The promise of social signal processing for research on decision-making in entrepreneurial contexts.
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| Authors: | Liebregts, Werner1,2 (AUTHOR) W.J.Liebregts@uvt.nl, Darnihamedani, Pourya3 (AUTHOR), Postma, Eric1,4 (AUTHOR), Atzmueller, Martin1,4 (AUTHOR) |
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| Source: | Small Business Economics. Oct2020, Vol. 55 Issue 3, p589-605. 17p. 1 Diagram. |
| Subject Terms: | Signal processing, Social processes, Behavioral assessment, Facial expression, Social interaction |
| 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. [ABSTRACT FROM AUTHOR] |
| Database: | Entrepreneurial Studies Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: ent DbLabel: Entrepreneurial Studies Source An: 145257421 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liebregts%2C+Werner%22">Liebregts, Werner</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> W.J.Liebregts@uvt.nl</i><br /><searchLink fieldCode="AR" term="%22Darnihamedani%2C+Pourya%22">Darnihamedani, Pourya</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Postma%2C+Eric%22">Postma, Eric</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Atzmueller%2C+Martin%22">Atzmueller, Martin</searchLink><relatesTo>1,4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Small+Business+Economics%22">Small Business Economics</searchLink>. Oct2020, Vol. 55 Issue 3, p589-605. 17p. 1 Diagram. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Social+processes%22">Social processes</searchLink><br /><searchLink fieldCode="DE" term="%22Behavioral+assessment%22">Behavioral assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Facial+expression%22">Facial expression</searchLink><br /><searchLink fieldCode="DE" term="%22Social+interaction%22">Social interaction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ent&AN=145257421 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11187-019-00205-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 589 Subjects: – SubjectFull: Signal processing Type: general – SubjectFull: Social processes Type: general – SubjectFull: Behavioral assessment Type: general – SubjectFull: Facial expression Type: general – SubjectFull: Social interaction Type: general Titles: – TitleFull: The promise of social signal processing for research on decision-making in entrepreneurial contexts. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liebregts, Werner – PersonEntity: Name: NameFull: Darnihamedani, Pourya – PersonEntity: Name: NameFull: Postma, Eric – PersonEntity: Name: NameFull: Atzmueller, Martin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 0921898X Numbering: – Type: volume Value: 55 – Type: issue Value: 3 Titles: – TitleFull: Small Business Economics Type: main |
| ResultId | 1 |
