Cutting-Edge Research

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.

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.

Our educational model is predicated on an expert lecturing to a class of attentive disciples. We rely on standardized repeated exams. As a junior accounting professor, I was encouraged to take a senior faculty member’s teaching notes and simply go and deliver the material. Rely on older exams or some variant thereof. Have a standard key to grade the exams. In essence, minimize the time spent on teaching so that I can work on research and get tenure. To be fair, my senior professors were watching out for me and were trying to put me on the shortest path to tenure.

Language is a uniquely human trait at the core of human interactions. The language people use often reflects their personality, intentions and state of mind. With the integration of the Internet and social media into everyday life, much of human communication is documented as written text. These online forms of communication (for example, blogs, reviews, social media posts and emails) provide a window into human behaviour and therefore present abundant research opportunities for behavioural science.

The growing prominence of LLMs has led to an increase in the development of AI tutoring systems. These systems are crucial in providing underrepresented populations with improved access to valuable education. One important area of education that is unavailable to many learners is strategic bargaining related to negotiation. To address this, we develop a LLM-based Assistant for Coaching nEgotiation (ACE). ACE not only serves as a negotiation partner for users but also provides them with targeted feedback for improvement.