Journal of Advertising (JA) (Special issue)

The submission deadline: November 30, 2024

Manuscripts are currently being solicited for a special issue of the Journal of Advertising (JA) dedicated to Contributions of Biometrics to Advertising Research.

BACKGROUND

Broadly speaking, biometric science uses physical, physiological, neurophysiological, or behavioral measures to observe second-by- second responses to stimuli and contexts. Biometrics can reveal implicit psychological processes that help to further understand audiences’ responses to advertising (Mundel et al., 2021). Advertising researchers have used biometrics measures for decades (e.g., Karslake 1940); employing techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), electrodermal activity (EDA), facial electromyography (fEMG), electrocardiography (ECG), facial expression analysis (FEA), and eye tracking (ET) to examine complex or subtle psychological processes that are unable to be captured via self-report (Beard et al., 2024; Bellman et al., 2016; Beuckels et al., 2021; Holiday et al., 2023; Lee et al., 2023; Pozhaliev et al., 2017; Read et al., 2018).

While the frequency with which biometrics have appeared in the literature has fluctuated, their value to the discipline of advertising has solidified in the past decade, particularly given recent advances in advertising theory and practice facilitated via these measures (e.g., Floyd and Weber 2020), and advances in technology – resulting in less expensive and more accessible biometric equipment. Increasingly, advertising researchers are finding new ways to employ biometric technologies, such as collecting data remotely through webcams (e.g., Mancini et al., 2023), or applying older biometric approaches to new contexts (e.g., using FEA to examine the facial displays of influencers; Holiday et al., 2023).

Scholarship in the first years of the recent wave of advertising biometric research was characterized by introducing these methods and describing their potential applications to the field (e.g., Plassman et al., 2015). Now, after a decade of contemporary biometrics advertising research, it is time to look back and assess the contributions of these methods to advertising theory and practice. Many of the theories that explain advertising effectiveness, such as the Elaboration Likelihood Model (ELM), were formulated using traditional, self-report measures such as thought listing (elaboration). Biometrics have the potential to disentangle, for example, different attentional processes proposed by the ELM as they occur over time (Cacioppo et al. 2012; MacInnis and Jaworski 1989). The unique advantages of biometrics data in furthering understanding of advertising theory and practice include pinpointing biological mechanisms, dissociating emotional and cognitive processes as they occur over time, measuring implicit responses, and improving behavioral predictions (Plassman et al., 2015). For this reason, despite claims that AI and computational research eliminate the need for human subjects in research (e.g., www.alpha.one), biometrics studies conducted by trained researchers in controlled laboratories or natural settings, using actual consumers, are essential to furthering understanding of the dynamics behind how advertising works.

For this Special Issue, we seek manuscripts addressing the contributions of biometrics to our understanding of advertising, both those that focus on results and theory-building (i.e., “what have we learned?”) and ones exploring the methods themselves (i.e., “what could we do?”). For example, has recent research using biometrics supported core advertising theories like the ELM? Or has biometrics research provided evidence for competing theories based on brain function, physiology, and evolutionary processes (e.g., Jones, 2019; Lang, 2014; Lee et al., 2020; Lee et al., 2023) – which are more appropriately tested by biometrics than self-report?

 Now that we can observe fleeting attention to advertising in real-time, compared to assessing attention post-hoc via self-report, what data-driven recommendations can researchers provide to advertisers for increasing the effectiveness of creative and media? How can AI improve biometric data analysis to better understand advertising theory? This Special Issue aims to investigate these questions (and others) while reflecting on the contributions of biometrics to advertising theory and practice, identifying knowledge gaps in the field, and devising new ways biometrics can address these and push the discipline forward.

POTENTIAL TOPICS

To contribute to our understanding of advertising, manuscripts considered for this special issue must connect biometric research solidly to theory and extant literature. While some processes that fall under the purview of computational social science may be applicable to biometrics research, authors employing computational approaches must also ground their hypotheses in theories of persuasion, advertising, and/or communication science. We welcome submissions with diverse approaches to relevant topics, including literature reviews, meta-analyses, and empirical research. Potential topics include:

• Overview of established or novel theoretical perspectives that inform advertising biometrics research,

• Core principles and issues in the application of advertising theory to biometrics research (how insights drawn from biometrics may differ from traditional measures, issues in conceptualization and operationalization of variables with biometric measures, the unique contributions and/or drawbacks of using biometrics measures to inform advertising theory, etc.),

• Literature reviews/meta-analyses of what has been learned from the current wave of biometrics research (e.g., theories supported),

• Conceptual literature reviews assessing the strengths and weaknesses of current theories,

• Identifying gaps in theory, and proposing extensions or new theory informed by biometrics data, along with hypotheses future research could test,

• Theoretically grounded systematic reviews of case studies and applied research identifying the most effective uses of biometrics by advertisers (e.g., ad testing, attention metrics),

• Methodological innovations particularly applicable to better understanding processes associated with the advertising creation process or message reception,

• Best practices and ethical guidelines for biometrics data collection and reporting to contribute to advertising theory (e.g., acknowledging/integrating the correlational nature of biometrics data into theory development, theoretically driven interpretation of biometrics data).

SUBMISSION GUIDELINES

Submissions should follow the manuscript format guidelines for the JA found at https://www.tandfonline.com/action/authorSubmission?show=instructions&journalCode=ujoa20. The word count should be 12,000 words maximum (including references, tables, figures, etc.).

The submission deadline is November 30, 2024.

All manuscripts should be submitted through the JA Submission Site between November 1, 2024 and November 30, 2024. The link to the submission site can be found at the JA’s website https://www.tandfonline.com/journals/ujoa20 (“Go to submission site”). Authors should select “Article Type” (e.g., research article, literature review, research note) on the first page of the submission website. On the second page, authors will be asked if this is for a specific special issue or article collection. Select “Yes” and select “SPECIAL ISSUE: Contributions of Biometrics to Advertising Research” from the drop-down menu. Please note in the cover letter that the submission is for the Special Issue on Contributions of Biometrics to Advertising Research.

• Articles will undergo blind peer review by at least two reviewers.

• The anticipated date for publication of the Special Issue is August 2025.

Please send questions about the Special Issue to the guest editors: Drs. Robert F. Potter, Steve Bellman, and Glenna L. Read at JAbiometrics@gmail.com.

REFERENCES

Beard, E. C., N. M. Henninger, and V. Venkatraman. 2024. “Making Ads Stick: Role of Metaphors in Improving Advertising Memory.” Journal of Advertising 53 (1):86-103. doi:10.1080/00913367.2022.2089302

Bellman, S., B. Wooley, and D. Varan. 2016. “Program–Ad Matching and Television Ad Effectiveness: A Reinquiry Using Facial Tracking Software.” Journal of Advertising 45 (1):72-7. doi:10.1080/00913367.2015.1085816

 Beuckels, E., L. Hudders, V. Cauberghe, K. Bombeke, W. Durnez, and J. Morton. 2021. “To Fit In or to Stand Out? An Eye-Tracking Study Investigating Online Banner Effectiveness in A Media Multitasking Context.” Journal of Advertising, 50 (4):461-78. doi:10.1080/00913367.2020.1870053

Cacioppo, J. T., G. G. Berntson, C. J. Norris, and J. K. Gollan. 2012. “The Evaluative Space Model.” In Handbook of Theories of Social Psychology, vol. 1, edited by P. A.M. Van Lange, A. W. Kruglanski, and E. T. Higgins, 50-72. London: Sage. doi:10.4135/9781446249215.n4

Floyd, K., and R. Weber. 2020. The Handbook of Communication Science and Biology. New York: Routledge. doi:10.4324/9781351235587

Holiday, S., J. L. Hayes, H. Park, Y. Lyu, and Y. Zhou. 2023. “A Multimodal Emotion Perspective on Social Media Influencer Marketing: The Effectiveness of Influencer Emotions, Network Size, and Branding on Consumer Brand Engagement Using Facial Expression and Linguistic Analysis.” Journal of Interactive Marketing 58 (4):414-39. doi:10.1177/10949968231171104

Jones, M. R.. 2018. Time Will Tell: A Theory of Dynamic Attending. New York: Oxford University Press. doi:10.1093/oso/9780190618216.001.0001,

Karslake, J. S. 1940. “The Purdue Eye-Camera: A Practical Apparatus for Studying the Attention Value of Advertisements.” Journal of Applied Psychology 24 (4):417-40. doi:10.1037/h0054171

Lang, A. (2014). “Dynamic Human-Centered Communication Systems Theory.” The Information Society, 30(1), 60-70. https://doi.org/10.1080/01972243.2013.856364

Lee. H., B. Bellana, and J. Chen. 2020. “What can Narratives Tell us about the Neural Bases of human Memory?” Current Opinion in Behavioral Sciences 32:111-19. doi:10.1016/j.cobeha.2020.02.007

Lee, S., J. Kim, G. L. Read, and S. Kim. 2023. “The Effects of In-Stream Video advertising on Ad Information Encoding: A Neurophysiological Study.” Journal of Advertising. doi:10.1080/00913367.2023.2222782

MacInnis, D. J. and B.J. Jaworski. 1989. “Information Processing from Advertisements: Toward an Integrative framework. Journal of Marketing, 53(4), 1-23. doi.org/10.1177/002224298905300401

Mancini, M., P. Cherubino, A. Martinez, A. Vozzi, S. Menicocci, S. Ferrara, A. Giorgi, P. Aricò, A. Trettel, and F. Babiloni. 2023. "What is Behind In-Stream Advertising on YouTube? A Remote Neuromarketing Study Employing Eye-Tracking and Facial Coding Techniques.” Brain Sciences 13 (10):1481. doi:10.3390/brainsci13101481

Mundel, J., G. Read., A. Almond, S. Alhabash, and J. Wilson. 2021. “Translating Consumer Neuroscience into Advertising Research and Education. American Academy of Advertising Conference Proceedings (online):88-91.

Noton D, and L. Stark. 1971. “Scanpaths in Eye Movements During Pattern Perception.” Science 171 (3968):308-11. doi:10.1126/science.171.3968.308

Plassmann, H., V. Venkatraman, S. Huettel, and C. Yoon. 2015. “Consumer Neuroscience: Applications, Challenges, and Possible Solutions.” Journal of Marketing Research 52 (4):427-35. 427-35. doi:10.1509/jmr.14.0048

Pozharliev, R., W. Verbeke, and R. P. Bagozzi. 2017. “Social Consumer Neuroscience: Neurophysiological Measures of Advertising Effectiveness in a Social Context.” Journal of Advertising 46 (3):351-62. doi:10.1080/00913367.2017.1343162

Read, G. L., I. I. Van Driel, and R. F. Potter. 2018. “Same-Sex Couples in Advertisements: An Investigation of the Role of Implicit Attitudes on Cognitive Processing and Evaluation.” Journal of Advertising 47 (2):182-97. doi:10.1080/00913367.2018.1452653