The promise of social signal processing for research on decision-making in entrepreneurial contexts

Abstract

In this conceptual paper, we demonstrate how modern data science techniques can advance our understanding of important decisions in the context of entrepreneurship that involve social interactions. We know that individuals’ decision-making is strongly affected by nonverbal behavior. The emerging domain of social signal processing aims at accurate computerized analysis of such behavior. Behavioral cues stemming from, for example, gestures, posture, facial expressions, and vocal expressions can now be detected and analyzed by state-of-the-art technologies utilizing artificial intelligence. This paper discusses and illustrates their potential value for future research on decision-making by entrepreneurs as well as by others yet directly affecting them (e.g., investors). In brief, social signal processing is more accurate and more efficient than conventional research methods and may reveal important characteristics that so far have been omitted in explaining decisions that are vital for firm survival and growth. We derive a total of five propositions from our newly developed conceptual framework, which we hope will be subject to extensive empirical scrutiny in future research.