AI@CBS

Generative Artificial Intelligence (GenAI) tools based on Large Language Models (LLMs) are quickly reshaping how researchers conduct surveys and experiments. From reviewing the literature and designing instruments, to administering studies, coding data, and interpreting results, these tools offer substantial opportunities to improve research productivity and advance methodology. Yet with this potential comes a critical challenge: researchers often use these systems without fully understanding how they work.

The rise of low-cost artificial intelligence (AI) technologies offers significant potential for businesses globally, yet AI adoption remains uneven. What shapes this unequal adoption? While prior work attributes adoption patterns to demand-side factors including physical costs and complementary assets, we theorize that AI entrepreneurs' strategic choice to target specific markets creates both search and perceived-fit frictions for firms outside of those markets.

Language plays a crucial role in marketing, influencing outcomes such as consumer engagement and decision-making. Although prior research has extensively analyzed the relationship between linguistic features and business outcomes, most approaches have been descriptive or predictive, limiting their value for crafting more effective content. Understanding the causal effects of specific linguistic features is essential but challenging because, in real-world settings, the focal textual feature often changes simultaneously with other confounding factors.

Geoeconomic pressure—the use of existing economic relationships by governments to achieve geopolitical or economic ends—has become a prominent feature of global power dynamics. This paper introduces a methodology using large language models (LLMs) to systematically extract signals of geoeconomic pressure from large textual corpora. We quantify not just the direct effects of implemented policies but also the off-path threats that induce compliance without formal action. We systematically identify governments, firms, tools, and activities that are involved in this pressure.

How has Wikipedia activity changed for articles with content similar to ChatGPT following its introduction? We estimate the impact using differences-in-differences models, with dissimilar Wikipedia articles as a baseline for comparison, to examine how changes in voluntary knowledge contributions and information-seeking behavior differ by article content. Our analysis reveals that newly created, popular articles whose content overlaps with ChatGPT 3.5 saw a greater decline in editing and viewership after the November 2022 launch of ChatGPT than dissimilar articles did.

In this letter, we summarize our recent work on the welfare impact of recommendation algorithms and propose questions for further study. We model recommendation algorithms as an information structure, which shapes how a third party takes actions that affect the welfare of different individuals in a population. Each recommendation algorithm thus induces a welfare profile, describing the expected payoffs of different individuals when the third party takes actions following the algorithm.

We aim to stimulate discussion on how innovation research within marketing can use a better world (BW) perspective to help innovation become a driver of positive change in the world. In this "Challenging the Boundaries" series paper, we hope to provide purposeful research opportunities for scholars seeking to bridge innovation research with the BW movement. We frame our discussion with four areas of innovation research in marketing that are particularly relevant to BW objectives.