The 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.
The prospect theory and grey relational analysis are used to develop the proposed two-sided matching decision framework.
By 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.
The 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.
The proposed model can be applied to achieve a better matching between technological knowledge suppliers and users in a broker-assisted MFT.
A better matching between technological knowledge suppliers and users can enhance the success of open innovation, thereby contributing to the betterment of the society.
This 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.
This work is partially funded by the National Natural Science Foundation of China (71503103, 71572040), Natural Science Foundation of Jiangsu Province (BK20150157); Social Science Foundation of Jiangsu Province (14GLC008), the Fundamental Research Funds for the Central Universities (2017JDZD06) and a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant.
Liu, Y. and Li, K.W. (2017), "A two-sided matching decision method for supply and demand of technological knowledge", Journal of Knowledge Management, Vol. 21 No. 3, pp. 592-606. https://doi.org/10.1108/JKM-05-2016-0183
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