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

Cindy Kreuels (), Aida Stelter (), Bastian Kordyaka () and Björn Niehaves ()
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Cindy Kreuels: University of Siegen
Aida Stelter: University of Siegen
Bastian Kordyaka: University of Bremen
Björn Niehaves: University of Bremen

A chapter in Shaping the Digital Future Through Innovation and Practice, 2026, pp 61-76 from Springer

Abstract: 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.

Keywords: Big data; Store-based retail; Mixed-method study; Smart town; Digital transformation (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-032-08489-7_5

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DOI: 10.1007/978-3-032-08489-7_5

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