Diginomics Working Paper
Are investors willing to give up a higher return if the investment generates positive environmental impact? We investigate this question with a decision experiment among crowdfunders, where they choose between a higher return or environmental impact. Overall, 65% of investors choose environmental impact at the expense of a higher return for sufficiently large impact, 14% choose impact independent of the magnitude of impact, while 21% choose the higher return independent of impact. Combining the experimental data with historical investments, we find that investors allocate a larger share of funds to green projects if they value environmental impact more, and if they expect green projects to be more profitable. These findings suggest that investors have a preference for positive environmental impact, and satisfy it by investing in green projects. We further show that the preference for environmental impact is distinct from a preference for positive social impact. Finally, we introduce new survey measures of impact for future use, which are experimentally validated and predict field behavior.
We study the impact fintech startups have on the performance and the default risk of traditional financial institutions. We find a positive relationship between fintech startup formations and incumbent institutions’ performance for the period from 2005 to 2018 and a large sample of financial institutions from 87 countries. We further analyze the link between fintech startup formations and the default risk of traditional financial institutions. Fintech startup formations decreases stock return volatility of incumbent institutions and decreases the systemic risk exposure of financial institutions. Our findings indicate that the development of fintech startups should be monitored very closely by legislators and financial supervisory authorities, because fintechs not only have a positive effect on the financial sector’s performance, but can also improve financial stability relative to the status quo.
In this study, we investigate whether and to what extent community managers in online collaborative communities can stimulate community activities through their engagement. Using a novel data set of 22 large online idea crowdsourcing campaigns, we find that moderate but steady manager activities are adequate to enhance community participation. Moreover, we show that appreciation, motivation, and intellectual stimulation by managers are positively associated with community participation but that the effectiveness of these communication strategies depends on the form of participation community managers want to encourage. Finally, the data reveal that community manager activities requiring more effort, such as media file uploads (vs. simple written comments), have a stronger effect on community participation.
When using digital devices and services, individuals provide their personal data to organizations in exchange for gains in various domains of life. Organizations use these data to run technologies such as smart assistants, augmented reality, and robotics. Most often, these organizations seek to make a profit. Individuals can, however, also provide personal data to public databases that enable nonprofit organizations to promote social welfare if sufficient data are contributed. Regulators have therefore called for efficient ways to help the public collectively benefit from its own data. By implementing an online experiment among 1,696 US citizens, we find that individuals would donate their data even when at risk of getting leaked. The willingness to provide personal data depends on the risk level of a data leak but not on a realistic impact of the data on social welfare. Individuals are less willing to donate their data to the private industry than to academia or the government. Finally, individuals are not sensitive to whether the data are processed by a humansupervised or a self-learning smart assistant.
We study the extent of fraud in initial coin offerings (ICOs), and whether information disclosure prior to the issuance predicts fraud. We document different types of fraud, and that fraudulent ICOs are on average much larger than the sample average. Issuers that disclose their code on GitHub are more likely to be targeted by phishing and hacker activities, which suggests that there are risks related to disclosing the code. Generally, we find it extremely difficult to predict fraud with the information available at the time of issuance. This calls for the need to install a thirdparty that certifies the quality of the issuers, such as specialized platforms, or the engagement of institutional investors and venture capital funds that can perform a due diligence and thus verify the quality of the project.