to exercise greater information control through digital policy and puts rules of regulation against journalists.

Tsuneaki Kenzaka, Shinsuke Yahata, Ken Goda et al.

We devised and assessed open recruitment of host families for medical student homestays in a rural area of Hyogo Prefecture, Japan, so that program organizers would not have to depend on professional and personal connections. The duration of the homestays was one night and two days, and they were conducted in August 2016, 2017, and 2018. The purpose of this community-based medical education program was to promote interactions between medical students and residents of Tamba area. The study asked one family member from each host family to complete a questionnaire after the homestay, and their experiences were evaluated in the study. The questionnaire results were analyzed using a visual analog scale (VAS; 0-100 mm). Thirty-three host families participated in the homestay program over three years. Results showed that VAS scores were high for enjoyment of homestays (VAS; 92.4 ± 13.0), continuation of the homestay program (91.7 ± 12.7), continuation of participation in the homestay program (89.2 ± 16.2), and desire for the homestay students to work in the area in the future (95.4 ± 6.3). The recruitment of host families through advertising was an efficient method for this community-based medical education homestay program. The results indicate that it is possible to attract more host families through open recruitment, which will contribute to the sustainability of the homestay program. Further research, including a follow-up of the students who participated and whether they chose a rural area or Tamba to practice is needed in the future. Since this is an ongoing program, further research in a similar format can be conducted in the future.

Lianghai Xiao, Yixing Zhao, Jiwei Chen

The online advertising management platform has become increasingly popular among e-commerce vendors/advertisers, offering a streamlined approach to reach target customers. Despite its advantages, configuring advertising strategies correctly remains a challenge for online vendors, particularly those with limited resources. Ineffective strategies often result in a surge of unproductive ``just looking'' clicks, leading to disproportionately high advertising expenses comparing to the growth of sales. In this paper, we present a novel profit-maximing strategy for targeting options of online advertising. The proposed model aims to find the optimal set of features to maximize the probability of converting targeted audiences into actual buyers. We address the optimization challenge by reformulating it as a multiple-choice knapsack problem (MCKP). We conduct an empirical study featuring real-world data from Tmall to show that our proposed method can effectively optimize the advertising strategy with budgetary constraints.

Wenjie Li, Qiaolin Xia, Junfeng Deng et al.

As an emerging secure learning paradigm in lever-aging cross-agency private data, vertical federatedlearning (VFL) is expected to improve advertising models by enabling the joint learning of complementary user attributes privately owned by the advertiser and the publisher. However, there are two key challenges in applying it to advertising systems: a) the limited scale of labeled overlapping samples, and b) the high cost of real-time cross-agency serving. In this paper, we propose a semi-supervised split distillation framework VFed-SSD to alleviate the two limitations. We identify that: i)there are massive unlabeled overlapped data available in advertising systems, and ii) we can keep a balance between model performance and inference cost by decomposing the federated model. Specifically, we develop a self-supervised task MatchedPair Detection (MPD) to exploit the vertically partitioned unlabeled data and propose the Split Knowledge Distillation (SplitKD) schema to avoid cross-agency serving. Empirical studies on three industrial datasets exhibit the effectiveness of ourmethods, with the median AUC over all datasets improved by 0.86% and 2.6% in the local andthe federated deployment mode respectively. Overall, our framework provides an efficient federation-enhanced solution for real-time display advertising with minimal deploying cost and significant performance lift.

Zhimeng Jiang, Kaixiong Zhou, Mi Zhang et al.

Real-time bidding (RTB) has become a major paradigm of display advertising. Each ad impression generated from a user visit is auctioned in real time, where demand-side platform (DSP) automatically provides bid price usually relying on the ad impression value estimation and the optimal bid price determination. However, the current bid strategy overlooks large randomness of the user behaviors (e.g., click) and the cost uncertainty caused by the auction competition. In this work, we explicitly factor in the uncertainty of estimated ad impression values and model the risk preference of a DSP under a specific state and market environment via a sequential decision process. Specifically, we propose a novel adaptive risk-aware bidding algorithm with budget constraint via reinforcement learning, which is the first to simultaneously consider estimation uncertainty and the dynamic risk tendency of a DSP. We theoretically unveil the intrinsic relation between the uncertainty and the risk tendency based on value at risk (VaR). Consequently, we propose two instantiations to model risk tendency, including an expert knowledge-based formulation embracing three essential properties and an adaptive learning method based on self-supervised reinforcement learning. We conduct extensive experiments on public datasets and show that the proposed framework outperforms state-of-the-art methods in practical settings.

Soichiro Murakami, Peinan Zhang, Sho Hoshino et al.

Writing an ad text that attracts people and persuades them to click or act is essential for the success of search engine advertising. Therefore, ad creators must consider various aspects of advertising appeals (A$^3$) such as the price, product features, and quality. However, products and services exhibit unique effective A$^3$ for different industries. In this work, we focus on exploring the effective A$^3$ for different industries with the aim of assisting the ad creation process. To this end, we created a dataset of advertising appeals and used an existing model that detects various aspects for ad texts. Our experiments demonstrated that different industries have their own effective A$^3$ and that the identification of the A$^3$ contributes to the estimation of advertising performance.