Journal of Current Issues & Research in Advertising

1. Definition and scope of computational advertising

The Journal of Current Issues in Advertising (JCIRA) is calling for articles that discuss emerging issues and advances in computational advertising. Over the last decade, computational advertising has been praised for replicating “what humans might do if they had the time to read Web pages to discern their content and find relevant ads among the millions available” (Essex, 2009, p. 16). Computational advertising has expanded to become “a broad, data driven advertising approach relying on or facilitated by enhanced computing capabilities, mathematical models/algorithms, and the technology infrastructure to create and deliver messages and monitor/surveil” individual behaviors (Huh & Malthouse, p. 1).

By handling massive data in real time, computational advertising quantifies consumer characteristics and experiences to personalize advertising messages, target media content, and simplify consumer decision making. Algorithms drive targeted content to maximize message frequency, reach, ROI, and lift.

The rapidly growing field of computational advertising involves numerous systems including information retrieval, behavioral analytics, and decision making (Yang et al., 2017) and is thus relevant for interdisciplinary research such as advertising, marketing, computer science, linguistics, and economics.

2. Issues in the advertising landscape

Beyond its use as a marketing tool, computational advertising can be socially influential. First, across platforms, consumers are inundated with disruptive and frustrating advertisements. Despite state-of-the-art digital ad targeting models, Millennials and Gen Zs particularly disparage digital advertising for being irrelevant, useless, and deceptive (Lineup, 2021). Nevertheless, by synthesizing relevant messages based on consumer and/or context information, computational advertising is potentially able to overcome negative perceptions.

Second, marketers and advertisers are widely disdained for providing disinformation. A NewsGuard and Comscore study of programmatic advertising found that brands spend billions on algorithms intended to provide advertisements that maximize engagement, but unfortunately often amplify misinformation (Eisenstat, 2019; Skibinski, 2022). Computational advertising, however, can enhance brand safety by identifying inappropriate or incorrect content and preventing brands from misplacing ads next to reputation-harming content. Furthermore, targeting techniques can be used to correct disinformation or create public service announcements that promote media literacy so that consumers learn about consequences associated with data breaches, algorithmic biases, or mis/disinformation.

Third, advertisers and researchers can potentially use innovative new computational methods to measure key interests such as attitudes and emotions. For example, affective computing examines emotions by analyzing online activities of thousands of individuals in natural settings (D’Mello et al., 2018). It can be used to detect, interpret, and respond to human emotions before, during, and after ad exposure. Consequently, affective computing could be used to overcome challenges such as response biases and sampling errors. Simultaneously, as abstract concepts, emotions and affect are difficult to link with appropriate indicators or to map with proxies (Roy et al., 2013). Despite multiple challenges, future developments will enable affective computing to better respond and adapt to emotional states.

Consumers are increasingly concerned about privacy violations, lost control over personal information (Auxier et al., 2019), and biases built into algorithms and targeted advertising (e.g., Hao, 2019; Kant, 2021). Advertising ethicists have called targeted advertising “one of the world’s most destructive trends” (Mahdawi, 2019) because computational methods can be used to predict individual personalities, needs, or emotional states and use those insights to drive political preferences. The Cambridge Analytica scandal particularly exposed personalized advertising as a prejudicial force in the 2016 U.S. Presidential Election and the Brexit referendum (e.g., Cadwalladr & Graham-Harrison, 2018; Grassegger & Krogerus, 2017). Can computational advertising be used ethically to create relevant messages without violating privacy or enhancing biases?

Finally, computational advertising struggles to establish its worth. Attribution modeling, long challenged for inaccuracy, has become increasingly difficult under new privacy regulations and settings. Authors such as Tim Hwang (2020) argue that digital advertising is ineffective. Indeed, effectiveness is difficult to establish (e.g., Edelman, 2020; Frederik & Martijn, 2019), but attribution modeling is expected to evolve in its capacity to create, execute, and evaluate advertising programs (Yun et al., 2020).

3. Potential topics for the special issue on emerging issues in computational advertising

This special issue will publish original, high-quality papers that examine the theoretical, methodological, ethical, or practical implications of computational advertising. Suggested topics are listed below, but we are open to other relevant themes regarding computational advertising:

• Definitions and measurements of concepts
• Computational advertising and its relation to disinformation
• Brand safety in the age of computational advertising
• Ethical issues related to computational advertising
• Consumer privacy in the age of computational advertising
• Authentic versus fake advertising
• Measurement issues in computational advertising
• Societal value of computational advertising
• Algorithmic synthesis of creatives
• Short-term behaviors versus long-term valuations
• Trust and its role in computational advertising

• Select the special issue title "Emerging Issues in Computational Advertising" when submitting your paper to ScholarOne.
• Purely conceptual papers are welcome as well as papers using any methodological approach including quantitative, qualitative, computational, and mixed methods.
• Authors from underrepresented regions are particularly encouraged to submit their work.
• Authors interested in publishing their work in this special issue are encouraged to submit their extended abstract to the special track of the 2023 Global Marketing Conference. Details can be found in the following page: https://2023gmc.imweb.me/index.

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