Ivo Blohm

Crowdsourced 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.

Crowd 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.

Research 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.

While the crowdsourcer’s job is to encourage valuable contributions and sustained commitment in a cost-effective manner, it seems as if the primary attention of management and research is still centered on the evaluation of contributions rather than the crowd. As many crowdsourcers lack the resources to successfully execute such projects, crowdsourcing intermediaries play an increasingly important role. First studies dealt with internal management challenges of incorporating an intermediary. However, the issue of how intermediaries influence crowdsourcees’ psychological and behavioral responses, further referred to as engagement, has not been addressed yet. Consequently, two leading research questions guide this paper: (1) How can the engagement process of crowdsourcees be conceptualized? (2) How and why do crowdsourcing intermediaries impact crowdsourcees’ engagement? This study extends existing knowledge by offering IS-researchers a process perspective on engagement and exploring the underlying mechanisms and IT-enabled stimuli that foster value-creation in a mediated and non-mediated setting. A theoretical process model is first conceptualized and then explored with insights from two common cases in the growing field of crowd testing. By triangulating platform and interview data, initial propositions concerning the role of specific stimuli and the intermediary within the engagement process are derived. It is proposed that crowdsourcing enterprises, incorporating intermediaries, have the potential to generate a desired engagement state when perceived stimuli under their control belong to the so-called group of “game changers” and “value adders”, while the intermediary controls mainly “risk factors” for absorbing negative experiences. Apart from the theoretical relevance of studying mediated engagement processes and explaining voluntary use and participation in a socio-technical system, findings support decisions on how to effectively incorporate platform intermediaries.