My research focuses on explaining the behavior of organizations and their top management teams (TMTs) when they are facing existential threats. All TMTs encounter at some point during their tenure a threat that may severely harm the organization, or even lead to bankruptcy. Such harm is different from a mere performance shortfall, as in contrast to a shortfall, it is not possible to (easily) recover from it. What fascinates me is that there is a considerable heterogeneity across TMTs’ perceptions of the likelihood that such a fate befalls them, and of what the consequences would be for themselves and their organizations. The inability to notice threats, an erroneous perception, or the lack of a (suitable) response resulted in many organizations being driven to bankruptcy.
I employ a variety of methodologies, particularly focusing on big data tools to uncover the socio-psychological mechanisms underlying the response of the TMT to serious threats to their organization. For instance, how executives strategically use their tone of voice to discuss important topics. For this research, I have collected audio and transcripts for over 60,000 earnings calls from U.S. listed organizations. I developed a variety of algorithms which allow me to determine what was said, who said it, and when it was said. These data allow me to study dynamics during the earning calls, and made it possible for me to construct an effective Deep Neural Network model to classify emotions in the speaker’s verbal tone. Furthermore, I have constructed a dataset on the U.S. commercial banking industry, which consists of extensive data on their financials, and information on mortality in the sample. This dataset allows me to investigate what behavior affects the likelihood of bankruptcy and how the threat of bankruptcy affects the bank’s behavior.