Green oriented crowdfunding campaigns: Their characteristics and diffusion in different institutional settings
Introduction
An increasing body of research has shown that green entrepreneurial initiatives, defined as initiatives that have an environmental mission as their primal goal (Ortas et al., 2013; Thompson et al., 2011), face significant challenges in accessing financial resources (Fedele and Miniaci, 2010; Gaddy et al., 2017; Ghosh and Nanda, 2010; Lehner, 2013; Petkova et al., 2014; Ridley-Duff, 2009). In this scenario, crowdfunding potentially offers a new channel for green entrepreneurs to overcome these challenges. Previous studies have shown that differently from other entrepreneurial financial actors, backers of crowdfunding campaigns are not moved by profit motivations alone and select projects based also on their wish to help others and to support causes they care for (Belleflamme et al., 2014; Gerber and Hui, 2013). Moving from these arguments, a number of recent studies has investigated green initiatives launched on crowdfunding platforms, focusing on the likelihood of success of crowdfunding campaigns with an environmental focus (e.g., Bartenberger and Leitner, 2013; Bonzanini et al., 2015; Calic and Mosakowski, 2016; Candelise, 2016; Hörisch, 2015; Lam and Law, 2016). Comparatively, little attention has been given to factors that influence the propensity to launch green initiatives on crowdfunding platforms. This is an important gap in the literature given the crucial role that crowdfunding may play in financing green entrepreneurial initiatives. The aim of this study is to contribute to filling this gap.
For this purpose, we take inspiration from the view that the institutional setting contributes to inform the behavior of individuals and notably of entrepreneurs (Bruton et al., 2010). Particularly, we consider the environmental sustainability orientation of the institutional setting in a given country. We define the environmental sustainability orientation as the presence of formal (i.e., policy and regulation) and informal (i.e., norms, values, beliefs and practices) institutions,1 that are concerned not only with the current level of economic and non-economic well-being but also with its sustainability over time i.e. the ability to pass natural, physical, human, and social resources to future generations (Stiglitz et al., 2010). We argue that the environmental sustainability orientation of the institutional setting influences both entrepreneurs' willingness to launch a green-oriented entrepreneurial initiative and their inclination to use crowdfunding to finance such initiatives. In countries where the institutional setting has a stronger environmental sustainability orientation, one may expect that there are more green-related business opportunities. Moreover, as green initiatives are more legitimized, entrepreneurs may be more inclined to resort to crowdfunding to finance their green initiatives as they anticipate a greater likelihood of success of their campaigns. However, in these countries, entrepreneurs may also find it easier to finance their green initiatives through traditional channels. The ease of access to traditional channels may have a direct negative effect on their inclination to use crowdfunding. Moreover, because of adverse selection, green crowdfunding campaigns may be perceived by potential backers as having low quality, which would make entrepreneurs even more reluctant to finance their green initiatives through crowdfunding. Considering that opposed forces are at work, it is difficult to argue a priori whether the environmental sustainability orientation of a country's institutions is positively or negatively associated with the diffusion in this country of green-oriented crowdfunding campaigns. In this paper, we investigate the relative explanatory power of these competing hypotheses.
Answering this research question raises a serious methodological problem: identifying green-oriented campaigns. Prior studies have accomplished this task by either focusing on a limited number of campaigns and providing a subjective evaluation of their green orientation (Calic and Mosakowski, 2016) or using the classification of green campaigns provided on crowdfunding platforms (e.g., Hörisch, 2015). These strategies have important drawbacks. The subjective evaluation of green campaigns based on the textual description of the project has the advantage of being very precise (Kononenko and Bratko, 1991), particularly if made by a sufficient number of well-trained researchers, as in the case of Calic and Mosakowski (2016). However, this approach is only applicable to a limited sample of campaigns, due to the considerable time required to perform the evaluation. At the same time, using the classification of green campaigns provided by platforms has the clear advantage of relying on an easy to access and publicly available classification. However, it entails the risk of overlooking a large number of green-oriented campaigns. Consider, for instance, a movie produced through environmental sustainable techniques or a piece of art created with the intent of warning people about an environmental threat. It is hard to argue that these initiatives are not green. This notwithstanding, they are unlikely to be labeled as green by platforms, considering that platforms typically provide a single label to a campaign (see e.g., Mollick, 2014). An interesting step forward is the approach by Cumming et al. (2017). They implement a computer-based text analysis technique aimed at searching for a predefined set of related keywords in the description of the focal project. The accuracy of the classification clearly depends on the comprehensiveness and uniqueness of the chosen set of keywords. In this paper, we considerably improve this methodology by employing a robust machine-learning algorithm to create a content-specific classifier of green-oriented crowdfunding campaigns, based on the project descriptions posted on the crowdfunding platform. Compared to the keywords-based algorithm implemented in Cumming et al. (2017), the machine-learning approach offers a great advantage. Indeed, the set of most informative keywords used to discriminate between green and non-green campaigns is automatically extracted from a large training corpus of labeled descriptions of crowdfunding projects. Therefore, keywords are not selected in advance, but they are learnt from the specific textual domain.
We apply the classifier to the population of 48,598 crowdfunding campaigns launched on Kickstarter in the period between July 1, 2009 and July 1, 2012. Approximately 9.5% of all the campaigns presented on Kickstarter are identified by the algorithm as green initiatives. Our findings show that these campaigns significantly differ from other campaigns along several dimensions. Specifically, green crowdfunding campaigns have a larger capital goal, provide more information (both visual and verbal), are launched by creators with significantly larger amounts of social capital developed within the crowdfunding platform (Colombo et al., 2015), and a smaller external network (Mollick, 2014). More importantly, our econometric estimates show that green campaigns are proportionally more common in countries presenting a lower environmental sustainability orientation.
The paper is organized as follows. Section 2 discusses the theoretical background and develops hypotheses. In Section 3, we present data, methodology and descriptive statistics. Next, we present the main Results. The last section concludes the paper and discusses the implications for practice, policy, and future research.
Section snippets
Conceptual background and hypotheses
Similar to other forms of entrepreneurial activity, green-oriented ventures require significant initial investments. However, raising external finance has proven even more difficult for green ventures than for others (Agrawal et al., 2010; Fedele and Miniaci, 2010; Ridley-Duff, 2009). These difficulties arise from a combination of greater information asymmetries (Ghosh and Nanda, 2010; Petkova et al., 2014), more uncertain returns due to higher political risk (Bürer and Wüstenhagen, 2009; Foxon
The context of the study
For this study, we develop an original dataset including all the projects launched on Kickstarter from July 1, 2009 to July 1, 2012. Kickstarter is among the largest crowdfunding providers worldwide (Colombo et al., 2015), and data coming from this platform have been used in several prior studies (e.g., Pitschner and Pitschner-Finn, 2014; Mollick, 2014; Colombo et al., 2015; Butticè et al., 2017a; Courtney et al., 2017).
Kickstarter is a reward-based crowdfunding platform. In other words,
The association between the environmental sustainability orientation of countries' institutions and the probability of launching a green campaign
To test the hypotheses presented in Section 2, we run a set of logit estimates with robust standard errors that account for possible biases due to heteroscedasticity and used the probability of observing a green (vs. non-green) campaign on the Kickstarter platform, as the dependent variable. Table 6 reports the results of our estimates. First, we consider control variables (see Model I). The multivariate analysis confirms the results illustrated in the previous section. Green crowdfunding
Concluding discussion
In this paper, we have studied how the institutional setting of a given country countries as regards the environmental sustainability orientation of the country's institutions, might affect the emergence of green initiatives on crowdfunding platforms. Using data collected from Kickstarter and other public sources, we show that the probability of a crowdfunding campaign being green is, indeed, lower in countries where institutions are more oriented towards environmental sustainability, as
Vincenzo Butticè is Assistant Professor of entrepreneurial finance at the School of Management of Politecnico di Milano. His research is about crowdfunding and entrepreneur's performance. Vincenzo is teaching assistant of entrepreneurial finance, and business and industrial organization at Politecnico di Milano and lecturer of entrepreneurial finance at the MIP school of management.
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2023, Journal of Business ResearchCitation Excerpt :This is the case of health care start-ups that rely on artificial intelligence to better diagnose, prevent, and cure diseases (Garbuio & Lin, 2019). Sustainable entrepreneurs tend to use crowdfunding platforms more extensively because they have less access to traditional modes of financing, especially in countries where institutions are less concerned with environmental and societal issues (Butticè, Colombo, Fumagalli, & Orsenigo, 2019). A neural network and natural language processing approach can predict the outcome of crowdfunding campaigns based on content analysis of startup project presentations using text, speech, and video (Kaminski & Hopp, 2020).
Vincenzo Butticè is Assistant Professor of entrepreneurial finance at the School of Management of Politecnico di Milano. His research is about crowdfunding and entrepreneur's performance. Vincenzo is teaching assistant of entrepreneurial finance, and business and industrial organization at Politecnico di Milano and lecturer of entrepreneurial finance at the MIP school of management.
Massimo Colombo is a Full Professor of Innovation Economics, Entrepreneurship and Entrepreneurial Finance at Milan Polytechnic. His main research interests are in the organization, financing and growth of high-tech entrepreneurial ventures, the economics of organizational design, strategic alliances, acquisitions and open innovation, and the diffusion and performance impact of advanced technologies. He is the Associate Editor for Technology Strategy of the Journal of Small Business Management and Editor-in Chief of Economia e Politica Industriale-Journal of Industrial and Business Economics. Massimo Colombo is author (or co-author) of numerous books and articles in journals such as the Cambridge Journal of Economics, Economics Letters, Entrepreneurship Theory & Practice, Industrial and Corporate Change and the International Journal of Industrial Organization.
Carlotta Orsenigo is Assistant Professor of Computer Science at the School of Management of Politecnico di Milano, where she teaches courses in Optimization Methods for Operations Research and Business Intelligence and Data Mining. Her current research interests include the development of models and algorithms for pattern recognition and manifold learning, and their application to problems arising in several contexts such as biolife sciences, finance, social network analysis.
Elena Fumagalli is Associate Professor of Energy Economics at Politecnico di Milano, Dep. of Management, Economics and Industrial Engineering, Teaching Area Coordinator at MIP Politecnico di Milano, Research Fellow at Bocconi University, Center for Research on Energy and Environmental Economics and Policy (IEFE) and member of the extended faculty at the Florence School of Regulation, Energy and Climate. Her research interests include incentive regulation in energy networks, competition in electricity markets, technology policy and diffusion.