CALL FOR PAPERS

2023 EMAC-GAMMA JOINT SYMPOSIUM at SEOUL

“ARTIFICIAL INTELLIGENCE (AI) IN MARKETING AND MANAGEMENT”

Submission Deadline: February 28, 2023

Conference Dates: July 20-23, 2023

Symposium Co-Chairs:

Roland T. Rust (University of Maryland) & Sue Ryung Chang (Yonsei University)

The European Marketing Academy and the Global Alliance of Marketing and Management Associations is pleased to announce the 2023 EMAC-GAMMA Joint Symposium on “Artificial Intelligence (AI) in Marketing and Management” to be held at the 2023 Global Marketing Conference at Seoul (https://2023gmc.imweb.me/). We are interested in providing opportunities to share research findings and conceptual papers that explore and investigate issues related to the practical application of artificial intelligence.

Drawing from Huang and Rust (JAMS 2021):

Artificial intelligence (AI) in marketing and management is currently gaining importance, due to increasing computing power, lower computing costs, the availability of big data, and the advance of machine learning algorithms and models. We see wide applications of AI in various areas of marketing. For example, Amazon.com’s Prime Air uses drones to automate shipping and delivery. Domino’s pizza is experimenting with autonomous cars and delivery robots to deliver pizza to the customer’s door. RedBalloon uses Albert’s AI marketing platform to discover and reach new customers. Macy’s On Call uses natural language processing to provide an in-store personal assistant to customers. Lexus uses IBM Watson to write its TV commercial scripts, “Driven by Intuition.” Affectiva, based on affective analytics, recognizes consumers’ emotions while they are watching commercials. Replika, a machine learning-based chatbot, provides emotional comfort to consumers by mimicking their styles of communication. It has even been asserted that AI will change the future of marketing substantially (Davenport et al. 2020; Rust 2020). However, academic marketing research to date provides insufficient guidance about how best to leverage the benefits of AI for marketing impact.

The academic literature on AI in marketing ad management may be sorted into four main types. These are (1) technical AI algorithms for solving specific marketing problems (e.g., Chung, Rust and Wedel 2009; Chung, Wedel, and Rust 2016; Dzyabura and Hauser 2011, 2019), (2) customers’ psychological reactions to AI (e.g., Luo et al. 2019; Mende et al. 2019), (3) effects of AI on jobs and society (e.g., Autor and Dorn 2013; Frey and Osborne 2017; Huang and Rust 2018; Huang, Rust and Maksimovic 2019), and (4) managerial and strategic issues related to AI (e.g., Fontaine, McCarthy, and Saleh 2019; Huang and Rust 2020).

The fourth literature stream, managerial issues related to AI, is currently dominated by consultants gravitating to the latest hot topic, and largely lacks a solid academic basis, albeit there are some recent studies trying to tackle strategic marketing issues. Examples include unstructured data for various areas of marketing (Balducci and Marinova 2018), analytics for consumer value in healthcare (Agarwal et al. 2020), machine learning prediction for mobile marketing personalization (Tong, Luo, and Xu 2020), in-store technology (e.g., robots, smart displays, or augmented reality) for convenience or social presence (Grewal et al. 2020), and AI for personalized customer engagement (Kumar et al. 2019).

This symposium aims to study the growing influence of artificial intelligence in marketing and management. Thus, we call for conceptual and empirical papers that offer answers to the following inclusive but not exclusive list of AI research areas:

Managerial issues in the application of AI

Applications of machine learning models (Ma and Sun 2020)

Natural language processing applications in marketing and management (e.g., Rust et al. 2021)

How consumers respond to AI (e.g., Longoni, Bonezzi and Morewedge 2019)

Diffusion of AI and how it affects the society

Chatbots

Robots

Societal impact of AI algorithms

Algorithmic bias (e.g., Lambrecht and Tucker 2019; Ukanwa and Rust 2020)

Feeling AI (Rust and Huang 2021)

AI as customer (e.g., Rust 1997; Huang and Rust forthcoming

*** Topics are only intended to be thought provokers; additional areas and perspectives are encouraged.

Submission to:

Please submit your paper to ‘2023 EMAC-GAMMA Joint Symposium’ through the submission system of the 2023 GMC at Seoul.

- Submission Link of the 2023 GMC at Seoul: https://2023gmc.imweb.me/22

- Submission Guidelines:

https://2023gmc.imweb.me/35/?q=YToyOntzOjEyOiJrZXl3b3JkX3R5cGUiO3M6MzoiYWxsIjtzOjQ6InBhZ2UiO2k6MTt9&bmode=view&idx=12780977&t=board

For More Information:

- Symposium Co-Chairs:

Roland T. Rust (University of Maryland),  rrust@umd.edu

Sue Ryung Chang (Yonsei University), suechang@yonsei.ac.kr

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

- Central Office, Global Alliance of Marketing & Management Associations

gammacentraloffice@gmail.com

References

Agarwal, R., Dugas, M., Gao, G., & Kannan, P. K. (2020). Emerging technologies and analytics for a new era of value-centered marketing in healthcare. Journal of the Academy of Marketing Science, 48(2), 9-23.

Autor, D. H., & Dorn, D. (2013). The growth of low-skill service jobs and the polarization of the US labor market. American Economic Review, 103(5), 1553-1597.

Balducci, B., & Marinova, D. (2018). Unstructured data in marketing. Journal of the Academy of Marketing Science, 46(4), 557-590.

Chung, T. S., Wedel, M., & Rust, R. T. (2016). Adaptive personalization using social networks. Journal of the Academy of Marketing Science, 44(1), 66-87.

Chung, T. S., Rust, R. T., & Wedel, M. (2009). My mobile music: An adaptive personalization system for digital audio players. Marketing Science, 28(1), 52-68.

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(2), 24-42.

Dzyabura, D., & Hauser, J. R. (2011). Active machine learning for consideration heuristics. Marketing Science, 30(5), 757-944.

Dzyabura, D., & Hauser, J. R. (2019). Recommending products when consumers learn their preferences weights. Marketing Science, 38(3), 365-541.

Fountaine, T., McCarthy, B., & Saleh, T. (2019). Building the AI-powered organization. Harvard Business Review, July-August, 63-73.

Frey, C. B., & Osborne, M. A. (2017). The future of employment: how susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114(January), 254-280.

Grewal, D., Noble, S. M., Roggeveen, A. L., & Nordfalt J. (2020). The future of in-store technology. Journal of the Academy of Marketing Science, 48(2), 96-113.

Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172.

Huang, M. H. & Rust, R. T. (2020). Engaged to a robot? The role of AI in service. Journal of Service Research, online first, DOI: 10.1177/1094670520902266.

Huang, M.H. & Rust, R. T. (2021) A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30-50.

Huang, M.H. & Rust, R.T. (forthcoming) AI as customer. Journal of Service Management.

Huang, M.H., Rust, R. T., & Maksimovic, V. (2019). The feeling economy: Managing in the next generation of artificial intelligence (AI). California Management Review, 61(4), 43-65.

Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135-155.

Lambrecht, A. & Tucker, C. (2019). Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science, 65(7), 2966-2981.

Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46, 629-650.

Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines versus humans: The impact of ai chatbot disclosure on customer purchases. Marketing Science, 38(6), 937-947.

Ma, L., & Sun, B. (2020). Machine learning and AI in marketing – connecting computing power to human insights. International Journal of Research in Marketing, in press, DOI: 10.1016/j.ijresmar.2020.04.005.

Mende, M., Scott, M. L., van Doorn, J., Grewal, D., & Shanks, I. (2019). Service robots rising: How humanoid robots influence service experiences and elicit compensatory consumer responses. Journal of Marketing Research, 56(4), 535-556.

Rust, R. T. (1997). The dawn of computer behavior: Interactive service marketers will find their customer is not human. Marketing Management, 6(fall), 31-34.

Rust, R. T. (2020). The future of marketing. International Journal of Research in Marketing, 37(1), 15-26.

Rust, R. T., & Huang, M. H. (2021). The Feeling Economy: How Artificial Intelligence Is Creating the Era of Empathy. Palgrave-Macmillan.

Rust, R. T., Rand, W., Huang, M. H., Stephen, A. T., Brooks, G., & Chabuk, T. (2020). Real-time brand reputation tracking using social media. working paper.

Tong, S., Luo, X., & Xu, B. (2020). Personalized mobile marketing strategies. Journal of the Academy of Marketing Science, 48(2), 64-78.

Ukanwa, K., & Rust, R. T. (2020). Discrimination in service. working paper.