nen and O. Mazhelis (eds.), Vertical Software Industry Evolution, Contributions to Management Science, DOI 10.1007/978-3-7908-2352-3_10 C Springer-Verlag Berlin Heidelberg 2009 O. Mazhelis and P. Tyrväinen innovations are more likely to be successful in (1) those modules that are rather independent, or in (2) an inter-dependent module if the set of interdependent modules are under the control of a single organization thereby allowing it to coordinate the innovation activities spanning several modules. In order to study the inter-dependencies within a system, we use the so-called system modularity introduced by Frenken (2006). It indicates how independent the system's modules are from each other, and is calculated as: where N is the number of modules in the system, and P is a system pleiotropy evaluated as P = log, II P " Here, Pn E {1,F} denotes pleiotropy of module n (the number of functions affected by a change in the module); and F designates the number of functions performed by the system. The minimum value of the modularity is obtained when each module in the system has the pleiotropy equal to the total number of functions in the system, i.e., when changes in any single module would affect all the functions implemented by the system. The maximum modularity is achieved when one of the modules has the maximum pleiotropy, while all the others have pleiotropy equal to one; the module with the maximum pleiotropy plays in this case the role of a global interface mediating the interdependencies between all the other modules. An organization is likely to integrate closely interdependent modules into a core module, which encapsulates several dependent modules, hides the complexity of interconnections among its constituents, and provides a single interface to their functionality. After the integration, the value of the system's modularity indicator should rise, thereby reflecting the decrease of the overall system complexity. Using the system modularity, different integration scenarios may be compared: the greater the