The Internet and the space–time flexibility of daily activities: A case study of Beijing, China

Cities 97 (2020) 102493

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The Internet and the space–time flexibility of daily activities: A case study of Beijing, China

T

Yue Shena,b, Na Tac,d,*, Yanwei Chaie a

Centre for Modern Chinese City Studies & School of Urban and Regional Science, East China Normal University, Shanghai, 200062, China Institute of Eco-Chongming, East China Normal University, Shanghai, 200062, China c Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai, 200241, China d School of Geographic Sciences, East China Normal University, Shanghai, 200241, China e Department of Urban and Economic Geography, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China b

ARTICLE INFO

ABSTRACT

Keywords: ICTs Space–time constraints Activity Structural equation modeling Beijing

Although most existing studies support that information and communication technologies (ICTs) can relax traditional space–time constraints in relation to activities, direct empirical studies that measure the level and influenced extent of such constraints are insufficient. Additionally, the variation and interrelations between spatial and temporal dimensions of fixity constraints are rarely taken into account. This study uses self-reported flexibility from activity diary data to capture two key dimensions of space–time constraints: (1) how the Internet became embedded differently in various types of activities and (2) the interaction relationships between Internet use, temporal flexibility, and spatial flexibility. From 709 respondents in Beijing in 2012, 7-day GPS-facilitated activity diaries were collected and used to assess perceived space–time flexibility and Internet use for each activity. A structural equations model was applied to investigate how Internet use influences the temporal and spatial flexibility of activities, after controlling for individuals’ socio-economic attributes, activity features, and spatial factors. The findings suggest that Internet use increases the temporal flexibility of simultaneous activities, as well as the spatial fixity of activities. This supports that ICT use could lead to new constraints due to the need to maintain connectivity, thus making some activities more fixed in space.

1. Introduction Information and communication technologies (ICTs) play a significant role in everyday life, and extensive research has been conducted on the relationship between ICTs and daily activity-travel behavior. Different types of impacts such as substitution, complementarity, modifications, and neutrality have been proposed in attempting to reveal the connections between online pursuits and their corresponding activities and travel in the real world (e.g., teleworking versus working/commuting, e-shopping versus shopping, teleleisure versus leisure) (Andreev, Salomon, & Pliskin, 2010; Gössling, 2018; Kwan, Dijst, & Schwanen, 2007; Salomon, 1986; Schwanen, Dijst, & Kwan, 2008; Loo & Wang, 2018). Recently, the limitations of the complementarity/ substitution debate have been discussed, and it has been argued that it would be better to examine the impacts of ICT in terms of their mechanism (Aguiléra, Guillot, & Rallet, 2012; Ben-Elia & Feng, 2018; Mokhtarian & Tal, 2013). The association between ICTs and the fragmentation of activities, multi-tasking, physical mobility, and social networks has also

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garnered a great deal of attention (Alexander, Ettema, & Dijst, 2010; BenElia, Alexander, Hubers, & Ettema, 2014; Hubers, Schwanen, & Dijst, 2008; Kenyon & Lyons, 2007; Ling & Stald, 2010; Line, Jain, & Lyons, 2011). However, most existing studies have focused on the interrelations between ICT use and revealed activity-travel behavior, while little research has explored changes in space–time constraints, which reflect the impact mechanism of ICTs and determine individual decision-making, as well as the implementation of daily activities and trips. The space–time fixity constraint has long been recognized as an important concept in human geography and transport studies, because activities that are fixed in space and time anchor individuals’ space–time “prism” in their daily activity schedule, and thus, impose important restrictions on their accessibility and mobility (Hägerstrand, 1970; Kwan, 2000a; Schwanen, Kwan, & Ren, 2008). This is also a critical perspective from which to understand how ICTs change activitytravel behavior and everyday life (Kwan, 2007; Schwanen & Kwan, 2008). ICTs are expected to weaken the conventional links between activities, place, and time, and thus ease up the space–time fixity

Corresponding author at: No. 500 Dongchuan Road, Shanghai, 200241, China. E-mail addresses: [email protected] (Y. Shen), [email protected] (N. Ta), [email protected] (Y. Chai).

https://doi.org/10.1016/j.cities.2019.102493 Received 6 November 2018; Received in revised form 28 September 2019; Accepted 22 October 2019 0264-2751/ © 2019 Published by Elsevier Ltd.

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constraints, but the debate continues as to whether ICTs can truly loosen these constraints and the extent of the relaxation (Couclelis, 2004, 2009; Schwanen & Kwan, 2008). While existing research has discussed and shown the complex links between ICTs and space–time constraints, extant empirical studies are rather limited, and empirical studies on the degree to which space–time constraints are altered are even scarcer. In the analytical framework of time geography, space–time constraints can be categorized into three main types (i.e., capability constraints, coupling constraints, and authority constraints) under a series of basic assumptions and conditions (Hägerstrand, 1970, 1975). However, some of the conditions are modified by ICT use; for example, the binary corporeal presence/absence characteristic of an individual in classic time geography (Adams, 1995; Schwanen & Kwan, 2008). Thus, space–time constraints need to be measured and studied using different dimensions in empirical studies (Shen, Kwan, & Chai, 2013). Space and time are two key aspects of constraints, particularly in imposing essential restrictions on accessibility. Yet these two dimensions are often thought to be mutually inclusive and influence people’s daily activities in a similar direction; in other words, an activity that is fixed in time is usually considered to be fixed in space. For example, working and education are usually regarded as fixed in both space and time, while shopping and leisure activities are thought of as spatially and temporally flexible in transportation studies (Arentze & Timmermans, 2000; Schwanen & Dijst, 2003). However, the temporal and spatial facets of constraints may vary and interact with each other; thus, it is vital to examine the relationship between these two dimensions of constraints under the influence of ICTs. This paper studies the effects of Internet use on people’s daily lives from the perspective of space-time fixity constraints. In addition, it explores the changes in spatial and temporal constraints separately and examines how they interact with each other. In so doing, it seeks to answer the following questions: (1) How is the Internet embedded differently in various types of activities? (2) How does simultaneous Internet use influence the spatial and temporal flexibility of individuals’ activities? (3) How do the effects vary and interact between temporal flexibility and spatial flexibility? Analyses of these issues will be of great value to understanding the mechanism of ICTs in people’s daily activities. The findings of this study would be also helpful for urban planners and policymakers to improve quality of life and to address urban problems such as traffic congestion through reducing space–time constraints, especially by means of ICTs. This article is organized into five sections. Section 2 reviews previous studies. Section 3 describes the study area, data collection procedures, sample characteristics, and methodology. Section 4 reports the results of the empirical analysis of the Beijing case. Finally, Section 5 concludes and discusses the results.

research; for example, in transportation studies, activities are commonly defined as either fixed or flexible, based on their purpose or type, partly because this dichotomy is easy to operationalize in accessibility and mobility research (e.g., Arentze & Timmermans, 2000; Jones, Dix, Clarke, & Heggie, 1983; Schwanen & Dijst, 2003). However, it has been asserted that the fixity level may vary greatly, not only among different activity types but also within them (Axhausen, Zimmermann, Schönfelder, & Rindsfüser GandHaupt, 2002; Cullen & Godson, 1975; Schlich & Axhausen, 2003). While, on the one hand, the binary scheme cannot adequately capture this variability, on the other hand, the “objective” definition of fixity through activity type ignores the differences within activity types and may be influenced by how activities are categorized; this makes the degree of the space–time fixity of activities a critical issue (Doherty, 2006; Schwanen, Kwan et al., 2008; Shen et al., 2015). Several empirical studies have directly examined the extent of the space–time fixity of individuals’ activities; the most commonly used measures are activities’ perceived fixity/flexibility. A number of questions about spatial and temporal fixity have been asked in activity travel diaries, requesting respondents to rate the difficulty level of changing the location or time of each activity(i.e., the subjective space–time fixity /flexibility) (Cullen, Godson, & Mayor, 1972; Kwan, 2000a; Shen et al., 2015). Compared to the observed space–time fixity/flexibility (measured through the spatial and temporal intra-personal variations of activities) used by Doherty (2006) and Shen et al. (2013), self-reported, subjective space–time fixity/flexibility is more representative of time–geographic conceptualizations, because subjective fixity/ flexibility reveal the potential possibility before activities are conducted, and play a central role in scheduling respondents’ everyday activities (Cullen et al., 1972; Schwanen, Kwan et al., 2008; Shen et al., 2015). Using data from subjective fixity/flexibility ratings, attention has been paid to spatial and temporal fixity levels, as well as their relationships with activity type and other characteristics of activities and individuals. Cullen et al. (1972) first proposed a framework to measure and analyze space–time constraints using subjective fixity ratings, and argued that activities with a high level of space–time fixity serve as “pegs” around which other activities are organized. Cullen and Godson (1975) found spatial fixity constraints to be more frequent than temporal constraints, and that fixity levels differ with the type (and other attributes) of activities, in addition to respondents’ backgrounds. Kwan (2000a) focused on gender differences, based on an examination of the fixity degree of out-of-home non-employment activities, and showed that women experience more fixity constraints than men, since activities associated with household responsibilities are more likely to be fixed. Schwanen, Kwan et al. (2008) provided a comprehensive model to investigate how the space–time fixity levels of everyday activities are associated with different types of contextual factors; meanwhile, activity purpose, time, and location, the other persons involved in the activity, as well the personal, household, and geographic context have effects. On these bases, Shen et al. (2015) centered more on geographic context, examining how the impacts of the built environment vary between residential and activity places, as well as between different types of residential communities. They revealed the influence of institutional, social, cultural, and family norms in transitional China.

2. Literature review: ICTs and space–time fixity constraints 2.1. Space–time fixity constraints As mentioned above, the space–time constraint is a key concept in time geography and can be classified under three main types: capability constraints, coupling constraints, and authority constraints (Hägerstrand, 1970). Due to these space–time constraints, some activities need to take place at a particular time and/or location, making them relatively fixed in space and/or time. Activities with a high degree of space–time fixity restrict the organization of other activities and individuals’ daily activity schedules, thus limiting the choice of activities within a feasible region and delimiting a person’s accessibility and mobility (Kwan, 2000a; Schwanen, Kwan et al., 2008; Shen, Chai, & Kwan, 2015). The space–time fixity of activities was originally measured using a binary distinction between fixed and flexible activities to explain the notions of the space–time constraint and the space–time prism (Hägerstrand, 1970). This dichotomy was frequently used in subsequent

2.2. ICTs and space–time constraints The relationship between ICTs and space–time constraints remains an important issue. On the one hand, the rapid developments and wide applications of ICTs have drawn renewed attention to space–time constraints and the framework of time geography, because space–time conditions and the conventional links between activities, place, and time have shifted in the age of ICTs (Couclelis, 2009; Miller, 2005; Shaw & Yu, 2009). Efforts have been made to extend the time–geographic analytical framework for a hybrid physical–virtual space, and to rethink basic conditions and concepts such as space–time constraints, paths, prisms, and projects in light of the new reality (Couclelis, 2009; Kwan, 2000b; 2

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Schwanen & Kwan, 2008; Shaw & Yu, 2009; Yin, Shaw, & Yu, 2011). On the other hand, the idea of space–time constraints provides a crucial angle from which to grasp how ICTs affect the fixity/flexibility of activities in space and time, and impact both the decision-making of activities and travel, as well as accessibility. In addition, how these effects vary between individuals and contexts reflects the interaction between ICT use and processes of social and spatial differentiation (Kwan, 2000a, 2000b Schwanen & Kwan, 2008; Shen et al., 2015). However, only a few empirical studies have examined how ICTs directly influence the space–time fixity of daily activities. Much existing research argues that modern ICTs are capable of loosening traditional space–time constraints on activities. Regarding the spatial aspect, ICTs allow for a variety of absent presences through e-shopping, e-banking, e-mails, and phone calls (etc.), and expand the opportunities for action at a distance, so that activities no longer need to be performed at certain locations (Cao, Chen, & Choo, 2013; Schwanen, 2007; Shaw & Yu, 2009; Yin et al., 2011). Also, ICTs enable the acceleration of movement in physical space by substituting travel through activities in virtual space, or assisting individuals with trip planning, resulting in reduced travel demand and travel outcomes (Miller, 2005; Jamal & Habib, 2019). In terms of the temporal dimension, first, ICTs could relax the link between activities and certain times; for example, online services might be still available when offline stores are closed, and communications could take place asynchronously (Miller, 2005). Second, ICTs make it easier for individuals to participate in multiple tasks and activities simultaneously, which might lead to time being spent more productively, leaving more time for other activities (Ettema & Verschuren, 2007; Kenyon & Lyons, 2007; Kwan, 2007). For the spatial-temporal facet, ICTs (particularly mobile ones) make it possible to rearrange activity schedules more often; even improvisation is becoming more prevalent due to permanent reachability, which might lead to more flexible spatial and temporal arrangements of activities in both the private and professional spheres (Aguiléra et al., 2012; Line et al., 2011; Townsend, 2000). However, this hypothesis remains a subject of debate, and some researchers take the view that it is difficult to conclude that ICTs can remove the space–time constraints from activities. Regarding the spatial dimension, ICT use could lead to new constraints by maintaining connectivity, because information technologies themselves are bound by certain physical requirements (hardware, network infrastructure, the ability to recharge batteries, and so on) (Fiore, Mokhtarian, Salomon, & Singer, 2014; Schwanen & Kwan, 2008). As for the temporal aspect, the time people spend using ICTs may reduce the time they have available to pursue other activities, and lead to more fixed arrangements of activities and travel (Kwan, 2007). In addition, activity schedules are highly structured by relatively firm social and institutional norms both spatially and temporally, so they are not that easy to change (Aguiléra et al., 2012; Green, 2002). A few studies have also pointed out the complex relationship between ICT use and space–time constraints, while other factors need to be considered such as activity attributes, users’ socio-economic status, technology type, and social, cultural, institutional, and physical contexts (Kwan, 2007; Schwanen & Kwan, 2008; Valentine & Holloway, 2002). For example, Schwanen and Kwan (2008) found that the Internet has done little to improve space–time constraints on women and that gender differences exist in the connections between ICTs and the space–time constraints associated with everyday activities. They also revealed different effects of ICTs on spatial flexibility and temporal flexibility; yet, not enough attention has been paid to this issue.

studies that measure the level and influenced extent of space–time constraints are rare. Second, most scholars support ICTs’ capability to loosen traditional space–time constraints on activities, but the debate continues, and the interrelations between spatial and temporal dimensions are rarely taken into account. If we consider the spatial constraints and temporal constraints respectively, the phenomenon and mechanism of how ICTs relieve these two facets of constraints vary a lot. Hence, more research is needed on this matter. Third, some studies have demonstrated the complicated relationship between ICT use and space–time constraints, while extant empirical studies are far from sufficient. To fill these knowledge gaps, this study aims to construct a research framework to examine the complex interactions between Internet use, temporal flexibility, and spatial flexibility when controlling for other factors such as activity attributes, personal qualities, and spatial contexts. We believe that this empirical study on the activity level will not only offer insight into the impact of ICTs on people’s daily activities and travels, but also provide a model with which to understand space-time constraints from the angles of space-time integration and space-time interaction. 3. Research design and methodology 3.1. Data and study area We used data from an activity-travel survey dataset collected in a suburban area of Beijing in 2012.AsChina’s political and cultural center, Beijing had a total population of 20.69 million in 2012, of whom Internet users accounted for 72.2 %, thus ranking it the foremost amongst Chinese cities (Beijing Municipal Bureau of Statistics, 2013; CNNIC (China Internet Network Information Center), 2013). This research focuses on the Shangdi-Qinghe area of Beijing, which is an inner suburban region located outside the 5th Ring Road (Fig. 1). With 240,000 residents and 140,000 employees, the Shangdi-Qinghe area is one of the largest residential zones in Beijing and contains a substantial employment center. It covers a 16 square-kilometer area with mixed land use that includes residential, commercial, industrial, and retail functions. Data were collected via person-based global positioning system (GPS) tracking and a web-based activity-travel survey. The respondents were asked to carry a GPS tracking device and log their activities on a survey website for seven consecutive days. The GPS technology logged the space–time coordinates of each participant every 2 min. Each participant was asked to take part in a seven-day activity-travel diary survey. From the activity-travel diary survey, we extracted information about thetypes and basic attributes of respondents’ activity-travel behavior, (e.g., the start and end time of the activity/trip, purpose and location of the activity, travel mode and companions, etc.). For each activity in the diary, Internet use during the activity was asked about using two questions (i.e., whether or not the Internet was used and how longit was used for). Also, a series of questions that assessed the levels of an activity’s perceived space–time flexibility was posed. For example, “How easy is it for you to change the time for this activity?’’ and “How easy is it for you to change the location of this activity?’’ The respondents provided their answers using a5-point scale, ranging from difficult (-2) to easy (2). From the survey, we obtained activity-travel data with high levels of spatialand temporal resolution and adequate information about the space–time flexibility of activities. Although the method of capturing Internet use without considering heterogeneity (e.g., device type, connection quality, requirements for access) is somewhat rudimentary, it could still bring forth new ideas by obtaining information about Internet use and space-time flexibility simultaneously on the activity level, which offer important data sources for insight into the interrelations between Internet use and space-time fixity constraints. The subsample of residents and employees was recruited through multi-stage cluster sampling based on 0.5–1 % of the total population in some of the communities and companies in the Shangdi-Qinghe area. A

2.3. Summary First, the concept of space–time constraints – which may be reflected in people’s perceptions of fixity/flexibility in their daily activities – provides a vital perspective from which to understand the interaction between ICTs and daily activities and travel. However, direct empirical 3

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As Table 1 shows, when compared to the population traits based on the census data of the Shangdi-Qinghe area, there is an over-representation of residents with Beijing hukou; they have a higher level of education and fall under the middle-aged group. However, despite the sampling bias, the results still seem to be quite informative of the situation of the sample group. 3.2. Theoretical framework and hypotheses The main aim of this study is to explore whether the Internet could relax space-time fixity constraints and the extent of that influence. We assume that when people engage in activities, they are faced with spatial and temporal fixity constraints, which depend on their socioeconomic characteristics (gender, age, level of education, etc.), along with activity attributes (time, duration, purpose, etc.) and spatial factors. Individuals could choose whether to use the Internet to conduct the activity, and they could determine the intensity of Internet use. Since the Internet expands the opportunities for action at a distance or different time, it may alter the link between activities and certain times and locations. That is to say, the intensity of Internet use affects the degrees of spatial flexibility and temporal flexibility. Fig. 2 illustrates the hypothesized links between these variables. Internet use was assumed to have direct effects on the flexibility of activities, while the impacts on temporal and spatial flexibility were expected to differ. An activity’s temporal and spatial flexibility were hypothesized to directly influence each other; that is, if an activity is quite flexible in time, it is more likely to be flexible in terms of space, and vice versa. Thus, through its direct impact on temporal flexibility, Internet use may also indirectly affect spatial flexibility. Similarly, through its direct influence on spatial flexibility, Internet use may also indirectly impact temporal flexibility. Based on the literature review, we included socio-economics, activity attributes, and spatial factors as exogenous variables. All exogenous variables were hypothesized to both directly and indirectly impact all endogenous variables, including Internet use (intensity), temporal flexibility, and spatial flexibility (self-reported, perceived level). This is because personal, household, activity, and environmental contexts may determine the probability and intensity of Internet use. For instance, a younger person is more likely to use the Internet to conduct an activity. These different contexts may also explain the temporal and spatial flexibility of activities (Schwanen & Kwan, 2008; Shen et al., 2015).

Fig. 1. Location of the study area.

total of 791 participants from 23 communities and 19 companies participated in the survey, while 709 completed the entire survey. Table 1 shows the overall sample characteristics. For all respondents, the share of females (53.3 %) was slightly higher than males (46.7 %). Most respondents were under 50, and more than 70 % had Beijing’s hukou (i.e., China’s household registration system), indicating that they were local residents. Most of the respondents were well educated, with 41 % having driver’s licenses. Nearly 90 % had full-time jobs, and 62.9 % had a monthly income of between RMB 2000 and RMB 6000, which represents mid-level income in Beijing. The average household size was 2.8 persons, and the average job-housing distance was 8.1 km.

3.3. Methodology

Table 1 A summary of sample characteristics. Variables

Classification

N

%

Census

Gender

Male Female 15-29 30-49 > =50 With Beijing hukou Without Beijing hukou High school or less College / university Graduate school Has a driver’s license Does not have a driver’s license Fully employed Other 2000 RMB or less 2000-6000 RMB More than 6000 RMB

331 378 241 404 64 501 208 107 500 102 291 418

46.7 53.3 34.0 57.0 9.0 70.7 29.3 15.1 70.5 14.4 41.0 59.0

53.8 46.2 42.7 34.2 23.1 44.5 55.5 59.9 36.1 4.0

633 76 112 446 151 2.8 8.1

89.3 10.7 15.8 62.9 21.3

709

100.0

Age Hukou Level of education Driver’s license Employment status Monthly income Household size (mean) Job-housing distance (mean/km) Total

Structural equation modelling (SEM), an approach that has recently gained popularity in transport studies (see Lu & Pas, 1998; Golob, 2003; Wang & Chai, 2009), was used to test the hypothesis. SEM is a confirmatory technique used to capture the causal influences of exogenous variables on endogenous variables, and those of endogenous variables upon one another. It is a system with simultaneous equations combining path, factor, and regression analyses. SEM is suitable for exploring the causal relations among a number of factors that are supposed to influence each other. More details about the structural equations model can be found in Bollen (1989) and Golob (2003). Structural models are powerful tools for studying the causal influences of exogenous variables on endogenous variables, and the effects of endogenous variables on each other (Cao, Xu, & Douma, 2012; Farag, Schwanen, Dijst, & Faber, 2007; Golob, 2003; Wang & Law, 2007), as well as for distinguishing between the indirect, direct, and total effects of variables (Ben-Elia et al., 2014). A total effect equals the sum of all direct and indirect impacts. In this study, we used the Analysis of Moment Structures (AMOS) software. AMOS computes the discrepancy function between a model covariance matrix and a sample covariance matrix, while minimizing the differences between the predicted and observed values in an iterative way. 4

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Fig. 2. Theoretical framework.

working and connecting with others. There is also a considerable proportion of Internet use during leisure (32.54 %) and housework (19.91 %). Personal care usually comprises tasks such as eating or washing, so the Internet is rarely used in these instances (7.23 %). The share of activities conducted with the Internet was only 7.56 % for shopping, which is lower than the actual proportion of online shopping in China. This might be because online shopping can be conducted simultaneously with other types of tasks such as work, social, or leisure activities, and respondents tended to choose other activity types instead of shopping. With respect to the temporal and spatial flexibility of activities, Table 2 indicates that respondents perceived the time and location of activities in general as considerably fixed, and spatial fixity constraints were more extensive than their temporal counterparts, which is consistent with former studies (Cullen & Godson, 1975; Schwanen & Kwan, 2008; Shen et al., 2015). According to the analysis of variance (ANOVA), there were statistically significant differences between the flexibility of activities conducted with and without Internet. Activities carried out using the Internet were more temporally and spatially fixed, which contradicts the previous consensus that ICTs are capable of loosening the traditional space–time constraints of activities. This is due to different shares of Internet use for various activity types (e.g., the Internet is mostly used for work, which is also the most fixed activity), and reminds us that it is vital to consider activity type when discussing the links between the Internet and the temporal-spatial flexibility of activities. For different activity types, large variations can occur in the relationships between the Internet and activities’ temporal-spatial flexibility (Table 2). The respondents believed it easier to change the time for shopping, leisure, and social activities, and more difficult for work and personal care. The differences between activities conducted with and without the Internet in terms of temporal flexibility were not significant for most activity types, except for leisure and social activities. Such activities carried out with the Internet were perceived as more temporally flexible. With regard to spatial flexibility, most activities were relatively fixed in terms of location (particularly work and housework), yet shopping was relatively flexible. Work, leisure, and social activities conducted with the Internet were perceived as more fixed in location than those done without Internet use, which might be due to the need to access the Internet or relevant devices. In general, activities carried out with the Internet tended to be more flexible in

3.4. Operationalization of the variables Based on the theoretical framework, data availability, and literature, a total of three types of exogenous and three endogenous variables were specified in the model. All variables employed in the estimation are observed. Socio-economic attributes that may influence Internet use and the space–time flexibility of activities were incorporated as one type of exogenous variable, including gender, age, hukou status, level of education, having a driver’s license, employment status, income, household size, and household structure. Activity attributes covered the temporal quality (weekday or weekend), spatial feature (at home or outside the home), and type (personal care, work, housework, shopping, leisure, social activity, or other) of activity. Spatial factors included facility density at the residential location, facility density at work, and the job-housing distance of the individual. Using ArcGIS10.2, facility density was derived based on the number of facilities (such as retail stores and entertainment and public service facilities) within a 1,000-meterbuffer area around each participant’s home / workplace. For each activity, the respondents evaluated the three endogenous variables of Internet use (time spent on the Internet during an activity divided by the activity’s duration), temporal flexibility (the respondent’s perceived temporal flexibility level), and spatial flexibility (the respondent’s perceived spatial flexibility level). 4. Results 4.1. Descriptive analysis According to existing studies, the temporal and spatial flexibility of activities are highly correlated to activity type or purpose (Schwanen & Kwan, 2008; Shen et al., 2015). Table 2 shows the summary statistics for the flexibility levels of activities by activity type and whether the Internet was used during the activity. Overall, the average share of activities conducted with the Internet was approximately 25 %, which reflects the situation of ICT use in Beijing in 2012. At the same time, there were significant differences between activity types (Table 2).The Internet was mostly used during work and social activities, at percentages of 52.41 % and 59.79 %, respectively, indicating that the Internet has become a key means of Table 2 Flexibility levels of activities by activity type and ICT use. Activity type

Personal care Work Housework Shopping Leisure Social activity Other activity Total

Number of activities

Share of activities conducted with the Internet

Temporal flexibility

Spatial flexibility

With the Internet

Without the Internet

ANOVA (pvalue)

Total

With the Internet

Without the Internet

ANOVA (pvalue)

Total

12,194 5650 3431 652 4164 1930 744 28,765

7.23% 52.41% 19.91% 7.67% 32.54% 59.79% 10.62% 24.91%

−0.137 −1.168 0.068 0.340 0.451 0.502 0.103 −0.326

−0.179 −1.125 −0.021 0.435 0.314 0.189 0.003 −0.175

0.364 0.150 0.126 0.583 0.002 0.000 0.559 0.000

−0.176 −1.149 −0.002 0.427 0.362 0.388 0.015 −0.215

−0.708 −1.522 −1.036 0.240 −0.849 −0.941 −0.667 −1.133

−0.920 −1.413 −1.112 0.159 −0.583 −0.566 −0.955 −0.920

.000 .000 .150 .669 .000 .000 .070 .000

−0.904 −1.474 −1.096 0.166 −0.677 −0.805 −0.922 −0.977

5

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Table 3 Goodness-of-fit statistics of the model and reference values. Goodness-of-fit measures

ICT use

Minimum chi-square (χ ²) Degrees of freedom χ²/df RMSEA CFI

21.674 13 1.667 0.005 1.000

Internet use -0.018

0.017

Temporal flexibility

0.387 0.088

Spatial flexibility

Fig. 3. The standardized direct effects of endogenous variables upon each other.

time and more fixed in space when activity type was taken into account, and the effects on spatial flexibility were more significant.

suggesting that it could result in activities that are more fixed in place. This might be because people need to conduct some activities in certain locations to maintain good connectivity to the Internet or devices, such as wired networks and desktop computers. This supports the proposition that ICT use could lead to new constraints (Schwanen & Kwan, 2008). Temporal flexibility and spatial flexibility, two key dimensions of space–time constraints, were highly interconnected. However, the influence of temporal flexibility on spatial flexibility (direct effect: 0.387) was much stronger than in the inverse direction (direct effect: 0.088). This finding indicates that if an activity is flexible in time, it is likely flexible in place. When an activity is flexible in place, it can be flexible in time, yet that possibility is weaker. The interrelationship between temporal and spatial flexibility led to the reverse indirect effect of Internet use on temporal and spatial flexibility, resulting in the total effects being weaker than the direct effects. To sum up, it is difficult to state conclusively that Internet can loosen the space–time constraints of activities, because the different effects and interactions of different aspects of space–time constraints need to be considered.

4.2. Modeling results Table 3 presents the model’s goodness-of-fit statistics. The model produces plausible results and good fits to the observed data with a relative chi-square below 2, a root mean square error of approximation (RMSEA) below 0.01, and a confirmatory fit index (CFI) above 0.9.The standardized direct effects among the endogenous variables are illustrated in Fig. 2. Table 4 lists the standardized direct and total effects of both the endogenous and exogenous variables on the endogenous variables. In the following paragraphs, we explain the modeling results presented in the tables and interpret the findings. 4.3. Internet use and space–time flexibility Fig. 3 and Table 4 show that most of the hypothetical links among the endogenous variables were statistically significant. Internet use had a positive, significant direct effect on temporal flexibility, suggesting that Internet use could lead to activities that are more flexible in time, which seems to support the hypothesis that ICTs are capable of loosening traditional constraints (Kwan, 2007; Shaw & Yu, 2009). However, Internet use had a negative, significant direct effect on spatial flexibility,

4.4. The influence of socio-economic attributes Table 4 also shows the direct and total effects of exogenous variables on endogenous variables. Among individual and household socio-

Table 4 Standardized direct and total effects for the model. Variables Endogenous variables ICT use Temporal flexibility Spatial flexibility Exogenous variables Socio-economics Female Age Hukou Level of education Driver’s license Part time job High-income Low-income Household size Living with children Living with elderly relatives (> 50) Activity attributes Weekend Duration Outsidethe home Work Housework Shopping Leisure Social activity Other activity Spatial factors Facility density at one’s residence Facility density at work Job–housing distance Squared multiple correlations

ICT use

Temporal flexibility

Spatial flexibility

n.a. – –

0.017*(0.016) n.a. 0.088*(0.091)

−0.018***(-0.012) 0.387***(0.401) n.a.

0.017***(0.016)

−0.010**(-0.004) −0.026***(-0.027) −0.013**(-0.019) −0.010*(0.010)

−0.019***(-0.019) −0.060***(-0.060) 0.079***(0.079) 0.027***(0.027) −0.046***(-0.046) 0.012***(0.012)

−0.015***(-0.018) 0.053***(0.055) −0.025***(-0.025) 0.023***(0.028) −0.012*(-0.008)

−0.040***(-0.040) 0.153***(0.153) −0.096***(-0.096) 0.352***(0.352) 0.062***(0.062) 0.022***(0.022) 0.169***(0.169) 0.280***(0.280) 0.024***(0.024)

0.032***(0.031)

0.039***(0.050) 0.051***(0.048) −0.065***(-0.067) −0.026***(-0.014)

0.104***(0.107) −0.041***(-0.038) −0.137***(-0.126) −0.154***(-0.170) 0.029***(0.027) 0.079***(0.087) 0.126***(0.135) 0.095***(0.100) 0.045***(0.043)

0.016**(-0.002) 0.185***(0.138) −0.172***(-0.245) −0.042***(-0.033) 0.059***(0.092) 0.016**(0.066) −0.021***(0.013) −0.042***(-0.026)

−0.015**(-0.018)

0.022***(0.022) −0.015**(-0.015) 0.244

−0.032***(-0.037) 0.231

6

0.031***(0.032)

−0.032***(-0.039) −0.030***(-0.032) −0.037***(-0.051) 0.262

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economic attributes, gender significantly impacted temporal and spatial flexibility. The activities of females were more flexible in time and more fixed in place, which differs from the outcomes of previous studies in Western countries (see Schwanen & Kwan, 2008) and reflects the circumstances in China. Younger people preferred to use the Internet more during activities, which is consistent with common sense, and their activities were more flexible in place, suggesting they have greater choice of activity location than older people. Those with local hukou used the Internet less and their activities were more fixed in time and place, which shows that local residents have more stable lives than migrants. Level of education had a significant, positive effect on Internet use and temporal flexibility. This indicates that people who are better educated prefer to use the Internet to conduct activities and have more choice in terms of activity time. Level of education had a negative, direct impact on spatial flexibility, but the overall impact was positive due to the indirect impact through Internet use and temporal flexibility. Parttime workers used less Internet during activities, and their activities were more flexible in terms of place. Similar to well-educated respondents, high-income earners preferred to use the Internet during activities, but their activities were more fixed in time, probably because they face more pressure at their jobs. Correspondingly, the activities of low-income earners were more flexible regarding both time and location. With respect to household attributes, there was no significant correlation between Internet use and household structure, suggesting that Internet use is quite personal. However, household structure had significant impacts on temporal and spatial flexibility. The activities of those living with more family members were more fixed in time yet more flexible in place, probably due to the negotiation in terms of time and the diversified demand related to where family members would like to carry out the activity.

facility density at work also had a significant, negative effect on spatial flexibility. This indicates that when facilities near people’s homes were adequate, their activities tended to be more temporally and spatially fixed, and the activity locations were probably close to their homes. The situation was similar to the workplace; people’s activity locations tended to be more fixed and closer to their workplace if there were more facilities, but the impact on activity time was not significant since activities around the workplace may be influenced more easily by other factors (such as activity schedule) than tasks around the home. Longer job-housing distance led to higher levels of temporal and spatial fixity of activities, which implies that the job-housing distance results in stronger fixity constraints in both space and time, which is in line with general expectations and previous studies (Shen et al., 2015). 5. Conclusion and discussion ICTs have long been considered one of the most powerful forces in shaping the 21stcentury.Since the rise of the Internet, ICTs have become a critical part of daily life, resulting in much theoretical debate and empirical research on the association between ICTs and activity-travel behavior. The concept of space–time constraints in Hägerstrand’s timegeography provides an important angle from which to understand the effects of ICTs on daily life (Hägerstrand, 1970). Although most existing studies support the view that ICTs may relax the traditional space–time constraints in which people carry out tasks, direct empirical studies that measure the level and influenced extent of space–time constraints are insufficient, and the variation and interrelations between the spatial and temporal dimensions of fixity constraints are rarely taken into account. This study used self-reported temporal and spatial flexibility in activity diary data to reflect two key facets of space–time constraints, and examined how the Internet has become embedded differently in various types of activities, as well as the interaction relationships between Internet use, temporal flexibility, and spatial flexibility. The findings show variations of Internet use among different social groups and types of activities. According to the analysis, nearly 25 % of activities are performed while using the Internet, suggesting that the Internet has become a major means of assisting people with conducting activities, especially for work, social, and leisure activities. Likewise, people prefer traditional means over the Internet for activities such as personal care, shopping, and housework. There are also significant variations in Internet use across social groups. The findings show that people who are younger, better educated, and with higher incomes prefer to use the Internet during activities, which may be relevant to the skills or hardware conditions required to use the Internet. There is no significant gender difference for Internet use, which is not consistent with what has been observed in Western countries, but rather reflects the situation and social norms in China (Ren and Kwan, 2009). Regarding the interaction relationships between Internet use and space–time flexibility, Internet use has significant – but opposite – direct effects on temporal and spatial flexibility, while temporal and spatial flexibility are highly interconnected. Thus, the indirect effects of Internet use on temporal or spatial flexibility may partly weaken the direct effects. Internet use increases the temporal flexibility of simultaneous activities, which is in line with previous findings and expectations. However, Internet use expands the spatial fixity of activities. On the one hand, it has the potential to increase activities that are more spatially fixed (such as home-based tasks); on the other hand, it could lead to new constraints in order to maintain connectivity, making activities more fixed in space (Schwanen & Kwan, 2008). The analysis indicates that the relationship between Internet use and space–time constraints is quite complex, so it is vital to consider the spatial and temporal aspects of constraints, respectively. These results are useful in the transportation planning and policy context. ICTs were once expected to eliminate travel and solve traffic congestion, since activities could take place regardless of location with the help of ICTs (Andreev et al., 2010; Mokhtarian, 2003). However,

4.5. The influence of activity attributes The results of the model show a significant correlation between Internet use, space–time flexibility, and activity attributes, especially activity type. In the model, personal care was chosen as the reference category. According to the findings, compared to other exogenous variables, activity type had the strongest impact on Internet use, and work was the activity type for which people most frequently used the Internet, followed by social activities and leisure. People sometimes used the Internet during housework and shopping and used it least during personal care. With respect to temporal and spatial flexibility, work was the most fixed activity type for both time and space, while shopping and leisure were quite flexible, as is consistent with previous studies (Schwanen & Kwan, 2008; Shen et al., 2015). Housework and social activities were relatively flexible in time but fixed in space. As for other temporal and spatial features of activities, the respondents said they prefer to use the Internet to conduct activities; those tasks were more fixed in time during weekdays. The Internet was used more frequently for longer lasting activities, which were more fixed in time and space. People used more Internet when carrying out tasks at home since it is easier to access the Internet and devices there; activities at home were more flexible in time and fixed in place. 4.6. The influence of spatial factors Regarding the effects of spatial factors, facility density at work had a significant, positive effect on Internet use, suggesting that people who work at places with higher facility density prefer to use the Internet. This may be relevant to Beijing’s spatial structure, wherein jobs that rely more on the Internet (e.g., the information technology [IT] industry, the service industry, etc.) are usually located in higher density areas. Job-housing distance had a significant, negative effect on Internet use, revealing that a longer distance could restrict Internet use since it takes more time to travel. The results of the model show that facility density at one’s residence had significant, negative effects on both temporal and spatial flexibility; 7

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these expectations have not yet been realized, and the different impacts of ICTs on personal activities and travel remain an important topic in transportation and urban studies. This study provides empirical evidence for space–time constraints, thus adding to our understanding of why the Internet cannot eliminate travel as expected, because location is even more important for activities conducted with the Internet. Some efforts that could weaken the link between Internet use and certain places, such as the construction of wireless networks, might strengthen the substitution impact of the Internet and reduce relative travel time. Moreover, since the Internet could lead to activities being more temporally flexible, more attention should be paid to the temporal dimension in urban and transport management in order to relieve constraints, and to provide people with more choices so they can avoid urban problems (such as traffic congestion). With regard to the built environment, the findings indicate that increasing facility density may not be useful in relaxing space–time constraints in Beijing. This is because, while on the one hand, density is quite high in big cities in China, on the other hand, more facilities around residential and workplaces could make activities more regular and fixed. Furthermore, the job-housing distance is a significant spatial factor in restricting Internet use and increasing space–time constraints, suggesting that job-housing balance is a key direction in terms of loosening the space–time constraints in the city. Several limitations to our study and directions for future research are worth mentioning. First, this study did not consider the heterogeneity of Internet use. We only gathered information as to whether and how long the Internet is used for each activity based on the respondents’ answers, but we did not inquire as to the type and purpose of ICT use, considering the burden this may have placed on the respondents. Nevertheless, we believe that device type (mobile phone, laptop, or desktop computer), network type (wired or wireless), and usage purpose will significantly impact the links between Internet and space–time constraints. Future investigations should address this issue using more detailed data. Second, this study mainly explored whether and how the Internet changes space-time fixity constraints, and Internet use was considered as a concomitant activity or a way of carrying out a task. However, since the Internet is deeply embedded in people’s daily lives, Internet use itself could be taken as an activity, which is influenced by space–time constraints. Hence, further study is required to reveal the interactions between Internet use and space-time constraints. Third, this study mainly focused on how Internet use influences the space–time flexibility of activities conducted simultaneously, but the effects of Internet use may lag behind simultaneous activities and directly impact subsequent tasks or travel. For this reason, effects in the temporal dimension need to be discussed in more depth. Furthermore, with the emergence of mobile communication devices and the availability of wireless Internet services, mobile informatization has become the new trend (Loo, 2012; Wang, Zhen, Wei, Guo, & Chen, 2015).Mobile ICTs make it easier to rearrange activity schedules more often and could revolutionize the relationships between activity and location. Thus, the relationships between mobile ICTs, space–time constraints, and activity-travel behavior would be an interesting issue for future research.

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Funding This research was supported by the National Natural Science Foundation of China (41871166; 41601159). Declaration of Competing Interest None. Acknowledgements This research was supported by the National Natural Science Foundation of China (41871166; 41601159). Insightful comments from the three anonymous reviewers and Prof. Pengjun Zhao are gratefully acknowledged; they significantly improved the manuscript. 8

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