Abstract

Despite growing prevalence of online shopping, its impacts on mobility are poorly understood. This partially results from the lack of sufficiently detailed data. In this paper we address this gap using consumer panel data, a new dataset for this context. We analyse one year long longitudinal grocery shopping purchase data from London shoppers to investigate the effects of online shopping on overall shopping activity patterns and personal trips. We characterise the temporal structure of shopping demand by means of the duration between shopping episodes using hazard-based duration models. These models have been used to study inter-shopping spells for traditional shopping in the literature, however effects of online shopping were not considered. Here, we differentiate between shopping events and shopping trips. The former refers to all types of shopping activity including both online and in-store, while the latter is restricted to physical shopping trips. Separate models were estimated for each and results suggest potential substitution effects between online and in-store in the context of grocery shopping. We find that having shopped online since the last shopping trip significantly reduces the likelihood of a physical shopping trip. We do not observe the same effect for inter-event durations. Hence, shopping online does not have a significant effect on overall shopping activity frequency, yet affects shopping trip rates. This is a key finding and suggests potential substitution between online shopping and physical trips to the store. Additional insights on which factors, including basket size and demographics, affect inter-shopping durations are also drawn.

Highlights

  • Information and communication technologies (ICT) are transforming the retail sector with a proliferation of new channels and virtual alternatives

  • This paper presented the use of consumer panel data and hazard based duration models to explore the potential impacts of online shopping separately on shopping trip frequency and overall shopping activity patterns

  • Methods presented can be used for predicting online order or shopping trip, which is crucial for activity generation models used for travel demand predictions and for delivery operations

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Summary

Introduction

Information and communication technologies (ICT) are transforming the retail sector with a proliferation of new channels and virtual alternatives This change is observed in spending patterns: online retailing in the UK accounted for 14% in 2014 of all sales in 2014 up from 2.1% in 2002, Office for National Statistics 2015, and in the USA share of online retailing reached 7% of all sales up from only 2.8% in 2006 (Bucchioni et al 2015). The overall aim is to gain insights into how shopping patterns are changing with the increasing use of online channels for shopping This will provide insights to substitution question between physical and virtual activity in the context of grocery shopping, and contribute to multiday activity generation modelling work

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