Crowd Science: Measurements, Models, and Methods

Abstract

The increasing practice of engaging crowds, where organizations use IT to connect with dispersed individuals for explicit resource creation purposes, has precipitated the need to measure the precise processes and benefits of these activities over myriad different implementations. In this work, we seek to address these salient and non-trivial considerations by laying a foundation of theory, measures, and research methods that allow us to test crowdengagement efficacy across organizations, industries, technologies, and geographies. To do so, we anchor ourselves in the Theory of Crowd Capital, a generalizable framework for studying IT-mediated crowd-engagement phenomena, and put forth an empirical apparatus of testable measures and generalizable methods to begin to unify the field of crowd science.

Wikidatians are Born: Paths to Full Participation in a Collaborative Structured Knowledge Base

“…Gaining insights over these aspects would also be beneficial to assess the crowd capital of Wikidata, i.e. the organisational-level resource generated by its community [27], but to the best of our knowledge they have not been investigated yet in this project. This paper seeks to fill this gap by analysing the pathways to full participation of Wikidata editors.…”

Tailoring a Points Scoring Mechanism for Crowd-Based Knowledge Pooling

“…The body of literature on crowdsourcing has been rapidly growing [11] offering four categories to describe crowdsourcing systems: crowdsolving, crowdcreation, crowdrating and crowdprocessing [16] and a conceptual framework of crowd capital [13,17]. An active crowd of participants is crucial for such systems, raising the question of providing tailored incentives that will promote user motivation [18,19].…”

One for All? Managing External and Internal Crowds through a Single Platform - A Case Study

“…According to Prpić et al [23], in pursuit of crowd capital, executives should not think of siloed potentialities but rather of hypothetically overlapping tools in an overall crowdsourcing mix. Knowledge contained in any particular crowd is never static either [21]. With this case study, we aim at shedding light on such a mixing of the potential of boths external and internal crowds via one platform.…”