Article
Time & Society
2021, Vol. 30(3) 273–301
© The Author(s) 2020
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DOI: 10.1177/0961463X20980645
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A pace of life indicator.
Development and
validation of a General
Acceleration Scale
Jens Bergener
Department of Social Transformation and Sustainable Digitalization, Technical University
of Berlin, Institute for Ecological Economy Research, Berlin, Germany
Tilman Santarius
Department of Social Transformation and Sustainable Digitalization, Technical University
of Berlin, Institute for Ecological Economy Research, Einstein Centre Digital Futures,
Berlin, Germany
Abstract
Ever since Georg Simmel (1895) introduced the notion into sociological accounts
of modernity, scholars have tried to empirically test the claim of an increasing
“speed of life”in modern society. The acceleration of speed or pace of life has
been characterized as an “intensification”of our experience of time, a “time
squeeze,”and “hurriedness”in leisure time. However, to date, no comprehensive
instrument, scale, or indicator has been developed that is grounded in solid
theory and serves to empirically measure and compare the pace of life in a
straightforward manner. The purpose of this research is to develop and validate
a scale-based measure that reveals whether individuals pursue a fast or slow pace
of life in a leisure-time context. The result is the fifteen-item General Accel-
eration Scale (GAS), which conceptually rests on the comprehensive theory of
social acceleration by Hartmut Rosa (2013). The scale systematically tests the
pace of life along four temporal strategies of speedup: performing activities faster,
Corresponding author:
Tilman Santarius, Department of Social Transformation and Sustainable Digitalization, Technical
University of Berlin, Institute for Ecological Economy Research, Einstein Centre Digital Futures,
Marchstraβe 23, D-10578 Berlin, Germany.
Email: [email protected]
doing multitasking, replacing time-consuming by time-saving activities, and filling
breaks or waiting times with productive activities. If these temporal strategies
form a consistent pattern, they consequently lead to an increase in the rate,
speed, or relative density of experiences and activities per unit of time and thus to
an increase in the pace of life. Validation of the GAS was completed by a large
sample (N= 1161) as part of a self-report online survey in Germany in 2019. We
examined the convergent and discriminant validity as well as internal consistency
reliability of the scale and conducted a confirmatory factor analysis via maximum
likelihood estimation. Control variables and discriminant measures were included
to access construct validity. Overall, we can validate the GAS as a reliable
measure of time use that can be used as a straightforward pace of life indicator.
Keywords
Pace of life, social acceleration, time use, leisure time, quantitative research
Manifold studies have addressed the “speedup of life”(Garhammer, 2002;
Robinson and Godbey, 1997) in modern society, which has led to a sense of
“intensification”of our experience of time, feelings of “busyness”(Hassan,
2009), a “time squeeze”(Southerton and Tomlinson, 2005), and “hurriedness”
in leisure time (Linder, 1970;Schor, 1991). While these studies mark important
steps for the understanding of the concept, some scholars have addressed the need
for more refined ways of theorizing the concept of “social acceleration”(Hassan,
2009) and the demand to connect this part of social theory with detailed empirical
studies (Wajcman 2008: 59). This study intends to make a contribution to both of
these issues.
Rosa’s (2003,2013,2016) work serves as a complex and descriptive con-
tribution to the social acceleration debate. Building on manifold previous em-
pirical and theoretical studies, he suggests that societies in late modernity are
characterized by an increased frequency of activities per unit of time and hence by
an acceleration of the pace of life (Rosa, 2013). Moreover, Rosa’s theory of social
acceleration offers an empirically verifiable approach toward the speeding up of
the pace of life and a solid conceptual foundation.
Rosa specifically describes four temporal strategies that lead to an acceleration
of the pace of life (2013: 128–129). Rosa’s“four ways”include (i) speeding up
the pace with which activities are performed. For example, individuals can
shorten the duration of eating by chewing or swallowing faster, or likewise they
can increase the speed of typing a given text on a keyboard. A second strategy to
accelerate the pace of life is to (ii) perform several activities simultaneously, that
is, “multitasking (MT)”. For example, individuals can stream videos and do
household work at the same time. Third, a faster pace of life can be achieved by
274 Time & Society 30(3)
(iii) reducing breaks and downtime between activities. For example, waiting times
(WTs) at the doctor’s or a ride on public transportation can be used to check
a smartphone, read a book, or engage in other productive activities. Finally, (iv)
time-consuming activities can be replaced by time-saving activities, for example,
taking a plane instead of a train or car to travel a long distance.
Yet while Rosa’s work is widely cited, it has been recently criticized from
various epistemic and methodological positions (Gershuny and Sullivan, 2017;
Sharma, 2014;Vostal, 2014;Wajcman, 2015). Moreover, qualitative and theo-
retical reexaminations of Rosa’s theory have been produced (see, e.g. Hsu and
Elliott, 2014;Torres, 2016;Vostal, 2014,2019). Even so, the empirical evaluation
of Rosa’s“four ways”to accelerate the pace of life has not yet been addressed;
only a few studies have tried to empirically verify certain other aspects of Rosa’s
theory (Lorenz-Spreen et al., 2019;Sch¨
oneck, 2018;Ulferts et al., 2013).
In the empirical literature, various measurements for the analysis of temporal
dimensions are available, some of which are related to speed. However, only a
few studies have developed (quantitative) indicators to account for aspects of an
increasing speed of social actions. Moreover, no reliable instrument, scale, or
indicator exists to empirically measure the actual pace of life in a straightforward
manner. In this study, we therefore introduce a new scale-based measure–the
General Acceleration Scale (GAS). We deliberately operationalize Rosa’s“four
ways”to represent strategies to accelerate people’s pace of life. We validate the
GAS and critically discuss its potentials and pitfalls vis-`
a-vis other existing scales
that aim to account for an acceleration of the pace of life. As a contribution to time
use theories, the GAS serves to empirically test and validate a significant part of
Rosa’s theory. As for empirical research on time use, it provides a new tool for
measuring, or even a straightforward indicator of, the pace of life.
This study is structured as follows: in the second section of the study, we
provide a brief review of the literature with a particular focus on empirical re-
search on the pace of life. In the third section, we present the conceptual
framework from which we develop the GAS, and we define key terms used
throughout our analysis. The fourth section outlines the operationalization of our
methodology and defines all measures applied. The remaining sections present
our empirical results and draw conclusions from our analysis, including a dis-
cussion on limitations and future research needs.
State of literature on the acceleration of the pace of life
One of the earliest attempts to measure elements of speed in social action was the
approach by Jahoda et al. (1933). As part of their research in the small town of
Marienthal in 1930s Austria, they measured people’s speed of walking and re-
ported a markedly slow walking pace and frequent stopping in their tracks for
unemployed men on the streets. Although offering foundational research for the
Bergener and Santarius 275
social scientific understandings of unemployment, the validity of the assumed
causality of unemployment and walking speed has been criticized. Since then,
measuring individual time-related attitudes or behavior toward time has been
a long interest in various disciplines of social science research up to this day.
Empirically grounded and solid approaches have focused particularly on
work-life contexts and individual time preferences. Questionnaires for in-
vestigating differences of time perception on the individual level include the
Temporal Personality Inventory (Francis-Smythe and Robertson, 1999). This 43-
item scale assesses the behaviors, cognitions, and emotions of five dimensions
of temporal personality: awareness of free time, punctuality, planning, poly-
chronicity, and impatience. Francis-Smythe (2006) proposes impatience as the
preference for the speed at which to complete a task. High scorers on this factor
are associated with controlling the speed of their interactions with other people.
At the individual level, speed has been associated with personality traits like
impulsivity (Ortet et al., 2002), sensation-seeking and risk-taking (Westaby and
Lowe, 2005), perpetual activation (Wright et al., 1992), and time urgency (Conte
et al., 1998). Time urgency characterizes respondents concerned with the passage
of time and the way in which they can most efficiently fill that time with pro-
ductive activity (Landy et al., 1991). For example, the “general hurry”time
urgency dimension is defined as the extent to which individuals rush when
performing activities. Time urgency dimensions were also related to Type A
behavior pattern (Conte et al., 2001) and health problems (Menon et al., 1996).
Time urgency constructs particularly allow valuable insights into the extent to
which individuals feel pressed for time when performing activities. However,
they lack a body of reliability and validity data relating them to the pace of life,
while they have also been criticized for their theoretical propositions (Conte,
2007). Constructs of perpetual activation are used to analyze whether individuals
generally and intrinsically feel pressured to perform activities fast rather than
slow. So far, they have also mainly been related to health issues too (Wright et al.,
1992). The F-A-S-T Time Orientation Test by Alreck and Settle (2002) measures
the activity level of respondents and was linked to consumer behavior as an
attempt to “buy time.”The just mentioned studies represent extremely nuanced
and relevant directions of personality trait–driven research. However, their results
have been questioned over whether their linkage to the preferences for speed
affects the actual performance of tasks (Bluedorn and Jaussi, 2007: 194).
Usunier and Valette-Florence (1991,1994,2007) developed a scale to capture
individual time orientation (the Time Styles Scale). The item base was drawn from
interdisciplinary research on time and existing scales, like the F-A-S-T Time
Orientation Test (Alreck and Settle, 2002) and the Time Structure Questionnaire
of Bond and Feather (1988), and was elicited by generating items from interviews
on time perception. The scale consists of four main dimensions with two sub-
dimensions, including: the linearity and economic use of time (the preference for
276 Time & Society 30(3)
organized/economic or nonorganized time), temporal orientations (projections
toward the past and the future), obedience or compliance to time (time sub-
missiveness and the perceived usefulness of time), and motivational aspects of
time (tenacity and preference for a quick return). An economic value of time has
been linked to people’s behavior toward WTs and decision-making (Leclerc et al.,
1995). In this framework, “waiting”would be coded as a loss of time if the wait
exceeded some expected waiting time. While these scale dimensions offer im-
portant insight into various individual preferences toward time, none addressed
actual behavior toward speeding up tasks. Gevers et al. (2013) introduced a scale-
based instrument to capture how individuals pace themselves before a deadline.
Gevers et al. suggest three different pacing styles: deadline (completing work in
a short time period just before the due date), steady (spreading task activities
evenly over time), and U-shaped action styles (investing most of the effort at the
start and finish of a task, with a break in between).
Few studies have developed an (quantitative) indicator to account for the
actual pace of life. Levine (1988) studied the question in different cities across
countries using the following three indicators: walking speed (measuring the
average walking speed to cover one hundred feet or about 30 m), clock accuracy,
and postal speed (measuring the time it takes postal workers to complete
a standard request for stamps). Using the same three components, Levine and
Norenzayan compared the speed of life in 31 countries (Levine, 1997;Levine and
Norenzayan, 1999). Levine and Norenzayan’s research is a highly valuable
approach to explore the topic as it highlights the interconnectedness of the pace of
life with socio-cultural factors in defining a comparative “geography of time”
related to different places and cultures. Nevertheless, the selection of the in-
dicator’s three components lacks grounding in theory and appears somewhat
arbitrary.
Several researchers have identified pace or speed as a temporal dimension in
the relationship of work and time in organizational culture including the ex-
amination of the hurriedness of work groups and the impact of pacing congruence
on how fast things get done (the Hurriedness Scale,Jansen and Kristof-Brown,
2005). However, many approaches did not offer a validation of the actual tempo
aspect. Another prominent example, Schriber and Gutek (1987: 643) defined pace
as “the rate at which activities can be accomplished (i.e. the speed of activity or
the number of activities that can be done within a given interval).”They de-
veloped an instrument to measure work pace in a questionnaire with five Likert-
style items (Schriber and Gutek, 1987: 646). The items concerned with the speed
or pace of work accounted for 3.8% of the variance in their Time Dimension Scale
and showed marginal reliability (Cronbach’s alpha of 0.60). Although Schriber
and Gutek’s instrument enables explicit insights into the speed of work, its use is
limited to the organizational, strategic, or industry level and has been criticized as
rather conceptual (Conte, 2007).
Bergener and Santarius 277
Multitasking and polychronicity have received a considerable amount of
attention in the sociology of work. According to many studies, MT has greatly
increased over the last quarter of the 20th century (Bianchi et al., 2006;Bittman
and Wajcman, 2000;Mattingly and Bianchi, 2003) and is viewed as a fruitful
indicator for the acceleration of the pace of life (Wajcman, 2015: 80). Onken
(1999) used items from Schriber and Gutek’s (1987) scales to measure the value
placed on doing things quickly in an organization. This study found a substantial
correlation between polychronicity and speed, r = 0.44, that was significant at the
0.05 level. Bluedorn and Martin (2008) also used Schriber and Gutek’sfive-item
work pace scale and also found a statistically significant positive correlation (r =
0.33) between an entrepreneurs’levels of polychronicity and their preferences for
working fast. Schriber and Gutek’s nine-item schedules and deadlines scale and
their four-item punctuality scale were also shown to be related to an emphasis on
the pace of work. Notably, they discovered that Schriber and Gutek’s work pace
scale appeared to measure two variables–the perceived temporal flexibility and
the preference for working fast–rather than just the pace of work (Bluedorn and
Martin, 2008: 8). Given the established stature of Schriber and Gutek’s scale,
Bluedorn and Martin’s results provided motivation for additional scale de-
velopment as there were only two items measuring the preference for working
fast. Moreover, the alpha coefficients reported in their study were lower than most
researchers would prefer (Bluedorn and Martin, 2008: 14).
Time budget studies have been another prominent and useful way to em-
pirically reflect the allocation of time use and time shortages. Several time diary
studies were able to show an ongoing “speedup of life”(Garhammer, 1999,2002;
Gershuny, 2005;Robinson and Godbey, 1999) or presented empirical evidence of
the density of leisure denoting a fast pace of life (Katz-Gerro and Sullivan, 2007,
2010;Sullivan, 2008). Yet, such studies are concerned with how much time
people report on specific activities rather than general behavioral strategies to-
ward time. Nevertheless, in their time budget study, Robinson and Godbey (1999)
as well as Garhammer (2002) also introduced scale-type instruments to measure
subjective aspects of everyday speed. Robinson and Godbey’sTime Crunch Scale
(1999) examined broad aspects of time stress and work-life balance, while
Garhammer’sTime Pressure Index (2002) aimed to measure hurriedness, time
pressure, and scheduling difficulties at the same time and thus was criticized to
mix possible causes and consequences. Other scale instruments to measure
subjective feelings of time stress are the Time Pressure Scale (Roxburgh, 2004)
and measures of time affluence (see e.g. Kasser and Sheldon, 2009).
To sum up our brief literature review: the studies listed in this overview mark
important approaches for the analysis of various temporal dimensions, some of
which are related to speed. However, most studies only analyze certain aspects of
an accelerating life and do not intend to be generalized for the pace of life as such.
278 Time & Society 30(3)
Moreover, many studies analyze time use at work, and findings should not be
transferred to the pace of life outside of organizational culture, for example, to
work done from home or the temporal structure of leisure time. The only relevant
approach that deliberately claims to present a “pace of life indicator”(Levine,
1997;Levine and Norenzayan, 1999) has theoretical shortcomings, while its data
are now more than two decades old. Hence, to the present date, while existing
accounts deserve systematic credit, no instrument, scale, or indicator exists to
empirically measure and compare the pace of life.
Definition of terms and conceptual framework
In the social science literature, the term “pace”refers to the frequency of a specific
domain of activities in a social unit of time (Lauer, 1981). Several different terms
have been applied to this construct, such as speed (Bluedorn, 2002;Bluedorn and
Jaussi, 2007;Onken, 1999), tempo (Harvey and Novicevic, 2001), or time-
deepening (Garhammer, 1999;Robinson and Godbey, 1999). As a conceptual
framework for our empirical investigation, we follow Rosa’s understanding of the
pace of life and define it with Levine and Norenzayan (1999: 178) as the “rate,
speed, and relative rapidity or density of experiences (…) and activities”(making
reference to Amato, 1983;Lauer, 1981 and; Werner et al., 1985). Acceleration is
concerned with everyday practices that function “as a temporal lathe with which
to modify the contour of one’s personal experience (of time)”and thus, include
strategies that speed up the duration and frequency of activities (Flaherty,
2011:15).
As discussed above, Rosa identifies four manifestations of the acceleration of
the pace of modern life: “the speeding up of individual actions, the elimination of
breaks, the temporal overlapping of activities (multitasking), and the replacement
of temporally costly with time-saving activities”(2013: 128–129). Rosa suggests
that if such temporal strategies form a consistent pattern, they consequently lead
to an increase in the density of episodes of experience or actions per unit of time
(be it a day, a month, a year, or a lifetime) and thus lead to an increase in the pace
of life (Rosa, 2016: 35). We operationalize these four temporal strategies, in-
cluding the following definitions.
Performing activities faster (FA) means that strategies are used to shorten the
duration of activities by speeding up their pace. This conception is also found
within an accelerated form of monochronic behavior established by Bluedorn and
Jaussi (2007), who distinguish a one-thing-at-a-time approach performed at a
slow, measured pace from an accelerated form involving a laser beam-like focus
on a single task performed at a fast pace.
We define “break and downtime,”transfer times, or WTs as time periods in
which no activities are performed actively. As Flaherty has interpreted it, when
Bergener and Santarius 279
“we”(individuals) “try to accelerate the lived duration of travel and delay”(2011:
29), we are “filling the otherwise ‘empty’intervals that stretch between what we
have and what we want”(2011: 26).
According to Kenyon, Rosa, and many other authors, we define MT as “the
simultaneous conduct of two or more activities during a given time period”
(Kenyon, 2008: 286). For Rosa, MT still enables faster completion of activities in
their entirety, although it may de facto reduce the pace of individual activities.
Whoever performs several activities at the same time will probably be relatively
time inefficient concerning each activity, but may still finish faster than in the case
of a sequential completion of the corresponding actions (Rosa, 2013). Moreover,
Bluedorn and Jaussi (2007) conceptually associate MT behavior with an ac-
celerated speed of tasks because one wants to get more done in the same amount
of time.
When investigating whether a replacement of time-intensive activities through
time-saving activities (RE) takes place, we stipulate that such a replacement
requires both activities to serve similar ends. Hence, this strategy reflects the
customization of activities/experiences while replacing them with similar but
faster alternatives.
For reasons of conceptual clarity, context is an important issue worth
attending to. First, with the GAS as an indicator of the pace of life, we are not
aiming at measuring beliefs, norms, or preferences on a cultural or orga-
nizational level as they do not necessarily address individual behavior
(Poposki and Oswald, 2010). In an organizational and work context, even if
individuals prefer to, that is, perform only one task at a time, they may be
forced to behave differently, that is, perform multiple tasks by the re-
quirements of the job. Thus, while much of the research on time use focuses
on the impact of working practices, the empirical contribution of the GAS
lies in its focus on individual temporal strategies and behavior in a free or
leisure time context. Indeed, “what is a more fundamental process strategy
than the choice of the pattern for one’s activities?”(Bluedorn, 2002: 48). In
line with many other authors, we define leisure time or free time “as the
remaining time after time spent in market and nonmarket work and meeting
physiological needs (…) is deducted”(Wajcman, 2015: 194). Note that
temporal strategies attached to free time may not be as “free”as we imagine
them to be. Regarding Shir-Wise (2019), free time is understood here not as
a complete empty after-work space to be filled by actors as they wish, but as
the nonmarket or nonjob-related time that may still be shaped by cultural and
social forces. Second, the distinction between preference and behavior is an
important one as shown in discussions about definitions of polychronicity
and actual MT (Poposki and Oswald, 2010). Including preference in the
definition of any of the four ways of acceleration might be problematic as we
aim to measure actual behavior.
280 Time & Society 30(3)
Operationalization and methods
Measures
The General Acceleration Scale. To measure the four ways of accelerating the pace
of life, we have developed a GAS asking how individuals use their time in
a typical hour of leisure time. We develop this scale because scales are
a manifestation of latent constructs; they measure behaviors, attitudes, or
hypothetical scenarios that are expected to exist as a result of theoretical
reasoning. The GAS that we present in this study measures to what extent
individuals pursue strategies to accelerate their pace of life, and hence, the scale
as an aggregate indicates whether individuals live a comparatively fast or slow
pace of life.
As shown in Table 1, the GAS consists of 16 items, with four subscales. These
subscales each consist of groups of four items, asking whether individuals
generally tend to FA, engage in MT, replace time-intensive activities through
time-saving activities (RE), and fill breaks or WTs with productive activities.
Table 2 shows the list of items we operationalized for the final study. Note that all
items were originally developed (and empirically tested) in German. They have
been translated for information in this article, but the English translation has not
been empirically verified.
For the GAS’subscale on MT, we used items from existing scales. Three
items (e.g. “… I do several things at a time.”) were taken from K¨
onig et al.
(2010), who investigated time use at the workplace; hence, we slightly adapted
the formulation to make them applicable to leisure time. The items regarding
RE, FA, and WT were developed by ourselves. All items of the GAS were
measured on a five-point Likert scale, ranging from “never”to “always”and
contained the item stem “during a typical hour of my leisure time ….”All
items were designed to address temporal strategies in a general manner to
reflect a variety of everyday activities and tasks which are performed to
manipulate the duration of activities in leisure time (Flaherty, 2011). The five-
point response scale was designed to indicate how often the temporal strat-
egies were used, thereby reflecting the respondents’control over the frequency
of the strategies. Additionally, participants could mark the option “does not
apply.”
The responses on all subdimensions reflect a continuum with higher scores
indicating an intensified performance of the respective temporal strategies. Again,
Rosa explicitly theorized that if these strategies form a consistent pattern, they
will lead to a compression of activities and an increase in the pace of life. Thus,
scores of the subdimensions were summed, with the higher scores representing
a faster pace of life. At the other end of the continuum, we find participants who
do not engage much in speeding up their pace of life.
Bergener and Santarius 281
Table 1. Sociodemographic characteristics of the study sample (n= 1161).
N%
Gender
Male 581 50
Female 580 50
Age
18 to 39 years 315 27.1
40 to 59 years 456 39.3
60 to 89 years 390 33.6
Monthly net income (Euro)
0 to 999 241 20.8
1000 to 1999 365 31.4
2000 to 2999 307 26.5
3000 to 3999 141 12.1
4000 to 4999 67 5.8
5000 + 40 3.4
Work time per week (h)
1to10 81 7
10 to 20 86 7.4
20 to 30 100 8.6
30 to 40 336 28.9
40 to 50 187 16.1
50+ 28 2.4
No working time 291 29.4
Missing 2 0.2
Household size (people)
1 279 24
2 545 46.9
3 185 15.9
4 109 9.4
5 30 2.6
6 5 0.4
6 + 6 0.5
Other/number of people varies 2 0.2
Number of children
1 134 11.5
2 64 5.5
3 14 1.2
4 1 0.1
5 1 0.1
No children 947 81.6
(continued)
282 Time & Society 30(3)
Criterion variables. The literature review has revealed speed as a multidimensional
construct. Not only it is related to MT or fast monochronic behavior such as quick
decision-making and speedy performance of tasks but also to the use of breaks in
work time. In order to establish criterion validity, we included scales that measure
related/comparable aspects to control for the four subscales of the GAS (i.e. each
of Rosa’s four ways of accelerating the pace of life).
To validate our subscale on MT, we included a polychronicity scale that has
been used to identify an individual’spreference for MT (see K¨
onig et al., 2010).
The five items of that German polychronicity scale match the polychronicity–
monochronicity tendency scale (PMTS) in English, which has been developed
and validated by Lindquist and Kaufman-Scarborough (2007).
Economic use of time represents a dimension of time perception that is in-
dependent of polychronicity (Usunier and Valette-Florence, 1991), while it is
linked to people’s behavior during WTs (Leclerc et al., 1995). Following Usunier
and Valette-Florence (2007), we included a measure asking participants to rate
how strongly they agreed with statements like “time is money”or “one should
always make the most out of the time one has”on a five-point Likert scale. We
suspect that individuals for whom time is optimizable will attach great importance
to filling WTs with further activities.
To control for our subscale on how fast activities are performed (FA), we
included a construct measuring “perpetual activation,”taken from the Jenkins
Activity Survey (Jenkins et al., 1979). Constructs testing for perpetual activation
are used to analyze whether individuals generally and intrinsically feel pressured
to perform activities rather fast than slow (for more information see Wright et al.,
1992).
To empirically test whether the GAS was sufficiently discriminatory of other
time measures, we included the General Procrastination Scale adapted from Lay
Table 1. Continued
N%
Time spent doing care work per week (h)
1 to 10 145 12.5
10 to 20 44 3.8
20 to 30 21 1.8
30+ 71 6.1
0/not applicable 880 75.8
Educational attainment
Still in school 6 0.5
Elementary school 196 16.9
Secondary school 429 37
Graduation (university entrance qualification) 530 45.7
Bergener and Santarius 283
Table 2. Means, standard deviations, and other indices of items for the General Acceleration Scale.
Item Mean Standard
deviation
Variance Skewness Kurtosis Valid
cases
Missing
values
Dimension Code
…I do several things at a time 2.93 0.989 0.979 0.042 0.585 1149 12
Multitasking (MT)
MT_1
…I perform more than one activity 2.93 0.985 0.971 0.080 0.543 1145 16 MT_2
…I do multitasking 2.72 1.086 1.180 0.003 0.805 1134 27 MT_3
…I handle several tasks simultaneously 2.90 1.011 1.022 0.073 0.585 1153 8 MT_4
…I decide to do time-saving activities rather than time-
consuming activities
3.02 0.888 0.789 0.160 0.005 1134 27 Replacing time-
consuming by
time-saving
activities
(REs)
RE_1
…I replace time-intensive tasks to save time 2.90 0.898 0.807 0.169 0.046 1119 42 RE_2
... I try to replace time-consuming activities with other
activities that save time
2.91 0.901 0.812 0.177 0.162 1127 34 RE_3
... I prefer to choose activities that do not last long rather
than time-consuming activities
3.09 0.869 0.756 0.268 0.021 1135 26 RE_4
…I do things very quickly 3.17 0.942 0.887 0.219 0.328 1148 13 Performing
activities
faster (FA)
FA_1
…I perform activities most speedy 3.48 0.890 0.792 0.441 0.156 1152 9 FA_2
…I bring things to an end as quickly as possible 3.48 0.865 0.749 0.421 0.166 1148 13 FA_3
…I get things done as fast as possible 3.41 0.907 0.823 0.365 0.032 1152 9 FA_4
... I use waiting times for other activities 3.01 0.974 0.949 0.180 0.238 1142 19 Filling break or
waiting times
with activities
(WTs)
WT_1
…I use downtime and breaks for additional activities 3.00 0.939 0.881 0.238 0.309 1142 19 WT_2
…I try to fill breaks with as productive occupations as
possible
3.04 1.004 1.009 0.184 0.443 1144 17 WT_3
…I make use of transfer times to get things done 2.79 0.998 0.997 0.025 0.420 1114 47 WT_4
Note: respondents who choose “doesn’t apply”were coded as missing values.
284 Time & Society 30(3)
(1986), which has been shortened and validated for scientific use in German by
Klingsieck and Fries (2012)
Participants and procedure. To empirically test our scales and hypotheses, we
conducted an online survey. For the iterative development of the GAS, we ran
four pretests (n= 52, n= 114, n= 33, and n= 115) over the course of 18 months
in 2018 and 2019. A total of 32 different items were tested. As suggested, for
example, by Ziegler (2014), items with low factor loadings were iteratively
excluded. In addition, we evaluated each item to maintain the content of the
construct. Thinking aloud was used to improve scale items and to eliminate
ambiguity. The pretests particularly served the purpose of developing simple,
specific, concise, and easy to understand items. Following suggestions by
Podsakoff et al. (2012), minimum steps were taken to reduce the effects of
proximity and bias within the survey. Measures of predictor and criterion var-
iables were separated within the survey. Common scale properties were never
shared within one part of the survey. Answers were sorted in a random order for
every participant wherever possible. Every point on response scales was labeled.
Recoded items were tested but excluded from the final survey due to high error
covariance in all pretests. Balancing positive and negative items did not work as
negatively worded items and reversing the wording of some items may have
altered their content.
The final survey (n= 1393) was conducted together with a panel organization
(https://norstat.de). We introduced participants with a short cover story for their
motivation to fill out the questionnaire with great care and offered the option to
contact us for any further questions. Participation was remunerated. The panel
institute had no relationship to this study other than recruiting participants based
on screen out criteria to ensure the representativeness of the German population.
The self-report questionnaire could be accessed from any device with an internet
connection. We followed a cross-sectional research design where participants
completed the survey in their everyday environment. Unipark was used as an
online survey tool ensuring standards of data security. The sample of the final
survey was refined to ensure minimum standards of data quality. As proposed by
Meade and Craig (2012), two control questions were applied, due to which n=72
participants were excluded (see also Berinsky et al., 2012). Furthermore, we
controlled for response time as a link between very quick responses, and low data
quality has been supported with evidence (Callegaro et al., 2009;Malhotra, 2008;
Rossmann, 2010). Regarding Huang et al. (2012), we excluded n= 65 participants
answering faster than 2 s per item. Following Greszki et al. (2014), we excluded
another n= 84 participants who filled out the entire questionnaire in less than half
of the median time of all participants. With an additional of n= 11 participants not
delivering on the quotation questions, we arrived at a final sample of n= 1161. As
Table 1 shows more details, the age of participants ranged between 18–89 years
Bergener and Santarius 285
(M = 49.9; SD = 15.7 years). Half of the participants considered themselves men
and women, respectively. The socio-economic status of all participants, screened
by income as well as achieved level of education, represents a normal distribution.
Statistical analysis
Statistical analyses was conducted using IBM SPSS statistics version 25 and
AMOS version 26. Confirmatory factor analysis was performed in AMOS with
maximum likelihood (ML) estimation. Bivariate correlations were computed
using Pearson’s correlation procedure. Tests for mean differences were cal-
culated through t-test procedures. Before conducting statistical tests, the data
was tested for univariate and multivariate normality (Child, 2006) and screened
for univariate and multivariate outliers (Field, 2009). The assumption of linear
relationship was determined (Gorsuch, 1983). The absence of multicollinearity
was checked by looking at the variance inflation factor and tolerance (O’Brien,
2007).
Missing data
To reduce noise in our dataset, we urged participants to record their scores only if
they were certain. We explicitly offered the option that items did not apply to
them. Due to this constraint, these answers were missing by design and treated as
incomplete questionnaire items. Incomplete values made up 1.4% of the overall
values in our dataset and were found in 9.9% of the overall cases (at least one
value missing in any single item). The items of the GAS showed nonresponse
rates between 0.7 and 4%. We checked if incomplete values for missingness
patterns and these were related to observed values on other variables in our
dataset. A t-test revealed no significant mean difference between the complete and
incomplete datasets when compared to their mean score for various socio-
demographic features (age, income, work time per week, household size, number
of children, and time spent doing care work). Incomplete responses in our dataset
are thus assumed to be missing completely at random. We decided to deal with
incomplete cases through pairwise and listwise deletions in SPSS and AMOS,
respectively. Listwise and pairwise deletions have the potential to introduce bias
and reduce explanatory power. Considering our sample size and our un-
derstanding of the incomplete cases, this decision may not be regarded as a threat
to validity.
Results
Table 2 Presents all items of the GAS as well as among others, the means, and
standard deviations.
286 Time & Society 30(3)
Confirmatory factor analysis (CFA)
Confirmatory factor analysis (CFA) is a particularly useful analytic tool for
developing and refining measurement instruments, assessing their validity, and
confirmation of theories. The approach also allows for testing the relative fitof
competing factor models. The CFA was conducted using AMOS with (ML
extraction to estimate the model (Jackson et al., 2009). The proposed four-factor
structure of the GAS–that is, Hartmut Rosa’s four strategies to accelerate the pace
of life–was evaluated with multiple indices (Kline, 2008), including χ
2
statistics
(chi-square with degrees of freedom), the root mean square error of approxi-
mation (RMSEA), the Comparative Fit Index (CFI), the Tucker–Lewis Index
(TLI), the standardized root mean square residual (SRMR), and the p-values of
close fit (PClose). Values of CFI and TLI approaching 0.95 and values no higher
than 0.08 for RMSEA and SRMR indicate a good model fit(Hu and Bentler,
1999). If values of pare greater than 0.05 (i.e. not statistically significant), then it
is concluded that the fit of the model is close (PClose). Moreover, we used the
Akaike information criterion (AIC) to compare different models as suggested by
Hu and Bentler (1999). After testing the hypothesized four-factor model, we also
tested a four-factor model with a latent second-order factor. Moreover, we tested
three-factor, two-factor, and one-factor models, collapsing the GAS and its
subscales in all combinations. The χ
2
provided a statistical basis for comparing the
relative fit of these models (Bollen, 1989). Finally, internal consistency reli-
abilities were calculated.
Model fit statistics
In a nutshell: the fit indices supported the four-factor structure of the GAS,
meeting the cutoff criteria. See Table 3 for a summary of model fit indices for
different four-factor models of the GAS. The various collapsed factor models
provided a significantly worse fit to the data than the hypothesized four-factor
model indicated by significant differences in the χ
2
statistics. Moreover, the re-
liability coefficients indicated that each of the four dimensions of the GAS
possessed adequate internal consistency (see Table 4).
We ran a CFA model with covariances between the latent factors of the four
subdimensions (see Figure 1, Model A) and another four-factor model with the
GAS as a latent second-order factor (see Figure 2,ModelB).Themodels
showed a similarly good fit. The 2nd model is a bit weaker considering the
AIC.
The four-factor model with all 16 items revealed strong path loadings of the
GAS with the latent factors FA, r = 0.65, and RE, r = 0.65, and the highest to MT,
r = 0.80, and filling waiting times with activities (WTs), r = 0.88. The following
measures were conducted to test for validity and reliability: composite reliability
Bergener and Santarius 287
(CR), average variance extracted (AVE), maximum shared variance (MSV), and
average shared variance (ASV). The analysis showed concerns with convergent
validity for the factor RE: the AVE was 0.457 and somewhat less than the
threshold of 0.50 (Henseler et al., 2015). There were no other warnings for the
HTMT analysis (the heterotrait–monotrait ratio of correlations) and discriminant
validity. Note that Malhotra and Dash (2011) argued that AVE is often too strict
and reliability can be established through CR alone. Although showing a good fit
for the model, the results suggested removing the item RE_4 (see Table 2)to
improve the AVE.
As a next step, both four-factor models were replicated to be tested in
confirmatory factor analysis with 15 items (RE_4 was excluded, see Figures 3
and 4for Model C and D). The model fit statistics improved for both models.
Using the AIC, Model D proved to be empirically the best model in this dataset.
Table 3. Summary of quality of fit for different four-factor models of the General
Acceleration Scale.
Model χ
2
df χ
2
/df CFI TLI SRMR RMSEA PClose AIC
A 16 items 286.7 98 2.93 0.976 0.971 0.041 0.043 0.977 362.7
B 16 items +2nd order
factor
307.9 100 3.08 0.974 0.969 0.045 0.045 0.938 379.9
C 15 items 190.6 84 2.27 0.986 0.982 0.032 0.035 1.00 292.6
D 15 items +2nd order
factor
206.9 86 2.41 0.984 0.981 0.036 0.037 1.00 274.9
Note: The p-value for all models was <0.001, numbers have been rounded. CFI = Comparative Fit
Infex, TLI = Tucker–Lewis Index, SRMR = standardized root mean square residual, RMSEA = root
mean square error approximation, AIC = Akaike information criterion.
Table 4. Results of the reliability and validity analysis for Model C (see Figure 3)
HTMT analysis with correlation matrix
CR AVE MSV MaxR(H) WT FA RE MT
WT 0.812 0.519 0.516 0.816 0.721
FA 0.851 0.588 0.288 0.855 0.537 0.767
RE 0.762 0.518 0.344 0.774 0.587 0.511 0.720
MT 0.903 0.700 0.516 0.910 0.719 0.510 0.509 0.837
Note: MT = multitasking, RE = replacing time-consuming by time-saving activities, FA = performing
activities faster, WT = filling break or waiting time with activities, CR = composite reliability, AVE =
average variance extracted, MSV = maximum shared variance, and MaxR(H) = maximum reliability.
Significance of correlations: =p< 0.001; the square root of the AVE for every subdimension is
highlighted in bold within the correlation matrix.
288 Time & Society 30(3)
The results are consistent with suggestions to keep preference-related items
(RE_4) out of the scale as the scope was to measure actual behavior (Poposki
and Oswald, 2010).
Convergent validity, discriminant validity, and reliability
Table 4 shows the measures that were conducted to test for validity and reliability
of Model C: CR, AVE, MSV, and MaxR(H).
Figure 1. Model A with standardized estimates.
Bergener and Santarius 289
We examined discriminant validity within the four-factor GAS by calculating
the HTMTamong the four factors to indicate discriminant validity (see Table 4).
Results showed that the HTMT ratios between each of the four factors in the
GAS were less than 0.85, providing initial evidence of discriminant validity
within the GAS (Henseler et al., 2015). The second-order construct (the GAS)
with the four first-order subscales (Model D, see Figure 4) also showed good
results for CR (0.836), AVE (0.565), and MaxR(H) (0.869). There were no
warnings for the HTMT analysis either. We calculated Cronbach’salphaforthe
Figure 2. Model B with standardized estimates.
290 Time & Society 30(3)
15 items of the GAS as a composite scale in SPSS and found an excellent score
(α= 0.902).
Evaluating measurement invariance
Before creating path analysis, configural and metric invariance was tested.
Configural invariance was established testing for homogeneity of variance which
showed nonsignificant differences across different groups (time to complete the
survey and date of survey). Metric invariance was also good as evidenced by an
Figure 3. Model C with standardized estimates.
Bergener and Santarius 291
excellent model fit and a nonsignificant chi-square difference test between unconstrained
and fully constrained models where the regression weights were constrained. The test for
metric invariance showed that the indicators were related to the latent variables in the
same way across the different groups tested. Scalar invariance was established observing
nonsignificant differences in the measurement intercepts and structural covariance for
these groups. To test for common method bias (CMB), we used Harman’s single factor
score, in which all items were loaded into one common factor. The total variance for
a single factor was around 39%, suggesting that CMB did not affect our measurement.
Figure 4. Model D with standardized estimates.
292 Time & Society 30(3)
Correlates
Criterion-related validity. Table 5 shows the Pearson correlations between the
subdimensions of the GAS and other time-related measures. We found a high
positive bivariate correlation between MT and polychronicity, r (1121) = 0.680,
p< 0.01. MT represents the actual performance of everyday MT strategies,
whereas polychronicity measured the preference of MT. Hence, the test for
concurrent validity showed a satisfying result for the subdimension MT. Note that
polychronicity cannot be used as a “gold standard”for criterion validity but is
viewed as an appropriate criterion to validate this specific subscale. The test also
supported the hypotheses that the other three subscales (FA, RE, and WT) are
discriminant from measures of polychronicity. The measure of perpetual acti-
vation showed not only a small significant influence on FA but also on MT and
WT. As a marker of subjective discomfort with dysfunctional time delay, the
General Procrastination Scale showed a significant small negative bivariate
correlation with the GAS, r (1045) = 0.88, p< 0.01. Hence, it can also be viewed
as a discriminant measure of time use regarding the GAS.
Finally, and most interestingly, the economic use of time showed a moderate
correlation with all subscales of the GAS. Moreover, the economic use of time
correlated positively with the GAS as a composite variable, (r (953) = 0.388, p<
0.001). We ran a multiple linear regression (MLR) to explore if the economic use
of time serves as a predictor of the GAS. The results of the MLR indicated that the
model explained 14.9% of the variance and that the model was a significant
predictor, F (1, 952) = 168.197, p< 0.001, R
2
= 0.388, and R
2
adjusted = 0.149.
Table 5. Pearson correlations between the composite factor scores of the
subdimensions, the General Acceleration Scale as a composite score, and other
time-related measures.
Polychronicity (n) Economic use of time (n) Perpetual activation (n) Procrastination (n)
GAS 0.552 0.388 0.188 0.082
(1039) (953) (1046) (1046)
MT 0.680 0.272 0.181 0.004
(1121) (1018) (1128) (1128)
RE 0.236 0.296 0.032 0.081
(1084) (987) (1091) (1091)
FA 0.323 0.345 0.176 0.265
(1132) (1027) (1141) (1141)
WT 0.464 0.340 0.168 0.076
(1090) (995) (1098) (1098)
Note: GAS = General Acceleration Scale, MT = multitasking, RE = replacing time-consuming by time-
saving activities, FA = performing activities faster, and WT = filling break or waiting time with activities.
Significance of correlations =p< 0.01(2-tailed), =p< 0.05(2-tailed).
Bergener and Santarius 293
On the whole, the results of the correlates applied show a limitation of ex-
amining concurrent validity of the newly developed four subscales of the GAS.
Appropriate criterion variables are not yet available for all the subscales.
Conclusions
Ever since Georg Simmel (1895,1957,2004) introduced the notion into so-
ciological accounts of modernity, scholars have tried to empirically test the claim
of an increasing “speed of life”in modern society. However, as our literature
review of manifold empirical studies has shown, no comprehensive indicator has
so far been developed that measures the pace of life on a sound theoretical basis.
So far, quantitative empirical studies have either analyzed the pace of life by way
of very specific items (e.g. “how fast do you respond to Emails”or the like),
addressed the demands of working life as central to the need for speeding up
(Ulferts et al., 2013), or presented a pace of life indicator that was not grounded on
sound theory (Levine and Norenzayan, 1999).
With the “GAS”, we have developed and presented a new measure that can be
used as a straightforward pace-of-life indicator. The structure of our GAS, that is,
its subscales, is built on sound theory (Rosa, 2013). And, its items are formulated
in a general manner so that it applies to various contexts. For instance, the GAS
can be used as a tool to create a new, comparative “geography of time,”or it can be
used as a tool to measure impacts of (or at least, correlations with) various other
factors influencing everyday life, such as globalization or digitalization.
Compared to the symmetrical outline of the GAS with 16 items (four each of
Rosa’s“four ways”of accelerating the pace of life), our analysis showed that a
version with 15 items (i.e. deleting the preference-related item on the dimension
of “replacing time-intensive by time-saving activities”; RE) had the highest
validity. Given the conditions of our survey and our statistical tests, including
discriminate and control variables, the GAS has been proved valid and reliable.
For social acceleration theory, our empirical results prove an important part of
Rosa’s theory. The confirmatory factor analysis of the GAS fully supports Rosa’s
suggestion that the pace of life could be accelerated in four particular ways.
Moreover, it shows that these are indeed four distinct temporal strategies that
influence the intensity of time use; each strategy can be considered a legitimate
construct and can be clearly distinguished from each of the other strategies. The
pace of life is not only related to a polychronic sense of time, in which one keeps
several operations going at the same time (MT). Rather, a fast pace of life includes
several temporal acceleration strategies toward time use (MT, RE, FA, and WT).
In addition, correlations between the subdimensions of the GAS and other
time-related scales, that is on polychronicity, perpetual activation, and pro-
crastination, have proved that Rosa’s“four ways”are distinct, yet significantly
supported by these existing empirical foundations. The economic use of time was
294 Time & Society 30(3)
positively correlated to each of the subscales and the GAS as a composite
variable. It explained a significant amount of variance of the GAS. This finding
allows for the conclusion that acceleration strategies serve in part as an answer to
a cultural and economic logic of speed in modern societies (“time is money”), that
is, as old as capitalism itself. The results specifically back up Rosa’s claim that an
economic logic of time is a particular driving force that propels social acceleration
(Rosa, 2013).
The acceleration logic has been criticized as individuals seem to be passive
actors in a larger temporal feedback loop (Vostal, 2014). Rather than being totally
out of control, engaging in everyday temporal strategies can also be regarded as
a form of agency toward individual time use (Flaherty, 2011). Hence, the GAS
could not only serve as a straightforward pace of life indicator but also as a
starting point for a task that has been considered “overdue”in social sciences
(Vostal, 2014): the empirical investigation of acceleration and temporal agency
(Flaherty, 2011;Wajcman, 2008).
To further explore the temporal strategies validated in this study, the re-
lationship of the GAS and its subdimensions with various other dimensions of
temporal personality (Francis-Smythe, 2006), activity level (Alreck and Settle,
2002), time orientation (Usunier and Valette-Florence, 2007), and other
individual-level measures, such as time urgency (e.g. Conte et al., 2001), could be
evaluated. Other possible fields of study could be to explore a possible re-
lationship with consumption patterns or well-being.
Limitations and future research needs
Notwithstanding the validity and reliability of our GAS, this study contains some
limitations. First, it relies on self-reported data for both the independent and
dependent variables. One-time surveys can be sources of systematic measurement
error. Although steps to tackle and test for method bias were carried out, factors
such as common method variance could have influenced the data in this study.
Moreover, different perspectives of the respondents on what constitutes their
understanding of a particular item in their everyday life might also play a role in
the magnitudes of correlations. That the world is speeding up is as much a popular
cultural concern as it is a matter of contemporary theoretical importance (Sharma,
2014). Measuring the pace of life in a culture that is dominated by a narrative that
the world is speeding up could also be influenced by social desirability.
Moreover, the GAS serves as an easy-to-use pace of life indicator but not as
a measure of subjective and objective acceleration and hence, cannot be used to
validate Rosa’s entire theory of social acceleration. Such interrogations would
require future research.
Regarding the validation of our GAS, future studies could strengthen the
reliability analysis conducted in this survey examining its test–retest reliability.
Bergener and Santarius 295
Applying the PMTS served as a reliable control variable for the GAS’subscale on
MT. Yet, further specific control variables for the other subscales of the GAS (i.e.
for RE, FA, and WT) can improve reliability.
Acknowledgements
We would like to thank Vivian Frick, Sonja Geiger, Jan Osenberg, Jana Wilbert as well as
the entire team of the research group “Digitalization and Sustainability”for their support
and their comments on the research behind this publication.
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) disclosed receipt of the following financial support for the research, au-
thorship, and/or publication of this article: This publication is based on research in the
project “Digitalization and Sustainability”, which is funded by the German Federal
Ministry of Education and Research as part of its “Research for Sustainable Development
Framework Program”/”Social- Ecological Research”, Funding no. 01UU1607A.5.
ORCID iD
Tilman Santarius https://orcid.org/0000-0002-6026-3019
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