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Getting Ahead and Getting Along in Entrepreneurial Networks : = Network Effects of the "Dark" and "Light" Sides of Personality in New Venture Performance.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Getting Ahead and Getting Along in Entrepreneurial Networks :/
其他題名:	Network Effects of the "Dark" and "Light" Sides of Personality in New Venture Performance.
作者:	Yates, Victoria Antin.
面頁冊數:	1 online resource (118 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
標題:	Personality psychology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29063585click for full text (PQDT)
ISBN:	9798438791867

Getting Ahead and Getting Along in Entrepreneurial Networks : = Network Effects of the "Dark" and "Light" Sides of Personality in New Venture Performance.
Yates, Victoria Antin.

Getting Ahead and Getting Along in Entrepreneurial Networks :Network Effects of the "Dark" and "Light" Sides of Personality in New Venture Performance. - 1 online resource (118 pages)

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--Mississippi State University, 2022.

Includes bibliographical references

This dissertation explores how dark and light personality traits influence venture performance via mediating effects of social structure. Because entrepreneurship is an inherently social process, theoretical perspectives of social network theory and social comparison theory are used to examine how entrepreneurs' dark and light personality traits interact with personality traits of socially proximal others to influence venture performance via network structure. These perspectives are drawn together using socioanalytic theory. This dissertation argues that agreeableness and narcissism interact with the personality traits of network others at the group-level to influence structural hole positioning within entrepreneurial networks; in turn, structural holes are hypothesized to exert a positive effect on venture revenue. This study offers contributions to the fields of entrepreneurship, entrepreneurial personality, social networks, and social comparison processes. Hypotheses are tested using a dataset of 234 entrepreneurs nested within 24 groups; analyses are conducted via linear mixed effects models and Monte-Carlo approximation of mediation confidence intervals (Model 1) and OLS regression and non-parametric bootstrapping approximation of mediation confidence intervals (Model 2).

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798438791867Subjects--Topical Terms:

2144789
Personality psychology.
Subjects--Index Terms:

PerformanceIndex Terms--Genre/Form:

542853
Electronic books.

Getting Ahead and Getting Along in Entrepreneurial Networks : = Network Effects of the "Dark" and "Light" Sides of Personality in New Venture Performance.
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502 $a Thesis (Ph.D.)--Mississippi State University, 2022.
504 $a Includes bibliographical references
520 $a This dissertation explores how dark and light personality traits influence venture performance via mediating effects of social structure. Because entrepreneurship is an inherently social process, theoretical perspectives of social network theory and social comparison theory are used to examine how entrepreneurs' dark and light personality traits interact with personality traits of socially proximal others to influence venture performance via network structure. These perspectives are drawn together using socioanalytic theory. This dissertation argues that agreeableness and narcissism interact with the personality traits of network others at the group-level to influence structural hole positioning within entrepreneurial networks; in turn, structural holes are hypothesized to exert a positive effect on venture revenue. This study offers contributions to the fields of entrepreneurship, entrepreneurial personality, social networks, and social comparison processes. Hypotheses are tested using a dataset of 234 entrepreneurs nested within 24 groups; analyses are conducted via linear mixed effects models and Monte-Carlo approximation of mediation confidence intervals (Model 1) and OLS regression and non-parametric bootstrapping approximation of mediation confidence intervals (Model 2).
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