Digitizing Local Search: An Empirical Analysis of Mobile Search Behavior in Offline Shopping

35 Pages Posted: 8 May 2015 Last revised: 25 Jul 2022

See all articles by Dominik Molitor

Dominik Molitor

Fordham University - Gabelli School of Business

Stephan Daurer

DHBW Ravensburg; Ludwig Maximilian University of Munich (LMU) - Faculty of Business Administration (Munich School of Management)

Martin Spann

Ludwig Maximilian University of Munich (LMU) - Faculty of Business Administration (Munich School of Management)

Puneet Manchanda

University of Michigan, Stephen M. Ross School of Business

Date Written: July 24, 2022

Abstract

Mobile devices have become increasingly prevalent in the context of consumers’ offline search and shopping behavior. Compared to desktop PCs or laptops, the unique characteristics of mobile devices—including portability and real-time location sensing—can substantially influence consumers’ local offline search behavior. In this paper, we theorize and investigate the interplay between consumers’ real-time location and their search behavior when shopping offline. We develop two location-specific measures based on mobile device usage to analyze the association between a consumer’s real-time location and their search behavior: (1) present store distance and (2) present store density. We apply these measures to a unique dataset from a leading mobile product information app that includes 67 million unique search activities of 2.5 million consumers searching for 1.8 million different products. Our analyses provide strong evidence that store distance and store density can explain significant variation in consumers’ search behavior: detailed searches for product-specific attributes (search depth) increase with decreasing store distance and increasing store density. In addition, the number of jointly searched products within the same category (search breadth) increases with increasing store distance and increasing store density. Furthermore, we find that durable products reduce the main effects of store distance and density with search depth while amplifying search breadth relationships. These results can help offline retailers to better understand consumers’ location-specific demands for products, which could be used for more precise mobile in-app targeting and product assortment decisions.

Keywords: Consumer Search, Mobile Devices, Location-Based Services, Big Data, Random Coefficient Model

JEL Classification: M31, D83, C23, C25, L86

Suggested Citation

Molitor, Dominik and Daurer, Stephan and Spann, Martin and Manchanda, Puneet, Digitizing Local Search: An Empirical Analysis of Mobile Search Behavior in Offline Shopping (July 24, 2022). Ross School of Business Paper No. 1275, Available at SSRN: https://ssrn.com/abstract=2603242 or http://dx.doi.org/10.2139/ssrn.2603242

Dominik Molitor

Fordham University - Gabelli School of Business

113 West 60th Street
Bronx, NY 10458
United States

HOME PAGE: http://www.dominikmolitor.com

Stephan Daurer (Contact Author)

DHBW Ravensburg

Marienplatz 2
Ravensburg, Baden-Wuerttemberg 88212
Germany
+49.751.18999.2700 (Phone)
+49.751.18999.2701 (Fax)

HOME PAGE: http://www.dhbw-ravensburg.de/

Ludwig Maximilian University of Munich (LMU) - Faculty of Business Administration (Munich School of Management)

Geschwister-Scholl-Platz 1
Munich, Bavaria 80539
Germany

HOME PAGE: http://www.en.bwl.uni-muenchen.de

Martin Spann

Ludwig Maximilian University of Munich (LMU) - Faculty of Business Administration (Munich School of Management) ( email )

Ludwigstr. 28
Munich, 80539
Germany

Puneet Manchanda

University of Michigan, Stephen M. Ross School of Business ( email )

701 Tappan Street
Ann Arbor, MI MI 48109
United States
734-936-2445 (Phone)
734-936-8716 (Fax)

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