Data-driven digital advertising: benefits and risks of online behavioral advertising

Abstract

PurposeThe research aims to investigate how individuals can be persuaded to make purchases through repeated and personalized messages. Specifically, the study proposes a framework of the potential benefits and risks of the online behavioral and data-driven digital advertising (OBA), which can help researchers and practitioners to better understand shopping behavior in the online retailing setting. In addition, the research focuses on the role of privacy concerns in affecting avoidance or adoption of OBA.Design/methodology/approachThe authors apply a structural equation modeling (SEM) approach with partial least square (PLS) regression method to test the research hypotheses through data coming from a structured questionnaire.FindingsOBA is a controversial type of advertising that activates opposing reactions on consumers' perspective. Specifically, acceptance of the OBA is positively related to relevance, usefulness and credibility of the personalized advertisements, while the intention to avoid personalized ads is strictly related to the privacy concerns. Consequently, OBA acceptance and avoidance affected the click intention on the ad and the behavioral intention that are decisive for the success of data-driven digital advertising.Originality/valuePrior research came up with complex theoretical frameworks that explain antecedents of OBA focusing only on ethical issues in marketing, on the effectiveness of a single OBA campaign or on how to create a successful advertising campaign. However, no study focuses on the intended or actual behavior of shoppers. Specifically, filling the gap in the existing literature, our research applies an SEM approach to identify both benefits and risks and the antecedents of the actual behavior of individuals in terms of actual purchases promoted by OBA.

Personalization in personalized marketing: Trends and ways forward

“…Specifically, a strategic diagram was curated using keywords to map the most prominent topics in the field across four quadrants on the criteria of centrality and impact. The instill customer confidence and improve their perception toward data sharing (Aguirre et al, 2015;Aiolfi et al, 2021;Kang & Namkung, 2019). To extend this emerging and important line of inquiry, future research is encouraged to explore new ways to promote self-disclosure and the sharing of personal data to customers for personalized marketing.…”

“…Third , personalization depends on the accessibility and quality of customer data, but the sharing of personal data can raise vulnerability concerns among customers, which is known as the personalization–privacy paradox. Past researchers have recommended the explicit rather than implicit collection of customer data alongside trust‐building strategies and the promotion of personalization usefulness to instill customer confidence and improve their perception toward data sharing (Aguirre et al, 2015; Aiolfi et al, 2021; Kang & Namkung, 2019). To extend this emerging and important line of inquiry, future research is encouraged to explore new ways to promote self‐disclosure and the sharing of personal data to customers for personalized marketing.…”

Evaluation Expediency of Eco-Friendly Advertising Formats for Different Generation Based on Spanish Advertising Experts

“…(2021) [35] x Alonso-Mosquera and Sánchez-Martínez (2011) [36] x Álvarez (2020) [37] x Fernández-Camacho (2020) [44] x x…”

“…On the one hand, thanks to online behavioral advertising, the users receive advertisements that match their interests and needs. On the other hand, their privacy is at risk since this technique monitors their behavior [35]. Following the European legislation, Spain deployed a broad legislative framework around this form of advertising to protect users' privacy.…”

Research on the Influence Mechanism of Consumers’ Perceived Risk on the Advertising Avoidance Behavior of Online Targeted Advertising

“…In 2020, the online advertising market reached 766.6 billion Yuan, up 18.6% year-on-year; online advertising accounted for 87.2% of all advertising revenue; and 76.6% of advertisers increased their online advertising spending ( iResearch, 2021 ). Online advertising employs user datasets to infer user demographics, habits and preferences, thereby enabling advertising to be targeted more precisely ( Wijenayake and Pathirana, 2019 ; Aiolfi et al, 2021 ; Li and Yin, 2021 ). Targeting consumers is also regarded as the essential difference between online and traditional advertising ( Goldfarb, 2014 ).…”