As deep learning and big data increasingly shape modern artificial intelligence (AI) tools, it is essential to consider the broader impact of integrating AI into workplaces. While AI applications can optimize processes and improve productivity, their long-term effects on workers’ learning curves and overall performance are still underexplored. This paper investigates the intricate relationship between AI-enabled technology and workers’ learning dynamics through a large-scale randomized field experiment conducted on the Instacart platform.

The case study "Electronic Financial-Advisor for Tech Savvy" (EFforTS, or Efforts) examines a Robo-Advisor start-up based in Raleigh, North Carolina, founded by tech-industry entrepreneurs. Efforts developed an algorithm-based online investment platform tailored for technology professionals, gaining attention through successful social media marketing.

The market for initial coin offerings (ICOs) is currently unregulated, and many blockchain companies use "digital hype" as a marketing tool to promote their products. To predict the effects of digital hype during ICOs on blockchain-based projects and outcomes, this study adopts an information economics perspective.

Ride-hailing services are an essential mode of transportation for millions worldwide. The rapid growth of this service has raised concerns about its environmental impact. To address these concerns, ride-hailing companies are adding or introducing environmentally friendly vehicles (e.g., electric vehicles) to their platforms. However, it is not clear how adding such "green" vehicles will affect the environment and customers. To our knowledge, this paper is the first to analyze this question theoretically.

The paper studies the nowcasting of Euro area Gross Domestic Product (GDP) growth using mixed data sampling machine learning panel data regressions with both standard macro releases and daily news data.

Building on the literature in linguistics showing that the manner in which individuals speak provides context incremental to the actual spoken words, we study whether uncertainty expressed via the acoustic features of managerial speech in conference calls impacts analyst behavior. Using a novel measure of managerial acoustic uncertainty, we find that when managers sound more uncertain in their responses to analyst questions, analyst forecast dispersion increases, even after accounting for characteristics of the actual language being used by managers and analysts.

This article examines the role of Generative Artificial Intelligence (GenAI) in the context of marketing education, highlighting its substantial impact on the field. The study is based on an analysis of how GenAI, particularly through the use of Large Language Models (LLMs), functions. We detail the operational mechanisms of LLMs, their training methods, performance across various metrics, and the techniques for engaging with them via prompt engineering.