Crowdsourcing, open innovation and collective intelligence in the scientific method : a research agenda and operational framework

Open Access PublikationThe lonely researcher trying to crack a problem in her office still plays an important role in fundamental research. However, a vast exchange, often with participants from different fields is taking place in modern research activities and projects. In the “Research Value Chain” (a simplified depiction of the Scientific Method as a process used for the analyses in this paper), interactions between researchers and other individuals (intentional or not) within or outside their respective institutions can be regarded as occurrences of Collective Intelligence. “Crowdsourcing” (Howe 2006) is a special case of such Collective Intelligence. It leverages the wisdom of crowds (Surowiecki 2004) and is already changing the way groups of people produce knowledge, generate ideas and make them actionable. A very famous example of a Crowdsourcing outcome is the distributed encyclopedia „Wikipedia“. Published research agendas are asking how techniques addressing “the crowd” can be applied to non-profit environments, namely universities, and fundamental research in general. This paper discusses how the non-profit “Research Value Chain” can potentially benefit from Crowdsourcing. Further, a research agenda is proposed that investigates a) the applicability of Crowdsourcing to fundamental science and b) the impact of distributed agent principles from Artificial Intelligence research on the robustness of Crowdsourcing. Insights and methods from different research fields will be combined, such as complex networks, spatially embedded interacting agents or swarms and dynamic networks. Although the ideas in this paper essentially outline a research agenda, preliminary data from two pilot studies show that nonscientists can support scientific projects with high quality contributions. Intrinsic motivators (such as “fun”) are present, which suggests individuals are not (only) contributing to such projects with a view to large monetary rewards

Conceptualisation of "crowdsourcing" term in management sciences

Background. Crowdsourcing is a relatively new concept, nonetheless it has been raising more and more interest with researchers. This is a result of its potential since it enables improving business processes, creating open innovations, building of competitive advantage, access to experience, information, crowd skills and work, problem solving, crisis management, expanding the organisation's existing activity and offer, creating the organisation's image, improving communication with the surroundings, optimising costs of the organisation's activity. However, although the subject of crowdsourcing constitutes one of the currently emerging directions of research on the basis of management sciences, one observes a peculiar exploration difficulty. It may result from incoherence in conceptualisation or explication of this term. Research aims. The aim of this article is an attempt, basing on the existing research efforts, to conceptualise crowdsourcing based on management sciences. In the article a proposal of conceptualising the notion of crowdsourcing was presented including its levels. Methodology. For the needs of specifying, evaluation, and identification of the existing state of knowledge on crowdsourcing, a systematic literature review was conducted. It enabled getting familiar with the results of similar research, its selection and critical analysis and based on that it was used for expanding the earlier findings of other researchers. The biggest, full text databases i.e Ebsco, Elsevier/Springer, Emerald, Proquest, Scopus, and ISI Web of Science, which include the majority of journals on strategic management were analysed. In order to establish the state of knowledge and existing findings a review of databases in Poland: BazEkon and CEON was also conducted. 54 elaborations of English language databases and 41 from Polish language databases from the period of 2006-2017 were analysed. Key findings. A review of the scientific output revealed incoherence in the conceptualisation of the term of crowdsourcing. The approaches proposed in the existing literature are inadequate and do not allow for full understanding of crowdsourcing

Human Computation and Convergence

Humans are the most effective integrators and producers of information, directly and through the use of information-processing inventions. As these inventions become increasingly sophisticated, the substantive role of humans in processing information will tend toward capabilities that derive from our most complex cognitive processes, e.g., abstraction, creativity, and applied world knowledge. Through the advancement of human computation - methods that leverage the respective strengths of humans and machines in distributed information-processing systems - formerly discrete processes will combine synergistically into increasingly integrated and complex information processing systems. These new, collective systems will exhibit an unprecedented degree of predictive accuracy in modeling physical and techno-social processes, and may ultimately coalesce into a single unified predictive organism, with the capacity to address societies most wicked problems and achieve planetary homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added references to page 1 and 3, and corrected typ

Theoretical Underpinnings and Practical Challenges of Crowdsourcing as a Mechanism for Academic Study

Researchers in a variety of fields are increasingly adopting crowdsourcing as a reliable instrument for performing tasks that are either complex for humans and computer algorithms. As a result, new forms of collective intelligence have emerged from the study of massive crowd-machine interactions in scientific work settings as a field for which there is no known theory or model able to explain how it really works. Such type of crowd work uses an open participation model that keeps the scientific activity (including datasets, methods, guidelines, and analysis results) widely available and mostly independent from institutions, which distinguishes crowd science from other crowd-assisted types of participation. In this paper, we build on the practical challenges of crowd-AI supported research and propose a conceptual framework for addressing the socio-technical aspects of crowd science from a CSCW viewpoint. Our study reinforces a manifested lack of systematic and empirical research of the symbiotic relation of AI with human computation and crowd computing in scientific endeavors

Management of «Systematic Innovation»: A kind of quest for the Holy Grail!

In this paper, authors propose a contribution for improving the open innovation processes. It shows the necessity to get an efficient methodology for open innovation in order to build a computer aided tool for inventive design in Process Systems Engineering (PSE). The proposed methodology will be evocated to be fully used in the context of the “revolutionary” concepts around the so-called factory for the future, also called integrated digital factory, innovative factory… As a result the main contribution of this paper is to propose a software prototype for an Open Computer Aided Innovation 2.0. By definition this open innovation relies on collaboration. This collaboration should enable a community, with a very broad spectrum of skills, to share data, information, knowledge and ideas. As a consequence, a first sub objective is to create a methodological framework that takes advantages of collaboration and collective intelligence (with its capacity to join intelligence and knowledge). Furthermore, the raise of the digital company and more particularly the breakthroughs in information technologies is a powerful enabler to extend and improve the potential of collective intelligence. The second sub objective is to propose a problem resolution process to impel creativity of expert but also to develop, validate and select innovative solutions. After dealing with the importance of Process Innovation and Problem solving investigation in PSE, the proposed approach originally based on an extension of the TRIZ theory (Russian acronym for Theory of Inventive Problem Solving), has been improved by using approach such as case-based reasoning, in order to tackle and revisit problems encountered in the PSE. A case study on biomass is used to illustrate the capabilities of the methodology and the tool