Abstract:Since the advent of artificial intelligence, researchers have been trying to create machines that emulate human behaviour. Back in the 1960s however, Licklider (1960) believed that machines and computers were just part of a scale in which computers were on one side and humans on the other (human computation). After almost a decade of active research into human computation and crowdsourcing, this paper presents a survey of crowdsourcing human computation systems, with the focus being on solving micro-tasks and … Show more
“…Seekers must then merge the many submissions for the same micro‐task, as well as integrate the results of all the micro‐tasks for the main initial task to be completed (Luz et al . ). This is another critical operation for obtaining valuable results from a crowdsourcing session.…”
Section: Avenues For Future Research On Crowdsourcingmentioning
confidence: 97%
“…While the literature contains some empirical evidence on the techniques used in crowdsourcing platforms to aggregate the results of micro‐tasks (Luz et al . ), there are no systematic studies on how this activity can be performed effectively and how one technique may be more appropriate than another in specific applications. As a consequence, we propose the seventh research question: SRQ7 : How can the friction that emerges when seekers aggregate micro‐tasks accomplished by different individuals be overcome in an effective manner?…”
Section: Avenues For Future Research On Crowdsourcingmentioning
As academic and practitioner studies on crowdsourcing have been building up since 2006, the subject itself has progressively gained in importance within the broad field of management. No systematic review on the topic has so far appeared in management journals, however; moreover, the field suffers from ambiguity in the topic's definition, which in turn has led to its largely unstructured evolution. The authors therefore investigate the existing body of knowledge on crowdsourcing systematically through a penetrating review in which the strengths and weakness of this literature stream are presented clearly and then future avenues of research are set out. The review is based on 121 scientific articles published between January 2006 and January 2015. The review recognizes that crowdsourcing is ingrained in two mainstream disciplines within the broader subject matter of innovation and management: (1) open innovation; and (2) co-creation. The review, in addition, also touches on several issues covered in other theoretical streams: (3) information systems management; (4) organizational theory and design; (5) marketing; and (6) strategy. The authors adopt a process perspective, applying the 'Input-Process-Output' framework to interpret research on crowdsourcing within the broad lines of: (1) Input (Problem/Task); (2) Process (session management; problem management; knowledge management; technology); and (3) Outcome (solution/completed task; seekers' benefits; solvers' benefits). This framework provides a detailed description of how the topic has evolved over time, and suggestions concerning the future direction of research are proposed in the form of research questions that are valuable for both academics and managers
“…Seekers must then merge the many submissions for the same micro‐task, as well as integrate the results of all the micro‐tasks for the main initial task to be completed (Luz et al . ). This is another critical operation for obtaining valuable results from a crowdsourcing session.…”
Section: Avenues For Future Research On Crowdsourcingmentioning
confidence: 97%
“…While the literature contains some empirical evidence on the techniques used in crowdsourcing platforms to aggregate the results of micro‐tasks (Luz et al . ), there are no systematic studies on how this activity can be performed effectively and how one technique may be more appropriate than another in specific applications. As a consequence, we propose the seventh research question: SRQ7 : How can the friction that emerges when seekers aggregate micro‐tasks accomplished by different individuals be overcome in an effective manner?…”
Section: Avenues For Future Research On Crowdsourcingmentioning
As academic and practitioner studies on crowdsourcing have been building up since 2006, the subject itself has progressively gained in importance within the broad field of management. No systematic review on the topic has so far appeared in management journals, however; moreover, the field suffers from ambiguity in the topic's definition, which in turn has led to its largely unstructured evolution. The authors therefore investigate the existing body of knowledge on crowdsourcing systematically through a penetrating review in which the strengths and weakness of this literature stream are presented clearly and then future avenues of research are set out. The review is based on 121 scientific articles published between January 2006 and January 2015. The review recognizes that crowdsourcing is ingrained in two mainstream disciplines within the broader subject matter of innovation and management: (1) open innovation; and (2) co-creation. The review, in addition, also touches on several issues covered in other theoretical streams: (3) information systems management; (4) organizational theory and design; (5) marketing; and (6) strategy. The authors adopt a process perspective, applying the 'Input-Process-Output' framework to interpret research on crowdsourcing within the broad lines of: (1) Input (Problem/Task); (2) Process (session management; problem management; knowledge management; technology); and (3) Outcome (solution/completed task; seekers' benefits; solvers' benefits). This framework provides a detailed description of how the topic has evolved over time, and suggestions concerning the future direction of research are proposed in the form of research questions that are valuable for both academics and managers
“…A VLM is an IT-mediated market where individuals can provide online services that can be performed anywhere (often by engaging in spot labour), offered by organisations generally through micro-tasks, typifying the production model of crowdsourcing (Brabham 2008), in exchange for monetary compensation (Prpić, Taeihagh and Melton 2014;Luz, Silva and Novais 2015).…”
What are the similarities and differences between crowdsourcing and sharing economy? What factors influence their use in developing countries? In light of recent developments in the use of IT-mediated technologies, such as crowdsourcing and the sharing economy, this manuscript examines their similarities and differences, and the challenges regarding their effective use in developing countries. We first examine each individually and highlight different forms of each IT-mediated technology. Given that crowdsourcing and sharing economy share aspects such as the use of IT, a reliance on crowds, monetary exchange, and the use of reputation systems, we systematically compare the similarities and differences of different types of crowdsourcing with the sharing economy, thus addressing a gap in the current literature. Using this knowledge, we examine the different challenges faced by developing countries when using crowdsourcing and the sharing economy, and highlight the differences in the applicability of these IT-mediated technologies when faced with specific development issues.
“…During the last decade, crowdsourcing has become popular among companies, institutions and universities as a promising on-line problem solving paradigm tapping the intelligence of the crowd. Crowdsourcing platforms such as Mechanical Turk have been widely applied to solve various tasks such as data collection, image labeling, recognition and categorization, translation [24], etc. Basically, the studies on crowdsourcing mainly focus on: (1) definitions and taxonomy; (2) applications and systems; (3) motivations and incentives; (4) task designing and assignment; (5) answers aggregation and quality control.…”
Section: Task-oriented Crowdsourcingmentioning
confidence: 99%
“…As crowdsourcing has become a powerful paradigm for large scale problem-solving especially for those tasks that are difficult for computers but easy for human [8,24], we make use of crowdsourcing to identify and extract those Knowledge Cells as well as their relevant key information and relationships from huge amount of PDFs. It is notable that during the process of identification and extraction, some activities can generally be broken into small tasks which are often repetitive and do not require any specific expertise.…”
Scientific literature contains a lot of meaningful objects such as Figures, Tables, Definitions, Algorithms, etc., which are called Knowledge Cells hereafter. An advanced academic search engine which could take advantage of Knowledge Cells and their various relationships to obtain more accurate search results is expected. Further, it's expected to provide a fine-grained search regarding to Knowledge Cells for deep-level information discovery and exploration. Therefore, it is important to identify and extract the Knowledge Cells and their various relationships which are often intrinsic and implicit in articles. With the exponential growth of scientific publications, discovery and acquisition of such useful academic knowledge impose some practical challenges For example, existing algorithmic methods can hardly extend to handle diverse layouts of journals, nor to scale up to process massive documents. As crowdsourcing has become a powerful paradigm for large scale problem-solving especially for tasks that are difficult for computers but easy for human, we consider the prob
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