Crowd vs. experts

Oosterman, Jasper; Bozzon, Alessandro; Houben, Geert‐Jan; Nottamkandath, Archana; Dijkshoorn, Chris; Aroyo, Lora; Leyssen, M.H.R.; Traub, Myriam C.

doi:10.1145/2567948.2576960

Cited by 12 publications

(5 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…e KIC intermediaries are typical two-or multisided markets, where the network effects will attract an increasing number of participants from both supply and demand sides, to join the platform for valuable advantages [26]. Oosterman et al [27] suggested that the KIC has the advantage of low cost for service requesters; therefore, it allows cost-saving and efficient use of resources. Additionally, by inviting a crowd of customers to new product development through a crowdsourcing practice, taking Dell IdeaStorm [28] for example, innovative product or service ideas can be generated and then applied to the production process, thus making the products more attractive to markets and adding value to companies' business [19].…”

Section: Knowledge-intensivementioning

confidence: 99%

Understanding Service Providers’ Competency in Knowledge‐Intensive Crowdsourcing Platforms: An LDA Approach

2021

View full text Add to dashboard Cite

Knowledge-intensive crowdsourcing (KIC) is becoming one of the most promising domains of crowdsourcing by leveraging human intelligence and building a large labor-intensive service network. In this network, the service providers (SPs) constitute the backbone of the KIC platform and play an important role in connecting the platform and service requesters. The SPs are a group of distributed crowds with a complex composition and high level of uncertainty, resulting in great challenges in service quality and platform management. Understanding the SPs’ competency is an effective way for the platform to manage them. Therefore, we attempt to connect the competency analysis to the environment of KIC to investigate and identify the criteria of SPs’ competency (i.e., the competency factors and dimensions required for being competent for the SPs’ business). To this end, we leverage the Latent Dirichlet Allocation (LDA) model to explore and extract hidden competency dimensions from online interview records. We then introduce the competency theory to identify and label the competency factors and dimensions and construct the three-level KSAT competency model, which presents a comprehensive vision of the SPs’ performance standards in the context of KIC. Given the competency criteria in the KSAT competency model, we use the Best-Worst Method (BWM) to determine their weights, which reflect their importance when evaluating the SPs’ competency from the platforms’ perspective. The results show that skill and knowledge are the two most important competency factors, and customer relationship management and communication ability are the two most valuable competency dimensions when evaluating the SPs’ competency. Furthermore, the KSAT competency model can be applied to analyze the competency of individuals or organizations in many other industries as well.

show abstract

Section: Knowledge-intensivementioning

confidence: 99%

Understanding Service Providers’ Competency in Knowledge‐Intensive Crowdsourcing Platforms: An LDA Approach

2021

View full text Add to dashboard Cite

show abstract

“…Traditionally, new knowledge can be acquired through research and learning (Huber 1991) and/or by hiring new employees (Davenport and Prusak 2000) with distinct knowledge and expertise, evidenced by their curriculum and qualifications. In citizen science, however, recruiting can be either to acquire new knowledge and learn from the public (Afuah and Tucci 2012; Oosterman et al 2014) or to access and use specific skills of the public.…”

Section: Accessing Versus Acquiring Knowledgementioning

confidence: 99%

A Knowledge Perspective on Quality in Complex Citizen Science

Lopez

Soekijad

Berends

et al. 2020

Citizen Science: Theory and Practice

View full text Add to dashboard Cite

This article examines how project leaders in complex citizen science projects ensure the quality of project outcomes given the challenges of involving citizens, whose knowledge is diverse and unknown beforehand. To this aim, a qualitative multiple-case study was carried out to compare the practices of five collaborative online citizen science projects in the humanities in which citizens transcribe, translate, and annotate handwritten manuscripts from the 17th, 18th, and 19th centuries. The choices made to access knowledge, thus to recruit participants either through open or targeted calls, seem to be fundamental for the different configurations of knowledge management activities that project leaders apply to ensure quality outcomes. Other factors influencing these knowledge management configurations are citizens' proximity, knowledge characteristics, technology affordances, and the extent to which project leaders are aware of citizens' backgrounds and skills. This study adds to earlier frameworks proposed to advise the design and management of citizen science projects. By taking a knowledge perspective, this article provides practical directions for project leaders involved in citizen science and highlights the need to put time and effort in managing knowledge processes.

show abstract

“…The early work [27] defined the problem of artwork annotation in the context of the Rijksmuseum Amsterdam in the Netherlands. The subsequent works [8,28] introduced an experimental methodology, and reports on preliminary results mainly focused on Artwork-centric knowledge extraction. To the best of our knowledge, our work is the first one that systematically studies the performance of Artwork-centric and Class-centric knowledge extraction techniques in human computation platforms, and assess their identification and location performance with respect to a high-quality ground truth.…”

Section: Related Workmentioning

confidence: 99%

“…Crowdsourced artwork annotation is a representative example of a Crowdsourced Knowledge Creation (CKC) task [8], i.e. a class of crowdsourcing tasks where workers are requested to stress their highlevel cognitive abilities (e.g., knowledge synthesis, data interpretation), and draw from their experience or education, in order to solve problems for which a unique, factual solution might not exist.…”

Section: Introductionmentioning

confidence: 99%