Abstract: Marketing, customer, employee, firm disclosure data have become increasingly multimodal, simultaneously conveying visual, auditory, and textual information. Most prior studies adopt a “signal-by-signal” AI approach, using LLM to extract visual, textual, and audio features separately. Yet

Abstract: Artificial intelligence (AI) is rapidly transforming how employees approach their work—but not without emotional and interpersonal consequences. This research sheds light on these consequences by drawing from the appraisal theory of emotions to examine how employees feel about their own

Abstract: Recent advances in large language models (LLMs) are rapidly reshaping the research landscape in the social sciences. In this talk, I discuss how generative AI can augment multiple stages of the research process in management and related disciplines. In particular, I focus on how LLMs

Abstract: Proactivity at work involves self-initiated actions aimed at making changes to improve the situation or oneself. In this presentation, Professor Wu will share findings from his research program on proactivity in two topics. First, whether and how can organizations promote and build a

Abstract: In this seminar, I present a working paper that examines how organizations respond to external scrutiny and reputational challenges in the public sphere. The study explores how firms translate social attention and legitimacy pressure into changes at the leadership level, and how these

Abstract: Research on the governance of new ventures has traditionally focused on mitigating incentive misalignment between founders and investors through boards’ incentive design, monitoring, and control. In this paper, we identify a distinct governance problem: managing cognitive misalignment —