This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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
* Corresponding author
Phone: +31-30-2532407
Fax: +31-30-2532037
E-mail: C.Hubers@geo.uu.nl
1
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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:
Activity fragmentation; Information and Communication Technologies; The Netherlands
2
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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).
However, there is almost no empirical support for the propositions about activity
fragmentation. Therefore, it is not known to what extent such fragmentation is indeed
occurring, how it takes place and what the role of ICT in this process is. This is among
others due to the fact that activity fragmentation as a concept is intuitively sensible but
difficult to grasp theoretically, methodologically and empirically. There is not only a lack
of a clear framework for analysing and measuring fragmentation but also of appropriate
data. A first attempt to measure fragmentation empirically has been made by Lenz and
Nobis (2007). Although these authors find some evidence of the occurrence of activity
3
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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
fragmentation for paid labor, shopping for daily and non-daily goods, and leisure
activities, and assess whether fragmentation varies systematically with ICT usage, and
possible other factors that as we will argue below might have an influence on the level of
fragmentation. Note that the exploratory analysis in the current paper will be confined to
the fragmentation of activities across time. Although activity fragmentation is usually
discussed as a phenomenon in both space and time, the unravelling of temporal patterns
is so complex that a separate paper is warranted. The paper ends with conclusions and a
discussion.
4
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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 landuse 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:
a process whereby a certain activity is divided into several smaller pieces, which are
performed at different times and/or locations
5
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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
throughout that single day. The term ‘activity location’ represents the different locations
at which the smaller fragments of the sub-task are performed. Again taking the sub-task
of searching and evaluating product information as an example, we may say this sub-task
6
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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 multitasking, 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
relevant to (future studies of) activity fragmentation. The term balkanization refers to the
division of a place or country into several small political units that are often unfriendly to
one another (New Dictionary of Cultural Literacy, 2002). With respect to daily activities,
balkanization is concerned with the ways people evaluate activity fragmentation. Mark et
al. (2005) showed that an interruption of a certain work activity was evaluated more
negatively when the interrupting task had nothing to do with the original task. The
interruption was, however, evaluated less negatively and sometimes even considered
beneficial, when the interrupting and disrupted tasks were associated with one another. It
also mattered whether the interruption was self-induced or imposed externally with the
former being less negatively evaluated than the latter. Contamination, or multitasking, is
7
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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
fragmentation in ecology (Rutledge, 2003). While Rutledge acknowledges that, in a strict
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This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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.
In short, we believe that the three dimensions should be analysed simultaneously even
though they capture different aspects of activity fragmentation. The first two dimensions
describe how much a certain activity is fragmented, whereas the third explains in what
way the activity is fragmented and provides valuable insights into the spatial and
temporal patterns that are formed by the different activity episodes and locations.
9
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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
fragmentation will probably increase with the number and variety of ICT devices owned
and services used, and the purpose of use. The daily activity pattern of a person who
owns multiple ICT devices (laptop, desktop computer and mobile phone, for instance)
and uses them throughout the day and for different goals (communication, e-shopping,
work) is more likely to be fragmented by ICTs than the activity pattern of a person who,
say, only owns a desktop computer, is not very accustomed to using it, and only uses it to
send e-mails. Sociodemographic characteristics can also affect activity fragmentation,
both directly and indirectly (Hanson, 1982; Lu and Pas, 1999). The relatively strong level
of space-time fixity of caring tasks (for instance, the chauffeuring of children), which are
still largely a female responsibility (Dijst, 1999; Kwan, 1999; Schwanen et al., 2007),
make us hypothesize that women’s activities will be more fragmented than those of men.
Activity fragmentation could be seen as a strategy for women to reconcile the different
10
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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
experience of time pressure can induce people to bundle their shopping activities
resulting in less fragmentation.
11
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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).
Since the mean is sensitive to outliers and very different episode combinations can have
an identical mean episode size, we will also look at the size of the largest episode and the
standard deviation of the episode sizes. A small standard deviation and small size of the
largest episode indicate that the episodes are more equal in size and therefore more
12
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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
4. The standard deviation of the temporal distance to the nearest neighbouring
episode (SD NTD).
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This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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 NovemberDecember 2003. As the research was targeted toward shopping activities, the diaries were
completed on a Friday and Saturday – the days of the week that most shopping is done in
The Netherlands. It is important to notice that non-Internet users were excluded from the
study, rendering it impossible to compare Internet users and non-Internet users in our
study. Due to the thorough measurement of the frequency of Internet use, we are
nonetheless capable of comparing frequent with infrequent Internet users. The research
area consisted of four municipalities located in the heart of the Netherlands: Utrecht (270
243 inhabitants and a high level of shop availability); Nieuwegein (61 806 inhabitants,
low level of shop availability and located 7 kilometres from Utrecht); Culemborg (26 613
inhabitants, high level of shop availability and located 17 kilometres from Utrecht); and
Lopik (13 869 inhabitants, low level of shop availability and located 18 kilometres from
Utrecht). 826 Respondents completed both a shopping questionnaire and a travel diary, of
whom 44 percent participated online, and the rest used paper-and-pencil surveys. There
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This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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
Activity episodes were determined as follows. In the travel diaries respondents could
report a maximum of 15 trips per day. For each trip made they reported the destination
type (school, work, home, supermarket, etc.) and street address information of this
destination, as well as the exact departure and arrival time. The time spent at a certain
destination type was considered an activity episode, so an episode is a sojourn at a single
spatial location. If a trip to a certain destination type was followed by a trip to an identical
destination type (for instance going to the baker’s shop followed by a trip to the butcher’s
shop, both daily shopping activities) these were defined as two separate activity episodes
(Burnett and Hanson, 1982; Harvey, 2003).
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This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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.
1
Percentage represents part of the total general activity that was spent at this specific destination type.
16
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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.
Somewhat surprising is the finding that the time-spans between daily shopping episodes
are also quite long (one hour and 35 minutes), since it would appear to be more efficient
to chain these different daily shopping episodes together. Perhaps the large MTD for
daily shopping is a result of the long opening hours of supermarkets that allow one to do
some groceries during the lunch break and the remaining groceries in the evening after
work (see Figure 2). The large temporal distances may also reflect that needs for (certain)
17
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
daily products manifest themselves at different moments during the day. The relatively
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
shopping and leisure which have more extended business hours, the MTD for paid labor
is rather low (see also Figures 2 and 3). Non-daily shopping episodes are also globally
more clustered than daily shopping and leisure episodes. Leisure and daily shopping have
the largest time intervals between episodes, which in combination with the considerable
variation in the temporal distance indicators seems to be indicative of the existence of
local clusters in combination with an outlier (see Figure 1, configuration E).
18
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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 R2 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
fragmentation of paid labor. People with more hours of paid labor not only have more
work episodes (NAE), but these episodes also have longer durations (MES). There is also
a relation between the residential context and the number of work episodes in that people
19
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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
shopping episodes on average last shorter (MES) than those of persons who primarily use
the Internet at home or at the office. This is probably due to the fact that the group of
respondents who most often use the Internet at other locations than in the home or at
work largely consists of students and mobile professionals who are known to have more
fragmented daily shopping patterns (Kakihara and Sorenson, 2002). In this case the
primary location at which Internet is used is more likely to function as a proxy for
occupation type than having an autonomous effect on the fragmentation of the daily
shopping activity. It therefore does not contribute any knowledge regarding the question
whether ICTs are positively associated with activity fragmentation. Further, persons who
search for product information on the Internet, but buy the product in-store, have fewer
but at the same time longer daily shopping episodes. At first glance, this result is not as
20
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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 nonwork 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).
The number of daily shopping episodes is also larger for women and highly educated
persons and for the latter these episodes are also more clustered than for their
counterparts. As is well known, women tend to perform more daily shopping episodes
21
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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
fragmentation of daily shopping. On workdays the number of shopping episodes as well
as the average duration of these shopping episodes is smaller, reflecting that paid labor
22
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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.
On the website of Culemborg, the city centre is typified as “a convenience-centre, where not only can the
consumer shop for daily products, but also do some recreational shopping.” (Gemeente Culemborg, 2007).
2
23
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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.
People with children under the age of twelve tend to have a more fragmented leisure
pattern. Not only do their leisure episodes last 36 minutes shorter on average than those
of people without children in this age class, the time-spans between the different leisure
episodes also are much longer, indicating less clustering of episodes. This probably
reflects the fact that people with small children have very little leisure time left after all
paid and unpaid labor is done (SCP, 2004). In addition, a lot of the leisure time of parents
is spent with and also for their children, for instance when attending the soccer match of a
child on Saturday morning. Whereas working people without young children at least have
the evenings for themselves, the evenings of people with children are taken up with
caring for their children.
24
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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.
Our findings suggest that there is no straightforward relation between ICT-usage and
temporal fragmentation, but rather a complex set of sometimes counter-acting relations.
25
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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.
In future studies, an analysis of temporal fragmentation of activities should preferably be
based on activity diaries which allow us to consider the different activity types performed
at a certain destination. The data should also enable the analysis of the series of sub-tasks
and activity episodes that constitute a given activity. The general activity of shopping, for
instance, should be unravelled into the sub-tasks of searching for product information and
the purchasing of the product. Furthermore, such data should also allow studying the
related concept of multitasking which was briefly discussed in Section 2.1. Information
on primary and secondary activities that are performed simultaneously could help
26
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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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30
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
FIGURE 1 Three dimensions of fragmentation.
31
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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
Mean temporal distance between
MTD
Divides the sum of all temporal distances between episodes by the number of temporal distances
Configuration
episodes
Variation in temporal distance
between episodes
SD TD
Calculates the standard deviation of the temporal distance between episodes
MNTD
Divides the sum of all temporal distances to nearest neighbouring episode by the number of temporal
between episodes
Mean temporal distance from one
episode to its nearest neighbouring
distances to its nearest neighbouring episode
episode
Variation in temporal distance to the
SD NTD
Calculates the standard deviation of the temporal distances to its nearest neighbouring episode
nearest neighbouring episode
32
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
TABLE 2 Fragmentation Patterns and their Indicator Values
Temporal fragmentation
Pattern
0
0
0
0
0
Indicator values
24
24
24
24
24
MTD
SD TD
MNTD
SD NTD
low
low
low
low
MTD
SD TD
MNTD
SD NTD
high
low
high
low
MTD
SD TD
MNTD
SD NTD
high
high
low
low
MTD
SD TD
MNTD
SD NTD
low
medium
medium
medium
MTD
SD TD
MNTD
SD NTD
high
high
high
high
Description of fragmentation
pattern
A: Global clustering
B: Spread out evenly
C: Multiple local clusters
D: Global cluster and outlier
E: Multiple local clusters and an
outlier
33
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
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
Paid labor
Mean
N obs.
1.27
380
363.1
380
109.3
81
92.2%
380
50.6
81
33.3
19
45.7
81
SD
NTD
19.0
19
Daily
Shopping
Mean
N obs.
1.54
623
25.6
623
12.1
225
88.4%
623
94.4
225
67.1
67
81.1
225
50.6
67
Non-daily
Shopping
Mean
N obs.
2.12
459
32.2
459
13.9
230
78.6%
459
58.3
230
50.1
125
37.2
230
29.5
125
Leisure
Mean
N obs.
1.62
561
140.5
561
81.4
227
86.7%
561
140.7
227
102.2
79
117.2
227
81.0
79
NAE
MES
SD ES
LEI
MTD
SD TD
MNTD
34
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
FIGURE 2 Timing of the four different activities on Friday
150
Frequency
125
100
Work
Daily shopping
Non-daily shopping
Leisure
75
50
25
0
1
3
5
7
9
11 13 15 17 19 21 23
Starting time
35
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
FIGURE 3 Timing of the four different activities on Saturday
150
Frequency
125
100
Work
Daily shopping
Non-daily shopping
Leisure
75
50
25
0
1
3
5
7
9
11 13 15 17 19 21 23
Starting time
36
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
TABLE 4 Regression Analyses for the Fragmentation of Work and Daily Shopping Activities
Paid labor
Number of
episodes
B
Constant
Mean episode size
β
0.641***
B
β
336.2***
Gender
Handicap
Frequency
internet use
Primary Internet
use not at home
or work
Search online,
buy in-store
Efficiency daily
shopping
Risk-averse
personality
Number of days
working from
home
Number of ICT
devices owned
Nieuwegein
Lopik
Saturday
Workday
Commute by car
0.089**
0.045**
0.11
6
24.6**
B
β
41.8***
Number of
episodes
B
Mean episode size
β
1.059***
B
β
22.7***
0.012***
0.164
-0.149*
-0.077
0.190**
0.103
Mean temporal
distance between
the episodes
B
β
209.4***
Mean temporal
distance episode
to its nearest
neighbouring
episode
B
β
211.1***
-1.6**
0.179
-1.8**
0.202
-43.1**
0.189
-51.4***
0.230
27.0**
0.163
26.3**
0.163
-40.5**
0.177
-44.6**
0.199
-61.0***
0.265
-.57.8***
0.257
0.109
18.9***
0.158
0.13
0
-13.2***
0.161**
Mean temporal
distance between
the episodes
46.3***
Age
Male
Number of hours
paid labor
High education
Daily shopping
Mean temporal
distance episode
to its nearest
neighbouring
episode
B
β
0.356*
0.072
-11.1*
-0.075
-0.194**
-0.099
8.4***
0.145
3.2***
0.122
3.6***
0.131
-0.160
0.10
4
13.5***
-75.5***
0.160
-0.164
-0.219**
-0.104
-.6.9***
-0.110
37
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
Picking
up/bringing away
once
Picking
up/bringing away
two or more
times
R2
N
0.322***
0.20
0
0.087
380
-101.8***
-0.214
-87.0***
-0.165
0.131
380
34.6**
0.064
81
0.25
3
31.6**
0.065
81
0.255
0.073
623
0.119
623
0.172
225
0.194
225
*p < 0.1; ** p < 0.05; *** p < 0.01
38
This is a pre-publication version of the following article:
Hubers, C., Schwanen, T. And Dijst, M. (2008). ICT and temporal
fragmentation of activities: An analytical framework and initial
empirical findings. Jtijdschrift voor economische en sociale
geografie, 99(5), 528-546.
TABLE 5Regression Analyses for the Fragmentation of Non-daily Shopping and Leisure Activities
Non-daily shopping
Number of
episodes
B
Constant
Mean episode size
β
1.761***
B
β
37.2***
Leisure
Mean temporal
distance between
the episodes
B
β
72.6***
Mean temporal
distance episode
to its nearest
neighbouring
episode
B
β
57.3***
0.010***
-14.8***
-0.185
-21.4*
-0.132
-21.4**
-3.8*
0.162*
0.698***
Urbanization
level
Workday
1.7*
B
β
160.7***
0.106
0.191*
0.094
0.268***
-0.128
β
26.1
-0.127
0.206**
B
-36.0**
-0.112
-26.4**
-0.097
-5.8
2.6***
.203
2.2***
0.189
90.3***
.220
64.4**
0.169
27.6**
0.138
-.162
-.131
0.083
0.430***
0.039
459
β
Mean temporal
distance episode
to its nearest
neighbouring
episode
B
Β
0.07
6
0.18
5
Commute by
public transport
Number of cars
in household
Picking
up/bringing away
two or more
times
R2
N
B
Mean episode size
1.889***
Age
Two or more
children under 12
years
Gender
Male
High education
Primary Internet
use at work
Number of days
working from
home
Number of ICT
devices owned
Culemborg
Number of
episodes
Mean temporal
distance between
the episodes
0.037
459
0.017
230
0.051
230
0.071
561
0.176
-51.8***
0.054
561
-0.176
0.085
227
0.100
227
*p < 0.1; ** p < 0.05; *** p < 0.01
39