The determinants of social promotion success : A case study of crowdfunding projects
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 251-265 |
Journal / Publication | Applied Stochastic Models in Business and Industry |
Volume | 37 |
Issue number | 2 |
Online published | 25 Sept 2020 |
Publication status | Published - Mar 2021 |
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Abstract
Crowdfunding has emerged as a major way to raise financial supports through collaborative contributions of the general public. In recent studies, promotional activities are found to be highly influential to the final outcome of crowdfunding projects. In order to conduct effective and efficient promotional campaigns, it is essential to contiguously monitor their progress status and investigate the determinants to their success during the whole fundraising process. It can help project starters and fundraisers to make decisions on whether and how to adjust the promotional campaigns to make them more useful. To this end, we propose a prediction framework for social influence, an evaluation for success of social promotions, and utilize knowledge gained from both crowdfunding websites and social networks to facilitate the promotional activities. Under two influence definitions based on reshare count and followers count, we find that knowledge gained from aggregate data rather than incremental data is better indicators of the social influence growth. We also discover that some features, including “value of pledged funds” and “number of total promoters,” are of great importance to social influence within the fundraising durations. All these findings could help project starters and fundraisers to make decisions on devising effective social promotions.
Research Area(s)
- crowdfunding, prediction, social influence, social networks, social promotion
Citation Format(s)
The determinants of social promotion success: A case study of crowdfunding projects. / Wang, Feifei; Yang, Yang; Tso, Geoffrey K. F. et al.
In: Applied Stochastic Models in Business and Industry, Vol. 37, No. 2, 03.2021, p. 251-265.
In: Applied Stochastic Models in Business and Industry, Vol. 37, No. 2, 03.2021, p. 251-265.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review