Abstract
The growing complexity of markets, business development and administration has fostered the application of more sophisticated quantitative methods aiming at the analysis of common features and differences amongst different businesses. Amongst those quantitative methods, Neural Networks are gaining support of both practitioners and scholars. This is due to their generalisation capabilities which make them apt to be used without any preliminary assumptions about the variables at hand or about the specific types of the corresponding models. To this extent, we are using them to classify firms w.r.t. the relationship between the perception of their innovativeness and the degree of their involvement in value co-creation activities—the extent to which they involve end users in the definition of their final products and services. We will show that businesses from specific sectors could have a higher degree of involvement in value co-creation. The mapping between the type of firms and the degree of their involvement in value co-creation is of particular interest since they describe attributes and activities and a completely different heuristic level. We have also studied businesses belonging to stock Exchange indexes, which are regarded as the specimen of the economic and financial situation of a Country. Our main contribution will be in translating the applicability of ANN in innovation research.
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Notes
- 1.
As for the Principal Component Analysis we have used the Vari-max rotation method. Parameters for accepting the PCA analysis were: \(correlation~ table~ determinant~ > ~\frac{10}{-5},~ Kaiser-Meyer-Olkin measure~ > ~0.5,~ Bartletts ~significance~ level~ < ~0.05.\)
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di Tollo, G., Tanev, S., Slim, K.M., De March, D. (2014). Determining the Relationship Between Co-creation and Innovation by Neural Networks. In: Faggini, M., Parziale, A. (eds) Complexity in Economics: Cutting Edge Research. New Economic Windows. Springer, Cham. https://doi.org/10.1007/978-3-319-05185-7_3
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