Journal article
Predictive Crowding as a Concept to Support the Assessment of Disruptive Ideas: A Conceptual Framework
Journal of New Business Ideas and Trends, Vol.12(2), pp.1-13
2014
Abstract
Purpose - The purpose of this paper, is to develop a conceptual framework for a holistic view on how to use crowd intelligence to identify the logic of sequences to fully address the potential of crowds, and contest the common assumption that one crowd fits all challenges. Design/methodology/approach - This conceptual development is based on both deductive and inductive reasoning and is the result of interdisciplinary collaboration of partner universities and research institutions. Findings - A number of research postulations are presented, opening a future research stream to provide a new perspective on application possibilities of crowdsourcing in SMEs, and encourage further discussion on crowd definition and crowd selection for varying applications. Research limitations/implications - Subsequent empirical work is called for to test various research postulations. Practical Implications - The conceptual framework demonstrates the applicability of crowd intelligence for predictive assessment of disruptive ideas, and adds to the literature on how SMEs could use Predictive Crowding (Expert Crowds) to assess disruptive ideas
Details
- Title
- Predictive Crowding as a Concept to Support the Assessment of Disruptive Ideas: A Conceptual Framework
- Authors
- Thomas Peisl (Author) - Munich University, GermanyWillem Selen (Author) - University of the Sunshine Coast - Faculty of Arts and BusinessRobert Raeside (Author) - Edinburgh Napier University, United KingdomTatiana Albera (Author) - Edinburgh Napier University, United Kingdom
- Publication details
- Journal of New Business Ideas and Trends, Vol.12(2), pp.1-13
- Publisher
- Australian Business Education Research Association
- Date published
- 2014
- ISSN
- 1446-8719; 1446-8719
- Copyright note
- Copyright © 2014 JNBIT. Reproduced with permission of the publisher.
- Organisation Unit
- University of the Sunshine Coast, Queensland
- Language
- English
- Record Identifier
- 99448760102621
- Output Type
- Journal article
- Research Statement
- false
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