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Social media users are frequently targeted by commercial and political advertisements, which many perceive as a privacy threat. Although commercial and political targeting intend to influence users in different ways, comparative studies about user perceptions and responses to different kinds of targeting are scarce. Moreover, notwithstanding the legal obligation to make people aware of targeting in the European Union, current transparency cues do not immediately inform about the use of personal data. The present preregistered between-subjects online experiment (N = 1,248) investigates social media users' privacy perceptions and reactions to commercial and political targeting using an expanded protection motivation theory approach. Results revealed that more salient transparency cues can increase users' perception of ad manipulativeness, which in turn positively relates to privacy protection motivation. Perceiving privacy risks from targeting and protecting against it as effective positively relates to both protection motivation and desired legal regulation of targeting. Differences in perceptions of and reactions to commercial and political ads were marginal. [ABSTRACT FROM AUTHOR]

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

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

The 4th Davos Declaration was developed during the Global Allergy Forum in Davos which aimed to elevate the care of patients with atopic dermatitis (AD) by uniting experts and stakeholders. The forum addressed the high prevalence of AD, with a strategic focus on advancing research, treatment, and management to meet the evolving challenges in the field. This multidisciplinary forum brought together top leaders from research, clinical practice, policy, and patient advocacy to discuss the critical aspects of AD, including neuroimmunology, environmental factors, comorbidities, and breakthroughs in prevention, diagnosis, and treatment. The discussions were geared towards fostering a collaborative approach to integrate these advancements into practical, patient‐centric care. The forum underlined the mounting burden of AD, attributing it to significant environmental and lifestyle changes. It acknowledged the progress in understanding AD and in developing targeted therapies but recognized a gap in translating these innovations into clinical practice. Emphasis was placed on the need for enhanced awareness, education, and stakeholder engagement to address this gap effectively and to consider environmental and lifestyle factors in a comprehensive disease management strategy. The 4th Davos Declaration marks a significant milestone in the journey to improve care for people with AD. By promoting a holistic approach that combines research, education, and clinical application, the Forum sets a roadmap for stakeholders to collaborate to improve patient outcomes in AD, reflecting a commitment to adapt and respond to the dynamic challenges of AD in a changing world. [ABSTRACT FROM AUTHOR]

With the widespread adoption of mobile internet, users generate vast amounts of location-based data across multiple social networking platforms. This data is valuable for applications such as personalized recommendations and targeted advertising. Accurately identifying users across different platforms enhances understanding of user behavior and preferences. To address the complexity of cross-domain user identification caused by varying check-in frequencies and data precision differences, we propose HTEGAT, a hierarchical trajectory embedding-based graph attention network model. HTEGAT addresses these issues by combining an Encoder and a Trajectory Identification module. The Encoder module, by integrating self-attention mechanisms with LSTM, can effectively extract location point-level features and accurately capture trajectory transition features, thereby accurately characterizing hierarchical temporal trajectories. Trajectory Identification module introduces trajectory distance-neighbor relationships and constructs an adjacency matrix based on these relationships. By utilizing attention weight coefficients in a graph attention network to capture similarities between trajectories, this approach reduces identification complexity while addressing the issue of dataset sparsity. Experiments on two cross-domain Location-Based Social Network (LBSN) datasets demonstrate that HTEGAT achieves higher hit rates with lower time complexity. On the Foursquare-Twitter dataset, HTEGAT significantly improved hit rates, surpassing state-of-the-art methods. On the Instagram-Twitter dataset, HTEGAT consistently outperformed contemporary models, showcasing its effectiveness and superiority. [ABSTRACT FROM AUTHOR]

As contemporary churches continue to hire media teams to create and disseminate growing amounts of online content, questions of safety, security, and privacy are warranted. This study focuses on the case of a church in Ghana and explores the complex interplay between, ethical, security, and privacy issues in the online church. Data collection involved 170 survey respondents and eight (8) interview participants. The study found that convenience, flexibility, and device accessibility drove congregants’ reliance on the digital platforms of the church. However, there were discernible privacy and safety concerns like invasion of privacy, unauthorized access to personal information, potential for identity theft, and misuse of personal data for targeted advertising. While diverse perspectives were held by members of the media department of the church, there was a general lack of concern about the safety and security ramifications of disclosing personal sensitive information to audiences outside of the physical church as it was viewed as a potential point of inspiration for others. We recommend that churches develop guidelines around concerns raised by congregants to optimize congregants’ online security and safety, while also providing continuous awareness programs for congregants to protect their security and safety and be ethical users of digital platforms. [ABSTRACT FROM AUTHOR]

Oral Janus kinase inhibitors (JAKi), a class of advanced targeted systemic therapy, have demonstrated efficacy and safety in the treatment of moderate-to-severe atopic dermatitis (AD). Like other small molecules, oral JAKi have the potential for off-target effects including laboratory-related adverse events (AEs). Product labels for oral JAKi recommend an initial laboratory assessment and follow-up 4–12 weeks later to monitor for potential changes, based on evidence from clinical trials across therapeutic indications for oral JAKi, which may not reflect a population of moderate-to-severe AD patients typically seen in routine clinical practice. To address this gap, a panel of eight dermatologists with clinical and research experience with oral JAKi for the management of AD conducted a targeted review of the literature focused on key laboratory-related AEs associated with oral JAKi in the moderate-to-severe AD population. Based on the synthesis of evidence and informed opinion, a set of best practice statements related to fundamental standards of care and consensus recommendations on laboratory monitoring were suggested, and level of agreement was ascertained using a Likert scale from 0 to 100. There was a high level of agreement on three of the four suggested recommendations related to assessment and monitoring of key laboratory parameters and to dose reduction or switching in response to laboratory changes; there was a lower level of agreement related to the frequency of ongoing laboratory monitoring. Appropriate patient selection and laboratory assessment is an important strategy to mitigate the potential risks associated with oral JAKi when treating AD. [ABSTRACT FROM AUTHOR]

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

Historically, advertising allocation decisions have operated mostly at a macro level, comprising determination of the total budget followed by apportionment among media channels. Owing to the rapid and sustained rise of digital media, an additional decision now operates at a micro level, namely, which specific customers to target with advertising. In the macro case, optimal control theory provides a powerful framework for firm profit maximization, specifically allowing for ad response, cost per medium, and discount rate, all in the presence of multiple competing brands. However, optimal control theory has never been applied to the situation of microtargeting individual customers. Consequently, in this study the author shows how optimal control theory can be adapted for application to individual customers. The author estimates a multinomial logit model with individual-specific advertising response parameters. In turn, these parameters are used to determine the optimal number of exposures each customer should receive for each advertising medium. An empirical example demonstrates that using the optimal microtargeting method improves firm profits over existing ad scheduling methods by between 150% and 183%. [ABSTRACT FROM AUTHOR]

Atopic dermatitis (AD) is the most common chronic inflammatory skin disease worldwide. AD is a highly complex disease with different subtypes. Many elements of AD pathophysiology have been described, but if/how they interact with each other or which mechanisms are important in which patients is still unclear. Langerhans cells (LCs) are antigen‐presenting cells (APCs) in the epidermis. Depending on the context, they can act either pro‐ or anti‐inflammatory. Many different studies have investigated LCs in the context of AD and found them to be connected to all major mechanisms of AD pathophysiology. As APCs, LCs recruit other immune cells and shape the immune response, especially adaptive immunity via polarization of T cells. As sentinel cells, LCs are primary sensors of the skin microbiome and are important for the decision of immunity versus tolerance. LCs are also involved with the integrity of the skin barrier by influencing tight junctions. Finally, LCs are important cells in the neuro‐immune crosstalk in the skin. In this review, we provide an overview about the many different roles of LCs in AD. Understanding LCs might bring us closer to a more complete understanding of this highly complex disease. Potentially, modulating LCs might offer new options for targeted therapies for AD patients. [ABSTRACT FROM AUTHOR]

Wearable smart devices have gradually become indispensable devices in people's lives. Their security and privacy have gained increasing popularity among the public due to their ability to monitor and record various aspects of users' daily activities and health data. These devices maintain a wireless connection with mobile phones through periodic signal transmissions, which can be intercepted and analyzed by external observers. While these signal packets contain valuable information about the device owner, the identity of the actual user remains unknown. In this study, we propose two approaches to link wearable smart devices with users' mobile phones, which serve as electronic identities, to enable novel applications such as multi-device authentication and user-device graph construction for targeted advertising. To establish this linkage, we propose two approaches: a passive-sniffing-based linking approach and an active-interference-based linking approach, which solve the problem of sniffing Bluetooth Low Energy broadcast packets in two stages of Bluetooth Low Energy communication. Through experiments conducted across three scenarios, we demonstrate that seven wearable devices can be successfully linked with an accuracy rate exceeding 80%, with accuracy rates approaching 100% when a device is recorded more than 11 times. Additionally, we find that four wearable devices can be linked via an active-interference-based linking approach with an accuracy rate exceeding 70%. Our results highlight the potential of wearable devices and mobile phones as a means of establishing user identities and enabling the development of more sophisticated applications in the field of wearable technology. [ABSTRACT FROM AUTHOR]

A monopolist platform uses data to match heterogeneous consumers with multiproduct sellers. The consumers can purchase the products on the platform or search off the platform. The platform sells targeted ads to sellers that recommend their products to consumers and reveals information to consumers about their match values. The revenue-optimal mechanism is a managed advertising campaign that matches products and preferences efficiently. In equilibrium, sellers offer higher qualities at lower unit prices on than off platform. The platform exploits its information advantage to increase its bargaining power vis-à-vis the sellers. Finally, privacy-respecting data-governance rules can lead to welfare gains for consumers. (JEL D11, D42, D44, D82, D83, M37) [ABSTRACT FROM AUTHOR]

Data-driven political campaigning strategies often remain a black box for citizens; however, educational interventions provide a means to enhance understanding, conscious evaluations, and skills. In this context, we term this combination digital campaign competence (DCC). We conducted an online pre-registered experiment in Austria (N = 553) using a 2 × 2 between-subject design to compare intervention formats (reading a voter guide vs. playing a campaign game) and content framing (emphasizing risks vs. benefits of data-driven campaigning) plus a control condition. Results show no significant differences in framing on DCC. However, variations are observed among different formats, with the non-interactive voter guide proving to be the most effective one. Contrary to our expectations, the voter guide emphasizing the risks of data-driven political campaigning enhanced conceptual understanding levels, influenced evaluative perceptions, and aided skill development to detect highly targeted ads. We argue that innovative interventions do not always guarantee success in enhancing competencies. [ABSTRACT FROM AUTHOR]

Computational advertising has emerged as a new paradigm in the field, utilizing enhanced computing capabilities and mathematical models to create and deliver personalized advertising messages. It has the potential to overcome negative perceptions of digital advertising by synthesizing relevant messages based on consumer information. However, it must be cautious not to infringe on privacy and autonomy. Computational advertising also has the ability to combat the spread of disinformation and improve research methods and measurements in advertising. The special issue of the Journal of Advertising explores these opportunities and challenges, with articles covering topics such as sustainability messaging, privacy-preserving alternatives, credibility and trust, and the impact of researcher choices in data collection. Collaboration across disciplines and with industry, rethinking advertising's goals, and addressing methodological challenges are identified as important areas for future research. [Extracted from the article]

We document evidence that firms systematically increase specialized, locally targeted advertising following the firm being taken to trial in that given location, precisely following initiation of the suit. In particular, we use legal actions brought against publicly traded firms over the 20-year sample period that progress to trial between 1995 and 2014. In terms of magnitude, the increase is sizable: targeted local advertising increases by 23% (t = 4.37) following the suit. They focus their advertisement spikes specifically toward jury trials, and in fact specifically toward the most likely jury pool. Last, we document that these advertising spikes are associated with verdicts, increasing the probability of a favorable outcome. This paper was accepted by Tomasz Piskorski, finance. Funding: We gratefully acknowledge funding from the National Science Foundation [Grant SciSIP-1535813]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4896. [ABSTRACT FROM AUTHOR]

The blood brain barrier (BBB) limits the application of most therapeutic drugs for neurological diseases (NDs). Hybrid cell membrane‐coated nanoparticles derived from different cell types can mimic the surface properties and functionalities of the source cells, further enhancing their targeting precision and therapeutic efficacy. Neuroinflammation has been increasingly recognized as a critical factor in the pathogenesis of various NDs, especially Alzheimer's disease (AD). In this study, a novel cell membrane coating is designed by hybridizing the membrane from platelets and chemokine (C–C motif) receptor 2 (CCR2) cells are overexpressed to cross the BBB and target neuroinflammatory lesions. Past unsuccessful endeavors in AD drug development underscore the challenge of achieving favorable outcomes when utilizing single‐mechanism drugs.Two drugs with different mechanisms of actions into liposomes are successfully loaded to realize multitargeting treatment. In a transgenic mouse model for familial AD (5xFAD), the administration of these drug‐loaded hybrid cell membrane liposomes results in a significant reduction in amyloid plaque deposition, neuroinflammation, and cognitive impairments. Collectively, the hybrid cell membrane‐coated nanomaterials offer new opportunities for precise drug delivery and disease‐specific targeting, which represent a versatile platform for targeted therapy in AD. [ABSTRACT FROM AUTHOR]

This research investigates whether consumers anticipate any negative financial consequences arising from personalization practices, and examines how individuals respond to messages about its financial welfare implications, to see what factors increase blame toward firms for the impact their tactics can have on vulnerable populations. Results from a pilot study show that while privacy risks of personalization are highly accessible, few consider its potential to influence spending behavior. In addition, experimental studies reveal that individuals are more likely to blame firms for increases in teen consumer spending when they learn how online tactics limit and determine consumer choices, compared with when messages focus on firm's use of personal information. Additional studies also introduce a moderator for this effect. Overall, this research highlights limitations in consumers' ability to consider consequences of personalization, and provides guidance for advocates on how to better educate the public about potential issues associated with online marketing tactics. [ABSTRACT FROM AUTHOR]

Whilst data-driven strategies are allegedly prevalent in political campaigns, evidence regarding their actual effectiveness is scarce. This study investigates, from an individual perspective, the effect of issue congruency in political ads on immediate responses and voting behaviors. To reach our goal, we combined different types of data collection: mobile experience sampling method (mESM), panel survey, and content analysis. The combined approach allowed us to effectively study targeted ads within the cross-device and cross-platform environment. The results showed that voters perceive online political ads that are about a topic that they care about as more interesting, informative, and persuasive regardless of their partisanship. This positive ad perception subsequently leads to a higher probability of voting for the promoted party in the ad. We also found that an ad discussing a topic in line with the receiver's concerns positively affects the evaluation of the promoted party in the ad only when the party is already favored by the voter. Taken together, this study provides insights into the conditional effectiveness of data-driven strategies in political campaigns. [ABSTRACT FROM AUTHOR]

[Purpose] To conduct a comprehensive analysis of the essence, types of targeted advertising, and features of personal data protection in the context of using targeted advertising with the aim of aligning Ukrainian legislation with the requirements of the European Union. In particular, the study aims to analyze the nature of advertising and the role of targeted advertising in the modern world, identify the main advantages and disadvantages of using targeted advertising, characterize different types of targeted advertising (such as contextual advertising, advertising in social networks, actually targeted advertising, and retargeting), analyze problematic issues related to the protection of personal data of users during the use of targeting technologies in Ukraine, describe key regulatory documents in the field of personal data protection in the online space that is active in the European Union, and propose measures that can be adopted from Ukrainian legislation to enhance personal data protection during the use of targeted advertising. [Methodology/Approach/Design] The analysis and synthesis method facilitated the examination of the essence of advertising and targeted advertising. Also, this method was used to characterize the different types of targeted advertising. The formal-legal and comparative-legal methods were utilized to analyze the norms of the European Union related to the protection of personal data. Lastly, the scenario method was employed to propose measures that could be adopted from Ukrainian legislation to enhance personal data protection during the use of targeted advertising. These methodological choices aimed to provide a comprehensive and well-founded analysis of targeted advertising and the protection of personal data within the context of aligning Ukrainian legislation with European Union requirements. [Findings] Ensuring the protection of personal data during targeted advertising involves adhering to specific rules and principles. These include ensuring that the collection of information is done in a transparent and voluntary manner. Only relevant and necessary information should be collected, and it is important to guarantee the protection of the data once it has been obtained.

In the conducted research, the authors describe the features of brand promotion using Internet marketing tools. The key types of online advertising and their differences are given alongside with the data on the Internet advertising market and its specifics. The purpose of the study was to determine the features of brand promotion with the help of online advertising. To achieve it, the following objectives were set: to consider the available types of online advertising; to analyse the cases of 5 Russian companies; to determine the specifics of promoting companies through advertising on the Internet. The research methodology was based on the works on Internet marketing and brand promotion. The authors analysed 5 cases of large Russian companies related to Internet marketing: Aviasales, Yota, Dodo Pizza, Tinkoff Bank, Samokat. In particular, the effective influence marketing of Aviasales and its integration with Yandex were noted alongside with the use of contextual, targeted and unusual advertising by Yota to increase awareness. In addition, the features of managing SMM (social media marketing) by Dodo Pizza, effective advertising campaigns of Tinkoff Bank and commercials based on the research of the target audience of Samokat were highlighted. The following characteristics of brand promotion using online advertising were identified: interactivity, the ability to test advertising creatives, simplicity of calculating results, the need to create bright advertising campaigns in order to increase brand competitiveness.

Supply Chain Analytics (SCA) has emerged as a critical factor in determining the success of electronic commerce (E-commerce) companies. This review investigates the significant impact that SCA has had on the advertising landscape in the e-commerce industry. This article examines the complex correlation between electronic vendor (E-vendor) targeted advertising strategies and SCA by extensively reviewing critical scholarly works. By harnessing sophisticated analytics methodologies, organisations can acquire intricate understandings of consumer behaviour, cultivating heightened customer engagement and loyalty levels. Furthermore, the review highlights the significance of anticipating and resolving potential roadblocks that may arise during the deployment of SCA, such as financial consequences and external disruptions. Ultimately, the broad application of SCA facilitates customised advertisements.

Decades after data-driven consumer surveillance and targeted advertising emerged as the economic engine of the internet, data commodifcation remains controversial. The latest manifestation of its contested status comes in the form of a recent wave of more than a dozen state data protection statutes with a striking point of uniformity: a newly created right to opt out of data sales. But data sales as such aren’t economically important to businesses; further, property-like remedies to privacy problems have long and repeatedly been debunked by legal scholars, just as the likelihood of effcient privacy markets has been undercut by an array of experimental fndings from behavioral economics. So, why are data sales a dominant point of focus in recent state legislation? This work proposes a cultural hypothesis for the recent statutory and political focus on data sales, and explores this hypothesis with an experimental approach. Inspired by the taboo trade-offs literature, a branch of experimental psychology looking at how people handle morally uncomfortable transactions, this work describes two experiments that explore reactions to data commodifcation. The experimental results show that selling data is far more contested than selling a traditional commodity good, suggesting that selling data fts within the domain of a taboo transaction. Further, various potential modifcations to a data sale are tested, but in each case the initial resistance to the taboo transaction remains. The experimental results show a robust resistance to data commodifcation, suggesting that newly enacted state-level sales opt-out rights provide a culturally powerful balm to consumers. The results also suggest a new framework for analyzing economic measurements of privacy preferences, suggesting a new possibility for interpreting those fndings in light of the tabooness of data commodifcation. More broadly, the normative implications of the results suggest the need for culturally-responsive privacy reform while keeping an eye to the possibility for taboos to distort technology policy in ways that ultimately fail to serve consumer protection interests. [ABSTRACT FROM AUTHOR]

[Purpose] To conduct a comprehensive analysis of the essence, types of targeted advertising, and features of personal data protection in the context of using targeted advertising with the aim of aligning Ukrainian legislation with the requirements of the European Union. In particular, the study aims to analyze the nature of advertising and the role of targeted advertising in the modern world, identify the main advantages and disadvantages of using targeted advertising, characterize different types of targeted advertising (such as contextual advertising, advertising in social networks, actually targeted advertising, and retargeting), analyze problematic issues related to the protection of personal data of users during the use of targeting technologies in Ukraine, describe key regulatory documents in the field of personal data protection in the online space that is active in the European Union, and propose measures that can be adopted from Ukrainian legislation to enhance personal data protection during the use of targeted advertising. [Methodology/Approach/Design] The analysis and synthesis method facilitated the examination of the essence of advertising and targeted advertising. Also, this method was used to characterize the different types of targeted advertising. The formal-legal and comparative-legal methods were utilized to analyze the norms of the European Union related to the protection of personal data. Lastly, the scenario method was employed to propose measures that could be adopted from Ukrainian legislation to enhance personal data protection during the use of targeted advertising. These methodological choices aimed to provide a comprehensive and well-founded analysis of targeted advertising and the protection of personal data within the context of aligning Ukrainian legislation with European Union requirements. [Findings] Ensuring the protection of personal data during targeted advertising involves adhering to specific rules and principles. These include ensuring that the collection of information is done in a transparent and voluntary manner. Only relevant and necessary information should be collected, and it is important to guarantee the protection of the data once it has been obtained. [ABSTRACT FROM AUTHOR]

Consumer privacy information is used to target persuasive advertising in E-commerce, which raises consumer privacy concern. Consumer privacy concern is relative to consumer's intrinsic privacy concern and social concern over the application of consumer privacy information, known as the anchoring effect of privacy. The anchoring effect of privacy influences targeted persuasive advertising competition. With a game-theoretical model, this paper studies how asymmetric duopolistic firms compete with targeted persuasive advertising considering the anchoring effect of privacy. The results indicate that when the advantageous firm increases its advertising level, the advantageous firm gets more profit, and the disadvantageous firm gets less profit only if the anchoring effect of privacy is greater than zero. Otherwise, the disadvantageous firm gets more profit, but the advantageous firm gets less profit. Then, in equilibrium, the increase in the expected anchoring effect of privacy, expected consumer's intrinsic privacy concern, and advertising influence power reinforce the competition asymmetry. Whereas, the increase in social concern over the application of consumer privacy information weakens the competition asymmetry. [ABSTRACT FROM AUTHOR]

This article explores the effectiveness of applying auto encoders in the context of classical machine learning tasks, particularly classification tasks on tabular data. Significant emphasis is placed on a detailed comparison of this approach with other widely adopted methods in the field. As an applied task, the creation of a look-alike model for targeted advertising is pursued. Research into the requirements for this model reveals that conventional approaches to processing tabular data and model training cannot yield a solution that qualitatively meets all specified criteria. Evaluation is conducted on a closed dataset provided by a telecommunications operator. This de-identified dataset includes subscriber descriptions, their tariff plans, and mobile devices. The research goal is to assess non-traditional approaches from the domain of deep learning and neural networks in the context of classification tasks on tabular data and compare them with established methods in this context. The research findings demonstrate that the investigated approach yields a solution that surpasses traditional approaches across all specified criteria. Furthermore, the study provides insights into the universality of this approach, indicating its applicability in various domains for tabular data classification tasks. The ability to compress information into vector representations without significant loss and identify new hidden nonlinear dependencies in data enhances the practical value of the approach. Future research will address the formation of multiple autoencoders in different knowledge domains and their integration for specific tasks. Additionally, efforts will focus on identifying the most effective algorithm for finding similar subscribers based on the vector representations obtained from autoencoders. [ABSTRACT FROM AUTHOR]

Mobile app users are often exposed to a sequence of short-lived marketing interventions (e.g., ads) within each usage session. This study examines how an increase in the variety of ads shown in a session affects a user's response to the next ad. The authors leverage the quasi-experimental variation in ad assignment in their data and propose an empirical framework that accounts for different types of confounding to isolate the effects of a unit increase in variety. Across a series of models, the authors consistently show that an increase in ad variety in a session results in a higher response rate to the next ad: holding all else fixed, a unit increase in variety of the prior sequence of ads can increase the click-through rate on the next ad by approximately 13%. The authors then explore the underlying mechanism and document empirical evidence for an attention-based account. The article offers important managerial implications by identifying a source of interdependence across ad exposures that is often ignored in the design of advertising auctions. Furthermore, the attention-based mechanism suggests that platforms can incorporate real-time attention measures to help advertisers with targeting dynamics. [ABSTRACT FROM AUTHOR]

From the perspective of the user's psychological load, this study examined the cognitive mechanism by which privacy interpretation information affects the willingness to accept targeted advertising. Our findings provide a reference for enterprises seeking to develop and improve privacy interpretation information and reduce advertising avoidance behaviour. Based on information processing theory and cognitive load theory, this study experimentally collected volunteers' electroencephalography (EEG) data, interpreted the EEG results as representing emotions and cognitive load, and analysed the impact of the normativeness and intelligibility of privacy interpretation information on the willingness to accept targeted advertising. Intelligibility significantly affected this willingness. In the case of acceptable transparency, users were more inclined to avoid targeted advertising under low interpretability. Low interpretability caused them to expend more cognitive effort, which induced a larger N2 amplitude. In the case of unacceptable transparency, the opposite conclusion was drawn. In addition, privacy information with high interpretability was more likely to arouse users' emotions and induce a greater amplitude of late positive potential. [ABSTRACT FROM AUTHOR]

On many multisided app platforms, the supply-side monetizes their work with targeted advertising. The targeting of ads has raised concerns over user privacy and has led to calls for platform firms and regulators to bar this practice. Important for this debate is to understand the consequences that a ban on targeted advertising would have for app development. To inform, we exploit that Google, in 2019, banned targeted advertising in Android children's games. This setting represents an ideal real-world laboratory and permits a quasi-experimental research design. Our overall finding is that the ban on targeted advertising caused substantial app abandonment. The ban reduced the release of feature updates, particularly for games of young, undiversified, and advertisement-dependent firms. Only games of exceptionally high quality and demand showed an increase in development. Corroborating this picture, affected games were more likely to be delisted. Developers shifted their efforts toward their unaffected games and released fewer new games on average. Further tests substantiate that targeted advertising represented a crucial form of monetization for affected games and that the ban obliterated ad revenues used for app development. Our findings have several implications. To avoid a loss in app innovation, platform firms should consider implementing measures to reduce the burden on developers, especially by creating alternative monetization opportunities. Consumers and policymakers should be aware that targeted advertising plays a crucial role for app development and can use our estimates for designing policies. Thus, consumers' demand for privacy can conflict with platform firms' goal to foster app innovation. This paper was accepted by Hemant Bhargava, information systems. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4726. [ABSTRACT FROM AUTHOR]

Recent research suggests that targeted promotions have positive effects on targeted customers and negative effects on untargeted customers. However, the advent of market transparency increases the likelihood of untargeted customers' negative reactions reaching targeted customers. We examine this issue with four online experiments and find that for promotions targeted on infrequent customers, exposure (vs. no exposure) to negative word‐of‐mouth (WOM) from untargeted frequent customers does not affect targeted infrequent customers' current purchase intentions, but does reduce their intentions to remain with the marketer. Furthermore, we identify two moderators, such that when managers mention luck in the promotion delivery stage and when managers respond to negative online word‐of mouth (eWOM) from untargeted frequent customers and mention promotions targeted on frequent customers, they mitigate this negative effect. Our findings offer a new direction for the undesired effect of targeted promotions and identify the possibility that WOM diffusion reduces targeted customers' retention. We also provide valuable managerial insights on how to mitigate the negative effects of targeted promotions by framing the promotion as a result of luck, and by responding to and mentioning promotions targeted on frequent customers to negative eWOM from untargeted customers. [ABSTRACT FROM AUTHOR]

The method of targeting advertising on Internet sites based on a structured self-learning database is considered. The database accumulates data on previously accepted requests to display ads from a closed auction, data on participation in the auction and the results of displaying ads – the presence of a click and product installation. The base is structured by streams with features – site, place, price. Each such structural stream has statistical properties that are much simpler compared to the general ad impression stream, which makes it possible to predict the effectiveness of advertising. The selection of bidding requests only promising in terms of the result allows to reduce the cost of displaying advertising.

Communicators frequently make adjustments to accommodate receivers' characteristics. One strategy for accommodation is to enhance the relevance of communication for receivers. The current work uses information targeting —a communication strategy where information is disseminated to audiences believed to experience heightened risk for a health condition—to test whether and why targeting health information based on marginalized racial identities backfires. Online experimental findings from Black and White adults recruited via MTurk (Study 1) and Prolific Academic (Study 2) showed that Black Americans who received targeted (vs. nontargeted) health messages about HIV or flu reported decreased attention to the message and reduced trust in the message provider. White Americans did not differentially respond to targeting. Findings also demonstrated that (a) these negative consequences emerged for Black Americans due to social identity threat, and (b) these consequences predicted downstream cognitive and behavioral responses. Study 2 showed that these consequences replicated when the targeting manipulation signaled relevance directly via marginalized racial identities. Collectively, findings demonstrate that race-based targeting may lead to overaccommodation, thus precluding the expected benefits of relevance. [ABSTRACT FROM AUTHOR]

We study list price competition when firms can individually target consumer discounts (at a cost) afterwards, and we address recent privacy regulation (like the GDPR) allowing consumers to choose whether to opt-in to targeting. Targeted consumers receive poaching and retention discount offers. Equilibrium discount offers are in mixed strategies, but only two firms vie for each contested consumer and final profits on them are Bertrand-like. When targeting is unrestricted, firm list pricing resembles monopoly. For plausible demand conditions and if targeting costs are not too low, firms and consumers are worse off with unrestricted targeting than banning it. However, targeting induces higher (lower) list prices if demand is convex (concave), and either side of the market can benefit if list prices shift enough in its favour. Given the choice, consumers opt in only when expected discounts exceed privacy costs. Under empirically plausible conditions, opt-in choice makes all consumers better off. [ABSTRACT FROM AUTHOR]

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The article explores a method for targeting advertising on internet sites using a self-learning database. By analyzing data on previous ad requests and results, advertisers can predict the effectiveness of their ads and select bidding requests that are more likely to result in clicks and product installations, reducing costs. The article discusses various models and algorithms that can be used for prediction and bidding, as well as the importance of estimating bid effectiveness. The research aims to improve the efficiency of the advertising process and reduce costs. [Extracted from the article]

Copyright of lnternational Journal of Geography & Geography Education is the property of Marmara University 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.)

Data law and policy assume that harms to individuals can result only from personal data processing. Conversely, generation and use of non-personal data supposedly create new value while presenting no risk to individual interests or fundamental rights. Consequently, the law treats these two categories differently, constraining generation, use, and sharing of the former while incentivizing the latter. This article challenges this assumption. It proposes to divide data-related harms into two high-level categories: unwanted disclosure and detrimental use. It demonstrates how personal/non-personal data distinction prevents unwanted disclosure but fails to capture, and unintendedly enables, detrimental use of data. As a remedy, the article proposes a new concept – data about humans – and illustrates how it could advance data law and policy.

This paper explores targeted advertising, focusing on voice recognition technology and its potential effects on user privacy, assessing the advantages and challenges of such advertising methods. By emphasizing the need to balance marketing efficacy and user privacy protection, the analysis delves into the evolution, current state, and potential applications of voice recognition technology in targeted advertising while considering ethical and legal aspects. Security implications of overheard conversations are also addressed, highlighting the importance of user consent, awareness, and legal frameworks. Strategies for preserving user privacy are proposed, encompassing privacy-enhancing technologies, policy recommendations, consumer awareness, and education efforts. This paper contributes to the ongoing discourse on balancing advertising efficiency and user privacy in today's digital landscape. It provides a comprehensive analysis and valuable insights for stakeholders, including advertisers, policymakers, and users.

The rise of targeted advertising has led to frequent privacy data leaks, as advertisers are reluctant to share information to safeguard their interests. This has resulted in isolated data islands and model heterogeneity challenges. To address these issues, we have proposed a C-means clustering algorithm based on maximum average difference to improve the evaluation of the difference in distribution between local and global parameters. Additionally, we have introduced an innovative dynamic selection algorithm that leverages knowledge distillation and weight correction to reduce the impact of model heterogeneity. Our framework was tested on various datasets and its performance was evaluated using accuracy, loss, and AUC (area under the ROC curve) metrics. Results showed that the framework outperformed other models in terms of higher accuracy, lower loss, and better AUC while requiring the same computation time. Our research aims to provide a more reliable, controllable, and secure data sharing framework to enhance the efficiency and accuracy of targeted advertising. [ABSTRACT FROM AUTHOR]