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Supporting the Store-Based Retail with Big Data in German Towns—A Longitudinal Mixed-Method-Study

In: Shaping the Digital Future Through Innovation and Practice

Author

Listed:
  • Cindy Kreuels

    (University of Siegen)

  • Aida Stelter

    (University of Siegen)

  • Bastian Kordyaka

    (University of Bremen)

  • Björn Niehaves

    (University of Bremen)

Abstract

Store-based retailers face the challenge of meeting increased customer demands and compete with online marketplaces. Big data (BD) can support store-based retail and engage customers locally. We are therefore conducted a three-year mixed-method-study to identify relevant factors for the German store-based retail in three successive phases. Firstly, we qualitatively identify effectiveness-factors using BD through 13 semi-structured-interviews. Secondly, we quantitatively evaluate the relevance of the identified effectiveness-factors (i.a., over 2.8 million data points) using the 7P-Marketing-Mix, and thirdly analyzed significant factors. Our findings show that many store-based retailers lack knowledge about smart town and BD-use, e.g., by creating a network or running joint retail campaigns to increase the towns attractiveness. We provide an overview and guidance on how BD-analysis can effectively influence the store-based retail transformation in smart towns. Using a mixed-method-study in a German town, we were able to identify which factors influence store-based retail in a smart town and suggest promising interventions.

Suggested Citation

  • Cindy Kreuels & Aida Stelter & Bastian Kordyaka & Björn Niehaves, 2026. "Supporting the Store-Based Retail with Big Data in German Towns—A Longitudinal Mixed-Method-Study," Lecture Notes in Information Systems and Organization, in: Christoph M. Flath & Gunther Gust & Frédéric Thiesse & Axel Winkelmann (ed.), Shaping the Digital Future Through Innovation and Practice, pages 61-76, Springer.
    • Handle: RePEc:spr:lnichp:978-3-032-08489-7_5
    DOI: 10.1007/978-3-032-08489-7_5
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