Title
Prof. Dr.
Last Name
Blohm
First name
Ivo
Email
ivo.blohm@unisg.ch
ORCID
Phone
+41 71 224 3321
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PublicationHow Lufthansa Shapes Data-Driven Transformation Leaders( 2024-04-18)
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PublicationHedonic Signals in Crowdfunding: A Comparison across Crowdfunding Platform TypesThis study draws on signaling theory to investigate the effect of hedonic signals in crowdfunding projects on funding performance. It compares the effect of hedonic signals across reward-, equity-, and donation-based crowdfunding platforms by combining archival data from 18 platforms and a large-scale panel of 64 experts that rate the strength of hedonic signals in 108 crowdfunding projects. Through the application of mixed linear modeling, the findings indicate a positive influence of stronger hedonic signals on funding performance. However, there are substantial differences across platform types. Increasing the strength of hedonic signals by one standard deviation increases funding performance by 28.9% on reward platforms, while there are no systematic effects on equity and donation platforms. This study contributes to existing crowdfunding research by clarifying the role of hedonic signals in crowdfunding and shedding light on the increasing need to better consider the characteristics of different crowdfunding platforms in crowdfunding research.Type:Journal:Volume:
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Scopus© Citations 30 -
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PublicationHuman Machine Creativity. How AI Can Influence Human Creativity in Open Innovation( 2022-06)
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PublicationDo Algorithms Make Better — and Fairer — Investments Than Angel Investors?(Harvard Business Review, 2020-11-02)
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PublicationHow to Manage Crowdsourcing Platforms EffectivelyCrowdsourced tasks are very diverse – and so are platform types. They fall into four categories, each demanding different governance mechanisms. The main goal of microtasking crowdsourcing platforms is the scalable and time-efficient batch processing of highly repetitive tasks. Crowdsourcing platforms for information pooling aggregate contributions such as votes, opinions, assessments and forecasts through approaches such as averaging, summation, or visualization. Broadcast search platforms collect contributions to solve tasks in order to gain alternative insights and solutions from people outside the organization, and are particularly suited for solving challenging technical, analytical, scientific, or creative problems. Open collaboration platforms invite contributors to team up to jointly solve complex problems in cases where solutions require the integration of distributed knowledge and the skills of many contributors. Companies establishing crowdsourcing platforms of any type should continuously monitor and adjust their governance mechanisms. Quality and quantity of contributions, project runtime, or the effort for conducting the crowdsourcing project may be good starting points.Type:Journal:Volume:Issue:
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PublicationThe Nature of Crowd Work and its Effects on Individuals’ Work PerceptionCrowd work reflects a new form of gainful employment on the Internet. We study how the nature of the tasks being performed and financial compensation jointly shape work perceptions of crowdworkers in order to better understand the changing modes and patterns of digital work. Surveying individuals on 23 German crowd working platforms, this work is the first to add a multi-platform perspective on perceived working conditions in crowd work. We show that crowd workers need rather high levels of financial compensation before task characteristics become relevant for shaping favorable perceptions of working conditions. We explain these results by considering financial compensation as an informational cue indicating the appreciation of working effort that is internalized by well-paid crowd workers. Resulting boundary conditions for task design are discussed. These results help us understand when and under what conditions crowd work can be regarded as a fulfilling type of employment in highly developed countries.Type:Journal:Volume:Issue:
Scopus© Citations 59 -
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Scopus© Citations 48 -
PublicationPredicting new venture survival: A Twitter-based machine learning approach to measuring online legitimacyResearch indicates that interactions on social media can reveal remarkably valid predictions about future events. In this study, we show that online legitimacy as a measure of social appreciation based on Twitter content can be used to accurately predict new venture survival. Specifically, we analyze more than 187,000 tweets from 253 new ventures' Twitter accounts using context-specific machine learning approaches. Our findings suggest that we can correctly discriminate failed ventures from surviving ventures in up to 76% of cases. With this study, we contribute to the ongoing discussion on the importance of building legitimacy online and provide an account of how to use machine learning methodologies in entrepreneurship research.Type:Journal:Volume:Issue:
Scopus© Citations 50