While researchers in social psychology often explore space and time in isolation, the relations between these dimensions are rarely considered. To address this gap, we explore a model of Time–Space Distanciation, the extent to space and time are abstracted from one another in the cultural coordination of activity. We introduce this construct with an emphasis on its interdisciplinary roots and its status as a feature of both group- and individual-level psychology. We then offer three studies providing initial evidence of the distinctiveness of this variable at both levels. We find that (1) state-level time–space distanciation is related to, but distinct from, collectivism and cultural tightness and (2) it has important implications for collective well-being. We further found that (3) individual-level time–space distanciation is associated with a wide range of trait differences. We conclude by describing the implications of this research for the study of time, space, and their connection.
Introduction
For anyone who commutes to work, there is a moment that is instantly recognizable: one finds oneself sitting in traffic, barely moving, watching the clock tick as any chance of making the next appointment on time dissolves. There may be a bottleneck ahead or the rail might be delayed due to a malfunction. Familiar feelings arise: anger, frustration, even righteous indignation. Some idiot couldn’t stow a trailer properly, and now other idiot gawkers are wasting your precious time—time that you carefully budgeted but are losing by the minute.
This frustration is contingent on a set of unique assumptions about the world. It requires the notion that time is some sort of resource, and therefore can be wasted. Moreover, it relies on thinking of time as somehow “empty,” and therefore capable of being filled with different activities—some useful and some wasteful. It requires a kind of budgeting of one’s activities in space, too, because one must organize one’s movements through space to spend time effectively.
While all these assumptions may seem quite natural, consider by contrast Ecclesiastes 3: “There is a time for everything, a season for every activity under the heavens … a time to tear down and a time to build…” The sensibility of Ecclesiastes relies on different assumptions. Time is not a resource—rather, its passage is commensurate with the events that unfold in the world. And, indeed, this view of time is resistant to the notion of budgeting: things transpire “in their own good time.” Lest this perspective seem remote, it is worth recalling that it arises out of an agrarian lifestyle—a lifestyle that is still recognizable in countries like the United States today.
Social scientists have noted differences in how social groups organize their members’ activities in time and space, as well as how they tend to conceive of these dimensions. Even within the United States, many factors—career, socioeconomic status, religious affiliation, access to technology, and institutions—generate very different experiences of time and space. Synthesizing the disparate research in this domain in order to clarify the importance of structural factors for the social psychology of temporal and spatial experience, the authors (Palitsky et al., 2016; Sullivan et al., 2016) have proposed a theoretical analysis centered on time–space distanciation (TSD).1 This construct refers to broad variation in how differing groups shape people’s experience and conceptualization of time, space, and their relationship.
We begin by outlining the TSD construct itself at its two mutually reinforcing levels: group and individual. We then offer the first quantitative, empirical investigation of this novel analysis. We do so using a two-pronged approach based on our multilevel perspective. First, we assess group-level variability in TSD in order to test for its uniqueness and implications at the aggregate level. Second, we assess individual-level variation in the experience of TSD in order to test a number of claims specified by our analysis.
TSD: A multilevel, integrative construct
Broad differences have been documented in the reckoning of time and space, from the use of natural light and stars for scheduling and navigation to artificial lighting, maps, and GPS (Birth, 2012; Heft, 2013). The abstract, uniform conception of time typical of the developed world stands in contrast to other representations of time as “right-to-left, near-to-far, far-to-near, east-to-west, west-to-east, uphill” (Boroditsky and Gaby, 2010; Majid et al., 2013). Egocentric conceptions of space (“my left,” “your right,” and so on) are prominent among some groups, while others rely on external referents such as cardinal directions (Levinson, 1998).
The influence of cultural, economic, and societal factors on temporal and spatial variables has been of interest to social scientists from the early days of anthropology (Munn, 1992), sociology (Bergmann, 1992; Gieryn, 2000), and geography (Jakle, 1971), and these variables have received considerable interest in areas such as management and organizational behavior (Ancona et al., 2001). Geography, in particular, has played a groundbreaking role in furthering our understanding of how the activities of individuals and groups are precisely and routinely organized in time and space (e.g. Hägerstrand, 1975; Kwan, 2013; Parkes and Thrift, 1975). Yet in social–cultural psychology no comprehensive framework has emerged for empirically investigating this phenomenon. This is unfortunate, since this field is well equipped to research links between social structural factors and the individual’s psychological experience of time and space.
Derived from the theorizing of Giddens (1990) and Harvey (1990), TSD refers to the extent to which (1) time and space are abstracted from one another within a social environment through their precise measurement and control as separate, quantifiable dimensions, and (2) activities tend to be abstracted and organized across large distances and long spans of time (Sullivan et al., 2016). For individuals belonging to lower-TSD groups, or socialized in lower-TSD environments, time and space are only slightly differentiated: spaces are defined and shaped by the activities that occur within their boundaries, and time is not very sharply conceptualized beyond present activities. In such groups and environments, concepts of time are often intricately bound up with concepts of space (e.g. determining time via the position of the sun). In higher-TSD social groups and environments, by contrast, time and space are understood as separate dimensions that exist independent of immediate human activity (e.g. time can be “wasted” if it is not properly “filled”; Rosa, 2015). Facilitated by this separation (or “distanciation”), members of higher-TSD groups systematically, routinely, and precisely coordinate time and space using abstract standardized units (Palitsky et al., 2016). Train time tables and time zones pegged to Coordinated Universal Time, for example, bring together variable units of time and distant spaces into routinized coordination (Giddens, 1990; Zerubavel, 1982).
Different settings afford different technologies and resources for distributing individual activity across time and space (e.g. airplanes, GPS). The resulting trends in social organization make certain demands of individuals, shaping opportunity and constraining choice. Individuals, however, mold their own behavior around these technologies and resources to adapt to cultural demands or to exploit opportunities in pursuit of their own goals (e.g. by working remotely). As a result, we propose that TSD can be meaningfully studied as a feature of both social groups or environments and individuals. Below we specify in greater detail the causes and components of TSD, with an eye toward developing a model of the two levels and their relationship. This model is summarized in Figure 1.
Cultural modernization and the distal, social structural causes of TSD
Cultural variability in time–space experience has been driven by an array of diachronic developments in social–structural factors (Palitsky et al., 2016). Factors increasing TSD fall under the umbrella term cultural modernization (Allan, 2012; Giddens, 1990; Greenfield, 2013; Harvey, 1990). They include historical events and circumstances such as the influence of the Renaissance and Enlightenment on philosophical and scientific understandings of space and time. They also include technological developments, such as the increasingly widespread manufacture and distribution of devices for reckoning time and space as well as advanced means of travel. Global commerce, fueled by the advent of the Internet, has led to the overcoming of spatial distances and temporal requirements in a process typically referred to as globalization (Waters, 2001). Nevertheless, the pattern of globalization and TSD has been far from perfectly linear or all encompassing (Bauman, 1998). The urban poor, for example, may not have the same degree of mobility or agency in time use as more privileged groups (Atkinson, 2010; Harvey, 2005).
It is worth noting that the effects of cultural modernization are not specific to TSD. For instance, modernization processes have contributed to the rise of cultural individualism (Greenfield, 2013). Higher TSD emerged historically with these other factors in a complex, dynamic process of capitalist modernization. We thus would expect other outcomes of modernization to be related to TSD at both the group and individual levels, and potentially to be subsumable under a higher-order factor reflecting the shared influence of cultural modernism.
Group-level TSD
TSD has a theoretically specifiable group level that consists of the aggregate behaviors of multiple individuals in a social environment as well as stable characteristics of that environment. These characteristics are related to, but analytically separable from, the psychological experiences of individuals within those settings. In higher-TSD settings, time and space are independently measured and commodified. This refers to the relative prevalence in a social group of technology for measuring and organizing time and space (Heft, 2013; Levine and Norenzayan, 1999), as well as the development of a market on which temporal and spatial units are exchanged for goods and services (e.g. wage labor, rent).
Social groups and environments also vary in the extent to which activities of individuals within the group or environment are extended across and tightly organized in time–space. In lower-TSD social environments activities tend to be restricted to periods of daylight, whereas in higher-TSD environments technology (e.g. electric lighting) permits many activities on a 24-hour basis (Sonnentag et al., 2014). In higher-TSD groups, individuals tend to carry out activities across a broader range of spaces as well: On average, more individuals in a higher-TSD social group will be more widely traveled. But behavior is not only extended across space and time among members of higher-TSD groups; it is also more tightly organized into discrete units (e.g. payable hours for work at the office, half-hour meetings in a conference room) using available technologies.
It is worth noting that group-based differences in TSD can be identified on the basis of a wide array of factors. Some are features of the social environment that constrain or afford the opportunity to extend behavior over space and time, such as access to public transportation or urban density. Other factors are associated with group membership. For instance, TSD will be higher among more modernized, upper-class individuals who have the resources to make considerably more choices about their positions in space and time (Kaufmann et al., 2004). Other research demonstrates that social status plays an important role: Women on average complete more activities that are spatially and/or temporally fixed to a given time or place than men (Kwan, 2000).
The fact that TSD at the aggregate level arises from both group-based and environmental factors broaches the interesting possibility that individuals may be subject to conflicting TSD-related influences as a function of the intersectionality of social identity and location in a globalizing world. Depending on the point of comparison, these different types of factors will be largely correlated and reinforcing; for example, when comparing the pastoral Duna of Papua New Guinea to urban Chicagoans, environmental and group-based TSD differences are largely conflated (Sullivan et al., 2016). Naturally, however, given individuals may find themselves subject to contradictory forces; consider the case of lower-class workers who commute to spend much of their lives working in elite hotels, or a person socialized into a high-TSD psychology who adopts the role of primary caregiver and suddenly feels increased temporal–spatial boundedness and constraint.
Though such cases provide interesting possibilities for future research, for simplicity in our initial investigation of this construct we focus on the reinforcing influence of congruent environmental and group factors by analyzing aggregate social behavior as an indicator of group-level TSD.
In short, we characterize lower- (versus higher-) TSD groups as those that, on the whole, tend to have less (versus more) agency with respect to the organization and use of space and time. Because we use aggregate data from clearly defined groups in clearly defined environments (specifically, state-level data), we are at present agnostic as to whether TSD differences arise primarily from group-based or environmental factors, which we assume to be typically reinforcing.
Individual-level TSD
TSD is not only a feature of social environments. Group-based differences are related to, yet can be distinguished from, the experiences of individuals within those groups. At one level, TSD reflects social patterns of behavior in a setting, regardless of its occupants. However, at the individual level, TSD describes the extent to which people themselves separate space from time and how they organize their behavior within those dimensions.
For higher-TSD individuals, time and space are understood as consisting of standardized, quantifiable, interchangeable, and commodified units. Such individuals are more prone to thinking in terms of “clock time” than “event time” (Brislin and Kim, 2003; Levine, 1997; White et al., 2011), meaning that they see temporal units (minutes, hours) as “empty” blocks which can be filled in any way because they are not predefined by particular spaces or activities. Furthermore, higher-TSD individuals tend to perceive locations more in terms of “space” than “place” (Lewicka, 2011b; Tuan, 2007), meaning that they believe most spaces to be commodified units that permit a variety of activities, which may be pursued without any prior personal history of engagement with the space.
Additionally, the activities of higher-TSD individuals are extended across and performed independently of time cycles and particular spaces. Technological and organizational developments in higher-TSD social environments have altered patterns of activity such that many workers operate on night shifts and some businesses are open around the clock (Birth, 2007). By contrast, lower-TSD individuals tend to perform particular activities at particular times within diurnal and seasonal cycles. A further implication is that, in higher-TSD environments, many individuals pursue distal goals across broad swaths of space and projected time. Artifacts like money and systems of wage labor encourage projection of intentions farther into the future (Simmel, 1982).
Implications of TSD for well-being
Assuming that the theoretical construct of TSD can be operationalized and verified at both the group and individual level, an additional practical question remains: what are the implications of TSD for well-being? On the one hand, globalization advocates propose that the factors facilitating TSD—such as technological development and greater wealth—should also increase basic access to life-enhancing resources, like modern health care and a variety of sources of nutrition (Giddens, 1990). Supporting evidence shows that both regional and personal indicators of social class, which we would expect to correlate with greater TSD, predict decreased risk of heart disease and lower mortality overall (Smith et al., 1998). Furthermore, greater time spent away from home in less disadvantaged nonresidential spaces (e.g. work, shopping) predicted better self-rated health (Inagami et al., 2007). Advocates for development and modernism might further suggest that experiencing higher TSD could be associated with a greater sense of freedom to pursue one’s intrinsic goals, leading to an overall enhanced level of life satisfaction or psychological well-being (Cudd, 2011).
On the other hand, many authors have argued that higher-TSD conditions induce psychological stress as workers are increasingly expected to complete more work with the aid of “time-saving” technologies (Lazzarato, 2015). Capitalist modernization has also resulted in tremendous economic inequality (Piketty, 2014), which negatively predicts well-being (Oishi et al., 2011). Thus, while aggregate higher TSD in an environment is likely associated with the presence of many resources that could facilitate life satisfaction (e.g. better access to healthcare), it is unclear whether TSD should directly predict well-being at the group or individual level.
The current studies
To date no research has directly examined the unique explanatory power of TSD in comparison to other group- or individual-level psychological variables. In Study 1, we demonstrate the uniqueness of, and variation in, group-level TSD, with a focus on between-state differences in the United States. To further test the significance of TSD as a group-based phenomenon, in Study 2 we explore the relations between state-level TSD and state-level indices of well-being. We expect that variation in TSD will be associated with access to resources that could facilitate well-being but not necessarily with subjective measures of life satisfaction. Finally, to provide further support for our multilevel account, in Study 3 we assess variation in TSD at the individual level. Using a latent variable modeling approach, we also demonstrate that variation in TSD and related psychological phenomena is partly due to shared variance attributable to cultural modernization.
Study 1
To assess variation in TSD, we examine between-state differences in the United States with data from the American Time Use Survey (ATUS) collected by the U.S. Department of Labor. ATUS asks individual respondents to recount their activities for the past 24 hours, and to provide estimates of the amount of time, to the minute, spent on each activity they performed. The sample includes a wide range of individuals interviewed throughout the week, effectively providing a representative sample of time use behavior within each state. We estimate an aggregate state-level indicator of TSD on the basis of these microdata.
Although these data do not contain enough data points for more fine-grained comparisons, this approach has some advantages. We acknowledge that any state-level analysis is limited as states tend to be somewhat heterogeneous in levels of development. However, we have state-level estimates of a range of important cultural (Study 1), economic (Study 1), and health-related (Study 2) outcomes. As a result, this approach allows us to take a first look at the extent to which state-level differences in TSD correspond in predicted ways with those variables.
For instance, in addition to providing evidence for state-level variation in TSD, we seek to test for the unique predictive validity of TSD as a psychological variable. Specifically, we compiled prior state-level data on individualism–collectivism (Vandello and Cohen, 1999) and tightness–looseness (Gelfand et al., 2006). Collectivism (versus individualism) refers specifically to the extent to which a setting (in this case, state) prioritizes conformity (versus uniqueness), tradition (versus novelty), and interdependence (versus independence). Tightness is a conceptually distinct cultural variable referring to the relative strength of social norms within a setting; higher tightness states are therefore characterized by a greater abundance of explicit and implicit social norms for behavior and more severe punishments for violating those norms.
We predict that state-level TSD will be correlated with, but ultimately distinct from, these factors. First, we expected that TSD will correlate with decreased state-level collectivism, as our account suggests that distal, social structural factors associated with the rise of cultural modernism have increased individualism as well as TSD (e.g. Greenfield, 2013; Oyserman et al., 2002). Similarly, we expected TSD to correlate with decreased cultural tightness. Tightness—the strength of cultural norms in a setting—is presumed to decrease as a function of the same modernization processes that increase TSD, because such processes lead to less normative regulation of behavior and greater protection from ecological threat (Gelfand et al., 2011; Inglehart, 1997).
Furthermore, to provide an initial test of TSD’s unique predictive validity, we conduct multiple regression analyses to determine whether TSD can uniquely predict state-level variation in these established cultural variables. Prior analyses suggest that relatively more collectivist social groups tend to be higher in tightness as well (Carpenter, 2000; Harrington and Gelfand, 2014). To the extent that TSD is a unique construct related to both lower tightness and collectivism, we expect that it will predict variation in tightness over and above variation in collectivism, as well as variation in collectivism over and above tightness.
Because economic and demographic factors such as income and population density are presumed to play a role in determining TSD and these cultural variables (Gelfand et al., 2011), we compiled state-level data on population size, density, median household income, inequality, and rates of entrepreneurship (Obschonka et al., 2013).
Method
Sample. The ATUS data set for 2003–2013 (United States Department of Labor Bureau of the Labor Statistics, 2013) contains a representative sample of 148,345 American citizens (Mage = 46.59, SD = 17.66; 56.3% Female/43.6% Male; 13.1% Hispanic/Latino; 86.8% Not Hispanic/Latino; 81.3% White, 13.4% Black, .7% Native American, 3.1% Asian) contacted by the US Department of Labor. The data and all relevant documentation are available at www.bls.gov/tus/.
Time–space distanciation (state-level TSD). Using ATUS data, we first totaled—for each individual in a state—the number of locations in which they had activities over the course of a 24-hour period. Second, we totaled the number of activities the average person reported over a day. These two indicators were correlated, r = .68, p < .001. A larger number of settings and a larger variety of activities reflects the extension of activity across space and time; additionally, we previously found that a similar measure at the individual level predicted attitudes toward time (Sullivan et al., 2015). To provide an estimate of a state’s average level of TSD, responses for each variable were averaged within each state to estimate the daily behavior of the average member of that state. After standardizing scores on these variables to put them on the same metric, the average was computed to provide an estimate of state-level TSD. A full ranking of states on this dimension is available in Table 1 and graphically presented in Figure 2 (map created using resources from Fitzpatrick (2012) and Murphy (2016)).
Table 1. TSD scores by state.
Rank
State
N
TSD score
1
UT
1482
1.89
2
MA
3177
1.57
3
NE
1110
1.45
4
CT
1862
1.28
5
MN
3424
1.22
6
IA
1907
1.13
7
WA
3303
1.06
8
IL
6277
0.77
9
OR
2186
0.74
10
WI
3444
0.71
11
AK
304
0.71
12
SD
485
0.58
13
KS
1773
0.53
14
WY
315
0.50
15
VT
338
0.49
16
CO
2864
0.49
17
HI
443
0.46
18
NH
746
0.42
19
RI
557
0.42
20
NJ
4049
0.41
21
IN
3260
0.33
22
ND
413
0.33
23
OH
5932
0.29
24
NY
8384
0.19
25
CA
14,758
0.16
26
VA
4211
0.16
27
MO
1534
0.16
28
MI
5310
0.08
29
OK
2055
0.08
30
NC
4567
−0.02
31
PA
6605
−0.15
32
MD
2899
−0.24
33
ID
863
−0.37
34
TN
2795
−0.40
35
NM
1141
−0.41
36
TX
10,346
−0.42
37
KY
2495
−0.59
38
AR
1552
−0.62
39
MT
565
−0.62
40
ME
757
−0.68
41
FL
8088
−0.74
42
AZ
2598
−0.77
43
NV
1249
−0.78
44
GA
3889
−1.06
45
SC
2310
−1.14
46
DE
472
−1.22
47
WV
1015
−1.22
48
LA
2105
−1.26
49
DC
365
−1.34
50
AL
2482
−1.65
51
MS
3284
−2.87
148,345
TSD: time–space distanciation.
Figure 1. A multilevel model of time–space distanciation.
Figure 2. The distribution of state-level TSD scores in the United States. Note: Higher scores indicate greater cultural TSD. Score bins represent standard deviations from 0. TSD: time–space distanciation.
Cultural tightness. Cultural tightness scores were collected for each state using previously validated scores from Harrington and Gelfand (2014).
Individualism-collectivism. Scores on the individualism–collectivism dimension were collected from the previously validated state-level data of Vandello and Cohen (1999).
Census data. Information about State Population, Median Household Income, and Population Density were collected from the 2013 census. State-level inequality was assessed on the basis of GINI coefficients per state in 2013, which the Census department also provides (Noss, 2014). This year was chosen since it was the most recent set of data available at the time of data analysis and the most recent year in the ATUS.
Entrepreneurship. As a further indicator of processes of capitalist modernization that we assume to be associated with TSD, we also collected entrepreneurship scores, reflecting the extent of business creation and self-employment in a given state (Obschonka et al., 2013).
Results
Correlations were computed between all included variables (Table 2). As predicted, state-level TSD—reflecting more spatially diffuse behavior in an average day—was correlated with both decreased collectivism (r =−.38, p = .006) and decreased cultural tightness (r = −.52, p < .0001). Supporting our model’s prediction that higher aggregate TSD is the result of distal factors like capitalist economic development, we also observed that wealthier states on average tended to be higher in state-level TSD (r = .50, p = .0001) and that higher-TSD settings showed marginally greater entrepreneurship (r = .26, p = .06).
Table 2. Observed correlations between state-level variables (Study 1).
Collectivism
Tightness
Population
Population density
Median household income
Inequality
Entrepreneurship
TSD
−.38**
−.52***
.03
−.18
.50***
−.38**
.26†
Collectivism
–
.23
.22
.24†
.10
.29*
−.34*
Tightness
–
−.08
−.26†
−.62***
.17
−.30*
Population
–
−.08
.06
.41**
.01
Population density
–
.29*
.50***
.34*
Median household income
–
−.07
.23
Inequality
–
−.08
Entrepreneurship
–
Note: †p < .10, *p < .05, **p < .01, ***p < .001.
To test the uniqueness of TSD as a state-level variable, we conducted regression analyses to test whether TSD would predict variation in collectivism after controlling for tightness. When both TSD and tightness were entered as simultaneous predictors, TSD predicted decreased collectivism (β = −.36, SE = 1.98, t = 2.27, p = .03) whereas tightness did not (β = .04, p = .80). Controlling for state population (β = .18, p = .24), population density (β = −.04, p = .85), median household income (β = .59, SE = .0002, t = 2.80, p = .008), inequality (β = .07, p = .69), entrepreneurship (β = −.16, p = .29), and tightness (β = .30, SE = .14, t = 1.86, p = .07), TSD remained a unique predictor of state-level collectivism (β = −.47, SE = 2.02, t = 2.89, p = .006).
We then regressed tightness scores onto both cultural TSD and collectivism. In this model, TSD predicted decreased tightness (β = −.51, SD = 1.87, t = 3.80, p = .0004) whereas collectivism did not (β = .03, SD = .15, t = .25, p = .80). In the full model, only collectivism (β = .26, SE = .15, t = 1.86, p = .07) and median household income (β = −.58, SE = .0002, t = 3.00, p = .004) significantly predicted tightness. TSD (β = −.05, p = .75), population (β = −.08, p = .57), population density (β = −.03, p = .89), inequality (β = −.01, p = .96), and entrepreneurship (β = −.10, p = .46) did not uniquely predict variation in tightness after controlling for collectivism and household income.
Discussion
The results of Study 1 provided broad support for our hypotheses. First, on the basis of observations of individual time use, we estimated state-level TSD by assessing the average number of settings inhabited and time spent traveling between settings for an average state resident in a day. To be clear, these data do come with one caveat: Our state-level analysis provides a convenient first test of whether TSD is associated with established cultural variables also measured at that state level, but only by neglecting the considerable variation in TSD we would expect within state and between individuals. For example, a state like New York encompasses both rural (low-TSD) and highly urbanized (high-TSD) spaces. Our estimate for the “average” member of that state is collapsing across these important differences. In the absence of more fine-grained measurements of TSD and the other variables included in this study, a more precise test is not practically available at this time. However, we fully acknowledge that this averaging across states is critically lacking in the nuance that is needed to develop this program of research.
Despite a relatively small sample (50 states and the Washington, D.C. area), we found that state-level TSD predicted variation in collectivism even after controlling for cultural tightness and vice versa. This both supports our predictions about TSD’s connection to other cultural shifts initiated by modernization and demonstrates that TSD is more than merely individualism or cultural looseness. It is worth noting that the unique ability of TSD to predict variation in tightness did not hold after controlling for collectivism and median household income, which were highly predictive of this tendency. This is likely due to the high degree of multicollinearity between TSD and income (r = .50), which is a more prominent issue in smaller samples (Cohen et al., 2002). Future research on TSD can work with larger, more spatially precise samples to potentially disentangle the closely related effects of TSD and income.
Study 2
While Study 1 provides evidence for TSD’s uniqueness as a group-level variable, in Study 2 we provide further evidence of both TSD’s distinctiveness and its substantive importance. In order to explore how state-level TSD might be related to various indicators of aggregate well-being, we test the extent to which variation in TSD, collectivism, and tightness uniquely predict well-being on a state-by-state basis. To accomplish this, we gathered secondary data assessing relative physical and psychological well-being across states in the United States. We test these relationships with and without controlling for state-level inequality (a predictor of well-being; Oishi et al., 2011) and wealth (reflected by median income).
Based on our theoretical analysis, we expected that higher-TSD settings would offer their residents greater access to health resources, and therefore might produce citizens with greater physical health. We did not have clear predictions regarding more subjective indicators of well-being at the group level. As described in the Introduction, some theorists have proposed that the modernization processes which give rise to TSD also tend to improve overall quality of life and personal freedom, which might lead to enhanced life satisfaction. However, others contend that higher-TSD conditions induce psychological stress and uncertainty.
Method
State-level data on TSD, collectivism, tightness, and inequality from Study 1 were used as predictors. State-level well-being indicators were calculated by Rentfrow et al. (2009) based on Gallup Organization data from 2008. These authors kindly provided their data for the analysis. We briefly review these indicators here.
Life evaluation was measured by two subjective items through which participants evaluated their current and near-future life situation. Work quality was a composite of items asking participants about their job satisfaction and relations to supervisors. Basic access measured participants’ access to resources that facilitate well-being, namely “clean water, medicine, affordable fruits and vegetables, and affordable health care” (Rentfrow et al., 2009: 1075). Healthy behavior assessed a variety of behaviors associated with physical health (e.g. exercise, smoking). Physical health was compiled based on a variety of indicators, including illness, energy, and body mass. Emotional health was an aggregate indicator of positive (as opposed to negative) daily affective experiences. The global well-being index was a composite score incorporating all six subindices. See Rentfrow et al. (2009) for further details.
Results
Zero-order correlations between the three cultural variables, median household income, inequality, and state-level indicators of well-being are presented in Table 3.
Table 3. Observed correlations between state-level variables (Study 2).
Collectivism
Tightness
Median household income
Inequality
Well-being Index
Life evaluation
Work quality
Basic access
Healthy behavior
Physical health
Emotional health
TSD
−.38**
−.52***
.50***
−.38**
.53***
.17
.22
.70***
.34*
.57***
.41**
Collectivism
–
.23
.10
.29*
−.07
.34*
−.41**
−.21
−.12
−.07
−.10
Tightness
–
−.62***
.17
−.52***
−.21
−.09
−.47***
−.74***
−.51***
−.32*
Median household income
–
−.07
.62***
.51***
−.11
.59***
.51***
.66***
.42**
Inequality
–
−.33*
−.15
−.38**
−.15
−.11
−.24†
−.57***
Well-being index
–
.80***
.45***
.51***
.78***
.83***
.79***
Life evaluation
–
.19
.11
.50***
.64***
.61***
Work quality
–
−.04
.32*
.07
.31*
Basic access
–
.38**
.56***
.31*
Healthy behavior
–
.60***
.48**
Physical health
–
.74***
Note: †p < .10, *p < .05, **p < .01, ***p < .001.
To test the unique contributions of collectivism, tightness, and TSD to state-level well-being, we conducted a multiple regression analysis for each well-being indicator (a full summary of results is presented in Table 4). Given that median household income and inequality were highly correlated with many well-being indicators, we conducted this analysis with and without these controls to address the possibility that the effects of the cultural variables are confounded with wealth or inequality.
Table 4. Standardized coefficients of multiple regression models predicting each state-level well-being indicator (Study 2).
Well-being index
Life evaluation
Work quality
Basic access
Healthy behavior
Physical health
Emotional health
Base model
TSD
.41**
.25
.11
.65***
−.06
.48**
.36*
Collectivism
.17
.48**
−.38*
.07
.04
.19
.07
Tightness
−.35*
−.18
.05
−.15
−.78***
−.31*
−.14
Controlling for inequality
TSD
.35*
.20
.03
.66***
−.06
.45**
.23
Collectivism
.22†
.52***
−.32*
.06
.04
.21
.17
Tightness
−.34*
−.19
.05
−.15
−.78***
−.31*
−.14
Inequality
−.23†
−.20
−.28*
.07
−.005
−.10
−.52
Controlling for inequality and median household income
TSD
.20
−.01
.17
.52***
−.12
.27†
.13
Collectivism
.08
.33*
−.21
−.07
−.01
.04
.08
Tightness
−.19
.04
−.09
−.002
−.72***
−.12
−.04
Inequality
−.20†
−.17
−.31*
.10
.005
−.07
−.50***
Income
.33†
.48*
−.29
.32†
.13
.41*
.22
TSD: time–space distanciation.
Note: †p < .10, *p < .05, **p < .01, ***p < .001.
Table 5. Items and item loadings for individual-level TSD scale (Study 3).
Item
Loading
SE
p
Unstandardized
Standardized
I often find myself scheduling tasks weeks ahead of time.
1.30
.75
.18
<.0001
I plan my activities carefully using planning devices (such as electronic calendars or day-planners) and timekeeping devices (such as a watch)
1.45
.74
.20
<.0001
I find it hard to know what I’ll be doing tomorrow without looking at my planner
.70
.43
.43
<.0001
When I’m headed to a place I don’t usually go, I often visualize a map as I navigate
.54
.32
.32
.001
If I postpone something important, I need to know when exactly it will get done
.46
.31
.14
.001
My time is a resource that I try to spend as effectively as possible
.34
.29
.11
.001
I feel perfectly comfortable moving through many different environments in the course of a day
.28
.20
.13
.04
I am capable of doing my work in any kind of environment
.26
.19
.13
.04
TSD: time–space distanciation.
Global well-being index. TSD (β = .41, SE = .27, t = 2.89, p = .005) and cultural tightness (β = −.35, SE = .02, t = 2.56, p = .01) emerged as significant predictors of better and worse global well-being, respectively. Controlling for the effect of state-level inequality (β = −.23, SE = 10.34, t = 1.89, p = .06), these effects remained significant. However, additionally controlling for the effect of median household income (β = .35, SE = .00003, t = 1.94, p = .06) accounted for the effects of the cultural variables (all ps > .20).
Life evaluation. We found that only collectivism (β = .48, SE = .05, t = 3.44, p = .001) predicted more positive life evaluations. This is consistent with the notion that a relative tendency toward collectivism provides individuals with a greater sense of social belongingness and support, which are associated with life satisfaction across the United States (Lim and Putnam, 2010). Controlling for the effect of inequality (β = −.20, SE = 28.61, t = 1.49, p = .14), collectivism remained a significant predictor. The addition of median household income to the model (β = .48, SE = .00009, t = 2.53, p = .01) did not eliminate the unique effect of collectivism (β = .33, SE = .05, t = 2.17, p = .03).
Work quality. Only collectivism (β = −.38, SE = .03, t = 2.64, p = .01) predicted poorer work quality. Adding inequality to this model (β = −.28, SE = 18.124, t = 2.03, p = .05), the effect of collectivism remained significant. Finally, the addition of median household income as a control (β = −.29, SE = .00006, t = 1.47, p = .15) rendered the effect of collectivism nonsignificant (β = −.21, p = .20).
Basic access. Only TSD (β = .65, SE = .34, t = 5.40, p < .001) predicted better access to resources that promote well-being. This effect remained significant (β = .66, SE = .35, t = 5.09, p < .001) even after controlling for the effect of inequality (β = .07, SE = 13.76, t = 0.66, p = .52). The addition of median household income in the model (β = .32, SE = .00004, t = 2.01, p = .05) did not eliminate the effect of TSD.
Healthy behavior. Cultural tightness uniquely predicted large deficits in healthy behavior (β = −.78, SE = .02, t = 6.80, p < .001), consistent with prior work showing that this variable predicts a wide range of preventable negative health outcomes (Harrington and Gelfand, 2014). Controlling for the effect of inequality (β = −.005, SE = 12.69, t = 0.04, p = .97), this large effect remained significant (β = −.78, SE = .02, t = 6.73, p < .001). Controlling for income (β = .13, SE = .00004, t = 0.83, p = .41), the unique effect of tightness remained significant (p < .001).
Physical health. TSD predicted better (β = .48, SE = .29, t = 3.44, p = .001) and tightness predictor poorer (β = − .31, SE = .02, t = 2.32, p = .03) physical health. The addition of inequality as a control (β = −.10, SE = 11.47, t = 0.79, p = .43) did not weaken either the effects of TSD (p = .002) or tightness (p = .03). Additionally controlling for the effect of income (β = .41, SE = .00004, t = 2.41, p = .02), the effect of tightness was eliminated (p = .43) but the effect of state-level TSD remained marginal (β = .27, SE = .31, t = 1.73, p = .09).
Emotional health. Only TSD (β = .37, SE = .25, t = 2.20, p = .03) predicted greater emotional health. This effect was eliminated (p = .11) after controlling for the strong effect of inequality (β = −.52, SE = 8.48, t = 4.26, p = .0001). The addition of income (β = .22, SE = .00003, t = 1.24, p = .22) did not affect the model.
Discussion
The results of Study 2 provide further evidence for the uniqueness of TSD as a group-level variable. We found that in comparison to collectivism and cultural tightness, state-level TSD uniquely predicted higher levels of basic access to health resources and better physical health. In contrast, collectivism (versus tightness and TSD) was highly predictive of individuals’ subjective life evaluations, and tightness (versus collectivism and TSD) predicted fewer healthy behaviors.
Beyond merely demonstrating the uniqueness of TSD, the current study also highlights the practical significance of TSD. Higher-TSD settings, presumed by our analysis to be more modernized and wealthy, may have important impacts on physical health and well-being that are unique from any shared effects of other modernization outcomes or income. However, these effects do not directly translate into better psychological well-being. All positive effects of state-level TSD on more subjective indicators seem to have been by-products of the fact that wealthier individuals tend to be more satisfied and to experience more positive affect (Aknin et al., 2009).
Study 3
While Studies 1 and 2 support our claims about TSD at the group level, in Study 3 we test several hypotheses about TSD at the individual level. The primary goal of this study is to establish TSD as a unique psychological construct with important associations to other established psychological variables. We first created and validated a brief measure designed to assess individual differences in TSD.
We also include prominent psychological measures of attitudes toward space and time, which tend to only assess attitudes toward these dimensions separately. Previous work on time perspective (Zimbardo and Boyd, 1999) demonstrates people vary in their psychological orientation toward the past, present, and future. With reference to cultural attitudes toward space, Lewicka (2011a) contrasts a more active, ideological form of place attachment (characteristic of cultural modernism) with a more traditional, rooted form. Because cultural modernism has been associated with a positive focus on the present and future (as opposed to the past) and on a more active, individualized form of place attachment (Giddens, 1990; Tuan, 2007), we expected individual-level TSD to be positively associated with these tendencies.
Further, we assess differences in several psychological constructs implicated by our analysis as associated with, but ultimately distinct from, individual-level TSD. Specifically, we expect that higher psychological TSD will be associated with greater use of technologies that reinforce the distanciation of behavior across spaces and times (e.g. email, cell phones). Additionally, given that individual-level TSD can ultimately be traced to modernization processes which have also generated greater individualism and psychological tendencies to abstract behavior from context (Giddens, 1990; Mishra et al., 1996), we expect all these variables to be correlated at the individual level.
Our account further suggests that the interrelationship between these constructs is a more complex matter than a series of isolated associations. Specifically, the process of cultural modernization (resulting from the distal, social structural factors presented in Figure 1) has simultaneously made certain technologies more prevalent, fostered greater TSD (as well as present/future orientation and active place attachment), increased individualism, and facilitated an abstract psychological orientation toward behavior. In short, we expect not only that TSD will correlate with these other variables at the individual level, but that variation in all of them may be partially explained by a single underlying factor reflecting the shared influence of modernism.
To test this conceptual model, we model the covariances between TSD and other facets of modernism as a single, common factor using a confirmatory factor analysis approach. In essence, this technique assumes the existence of a single variable (viz. Modernism) which explains individual variability in TSD and related variables. To the extent that this single-factor model fits the data reasonably well, it would support the idea that cultural modernism is a coherent phenomenon underlying many closely related effects on individual psychology.
Method
Sample. One-hundred fifty-nine American adults were recruited through Amazon’s Mechanical Turk service (Mage = 34.18, SD = 11.27; 48% Women, 80% White, 7% Asian, 6% Black, 4% Hispanic/Latino, 3% Other) for a study ostensibly researching personality and social attitudes (payment = $1.00). An additional subset of participants (n = 31, approximately 16% of those recruited) either failed attention checks or indicated concern about the length of the study; their data were excluded from the analyses a priori.
Materials
Individual-level TSD scale. First, participants were asked to complete a series of eight self-report items designed to assess individual differences in TSD. Participants rated their agreement (1 = Strongly disagree; 7 = Strongly agree) with items assessing (1) explicit conceptualizations of time and space as consisting of commodified, functionally interchangeable units; and (2) the extension of activities across time and space through planning. See Table 5 for all items.
Technology Use. We asked participants how frequently (1 = Never; 5 = All of the time) they used cell phones, social media (such as Facebook or Twitter), GPS, and e-mail.
Active Place Attachment. To assess active (i.e. nontraditional) place attachment toward their current city or town of residence, we then had participants complete the 6-item measure designed (and validated) by Lewicka (“place discovered”; 2011a). In this scale, participants are asked to what extent (1 = Strongly disagree; 5 = Strongly agree) they value their town of residence because it conforms to their personal values and goals (e.g. “I like to wander around this city or town and discover new places;” “I like to keep up with changes in my city”). The six items formed a reliable composite indicator of active place attachment (α = .75; M = 3.07; SD = 0.82).
Individualism. Next, participants rated their agreement (1 = Strongly disagree; 7 = Strongly agree) with six statements (e.g. “Competition is the law of nature”; “What happens to me is my own doing”) assessing individualism (Singelis et al., 1995). While weaker than the other measures, scores were somewhat reliable (α = .68) and were averaged (M = 4.70, SD = .90).
Action Identification. The tendency to construe behaviors more abstractly was assessed using the Behavior Identification Form (Vallacher and Wegner, 1989). Specifically, participants were provided with a list of 25 activities and asked which of two descriptions (concrete versus abstract) is more apt (e.g. “Reading. Following lines of print OR Gaining knowledge”). The measure is scored by summing the total number of abstract descriptions participants choose (M = 14.81; SD = 6.45)
Time Orientation. Finally, participants completed a short form of the Zimbardo Time Perspective Inventory (SZTPI-15; Zhang et al., 2013). The scale asks participants to rate (1 = Very untrue; 5 = Very true) three items each for five distinct factors of time perspective: a negative view of the past (past negative; e.g. “I think about the bad things that have happened to me in the past”), a positive view of the past (past positive; e.g. “Happy memories of good times spring readily to mind”), a fatalistic view of the present (present fatalism; e.g. “Since whatever will be will be, it doesn’t really matter what I do”), a hedonistic view of the present (present hedonism; e.g. “It is important to put excitement in my life”), and an orientation toward the future (future; e.g. “I complete projects on time by making steady progress”). The items corresponding to each factor, with the exception of present fatalism, formed reliable composites and their scores were averaged: Past negative (α = .91, M = 2.72, SD = 1.14); Past positive (α = .80, M = 3.59, SD = .86); Present fatalism (α = .34, M = 2.58, SD = .77); Present hedonism (α = .75, M = 2.85, SD = .91); Future (α = .68, M = 3.89, SD = .65). Given the very poor coherence of the present fatalism construct, we report tentative results for the sake of the reader but note that poor reliability makes interpretation difficult.
Results
Individual-level TSD scale. Because there was no empirical precedent for either our individual-level TSD measure or our technology use measure, we submitted both to a confirmatory factor analysis to ensure that the items demonstrated appropriate coherence. Fitting a measurement model in which the eight TSD items loaded onto a single factor and the four technology use items loaded onto a separate factor returned a model with weak to moderate fit, χ2 (53) = 119.59, RMSEA = .089 (95% CI: .068, .110), TLI = .61, CFI = .69, SRMR = .088.
Because our sample was relatively small and the subsequent models quite complex, we parceled the indicators of the individual-level TSD factor to improve power. Following Little et al. (2002), we averaged the highest and lowest loading indicators into one parcel, the second highest and second lowest into another parcel, and so on. Doing so simplified the model considerably and resulted in a vast improvement in model fit, χ2 (19) = 25.84, RMSEA = .048 (95% CI: .00, .09), TLI = .93, CFI = .95, SRMR = .056. Consistent with our analysis, we found that individual-level TSD and technology use were significantly correlated (r = .37, p = .003).
Correlations. Having established the acceptability of our new measures, we then proceeded to test their associations with the other observed variables in our dataset. We continued to model the novel factors as latent constructs and introduced the other variables as manifest constructs, scoring each scale according to the procedures specified by prior research.
The pattern of covariances was highly consistent with predictions (for a summary of correlations with individual-level TSD and technology use, see Figure 3; for full correlation table, see Table 6). Specifically, we found that TSD correlated with more active place attachment (r = .32, p < .001), greater individualism (r = .23, p = .01), more abstract construal of activity (r = .28, p = .004), more hedonistic orientation toward the present (r = .27, p = .003), and greater future orientation (r = .39, p < .001). We also note the correlation between active place attachment and present hedonism (r = .42, p < .001), which offers evidence for a direct psychological link between attitudes toward space and time. Additionally, technology use correlated with individualism (r = .24, p = .01) and a present hedonistic orientation (r = .26, p = .02).
Figure 3. Summary of correlates of TSD and technology use (Study 3). Note: Solid lines indicate significant associations (*p < .05, **p < .01, ***p < .001). Gray dashed paths indicate marginal associations (p < .10). TSD: time–space distanciation.
Table 6. Observed correlations between observed variables (Study 3).
Technology use
Active place attach.
Individ.
Action ID
Past negative
Past positive
Present hedonism
Present fatalism
Future
TSD
.35**
.32***
.23*
.28**
−.07
.16†
.27**
−.18†
.39***
Technology use
–
.15
.24*
−.19†
.15
.14
.26*
.14
.08
Active place attachment
–
.27**
.13
−.05
.12
.42***
.02
.05
Individualism
–
−.02
.02
.05
.29***
.02
.01
Action ID
–
−.03
−.04
−.06
−.10
.17*
Past negative
–
.06
.03
.29**
−.08
Past positive
–
.07
.16*
.11
Present hedonism
–
.15†
−.10
Present fatalism
–
−.27**
Future
–
TSD: time–space distanciation.
Note: †p < .10, *p < .05, **p < .01, ***p < .001.
The coherence model of cultural modernism. Finally, we tested our prediction that variation in individual-level TSD and related factors could be effectively modeled by a single common factor reflecting the shared influence of modernism. We modeled this underlying factor using CFA and including TSD and its correlates as indicators of this single latent construct. Fitting this model resulted in relatively poor model fit, χ2 (63) = 124.10, RMSEA = .078 (95% CI: .058, .098), TLI = .70, CFI = .76, SRMR = .085. This was partly due to the fact that future orientation and abstract action identification did not significantly load onto this underlying factor (λs = .10 and .08, respectively, ps = .37, .46).
Removing these nonloading indicators resulted in a model with a close fit to the data, χ2 (42) = 56.83, RMSEA = .047 (95% CI: .000, .076), TLI = .91, CFI = .93, SRMR = .058. For the parameters of the final model, see Figure 4.
Figure 4. Single factor model of observed psychological constructs (Study 3). Note: *p < .05, **p < .01, ***p < .001.
Discussion
The results of Study 3 supported predictions and provide a firm basis for future research on TSD as a psychological phenomenon. We validated a brief measure of individual-level TSD and found that scores on this measure correlated in moderate and expected ways with established cultural–psychological and social cognitive variables.
We also found tentative support for our broad claim that TSD at the individual level is but one consequence of shifts in modernism. Variation in TSD, along with individualism, a hedonic approach to the present, an active attachment to place, and the use of behavior-organizing technologies, could be sufficiently explained by a single latent factor. It should be noted that previous work has largely assumed these variables to be psychologically distinct phenomena. In contrast, our analysis offers an integrative understanding that draws theoretically grounded connections between these superficially distinct variables.
General discussion
Across three studies, we found broad support for predictions specified by our analysis of the independence and importance of TSD at both aggregate (state) and individual (psychological) levels. In Studies 1 and 2, we found that our indicator of state-level TSD (1) was associated with decreased collectivism and cultural tightness but was observed to be distinct from each of these constructs and (2) that each of these three cultural variables had unique predictive ability in assessing state-level differences in various forms of well-being, providing further evidence of the distinctiveness and practical significance of these variables. In particular, state-level TSD seems to be associated with access to resources that facilitate physical, but not psychological, well-being.
Finally, in Study 3 we provided the first empirical analysis of TSD at the individual level. Consistent with our predictions, we found that individual-level TSD was related to individualism, the use of space–time distancing technologies, orientation toward the present and future, abstract cognition, and a more active attachment to place. Consistent with our broader claims about the common origins of many of these variables in shifts toward increasing modernism, we found that there was broad coherence in a range of previously disconnected psychological phenomena.
Implications for psychology
Based on the results of Study 3, we propose that TSD holds the potential to integrate other, disparate areas of psychological research at the individual and interpersonal levels. To the extent that higher individual-level TSD is characterized by psychological distance from any particular setting (Fiedler et al., 2012), TSD may generally encourage more abstract conceptions of other people and their behaviors. Trope and Liberman (2010) note that higher-level construals of targets are less susceptible to change in the face of contextual variation, because they are less specific. This has important implications for social interaction in higher-TSD settings. For example, identifying a socially distant person by an abstract social role (e.g. waiter) allows an individual agent to call on a stereotype (McCrea et al., 2012) or an invariant script for interaction with that role (Stephan et al., 2010), rather than investing effort in considering idiosyncrasies of the person or the locale (Simmel, 1982). Supporting behavioral data show that wealthy individuals do indeed attend less to others in the social environment (Dietze and Knowles, 2016).
In higher TSD settings, people may also prefer more abstract conceptions of their environment. Consider the central importance given to considerations of monetary value in higher-TSD capitalist settings like the United States. Money is an abstraction capable of uniting the complexities of the social world, and it offers a common metric to describe the value of of a range of radically dissimilar targets: for example, a laborer’s time (i.e. a wage), a personal experience (Holbrook and Hirschman, 1982), or a commodity.
Implications for sociology and related disciplines
These implications are also indicative of the potential of TSD to bridge psychological research with lines of research which are more historically, contextually, or structurally oriented. It is likely no accident that higher-TSD settings appear to encourage a more cross-cutting economic or utilitarian view of the social world, although further research is needed to investigate this relationship. This possibility further suggests that TSD may have a role to play in research on interaction in both the private sector and the public sphere, for instance in research on the proliferation of rationalized norms of conduct (Meyer and Bromley, 2013; Meyer and Rowan, 1977) and the emergence of certain beliefs about good governance and corruption (Andvig, 2006; Drori et al., 2006; Granovetter, 2007; Rothstein and Teorell, 2008).
The theoretical link between TSD and capitalist modernization and the empirical link between group-level TSD and income suggest the importance of more comparative research. Recent work has articulated nuanced frameworks for characterizing the role of time in phenomena of sociological interest (Tavory and Eliasoph, 2013) but has paid less attention to related spatial considerations. For example, TSD may stimulate research in the sociology of work and the study of organizations, areas which have already demonstrated interest in the effects of technology (Barley, 1990; Kirkman and Mathieu, 2005) and the experience of time (Perlow, 1999; Waller et al., 2001; for a different use of Giddens’ work, see Jin and Robey, 2008). The importance of commodified perceptions of time and space varies across professions, even within urban areas of the developed world. Individuals in professions such as finance, law, or academia can be expected to have different orientations toward time and space than individuals working in lower-class professions, most visibly in the meaning and magnitude of compensation for demands on one’s temporal and spatial experience. Variation in attitudes toward time and space within a profession may also have important consequences for the individual or the organization (see, e.g. Chen and Nadkarni, 2016, for consequences of CEOs’ time perspectives).
Although our data did not show strong links between TSD and inequality at the state level, prior research and theorizing points to the fruitfulness of the intersection of TSD with inequality and stratification. These relationships may exhibit more subtle, moderated patterns visible to multilevel analyses. Poverty and precarity motivate a focus of attention away from an uncertain future (Fieulaine and Apostolidis, 2015), which may directly affect individual-level TSD. Other negative effects of distributing social behavior may disproportionately affect disadvantaged groups, including residential segregation (Hall et al., 2015), the “spatial mismatch” separating inner city inhabitants from suburban job opportunities (Gobillon et al., 2007), the spatial distribution of inequality (Tickamyer, 2000), urban/rural differences (Lobao and Saenz, 2002), and ethnic and national differences in attitudes toward time (e.g. Jones and Brown, 2005), which can equip some individuals for success while leaving others disadvantaged in environments varying in TSD. Additionally, research on the cultures of educational systems (Stephens et al., 2012; Willis, 1977) reinforces the intuition that some groups are lacking in the resources and opportunities needed to internalize higher levels of individual TSD, which is adaptive for higher-TSD environments. Factors that discourage consideration of the future or inhibit positive evaluations of the future may likewise worsen economic, academic, or health outcomes (Andersson, 2012; Hitlin and Johnson, 2015; Vuolo et al., 2012; Yowell, 2000).
Conclusion
Although they are only a first step, these three studies present solid evidence for the important but neglected link between time and space as dimensions of experience. They offer a number of novel insights gained from adopting a broad, interdisciplinary approach to the study of TSD. First, we see that TSD at the state level is unique from related cultural and economic variables (Study 1) and furthermore that it has important implications for health, even after accounting for those other established factors (Study 2). Study 3 offered a first look at how theorizing about aggregate factors in sociology, geography, and anthropology can inform our understanding of individual psychology.
The TSD construct establishes the unique predictive power of the interrelationship of time and space at both individual and group levels. The underlying theory, furthermore, predicts additional relationships between time–space attitudes, social structure, and psychological or socioeconomic outcomes. We encourage other scholars to join us in exploring these implications. Finally, we believe we have made a strong case for the merits of TSD as an integrative construct for the cultural psychology of time and space, a construct which can facilitate communication and collaboration between researchers in a variety of scholarly fields.
Acknowledgments
For their comments on an earlier version of this manuscript, the authors thank John W. Meyer, Michelle Jackson, Katie Wullert, Anna Lunn, Scott Westenberger, Jasmine Hill, Xueguang Zhou, and Cristobal Young. We thank Rentfrow and colleagues for sharing their health outcome data.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Footnote
1 The phenomena mapped by this construct have been identified by other names, including time–space compression (Harvey, 1990), time–space convergence (Janelle, 1968), and space–time contraction (Bretagnolle et al., 1997). For the sake of consistency, we will use Giddens’ term “distanciation” throughout the paper. For a fuller discussion of how the TSD construct relates to and incorporates these other notions, see Palitsky et al. (2016).
References
Aknin LB, Norton MI, Dunn EW (2009) From wealth to well-being? Money matters, but less than people think. The Journal of Positive Psychology 4(6): 523–527.
Allan K (2012) Runaway modernity: Anthony Giddens. In: Allan K (ed.) Contemporary Social and Sociological Theory, Thousand Oaks, CA: Sage, pp. 309–334.
Andersson M (2012) Identity crises in love and at work: Dispositional optimism as a durable personal resource. Social Psychology Quarterly 75(4): 290–309.
Bergmann W (1992) The problem of time in sociology: An overview of the literature on the state of theory and research on the ‘sociology of time’, 1900–82. Time & Society 1(1): 81–134.
Boroditsky L, Gaby A (2010) Remembrances of times east: Absolute spatial representations of time in an Australian Aboriginal community. Psychological Science 21(11): 1635–1639.
Bretagnolle A, Pumain D, Rozenblat C (1997) Space-time contraction and the dynamics of urban systems. Cybergeo: European Journal of Geography. 6–11. Available at: http://cybergeo.revues.org/373 (accessed 15 March 2017).
Cohen J, Cohen P, West SG, et al. (2002) Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd ed. Mahwah, NJ: Lawrence Erlbaum Associates.
Cudd AE (2011) For capitalism as a feminist ideal and reality. In: Cudd AE, Holmstrom N (eds) Capitalism, for and Against: A Feminist Debate, Cambridge: Cambridge University Press, pp. 3–130.
Dietze P, Knowles ED (2016) Social class and the motivational relevance of other human beings: Evidence from visual attention. Psychological Science 27: 1517–1527.
Fiedler K, Jung J, Wänke M, et al. (2012) On the relations between distinct aspects of psychological distance: An ecological basis of construal-level theory. Journal of Experimental Social Psychology 48(5): 1014–1021.
Fieulaine N, Apostolidis T (2015) Precariousness as a time horizon: How poverty and social insecurity shape individuals’ time perspectives. In: Stolarski M, Fieulaine N, van Beek W (eds) Time Perspective Theory; Review, Research and Application, New York: Springer International Publishing, pp. 213–228.
Granovetter M (2007) The social construction of corruption. In: Nee V, Swedberg R (eds) On Capitalism, Stanford, CA: Stanford University Press, pp. 152–172.
Hägerstrand T (1975) Space, time and human conditions. In: Karlqvist A, Lundquist L, Snickars F (eds) Dynamic Allocation of Urban Space, Farnborough: Saxon House, pp. 3–12.
Hall M, Crowder K, Spring A (2015) Neighborhood foreclosures, racial/ethnic transitions, and residential segregation. American Sociological Review 80(3): 526–549.
Hitlin S, Johnson MK (2015) Reconceptualizing agency within the life course: The power of looking ahead. American Journal of Sociology 120(5): 1429–1472.
Holbrook MB, Hirschman EC (1982) The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of Consumer Research 9(2): 132–140.
Inagami S, Cohen DA, Finch BK (2007) Non-residential neighborhood exposures suppress neighborhood effects on self-rated health. Social Science and Medicine 65(8): 1779–1791.
Inglehart R (1997) Modernization and Postmodernization: Cultural, Economic, and Political Change in 43 Societies Vol. 19, Princeton, NJ: Princeton University Press.
Jin L, Robey D (2008) Bridging social and technical interfaces in organizations: An interpretive analysis of time-space distanciation. Information and Organization 18(3): 177–204.
Jones JM, Brown WT (2005) Any time is Trinidad time! Cultural variations in the value and function of time. In: Strathman A, Joireman J (eds) Understanding Behavior in the Context of Time: Theory, Research, and Application, Mahwah, NJ: Lawrence Erlbaum Associates, pp. 305–323.
Kwan M-P (2013) Beyond space (as we knew it): Toward temporally integrated geographies of segregation, health, and accessibility. Annals of the Association of American Geographers 103(5): 1078–1086.
Little TD, Cunningham WA, Shahar G, et al. (2002) To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling 9(2): 151–173.
McCrea SM, Wieber F, Myers AL (2012) Construal level mind-sets moderate self-and social stereotyping. Journal of Personality and Social Psychology 102(1): 51.
Obschonka M, Schmitt-Rodermund E, Silbereisen RK, et al. (2013) The regional distribution and correlates of an entrepreneurship-prone personality profile in the United States, Germany, and the United Kingdom: A socioecological perspective. Journal of Personality and Social Psychology 105: 104–122.
Palitsky R, Sullivan D, Keefer LA, et al. (2016) The cultural backdrop to prospection: Exploring the relevance of time-space distanciation. Review of General Psychology 20(1): 86–100.
Rentfrow PJ, Mellander C, Florida R (2009) Happy states of America: A state-level analysis of psychological, economic, and social well-being. Journal of Research in Personality 43(6): 1073–1082.
Singelis TM, Triandis HC, Bhawuk DP, et al. (1995) Horizontal and vertical dimensions of individualism and collectivism: A theoretical and measurement refinement. Cross-Cultural Research 29(3): 240–275.
Smith GD, Hart C, Watt G, et al. (1998) Individual social class, area-based deprivation, cardiovascular disease risk factors, and mortality: The Renfrew and Paisley study. Journal of Epidemiology and Community Health 52: 399–405.
Sonnentag S, Pundt A, Albrecht A (2014) Temporal perspectives on job stress. In: Shipp AJ, Fried Y (eds) Time and Work Vol. 1, New York: Psychology Press, pp. 111–140.
Stephan E, Liberman N, Trope Y (2010) Politeness and psychological distance: A construal level perspective. Journal of Personality and Social Psychology 98(2): 268–280.
Stephens NM, Fryberg SA, Markus HR, et al. (2012) Unseen disadvantage: How American universities’ focus on independence undermines the academic performance of first-generation college students. Journal of Personality and Social Psychology 102: 1178–1197.
Sullivan D, Keefer LA, Stewart SA, et al. (2016) Time-space distanciation: An interdisciplinary account of how culture shapes the implicit and explicit psychology of time and space. Journal for the Theory of Social Behaviour 46(4): 450–474.
Sullivan D, Stewart SA, Diefendorf J (2015) Simmel’s time-space theory: Implications for experience of modernization and place. Journal of Environmental Psychology 41: 45–57.
United States Department of Labor Bureau of the Labor Statistics (2013) American time use survey: 2013. Available at: http://www.bls.gov/tus/datafiles_2013.htm (accessed 16 December 2014).
Vallacher RR, Wegner DM (1989) Levels of personal agency: Individual variation in action identification. Journal of Personality and Social Psychology 57(4): 660.
Vandello JA, Cohen D (1999) Patterns of individualism and collectivism across the United States. Journal of Personality and Social Psychology 77: 279–292.
Vuolo M, Staff J, Mortimer JT (2012) Weathering the great recession: Psychological and behavioral trajectories in the transition from school to work. Developmental Psychology 48(6): 1759–1773.
Waller MJ, Conte JM, Gibson CB, et al. (2001) The effect of individual perceptions of deadlines on team performance. The Academy of Management Review 26(4): 586–600.
White LT, Valk R, Dialmy A (2011) What is the meaning of “on time”? The sociocultural nature of punctuality. Journal of Cross-Cultural Psychology 42(3): 482–493.
Yowell CM (2000) Possible selves and future orientation: Exploring hopes and fears of Latino boys and girls. The Journal of Early Adolescence 20(3): 245–280.
Zimbardo PG, Boyd JN (1999) Putting time in perspective: A valid, reliable individual-differences metric. Journal of Personality and Social Psychology 77: 1271–1288.
I can access personal subscriptions, purchases, paired institutional access and free tools such as favourite journals, email alerts and saved searches.
