Performance distribution of TikTok contentpreneurs: A conceptual replication study
(2024) ENTN19 20241Department of Business Administration
- Abstract
- This study analyzes the performance distribution of TikTok contentpreneurs, along with its generative mechanism. It is a conceptual replication of Gala et al's (2024) “Star entrepreneurs on digital platforms: Heavy-tailed performance distributions and their generative mechanisms.” It focuses on contentpreneurship as a form of entrepreneurship that takes place among content creators on social media platforms. The study tests Gala et al’s hypotheses concerning the non-normality of entrepreneurial performance distributions on digital platforms, engendered by the prevalence of star entrepreneurs (or, contentpreneurs) who become outliers by outperforming the rest, skewing the platform’s distribution into a non-normal shape and creating a... (More)
- This study analyzes the performance distribution of TikTok contentpreneurs, along with its generative mechanism. It is a conceptual replication of Gala et al's (2024) “Star entrepreneurs on digital platforms: Heavy-tailed performance distributions and their generative mechanisms.” It focuses on contentpreneurship as a form of entrepreneurship that takes place among content creators on social media platforms. The study tests Gala et al’s hypotheses concerning the non-normality of entrepreneurial performance distributions on digital platforms, engendered by the prevalence of star entrepreneurs (or, contentpreneurs) who become outliers by outperforming the rest, skewing the platform’s distribution into a non-normal shape and creating a heavy-tail effect. Using randomly selected data of 1,046 user accounts from TikTok, elements of distribution shape and fit are analyzed through statistical tests of correlation and goodness-of-fit
tests. Longitudinal data from the same sample is used to identify the distribution’s generative mechanism. Comparing the results of this study with its original, some support for the original hypotheses is found, mainly for non-normal shape and lognormal fit, but no support is found for knowledge intensity as a performance predictor, and only conditional support is found for proportional differentiation as a generative mechanism. Interpretation of the findings connect the discrepancy in results between the two papers to the distinct nature, evolution, and audience of the digital platforms investigated. This study contributes theory and empirical evidence for non-normal distributions to be embraced in the field of entrepreneurship, and provides theoretical and practical insights based on the results of the analysis. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9165721
- author
- Kulchetscki, Luiza LU
- supervisor
- organization
- course
- ENTN19 20241
- year
- 2024
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- contentpreneurship, social media platforms, non-normal distribution, lognormal, generative mechanism, proportional differentiation, preferential attachment, Pareto effect.
- language
- English
- id
- 9165721
- date added to LUP
- 2024-06-19 17:34:25
- date last changed
- 2024-06-19 17:34:25
@misc{9165721,
abstract = {{This study analyzes the performance distribution of TikTok contentpreneurs, along with its generative mechanism. It is a conceptual replication of Gala et al's (2024) “Star entrepreneurs on digital platforms: Heavy-tailed performance distributions and their generative mechanisms.” It focuses on contentpreneurship as a form of entrepreneurship that takes place among content creators on social media platforms. The study tests Gala et al’s hypotheses concerning the non-normality of entrepreneurial performance distributions on digital platforms, engendered by the prevalence of star entrepreneurs (or, contentpreneurs) who become outliers by outperforming the rest, skewing the platform’s distribution into a non-normal shape and creating a heavy-tail effect. Using randomly selected data of 1,046 user accounts from TikTok, elements of distribution shape and fit are analyzed through statistical tests of correlation and goodness-of-fit
tests. Longitudinal data from the same sample is used to identify the distribution’s generative mechanism. Comparing the results of this study with its original, some support for the original hypotheses is found, mainly for non-normal shape and lognormal fit, but no support is found for knowledge intensity as a performance predictor, and only conditional support is found for proportional differentiation as a generative mechanism. Interpretation of the findings connect the discrepancy in results between the two papers to the distinct nature, evolution, and audience of the digital platforms investigated. This study contributes theory and empirical evidence for non-normal distributions to be embraced in the field of entrepreneurship, and provides theoretical and practical insights based on the results of the analysis.}},
author = {{Kulchetscki, Luiza}},
language = {{eng}},
note = {{Student Paper}},
title = {{Performance distribution of TikTok contentpreneurs: A conceptual replication study}},
year = {{2024}},
}