Worker Retention, Response Quality, and Diversity in Microtask Crowdsourcing: An Experimental Investigation of the Potential for Priming Effects to Promote Project Goals

Online microtask crowdsourcing platforms act as efficient resources for delegating small units of work, gathering data, generating ideas, and more. Members of research and business communities have incorporated crowdsourcing into problem-solving processes. When human workers contribute to a crowdsourcing task, they are subject to various stimuli as a result of task design. Inter-task priming effects - through which work is nonconsciously, yet significantly, influenced by exposure to certain stimuli - have been shown to affect microtask crowdsourcing responses in a variety of ways. Instead of simply being wary of the potential for priming effects to skew results, task administrators can utilize proven priming procedures in order to promote project goals. In a series of three experiments conducted on Amazon’s Mechanical Turk, we investigated the effects of proposed priming treatments on worker retention, response quality, and response diversity. In our first two experiments, we studied the effect of initial response freedom on sustained worker participation and response quality. We expected that workers who were granted greater levels of freedom in an initial response would be stimulated to complete more work and deliver higher quality work than workers originally constrained in their initial response possibilities. We found no significant relationship between the initial response freedom granted to workers and the amount of optional work they completed. The degree of initial response freedom also did not have a significant impact on subsequent response quality. However, the influence of inter-task effects were evident based on response tendencies for different question types. We found evidence that consistency in task structure may play a stronger role in promoting response quality than proposed priming procedures. In our final experiment, we studied the influence of a group-level priming treatment on response diversity. Instead of varying task structure for different workers, we varied the degree of overlap in question content distributed to different workers in a group. We expected groups of workers that were exposed to more diverse preliminary question sets to offer greater diversity in response to a subsequent question. Although differences in response diversity were revealed, no consistent trend between question content overlap and response diversity prevailed. Nevertheless, combining consistent task structure with crowd-level priming procedures - to encourage diversity in inter-task effects across the crowd - offers an exciting path for future study

Accelerating Innovation Through Analogy Mining

The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy, real-world repositories remains a persistent challenge for either human or automated methods. Previous approaches include costly hand-created databases that have high relational structure (e.g., predicate calculus representations) but are very sparse. Simpler machine-learning/information-retrieval similarity metrics can scale to large, natural-language datasets, but struggle to account for structural similarity, which is central to analogy. In this paper we explore the viability and value of learning simpler structural representations, specifically, "problem schemas", which specify the purpose of a product and the mechanisms by which it achieves that purpose. Our approach combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions. We demonstrate that these learned vectors allow us to find analogies with higher precision and recall than traditional information-retrieval methods. In an ideation experiment, analogies retrieved by our models significantly increased people's likelihood of generating creative ideas compared to analogies retrieved by traditional methods. Our results suggest a promising approach to enabling computational analogy at scale is to learn and leverage weaker structural representations.Comment: KDD 201

Crowdsourcing as a way to access external knowledge for innovation

This paper focuses on “crowdsourcing” as a significant trend in the new paradigm of open innovation (Chesbrough 2006; Chesbrough & Appleyard 2007). Crowdsourcing conveys the idea of opening the R&D processes to “the crowd” through a web 2.0 infrastructure. Based on two cases studies of crowdsourcing webstartups (Wilogo and CrowdSpirit), the paper aims to build a framework to characterize and interpret the tension between value creation by a community and value capture by a private economic actor. Contributing to the discussions on “hybrid organizational forms” in organizational studies (Bruce & Jordan 2007), the analysis examines how theses new models combine various forms of relationships and exchanges (market or non market). It describes how crowdsourcing conveys new patterns of control, incentives and co-ordination mechanisms.communauté ; crowdsourcing ; innovation ; formes organisationnelles hybrides ; plateforme ; web 2.0