[Commlist] CfP: JCIRA Special Issue "Emerging Issues in Computational Advertising"

Journal of Current Issues in Advertising Research -- Special Issue 
"Emerging Issues in Computational Advertising"

Submission deadline for special issue: November 1, 2023 (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; Extended Abstract Submission Deadline: 
January 16, 2023https://2023gmc.imweb.me/ <https://2023gmc.imweb.me/>)

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.

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).

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

Submission information

All manuscripts submitted must not have been published, accepted for 
publication, or be currently under consideration elsewhere.
No payment from the authors of manuscripts accepted for publication will 
be required.

Direct inquiries to the Special Issue Editors

Su Jung Kim  -- Assistant Professor, Public Relations, Annenberg School 
for Communication and Journalism, University of Southern California 
((sujung.kim /at/ usc.edu) <mailto:(sujung.kim /at/ usc.edu)>)

Ewa Maslowska -- Assistant Professor, Charles H. Sandage Department of 
Advertising, College of Media, University of Illinois at 
Urbana-Champaign ((ehm /at/ uiuc.edu) <mailto:(ehm /at/ uiuc.edu)>)

Joanna Strycharz -- Assistant Professor, Amsterdam School of 
Communication Research (ASCoR), University of Amsterdam 
((J.Strycharz /at/ uva.nl) <mailto:(J.Strycharz /at/ uva.nl)>)

For More Information:

Journal of Current Issues and Research in 
Advertising:https://www.tandfonline.com/journals/ujci20 
<https://www.tandfonline.com/journals/ujci20>

2023 Global Marketing Conference at Seoul:https://2023gmc.imweb.me/ 
<https://2023gmc.imweb.me/>

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