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Submission window: May 31, 2022 – Aug 31, 2022

Article type to select when submitting: AI and Advertising

MGE: Chen, Huan huanchen@jou.ufl.edu

In recent years, we have been witnessing dramatic changes in the field of advertising industry and academia in terms of meaning of advertising, media landscape, academic discipline, and scholarship focus (Nelson et al., 2017). Specifically, the emerging new technology of artificial intelligence (AI) has significantly shaped the industry, produced an in-depth impact on every aspect of the advertising process (Rogers, 2021), and created a new ecology of advertising businesses and operations (Li, 2019). Thus, new theories and innovative methods are in a dire need for us to better understand today’s advertising. Previous research suggests that multidisciplinary perspectives and interdisciplinary research are one of the effective ways to encourage breakthroughs and advance knowledge (Rotfeld & Taylor, 2009; Rogers, 2021).

Aboelela et al. (2006) defined interdisciplinary research as “any study or group of studies undertaken by scholars from two or more distinct scientific disciplines. The research is based upon a conceptual model that links or integrates theoretical frameworks from those disciplines, uses study design and methodology that is not limited to any one field, and requires the use of perspectives and skills of the involved disciplines throughout multiple phases of the research process” (p.341). Interdisciplinary research can resolve real-world issues, tackle complex problems, bring comprehensive theory-based perspectives, reduce one-dimensional evaluations, and offer thorough understandings of effective practices (Hyllegard et al., 2012). While many advertising scholars call for more interdisciplinary research, only limited numbers of research have been conducted in advertising using the interdisciplinary approach (for example Hovland, 2015; Hyllegard et al., 2012).

The lack of interdisciplinary approaches to advertising research has hindered the advancement of the advertising field. In health communication and marketing research, it is not uncommon to see interdisciplinary research that has adopted state-of-the-art methods like BERT models and transformers (Vaswani et al., 2017) to analyze social media opinion, topics, and influencer networks. However, the usage of earlier approaches, such as LDA topic modeling (Blei et al., 2003; Jelodar et al. 2019), VADER sentiment analysis (Hutto & Gilbert, 2014), CNN, Word2Vec, Glove (Pennington, Socher, & Manning, 2014), etc. is still scarce in advertising research methods. Meanwhile, technology advancement speeds up in areas of unsupervised learning, self-supervised learning, transfer learning, and explainable AI to improve the efficiency, accuracy, and interpretability of text and image processing for real-world usages including big data advertising research. Introduction of these state-of-the-art methods into advertising research requires interdisciplinary approach.

In addition, advertising research has to become a more meaningful co-creator of next-generation digital advertising by examining the social, ethical, and legal impacts of technology advancements, and providing future directions for technology innovation. The intertwined relationship between advertising and AI/data science is unprecedent. User’s data privacy, for example, has been one of the major criticisms of advertising research on behavioral targeting and online recommendation systems. Recently, Federal learning, Differential Privacy (DP) and graph neutral networks (GNN) have been examined to create online recommendation systems that provide the maximum protection of user’s privacy (Wu et al., 2021). Another area of exponential growth is online video advertising. Image segmentation and online action detection technologies are investigated to make video recommendation more accurate (Eun et al., 2020). All of these inventions have raised important research questions to advertising researchers. Interdisciplinary approach is needed to fully understand the societal implications of these newer technologies that may drastically change the digital advertising industry.

While we have begun to see formation of interdisciplinary research in advertising, multiple-layered difficulties and challenges exist during the interdisciplinary research process (Wang et al., 2021). In order to further advance the conceptualization and theorization of AI advertising and general advertising scholarship, we invite original manuscripts for this upcoming Special Issue of the Journal of Business Research dedicated to Interdisciplinary Research on AI and Advertising. Relevant topics and themes for this special issue might include, but are not limited to:

• Defining and examining the co-creation relationship between AI and advertising
• Examining the acceptance and perception of using AI/data science methods in studying advertising among advertising and/or AI/data science researchers and professionals.
• Interdisciplinary research that applies triangulated methods of using state-of-the-art AI/data science methods, and qualitative and/or quantitative communication research methods, to study advertising.
• Different stakeholders’ perception on AI and advertising, and potential legal and ethical concerns and issues
• New ecology of AI and advertising practices in industry
• Socio-technical assemblages, artifacts, and digital materiality within AI and advertising
• Dynamics and tensions of interdisciplinary research the field of AI and advertising
• Cross-cultural perspectives of AI and advertising

Guest Editors:

Huan Chen, Associate Professor, University of Florida, USA

Ye Wang, Associate Professor, University of Missouri – Kansas City, USA

Chen Lou, Assistant Professor, Nanyang Technological University, Singapore

Yugyung Lee, Professor, University of Missouri – Kansas City, USA

Manuscript submission information:

Papers targeting the special issue should be submitted through the , submission guidelines can be found at the journal’s 

Important dates

Submission system open: 30th May, 2022

Deadline for submissions: 31st August, 2022

Reference

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Eun, H., Moon, J., Park, J., Jung, C., & Kim, C. (2020). Learning to discriminate information for online action detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 809-818.

Hutto, C., & Gilbert, E. (2014, May). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the International AAAI Conference on Web and Social Media, 8(1).

Hovland, R. (2015). Alcoholic beverage advertising: An integrative interdisciplinary review,��Journal of Current Issues & Research in Advertising, 36(1), 88-114

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Li, H. (2019). Special section introduction: Artificial intelligence and advertising. Journal  of Advertising, 48(4), 333-337.

Nelson, M. R., Ham, C., & Ahn, R. (2017). Knowledge flows between advertising and other disciplines: A social exchange perspective. Journal of Advertising, 46(2), 309-332.

Pennington, J., Socher, R., & Manning, C. D. (2014, October). Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 1532-1543.

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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems. 5998-6008.

Wang, Y., Chen, H., Nagireddy, S., & Lee, Y. (2021). Social media advertising and big data at the intersection: A diversity perspective to interdisciplinary communication. ���ԲԳܲ�����AEJMC conference.

Werder, K. P., Nothhaft, H., Verčič, D., & Zerfass, A. (2018). Strategic communication as an emerging interdisciplinary paradigm. International Journal of Strategic Communication. 12(4), 333-351.

Wu, C., Wu, F., Cao, Y., Huang, Y., & Xie, X. (2021). Fedgnn: Federated graph neural network for privacy-preserving recommendation. arXiv preprint arXiv:2102.04925.