An Integrative Approach to Innovation Management: Patterns by Nicolas Zacharias

By Nicolas Zacharias

Nicole Zacharias identifies 4 types of innovation orientation and indicates that the main leading edge businesses are financially no longer the main profitable. moreover, he investigates optimistic and adverse shopper responses to diversified dimensions of innovativeness in addition to contingencies that would adjust those linkages.

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Cluster 3 (Proactive Customer-Oriented Innovators) The environmental uncertainty of these companies is mediocre: market-related dynamism is at a medium range, and technological turbulence exhibits the lowest score among all clusters. However, this type engages very actively in customer information acquisition activities, which is the type’s most dominant characteristic, despite the low environmental uncertainty. With regard to internal arrangements, this type mainly benefits from its comparatively high innovation orientation of culture and HR systems.

Second, customer-related boundaries may establish a communication platform that increases customer acceptance of new products (Schatzel/Calantone 2006; Sorescu/Shankar/Kushwaha 2007). , Calantone/Chan/Cui 2006; Danneels/Kleinschmidt 2001; Hauser/Tellis/ Griffin 2006). Thus, configuration and boundary theory both contribute to this study: Configuration theory indicates the need to consider internal and external elements to attain an integrated view of organizations. We consider both internal arrangements and environmental variables highly relevant for our investigation of companies’ innovation orientations.

Determining the appropriate number of clusters is central to cluster analysis (Milligan/Cooper 1987); we employed the elbow criterion and the pseudo-t² index (Duda/Hart 1973) in combination with the hierarchical clustering algorithm developed by Ward (1963). To assign observations to clusters, we adopted a hybrid approach, as recommended by Punj and Stewart (1983). We first applied Ward’s (1963) algorithm, then adopted the k-means method, which builds on the previous solution. The k-means procedure yields exceptional results if given a reasonable starting solution (Milligan/Cooper 1987).

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