Distant Search, Narrow Attention: How Crowding Alters Organizations’ Filtering of Suggestions in Crowdsourcing
By Henning Piezunka, Linus Dahlander. 2014
448 citations recorded by the source corpus.
Subfields: Communication, Computer Science Applications, Safety Research
View Paper: https://www.semanticscholar.org/paper/4e6ab471ad97f9a90bbd2638662479105c19c5c4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Communication
In Computer Science Applications
- Kevin J. Boudreau
- Gerard George
- Paola Criscuolo
- Karim R. Lakhani
- Louis Tay
- Joel West
- Marcel Bogers
- Amrit Tiwana
In Safety Research
- Kevin J. Boudreau
- Paola Criscuolo
- Karim R. Lakhani
- Martine R. Haas
- Gerard George
- Louis Tay
- Joel West
Citations in the corpus
- Which Problems to Solve? Online Knowledge Sharing and Attention Allocation in Organizations (2014, background): “…discussion forum, a central feature of the context that can be expected to influence the allocation of attention to a focal problem is problem crowding, in the form of the number of other problems concurrently posted to the forum (see Jones, David, & Rafaeli, 2004; Piezunka & Dahlander, 2014).”
- The open innovation research landscape: established perspectives and emerging themes across different levels of analysis (2016, unknown): “Since there may be the strong preference for outsiders’ knowledge sourcing and necessity to entice external contributors (Menon and Pfeffer 2003; Piezunka and Dahlander 2015), it is very important to develop certain mechanisms that will allow a smooth decision-making process, which will consist of…”
- Looking Across and Looking Beyond the Knowledge Frontier: Intellectual Distance, Novelty, and Resource Allocation in Science (2016, background): “The challenge of evaluating ideas has only grown with increasing use of “ideation,” platform-based contests, crowdsourcing, and crowdfunding as a means of generating a large number of proposals (Agrawal et al. 2014, Piezunka and Dahlander 2014, Mollick and Nanda 2016).”
- Text Preprocessing for Text Mining in Organizational Research: Review and Recommendations (2020, background): “…also applied text mining for a variety of other tasks, including understanding the similarity of texts (Banks et al., 2018), understanding context-specific “state” variables like attitudes (e.g., sentiment analysis), and predicting behavioral outcomes from text data (Piezunka & Dahlander, 2015).”