Forging the Future of Work

Learning from Earnings Calls: Graph-Based Conversational Modeling for Financial Prediction
Information Systems Research

Earnings conference calls are valuable venues for business communication. Empirical research has shown that the content of earnings calls contains predictive signals about future market risks, which has motivated a line of computational studies that utilize earnings transcripts for financial forecasting tasks. However, earnings call transcripts are typically very long, and the predictive signals within them are often sparsely distributed across different sections of the transcript. As a result, existing computational methods often fail to capture the essential information within the transcript that is relevant to market risks. In this work, we design a novel method to model earnings transcripts as a conversational graph where graph nodes represent discussed topics and graph edges indicate the similarity between topics. By doing so, we aim to explicitly model what is discussed (i.e., topical content), how it is discussed (e.g., cross-referencing or newly introduced topics), and in what manner it is discussed (e.g., sentiment and other linguistic features) within the transcript. We then leverage a graph neural network to learn transcript-level representations for financial risk forecasting. Experimental results show that the proposed method significantly reduces risk forecasting errors, demonstrating its capability of modeling earnings call transcripts. Moreover, this predictive power holds even after considering the firm’s fundamentals, and the performance gain over baseline models continues to grow as transcript length increases. The interpretability analysis shows that the proposed method identifies cross-referencing and newly introduced topics as influential for risk prediction. Moreover, the model tends to associate transcripts with a higher number of new topics in the Q&A section, more cross-referencing discussions, and more positive sentiment with lower predicted financial risks. This work contributes methodologically by proposing a novel predictive approach specifically tailored to the domain-specific challenge of transcript-based risk forecasting. We also discuss key design insights and implications.

Yi Yang (HKUST), Yixuan Tang (HKUST), Yangyang Fan (HK PolyU), and Kunpeng Zhang (UMD)

• AI
• FinTech

The GenAI Future of Consumer Research
Journal of Consumer Research

We develop a novel generative AI (GenAI) trajectory, “democratization-average trap-model collapse,” to identify data and model challenges posed by GenAI, from which we project the GenAI future of consumer research. This trajectory consists of three key phenomena: democratization broadens consumer participation, the average trap produces generic responses, and model collapses occurs when GenAI outputs lose human sensibilities. Data and model challenges arise as democratization enhances data representation but embeds real-world biases; the average trap, caused by next-token prediction algorithms, leads to generic outputs that lack individuality; and model collapse occurs when GenAI increasingly learns from its own outputs, amplifying machine bias and diverging from human behavior. To address these challenges, researchers can leverage democratization to study marginalized consumers and prioritize human-centered research over purely data-driven methods. The average trap can be mitigated by fine-tuning models with task-specific and marginalized consumption data while engineering responses for uniqueness. Preventing model collapse requires integrating human-machine hybrid data and applying theories of mind to realign AI with human-centric consumption. Finally, we outline three future research directions: preserving data distribution tails to support consumption democratization, countering the average trap in next-token prediction, and reversing the trajectory from democratization to model collapse.

Ming-Hui Huang, Distinguished University Professor, National Taiwan University
Roland T. Rust, Distinguished Professor and David Bruce Smith Chair in Marketing, University of Maryland

• AI
• Fairness and Equity
• Feelings Economy
• Personalization

Prompt Adaptation as a Dynamic Complement in Generative AI Systems

As generative AI systems rapidly improve, a key question emerges: How do users keep up—and what happens if they fail to do so. Drawing on theories of dynamic capabilities and IT complements, we examine prompt adaptation—the adjustments users make to their inputs in response to evolving model behavior—as a mechanism that helps determine whether technical advances translate into realized economic value. In a preregistered online experiment with 1,893 participants, who submitted over 18,000 prompts and generated more than 300,000 images, users attempted to replicate a target image in 10 tries using one of three randomly assigned models: DALL-E 2, DALL-E 3, or DALL-E 3 with automated prompt rewriting. We find that users
with access to DALL-E 3 achieved higher image similarity than those with DALL-E 2—but only about half of this gain (51%) came from the model itself. The other half (49%) resulted from users adapting their prompts in response to the model’s capabilities. This adaptation emerged across the skill distribution, was driven by trial-and-error, and could not be replicated by automated prompt rewriting, which erased 58% of the performance improvement associated with DALL-E 3. Our findings position prompt adaptation as a dynamic complement to generative AI—and suggest that without it, a substantial share of the economic value created when models advance may go unrealized.

Eaman Jahani, UMD
Benjamin Manning, MIT
Hong-Yi TuYe, MIT
Mohammed Alsobay, MIT
Christos Nicolaides, University of Cyprus
Siddharth Suri, Microsoft Research
David Holtz, Columbia

• AI