Green oriented crowdfunding campaigns: Their characteristics and diffusion in different institutional settings

Abstract

In this paper, we investigate how the institutional setting affects the diffusion of green crowdfunding campaigns across countries. To this aim, we develop and test two competing hypotheses about the association between country environmental sustainability orientation and the diffusion of green campaigns. To identify green campaigns, we develop an original machinelearning algorithm. We apply this algorithm to the population of 48,598 campaigns presented on Kickstarter between July 1, 2009 and July 1, 2012. By means of econometric estimates, we show that green campaigns differ from others along several dimensions and are more diffused in countries with a limited environmental sustainability orientation. Implications for research, practice, and policy are discussed.

Greening crowdfunding campaigns: an investigation of message framing and effective communication strategies for funding success

“…Despite the wide use of digital financial instruments by multiple organisations in this field, research that combines framing and the environmental literature is lacking (Jacobson et al, 2019), as is an application of framing theory to the entrepreneurial setting (Pan et al, 2020). Thus, our work expands upon both the crowdfunding literature about green campaign success determinants (Buttic e et al, 2019;Cumming et al, 2017;Kim and Hall, 2021;H€ orisch, 2018) and the literature about the importance of language in promoting new entrepreneurial projects and their funding activities (Kaminski and Hopp, 2020;Koh et al, 2020;Parhankangas and Renko, 2017).…”

Environmental Data Collection and Classification in Crowd-Funding Platforms - Evidence From Kickstarter

“…Numerous studies point to the most important determinant of environmental campaign success. Campaign goal, length of the funding period, staff-pick, quality and complexity of the project, number of social contact and friends, comments, updates and similar factors stand out [10][11][12][17][18][19][20][21][22].…”

“…Next, we eliminated campaigns from 2011 to 2019, as there was a small number of projects. Finally, in line with [9][10][11][12], we excluded projects with too small or too big goals (with a value below $5,000 and over $500,000). This led to a final dataset of 121,437 project campaigns.…”

“…This approach is simple to use and employable with large datasets. However, it potentially introduces a bias in the classification because it uses a dictionary of keywords arbitrarily defined by the authors [12]. Frequently, individual words can have multiple meanings.…”

“…In-stead, project creators can use the word green to allude to a characteristic of the campaign, referring to the colour. Certain authors use machine-learning algorithms to define automatically the dictionary of keywords used to discriminate between green and non-green campaigns [12,16]. In this paper, we used text analysis to define projects with environmental characteristics.…”