Using online textual data, principal component analysis and artificial neural networks to study business and innovation practices in technology-driven firms

Giacomo di Tollo, Stoyan Tanev, Giacomo Liotta, Davide De March

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

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.
OriginalsprogEngelsk
TidsskriftComputers in Industry
Vol/bind74
Udgave nummerC
Sider (fra-til)16-28
ISSN0166-3615
DOI
StatusUdgivet - sep. 2015

Fingeraftryk

Principal component analysis
Innovation
Self organizing maps
Neural networks
Industry
Semantics

Citer dette

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title = "Using online textual data, principal component analysis and artificial neural networks to study business and innovation practices in technology-driven firms",
abstract = "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.",
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Using online textual data, principal component analysis and artificial neural networks to study business and innovation practices in technology-driven firms. / di Tollo, Giacomo; Tanev, Stoyan; Liotta, Giacomo; De March, Davide.

I: Computers in Industry, Bind 74, Nr. C, 09.2015, s. 16-28.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Using online textual data, principal component analysis and artificial neural networks to study business and innovation practices in technology-driven firms

AU - di Tollo, Giacomo

AU - Tanev, Stoyan

AU - Liotta, Giacomo

AU - De March, Davide

PY - 2015/9

Y1 - 2015/9

N2 - 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.

AB - 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.

KW - Value co-creation

KW - Product-enabled services

KW - Perception of innovation

KW - Principal Component Analysis

KW - K-means clustering

KW - Self Organizing Map (SOM)

KW - Artificial Neural Network (ANN)

UR - http://www.sciencedirect.com/science/article/pii/S0166361515300361

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DO - 10.1016/j.compind.2015.08.006

M3 - Journal article

VL - 74

SP - 16

EP - 28

JO - Computers in Industry

JF - Computers in Industry

SN - 0166-3615

IS - C

ER -