In this paper we introduce a method that combines principal component analysis, correlation analysis, K-means clustering and self organizing maps for the quantitative semantic analysis of textual data focusing on the relationship between firms' co-creation activities, the perception of their innovation and the articulation of the attributes of their product-enabled services. Principal component analysis was used to identify the components of firms' value co-creation activities and service value attributes; correlation analysis was used to examine the relationship between the degree of involvement in specific co-creation activities, the online articulation of firms' service value attributes and the perception of their innovativeness. K-means and self organizing map (SOM) are used to cluster firms with regards to their involvement in co-creation and new service development, and, additionally, as complementary tools for studying the relationship between co-creation and new service development. The results show that, first, there is a statistically significant relationship between firms' degree of involvement in co-creation activities and the degree of articulation of their service value attributes; second, the relationship should be considered within the context of firms' innovation activities; third, OS Software-driven firms are the best example in terms of co-creation and new product-enabled service development, i.e. the collaborative principles built in their customer participation platforms should be adopted by other (non-software) firms interested in enhancing their innovation capacity through involvement in co-creation and new product-enabled service development.

Artificial neural network (ANN), K-means clustering, Perception of innovation, Principal component analysis, Product-enabled services, Self organizing map (SOM), Value co-creation
Computers in Industry
Sprott School of Business

Di Tollo, G. (Giacomo), Tanev, S, Liotta, G. (Giacomo), & De March, D. (Davide). (2015). Using online textual data, principal component analysis and artificial neural networks to study business and innovation practices in technology-driven firms. Computers in Industry, 74, 16–28. doi:10.1016/j.compind.2015.08.006