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The global importance of online advertising calls for a detailed understanding of consumer-specific responses to online ad repetitions. A key concern for advertisers is not only whether some consumers display degrees of "wearout" but also whether they can surpass a point at which additional exposures have a negative marginal effect: "weariness." The authors examine a large-scale advertising campaign aimed at driving viewers to a target website, which comprises more than 12,000 users across over 400 websites. These data are analyzed using a flexible discrete mixture specification that accommodates different response shapes over ad stock and timing and parcels ad viewers into response classes based on their internet usage metrics. The resulting classes display varying degrees of wearout, with one subgroup, accounting for about 24% of the sample, evincing weariness. The model also estimates differential publisher effectiveness, with the most effective publisher being nine times more effective than the one 26 places down. The authors demonstrate that the finding of weariness is robust to all the model's main components, with one key exception: heterogeneity in users' ad response. Analysis further suggests that an appropriate "profiling and capping" strategy can improve ad deployment by as much as 15% overall for these data. [ABSTRACT FROM AUTHOR]

We study an online display advertising planning problem in which advertisers' demands for ad exposures (impressions) of various types compete for slices of shared resources, and advertisers prefer to receive impressions that are evenly spread across the audience segments they target. We use the Gini coefficient measure and formulate an optimization problem that maximizes the spreading of impressions across targeted audience segments, while limiting demand shortfalls. First, we show how Gini-based metrics can be used to measure spreading that publishers of online advertising care about and how Lorenz curves can be used to visualize Gini-based spread so that managers can effectively monitor the performance of a publisher's ad delivery system. Second, we adapt an existing ad planning model to measure Gini-based spread across audience segments and compare and contrast our model to this baseline with respect to key properties and the structure of the solutions they produce. Third, we introduce a novel optimization-based decomposition scheme that efficiently solves our instances of the Gini-based problem up to 60 times faster than the commercial solver CPLEX directly solves a basic formulation. Finally, we present a number of model and algorithmic extensions, including (1) an online algorithm that mirrors the structure of our decomposition method to serve well-spread ads in real time, (2) a model extension that allows an aggregator buying impressions in an external market to allocate them to advertisers in a well-spread manner, and (3) a multiperiod model and decomposition method that spreads impressions across both audience segments and time.

Electronic devices consume energy both in the production and the use phase. Furthermore, the "hidden" impacts linked to their use are not frequently assessed and they depend on the behavior of the users, besides the servers and complex web networks. It must be underlined that many websites employ ads and trackers as part of their monetization strategy and, in order for online ads and trackers to work, they add an additional code to be executed on the users' machines, which in turn requires more processing power. Considering that the Internet had an estimated 4.9 billion users in 2021, the global energy and carbon impacts of online ads and trackers might be significant. To investigate this phenomenon, we designed a novel automated framework for bottom‐up estimation of greenhouse gas (GHG) emissions attributable to software using exclusively free and open source software. Our process involved the building of a random sample of global news websites which we visited with and without an ad‐blocker, each time collecting power usage in identical conditions. The gathered data were put into an ordinary least squares (OLS)‐based linear regression model, which showed that ads and trackers on news websites require on average an additional 6.13 W of power on personal computers. This result was then tuned to global environmental and technological parameters to estimate that in 2019, on the client side, ads and trackers on the news websites consumed 0.61 TWh of electrical energy, emitted 0.29 MtCO2eq of GHG, and cost all Internet users approximately 140 million USD (purchasing power parity) of electrical energy. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges. [ABSTRACT FROM AUTHOR]

Operating under both supply-side and demand-side uncertainties, a mobile-promotion platform conducts advertising campaigns for individual advertisers. Campaigns arrive dynamically over time, which is divided into seasons; each campaign requires the platform to deliver a target number of mobile impressions from a desired set of locations over a desired time interval. The platform fulfills these campaigns by procuring impressions from publishers, who supply advertising space on apps via real-time bidding on ad exchanges. Each location is characterized by its win curve, that is, the relationship between the bid price and the probability of winning an impression at that bid. The win curves at the various locations of interest are initially unknown to the platform, and it learns them on the fly based on the bids it places to win impressions and the realized outcomes. Each acquired impression is allocated to one of the ongoing campaigns. The platform's objective is to minimize its total cost (the amount spent in procuring impressions and the penalty incurred due to unmet targets of the campaigns) over the time horizon of interest. Our main result is a bidding and allocation policy for this problem. We show that our policy is the best possible (asymptotically tight) for the problem using the notion of regret under a policy, namely the difference between the expected total cost under that policy and the optimal cost for the clairvoyant problem (i.e., one in which the platform has full information about the win curves at all the locations in advance): The lower bound on the regret under any policy is of the order of the square root of the number of seasons, and the regret under our policy matches this lower bound. We demonstrate the performance of our policy through numerical experiments on a test bed of instances whose input parameters are based on our observations at a real-world mobile-promotion platform. This paper was accepted by Baris Ata, stochastic models and simulation. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2022.4441. [ABSTRACT FROM AUTHOR]

Online personalized advertising has been widely adopted in China in the recent years, leading to both positive and negative results. This study endeavors to examine the impact of perceived personalization of online advertising on consumers' attitudes toward advertising. A total of 472 questionnaires were administered and analyzed using Structural Equation Modeling. The results show that perceived personalization exerts a positive impact through perceived informativeness, and a negative impact through privacy concerns. However, the positive effect was determined to be stronger and thus perceived personalization had an overall positive impact. Additionally, the results showed that both effects were mediated by flow experience. The practical and theoretical implications of the findings were discussed. [ABSTRACT FROM AUTHOR]

Despite the popular use of online advertising by marketers, little is known about its role in the real estate market. This research aims to investigate how online advertising affects the sales of new houses and explores potential contingent factors and the underlying mechanism. Based on a rich secondary data set and econometric models, we find that online advertising increases the sales of new houses, and the effect is stronger with lower housing prices, higher residential incomes, and lower-tier cities. Additionally, the mechanism is that advertising generates an impact via processes, such as lowering housing prices, attracting more immigrants, and reducing emigration. Lastly, the spillover effect of online advertising may not exist in the housing market. We discuss theoretical contributions and important implications to practitioners and policy makers. [ABSTRACT FROM AUTHOR]

This research focuses on gender roles in advertising on YouTube using a content-analysis approach that addresses the unique functionalities and characteristics of online advertising formats and its consumers. The data was collected from Germany and Israel, based on 311 advertisements providing 473 central characters. The results provide evidence for stereotyping in online advertising. These findings are slightly different from previous studies on gender roles in traditional advertising. Stereotyping in advertising on YouTube mainly draws on physical characteristics (e.g. body shape, height), but not occupational status (e.g. professional). In contrast, in traditional advertising stereotyping draws more on occupational status. The degree of stereotyping based on role behaviours is similar in both traditional and social media advertising. The country-specific results show that gender roles in advertising show cross-cultural differences, defying the assumption of YouTube as a uniform globalized online platform for ads. These findings have implications for advertisers asking to improve the efficiency of online ads that use stereotypes and for researchers and regulators, underlining the importance of the ongoing political debate on what constitutes appropriate regulation. [ABSTRACT FROM AUTHOR]

Digital platforms such as Google, Facebook, and Microsoft are powerful firms, which benefit from having substantial resources and central positions in online industries. Although they are capable of defending their interests autonomously, they still get involved in and fund collective initiatives such as meta-organizations (MOs – i.e., organizations that have organizations as their members), particularly in the online advertising sector. In this article, which is based on an in-depth qualitative case study, we analyze what digital platforms gain from being members of MOs and, reciprocally, what the MOs gain from having these actors as members. We also investigate how these platforms act as MO members, paying attention to the existing literature on MOs. We focus on the Coalition for Better Ads MO, a collective initiative aiming to counter the rise of online ad-blocking. We show that digital platforms that operate in the online advertising market and as web browsers make a significant contribution to the MO. To this end, the MO delegates several organizational elements (i.e., monitoring and sanctioning) to these firms. This delegation reinforces the position of these members and helps them to change the organization of the whole market to their advantage as they control the advertising features (i.e., formats) of their rivals (publishers). The MO gains in credibility and efficiency, but, reciprocally, the MO gives legitimacy to the actions of the platforms, thereby reducing the risk of conflict with stakeholders.

This study presents a new type of advertisement—web-series ads. This undiscussed form of advertising is unique, as it presents an independent digital cultural field. By examining the main structural and narrative features of web-series ads and consumers’ responses, this research presents an integration model which explains how advertisements, as a commercial system, integrate into an independent new media system. Using thematic, narrative–structural, and textual analysis methods, this research shows how marketers convey messaging by introducing a free-from-constraints new online advertising media type. The use of polysystem theory offers a new theoretical perspective to media research and explains the findings. [ABSTRACT FROM AUTHOR]

Design plays a major role in online advertising. While specific aspects such as color or animation have been extensively studied, there is a surprising lack of comprehensive research on the overall impact of visual design and aesthetics. This study delved into the effects of varying levels of visual complexity in banner ads using the eye-tracking method. Out of 108 participants browsing test webpages with specially designed banners, data from 90 adequately measured data sets were used in the study. Notably, ads with a low level of visual complexity outperformed ads with a high level of visual complexity. While users noticed complex ads slightly faster (by 0.84%), they fixated on them significantly less (by 9.09%) and looked at them less frequently (by 4.79%). An implemented survey questionnaire examining the user perception of the banners reinforced the superiority of simple ads, as they were perceived as 4.40% more appealing in comparison. The study further delved into the correlation between objectively and subjectively evaluated data, exploring the credibility of subjective methods in the process. Considering our results and findings from other studies, it was evident that visually complex ads demanded more cognitive effort, could be more distracting, negatively impacted attention, could contribute to banner blindness and were perceived as less appealing.

Background and Aim: The right to health is among the most important human rights. This right can be influenced by certain factors such as the data induced through advertising. Therefore, legal systems have always taken approaches towards prohibiting or restricting the advertisements that endanger health. Throughout history, tobacco companies have advertised tobacco using various strategies, some of which have been subject to debate in terms of "rights" and at times, "ethics". Therefore, recognizing the advertising strategies in this field is of great importance. The purpose of conducting this research is to investigate whether some methods of tobacco advertising, in different eras, have resulted in "the violation of rights", or tobacco advertising is only an ethical issue. Methods: The present research is a narrative review. The data has been collected through library and internet sources, besides referring to legal documents, books and articles. Ethical Considerations: Throughout all the stages of writing this research, authenticity, honesty and integrity have been observed. Results: The analysis of tobacco companies' advertising strategies indicates that, with the aim of making more profit, these companies have managed to accumulate a significant portion of capital through quite known advertising strategies: targeting women and children; inducing the sense of freedom, power and independence and some other misleading methods. Conclusion: Prohibiting and restricting tobacco advertising is a crucial issue in protecting public health. Therefore, managing public affairs in the domain of health implies considering restrictions or prohibitions where necessary. Since advertising is a means of conveying specific messages and persuading the audience to achieve certain goals, it is essential to recognize the advertising mechanisms used by major tobacco companies. This knowledge can be used in future public actions such as determining precise prohibitions or reconsidering advertising restrictions. [ABSTRACT FROM AUTHOR]

The relevance of the research topic is that digital marketing 4.0 is a new type of marketing that has not been developed in the management and economic literature. Digital marketing 4.0 is developing in the era of new digital technologies, refers to a marketing method that uses the Internet, computer communication technologies and digital interactive media to achieve marketing goals. Digital marketing 4.0 uses advanced computer networking technology to seek the development of new markets and find new customers in the most efficient and cost-effective way. The term "digital marketing" first appeared in the 1990s, and since 2000, it has become more sophisticated and considered an effective way to create deeper and more interactive relationships with customers. The development of digital marketing continues to grow. The book "Marketing Revolution 4.0: From Traditional to Digital" emphasizes that digital marketing is another evolution of marketing in which demand management is the core of marketing. The purpose of the article is to analyze the theoretical and practical aspects of the formation and development of digital marketing in the era of marketing 3.0 and 4.0. Digital marketing 4.0 is the practice of promoting products and services through digital communication channels for timely, relevant, individual and cost-effective communication with consumers. Digital marketing includes many Internet marketing (network marketing) techniques and practices. Research result: It has been studied the evolution of the digital marketing concept from 1.0 to 4.0. The process of managing digital marketing as a complex social and communication system is revealed. The mechanism of digital marketing implementation is explained. The directions and strategies of digital marketing are analyzed. The typology of digital marketing is revealed. The future development of digital marketing is shown. Further research should be aimed at assessing the possibility and adaptation of digital marketing 4.0 developments for use in the digital economy and further development of domestic marketing in the digital age. [ABSTRACT FROM AUTHOR]

Copyright of Doxa Comunicación is the property of Fundacion Universitaria San Pablo - CEU 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.)

Businesses are increasingly delegating activities in the advertising process to dominant online advertising platforms. This delegation yields the ad platforms tremendous power, akin to the principal–agent dilemma discussed in economics. One of the major platforms is called Google Ads—this platform is the focal point of our study. Over the years, Google has made substantial changes to its platform's features, which, in turn, govern what is possible and what is not for the advertisers. These changes impact the advertisers' ability to act independently and make their own choices, referred to as human agency. To better understand this impact, we examined 362 industry news articles reporting changes in Google Ads from 2015 to 2020. The findings indicate that while most changes increase human agency, this effect is becoming weaker over time, driven by automation. To better understand advertisers' attitudes towards automation, we surveyed 193 advertisers with Google Ads experience. Contrary to the popular belief that marketers are afraid of being replaced by algorithms, we found this to not be the case. Even though most advertisers indicated appreciation for maintaining their human agency, they did not perceive this agency being violated by the ad platform. However, we did observe interesting variability among respondents, reflected in three computational advertising attitude types: tinkerers, instrumentalists, and shepherds. We discuss the implications for advertisers in terms of strategizing in the face of reduced human agency and for ad platforms in terms of designing features that advertisers perceive as fair. [ABSTRACT FROM AUTHOR]

This study develops a conceptual framework of advertising intrusiveness that extends previous research on the topic by examining both drivers and consequences. To achieve this, the work draws on in-depth interviews with 29 adult consumers who report experiencing intrusive advertising in their daily lives. Three drivers of advertising intrusiveness are identified - temporal disruption, visual disruption and flow disruption - along with three consequences - emotional response, behavioural response and cognitive response. From these drivers and consequences, a conceptual framework of advertising intrusiveness is developed based on ideas about psychological reactance. Based on this framework it is proposed that, while all advertisements might ultimately be perceived as intrusive, an advertisement’s characteristics, which are the identified subcomponents of the drivers of intrusiveness, exacerbate the resultant consequences. [ABSTRACT FROM AUTHOR]

In response to the challenge of replacing retiring workers with new talent, recruitment ads on recruitment platforms are becoming increasingly sophisticated and personalized. On some platforms, recruiters can now integrate users' names and photographs into recruitment ads. Previous research has frequently found that ad personalization improves advertising effectiveness; however, personalization is not always an effective tactic, as personalization effects can be nuanced depending on the consumer and the ad's personalized elements. A between-subjects experiment (N = 196) tested the effects of different types of recruitment ad personalization (integration of the recipient's name only or of both the recipient's name and photograph). This research found recruitment ads that were personalized with potential applicants' names and photographs increased organizational attractiveness for potential applicants who exhibited low involvement in the ad's message. In turn, their intentions to click on the ad and pursue the advertised job opportunity also increased. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Disciplines Economics & Administrative Scienves Studies is the property of International Journal of Disciplines in Economics & Administrative Sciences Studies 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.)

While real-time bidding brings a huge profit for online businesses, it also becomes a potential target for malicious purposes. In real-time bidding, the bid request traffic could be classified into two kinds: intentional and non-intentional. Intentional bid requests come from ordinal web users while non-intentional bid requests come from abnormal web users. From the perspective of a demand-side platform (DSP), the budget of advertisers should be used as effectively as possible by limiting non-intentional traffic. Therefore, it is essential to classify and predict these two kinds of bid request traffic. In this research, we propose a real-time filtering bid requests (RFBR) model to predict whether an incoming bid request is intentional or non-intentional from the DSP's viewpoint. Our model is built on three stages. In the first stage, we analyzed all potential attributes in the bid request scheme and figured out the relations between abnormal behaviors and their attributes; in the second stage, a classification model was built to classify normal and abnormal audiences by the extracted features and self-defined thresholds; in the third stage, a RFBR model was built to classify intentional and non-intentional bid requests. The experimental result shows that our system can effectively classify incoming bid requests. [ABSTRACT FROM AUTHOR]

In this work we consider a seller who sells an item via second-price auctions with a reserve price. By controlling the reserve price, the seller can influence the revenue from the auction, and in this paper, we propose a method for learning optimal reserve prices. We study a limited information setting where the probability distribution of the bids from bidders is unknown and the values of the bids are not revealed to the seller. Furthermore, we do not assume that the seller has access to a historical data set with bids. Our main contribution is a method that incorporates knowledge about the rules of second-price auctions into a multiarmed bandit framework for optimizing reserve prices in our limited information setting. The proposed method can be applied in both stationary and nonstationary environments. Experiments show that the proposed method outperforms state-of-the-art bandit algorithms. In stationary environments, our method outperforms these algorithms when the horizon is short and performs as good as they do for longer horizons. Our method is especially useful if there is a high number of potential reserve prices. In addition, our method adapts quickly to changing environments and outperforms state-of-the-art bandit algorithms designed for nonstationary environments. Summary of Contribution: A key challenge in online advertising is the pricing of advertisements in online auctions. The scope of our study is second-price auctions with a focus on the reserve price optimization problem from a seller's point of view. This problem is motivated by the real-life practice of small and medium-sized web publishers. However, the proposed solution approach is applicable to any seller who sells an item via second-price auctions and wants to optimize its reserve price during these auctions. Our solution approach is based on techniques from machine learning and operations research, and it would be beneficial especially for sellers who start the selling process without any historical data and can collect the data on the outcomes of the auctions while making reserve price decisions over time. History: Accepted by RamRamesh, Area Editor for Data Science & Machine Learning. Supplemental Material: The supplementary material is available at https://doi.org/10.1287/ijoc.2022.1199. [ABSTRACT FROM AUTHOR]

Scholars and practitioners are increasingly interested in the subject of integrated online marketing communications, i.e., the impacts of the sequence of online product information on customers' persuasion. In this study, we classify online reviews published by online interactive media into two types: online attribute-based review and online experiential-based review, and then examine the synergy caused by online advertising and the sequence of these two types of reviews. The matching of different product types and the sequence of online product information creates synergy based on the media congruence hypothesis and primacy effect. The results of two laboratory experiments (N1 = 160; N2 = 200) show that the matching between the sequence of online advertisement and attributebased review and product category affects message responses through cognitive responses; the matching between the sequence of online advertising and experience-based review and product category affects responses through cognitive responses or affective responses. The results complement and extend the relevant literature on synergy and matching effect and provide practical implications for marketers to develop and implement online communication plans. [ABSTRACT FROM AUTHOR]

With the rapid development of online advertising, click fraud is a serious issue for the internet market. Click fraud is a dishonest attempt to improve a website’s profit or deplete an advertiser’s budget by clicking on pay-per-click advertisements. For an extended period, this illegal act has a threat to the industrial sectors. As a result, these businesses hesitate to advertise their items on mobile apps and websites, as numerous groups attempt to take advantage of themes. To safely advertise their services and products online, a robust mechanism is needed for efficient click fraud detection. To tackle this issue, an ensemble architecture of machine learning and deep learning is proposed to detect click fraud in online advertisement campaigns. The proposed ensemble architecture consists of a Convolutional Neural Network (CNN), and a Bidirectional Long Short-Term Memory network (BiLSTM) is used to extract hidden features, while the Random Forest (RF) is used for classification. The main objective of the proposed research study is to develop a hybrid DL model for automatic feature extraction from clicks data and then process through an RF classifier into two classes, such as fraudulent and non-fraudulent clicks. Furthermore, a preprocessing module is developed to preprocess data by dealing with categorical attributes and imbalanced data to enhance the reliability and consistency of the clicks data. In addition, different evaluation criteria are used to evaluate and compare the performance of the proposed CNN-BiLSTM-RF with the ensemble and standalone models. The experimental results indicate that our ensemble architecture achieved the accuracy of 99.19 ± 0.08%, precision 99.89 ± 0.03%, sensitivity 98.50 ± 0.11%, F1-score 99.19 ± 0.08% and specificity 99.89 ± 0.03%. Furthermore, our proposed architecture produced superior results compared to other developed ensemble and conventional models. Moreover, our proposed ensemble architecture can be used as a safeguard against click fraud for pay-per-click advertising to facilitate industries for the safe and reliable promotion of their products.

One of the central challenges in online advertising is attribution, namely, assessing the contribution of individual advertiser actions such as emails, display ads, and search ads to eventual conversion. Several heuristics are used for attribution in practice; however, most do not have any formal justification. The main contribution in this work is to propose an axiomatic framework for attribution in online advertising. We show that the most common heuristics can be cast under the framework and illustrate how these may fail. We propose a novel attribution metric, which we refer to as counterfactual adjusted Shapley value (CASV), which inherits the desirable properties of the traditional Shapley value while overcoming its shortcomings in the online advertising context. We also propose a Markovian model for the user journey through the conversion funnel, in which ad actions may have disparate impacts at different stages. We use the Markovian model to compare our metric with commonly used metrics. Furthermore, under the Markovian model, we establish that the CASV metric coincides with an adjusted "unique-uniform" attribution scheme. This scheme is efficiently implementable and can be interpreted as a correction to the commonly used uniform attribution scheme. We supplement our theoretical developments with numerical experiments using a real-world large-scale data set. This paper was accepted by David Simchi-Levi, revenue management and market analytics. [ABSTRACT FROM AUTHOR]

Feature selection is a fundamental problem in online advertising, as features usually need to be purchased from third parties, and they are costly. Although many feature selection techniques can be used in online advertising and the general information systems (IS) domain, their performance is often context specific. Therefore, the literature of IS is suffering from a lack of adequate and generic methods. In this study, we address this issue by proposing a novel approach that employs ideas from the field of cooperative game theory. We derive a (continuous) second-order cone program that any convex programming solver can solve for determining the best subset of features. We show the efficacy of our proposed method on a real-life online advertising case study. We demonstrate that our proposed approach performs better in accuracy, precision, recall, and F-1 score than the best of the other approaches with much fewer features. Also, to illustrate that our method's benefits are not limited to the context of online advertising, we perform an extensive set of simulations and consider a well-established real-life data set drawn from the UCI Machine Learning Repository at the University of California, Irvine. Summary of Contribution: Selecting the best subset of features is an important problem in the context of online advertising and, more broadly, in the field of information systems because firms usually need to buy costly data to model and forecast economic outcomes. In this study, we propose a novel methodology for addressing this problem. The proposed method employs the concept of the Nash bargaining solution in cooperative game theory to create a good balance between maximizing the fit while minimizing the noise when selecting the best subset of features. We apply the method to a real-life online advertising case study, providing superior performance in predicting and interpreting the features. Moreover, we show that the proposed method applies to a broader range of feature selection problems. We conduct a comprehensive computational study on simulated regression data sets and other real-life classification data sets widely available in the machine learning domain. The result of these efforts indicates that our method is robust in terms of prediction accuracy by outperforming several state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

We generated advertisement creatives programmatically using deep neural networks. A landing page contains relevant text data, which can be used for generating advertisement creatives, i.e. ads. We treated the ad generation task as a text summarization problem and built a sequence to sequence model. In order to assess the validity of our approach, we conducted experiments on four datasets. Our empirical results showed that our model generated relevant ads on a template-based dataset with moderate hyperparameters. Training the model with more content increased the performance of the model, which we attributed to rigorous hyperparameter tune-up. The choice of word embedding used in the representation of the input altered the model's performance. When the source and the target shared common sequences during training, the model produced the best results. [ABSTRACT FROM AUTHOR]