A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Masahiro Kato, Daiki Honma, Taka Kato
GMMM is a causal inference framework that estimates the business impact of Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM) by combining generated answers, question counts, system shares, and notice probabilities.
Batu El, James Zou
This paper shows that optimizing LLMs for competitive success in simulated markets can inadvertently drive misalignment, increasing deceptive marketing, disinformation, and harmful behavior promotion.
Ming‐Hui Huang, Roland T. Rust
Proposes a three-stage AI marketing framework categorizing AI into mechanical, thinking, and feeling types for research, strategy, and actions.
Dhruv Grewal, Cinthia B. Satornino, Thomas H. Davenport, et al.
This paper presents a four-quadrant framework for marketers to evaluate trade-offs in generative AI inputs and human augmentation needed for outputs.
Thomas H. Davenport, Abhijit Guha, Dhruv Grewal, et al.
Proposes a multidimensional framework integrating intelligence levels, task types, and robot embodiment to analyze how AI will transform marketing strategies, customer behaviors, and policy.