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A two-sided matching decision method for supply and demand of technological knowledge

A two-sided matching decision method for supply and demand of technological knowledge PurposeThe purpose of this paper is to propose a novel prospect-based two-sided matching decision model for matching supply and demand of technological knowledge assisted by a broker. This model enables the analyst to account for the stakeholders’ psychological behaviours and their impact on the matching decision in an open innovation setting.Design/methodology/approachThe prospect theory and grey relational analysis are used to develop the proposed two-sided matching decision framework.FindingsBy properly calibrating model parameters, the case study demonstrates that the proposed approach can be applied to real-world technological knowledge trading in a market for technology (MFT) and yields matching results that are more consistent with the reality.Research limitations/implicationsThe proposed model does not differentiate the types of knowledge exchanged (established vs novel, tacit vs codified, general vs specialized) (Ardito et al., 2016, Nielsen and Nielsen, 2009). Moreover, the model focuses on incorporating psychological behaviour of the MFT participants and does not consider their other characteristics.Practical implicationsThe proposed model can be applied to achieve a better matching between technological knowledge suppliers and users in a broker-assisted MFT.Social implicationsA better matching between technological knowledge suppliers and users can enhance the success of open innovation, thereby contributing to the betterment of the society.Originality/valueThis paper furnishes a novel theoretical model for matching supply and demand in a broker-assisted MFT. Methodologically, the proposed model can effectively capture market participants’ psychological considerations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Knowledge Management Emerald Publishing

A two-sided matching decision method for supply and demand of technological knowledge

Journal of Knowledge Management , Volume 21 (3): 15 – May 8, 2017

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1367-3270
DOI
10.1108/JKM-05-2016-0183
Publisher site
See Article on Publisher Site

Abstract

PurposeThe purpose of this paper is to propose a novel prospect-based two-sided matching decision model for matching supply and demand of technological knowledge assisted by a broker. This model enables the analyst to account for the stakeholders’ psychological behaviours and their impact on the matching decision in an open innovation setting.Design/methodology/approachThe prospect theory and grey relational analysis are used to develop the proposed two-sided matching decision framework.FindingsBy properly calibrating model parameters, the case study demonstrates that the proposed approach can be applied to real-world technological knowledge trading in a market for technology (MFT) and yields matching results that are more consistent with the reality.Research limitations/implicationsThe proposed model does not differentiate the types of knowledge exchanged (established vs novel, tacit vs codified, general vs specialized) (Ardito et al., 2016, Nielsen and Nielsen, 2009). Moreover, the model focuses on incorporating psychological behaviour of the MFT participants and does not consider their other characteristics.Practical implicationsThe proposed model can be applied to achieve a better matching between technological knowledge suppliers and users in a broker-assisted MFT.Social implicationsA better matching between technological knowledge suppliers and users can enhance the success of open innovation, thereby contributing to the betterment of the society.Originality/valueThis paper furnishes a novel theoretical model for matching supply and demand in a broker-assisted MFT. Methodologically, the proposed model can effectively capture market participants’ psychological considerations.

Journal

Journal of Knowledge ManagementEmerald Publishing

Published: May 8, 2017

References