The interactions between e-shopping and traditional in-store shopping: an application of structural equations model

As the proliferation of e-commerce leads to ever greater numbers of on-line transactions, transportation planners are interested in the impacts of e-shopping on our strained transportation systems. Although the substitution effect of e-shopping is appealing, previous studies provided mixed results on its impact. Using 539 adult internet users in the Minneapolis-St Paul metropolitan area, this study applied a structural equations model to investigate the interactions among online purchases, in-store shopping, and product information search via internet. We found that online searching frequency has positive impacts on both online and in-store shopping frequencies and online buying positively affects in-store shopping. In particular, the marginal effects of online-buying frequency and online-searching frequency on in-store shopping frequency were estimated at 0.153 and 0.189, respectively. Since the internet as a shopping channel tends to have a complementary effect on in-store shopping, the rise of e-shopping is not likely to be a solution but a challenge to travel reduction.     

Shopping online and/or in-store? A structural equation model of the relationships between e-shopping and in-store shopping

Searching product information and buying goods online are becoming increasingly popular activities, which would seem likely to affect shopping trips. However, little empirical evidence about the relationships between e-shopping and in-store shopping is available. The aim of this study is to describe how the frequencies of online searching, online buying, and non-daily shopping trips relate to each other, and how they are influenced by such factors as attitudes, behaviour, and land use features. Questionnaire data were collected from 826 respondents residing in four municipalities (one urban, three suburban) in the centre of the Netherlands. Structural equation modelling was used to examine the variables’ multiple and complex relationships. The results show that searching online positively affects the frequency of shopping trips, which in its turn positively influences buying online. An indirect positive effect of time-pressure on online buying was found and an indirect negative effect of online searching on shopping duration. These findings suggest that, for some people, e-shopping could be task-oriented (a time-saving strategy), and leisure-oriented for others. Urban residents shop online more often than suburban residents, because they tend to have a faster Internet connection. The more shopping opportunities one can reach within 10 min by bicycle, the less often one searches online.     

A conceptual and methodological framework of leisure activity loyalty accommodating the travel context

As leisure travel continues to grow, it has become a critical subject for planners and decision-makers since it significantly impacts regional economic and social development as well as contributes to emission levels and congestion. Despite being a significant percentage of our travel, however, leisure travel behavior is still not very well understood. The goal of this article is to contribute to our understanding of leisure activity participation by considering leisure activity loyalty within the travel context. In particular, this study focuses on one specific dimension of travel context: travel extent (i.e., whether an individual participates in a leisure activity on a daily versus a long-distance basis). As such, this article first introduces a unified conceptual framework for measuring leisure activity loyalties within a travel context, based on two distinct dynamics of leisure loyalty behavior—destination attachment and activity involvement. Additionally, this article uses a unique 2001 NHTS dataset comprised of households’ daily and long-distance leisure activities to undertake a unique empirical analysis of five distinct leisure activities using the conceptual framework and a copula-based model methodology. The findings confirmed that households demonstrate significant loyalties to travel contexts across all leisure activities, especially resting and sightseeing.     

Shopping without travel or travel without shopping? an investigation of electronic home shopping

This study explores the growth of electronic home shopping in terms of likely transportation and communication interactions. Although opportunities exist to shop from home today, most consumers initiate travel trips to stores or markets. Widespread use of automobiles has facilitated the retailing configurations we know today but the development of new electronic networks could change this. This study establishes a baseline to explore shopping activities using two‐day travel activity data from a large U.S. metropolitan area. It is found that people who telework from home today spend more time engaged in shopping activities than other workers. Potentially, their saved work travel is converted into new trips. In the future, saved shopping travel might be converted into other types of travel, and modelling results show that for busy working women, there is a latent demand for maintenance‐related activities. The study results suggest that electronic home shopping will bring into play complex interactions between communications and transportation.     

A hazard-based approach to modelling the effects of online shopping on intershopping duration

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.     

Impact of different shopping stages on shopping-related travel behaviour: analyses of the Netherlands Mobility Panel data

From the moment e-shopping emerged, there have been speculations about its impact on personal mobility. A fair amount of research has already been carried out on Internet shopping itself as well as on its consequences for mobility. Most studies focus on the overall impact of online shopping on personal mobility. However, little is known about how personal shopping mobility can be characterised when differentiating its constituent stages, being browsing/orienting, comparing, selecting and purchasing products, and how this is affected by e-shopping. This will be the main topic of this paper. We will investigate this using recently collected data from the Netherlands Mobility Panel [in Dutch: MobiliteitsPanel Nederland (MPN)]. It is the unique combination of reported shopping trips in the three-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 this research. Using factor analysis, we explore the underlying factors related to the browsing and selection behaviour prior to the purchase of a product. Using these factors as a starting point, we apply cluster analysis resulting in three homogeneous groups of shoppers with different pre-purchase shopping behaviour. The groups differ clearly with respect to personal and household characteristics, in the frequency with which they buy and sell products online and in their perception of (dis-)advantages of online shopping. Once relevant groups have been distinguished and characterised, differences in shopping-related mobility between them are studied in two different ways. Firstly, we analyse statements from shoppers on how their shopping-related mobility has changed. Secondly, we analyse shopping trips reported in the three-day travel diary. Only one group, which consists of shoppers that rely on the Internet to search for product information, compare prices and get new product ideas, states that their shopping-related travel behaviour has changed since they started shopping online. Approximately 50% of all shoppers experienced no difference in their shopping mobility. The analysis of actual shopping mobility using the travel diary data showed only minor differences in shopping-related travel behaviour between the identified groups. Finally, we fit a multi-variate linear regression model of shopping trip distance to determine if (e)-shopping characteristics influence trip distances. The frequency with which people shop online as well as some stated changes in shopping-related travel behaviour (shopping in a similar manner and shopping longer) turn out to influence non-grocery shopping trip distance. No significant influence could be found of shopping cluster membership on shopping trip distances.

     

Investigating the temporal changes in the relationships between time spent on the internet and non-mandatory activity-travel time use

The amount of time we spend online has been increasing dramatically, influencing our daily travel and activity patterns. However, empirical studies on changes in the extent to which the amount of time spent online are related to changes in our activity and travel patterns are scarce, mainly due to a lack of available longitudinal or quasi-longitudinal data. This paper explores how the relationships between the time spent using the Internet, and the time spent on non-mandatory maintenance and leisure activities, have evolved over a decade. Maintenance activities include out-of-home activities such as shopping, banking, and doctor visits, while leisure activities include entertainment activities, visiting friends, sporting activities, and so forth. Our approach uses two datasets from two major cross-sectional surveys in Scotland, i.e. the 2005/06 Scottish Household Survey (SHS) and the 2015 Integrated Multimedia City Data (iMCD) Survey, which were similarly structured and formed. The multiple discrete–continuous extreme value (MDCEV) model and difference-in-differences (DD) estimation are applied and integrated to examine how the relationships between the time spent on the Internet and travel have changed over time and the direction and magnitude of the changes. Our findings suggest that the complementary associations between Internet use and individuals’ non-mandatory activity-travel time use are diminishing over time, whereas their substitutive associations are increasing. We additionally find that such temporal changes are significant in the case of those who spent moderate to high levels of time on the Internet (5 h or more online) per week.

     

E‐Shopping and its Relationship with In‐store Shopping: Empirical Evidence from the Netherlands and the USA

Abstract

Despite considerable examination of the impact of telecommunications on travel, little empirical evidence sheds light on the impact of e‐shopping on travel—a recent and increasingly popular form of telecommunications. This paper analyses determinants of online buying and their relationship with in‐store shopping, using empirical data obtained from Minneapolis, USA, and Utrecht, the Netherlands. Based on chi‐square tests and logistic and ordinary least‐squares regressions, the results indicate that online buying is affected by sociodemographics and spatial characteristics of people, their Internet experience, and their attitudes towards in‐store shopping. US respondents who prefer to see products in person are less likely to buy online. Dutch respondents are more likely to buy online as travel times to shops are shorter. At first sight, this counterintuitive result might be related to an urban, innovative lifestyle that supports e‐shopping. A more detailed analysis of Dutch online buyers reveals that they make more shopping trips than non‐online buyers and have a shorter shopping duration. The results indicate that the relationship between online buying and in‐store shopping is not one of substitution but of complementarity.     

Incorporating online shopping into travel demand modelling: challenges,progress, and opportunities

There is a large body of literature, spanning multiple disciplines, concerned with the relationship between traditional (physical) shopping and associated travel behaviour. However, despite the recent rapid growth of digital retailing and online shopping, the impact on travel behaviour remain poorly understood. Although the issue of the substitution and complementarity between conventional and virtual retail channels has been extensively explored, few attempts have been made to extend this work so as to incorporate virtual retail channels into modelling frameworks that can link shopping and mobility decisions. Here, we review the existing literature base with a focus on most relevant dimensions for personal mobility. How online activity can be incorporated into operational transport demand models and benefits of such effort are discussed. Existing frameworks of shopping demand are flexible and can, in principle, be extended to incorporate virtual shopping and the associated additional complexities. However, there are significant challenges associated with lack of standard ontologies for crucial concepts and insufficiencies in traditional data collection methods. Also, supply-side questions facing businesses and policy-makers are changing as retailing goes through a digital transformation. Opportunities and priorities need to be defined for future research directions for an assessment of existing tools and frameworks.     

Social networks and joint/solo activity–travel behavior

The social dimension of activity–travel behavior has recently received much research attention. This paper aims to make a contribution to this growing literature by investigating individuals’ engagements in joint activities and activity companion choices. Using activity–travel diary data collected in Hong Kong in 2010, this study examines the impact of social network attributes on the decisions between solo and joint activities, and for joint activities, the choices of companions. Chi-square difference tests are used to assess the importance of social network variables in explaining joint activity behavior. We find that the inclusion of social network attributes significantly improves the goodness-of-fit of the model with only socioeconomic variables. Specifically, individuals receiving emotional support and social companionship from family members/relatives are found to more likely undertake joint activities with their family members/relatives; the size of personal social networks is found to be a significant determinant of companion choices for joint activities; and activity companions are found to be significant determinants of travel companions. The findings of this study improve the understanding about activity–travel, especially joint activity–travel decisions.     

A structural equations analysis of commuters' activity and travel patterns

An exploratory analysis of commuters' activity and travel patterns was carried out using activity-based travel survey data collected in the Washington, DC metropolitan area to investigate and estimate relationships among socio-demographics, activity participation, and travel behavior. Structural equations modeling methodology was adopted to determine the structural relationships among commuters' demographics, activity patterns, trip generation, and trip chaining information. Three types of structural equations model systems were estimated: one that models relationships between travel and activity participation, another that captures trade-offs between in-home and out-of-home activity durations, and a third that models the generation of complex work trip chains. The model estimation results show that strong relationships do exist among commuters' socio-demographic characteristics, activity engagement information, and travel behavior. The finding that significant trade-offs exist between in-home and out-of-home activity participation is noteworthy in the context of in-home vs. out-of-home substitution effects. Virtually all of the results obtained in this paper corroborate earlier findings reported in the literature regarding relationships among time use, activity participation, and travel. This revised version was published online in June 2006 with corrections to the Cover Date.     

Trend analysis of activity generation attributes over time

Activity generation models are relatively poorly developed in activity-based travel demand modelling frameworks. This research investigates whether observed distributions of activity attributes (activity frequency, start time and duration) used as inputs in the activity generation component of an activity-based travel demand model have changed over time. This research empirically examines changes in the distributions of activity generation attributes over time in the Greater Montreal area (GMA), Quebec, Canada. It also focuses on how these attributes vary with peoples’ socio-demographic characteristics. This research relies on the 1998, 2003 and 2008 origin–destination (O–D) household travel surveys of the GMA. The comparative analysis at three time points in a 10-year period clearly reveals that distributions of activity attributes are significantly changing over time. Work and school activities show similar trends; frequency “1” has increased and frequency “2+” has decreased over time. The occurrence of shopping activity on weekdays is decreasing over time. Start time and duration distributions for each activity have also changed significantly over time. The research allows preparing activity attributes for the application of an activity-based model, TASHA, such that they reflect temporal changes in travel behaviour of the GMA.     

Redefining activity types: Who participates in which leisure activity?

This paper analyzes the activity choices of individuals and the links between socio-demographics, daily schedules and activity attributes using a new activity choice framework. Activities are first clustered into groups based on their salient attributes, such as duration, frequency, flexibility, planning times, and number of involved persons, rather than their functional types (work, leisure and household obligations), using a K-means cluster technique. This led to the creation of several new activity groups such as “long, temporally fixed, personally flexible activities”, “short and flexible activities”. These activity groups form the choice set for the mixed logit activity choice modeling structure developed for the leisure activities in the second part of the paper. The model results reveal the significant relationships between socio-demographics, temporal characteristics, and characteristics of the schedules on leisure activity choice. The results demonstrate how changing demographics and other activities in individuals’ schedules may affect the nature of the leisure activities and present the substitution and complimentary effects that these new activity groups have on one another.     

Representing and estimating interactions between activities in a need-based model of activity generation

Although several activity-based models made the transition to practice in recent years, modeling dynamic activity generation and especially, the mechanisms underlying activity generation are not well incorporated in the current activity-based models. For instance, current models assume that activities are independent, but to the extent that different activities fulfill the same underlying needs and act as partial substitutes, their interactions/dependencies should be taken into account. For example, recreational, leisure, and social activities tend to be partly substitutable since they satisfy a common need of relaxation, and when undertaken together with others, social needs will be satisfied as well. This paper describes the parameter estimation of a need-based activity generation model, which includes the representation of possible interaction effects between activities. A survey was carried out to collect activity data for a typical week and a specific day among a sample of individuals. The diary data contain detailed information on activity history and future planning. Estimation of the model involves a range of shopping, social, leisure, and sports activities, as dependent variables, and socioeconomic, day preference, and interaction variables, as explanatory variables. The results show that several person, household, and dwelling attributes influence activity-episode timing decisions in a longitudinal time frame and, thus, the frequency and day choice of conducting the social, leisure, and sports activities. Furthermore, interactions were found in the sense that several activities influence the need for other activities and some activities affect the utility of conducting another activity on the same day.     

Activity fragmentation,ICT and travel: An exploratory Path Analysis of spatiotemporal interrelationships

This paper focuses on the interrelationships between ICT, activity fragmentation and travel behaviour. The concept of fragmentation relates to how activities are spatiotemporally reorganized, by subdividing activities into smaller components that are then performed at different times and/or locations, in connection with ICT use. The association between ICT, activity fragmentation and travel relationships remains uncharted. Based on a two-day Dutch communication-activity-travel diary different associations between ICT use, paid work spatiotemporal fragmentation indicators and frequency of travel are specified and tested with Path Analysis Modelling accounting for sociodemographic and land use factors. The results demonstrate that the interrelationships between fragmentation, ICT and travel are quite complex. ICT and fragmentation apparently have a reciprocal relationship with mobile ICT use influencing the degree of spatial fragmentation whereas the usages of sedentary ICT are influenced by the degree of temporal fragmentation. Person-ICT attributes and ICT use mediate the participation in non-work activities, and can replace work and non-work travel. Fragmentation reduces work trips but at the same time restricts non-work personal travel possibilities and can reallocate time for leisure activity and travel.     

Transport-related CO2 effects of online and brick-and-mortar shopping: A comparison and sensitivity analysis of clothing retailing

This paper compares transport-related CO₂ emissions of online and brick-and-mortar shopping based on supply, delivery, order and travel data related to one multi-channel clothing retailer. A sensitivity analysis sheds more light on how situational factors, such as the customers’ travel distances, returns, the use of public transport modes and information behavior via different channels influence the outcome of this comparison. The results show that online retailing causes lower CO₂ emissions under many conditions. Nevertheless, the brick-and-mortar channel is more environmentally friendly when travel distances are small. The radius for which brick-and-mortar shopping has an advantage increases when returns, shifts in the use of public transport and information behavior are also considered.     

The impact of activity chaining on the duration of daily activities

There is a broad body of theoretical and empirical literature dealing with trip chaining behaviour. This paper adds to the literature while focusing on the impact of activity chaining on the duration of time spent on individual purposes. Two questions in particular are addressed: first, does an additional purpose added to a trip chain affect the duration of the activities included? Second, is there any pattern of included activities that explains differences in duration? Duration data models are employed using German data. We find evidence that the number of purposes influences duration significantly. Leisure, shopping and personal business activities are affected by the occurrence of obligatory activities (work, school/university). We cannot find any evidence that personal business or leisure activities influence the duration of shopping, whereas the opposite is supported. Therefore, in terms of daily activities, obligatory and shopping activities are superior to leisure and personal business. We conclude that activity chaining and especially the pattern of combined purposes affect the duration of activities allocated to single purposes while controlling for a wide range of other explanatory variables. The results can be used in transport and simulation models.     

An examination of the determinants of day-to-day variability in individuals' urban travel behavior

Day-to-day variability in individuals' travel behavior (intrapersonal variability) has been recognized in conceptual discussions, yet the analysis and modeling of urban travel are typically based on a single day record of each individual's travel. This paper develops and examines hypotheses regarding the determinants of intrapersonal variability in urban travel behavior.Two general hypotheses are formulated to describe the effects of motivations for travel and related behavior and of travel and related constraints on intrapersonal variability in weekday urban travel behavior. Specific hypotheses concerning the effect of various sociodemographic characteristics on intrapersonal variability are derived from these general hypotheses. These specific hypotheses are tested empirically in the context of daily trip frequency using a five-day record of travel in Reading, England.The empirical result support the two general hypotheses. First, individuals who have fewer economic and role-related constraints have higher levels of intrapersonal variability in their daily trip frequency. Second, individuals who fulfil personal and household needs that do not require daily participation in out-of-home activities have higher levels of intrapersonal variability in their daily trip frequency.     

An examination of the determinants of day-to-day variability in individuals' urban travel behavior

Day-to-day variability in individuals' travel behavior (intrapersonal variability) has been recognized in conceptual discussions, yet the analysis and modeling of urban travel are typically based on a single day record of each individual's travel. This paper develops and examines hypotheses regarding the determinants of intrapersonal variability in urban travel behavior.Two general hypotheses are formulated to describe the effects of motivations for travel and related behavior and of travel and related constraints on intrapersonal variability in weekday urban travel behavior. Specific hypotheses concerning the effect of various sociodernographic characteristics on intrapersonal variability are derived from these general hypotheses. These specific hypotheses are tested empirically in the context of daily trip frequency using a five-day record of travel in Reading, England.The empirical results support the two general hypotheses. First, individuals who have fewer economic and role-related constraints have higher levels of intrapersonal variability in their daily trip frequency. Second, individuals who fulfil personal and household needs that do not require daily participation in out-of-home activities have higher levels of intrapersonal variability in their daily trip frequency.     

Involvement in clubs or voluntary associations, social networks and activity generation: a path analysis

Leisure activities have received increasing attention from travel behavior researchers over the past decade. However, these activities are often treated as a single category, neglecting their differences. Whereas most leisure activities are flexible, club activities are usually scheduled longer in advance and are more fixed in time, location and company. Hence, trip-generating properties of club activities are likely to differ from those of other leisure activities. As very little is known about involvement in clubs or voluntary associations in relation to trip generation, voluntary association activities deserve further research in relation to travel. Therefore, in this paper a path analysis is conducted, analyzing the relationships between participation in clubs or voluntary associations, trip frequencies, and social network characteristics. The analyses are based on data collected in 2011 in Eindhoven in the Netherlands in a survey among 516 respondents. The results show interesting relationships between the social context and involvement in clubs. They indicate that people become club members through their social networks, and frequent club activities increase social network size. Family oriented people were found to go less often to clubs. Club membership and the frequency of going to club activities were also found to be affected by socio-demographics, such as gender, age, education, work, presence of young children in the household and owning a season ticket for public transport.