دانلود رایگان مقاله انگلیسی رویکرد مبتنی بر خطر برای مدل سازی تأثیرات خرید آنلاین در مدت خرید اینترنتی - اشپرینگر 2018

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.

Summary and conclusions

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. Results provide new insights to substitution or complementarity question. Additionally, methods presented can be used for predicting next online order or shopping trip, which is crucial for activity generation models used for travel demand predictions and also for delivery operations. Our study has a number of novel components in comparison with past work that focused on understanding the implications of wider use of ICT on personal travel.

First, while transport researchers have used hazard based duration models to analyse inter-shopping duration in the context of multi-day activity generation, they have not considered online shopping activity and its effects on overall activity generation. Our study accounts for the influence of online activity on temporal patterns of shopping activities.

Second, our analyses are based on a dataset of unprecedented richness in transport research with respect to capturing both online and physical shopping activities. In consumer panels, online shopping activity data are collected in an episode-based manner, rather than through retrospective questionnaires where respondents report how frequently they shop using a points scale. This enables development of more advanced models to help us better understand changes in behaviour triggered by ICT use. Additionally, shopping basket characteristics, likely to affect shopping behaviour yet often neglected in transport literature, are available and used in empirical estimations. Importantly, market research companies operate consumer panels in many countries and make it available to researchers. This is crucial for the generalisability of our proposed method.