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The New Boundaries of Retail Location Decision-Making View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Scholars Commons Wilfrid Laurier University Scholars Commons @ Laurier Theses and Dissertations (Comprehensive) 2019 Spatial Big Data Analytics: The New Boundaries of Retail Location Decision-Making Joseph M. Aversa Wilfrid Laurier University, [email protected] Follow this and additional works at: https://scholars.wlu.ca/etd Part of the Business Analytics Commons, and the Real Estate Commons Recommended Citation Aversa, Joseph M., "Spatial Big Data Analytics: The New Boundaries of Retail Location Decision-Making" (2019). Theses and Dissertations (Comprehensive). 2138. https://scholars.wlu.ca/etd/2138 This Dissertation is brought to you for free and open access by Scholars Commons @ Laurier. It has been accepted for inclusion in Theses and Dissertations (Comprehensive) by an authorized administrator of Scholars Commons @ Laurier. For more information, please contact [email protected]. Spatial Big Data Analytics: The New Boundaries of Retail Location Decision‐Making DISSERTATION Joseph Mattia Jr Aversa 2018 Submitted to the Department of Geography and Environmental Studies, Faculty of Arts in partial fulfillment of the requirements for Doctor of Philosophy in Geography Wilfrid Laurier University © Joseph Mattia Jr Aversa 2019 AUTHOR’S DECLARATION I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii ACKNOWLEDGEMENTS I would like to thank my supervisors Dr. Sean Doherty and Dr. Tony Hernandez for their guidance and support throughout this process. The completion of this thesis would not have been possible without my wife (Tania), my children (Rosalie and Gemma) and my parents. Thank you for your constant encouragement and support. iii ABSTRACT This dissertation examines the current state and evolution of retail location decision‐ making (RLDM) in Canada. The major objectives are: (i) To explore the type and scale of location decisions that retail firms are currently undertaking; (ii) To identify the availability and use of technology and Spatial Big Data (SBD) within the decision‐making process; (iii) To identify the awareness, availability, use, adoption and development of SBD; and, (iv) To assess the implications of SBD in RLDM. These objectives were investigated by using a three stage multi‐ method research process. First, an online survey of retail location decision makers across a range of sizes and sub‐sectors was administered. Secondly, structured interviews were conducted with 24 retail location decision makers, and lastly, three in‐depth cases studies were undertaken in order to highlight the changes to RLDM over the last decade and to develop a deeper understanding of RLDM. This dissertation 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. Traditional practices still dominate the RLDM process. While the adoption of SBD applications is starting to appear within retail planning, they are not widespread. Traditional data sources, such as those highlighted in past studies by Hernandez and Emmons (2012) and Byrom et al. (2001) are still the most commonly used data sources. It was evident that at the iv 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 v 1 TABLE OF CONTENTS Author’s Declaration ..................................................................................................................................... ii Acknowledgements ...................................................................................................................................... iii Abstract ………………………………………………………………………………………………………………………………………..iv List of Figures ………………………………………………………………………………………………………………………………………ix List of Tables …………………………………………………………………………………………………………………………………………x 1 Introduction .......................................................................................................................... 1 1.1 Research Aim and Objectives ........................................................................................................ 4 1.2 Structure of Dissertation ............................................................................................................... 5 2 Research Context .................................................................................................................. 6 2.1 History of Retail Location Decision‐Making .................................................................................. 6 2.1.1 Wave 1 of Retail Location Decision Making .......................................................................... 7 2.1.2 Wave 2 of Retail Location Decision Making: ....................................................................... 16 2.1.3 Wave 3 of Retail Location Decision Making: ....................................................................... 23 2.1.4 Wave 4 of Retail Location Decision Making: ....................................................................... 27 2.2 Emerging Applications ................................................................................................................ 41 2.2.1 Tracking Technologies ......................................................................................................... 42 2.3 Innovation and technological Assimilation and Adoption .......................................................... 43 3 Research Design and Methodology .................................................................................... 50 3.1.1 Questionnaire Design and Format ...................................................................................... 51 3.1.2 Sampling: Selecting Questionnaire Participants ................................................................. 53 3.1.3 Pre‐Testing, Distribution and Maximizing Response Rate .................................................. 54 3.2 Semi‐Structured Interviews ........................................................................................................ 55 3.2.1 Sampling: Selecting Participants for Semi‐Structured interviews ...................................... 57 3.2.2 Interview Design & Practices .............................................................................................. 59 3.2.3 Interview Data Coding and Analysis Techniques ................................................................ 62 3.3 Case Studies ................................................................................................................................ 64 3.3.1 Designing Case Studies ........................................................................................................ 65 3.3.2 Selecting the Cases .............................................................................................................. 67 4 Research Findings ............................................................................................................... 69 4.1 Online Retailer Survey Results .................................................................................................... 69 4.1.1 Spatial Big Data Adoption by Retailers: Types, Importance, Perceptions, and Changes .... 70 vi 4.1.2 Techniques and Methods Adoptions by Retailers: Types, Importance, Perceptions and Changes ............................................................................................................................... 80 4.1.3 Business Culture: Opportunities and Challenges for Awareness, Availability, Use and Adoption of Spatial Big Data ............................................................................................... 88 4.2 Structured Interviews ................................................................................................................. 90 4.2.1 Data Theme ......................................................................................................................... 90 4.2.2 Data Requirements for Method and Technique Implementation ...................................... 95 4.2.3 Organizational Culture ...................................................................................................... 102 4.3 Case studies .............................................................................................................................. 106 4.3.1 Case 1 – Developer/Brokerage/Leasing Company ............................................................ 106 4.3.2 Data and Technology Environment ................................................................................... 108 4.3.3 Retail Location Decision‐Making ....................................................................................... 111 4.4 Case 2 – Food Services .............................................................................................................. 118 4.4.1 Data and Technology
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