From the moment e-shopping emerged, there are speculations about the impact that would have on personal mobility. Questions about the impact of e-shopping on mobility increase due to media coverage on the sharp increase in turnover of Internet purchases and the increasing number of consumers that shops via the Internet. The increase in Internet shopping does not only occur in the Netherlands, but worldwide. The global online retail market showed an 14.8% average annual growth from 2007 to 2012, while the total retail growth was just a 0.9% over the same period (Cushman & Wakefield, 2013). In Europe almost one out of three have purchased goods and services over the Internet in 2013 (Ecommerce Europe, 2013). The questions is whether the overall personal shopping mobility indeed decreases as a result of the increase in e-shopping. A fair amount of research has already been carried out on Internet shopping itself, but much less on its consequences for transportation. Rotem-Mindali & Weltreveden (2013) made an overview of the available studies on a e-shopping and the impact of that on both personal and freight transport. These studies differ in definition of e-shopping, in data collection approach, in analysis approach, etc. Different hypothesis varying from a likely decrease, to no or only a small decrease or even an increase in personal mobility have been formulated and have been empirically tested over the last two decades. In this paper, we will provide insights into how shopping via the Internet changes shopping travel behaviour, rather than estimate the total impact of e-shopping on total personal shopping-induced mobility in the Netherlands. More specifically: we will unravel the different constituent processes of e-shopping, which are looking for, choosing, buying, retrieving and returning products, and argue what the impact on personal mobility of each of these stages of e-shopping is. we will discuss the results of fitting a regression model that we have developed and estimated in which the reported number of shopping trips is explained from personal, household and spatial characteristics as well as e-shopping behaviour (e.g. e-shopping frequency). The same will be done for the travel distance as well as the travel time related to these shopping trips. we will present a mode choice model in order to study what determines mode choice for shopping trips and whether, and if so, to which extent e-shopping influences this mode choice behaviour. The above mentioned contributions will be made using data from the Netherlands Mobility Panel (in Dutch: MobiliteitsPanel Nederland (MPN)). The MPN is a state-of-the-art household panel which main objectives are to determine short-run and long-run dynamics in travel behaviour of individuals and households, and to determine how changes in personal and household characteristics and in other travel-related factors (e.g. economic crisis, reduced taxes on sustainable transport, changes in land-use or the increased availability and use of ICT) correlate with changes in travel behaviour (Hoogendoorn-Lanser et al., 2014). In the scope of the MPN, in 2013, respondents 12 years and older from 2,500 complete households recorded their travel data using a three-day travel diary. Over the next four years, this will be repeated at least yearly with the same respondents. At the same time, different questionnaires were filled out offering a large amount of background information on respondents and their households. Additionally, a questionnaire was filled out that provides us with detailed information on respondents' e-shopping behaviour and the consequences for their personal shopping-related mobility. Besides the frequency with which they purchase different types of products via the Internet, for the last purchased item(s) the processes of looking for, choosing, buying, retrieving and returning products are mapped in detail, and the way in which both the Internet and physical stores play a role in each of these stages. It is the unique combination of reported shopping trips in the 3-day travel diary, the large amount of personal and household characteristics combined with the detailed information from the e-shopping questionnaire that enables us to perform the above mentioned research. Looking at some of the first insights stemming from our analyses, we found that consumers rarely collect Internet purchases at the dedicated collection points or at physical stores of web shops, which were generally put in place to reduce delivery transport. The large majority of purchased items is still delivered at home or received digitally. Types of products that have shown the largest growth in number of sales are often those products that do not need to be delivered physically, but find their way to consumers digitally. The sharp increase in e-shopping revenues does therefore not imply an equal growth in delivery transport - although this is often assumed. If products need to be collected or returned, people use generally the same transport modes: 51% uses car, 31% goes by bike and 12% go on foot. The share of public transport for collecting and returning products is minimal. For 65% of the items that need to be collected, the travel distance is less than 5 km. Our analyses furthermore show that e-shopping frequency influences the number of reported shopping trips in a different way than it does the travel distance and travel time related to shopping. E-shopping frequency has a significant impact on the number of shopping trips made by respondents. Although e-shopping frequency has a significant impact on the distance travelled to shops, this impact is smaller. Level of urbanisation of the respondent's residential area and the number of cars in the household turn out to be equally important. This is a likely explanation that - although people report to make less shopping trips due to online shopping - the decrease in total yearly shopping related mobility in the Netherlands is minimal. For specific subpopulations differences can be observed in total shopping mobility between those who frequently or infrequently buy products via the Internet. Men and elderly people make more shopping trips per person per day, travel longer and/or further for shopping purposes. For people living in rural areas and people that have a low or high income the opposite is true. For them, shopping mobility decreases with an increase in e-shopping frequency. When people are asked directly how e-shopping influences their shopping mobility, one third mentions their shopping-induced mobility has not changed since the also shop via the Internet. Two third however state that change have occurred. Over 30% nowadays makes fewer shopping trips; while 11% indicate that they travel more often for shopping. Cushman & Wakefield. Global perspective on retail: online retailing. Cushman & Wakefield, London, 2012. Ecommerce Europe. European B2C E-commerce Report 2014. Ecommerce Europe, Brussels, 2014. Hoogendoorn-Lanser, S., N. Schaap and M.J.T. Olde Kalter (2014). The Netherlands Mobility Panel: An innovative design approach for web-based longitudinal travel data collection. Paper to be presented at ISCTSC in Leura, Australia. Rotem-Mindali, O. & J.W.J. Weltevreden (2013). Transport effects of e-commerce: what can be learned after years of research? Transportation, 40 (5), pp 867-885.
|Title of host publication||Proceedings 14th International Conference on Travel Behaviour Research, 19-23 July 2015, Windsor, UK. (online)|
|Publication status||Published - 19 Jul 2015|
|Event||14th International Conference on Travel Behaviour Research, IATBR 2015 - Windsor, United Kingdom|
Duration: 19 Jul 2015 → 23 Jul 2015
Conference number: 14
|Conference||14th International Conference on Travel Behaviour Research, IATBR 2015|
|Period||19/07/15 → 23/07/15|