Modelling the Relationship between Multi-Channel Retail and Personal Mobility Behaviour Esra Suel A thesis submitted as fulfilment of the requirements for the degree of Doctor of Philosophy of Imperial College London Centre for Transport Studies Department of Civil and Environmental Engineering Imperial College London Abstract The nature of shopping activity is changing in response to innovation in retailing and the growth in online channels. There is a growing interest from transport researchers, policy makers, marketing and retail businesses in understanding the implications of this change. However, existing tools and techniques developed for analysing behaviour in tra- ditional retail environments do not adequately represent emerging complexities resulting from digital innovation. The overarching goal of this research is to advance the develop- ment of new modelling and data collection tools for studying choice behaviour in todays increasingly complex retail environments. While data collection and empirical applica- tions relate to grocery shopping in London, the conceptual discussions and modelling frameworks developed are generalisable to all shopping activity. The contributions of the presented work are at three levels. First, a comprehensive conceptual framework was developed incorporating interactions between multiple agents that drive the transformation of the industry, and individual choice behaviour within this broader perspective. Secondly, it is a significant challenge for researchers to find appro- priate data sets, which combine travel and shopping related information and also capture online activity. For empirical applications here, an augmented version of a widely accepted revealed preference consumer panel data was used in together with API based data min- ing tools that o ffer great potential for enrichment in discrete choice modelling. Third, discrete choice models were developed using gathered data for the joint choice of chan- nel, shopping destination, and travel mode. This extension to traditional destination and mode choice models is critical as it provides the tools to quantify the e ffects of increased online shopping on traditional store formats and travel patterns. Results revealed im- portant insights into how shoppers choose from online and in-store alternatives, and how mode choice fits in with these decisions. During our study we also identified and explored substantial limitations in empirical applications of discrete choice models. We analysed issues of identification caused by sample size constraints, potential estimation bias due to potentially restricting choice set generation assumptions, and challenges that arise when newly introduced innovative alternatives show low-adoption rates. 1 To my father and mother Declaration The research presented in this thesis is my own, except where the work of others is refer- enced. The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work 3 Acknowledgements First and foremost, I would like to thank my supervisor Professor John Polak. He gave me invaluable advice throughout my research, shared his knowledge, experience, and ideas. I am also grateful for the opportunities to work with him in various projects and all his support in finding funding for my doctoral research. I would also like to thank my examiners Professor Harry Timmermans and Professor Erkko Autio. Many thanks to everyone in the Centre for Transport Studies; I gratefully appreciate all of your friendship and support over the many years. Special thanks to Alireza Zolfaghari, Scott Le Vine, and Nicolo Daina for their contributions to my research. My warmest gratitude goes to Fionnuala Ni Dhonnabhain for all the ways she helped me throughout my time at Imperial. Thanks are due also to Jackie Sime and Amy Valentine for all their assistance. This work has been supported in part by the Rees Je ffreys Road Fund and The Sci- entific and Technological Research Council of Turkey; this support is gratefully acknowl- edged. I would also like to thank Kantar Worldpanel and Local Data Company for pro- viding the data used in this research. I have also been extremely fortunate to work with and benefit from a community of mentors outside Imperial. I especially want to thank Dr. Melsa Ararat and Professor Steven Skerlos, who also gave me invaluable support throughout di fficult times. Finally, the biggest thanks of all to my parents, Akin Suel and Emine Sonmez; they gave me endless support and love in every phase of my life. And thank you to my friends out there in the real world whom I love very much. Thank you, Ender, for being by my side. 4 Contents 1 Introduction 11 1.1 Background and Motivation . 11 1.2 ResearchAimsandObjectives . 15 1.3 OutlineoftheThesis............................... 17 2 Literature Review 19 2.1 Implications of Changes in Retail Environments on Travel . 20 2.2 Review of Modelling Methodologies . 25 2.3 Review of Application Areas . 46 2.4 Review of Data Types and Aggregation Levels . 61 2.5 Summary ..................................... 63 3 Research Context and Conceptual Framework 64 3.1 RetailMarket................................... 64 3.2 Issues in Modelling Choice Behaviour within a Discrete Choice Framework . 73 3.3 Factors Influencing Choices in Shopping . 76 3.4 Summary ..................................... 82 4 Data Collection and Enrichment 83 4.1 Preliminary Analysis: Living Costs and Food Survey . 85 4.2 Data Collection: Augmented Consumer Panel . 88 4.3 DataEnrichment: GeneratingtheChoiceSet . 99 4.4 Summary .....................................106 5 Modelling Store, Channel, and Travel Mode Choice at the Level of Ele- mental Alternatives 107 5.1 Data Preparation . 108 5.2 Model M1: Store Location and Channel Choice . 110 5.3 Model M2: Store, Channel, and Travel Mode Choice . 130 5.4 Model Validation . 136 5.5 Forecasting Analysis . 138 5 5.6 Summary .....................................147 6 Modelling Store, Channel, and Travel Mode Choice Using Aggregated Alternatives 150 6.1 Aggregation of Alternatives . 151 6.2 Model M3: Store and Channel Choice . 156 6.3 Model M4: Store, Channel, and Travel Mode Choice . 163 6.4 Model Validation . 168 6.5 Summary .....................................169 7 A Monte Carlo Simulation Study on Identification Issues 171 7.1 Introduction....................................171 7.2 MonteCarloExperiments: ModelSetting . 173 7.3 E ffects of Sample Size and Number of Parameters . 175 7.4 E ffects of Varying Magnitude of Coe fficients..................178 7.5 E ffects of Shared and Nest Specific Parameters . 181 7.6 E ffects of Availability or Adoption Rates . 184 7.7 Summary .....................................188 8 Conclusion 191 8.1 Summary .....................................191 8.2 FutureWork ...................................195 Bibliography 197 Appendices 223 A UK Grocery Market 223 A.1 KeyPlayers....................................223 A.2 Ownershipstructures. 224 A.3 Store Formats and Channels . 224 B Preliminary Analyses Using Living Costs and Food Survey 226 B.1 Data Preparation . 226 B.2 MethodsandResults...............................227 C Add-On Travel Survey 232 D Data Access Permissions 237 E List of Publications 241 6 List of Figures 1.1 Seasonally adjusted proportion of total sales made online over 2006-2015 . 12 2.1 Two-level nested logit structure with two nests . 33 3.1 Retail market: conceptual framework . 65 4.1 LCF Instructions for completing the expenditure diary . 86 4.2 Scanning and data collection in consumer panels . 91 4.3 Selected boroughs in London for the survey sample . 93 4.4 Example screen-shot for add-on travel survey: introduction . 95 4.5 Example screen-shot for add-on travel survey: travel mode choice . 96 4.6 Example screen-shot for add-on travel survey: shopping party . 96 4.7 Example screen-shot for add-on travel survey: store location . 96 4.8 Walking score categories: screen-shots from WalkScore.com . 102 4.9 Example screen-shots from Grocer 33 . 103 5.1 MNL structure for channel and store choice . 119 5.2 Two-level nested logit structure with online and in-store nests . 120 5.3 Alternative three-level nested logit structures for store choice . 121 5.4 Alternative logit structures for channel, store, and travel mode choice . 132 5.5 Forecasting results from adoption scenarios . 141 5.6 Forecasting results from store closure scenarios . 144 5.7 Forecasting results from store congestion scenarios . 146 6.1 Model structures for store and channel choice with aggregated alternatives . 159 6.2 Alternative logit structures for channel, store, and travel mode choice . 165 7.1 Monte Carlo experiments: varying the number of observations (N) and variables(M) ...................................177 7.2 Monte carlo experiments: varying the magnitude of coe fficients βm and µk and the number of observations (N) where M = 5 and fixed . 180 7.3 Monte Carlo experiments: varying the number of shared and nest-specific variables......................................183 7 7.4 Monte Carlo experiments: varying adoption rates for alternatives in the smallnest .....................................185 7.5 Monte Carlo experiments: varying adoption