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From data to value: A nine-factor framework for data-based value creation in information-intensive services

Author

Listed:
  • Lim, Chiehyeon
  • Kim, Ki-Hun
  • Kim, Min-Jun
  • Heo, Jun-Yeon
  • Kim, Kwang-Jae
  • Maglio, Paul P.

Abstract

Service is a key context for the application of IT, as IT digitizes information interactions in service and facilitates value creation, thereby contributing to service innovation. The recent proliferation of big data provides numerous opportunities for information-intensive services (IISs), in which information interactions exert the greatest effect on value creation. In the modern data-rich economy, understanding mechanisms and related factors of data-based value creation in IISs is essential for using IT to improve such services. This study identified nine key factors that characterize this data-based value creation: (1) data source, (2) data collection, (3) data, (4) data analysis, (5) information on the data source, (6) information delivery, (7) customer (information user), (8) value in information use, and (9) provider network. These factors were identified and defined through six action research projects with industry and government that used specific datasets to design new IISs and by analyzing data usage in 149 IIS cases. This paper demonstrates the usefulness of these factors for describing, analyzing, and designing the entire value creation chain, from data collection to value creation, in IISs. The main contribution of this study is to provide a simple yet comprehensive and empirically tested basis for the use and management of data to facilitate service value creation.

Suggested Citation

  • Lim, Chiehyeon & Kim, Ki-Hun & Kim, Min-Jun & Heo, Jun-Yeon & Kim, Kwang-Jae & Maglio, Paul P., 2018. "From data to value: A nine-factor framework for data-based value creation in information-intensive services," International Journal of Information Management, Elsevier, vol. 39(C), pages 121-135.
    • Handle: RePEc:eee:ininma:v:39:y:2018:i:c:p:121-135
    DOI: 10.1016/j.ijinfomgt.2017.12.007
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    References listed on IDEAS

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    2. Fabian Hunke & Daniel Heinz & Gerhard Satzger, 2022. "Creating customer value from data: foundations and archetypes of analytics-based services," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(2), pages 503-521, June.
    3. Kolomoyets, Yuliya & Dickinger, Astrid, 2023. "Understanding value perceptions and propositions: A machine learning approach," Journal of Business Research, Elsevier, vol. 154(C).
    4. Martin Schymanietz & Julia M. Jonas & Kathrin M. Möslein, 2022. "Exploring data-driven service innovation—aligning perspectives in research and practice," Journal of Business Economics, Springer, vol. 92(7), pages 1167-1205, September.
    5. Frederik Möller & Ilka Jussen & Virginia Springer & Anna Gieß & Julia Christina Schweihoff & Joshua Gelhaar & Tobias Guggenberger & Boris Otto, 2024. "Industrial data ecosystems and data spaces," Electronic Markets, Springer;IIM University of St. Gallen, vol. 34(1), pages 1-17, December.

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