ICT and temporal fragmentation of activities: An analytical framework and initial empirical finding

ICT and Temporal Fragmentation of Activities:

An analytical framework and initial empirical findings

Christa Hubers* Tim Schwanen

Martin Dijst

Utrecht University Faculty of Geosciences

Department of Human Geography and Planning PO Box 80.115

3508 TC Utrecht The Netherlands

ABSTRACT

It is commonly believed that the widespread use of Information and Communication Technologies (ICTs) facilitate the fragmentation of daily activities across times and spaces. However, a clear conceptualization of what fragmentation is and how it can be measured empirically has been lacking. As a consequence, hardly any empirical evidence has been provided for these notions. The goal of this paper is twofold: (1) to propose a theoretical and methodological framework for identifying and measuring activity fragmentation; and (2) to assess temporal fragmentation empirically and consider its associations with ICT usage while controlling for sociodemographic variables, residential context, day of the week, activity pattern characteristics and some attitudinal variables. Activity fragmentation is defined as a process whereby a certain activity is divided into several smaller pieces, which are performed at different times and/or locations. The proposed theoretical and methodological framework covers three main dimensions of fragmentation: the number of fragments; the distribution of the sizes of fragments; and the temporal configuration of fragments. Based on travel diary data from The

Netherlands the analytical results are insightful and promising. The framework is not only capable of detecting temporal activity fragmentation for various trip purposes, but there are also indications of a positive relation between ICT-usage and temporal fragmentation.

Keywords:

1. INTRODUCTION

It is commonly believed that, due to developments in Information and Communication Technologies (ICTs), “professional and social relations can be established and

maintained almost equally easily over any distance across the globe” (Couclelis, 1996, p. 388). As a consequence, activities seem to be getting less firmly linked to fixed spatial locations and times which might be manifested in the fragmentation of activities into tasks that are widely distributed over space and across time (Couclelis, 2000; Dijst, 2004). This so-called ‘activity fragmentation’ is foreseen to have considerable impacts on the daily life of individuals. The fragmentation of daily activities across times and spaces facilitates the blurring of the boundaries between previously separated life domains of work, care and leisure and may offer new opportunities as well as challenges to people juggling paid labor and care giving responsibilities. It is furthermore foreseen to have considerable impacts on transportation flows since the predicted increases in travel demand that may result from activity fragmentation may increase road congestion across time (especially during what are now considered non-peak hours) and space (new

bottlenecks in addition to existing ones). The demand for certain facilities and services may also decrease, manifest itself at other times, or facilities may experience alterations in their functions. E-shopping, for example, may ultimately reduce brick-and-mortar stores to showrooms for products that are than purchased on the Internet. Likewise, telecommuting may reduce the relevance of the physical nearness to the employment location when searching for a new residence (Ory and Moktharian, 2006).

fragmentation, their research does not provide a detailed insight into the ways in which activities are fragmented. It furthermore remains unclear what the relative strength of the relation is between ICT and activity fragmentation. There are reasons to believe that the extent of activity fragmentation is also related to sociodemographic factors or the

residential and temporal context (cf. Hanson, 1982; Yamamoto and Kitamura, 1999). We expect ICTs to function as facilitators of fragmentation because they create new choice sets for the performance of activities in space and time, rather than them being

determinants of fragmentation. We hypothesize that characteristics of both the activity and the individual determine whether or not these new options will actually be chosen (cf. Mokhtarian et al. 2006).

In order to address these issues we aim, firstly, to develop a theoretical and

methodological framework for measuring fragmentation of activities. Our second aim is to apply this framework on travel diary data in order to assess the relevance of ICT-usage for temporal activity fragmentation.

For the development of the theoretical and methodological framework, an

interdisciplinary approach is employed. This framework will be presented in Sections 2 and 3. Sections 4-5 introduce the empirical analysis in which we describe the

2. AN INTERDISCIPLINARY APPROACH TO FRAGMENTATION

2.1. Defining Fragmentation

To avoid re-inventing the wheel, we have conducted an interdisciplinary literature search about the nature of fragmentation and measurement approaches.Fragmentation is a notion used in social and natural sciences and applied on various divisible phenomena or objects. Sociologists have extensively studied the temporal fragmentation of (leisure) activities, often in relation to time pressure (e.g, Mattingly and Bianchi, 2003; Sullivan, 1997). In human geography, economic geography and spatial planning spatial

fragmentation refers often to the development of socio-spatial specialised zones in relation with segmentation of infrastructures (Graham and Marvin, 2001), segmentation and relocation of economic activities (Arndt and Kierzowski, 2001) or decentralised land-use governance (Ulfarsson and Carruthers, 2006). Computer scientists Mark et al. (2005) have investigated the extent of fragmentation of work activities to find out whether the development of computer software programs assisting workers in picking up their work after interruptions is warranted. But probably the most well known application of the fragmentation concept in the world of computers is concerned with hard disk

fragmentation (Diskeeper Corporation Europe, 2006).This fragmentation process implies that accessing fragmented computer files consumes more time and necessitates users to de-fragment their hard disks. Ecology is nonetheless the discipline contributing the most relevant insights for our study: a vast literature exists on forest and ecosystem

fragmentation, on different dimensions of fragmentation, and on measurement approaches(Rutledge, 2003).

While fragmentation has been studied in many research areas, each discipline employs its own specific definition to the concept. As a consequence, there is no unequivocal

definition of fragmentation. Inspired by Couclelis (2003, page 11), we define fragmentation as:

Generally speaking, two types of activity fragmentation can be discerned: temporal fragmentation – different times at which the smaller sub-tasks are performed – and spatial fragmentation – different locations at which the sub-tasks are performed. The current paper focuses on the temporal fragmentation.

An example helps to clarify the above definitions. Suppose a person wants to purchase a Flatscreen TV. She could start browsing the Internet for some general information on the types of Flat TVs available. She may then go to a brick-and-mortar electronics store to get a better grasp of the difference between Plasma and LCD technology and see with her own eyes the differences in the picture quality, and see which features she likes best. She may also read some independent product reviews on the Web or talk to Flat TV-owning friends, relatives or colleagues about their experiences. After having decided what TV to buy, she has to decide where to purchase it. Comparison sites on the Internet might be used to get the best bargain. Having chosen the dealer and consequently purchased the new Flat TV online, she finally has to determine how to have the product delivered, where and when.

Not all purchases will be made in this or similar ways. But the example shows clearly that the activity of shopping for a certain product comprises several sub-tasks (e.g. searching and evaluating product information; purchasing the product; and transporting the product or having it delivered) and this probably holds for the majority of shopping activities (see also Salomon and Koppelman, 1988). These sub-tasks can exist of smaller fragments. We use the term ‘activity episode’ to denote the different times at which these smaller fragments a sub-task consists of are performed. If on a given day the individual in the above example in the morning talks to some colleagues at the office about their Flat TVs, stops by an electronics store to view some possible TVs after work and later at night browses the Internet for some more product information from home, the sub-task of searching and evaluating product information consists of three activity episodes

is spatially fragmented across three activity locations: the employment location, the electronics store and the home. The example also indicates the significance of ICTs in this process. Due to ICTs, the number of times when and locations where activity episodes can be performed has increased dramatically now that they are no longer exclusively dependent on shop opening hours and locations.

The discussion so far has not touched upon two complicating factors. First, our example does not make clear when an activity is fragmented as opposed to being two separate activities. In our view, the answer to this question greatly depends on the objectives of the study. If, for example, it seeks to examine whether the paid work activity of people who work from home is more often alternated with maintenance activities (activities that are performed for the upkeep of the household, such as cooking, cleaning and shopping) than is the case for people working in an office, a more general classification of paid and maintenance activities suffices. However, if one wants to find out whether the process of shopping for shoes is more or less fragmented than the process of shopping for a Flat TV, more detailed information on the sub-tasks that constitute both shopping activities is necessary.

Second, there are several concepts, such as balkanization and contamination or multi-tasking, that are intimately associated with fragmentation. We will discuss these related concepts briefly to demarcate what topics will not be investigated but may still be

derived from the sociological literature and is concerned with the fact that several

activities can be performed simultaneously (Mattingly and Bianchi, 2003; Sullivan, 1997; Felker Kaufman et al., 1991). For instance, watching the television while eating, working on the train while travelling, or calling a friend with your cellular phone while standing in line at the grocery store. With multitasking the emphasis is on how at a single moment in time multiple activities are performed, whereas with temporal activity fragmentation the emphasis is on how a single activity is performed at multiple times and locations. Several transportation studies have indicated that Internet and mobile phone use stimulate

multitasking (Kenyon and Lyons, 2007; Schwanen and Kwan, 2008). Since the

description of temporal fragmentation is already very complex without addressing issues of how people experience it and the performance of multiple activities simultaneously, the latter two issues are left to future studies.

2.2 Dimensioning fragmentation

According to the ecological literature in particular, fragmentation can be seen as composed of three dimensions (Figure 1). The most commonly identified dimension is the number of fragments or segments in which a given object (activity, forest or hard disk) is divided (Mattingly and Bianchi, 2003; Sullivan, 1997; Rutledge, 2003). Rutledge (2003, page 7) gives a simple but telling example: “A plate that is broken into 100 pieces is more fragmented than a plate broken into 10 pieces.” In our framework this means that when a certain sub-task is performed, for example, four times a day, this activity type counts four fragments, also called activity episodes.

The second dimension concerns the distribution of sizes of the fragments. As Rutledge (2003, page 7) continues: “Similarly, a plate broken into 10 pieces of equal size is more fragmented than a plate broken into 10 pieces, one of which is 90% of the original plate.” This is also recognized in social science for employment-related (Mark et al., 2005) and leisure activities (Sullivan, 1997).

Finally, the configuration of fragments is considered an important dimension of

sense, configuration is not related to spatial fragmentation, he argues that the survival of plant and animal species depends on the configuration of their habitat fragments. If habitats become too isolated, the survival of plants and animals is threatened. Although it is probably not a matter of life and death in social sciences, studying the configuration of activity fragments can provide valuable insights into their timing. ICT use may imply that activity episodes become more spread out across the day. This is shown clearly in the example in Section 2.1 where the person gathers product information from her colleagues in the morning during office hours, after work just before the closing time of the

electronics store and again later at night on the Internet which conveniently has no closing time. Fragmentation can have a direct or indirect effect on the timing of activity episodes. As ICTs reduce the space-time fixity of activities they have a direct effect on the timing of activity episodes (Schwanen and Kwan, 2008). ICTs however may also increase the efficiency with which activities are performed, thereby reducing their duration. And since short activity episodes are more readily slotted into individuals’ activity schedules than longer ones, ICTs may have an indirect effect on the timing of activity episodes. Information on the number and/or duration of activities is insufficient for determining whether they are rescheduled to alternative moments. The distribution of the sizes of the activity episodes only tells us something about the duration of these activity episodes. By taking into account the temporal distances between the different activity episodes, which are calculated in the configuration dimension, the exact timing of these activity episodes can also be determined.

2.3 Factors Associated with Fragmentation

Based on previous research on determinants of activity and travel behaviour we expect that besides ICTs, other factors including sociodemographic factors, characteristics of the built environment (Hanson, 1982; Lu and Pas, 1999), factors concerning the day of the week (Yamamoto and Kitamura, 1999) and attitudinal variables (Farag et al., 2007) might also be related to the fragmentation of activities. Like Mokhtarian et al. (2006) we expect ICTs primary impact on activities is “to expand an individual’s choice set” (page 263), whereas characteristics of both the activity and the individual determine whether or not these new options will actually be chosen. Because the adoption of ICT and their use depends among others on socio-demographic factors, the effects of these variables should be controlled in an analysis of the associations between ICT ownership and use and activity fragmentation. This section provides a brief summary of the potential relations between other determinants of activity and travel behaviour and fragmentation.

Previous studies have discussed the impact of ICT on activity fragmentation in general terms (Couclelis, 1996; Lenz and Nobis, 2007). However, ICT is a highly differentiated category of information and communication devices and services, ranging from PCs to cell phones and from voice call to email. Therefore, it can be hypothesized that

claims on their time-space resources (Kwan, 1999). And since the caring task load is higher if adults have small children, we expect persons with young children to have more fragmented daily activity patterns than persons without young children. Other

sociodemographic variables that have been related to activity and travel behaviour and therefore may also be associated with the fragmentation of daily activity patterns are age and education (Hanson, 1982; Lu and Pas, 1999).

Residential context, such as the number and accessibility of shops and other facilities, define the opportunities and constraints for the fragmentation of activities (Dijst et al., 2002; Ritsema van Eck et al., 2005). Since these opportunities are substantially larger in urban than in suburban areas, we expect people living in urban areas to have more fragmented activity patterns than their counterparts in suburban areas (Schwanen, 2004). Furthermore, since travelling by car increases the accessibility of facilities, commute mode is also expected to be related to activity fragmentation. A temporal factor expected to be related to the fragmentation of activities, is the difference between week- and weekend days (Yamamoto and Kitamura, 1999). On weekdays, paid labor, activities related to personal care and the caring for children at home, as well as chauffeuring young children to their schools, impose major time constraints on activity-travel patterns (Cullen and Godson, 1975; Kwan, 2000a; Doherty, 2006; Schwanen and Kwan, 2008). Hence, it can be expected that activities on weekend days are less fragmented than on other days. Finally, where the fragmentation of the shopping activity is concerned, earlier findings by Farag et al. (2007) indicate that people with positive attitudes towards

shopping are willing to put more efforts into shopping. This may result in more shopping related activity episodes that require more or longer trips. On the other hand, the

3. FRAGMENTATION INDICES

Based on the dimensions of fragmentation discussed in Section 2.1, we now introduce the indicators developed for each dimension. Most of the indicators are based on the

literature in ecology and sociology (e.g. Sullivan, 1997; Mattingly and Bianchi, 2003; Rutledge, 2003) and have been adjusted to the specific measurement of temporal fragmentation. The term ‘activity episode’ is used as a synonym for ‘activity fragment’. Details on the exact definitions of the indicators are available in Table 1. The temporal distances between the fragments characterise the distribution or configuration of these fragments in a certain time span. An important benefit of the three dimensions discerned in Section 2.2, is that they are applicable on multiple temporal scales. Whereas in the current research paper they are applied to investigate the temporal fragmentation of daily activity patterns, they can also be employed to study the fragmentation of weekly,

monthly or even yearly activity patterns.

3.1 Number of Activity Episodes (NAE)

This dimension makes a first and simple distinction between more or less fragmented activities by counting the number of different episodes of a certain activity in a day. Its interpretation is straightforward: the greater the number, the greater the fragmentation.

3.2 Distribution of Sizes

The distribution of the sizes of the episodes is measured via three indicators: 1. The mean size of the different episodes an activity is divided into (MES); 2. The standard deviation of the episode sizes (SD ES), and;

3. The size of the largest episode (LEI).

fragmented. It is expected that the mean size of the episodes and the size of the largest episode are inversely related to the number of episodes. This would be in accordance with the work of Kitamura et al. (1981) who found that the number of episodes in a daily activity pattern and the duration per episode are negatively correlated.

3.3 Configuration

The configuration indicators measure whether a certain activity is more or less spread across time and in what way. Their value primarily lies in their ability to describe in what way a certain activity is fragmented. This exercise, however, is only fruitful when the indicators allow for a distinction between global and local clustering as well as outliers. Global clustering refers to the degree to which episodes are located near or far from one another at the level of the total pattern of all activity episodes collectively. The term local clusters is used to indicate the occurrence of several smaller subgroups of fragments within the total temporal pattern, located at a certain temporal distance from one another. Furthermore, outliers are defined as single fragments that are separated relatively far in time from the other fragments. It does not suffice to consider only the average temporal distance between all fragments. This would only reveal the amount of global clustering of an activity and might cancel out the differences in temporal distance between pairs of fragments. In this case configurations B and C portrayed in Figure 1 would have a roughly similar average temporal distance between the episodes, even though their configurations are clearly different. It is therefore also relevant to study the possible occurrence of local clusters or outliers as shown in pictures D and E. Four indicators have therefore been developed:

1. The mean temporal distance between the episodes (MTD);

2. The standard deviation of the temporal distance between the episodes (SD TD); 3. The mean temporal distance from one episode to its nearest neighbouring episode

(MNTD); and

The mean temporal distance between the episodes (MTD) measures the time intervals between each episode and all other episodes. If there are more than two episodes, say three, the temporal distance between the first and third episode is calculated by subtracting the starting time of the third from the ending time of the first episode and discounting the duration of the second episode in-between. The use of the four indicators discerned here enables us to detect various different kinds of configurations. Table 2 offers an overview of how fragmentation patterns can be represented by the different combinations of mean temporal distance between the episodes (MTD) and its nearest neighbouring episode (MNTD) and their standard deviations.

4. RESEARCH DESIGN

4.1. Data Description

The data used for the empirical analysis were originally gathered to examine the

relationships between e-shopping and in-store shopping (Farag et al., 2007). It consists of a shopping questionnaire and a two-day travel diary and was collected

appears to be some selection bias in that highly educated persons, females and older persons are over-represented. Further information about the data collection process is available in Farag (2007).

We have selected this data set because it allows us to assess the fragmentation of activities within daily activity patterns and its association with ICT use and other relevant factors. But since it was not originally designed to measure the fragmentation of

activities, the data also has some important limitations. The main limitation lies in the fact that the travel diaries only contain information on the main activity that is undertaken at a certain location and not on the possible other types of activities that have been carried out at that destination. Furthermore, the categorization of activities is not sophisticated enough to allow us to discern possible sub-tasks, like searching for product information and purchasing of the product for the activity of shopping. However, the unique and comprehensive set of independent variables available in the data was an important advantage. Not only does it include a range of ICT indicators, but also information on respondents’ attitudes towards shopping and several characteristics of respondents’ residential context. The total number of respondents (826) also constitutes an advantage. Daily activity patterns tend to be very heterogeneous and data for many days or

individuals is required if one wants to concentrate on specific activity types (paid labor, grocery shopping, etc.) of activities as we will do below.

4.2. Operationalisation of Variables and Analysis

While comparing the temporal fragmentation of, for example, shopping for two specific product types (e.g. clothing compared to electronics) is certainly interesting, we

concentrate on the differences in temporal fragmentation between more general activity categories in the current paper. It is expected that also on this broader level interesting differences between activity types are manifest. Therefore for our analysis four activity types were selected:

 paid labor: only includes visits to workplaces.

 daily shopping: market (10%1_{), supermarket (58%), a combined category (bakery, the}

greengrocery, the butcher’s store and the fish store (21%), and other (11%).

 non-daily shopping: clothing/footwear (13%), books/music (15%), department stores (13%), domestic appliances (10%), drug stores (9%), electronics (6%) and other (34%)  leisure: social visits (50%), restaurant/café (23%), sports and hobbies (17%),

theatre/cinema/museum (5%), other (6%).

The factors potentially associated with fragmentation (Section 2.2) were derived from the shopping questionnaire. In total 18 variables have been defined and tested, which belong to the following categories: socio-demographics (8 variables); ICT factors (4); residential context (2); day of the week (1); and activity pattern characteristics (3). Some behavioral and attitudinal characteristics were also available from the shopping questionnaire. Principal factor analysis was applied to derive certain attitudes from a list of statements of which respondents were asked whether they agreed or disagreed. Agreement could be stated on a scale of 1 (totally disagree) to 7 (totally agree). Both for shopping behavior as for personality, only the third component was part of the final regression models

presented in Section 5. With regard to shopping behavior this component seems to represent the efficiency of daily shopping. The personality component seems to reflect having a risk-averse personality. Unfortunately the shopping questionnaire did not contain any information on people’s evaluation of activity fragmentation, so the relation this might have with the amount of activity fragmentation cannot be assessed at this time.

To determine the extent of fragmentation of the four different activity types, the mean scores on the eight different fragmentation indicators were computed for all four

activities separately. Regression analysis was used to analyze the relative strength of the associations between the eight fragmentation indicators and the independent variables mentioned above. In the regression models discussed in Section 5 only variables that were statistically significant at p < 0.1 were included in the final model specifications.

5. RESULTS

5.1. Descriptive analysis

Based on the mean indicator values for the dimensions of the number of activity episodes and the distribution of episode sizes (Table 3), non-daily shopping appears to be the most fragmented activity of the four activity types considered. Not only does the activity of non-daily shopping consist of more different activity episodes than do paid labor, daily shopping and leisure (NAE), but these episodes also last shorter (MES) and are more equal in size (SD ES & LEI) than those of most other activities. As expected, the number of episodes is lowest and the duration per episode longest for paid labor. The

configuration of the activity episodes indicators provides information on the pattern formed by the activity episodes. According to the mean temporal distance between episodes (MTD) the time-spans between leisure episodes are the longest, namely two hours and twenty minutes on average.

short MTD of 58 minutes for non-daily shopping reflects that non-daily shopping episodes are often chained together (see also Figures 2 and 3).

It is noteworthy that the MTD and the mean temporal distance from one episode to the nearest neighbouring episode (MNTD) are quite similar for paid labor, daily shopping and leisure. This is because the majority of respondents participate in these activity types at most twice per day, in which case the MTD and the MNTD are identical to one

another. When the activity on average consists of several episodes the combination of the MTD and MNTD can provide insight into whether and how these episodes are spread across time. For example, since the MNTD for non-daily shopping is lower than the MTD, there is reason to believe that some activity episodes have smaller temporal distances than others, thereby forming one or more local clusters. This claim is

substantiated by the standard deviations of the temporal distances between the episodes (the SD TD and SD NTD).

In order to be able to compare the configurations of the activity episodes of paid labor, daily- and non-daily shopping, and leisure we have corrected the standard deviations for differences in mean temporal distances between the episodes of the three activity types by calculating the coefficient of variation (cv, results not shown here). Since paid labor has the lowest cv scores, this in combination with the other results tells us that paid labor is globally rather clustered (between 9:00 AM and 6:00 PM). Compared to daily

5.2. Multivariate Analysis

Due to data limitations, it was only possible to perform regression analyses for five of the eight indicators. Furthermore, since the majority of the activities consist of at most one or two episodes, the analyses for the number of activity episodes (NAE) and largest episode index (LEI) yielded strongly similar results. For the sake of efficiency, only the results of the analysis of the NAE are presented along with those for the mean episode size (MES), the mean temporal distance between the episodes (MTD) and the mean temporal distance from one episode to its nearest neighbouring episode (MNTD). Tables 4 and 5 present the significant unstandardized regression coefficients (B) and the standardized regression coefficients (β) for the different models. The βs allow comparisons of the relations of the different independent variables with the dependent variable. The R2 _{statistic, which can} range from 0 to 1, indicates the goodness-of-fit of the model. It should be noted,

however, that the R2 _{is influenced by the number of cases (N) in the model, in that fewer} cases usually result in a larger R2_{. As will be seen, the R}2 _{of the models on average is not} very high, which might indicate the absence of important variables in the model or a small amount of variance in the fragmentation indicators. When the variance in the dependent variable is rather small, the independent variables have to be much more sensitive to the finer nuances in the dependent variable. This usually results in fewer statistically significant relationships between dependent and independent variables in a regression model.

Paid Labor. ICT usage is positively related to the fragmentation of paid labor. Frequent Internet users appear to have more work episodes than infrequent Internet users.

However, the duration of these work episodes (MES), and the time-spans between them (MTD and MNTD) do not differ between frequent and infrequent Internet users. This is in line with the work of Couclelis (2004) and Lenz and Nobis (2007). The number of hours of paid labor is the only sociodemographic variable that is related to the

living in the suburb of Nieuwegein have more work episodes than people living in the other three municipalities

The variables with the largest βs, and therefore with the strongest relations with all fragmentation indicators for paid labor, are the activity pattern characteristics, in particular the bringing away or picking up of something or somebody (chauffeuring). It seems that chauffeuring tasks form an indicator of a generally high amount of care giving responsibilities among some respondents, like bringing and picking up their children from schools or day care centers and daily shopping at certain fixed moments in time. The juggling of these care giving responsibilities with other tasks results in the

fragmentation of work-related tasks (see also Hanson and Pratt, 1995; Kwan 1999; Schwanen, 2007). Other activity pattern characteristics related to the duration of work episodes (MES) are the number of days one works from home, and whether the workday is a Saturday. For people working from home, and those working on a Saturday work episodes are shorter, all as could be expected.

Daily Shopping. The results for daily shopping show a rather different picture. With regard to ICT factors, the models show that persons whose primary use of the Internet does not take place in the home or at work, show more fragmented daily shopping patterns. They have a larger number of daily shopping episodes (NAE) and these

expected since it seems to indicate that ICT usage is related to less instead of more fragmentation (less and longer episodes instead of more and shorter episodes). It appears that the persons who search online and buy in-store bundle or defragment these shopping episodes, resulting in fewer but longer shopping episodes. This is further substantiated by the fact that people who prefer to shop efficiently, and those who have a risk-averse personality (a factor that among others consists of the statement that one likes to combine shopping with other activities), also have longer shopping durations. The number of ICT devices a person owns is positively related to the temporal distances between daily shopping episodes (MTD and MNTD) which are about 27 minutes longer for owners of multiple ICT devices. The daily shopping patterns of owners of multiple ICT devices are temporally less clustered and thus differ from those of persons who own few or none ICT devices. These results thus seem to indicate that ICTs are more likely to be associated with the defragmenting of daily shopping activities, as well as to temporally less clustered daily shopping patterns.

Several of the sociodemographic variables are related to the fragmentation of daily shopping. Age, for instance, has the strongest relation with the number of daily shopping episodes (NAE) in that older people tend to have more of them. Their daily shopping episodes are also more clustered than those of younger people, as shown by the shorter time-spans between the shopping episodes, represented by the mean temporal distance between the episodes (MTD), and the mean temporal distance from one episode to its nearest neighbouring episode (MNTD). This seems to reflect that older people have larger time windows (time-blocks in which one is free to engage in out-of-home non-work activities and travel, Forer and Kivell, 1981) at their disposal for daily shopping, whereas younger people have several smaller time windows available for daily shopping, forcing the latter to split up and divide the daily shopping activity across the day

(Srinivasan and Bhat, 2005).

than men because they still take on the most responsibility for social reproductive

activities like daily shopping (Kwan, 1999; Schwanen et al., 2007). As for the differences between the educational levels, this might reflect a different taste in daily shopping goods, resulting in visits to various specialist shops by highly educated respondents, instead of a single supermarket.

Persons experiencing a mobility constraint in the form of a handicap or illness on average have 18 minutes longer daily shopping episodes compared to people without such a constraint because the handicap slows them down. Furthermore, of all the variables tested in the models, the commuting mode has the strongest relation with the mean temporal distance between the episodes (MTD) and its nearest neighbouring episode (MNTD) for daily shopping. The temporal intervals between the shopping episodes of car commuters are much shorter than those of persons who go to work on foot, bicycle or public

transport. People who commute by car work further away from their place of residence, resulting in a larger commute time, which leaves less time to do the shopping. Although people who commute by public transport are also likely to have long commute times, a car is more flexible and therefore better suited to visit different shopping locations than public transportation is.

Residential context has the strongest relation with the mean shopping episode duration (MES) for daily shopping. A shopping duration in Lopik on average lasts 13 minutes longer than in the other municipalities. This probably reflects the fact that the duration of an activity is related to the travel time associated with that activity (Dijst and Vidakovic, 2000). Longer trips are usually only undertaken for activities with a longer duration. Since Lopik has a low level of shop availability, its residents might be inclined to employ a bundling strategy to save on shopping related travel time and make the activity duration worth the long trip.

One activity pattern characteristic and the day of the week are also related to the

leaves less time for shopping. Furthermore, on Saturdays daily shopping episodes are more clustered which might reflect a bundling strategy of people who have little time to do the shopping on weekdays because of work obligations.

Non-Daily Shopping. Perhaps the most telling about the results for non-daily shopping (Table 5), is the fact that there are only a small number of statistically significant results. This gets even more puzzling when we take into account the fact that of all four

activities, non-daily shopping was the most fragmented one, as was shown in Section 5.1. The low model fit is probably due both to the absence of other important variables, and the small amount of variance in the fragmentation indicators. The ownership of ICT devices is the only ICT variable that is related to the fragmentation of non-daily shopping. As hypothesized, the more devices a person owns, the more non-daily shopping episodes he or she has. There is also a weak relation between the number of days a person works from home, an activity pattern characteristic, and the mean temporal distance between episodes. The more days one works from home, the stronger the

clustering of non-daily shopping episodes. Educational attainment is the only

sociodemographic variable related to the fragmentation of non-daily shopping. Being highly educated implies shorter and more clustered non-daily shopping episodes. This might be because highly educated persons tend to engage more in comparison shopping (Hanson, 1982).

Residing in Culemborg is one of the two residential context variables related to non-daily shopping. People from Culemborg have more non-daily shopping episodes than people living in the other three municipalities. This might reflect the town planning of

Culemborg which offers a somewhat unusual mix of stores for both daily and non-daily shopping2_{. Further analysis has shown that this is conducive to the chaining of daily and}

non-daily shopping episodes in Culemborg. In more urbanized areas the non-daily shopping episodes have a longer duration, possibly because they offer more and larger stores for the consumer to dwell in.

Leisure. The location where one uses the Internet most often is the only ICT variable related to the fragmentation of leisure activities. People who primarily make use of the Internet at their workplace tend to have more leisure episodes. Perhaps this is because these people are mainly to be found in certain professions related to the creative class, that are associated with lifestyles that are more focused on out-of-home leisure activities. If this is the case, job characteristics have an indirect relation with the fragmentation of leisure activities through the primary location of Internet use. This result is therefore not necessarily in line with the expectations that were formulated in other activity

fragmentation literature since the positive relation between ICTs and activity fragmentation may in fact be a proxy for job characteristics.

Several sociodemographic variables are related to the fragmentation of out-of-home leisure. Older people tend to have fewer leisure episodes, but it cannot be said that their out-of-home leisure activities are less fragmented than those of younger people, since the time-spans between the episodes are larger for older people. This seems to reflect that older people have more free time at their disposal and are therefore capable to combine a leisure activity in the morning with one in the afternoon, whereas people who have to work during the day will most likely postpone leisure activities until after work. Men also have more leisure episodes than women.

Because of the considerable commute time of public transport commuters, which leaves less time for leisure activities, their leisure episodes have a shorter duration than people who use other commuting modes. Furthermore, the more cars available in the household, the larger the time-span between one leisure episode, and the directly preceding or following leisure episode (MNTD). When there are more cars in the household, household members might be more inclined to visit leisure activities that are located further away from each other, resulting in more travel time and larger temporal distances between episodes. Two of the activity pattern characteristics are related to the

fragmentation of leisure. On a workday the number of leisure episodes is smaller. Having two or more chauffeuring duties is the most strongly related to the number of leisure episodes (NAE), and the average duration of these episodes (MES). The more chauffeuring tasks, the more leisure episodes, and the shorter the duration of these episodes.

6. CONCLUSIONS AND DISCUSSION

Given that the literature on activity fragmentation has so far been mainly conceptual, this paper had a double goal: proposing a theoretical and methodological framework for analyzing activity fragmentation; and empirically assessing the extent of temporal fragmentation and its associations with ICT usage, while controlling for

sociodemographic variables, residential context, day of the week, activity pattern characteristics and some attitudinal variables. A framework has been built around three dimensions of fragmentation: the number of fragments; the distribution of the sizes of fragments; and the temporal configuration of fragments. Applying the framework to existing travel-diary data has demonstrated its capability of distinguishing between more and less fragmented activities and differences in the configuration of these fragments.

The analysis has shown that for each of the four activity types considered ICTs are related to at least one fragmentation indicator, most often in a positive way. By and large, our analysis is consistent with the expectation in the literature (Couclelis, 2000; Dijst, 2004; Lenz and Nobis, 2007) that ICT ownership and use is associated with more activity fragmentation. More specifically, the number of work episodes for instance was

positively related to the frequency of Internet usage. For daily shopping several ICT variables were related to the fragmentation of daily shopping episodes, though contrary to our expectations one of them, searching online to buy in-store, had a negative sign. The number of ICT devices one owns is positively related to the number of non-daily

shopping episodes and the number of leisure episodes is related to the primary location of Internet use. So although the results show mostly positive relations between ICTs and fragmentation, these relations differ for the kind of ICT and kind of activity investigated. This, we believe, is an important notion which should be taken into account in the

development of future research on activity fragmentation.

Nonetheless, non-ICT variables were always related with fragmentation more strongly than ICT ownership and use in every model. Chauffeuring, for instance, had a strong impact on the fragmentation of paid labor and leisure activities, and in most models such sociodemographic variables as age, presence of children and educational level have more and stronger relations with fragmentation than ICTs have. Since we expect ICTs to create the necessary conditions for fragmentation to occur and thus function as facilitators rather than as determinants of fragmentation, these results are not surprising.

substantiate the claim put forward in several transportation studies that Internet and mobile phone use stimulate multitasking. Otherwise, when a person for instance works while travelling home by train, and commuting is the primary activity, the secondary work activity would not be detected. Finally, to determine whether fragmentation results in highly flexible daily activity patterns, or whether these patterns remain rather fixed even though they are more fragmented, the time scale of the data could be expanded from the two days of the current paper to preferably several weeks (Kitamura et al., 2006).

The framework proposed in this paper may also be extended in future research by considering the possible relations between the four different activity types that were analyzed in isolation from one another in the current study. Analysis of the total daily activity pattern they form may offer important insights into whether ICT ownership and use is, for example, associated with an increased alternation of paid labor with

maintenance and/or leisure activities. It is also important to address the ways in which respondents experience and evaluate activity fragmentation and not limit oneself to the study of actual behaviour as was done in the current study (Adams, 1995, 2000, 2005; Kwan, 2000b). After all, the technical feasibility to be able to fragment activities does not guarantee that people will actually do so. If people evaluate fragmentation negatively, they are probably less prone to fragment their activities, and the futuristic views of highly fragmented daily activity patterns will never actualize.

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TABLE 1 Description of Configuration Measures

Dimension Name Symbol Description

Number Number of activity episodes NAE Counts the number of activity episodes

Distribution Mean episode size MES Divides the total activity duration by the number of activity episodes. Results are always larger than 0

Episode size variation SD ES Calculates the standard deviation of the episode durations

Largest episode index LEI Divides the episode with the longest duration by the total activity duration and multiplies it by 100

Configuration Mean temporal distance between episodes

MTD Divides the sum of all temporal distances between episodes by the number of temporal distances between episodes

Variation in temporal distance between episodes

SD TD Calculates the standard deviation of the temporal distance between episodes

Mean temporal distance from one episode to its nearest neighbouring episode

MNTD Divides the sum of all temporal distances to nearest neighbouring episode by the number of temporal distances to its nearest neighbouring episode

Variation in temporal distance to the nearest neighbouring episode

24 0

24 0

24 0

24 0

24 0

TABLE 2 Fragmentation Patterns and their Indicator Values

Temporal fragmentation

Pattern Indicator values

Description of fragmentation pattern

MTD low

SD TD low

MNTD low

SD NTD low

A: Global clustering

MTD high

SD TD low

MNTD high

SD NTD low

B: Spread out evenly

MTD high

SD TD high

MNTD low

SD NTD low C: Multiple local clusters

MTD low

SD TD medium MNTD medium

SD NTD medium D: Global cluster and outlier

MTD high

SD TD high

MNTD high

SD NTD high

TABLE 3 Fragmentation of the Activity Types Paid Labor, Daily and Non-Daily Shopping, and Leisure

Number of activity episodes

Episode size Distribution

Configuration of activity episodes

NAE MES SD ES LEI MTD SD TD MNTD _NTDSD Paid labor Mean 1.27 363.1 109.3 92.2% 50.6 33.3 45.7 19.0

N obs. 380 380 81 380 81 19 81 19

Daily Mean 1.54 25.6 12.1 88.4% 94.4 67.1 81.1 50.6

Shopping N obs. 623 623 225 623 225 67 225 67

Non-daily Mean 2.12 32.2 13.9 78.6% 58.3 50.1 37.2 29.5 Shopping N obs. 459 459 230 459 230 125 230 125

Leisure Mean 1.62 140.5 81.4 86.7% 140.7 102.2 117.2 81.0

TABLE 4 Regression Analyses for the Fragmentation of Work and Daily Shopping Activities

Paid labor Daily shopping

Number of

episodes Mean episode size

Mean temporal distance between

the episodes

Mean temporal distance episode

to its nearest neighbouring episode

Number of

episodes Mean episode size

Mean temporal distance between

the episodes

Mean temporal distance episode

to its nearest neighbouring episode

B β B β B β B β B β B β B β B β

Constant 0.641*** 336.2*** 46.3*** 41.8*** 1.059*** 22.7*** 209.4*** 211.1***

Age _{0.012*** 0.164 -1.6**}

-0.179 -1.8**

-0.202 Gender

Male -0.149* -0.077

Number of hours

paid labor 0.089**

0.11

6 24.6** 0.109

High education _{0.190** 0.103 -43.1**}

-0.189 -51.4*** 0.230

-Handicap 18.9*** 0.158

Frequency

internet use 0.045**

0.13 0 Primary Internet

use not at home or work

0.356* 0.072 -11.1* -0.075

Search online,

buy in-store -0.194** -0.099 8.4*** 0.145

Efficiency daily

shopping 3.2*** 0.122

Risk-averse

personality 3.6*** 0.131

Number of days working from

home -13.2*** -0.160

Number of ICT

devices owned 27.0** 0.163 26.3** 0.163

Nieuwegein _0.161** 0.10

4

Lopik 13.5*** 0.160

Saturday _{-75.5*** -0.164 -40.5**}

-0.177 -44.6**

-0.199

Workday -0.219** -0.104 -.6.9*** -0.110

Commute by car _-61.0***

-Picking up/bringing away once

0.322*** 0.20₀ -101.8*** -0.214

Picking up/bringing away two or more times

-87.0*** -0.165 34.6** 0.25₃ 31.6** 0.255

R2 _{0.087 0.131 0.064 0.065 0.073 0.119 0.172 0.194}

N 380 380 81 81 623 623 225 225

TABLE 5Regression Analyses for the Fragmentation of Non-daily Shopping and Leisure Activities

Non-daily shopping Leisure

Number of

episodes Mean episode size

Mean temporal distance between

the episodes

Mean temporal distance episode

to its nearest neighbouring episode

Number of

episodes Mean episode size

Mean temporal distance between

the episodes

Mean temporal distance episode

to its nearest neighbouring episode

B β B β B β B β B β B β B β B Β

Constant 1.761*** 37.2*** 72.6*** 57.3*** 1.889*** 160.7*** 26.1 -5.8

Age

-0.010*** -0.127 2.6*** .203 2.2*** 0.189

Two or more children under 12 years

-36.0** -0.112 90.3*** .220 64.4** 0.169

Gender

Male 0.206** 0.106

High education -14.8*** -0.185 -21.4* -0.132 -21.4** -.162

Primary Internet

use at work 0.191* 0.094

Number of days working from

home -3.8* -.131

Number of ICT

devices owned 0.162*

0.07 6

Culemborg _0.698*** 0.18

5 Urbanization

level 1.7* 0.083

Workday

-0.268*** -0.128 Commute by

public transport -26.4** -0.097

Number of cars

in household 27.6** 0.138

Picking up/bringing away two or more times

0.430*** 0.176 -51.8*** -0.176

R2 _{0.039 0.037 0.017 0.051 0.071 0.054 0.085 0.100}

N 459 459 230 230 561 561 227 227