TY - JOUR
T1 - Analyzing the impact of social capital on US based Kickstarter projects outcome
AU - Onginjo, Joseph Ochieng
AU - Zhou, Dong Mei
AU - Berhanu, Tesema Fiseha
AU - Belihu, Sime Welde Gebrile
N1 - Publisher Copyright:
© 2021 The Authors
PY - 2021/7
Y1 - 2021/7
N2 - The essence of this paper is to analyse the ripple effects caused from the intertwining and complex relationship between the relational and structural dimensions of social capital on the US based Kick starter projects’ outcomes. This will be measured based on real time data collected from the Kick starter. com in form of 1157 projects organised in the structure of the number of backers, amount of time taken to fund the projects and the converted amount pledged towards the projects, as classified according to various project categories and geographical locations. This research applies qualitative and quantitative statistical analysis methods as well as data mining techniques; k-Nearest Neighbour, Naive Bayes and Decision Tree Algorithms. The results from this research confirm that relational social capital i.e. the number of backers involved in the projects, has significantly strong and positive impact on the converted amount pledged towards a project and the project outcome. This paper also offers a feasible decision-making model that will be used by the entrepreneurs in the future to determine which type of project categories an entrepreneur can choose to host and the project outcome.
AB - The essence of this paper is to analyse the ripple effects caused from the intertwining and complex relationship between the relational and structural dimensions of social capital on the US based Kick starter projects’ outcomes. This will be measured based on real time data collected from the Kick starter. com in form of 1157 projects organised in the structure of the number of backers, amount of time taken to fund the projects and the converted amount pledged towards the projects, as classified according to various project categories and geographical locations. This research applies qualitative and quantitative statistical analysis methods as well as data mining techniques; k-Nearest Neighbour, Naive Bayes and Decision Tree Algorithms. The results from this research confirm that relational social capital i.e. the number of backers involved in the projects, has significantly strong and positive impact on the converted amount pledged towards a project and the project outcome. This paper also offers a feasible decision-making model that will be used by the entrepreneurs in the future to determine which type of project categories an entrepreneur can choose to host and the project outcome.
KW - Crowdfunding
KW - Decision tree algorithm
KW - Naive Bayes
KW - Social capital theory dimensions
KW - k-nearest neighbor
UR - http://www.scopus.com/inward/record.url?scp=85110738864&partnerID=8YFLogxK
U2 - 10.1016/j.heliyon.2021.e07425
DO - 10.1016/j.heliyon.2021.e07425
M3 - Article
AN - SCOPUS:85110738864
SN - 2405-8440
VL - 7
JO - Heliyon
JF - Heliyon
IS - 7
M1 - e07425
ER -