Addressing antecedents’ importance of open innovation between industry and universities: A neural network-based importance-performance analysis with a fuzzy approach
Year: 2024
Type: article
Abstract: Determining the importance of major antecedents of open innovation between such distinct partners as industry and universities influences the decision-making regarding resources and effort allocation to their improvement, according to the strategic objectives of the firms. For this purpose, the present paper proposes an approach for conducting their importance-performance analysis based on fuzzy set theory and neural networks. Considering a hierarchical component model that integrates the components of the major antecedents, this study advances a research framework that first involves the operationalization of the collected data as fuzzy numbers. Then, the SHapley Additive exPlanation-based method estimates the derived importance of each component in the hierarchical component model using an optimal two-layers back-propagation network. Finally, a nine quadrants division of the importance-performance analysis developed on the basis of relevance and determinance measures of the analyzed antecedent components, delineates the prioritization of their potential improvements. A case study aims to demonstrate the developed research framework, illustrating its effectiveness and flexibility in decision-making related to the improvement of such antecedents.
Source: Alexandria Engineering Journal
Authors: Marius Băban, Călin-Florin Băban
Institution: University of Oradea
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,300
OpenAlex ID: W4401505500