The Assimilation of Spatial Big Data Analytics within Retail Location Decision-Making

Authors: Joseph Aversa*, Ryerson University, Tony Hernandez, Ryerson University, Sean Doherty, Wilfrid Laurier University
Topics: Business Geography
Keywords: Retail Location Decision-Making, Spatial Big Data
Session Type: Paper
Day: 4/4/2019
Start / End Time: 1:10 PM / 2:50 PM
Room: Stones Throw 3 - Mica, Marriott, Lobby Level
Presentation File: No File Uploaded


This paper examines the current state and evolution of retail location decision-making (RLDM) in Canada. The major objectives are: (i) To identify the awareness, availability, use, adoption and development of SBD; and, (ii) To assess the implications of SBD in RLDM. These objectives were investigated through three in-depth cases studies.

The paper found that within the last decade RLDM changed in three main ways: (i) There has been an increase in the availability and use of technology and SBD within the decision-making process; (ii) The type and scale of location decisions that a firm undertakes remain relatively unchanged even with the growth of new data; and, (iii) The range of location research methods that are employed within retail firms is only just beginning to change given the presence of new data sources and data analytics technology. This paper further identifies a conceptual framework for SBD assimilation as well as industry best practices.

While the adoption of SBD applications is starting to appear within retail planning, they are not widespread. It was evident that at the heart of SBD adoption is a data environment that promotes transparency and a clear corporate strategy. While most retailers are aware of the new SBD techniques that exist, they are not often adopted and routinized

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