Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 1-17 |
| Number of pages | 17 |
| Journal | Small Business Economics: An International Journal |
| DOIs | |
| Publication status | E-pub ahead of print - 17 Jun 2019 |
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Keywords
- Decision-making
- Entrepreneurial contexts
- Social interactions
- Nonverbal behavior
- Social signal processing
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The promise of social signal processing for research on decision-making in entrepreneurial contexts. / Liebregts, Werner; Darnihamedani, Pourya; Postma, Eric; Atzmueller, Martin.
In: Small Business Economics: An International Journal, 17.06.2019, p. 1-17.Research output: Contribution to journal › Article › Scientific › peer-review
TY - JOUR
T1 - The promise of social signal processing for research on decision-making in entrepreneurial contexts
AU - Liebregts, Werner
AU - Darnihamedani, Pourya
AU - Postma, Eric
AU - Atzmueller, Martin
PY - 2019/6/17
Y1 - 2019/6/17
N2 - 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.
AB - 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.
KW - Decision-making
KW - Entrepreneurial contexts
KW - Social interactions
KW - Nonverbal behavior
KW - Social signal processing
UR - https://link.springer.com/article/10.1007/s11187-019-00205-1
U2 - 10.1007/s11187-019-00205-1
DO - 10.1007/s11187-019-00205-1
M3 - Article
SP - 1
EP - 17
JO - Small Business Economics: An International Journal
JF - Small Business Economics: An International Journal
SN - 0921-898x
ER -