The impact of big data on innovation is not only driven by technology and analytics. It involves a transformation of the organizational culture, structures, processes, roles, and capabilities that underpin the innovation process. Understanding these factors is particularly important for service innovators, given the strong interdependence between the organizational context and technology in service companies. Moreover, in many of these organizations, the innovation process is still deeply rooted in a non-digital past. This study answers the call to understand what are the key characteristics of a systematic process for service innovation in data-rich environments. In particular, the authors investigate the primary factors that enable existing service organizations to capture the innovation potential inherent in data-rich environments. To this aim, the authors implemented a two-step research design. First, they integrated the service innovation and information systems literatures in a unified conceptual framework that articulates the relationship between data-rich environments and service innovation from an organizational perspective. Second, they carried out 40 semi-structured interviews in seven large service firms, which allowed them to refine and populate the initial framework with typologies, concepts, and examples from the field. A major contribution of this study is to articulate the concept of data density, as three distinct processes (pattern spotting, real-time decisioning, and synergistic exploration) connecting data-rich environments with service innovation opportunities. Finally, the authors identified a set of organizational enablers that facilitate the links among technology, data density processes, and service innovation. The findings of this study offer a roadmap for service managers who need to align the service innovation process of their organizations with the opportunities offered by data-rich environments.

Linking data-rich environments with service innovation in incumbent firms: a conceptual framework and research propositions

Troilo, Gabriele
;
De Luca, Luigi M.;Guenzi, Paolo
2017-01-01

Abstract

The impact of big data on innovation is not only driven by technology and analytics. It involves a transformation of the organizational culture, structures, processes, roles, and capabilities that underpin the innovation process. Understanding these factors is particularly important for service innovators, given the strong interdependence between the organizational context and technology in service companies. Moreover, in many of these organizations, the innovation process is still deeply rooted in a non-digital past. This study answers the call to understand what are the key characteristics of a systematic process for service innovation in data-rich environments. In particular, the authors investigate the primary factors that enable existing service organizations to capture the innovation potential inherent in data-rich environments. To this aim, the authors implemented a two-step research design. First, they integrated the service innovation and information systems literatures in a unified conceptual framework that articulates the relationship between data-rich environments and service innovation from an organizational perspective. Second, they carried out 40 semi-structured interviews in seven large service firms, which allowed them to refine and populate the initial framework with typologies, concepts, and examples from the field. A major contribution of this study is to articulate the concept of data density, as three distinct processes (pattern spotting, real-time decisioning, and synergistic exploration) connecting data-rich environments with service innovation opportunities. Finally, the authors identified a set of organizational enablers that facilitate the links among technology, data density processes, and service innovation. The findings of this study offer a roadmap for service managers who need to align the service innovation process of their organizations with the opportunities offered by data-rich environments.
2017
Troilo, Gabriele; De Luca, Luigi M.; Guenzi, Paolo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/4002094
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