The impact of privacy concern regarding online behavioral advertising in the context of social networking sites

Advertising on social networking sites is a much more complicated phenomenon than on traditional broadcasting media. Recent advances in Web technologies have made it possible for advertisers to track users’ online behaviours and browsing histories thus enabling advertisements to be customized and personalized for the individual. The growing privacy concerns have created a demand for providing users with choices and control related to online behavioural advertising (OBA). Research on privacy concerns related to OBA is still limited in IS literature. This research uses privacy calculus theory (PCT) to investigate the impact of users’ privacy concerns on OBA. This study will help to establish a new viewpoint and a deeper understanding of factors influencing privacy concerns regarding OBA in the context of social networking sites. Results are expected to support previous IS research with perceived entertainment, perceived informativeness and perceive intrusiveness will have positive influences on SNSs users\u27 acceptance-avoidance of OBA. Keywords Privacy Concerns, Social Networking Sites (SNSs), Online Behavioral Advertising (OBA), Privacy Calculus Theory (PCT)

Impression Effect vs. Click-through Effect: Mechanism Design of Online Advertising

Search advertising and display advertising are two major online advertising formats. Search advertising emphasizes ads’ click-through effect. Advertisers only pay when users click the link of their ads. Traditional display advertising emphasizes ads’ impression effect. Most display ads are charged based on the number of views on the ads. Considering that most online ads increase brand awareness (impression effect) and directly promote sales (click-through effect), the not-emphasized effect in search advertising or display advertising actually has a significant impact on the market outcome. However, these impacts have been largely ignored. In this paper, we examine various mechanisms in search and display advertising by considering both ads’ impression effect and click-through effect. Interestingly, we show a seesaw relationship between ads’ two effects in search advertising. The advertiser whose advertisement has a strong click-through effect benefits relatively less from its impression effect. In display advertising, the real-time-bidding (RTB) mechanism considers both ads’ impression effect and click-through effect. It allows a publisher to gain more surplus than that through a static auction. However, we show that RTB is associated with a high risk of market failure

Targeting of Online Advertising

The purpose of this study is to shed light on targeting in the online advertising industry and especially its recent development, and to find out what factors affect said development. The study includes a literature review, four case studies and an explorative small-scale study on how well targeting is currently done on YouTube. This study aims to answer the following research questions: 1) How is targeting of online ads currently done? 2) How effective is the targeting? 3) What are the limiting factors of targeting and why? 4) What things affect the future development of targeting

How to increase reach effeciency and effectiveness of MEO's Digital marketing campaings: programmatic buying & targeting techniques

Field lab: Business projectThis paper draws on two new trends in the digital marketing environment: programmatic buying and targeting techniques. These topics arose as the main responses to increase digital marketing campaigns’ efficiency and effectiveness, as requested during a Field Lab carried out at a leading Portuguese telecommunication and media services operator. Once the reader is introduced to the environment in which the project was performed and the reference literature, theoretical recommendations for a strategic implementation of these techniques, and a practical example to increase the targeting efficiency, are provided, passing through the conclusions of four main research techniques that led to these choices

selected case studies from the choice factory

A inovação tecnológica e digital, o surgimento do marketing digital e a generalização da utilização de dados para fins comerciais têm permitido que o marketing comportamental orientado para os dados esteja progressivamente mais presente no sector da publicidade. Devido à vontade das empresas e profissionais de aumentar as suas vendas, os marketers e os publicitários apresentam técnicas publicitárias baseadas em conclusões provenientes da ciência comportamental que podem não ser tão inofensivas como parecem. Como tal, os consumidores podem vir a ser vítimas de publicidade personalizada, enganosa ou agressiva, através de, por exemplo, violações do RGPD ou mesmo da utilização de padrões obscuros, que podem constituir práticas comerciais desleais ao abrigo da DPCD. Como Vender Mais, de Richard Shotton, autor e consultor, é um livro em que o autor emprega preconceitos comportamentais para sugerir técnicas práticas de marketing com o objetivo de convencer os consumidores a comprar mais. Posto isto, esta dissertação analisa quatro dessas técnicas de marketing e publicidade, sugeridas por Richard Shotton, para inferir se são inofensivas para o consumidor ou se, de facto, poderão constituir práticas ilícitas ao abrigo da DPCD. A análise foi dividida em três partes. Em primeiro lugar, esta dissertação reconhece e faz um esboço geral dos recentes desenvolvimentos legislativos no quadro da UE em matéria de proteção do consumidor que podem prevenir eventuais abusos na área do marketing e publicidade, fornecendo, em simultâneo, uma análise do conceito de marketing comportamental, da sua influência e do entendimento dos marketers sobre este tema. Em segundo lugar, uma vez que, de acordo com orientações recentes fornecidas pela CE sobre a interpretação e aplicação da DPCD e da própria DPCD, há necessidade de efetuar apreciações caso a caso, para garantir a segurança jurídica, esta dissertação pretende fazêlo, analisando as técnicas fornecidas no livro Como Vender Mais, à luz do contexto legal comunitário existente e explicando como algumas destas práticas comerciais poderiam ser consideradas desleais e, por isso, proibidas ao abrigo da DPCD. Finalmente, são feitos alguns comentários acerca das declarações de Richard Shotton sobre a ética aplicada à utilização da ciência comportamental para influenciar consumidores.Technological and digital innovation, the emergence of digital marketing and the widespread of data use for commercial purposes have allowed data-driven behavioural marketing to be progressively more present in the advertising sector. Due to businesses’ demand to sell more, marketers and advertisers come up with advertising techniques based on behavioural science findings that may not be as harmless as them seem. Hence, consumers may fall victim to over personalised, misleading or aggressive advertisements, for instance through violations of the GDPR or even the use of dark patterns, that may constitute unfair commercial practices under the UCPD. The Choice Factory, by Richard Shotton, bestselling author and consultant, is a book in which the author employs behavioural biases to suggest practical marketing techniques to sway consumers into buying more. Therefore, this dissertation analyses four of those marketing and adverting techniques, suggested by Richard Shotton, to further explore if they are harmless to the consumer or if, in fact, they are unlawful under the UCPD. The analysis is divided in three parts. Firstly, this dissertation acknowledges and makes an overview of recent developments in the EU’s legal framework regarding consumer protection that could prevent eventual marketing and advertising abuses towards consumers, while also providing an overview of behavioural marketing, its influence and the marketers’ perspective of it. Secondly, since according to recent Guidance provided by the EC on the interpretation and application of the UCPD and the UCPD itself, case-by-case assessments are required to ensure legal certainty, this dissertation intends to do precisely that, analysing the techniques provided in The Choice Factory in light of the existing EU’s legal framework and explaining how some of these commercial practices could be considered unfair and, therefore, prohibited under the UCPD. Lastly, some comments are made on the statements by Richard Shotton on the ethics regarding using behavioural science to influence consumers

Don’t Take It Personally: The Effect of Explicit Targeting in Advertising Personalization

Firms increasingly use consumers’ information to personalize their communication. Personalized advertisements, targeted based on users’ past behavior, offer users relevant product information that fits their preferences. In this study, we investigate the implications of explicit targeting, making the underlying targeting mechanism explicit to consumers, and ad message framing, in terms of utilitarian or hedonic product benefits. In a large-scale field experiment in which we run a campaign for a mobile application, we show that explicit targeting reduces advertising effectiveness pointing towards increased consumer privacy concerns. While utilitarian ad messages reinforce the negative effect of explicit targeting, the use of hedonic ad messages alleviates such a negative effect. Our study contributes to IS literature on advertising personalization and the personalization privacy paradox. We provide practical insights for firms that can be used in the design and implementation of personalized advertising campaigns

Using Machine Learning Models to Investigate Consumer Attitudes toward Online Behavioral Advertising

CC BY-NC-ND 4.0The technique of online behavioral advertising (OBA) is a strategy that has been widely used in the last decade by businesses and advertisers to deliver targeted advertising messages to internet users. It is done by utilizing technology to record the habits of online shoppers, including their searches and the content they visit. Users who browse the internet or use social media view advertisements relevant to their interests, recent searches, and location. We study Twitter users’ attitudes about targeted ads using five different machine learning models in this research, applying the CRISP-DM framework. Our primary focus is to develop a benchmark Twitter sentiment dataset related to targeted ads and implement highly accurate machine learning algorithms to predict tweet text sentiments when discussing targeted ads. The machine learning algorithms used are Logistic Regression, Random Forest, Multinomial Naïve Bayes, Multi-Layer Perceptron, and Decision Tree. We use accuracy, precision, recall, and the F1 measure to evaluate their performance. Logistic Regression using the content-based method provides the utmost accuracy of 0.88. We propose a model that allows real-time consumer attitude research regarding retargeting ads. The results show that logistic regression is the most accurate method for predicting customer responses to OBA campaigns and that retargeting and OBA often cause negative feelings in consumers