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

Research output: Contribution to journalArticleScientificpeer-review

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.
Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalSmall Business Economics: An International Journal
DOIs
Publication statusE-pub ahead of print - 17 Jun 2019

Fingerprint

Decision making
Individual decision making
Entrepreneurs
Conceptual framework
Investors
Firm survival
Firm growth
Artificial intelligence
Research methods
Entrepreneurship
Social interaction

Keywords

  • Decision-making
  • Entrepreneurial contexts
  • Social interactions
  • Nonverbal behavior
  • Social signal processing

Cite this

@article{589df82618574d07b5b7371ad88159b6,
title = "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.",
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year = "2019",
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doi = "10.1007/s11187-019-00205-1",
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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 journalArticleScientificpeer-review

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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.

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