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

Small Business Economics, Jun 2019

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.

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

Small Business Economics pp 1–17 | Cite as The promise of social signal processing for research on decision-making in entrepreneurial contexts AuthorsAuthors and affiliations Werner LiebregtsPourya DarnihamedaniEric PostmaMartin Atzmueller Open Access Article First Online: 17 June 2019 3 Shares 152 Downloads 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. KeywordsDecision-making  Entrepreneurial contexts Social interactions Nonverbal behavior Social signal processing  JEL codesC31 D81 D91 G11 L26 M51  1 Introduction By now, decision-making is a well-established topic of interest in the field of entrepreneurship research (e.g., Shepherd 2011; Shepherd et al. 2015). Entrepreneurs are required to make many decisions on a day-to-day basis and usually do so under conditions of high risk and uncertainty (e.g., Baron 1998). Given the inherent uncertainty involved in running a business and the decisions that come with it, entrepreneurs often rely on a set of flexible decision-making principles (Sarasvathy 2001, 2009; Dew et al. 2009). The use of heuristics is seen as a way to speed up the decision-making process (Busenitz and Barney 1997; Tversky and Kahneman 1974). However, if a decision concerns other individuals, and the entrepreneur lacks the desire or ability to retrieve more information about the opposing party, stereotyping can play a key role in shaping the entrepreneur’s judgments and decisions (Bodenhausen 1990, 1993; Greenwald and Banaji 1995). This is especially true if the decision-making process is influenced by one or more social interactions between the entrepreneur and whom it concerns (e.g., Huang et al. 2013; Loewenstein et al. 1989). Obviously, decisions that are relevant to entrepreneurs are not always made by entrepreneurs themselves (Baron and Markman 2000; Lechler 2001). Especially funding decisions by investors have also attracted a lot of attention in the extant entrepreneurship literature (e.g., Chen et al. 2009; Huang and Pearce 2015). While many decisions in the context of entrepreneurship are made with little or no social interactions involved, others are largely based on or at least influenced by human-to-human interaction. The available evidence unequivocally suggests that behavioral cues during such interactions strongly affect the ultimate decision (Ambady and Rosenthal 1992; Bonaccio et al. 2016; McNeill 1992, 2005). This has also been demonstrated extensively for decision-making in entrepreneurial contexts that involves social interactions. In the case of entrepreneurs making the decision, most attention has been paid to hiring decisions or employment decisions more broadly (e.g., Hollandsworth et al. 1979; Koch et al. 2015). For example, Barrick et al. (2009) conclude that self-presentation tactics by applicants influence the interviewers’ perceptions of the candidate, and, in turn, whom they hire (also see Hosoda et al. 2003). In the case of decisions made by others yet affecting entrepreneurs, a vast amount of studies focuses on how business angels and venture capitalists arrive at their investment decisions (e.g., Maxwell et al. 2011; Petty and Gruber 2011). Many of those have investigated entrepreneurial pitches as a particular setting in which investors judge business ideas and note that their funding decisions are generally influenced by what is being said and done by the entrepreneurs (Ciuchta et al. 2018; Clarke 2011; Clarke et al. 2019; Pollack et al. 2012). In particular, both the verbal content of the presentation and the presentation style are considered important (Chen et al. 2009; Clark 2008). Hence, both verbal and nonverbal behavioral cues during social interactions are shown to have a major influence on individuals’ decision-making processes in the context of entrepreneurship. Theoretical insig (...truncated)


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Werner Liebregts, Pourya Darnihamedani, Eric Postma, Martin Atzmueller. The promise of social signal processing for research on decision-making in entrepreneurial contexts, Small Business Economics, 2019, pp. 1-17, DOI: 10.1007/s11187-019-00205-1