Collective Intelligence Approach to Measuring Invisible Hand of the Market

2011 ◽  
pp. 435-444 ◽  
Author(s):  
Pawel Skrzynski ◽  
Tadeusz Szuba ◽  
Stanisław Szydło
Keyword(s):  
Collective Intelligence ◽  
Invisible Hand ◽  
Intelligence Approach

scholarly journals A decision support framework for remanufacturing of highly variable products using a collective intelligence approach

Procedia CIRP ◽  
2020 ◽  
Vol 90 ◽  
pp. 594-599
Author(s):  
Mickaël Bettinelli ◽  
Michel Occello ◽  
Damien Genthial ◽  
Daniel Brissaud
Keyword(s):  
Decision Support ◽  
Collective Intelligence ◽  
Decision Support Framework ◽  
Intelligence Approach

scholarly journals Classification of glomerular pathological findings using deep learning and nephrologist–AI collective intelligence approach

2020 ◽  
Vol 141 ◽  
pp. 104231 ◽  
Author(s):  
Eiichiro Uchino ◽  
Kanata Suzuki ◽  
Noriaki Sato ◽  
Ryosuke Kojima ◽  
Yoshinori Tamada ◽  
...  
Keyword(s):  
Deep Learning ◽  
Collective Intelligence ◽  
Pathological Findings ◽  
Intelligence Approach

Intellectual capital management in the fourth stage of IC research

2018 ◽  
Vol 19 (1) ◽  
pp. 157-177 ◽  
Author(s):  
Giustina Secundo ◽  
Maurizio Massaro ◽  
John Dumay ◽  
Carlo Bagnoli
Keyword(s):  
Intellectual Capital ◽  
Value Creation ◽  
Collective Intelligence ◽  
Fourth Stage ◽  
Structured Interviews ◽  
Content Type ◽  
Intelligence System ◽  
The University ◽  
Intelligence Approach

Purpose The purpose of this paper is to present a case study of a university that uses a collective intelligence approach for managing its intellectual capital (IC). Specifically, the authors investigate how one of Europe’s oldest business schools, Ca’ Foscari University of Venice (Italy), manages IC through stakeholder engagement to achieve academia’s third mission so contributing to social and economic development. Design/methodology/approach Data are collected through semi-structured interviews and Ca’ Foscari University’s strategic plan. Secundo et al.’s (2016) collective intelligence framework is used to analyse the data. Alvesson and Deetz’s (2000, pp. 19-20) critical management tasks – insight, critique and transformative redefinition – are adopted to frame and discuss the results. Findings On the assumption that a university is a collective intelligence system, the findings demonstrate that IC management needs to change to incorporate an ecosystem perspective, reflecting the fourth stage of IC research. The IC management at the university incorporates its core goal (what), the collective involvement of internal and external stakeholders to achieve the goal (who), the motivations behind the achievement of the goal (why) and, finally, the processes activated inside the university (how) and indicators to assess value creation. Research limitations/implications A new perspective for managing IC in universities that adopts a collective intelligence approach is further developed. Contributions to the fourth stage of IC research – IC in an ecosystem – are highlighted that expand the concept of IC value creation beyond universities into wider society. Practical implications Two key consequences of this case study are that more stakeholders have become involved in IC management and that IC management requires critical rethinking, given the universities’ evolving role. Originality/value This paper brings together issues that are usually dealt with in separate domains of the literature: IC management and collective intelligence in the university setting.

Managing intellectual capital through a collective intelligence approach

2016 ◽  
Vol 17 (2) ◽  
pp. 298-319 ◽  
Author(s):  
Giustina Secundo ◽  
John Dumay ◽  
Giovanni Schiuma ◽  
Giuseppina Passiante
Keyword(s):  
Public Sector ◽  
Intellectual Capital ◽  
Collective Intelligence ◽  
Future Research ◽  
Fourth Stage ◽  
Underlying Assumption ◽  
Content Type ◽  
The Public ◽  
The University ◽  
Intelligence Approach

Purpose – The purpose of this paper is to provide a new framework for managing intellectual capital (IC) inside a university considering the collective intelligence perspective. Design/methodology/approach – The research method uses the fourth stage of IC research and adopts the collective intelligence approach. The underlying assumption behind the framework is to consider the university as a collective intelligence system in which the tangible and intellectual assets are coordinated towards the achievement of strategic goals. Findings – The conceptual framework for IC management harnesses the power of IC, collectively created by the engagement of multiple stakeholders inside the university network. The main components are the final goal of a university (what); the collective human capital to achieve the goal (who); the processes activated inside the university (how); and finally the motivations behind the achievement of the goal (why). Research limitations/implications – The research is exploratory and the framework offers opportunities for refinement. Future research is needed to verify the application of the framework to other organisations in the public sector intended as collective intelligence systems. A new perspective for managing IC in universities adopting the collective intelligence approach is developed. Contribution to the fourth stage (ecosystem) of IC research is highlighted, expanding the concept of IC value creation beyond the university into wider society. Practical implications – The framework can be used to manage IC strategically in all the systems interpreted as collective intelligence systems in which the role of IC creation from multiple actors is relevant. This makes possible the understanding of how IC helps create value for the society and the region in which the university operates. Originality/value – The originality of the paper is in bringing together issues usually dealt within the literature in separate domains, such as IC management and collective intelligence perspective. The concept of collective intelligence remains an unexplored field in relation to IC management in the public sector. The collective intelligence approach provides a novel contribution to managing IC and is intended to inspire future research.

Identifying Important Segments in Videos: A Collective Intelligence Approach

2014 ◽  
Vol 23 (02) ◽  
pp. 1440010 ◽  
Author(s):  
Ioannis Karydis ◽  
Markos Avlonitis ◽  
Konstantinos Chorianopoulos ◽  
Spyros Sioutas
Keyword(s):  
Information Seeking ◽  
Collective Intelligence ◽  
Information Seeking Behavior ◽  
Video Content ◽  
Search Results ◽  
Detection Tool ◽  
Seeking Behavior ◽  
Collective Information ◽  
Intelligence Approach ◽  
User Experiment

This work studies collective intelligence behavior of Web users that share and watch video content. Accordingly, it is proposed that the aggregated users' video activity exhibits characteristic patterns. Such patterns may be used in order to infer important video scenes leading thus to collective intelligence concerning the video content. To this end, experimentation is based on users' interactions (e.g., pause, seek/scrub) that have been gathered in a controlled user experiment with information-rich videos. Collective information seeking behavior is then modeled by means of the corresponding probability distribution function. Thus, it is argued that the bell-shaped reference patterns are shown to significantly correlate with predefined scenes of interest for each video, as annotated by the users. In this way, the observed collective intelligence may be used to provide a video-segment detection tool that identifies the importance of video scenes. Accordingly, both a stochastic and a pattern matching approach are applied on the users' interactions information. The results received indicate increased accuracy in identifying the areas selected by users as having high importance information. In practice, the proposed techniques might improve both navigation within videos on the web as well as video search results with personalised video thumbnails.

Sign in / Sign up

Export Citation Format

Share Document