CFP: JCIRA on Emerging Issues in Computational Advertising

Journal of Current Issues and Research in Advertising – Special Issue

“Emerging Issues in Computational Advertising”

Extended Submission Deadline of Extended Abstract for the 2023 GMC at Seoul: February 28, 2023

The Emerging Issues in Computational Advertising Track of the 2023 Global Marketing Conference at Seoul is sponsored by the Journal of Current Issues and Research in Advertising. Selected papers from the track will be recommended for publication consideration by the Journal of Current Issues and Research in Advertising. All submissions from this track will still go through the regular review process of the Journal of Current Issues and Research in Advertising.

1. Definition and scope of computational advertising

The Journal of Current Issues and Research 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

4. Submission information

All manuscripts submitted must not have been published, accepted for publication, or be currently under consideration elsewhere.

  • Extended Submission Deadline for Extended Abstracts: February 28, 2023

Authors should submit their extended abstracts to the Co-Chairs of the track of Emerging Issues in Computational Advertising of the 2023 GMC at Seoul.

Track Chairs and the Guest Editors of this Special Issue: Su Jung Kim (University of Southern California),, Ewa Maslowska (University of Illinois at Urbana-Champaign),, Joanna Strycharz, (University of Amsterdam),

5. Direct inquiries to the Special Issue Editors

Su Jung Kim  – Assistant Professor, Public Relations, Annenberg School for Communication and Journalism, University of Southern California (

Ewa Maslowska – Assistant Professor, Charles H. Sandage Department of Advertising, College of Media, University of Illinois at Urbana-Champaign (

Joanna Strycharz – Assistant Professor, Amsterdam School of Communication Research (ASCoR), University of Amsterdam (

For More Information:

Journal of Current Issues and Research in Advertising:

2023 Global Marketing Conference at Seoul:


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Yang, Y., Yang, Y. C., Jansen, B. J., & Lalmas, M. (2017). Computational advertising: A paradigm shift for advertising and marketing?. IEEE Intelligent Systems, 32(3), 3-6.

Yun, J. T., Segjin, C. M., Pearson, S., Malthouse, E.C., Konstan, J. A., & Shankar, V. (2020). Challenges and future directions of computational advertising measurement systems. Journal of Advertising, 49(4), 446-458.