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The Fresh Start Effect: Temporal Landmarks Motivate Aspirational Behavior

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The popularity of New Year's resolutions suggests that people are more likely to tackle their goals immediately following salient temporal landmarks. If true, this little-researched phenomenon has the potential to help people overcome important willpower problems that often limit goal attainment. Across three archival field studies, we provide evidence of a "fresh start effect." We show that Google searches for the term "diet" (Study 1), gym visits (Study 2), and commitments to pursue goals (Study 3) all increase following temporal landmarks (e. g., the outset of a new week, month, year, or semester; a birthday; a holiday). We propose that these landmarks demarcate the passage of time, creating many new mental accounting periods each year, which relegate past imperfections to a previous period, induce people to take a big-picture view of their lives, and thus motivate aspirational behaviors. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.1901.
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The Fresh Start Effect: Temporal Landmarks Motivate
Aspirational Behavior
Hengchen Dai, Katherine L. Milkman, Jason Riis
To cite this article:
Hengchen Dai, Katherine L. Milkman, Jason Riis (2014) The Fresh Start Effect: Temporal Landmarks Motivate Aspirational
Behavior. Management Science ():. http://dx.doi.org/10.1287/mnsc.2014.1901
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MANAGEMENT SCIENCE
Vol. 60, No. 10, October 2014, pp. 2563–2582
ISSN 0025-1909 (print) ISSN 1526-5501 (online) http://dx.doi.org/10.1287/mnsc.2014.1901
© 2014 INFORMS
The Fresh Start Effect: Temporal Landmarks
Motivate Aspirational Behavior
Hengchen Dai, Katherine L. Milkman, Jason Riis
The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104
{hengchen@wharton.upenn.edu, kmilkman@wharton.upenn.edu, jriis@wharton.upenn.edu}
T
he popularity of New Year’s resolutions suggests that people are more likely to tackle their goals immediately
following salient temporal landmarks. If true, this little-researched phenomenon has the potential to help
people overcome important willpower problems that often limit goal attainment. Across three archival field studies,
we provide evidence of a “fresh start effect.” We show that Google searches for the term “diet” (Study 1), gym
visits (Study 2), and commitments to pursue goals (Study 3) all increase following temporal landmarks (e.g., the
outset of a new week, month, year, or semester; a birthday; a holiday). We propose that these landmarks demarcate
the passage of time, creating many new mental accounting periods each year, which relegate past imperfections to
a previous period, induce people to take a big-picture view of their lives, and thus motivate aspirational behaviors.
Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2014.1901.
Keywords: goals; motivation; self-control; temporal landmarks; mental accounting
History : Received January 20, 2013; accepted November 26, 2013, by Yuval Rottenstreich, judgment and decision
making. Published online in Articles in Advance June 23, 2014.
1. Introduction
The beginning of the year is widely documented as
a time when millions of people commit themselves
with atypical vigor to achieving their goals, such
as losing weight, eating more healthfully, quitting
smoking, obtaining a better education, and saving
more money (Marlatt and Kaplan 1972, Norcross et al.
2002). The U.S. government actually lists popular
New Year’s resolutions on its official website and
provides resources to help its citizens tackle their goals
in the coming year (USA.gov 2013). More broadly,
the notion that fresh starts are possible and offer
individuals an opportunity to improve themselves
has long been endorsed by our culture. For example,
Christians can be “born again”; Catholic confessions
and penance provide sinners with a fresh start; many
religious groups engage in ritual purification or ablution
ceremonies (e.g., Buddhists, Christians, Muslims, and
Jews); and the metaphorical phoenix rising from the
ashes is a ubiquitous symbol of rebirth. This suggests
a widely shared belief that we have opportunities
throughout our lives to start fresh with a clean slate,
with the “New Year’s effect” representing just one
example of a far broader phenomenon documented in
this paper. Specifically, we show that special occasions
and calendar events (e.g., a birthday, a holiday, the
beginning of a new week/month), which demarcate
the passage of time and create numerous “fresh start”
opportunities at the beginning of new cycles throughout
each year, are associated with subsequent increases in
aspirational behavior.
Understanding when people are most motivated to
pursue their aspirations is important for a number
of reasons. Aspirational behaviors are activities that
help people achieve their wishes and personal goals.
1
Examples of behaviors that people frequently aspire
to engage in more often include exercising, saving
money, studying, dating, and dieting (Khan et al. 2005).
Notably, we often lack the self-control to expend the
time and effort needed to achieve our aspirations and
instead postpone the work necessary to tackle our
goals until a later date (Bazerman et al. 1998, Milkman
et al. 2008, O’Donoghue and Rabin 1999). For example,
individuals often repeatedly procrastinate when it
comes to dieting, exercising, and quitting smoking.
Over time, such nearsighted decision making can result
in serious individual and societal problems, such as
high rates of obesity and cancer.
Many researchers have sought to understand situ-
ational factors that motivate people to pursue their
aspirations (e.g., Shiv and Fedorikhin 1999, Botti et al.
2008, Sela et al. 2009, Milkman 2012, Toure-Tillery and
Fishbach 2012, Townsend and Liu 2012). However,
sparse research has investigated naturally arising points
in time when people feel particularly motivated to
tackle their goals. Notable exceptions include past work
demonstrating increased attention to aspirations at
the outset of the new year (Marlatt and Kaplan 1972,
Norcross et al. 2002) as well as unpublished (Cross
1
Merriam-Webster Online, s.v. “aspiration,” accessed July 29, 2013,
http://www.merriam-webster.com/dictionary/aspiration.
2563
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Dai, Milkman, and Riis: Temporal Landmarks Motivate Aspirational Behavior
2564 Management Science 60(10), pp. 2563–2582, © 2014 INFORMS
et al. 2006, Fry and Neff 2010) and concurrent studies
(Ayers et al. 2014) suggesting that people are most
likely to think about their health on Mondays.
This paper empirically examines whether other points
in time, beyond (but including) the start of a new year
or week, are associated with increases in aspirational
behavior. Across three field studies, we demonstrate
that people are more likely to pursue various types
of aspirational behavior (e.g., dieting, exercising, goal
pursuit) at the start of “new epochs” initiated by
the incidence of temporal landmarks, including the
beginning of a new week, month, year, and school
semester, as well as immediately following a public
holiday, a school break, or a birthday. We use his-
torical Google search volume data, university gym
attendance records, and data from the goal-setting
website (http://www.stickK.com; hereafter referred to
as stickK) to document this phenomenon, which we
call “the fresh start effect.” Though much past research
assumes that self-control is a time-invariant trait (e.g.,
Shoda et al. 1990), we add to a growing body of recent
research suggesting that self-control capacity is variable
(Shiv and Fedhorkin 1999; Khan and Dhar 2006, 2007).
We postulate that temporal landmarks, including per-
sonally meaningful events (e.g., birthdays, job changes)
and socially constructed calendar partitions (e.g., the
outset of a new month, the observance of a public
holiday), demarcate the passage of time and open new
mental accounting periods. We propose two primary
explanations for the fresh start effect. Specifically, we
propose that naturally arising time markers (a) create
discontinuities in time perceptions that make peo-
ple feel disconnected from their past imperfections
(described in §2.2) and (b) disrupt people’s focus on
day-to-day minutiae, thereby promoting a big-picture
view of life (described in §2.3). We postulate that these
processes triggered by fresh start moments encourage
people to pursue their aspirations. We will address
and rule out a number of key alternative explanations
for our findings, but it is important to acknowledge
that our field data provide imperfect insights into the
mechanisms responsible for the fresh start effect and
thus additional future research on this topic would be
extremely valuable.
2. Conceptual Framework
2.1. Temporal Landmarks Segregate Life into
Numerous, Distinct Mental Accounting Periods
Past research on mental accounting has demonstrated
that “choices are altered by the introduction of
notional
000
boundaries” (Thaler 1999, p. 197) and has
largely focused on examining how the initiation of new
mental accounting periods affects financial outcomes
(for reviews, see Read et al. 1999, Thaler 1999, Soman
2004, Soman and Ahn 2011). Although this previous
research has shown that time is not treated as contin-
uous and fungible (Rajagopal and Rha 2009, Soman
2001), many implications of the nonlinear way in which
we experience time have not yet been explored. In this
paper, we investigate how people’s motivation to pur-
sue personal goals can be altered by the initiation of
new mental accounting periods, as demarcated by
temporal landmarks.
Temporal landmarks, or distinct events that “stand
in marked contrast to the seemingly unending stream
of trivial and ordinary occurrences that happen to us
every day” (Shum 1998, p. 423), have been shown to
structure our memories and experiences (Robinson
1986, Shum 1998). One type of temporal landmark
includes reference points on socially constructed and
shared timetables. Examples include the beginning
of an academic semester, secular and religious holi-
days, and time dividers on the yearly calendar (Kurbat
et al. 1998, Robinson 1986). Another type of temporal
landmark includes personally relevant life events that
demarcate our personal histories, such as develop-
mental milestones, life transitions, first experiences,
and occasions of recurrent significance (Robinson 1986,
Rubin and Kozin 1984). These temporal landmarks not
only influence the manner in which people recall mem-
ories, experiences, and time durations retrospectively
(Ahn et al. 2009, Rubin and Kozin 1984, Shum 1998,
Zauberman et al. 2010) but are also used to organize
current activities and future plans and to designate the
boundaries of temporal periods (LeBoeuf et al. 2014,
Peetz and Wilson 2013, Robinson 1986, Soster et al.
2010, Tu and Soman 2014). For example, when asked
to describe the periods into which they divide their
time, people frequently list cycles such as a day,week,
month,school semester, and school break (Soster et al.
2010). Furthermore, when a salient temporal landmark
(e.g., a public holiday, a birthday, a school event) in
between two points in time is highlighted, people
are more likely to perceive those two points in time
as arising in two distinct periods (Peetz and Wilson
2013, Soster et al. 2010, Tu and Soman 2014). Together,
this research suggests that temporal landmarks open
new mental accounts. We propose that when temporal
landmarks open new mental accounts, the beginning
of a new period stands in contrast to more typical days
in our lives. Below, we describe two perspectives on
why temporal landmarks may then motivate people to
pursue their aspirations.
2.2. Temporal Landmarks Relegate Past
Imperfections to a Previous Mental
Accounting Period
Individuals think of their past, current, and future
selves as interconnected but separable components of
their identity (Parfit 1984) and often compare these
selves to one another (Wilson and Ross 2001). For
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Dai, Milkman, and Riis: Temporal Landmarks Motivate Aspirational Behavior
Management Science 60(10), pp. 2563–2582, © 2014 INFORMS 2565
example, an individual might consider whether she is
a wiser person now than she was in the past, or she
might plan to be a better person in the future.
Past research has shown that the perceived connec-
tion between our present and past temporal selves can
be affected by (a) personally relevant events such as
a religious conversion (Libby and Eibach 2002, 2011;
Wilson and Ross 2003; Bartels and Rips 2010) and (b)
the salience of calendar landmarks (Peetz and Wilson
2013). Anecdotally, past researchers have noted that
people who change (e.g., receive a cancer diagnosis,
recover from an addiction) often describe their pre-
change self as a distinct person (Libby and Eibach 2002).
Wilson and Ross (2003) suggest that many real-life
experiences, ranging from personal milestones (e.g.,
a marriage or job change) to mundane changes in
appearance or possessions (e.g., getting a new haircut
or suit), can distance us from our past self. Together,
this research demonstrates that landmarks in people’s
lives generate a disassociation between present and
past selves.2
We propose that the psychological separation
between one’s present and past selves induced by
temporal landmarks motivates people to pursue their
aspirations. The theory of temporal self-appraisal con-
tends that people evaluate their past self in a manner
that flatters their current self (Wilson and Ross 2001).
In particular, people tend to disparage and attribute
their past failures to their former, distant self because
(a) faults of a remote, past self are less apt to tarnish
their present self-image and thus are less threaten-
ing and (b) criticizing a distant, inferior self implies
self-improvement over time, which is viewed as desir-
able (Wilson and Ross 2001). Importantly, temporal
landmarks—moments that psychologically disconnect
one’s past, current and future selves—lead people to
perceive a contrast between their disconnected selves
(Peetz and Wilson 2013). This facilitates a tendency to
view one’s past self as inferior and one’s current self
as superior (Wilson and Ross 2001).
We argue that by relegating previous imperfections
to a past self and generating a sense that the current
self is superior, temporal landmarks can alter people’s
decisions. Considerable past research has shown that
people are motivated to maintain a coherent self-image
(Epstein 1973, Markus et al. 1997, Kivetz and Tyler 2007).
2
Recent research has also shown that temporal landmarks affect
the perceived psychological distance between people’s present
and future selves. Bartels and Rips (2010) demonstrated that the
psychological connectedness between a person’s present and future
selves can be weakened by prompting them to imagine experiencing
landmark events (e.g., finding out that they were adopted, being
imprisoned as a political hostage). Also, recent work showed that
highlighting a future landmark event (e.g., a public holiday, a
birthday) induces a psychological separation between the current
self and the post-landmark future self (Peetz and Wilson 2013).
For example, if people perceive themselves as moral,
they are more likely to pursue moral actions (Aquino
and Reed 2002). Thus, when people perceive themselves
to be superior to a past self (e.g., more self-disciplined,
more extroverted, etc.), past research suggests they
will behave in accordance with those perceptions (e.g.,
study harder, become more active in social events,
etc.). Therefore, we hypothesize that when temporal
landmarks psychologically disconnect us from our
inferior, past self and make us feel superior, we will
be motivated to behave better than we have in the
past and strive with enhanced fervor to achieve our
aspirations.
It should be noted that some people may not see
their past self as inferior to their current self. However,
so long as the average person sees her past self as
more flawed than her current self, the fresh start effect
should emerge on average, albeit not necessarily for
every individual.
2.3. Temporal Landmarks Promote
a Focus on the Big Picture
In addition to psychologically separating people from
their past imperfections, temporal landmarks may
motivate people to pursue their aspirations by altering
the manner in which they process information and
form preferences. Specifically, by creating disconti-
nuities in our perceptions of time, experiences, and
activities, temporal landmarks may promote taking
a broader view of decisions. Liu (2008) shows that
interruptions to decision making (e.g., switching to a
new background task while pondering a focal decision)
change information processing. Specifically, interrup-
tions move people from a bottom-up, contextually rich
mode of thinking focused on concrete data to a higher
level, top-down mode guided by preexisting goal and
knowledge structures. Temporal landmarks may serve
as one type of disruption to decision making and thus
direct attention to high-level, goal-relevant information.
Indeed, there is some evidence that this is the case.
For example, Bhargave and Miron-shatz (2012) show
that people at milestone ages (e.g., 30, 40 years old) are
more likely than those at other ages to judge their life
satisfaction based on their overall achievements rather
than their daily emotions, highlighting that temporal
landmarks can lead to bigger picture thinking.
Past research has shown that high-level, big picture
thinking has important implications for goal motivation.
When induced to take a high-level view of a situation,
people are more likely to evaluate their actions based
on the desirability of the end state (or goal) they hope
to achieve rather than the time and effort required
to achieve it (Liu 2008, Rogers and Bazerman 2008,
Trope and Liberman 2003). As a result, high-level
thinking leads people to make choices that are more
oriented toward goal achievement (Liberman and
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Dai, Milkman, and Riis: Temporal Landmarks Motivate Aspirational Behavior
2566 Management Science 60(10), pp. 2563–2582, © 2014 INFORMS
Trope 1998, Liu 2008, Trope and Liberman 2003). We
therefore predict that when temporal landmarks serve
as interruptions, leading people to take a higher-level,
big picture view of their lives, people’s motivation to
achieve their aspirations will increase.
2.4. Hypothesis and Study Overview
Integrating the past literature described above, we
propose that temporal landmarks (a) separate people
from their past imperfections and (b) shift people to
think at a higher level about their lives and decisions.
Consequently, we hypothesize that people will exhibit
an increased tendency to pursue their aspirations
following temporal landmarks.
Across three field studies, we test the hypothesis that
temporal landmarks motivate aspirational behaviors
but that these effects weaken as people perceive them-
selves to be further from a temporal landmark. Based
on past research on landmarks in autobiographical
memory, we know that the beginning of a generic
calendar cycle (e.g., the beginning of a week, month, or
year); the beginning of a new period on an academic
or work calendar (e.g., the first month of a semester,
the first workday after a meaningful holiday); and the
beginning of a new period in one’s personal history
(e.g., immediately following a birthday) serve as salient
temporal landmarks (Robinson 1986, Soster et al. 2010).
We therefore predict that aspirational behaviors will
increase following these temporal landmarks.
3
The
aspirational behaviors we examine primarily involve
the initiation of behaviors that contribute to achieving
a goal and tend to require repeated effort (e.g., dieting,
exercising). Specifically, Study 1 uses daily Google
searches for the term “diet” to examine how public
interest in one particularly common aspirational activity
changes over time. Study 2 tests whether actual engage-
ment in an aspirational behavior (exercise) increases
following temporal landmarks using university gym
attendance records. Study 3 investigates the frequency
with which people commit to a broad set of goals
on the goal-setting website stickK. Our findings are
consistent with the hypothesis that we propose based
on the theories described above. Although the current
research primarily focuses on illustrating an important
phenomenon and does not provide a direct test of the
underlying mechanisms, these three field studies rule
out a number of uninteresting alternative explanations
for our findings, which we will discuss in the sections
below.
3
Note that we examine the impact of a set of temporal landmarks
that past research has shown demarcate the transition to a new
mental accounting period. However, we do not address precisely
what types of temporal landmarks produce fresh starts and what
types of temporal landmarks fail to do so in the current research.
This is a question worthy of future investigation.
3.
Study 1: Google Searches for “Diet”
In Study 1, we measure public interest in the adoption
of one aspirational behavior at different points in time.
Specifically, we explore whether Internet searches for
the term “diet” by the general population increase
following temporal landmarks. Maintaining a healthy
diet is considered one of the most effective methods
for maintaining an optimal body weight (Shai et al.
2008), and about two-thirds of adult Americans are
currently classified as overweight or obese (Centers for
Disease Control and Prevention 2013), making dieting
an important goal for most Americans. Indeed, dieting,
losing weight, and eating more healthfully are among
the most popular New Year’s resolutions listed on
the U.S. government’s website (USA.gov 2013). As
described above, we propose that temporal landmarks
motivate the pursuit of aspirations by making an
individual feel segregated from and superior to her
past, imperfect self and by triggering her to take a
big-picture view, which promotes a focus on goal
attainment. Therefore, we predict that people will
search for the term “diet” more frequently following
temporal landmarks than on other days but that this
increase will fade as the temporal landmark recedes
into the past.
3.1. Data
We obtained data from “Google Insights for Search”
(http://www.google.com/insights/search), a website
where it is possible to download the daily number of
Google web searches that include a given search term
dating back to 2004. Daily data on a given search term
can only be extracted in intervals of three months or
less. We downloaded data on the daily number of
Google searches in the United States for the term “diet”
over three-month intervals ranging from January 1,
2004, to June 30, 2012 (a time period including
3,104 days). Daily search volume data provided by
Google Insights for Search is both normalized relative
to the total number of daily searches (for any and
all terms) on Google and further scaled based on
search activity for the specific query in question over
the time period extracted (three months in this case).
More specifically, the day in a downloaded extraction
period with the highest number of searches (relative
to total Google queries) is assigned a scaled value
of 100, and other days receive values that are scaled
accordingly to fall between 0 and 100.
4
The relative
daily search volume ranges from 19 to 100 during the
4
Further, Google Insights for Search reports a search volume of “zero”
when actual volume falls below a certain, undisclosed threshold.
Zeros appear on seven days in our 3,104-day data set. To ensure that
these zero values did not spuriously magnify differences in search
volume over time, we replaced each zero value with the lowest
observed nonzero search frequency during the same extraction
period. However, all reported results are robust to retaining zeros in
our data set.
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Dai, Milkman, and Riis: Temporal Landmarks Motivate Aspirational Behavior
Management Science 60(10), pp. 2563–2582, © 2014 INFORMS 2567
study period
(M=64, SD =18)
. See Appendix A in
the electronic companion (available at http://opim
.wharton.upenn.edu/~kmilkman/mnsc_2014_1901
_electronic_companion.pdf) for Google’s description of
these data.
3.2. Analysis Strategy
We examine whether people are more interested in
dieting following temporal landmarks using ordinary
least squares (OLS) regression analyses. Our regression
models predict daily Google search volume for the term
“diet” as a function of a series of temporal landmark
predictor variables described below. We estimate these
regressions with fixed effects for the 34 three-month
intervals in our data because search data are scaled
within each interval and therefore cannot be compared
directly over time. We also cluster standard errors at
the three-month interval level.5
Because public holidays and the start of a new week,
a new month, and a new year all represent partitions
on the calendar, we expect that Internet searches for
the term “diet” will be highest immediately following
these temporal landmarks. Notably, individuals are
naturally aware of the (continuously measured) day
of the week (Monday–Sunday), day of the month
(1–31), and month of the year (1–12), which means
they are always aware of the time elapsed since the
last temporal landmark corresponding to a new week,
month, or year. However, calendars do not track the
number of days that have elapsed since the latest
holiday. Thus, we do not expect people to be aware of
how many days have elapsed since the last holiday they
celebrated, but we do expect them to be aware of how
far they are from weekly, monthly, and yearly fresh start
moments on the calendar. In light of this, the predictor
variables in our OLS regressions include measures
of a given day’s distance from the beginning of the
week, month, and year. However, when evaluating
the fresh start effect associated with public holidays,
we simply test whether searches for “diet” spike on
the first workday after a holiday compared with other,
mundane days. Specifically, we include the following
predictor variables in our regression analyses to test
for evidence of a fresh start effect:
Days since the start of the week. We construct a con-
tinuous predictor variable indicating the days elapsed
since the beginning of the current week (from 1 =
Monday to 7 =Sunday).
Days since the start of the month. We create a contin-
uous predictor variable indicating the days elapsed
since the beginning of the current month (min =1,
max =31).
Months since the start of the year. We include a
continuous predictor variable indicating the number of
5
Our results do not change qualitatively or in terms of statistical
significance if standard errors are not clustered.
months elapsed since the beginning of the current year
(from 1 =January to 12 =December).6
First day after a federal holiday. We focus on the
most widely celebrated U.S. holidays, or federal holidays,
which we define as one of the ten annual U.S. federal
holidays. We define the first workday after a federal
holiday as the first day when people return to work
after a federal holiday and include a dummy variable in
our regressions to indicate whether or not a given day
is the first workday after a federal holiday.
First workday ×fresh start score of federal holiday.
If, as hypothesized, temporal landmarks elicit fresh
start feelings and increase aspirational behavior, we
would expect search volume for the term “diet” to be
particularly high on days that feel more like a fresh
start. For a separate research project, we identified a list
of 26 holidays, 10 of which were the federal holidays
studied here. We asked 52 participants on Amazon’s
Mechanical Turk to rate the extent to which each of
these 26 holidays (or the day after it) felt like a fresh
start on a seven-point scale (1 =not at all; 7 =very
much) (see Appendix B in the electronic companion
for these 26 holidays and the exact wording of our
question). For the current study, we examine ratings of
the 10 federal holidays of interest. For each of these
10 holidays, we averaged participants’ ratings to form
a composite fresh start score and standardized this
score across the 10 holidays in our sample. We then
created the variable first workday ×fresh start score of
federal holiday by assigning the standardized rating
of fresh start feelings associated with each federal
holiday to the first workday after a corresponding
federal holiday and assigning 0 to other days. Note
that all reported results are robust to studying the set
of 26 holidays rated instead of focusing only on the
10 federal holidays.
3.3. Results
As predicted, we find that searches for the term “diet”
are most frequent at the start of each new calendar
cycle: the beginning of the week, month, and year
(see Model 1 in Table 1). First, searches for the term
“diet” are more common at the beginning of the week
and decrease as the week proceeds, as indicated by a
significant, negative coefficient on days since the start of
the week. Further, the significant, negative coefficients
on days since the start of the month and months since the
start of the year indicate that search volume for the term
“diet” decreases over the course of each month as well
as each year.
As we hypothesized, there is also an increase in
search volume for the term “diet” following federal
6
Because calendars count the time elapsed since the start of the year
in months rather than days, we specified our regressions accordingly,
but notably our results are robust to instead including a measure of
the days elapsed since the start of the year.
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Dai, Milkman, and Riis: Temporal Landmarks Motivate Aspirational Behavior
2568 Management Science 60(10), pp. 2563–2582, © 2014 INFORMS
Table 1 Ordinary Least Squares Regressions to Predict Daily Google Search Volume for Various Search
Terms (Study 1)
Google search term: Diet News Weather Laundry Gardening
Model 1 Model 2 Model 3 Model 4 Model 5
Generic calendar predictors
Days since the start of the week (Monday) 1063∗∗∗ 2009∗∗∗ 0072∗∗∗ 1089∗∗∗ 2023∗∗∗
400085 400115 400175 400105 400135
Days since the start of the month 0009∗∗∗ 000500091.8e−03 0007
400025 400025 400045 400025 400065
Months since the start of the year 3081∗∗∗ 0005 0093 10021029
400425 400455 400835 400415 410885
Work calendar predictors
First workday after a federal holiday 7040∗∗∗ 10760077 2089∗∗ 0029
400735 400845 400765 400945 410115
First workday ×Fresh start score of federal holiday 6078∗∗∗ 2019∗∗∗ 20270026 3025∗∗∗
400655 400545 400735 400395 400755
Fixed effects for each three-month download interval Yes Yes Yes Yes Yes
Observations 3,104 3,104 3,104 3,104 3,104
R20062 0081 0053 0033 0032
Notes. Model 1 reports the coefficients from an OLS regression predicting the relative Google search volume for “diet”
as a function of a given day’s proximity to a variety of calendar markers. Models 2–5 predict search volume for the
placebo terms “news,” “weather,” “laundry,” and “gardening,” respectively, using the same regression specification as
Model 1. Standard errors (in parentheses) are clustered at the three-month interval level.
p < 0010; p < 0005; ∗∗ p < 0001; ∗∗∗p < 00001.
holidays (see Model 1 in Table 1). Consistent with our
prediction that temporal landmarks stimulate increases
in aspirational behavior, there are more searches for
“diet” following federal holidays perceived as more
like a fresh start. Specifically, a one-standard-deviation
increase in a federal holiday’s fresh start rating is
associated with a 6.78 point increase in daily search
Figure 1
Changes in the Fitted Daily Search Volume for the Term “Diet” as a Function of the Date and Its Proximity to a Variety of Temporal Landmarks
3.51
6.78
7.40
2.61
9.78
036912
The day after the New York Times
released a report on a new diet pill
One-standard-dev iation increase in
a holiday’s fresh start score
On the first workday
after a federal holiday
In the first month of the year
(compared to the last)
On the first day of the month
(compared to the 31st)
On the first day of the week
(compared to the last)
Fitted change in the daily Google search volume for the word “diet”
(search volume scaled from 0 to100)
41.91
39 42
Note. These effects are compared with the effect of the New York Times releasing a report about a promising new diet pill (see Pollack 2005) on searches for the
term “diet.”
volume for the term “diet” (on a scale ranging from
0–100; p < 00001, see Model 1 in Table 1).
Figure 1 illustrates that the magnitude of these effects
is quite large when compared to the effect of the New
York Times releasing a report on the successful clinical
trial of an experimental diet pill in May 2005 (see
Pollack 2005), a benchmark event that we expected to
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dramatically alter searches for the term “diet” (and
that indeed increased “diet” search volume; p < 00001).
For example, the increase in daily search volume for
the term “diet” associated with the start of the week
(versus the end of the week) is about three times as
large as the increase in search volume caused by this
New York Times article.
Search Volume for Placebo Terms. It is important to
highlight that search volume for the term “diet” is
already scaled by Google Insights for Search to adjust
for the total number of daily Google queries, so
the detected relationships between daily searches for
“diet” and temporal landmarks cannot be attributed
to changes in Internet search volume. However, to
further exclude the possibility that our findings in
Study 1 can be attributed to general patterns of Internet
search over time, we compare searches for the term
“diet” with searches for two popular search terms,
“news” and “weather” (e.g., “news” was on Google’s
list of “hot searches” in the United States on July 23,
2012), which do not relate to aspirational behaviors.
Furthermore, to empirically address two alternative
explanations that may account for our findings in this
paper (discussed in detail in §6), we identified and
analyzed two additional placebo terms: “laundry” and
“gardening.”
7
We download daily search volume for
these four terms during the same period when searches
for the term “diet” are analyzed (from January 1, 2004,
to June 30, 2012). When we rerun our models with
the aforementioned placebo terms (news, weather,
laundry, and gardening), we neither predict nor find
that searches for these terms systematically increase
following the temporal landmarks examined in Model 1
(see Models 2–5 in Table 1).8
3.4. Discussion
The findings presented in Study 1 support our hypoth-
esis that public interest in one important aspirational
7
See §6 for details about the two alternative accounts as well as how
we identified these placebo terms.
8
Across our regressions with these four placebo terms, a few
coefficient estimates are statistically significant in the predicted
“fresh start” direction, whereas others show significant effects in
the opposite direction. Consistent with our hypothesis, we did
not observe reliable increases following temporal landmarks in
searches for any of these placebo terms—only for the term “diet.” The
coefficient on days since the start of the week is a negative and significant
predictor of daily searches for “news.” A closer examination reveals
that the negative coefficient on days since the start of the week, however,
is driven by a dramatic drop in “news” search volume on weekends
compared with weekdays, rather than by a gradual decline over the
course of a week as is the case with searches for “diet” (and as the
fresh start hypothesis predicts). In fact, people are significantly more
likely to search for “news” on each day from Tuesday to Friday
relative to Monday, whereas people are more interested in dieting on
Mondays than on all other days of the week (all p’s <00001; see
Models A1 and A2 in Appendix C of the electronic companion).
behavior—dieting—is higher following temporal land-
marks. Specifically, we find that relative to baseline
(Model 1 in Table 1), interest in dieting increases at
the start of a new week (by 14.4%), a new month (by
3.7%), and a new year (by 82.1%) and following federal
holidays (by 10.2%). The effects cannot be attributed to
general patterns of Internet traffic since the data we
analyze are already scaled to account for overall search
traffic on a given day and the search volume for other
popular terms (news, gardening, laundry, and weather)
does not exhibit the same systematic patterns.
Study 1 examines people’s tendency to search for
information about one particularly common aspira-
tional behavior. However, we predict that the fresh start
effect alters not only searches for information but also
actual decisions because motivations and intentions
are the first steps toward initiating actions and are
predictive of behaviors (Ajzen 1991, Gollwitzer 1999).
Our next study examines this prediction.
4. Study 2: Undergraduate Gym
Attendance
By creating a discontinuity in our time perceptions and
experiences, temporal landmarks can both psychologi-
cally separate individuals from their past imperfections
and promote high-level thinking. Such processes are
predicted to spur people to pursue aspirational behav-
iors following temporal landmarks. This is a hypothesis
that we test in Study 2 by examining the frequency of
engagement in one important aspirational behavior—
exercise. Increasing the frequency of exercise is one
of the three most popular New Year’s resolutions
(Norcross et al. 2002, Schwarz 1997). Like dieting, regu-
lar physical activity helps with weight loss and weight
maintenance (Catenacci and Wyatt 2007). However,
only about 50% of American adults exercise as often
as recommended by government guidelines (Centers
for Disease Control and Prevention 2007). Thus, for
many, exercise is an important but difficult-to-engage-in
aspirational behavior.
In addition to examining actual engagement in an
aspirational behavior (exercise), Study 2 also explores
an additional, important predictor variable that was
not available in Study 1. Specifically, in Study 2, we
are able to investigate the impact on exercise of both
calendar markers (e.g., holidays, the start of a new
week, month or year) and one type of personal temporal
landmark: birthdays.
4.1. Data
We obtained historical, daily gym attendance data for
every undergraduate member (N
members
=11,912) of a
fitness center affiliated with a large university in the
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2570 Management Science 60(10), pp. 2563–2582, © 2014 INFORMS
northeastern United States from September 1, 2010,
through December 9, 2011 (N
days
=442).
9
Attendance
was recorded automatically when students presented
a magnetic student identification card to enter this
facility. We also obtained information about the birth-
dates of a subset of these undergraduate members
(N
members_with_birthday_data
=2,076). The number of stu-
dents visiting the gym per day ranged from 31 to 2,270
during the study period (M=883, SD =470).
4.2. Analysis Strategy
We conduct two types of OLS regressions to analyze our
gym attendance data. The first aggregates attendance
records across all undergraduate gym members on a
daily basis. The outcome variable in this regression
specification is the total number of gym visits on a
given day divided by the number of hours the gym was
open on that day (or the average gym visits per hour),
which ranged from 5 to 142 (M=54, SD =27) in our
sample. Our second analysis examines the likelihood
that a given gym member visits the gym on each day in
our data set using an OLS regression model including
fixed effects for each gym member and clustering
standard errors at the date level.
10
The inclusion of
gym member fixed effects controls for the effects of
individual differences in time-invariant characteristics
(e.g., gender, race, birth month) on gym attendance. To
conduct this second analysis, we create a data set that
contains one observation for each gym member on each
day (N
person-days
=5,265,104). The dependent variable in
this analysis equals one if a given gym member visited
the gym on a given day and equals zero otherwise.
In both of our regression specifications, we include
predictor variables capturing the relationship between
a given calendar day and temporal landmarks, as
described below.
We predict that students will be more likely to visit
the gym immediately following calendar landmarks and
that their attendance will decline as these time markers
become less salient. As in Study 1, we include days since
the start of the week, days since the start of the month, and
months since the start of the year as predictor variables in
our regressions. However, unlike in Study 1, we do
9
During this period, the gym was closed on 19 days. No observations
about these days were therefore included in the raw data set that the
fitness center shared with us, and we thus exclude them from our
analysis.
10
We use an ordinary least squares regression model (rather than a
more computationally intensive logistic regression model) because we
include a large number of fixed effects and because logistic regression
models typically produce inconsistent estimates when fixed effects
are included unless data characteristics meet a stringent set of
assumptions (Wooldridge 2010). However, we obtain qualitatively
similar results when we rerun our analyses using logistic regression
models, though the significance of some predictors changes.
not expect federal holidays to be particularly salient
calendar markers in the Study 2 student population
because the university whose fitness center provided
data for our study only closes for a subset of public
holidays and has its own break schedule during the
academic cycle. Thus, we expect the set of holidays
and breaks recognized by this university to be more
relevant landmarks than are federal holidays for our
study population. As explained in Study 1, people do
not naturally track the number of days elapsed since a
recent holiday, so we measure the effects of holidays
by creating a dummy predictor variable to indicate
whether or not a day is the first day after any of the
breaks listed on the university’s academic calendar.
In addition, we expect the start of a new academic
semester and birthdays to be meaningful partitioning
points in the lives of the students included in our
gym data set. We predict that gym attendance will
be highest immediately following the outset of a new
semester and following an individual’s birthday and
will decline as the new semester or year of life proceeds.
We include the following predictor variables in our
regression analyses to test these hypotheses:
Months since the start of the semester. We include a
continuous predictor variable indicating the months
elapsed since the beginning of the current semester
(e.g., 1 =September or January; 4 =December or April).
Months since last birthday. We were able to obtain
information about the birthdates of a subset of 2,076
gym members, which we matched with their gym
attendance records. We define a birth year as a per-
sonalized year that starts on the first day following
an individual’s birthday and ends on his or her next
birthday. For each of the 2,076 students in our data
set with a known birthday, we include a continuous
predictor variable in our regressions indicating the
months elapsed since their last birthday. Specifically,
we calculate the distance in days between each date
in the study period and a given student’s previous
birthday. We convert this distance to units of months,
with each “month” taking on the actual length of the
appropriate calendar month (e.g., 1 =the 31 days imme-
diately following an individual’s birthday; 12 =the
31 days immediately preceding an individual’s birthday,
including the birthday itself).
We control for a number of other variables that may
affect a student’s likelihood of attending the gym. Since
college students are likely to be away from campus
during school breaks, we create one dummy variable to
indicate whether the university studied was in normal
class session (fall and spring semesters, excluding
school breaks) and another dummy variable to indicate
whether the university was in summer session on a
given date. Furthermore, since exam periods occur at
the end of the semester and the calendar year, it is
possible that month of the year and month of the semester
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Table 2 Ordinary Least Squares Regressions to Predict Daily Undergraduate Gym Attendance (Study 2)
Members with
Sample: All undergraduate gym members birthday information
Average gym Daily individual Daily individual
Regression outcome variable: visits per hour indicatorbindicatorb
Model 6 Model 7 Model 8
Generic calendar predictors
Days since the start of the week (Monday) 2012∗∗∗ 305e−03∗∗∗ 305e−03∗∗∗
400385 4608e−045 4705e−045
Days since the start of the month 0037∗∗∗ 403e−04∗∗∗ 309e−04∗∗
400095 4102e−045 4103e−045
Months since the start of the year 0066∗∗ 906e−04∗∗ 701e−04
400215 4208e−045 4300e−045
Academic calendar predictors
Months since the start of the semester 6097∗∗∗ 908e−03∗∗∗ 907e−03∗∗∗
400915 4101e−035 4103e−035
First day after a school break 15053∗∗∗ 0002∗∗ 0003∗∗
440415 4803e−035 4904e−035
Personal calendar predictor
Months since last birthday 509e−04∗∗∗
4100e−045
Controls for school sessionaYes YesbYesb
Fixed effects for each gym member No Yes Yes
Observations 442 5,265,104 722,362
Number of gym members 11,912 11,912 2,076
R20067 0.14 0.14
Notes. Models 6–8 report the results from OLS regressions in which the dependent measure is the daily average visits per hour at a
university gym (Model 6) and the likelihood that a given person visited the university gym on a given day (Models 7 and 8). Standard
errors (in parentheses) are clustered at the date level in Models 7 and 8. Predictor variables include measures of a given day’s proximity
to a variety of temporal landmarks.
a
School session control variables include normal school session indicator (during the fall and spring semesters), summer session
indicator, final exam period indicator, and days since the exam period starts.
bBesides school session control variables, the number of operating hours on each date is included as a control variable.
p < 0005; ∗∗p < 0001; ∗∗∗ p < 00001.
affect gym attendance because students are busier than
usual or more likely to have left school during exams.
To alleviate this concern, we control for whether each
date fell during the university’s final exam period. To
account for the fact that more students leave campus as
the exam period progresses, we also include a variable
in our regressions to indicate the number of days since
the start of the final exam period, which is coded
as zero for dates falling outside of the university’s
final exam period. All reported results are also robust
to excluding days falling during exam periods from
our data analysis. For the analyses at the level of
the individual gym member, we also control for the
number of hours that the gym was in operation on a
given calendar date.
4.3. Results
Models 6–8 in Table 2 present results from OLS regres-
sions exploring the statistical relationship between tem-
poral landmarks and (a) average gym visits per hour
across all gym members (Model 6) and (b) daily gym
attendance by individual members (Models 7 and 8).
First, as we observed with searches for the term
“diet,” we find that gym attendance increases at the
start of each new week, month, and year. As Models 6
and 7 in Table 2 show, days since the start of the week
takes on a significant, negative coefficient, indicating
that people visit the gym less as each week proceeds.
11
Further, the significant, negative coefficients on days
since the start of the month and months since the start of
the year in Models 6 and 7 suggest that gym attendance
decreases over the course of each month as well as each
year.
12
In addition, as hypothesized, Models 6 and 7
11
In separate regressions where we replace days since the start of the
week with six indicator variables—one for each day of the week
from Tuesday to Sunday (with Monday omitted)—we find that
hourly gym traffic is higher on Mondays than on all other days (all
p’s <0005 except the comparison with Tuesday; see Models A6–A8 in
Appendix D of the electronic companion).
12
It is worth noting that students in our study do not pay to use the
gym: all enrolled undergraduates are automatically granted mem-
berships at the university’s fitness facility. Therefore, the observed
decrease in usage over the course of a given month or semester could
not be attributed to gradually decreasing sensitivity to membership
payments as described by Gourville and Soman (1998).
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show that students exercise more both at the start of a
new semester (relative to the end of the semester)
13
and on the first day after a school break.
For the subset of 2,706 gym members whose birth-
dates were made available to us, we explore whether
the likelihood that a student visits the gym is higher
in the weeks and months immediately following a
birthday than later in the year. In an initial regression
analyzing daily gym attendance in this subpopulation,
we actually observe a positive correlation between
the variable months since birthday and gym attendance
(=30610
4
,p < 000001)—the opposite of our predic-
tion. However, when we examine this relationship more
closely, we find that gym members react dramatically
differently to their 21st birthdays than to other birth-
days. Specifically, students turning 21 tend to decrease
their gym attendance following this birthday. However,
for students celebrating other birthdays, we observe the
predicted, significant, and negative correlation between
months since birthday and gym attendance (see Model 8
in Table 2). This indicates that students exercise more
frequently right after most birthdays. The 21st birthday
exception may be because this birthday corresponds to
the date when students are first legally permitted to
purchase alcoholic beverages or because it is associated
with an increase in autonomy and social status, which
may reduce students’ urges to change themselves for
the better. Of course, although it is interesting that
the 21st birthday is qualitatively different from other
birthdays, it is important to highlight that potential
explanations are entirely speculative.
To confirm that the 21st birthday differs significantly
from other birthdays with respect to the predicted fresh
start effect, we ran a regression including observations
of all students with available birthdate data to predict
whether each student visited the gym on each date in
our data set. We added a dummy variable (age 21) to
indicate whether an observation corresponded to a day
in the year following a gym member’s 21st birthday
and interacted this dummy variable with all other
predictor variables in our model (including control
variables and person fixed effects). The interaction
between months since birthday and age 21 is significant
and positive in this model (=70910
4
,p < 0005),
which means that the coefficient on months since birthday
for observations associated with all birthdays other than
the 21st is significantly larger than the coefficient for
observations associated with students’ 21st birthdays.
Because we are interested in the effect of birthdays on
gym attendance at a typical age, we report the results
13
Importantly, the finding that gym attendance decreases over the
course of a semester, though consistent with our proposed fresh
start effect, may be because students become busier as the semester
proceeds. However, other temporal landmarks examined in this
study could not be explained by this alternative account.
from analyses of all other birthdays in Table 2 (see
Model 8). In a regression where we replace months
since birthday with 11 dummy variables to indicate each
month in a person’s birth year (with the half-birthday
month marker as the omitted reference month), we find
that people are more likely to exercise during the first
month after a birthday than during the half-birthday
month (=20910
3
,p < 0005), and they are also less
likely to exercise during the final month preceding
a birthday (= −30410
3
,p < 0005). We conclude
that birthday temporal landmarks typically motivate
exercise, and motivation declines over the course of
the year, reaching its lowest level in the final month
preceding a birthday.
Figure 2 illustrates the magnitude of these effects.
Specifically, these effects are compared to the impact
of extending the gym’s hours of operation by one
hour (which itself is a significant, positive predictor
of attendance; p < 00001). We observe that the effects
of temporal landmarks on gym attendance are quite
large in comparison with the effect associated with
extended hours. For example, the increase in an indi-
vidual’s probability of going to the gym in the month
immediately following a birthday (versus the month
immediately preceding a birthday) is equivalent to the
effect associated with keeping the gym open for two
extra hours.
4.4. Discussion
Study 2 shows that people are more likely to exer-
cise following temporal landmarks: the probability
of visiting the gym increases at the beginning of a
new week (by 33.4%), month (by 14.4%), year (by
11.6%), and semester (by 47.1%) as well as following
school breaks (by 24.3%), relative to baseline (Model 7
in Table 2). In addition to replicating the findings
of Study 1 with a consequential behavioral outcome,
Study 2 also demonstrates that personally relevant
temporal landmarks—namely, birthdays—are, like cal-
endar landmarks, associated with subsequent upticks
in aspirational behavior. In this case, the probability
of visiting the gym is increased by 7.5% following
birthdays besides the 21st (Model 8 in Table 2).
One alternative explanation for some of our findings
in Studies 1 and 2 is that people consume a larger
amount of food on certain temporal landmarks, such
as holidays and weekends. As a result, people might
try to reduce their caloric intake or exercise more
intensively following these “binges” in an attempt to
lose weight gained leading up to temporal landmarks.
This alternative account suggests that the tendency to
start healthier routines following temporal landmarks
is simply a physiological response to the health effects
of overindulgence. In light of the concern that some
federal holidays are excuses for gluttony, we conducted
robustness checks by removing Independence Day,
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Management Science 60(10), pp. 2563–2582, © 2014 INFORMS 2573
Figure 2 Changes in the Fitted Probability of Going to the Gym as a Function of the Date and Its Proximity to a Variety of Temporal Landmarks
0.33%
0.65%
2.58%
0.79%
1.18%
2.12%
0.0 0.5 1.0 1.5 2.0 2.5
One-hour increase in gym operating hours
In the first month following a non-21st
birthday (compared to the last month
preceding it)
On the first workday after a school
break
In the first month of the semester
(compared to the last)
In the first month of the year
(compared to the last)
On the first day of the month
(compared to the 31st)
On the first day of the week
(compared to the last)
Change in fitted probability of going to the gym (%)
4.0
3.89%
Note. These effects are compared with the effect of a one-hour increase in the gym’s operating hours on the likelihood of going to the gym.
Labor Day, Thanksgiving Day, and Christmas from the
list of public holidays and school breaks included in
our regression analyses. We found that daily Google
searches for the term “diet,” average gym visits per
hour, and the probability of visiting the gym are still
significantly higher on the first workday after a federal
holiday or a school break than on typical days. In spite
of this alternative explanation’s inability to account
for all of our empirical findings in Studies 1 and 2, to
more carefully address the possibility that the fresh
start effect is exclusively the product of overeating
on weekends and holidays, we conduct an additional
study.
5. Study 3: Commitment Contracts
The objective of Study 3 is to demonstrate that following
temporal landmarks, people take steps to tackle a broad
set of goals that they aspire to achieve, and increases in
the intensity of goal pursuit cannot be explained by the
physiological alternative explanation articulated above.
We expect temporal landmarks to propel the pursuit
of a broad set of goals because temporal landmarks,
by demarcating new mental accounting periods, can
both psychologically distance the current self from
past imperfections and direct an individual to focus on
high-level, goal-relevant ambitions.
5.1. Data
We obtained data from stickK (http://www.stickk.com),
a website that helps customers achieve their personal
goals. Specifically, stickK offers users an opportunity
to set personal goals and specify consequences that
will ensue if they fail to achieve those goals. It is
well-documented that goal-setting establishes reference
points (e.g., Heath et al. 1999, Sackett et al. 2012) and is
instrumental to goal achievement (Locke and Latham
1990). To create what stickK terms a “commitment
contract,” users first specify their goal and select a
date by which they contractually agree to accomplish
it. Next, users choose an amount of money to forego
if they fail to achieve their goal. When users put a
positive amount of money on the line, they also select
a recipient of these stakes (e.g., a friend, a charity),
should they fail to achieve their goal. Finally, users
have the option to (a) designate a third party to moni-
tor and verify their achievements and (b) designate
other stickK users as their supporters. When creating a
commitment contract, users can choose one of stickK’s
five standard goals (exercise regularly, lose weight,main-
tain weight, quit smoking, or run a race) or specify a
custom goal, which they are asked to classify into one
or more of the following categories: career, diet and
healthy eating, education and knowledge, exercise,
family and relationships, green initiatives, health and
lifestyle, home improvement, money and finance, per-
sonal relationships, quit smoking, religion, hobbies and
recreation, and weight loss.
The data that stickK provided for this study contain
66,062 records of commitment contracts that 43,012
unique users created between October 1, 2010, and
February 13, 2013 (N
days
=886). Table 3 lists summary
statistics for the number of contracts created per day
in each goal category.
5.2. Analysis Strategy
We conduct two types of analyses with data from
stickK. The first aggregates commitment contracts
across all users on a daily basis (N
contracts
=66,062,
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Dai, Milkman, and Riis: Temporal Landmarks Motivate Aspirational Behavior
2574 Management Science 60(10), pp. 2563–2582, © 2014 INFORMS
Table 3
Summary Statistics for Goal Contracts Created on stickK.com from October 1, 2010,
to February 13, 2013, by Goal Category
Total contracts Daily contracts
Sum % of all contracts Mean SD Max Min
Custom goal 281830 43064 33029 19058 174 3
Health-irrelevant custom goala151213 23003 17057 11025 94 1
Health-relevant custom goala121976 19064 15004 9025 78 0
Exercise regularly 101759 16029 12042 10073 140 0
Lose weight 231823 36006 27051 27014 349 4
Maintain weight 403 0061 0047 0070 4 0
Quit smoking 11500 2027 1073 1088 24 0
Run a race 747 1013 0065 1010 7 0
All types of goals 661062 100000 76028 55068 687 16
a
The data set does not contain subcategory information for all custom goals but instead for a
subset of 28,189 (or 98% of) custom goals.
N
users
=43,012, N
days
=866) and relies on OLS regression
models to predict the total number of contracts created
each day. The second method allows us to examine
the motivating effects of birthdays by examining the
likelihood that a given user creates a goal on each day
in our data set using an OLS regression model including
fixed effects for each of 42,913 users whose birthdates
were made available to us. We cluster standard errors
at the date level. As in Study 2, we create a data
set that contains one observation per user per day
(N
person-days
=37,162,658). We set the dependent variable
in this person-day analysis equal to one if a given
stickK user created a commitment contract on a given
day and zero otherwise.
As in Studies 1 and 2, we create a set of predictor
variables indicating a given calendar day’s proximity
to the beginning of the week (days since the start of the
week), the beginning of the month (days since the start
of the month), and the beginning of the year (months
since the start of the year). Using the same methods
described in §3.2, we construct (a) the dummy variable,
first workday after a federal holiday, to indicate whether a
given day is the first workday after a federal holiday
and (b) the variable first workday ×fresh start score of
federal holiday to indicate the extent to which each
federal holiday was rated as a fresh start. We again
expect that birthdays represent important personal
temporal landmarks and therefore promote a focus
on aspirations. For the subset of 42,913 stickK users
whose birthdates were available to us, we create the
additional predictor variable months since last birthday
using the method described in §4.2 (N
contracts
=65,845).
We control for stickK’s considerable growth in users
and contracts during our study period. For each cal-
endar day in our data set, we create a control vari-
able, days since launch, to indicate the number of
days elapsed since the start of our data set. We also
include its quadratic term to control for the potentially
nonlinear trend in the growth of the stickK customer
population.14
5.3. Results
5.3.1. All Types of Commitment Contracts. Con-
sistent with our hypothesis, we find that goal contracts
are created more frequently at the beginning of the
week than at the end of the week, as indicated by a
significant, negative coefficient on days since the start of
the week in our regression models (see Models 9 and 10
in Table 4).
15
Also, the significant, negative coefficients
on days since the start of the month and months since the
start of the year in Models 9 and 10 in Table 4 indicate
that people create more new goals at the beginning of
the month and year as compared with the end of the
month and year. Further, as we hypothesized, the total
number of commitment contracts increases immediately
following federal holidays, and the magnitude of this
increase is larger after holidays rated as more likely
to elicit fresh start feelings (see Models 9 and 10 in
Table 4).
We next turn to an exploration of whether the like-
lihood that a user creates a contract is higher in the
weeks and months immediately following his or her
birthday compared with later in the year. For the
42,913 users in our data set with a known birthdate,
the variable months since birthday is a negative and
marginally significant predictor of the likelihood that a
user will create a goal contract on a given day (see
14
Our independent variables of interests remained qualitatively
the same in terms of magnitude and statistical significance if we
excluded the quadratic term from our regression models.
15
In a separate regression where we replace days since the start of
the week with six indicator variables—one for each day of the week
from Tuesday to Sunday (with Monday omitted)—we find that the
number of commitment contracts created is significantly higher on
Mondays than on any other day of the week (all p’s <0005 except the
comparison with Tuesday; see Models A9 and A10 in Appendix E of
the electronic companion).
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Table 4 Ordinary Least Squares Regressions to Predict Daily Creation of Commitment Contracts on stickK.com in Aggregate
(Study 3)
Goal category: All categories Health-irrelevant custom goals
Did individual Did individual
Daily number create a goal ?Daily number create a goal ?
Regression outcome variable: of contracts (Y=1, N=0) of contracts (Y=1, N=0)
Model 9 Model 10aModel 11 Model 12a
Generic calendar predictors
Days since the start of the week (Monday) 5073∗∗∗ 102e−04∗∗∗ 1012∗∗∗ 100e−04∗∗∗
400795 4107e−055 400165 4102e−055
Days since the start of the month 0049∗∗ 100e−05∗∗ 0006 402e−06
400185 4302e−065 400045 4208e−065
Months since the start of the year 6009∗∗∗ 102e−04∗∗∗ 1024∗∗∗ 100e−04∗∗∗
400435 4101e−055 400085 4808e−065
Work calendar predictors
First workday after a federal holiday 41096∗∗∗ 800e−04∗∗ 5081∗∗ 409e−04
490345 4203e−045 410845 4202e−045
First workday ×Fresh start score of federal holiday 67074∗∗∗ 103e−03∗∗ 8097∗∗∗ 600e−04
480605 4307e−045 410705 4203e−045
Personal calendar predictor
Months since last birthday 304e−06508e−06
4109e−065 4307e−065
Days since launch 0001 108e−07 0001606e−07
400035 4501e−075 4500e−035 4405e−075
Days since launch2505e−05907e−10208e−05∗∗∗ 201e−09∗∗∗
4208e−055 4508e−105 4506e−065 4504e−105
Fixed effects for each stickK user No Yes No Yes
Observations 866 37,162,658 866 8,694,640
Number of stickK usersb43,012 42,913 10,074 10,040
R20.32 203e−03 0.35 205e−03
Notes. Models 9 and 11 predict the daily number of commitment contracts associated with all types of goals (Model 9) and health-irrelevant
custom goals (Model 11). Models 10
c
and 12 predict the likelihood that a given user created a goal contract on a given day for all types of
goals (Model 10) and for health-irrelevant custom goals (Model 12). Standard errors (in parentheses) are clustered at the date level for
Models 10 and 12. Across all models, independent variables include measures of a given day’s proximity to a variety of temporal
landmarks.
aThis regression model includes the 99.5% of users whose birthdates were available to us.
b
This represents the number of stickK users who created at least one commitment contract in a corresponding goal category and thus
were included in each corresponding regression model.
c
We only include regression results for the 42,913 users with a known birthdate because these users account for more than 99.5%
of all users in our data set. When we predict the likelihood of creating a commitment contract on a given day as a function of the
aforementioned predictors (with the exception of months since last birthday) for all 43,012 users in our data set, the regression results
we obtain are virtually identical.
p < 0010; p < 0005; ∗∗ p < 0001; ∗∗∗p < 00001.
Model 10 in Table 4). This suggests that people are
more motivated to pursue goals following a birthday
than preceding one.
16
In a regression where we replace
months since birthday with 11 dummy variables to indi-
cate each month in a person’s birth year (with the
half-birthday month marker as the omitted reference
month), we find that people are significantly more
likely to create a commitment contract during the
first month after a birthday than during the month
of their half-birthday (=70010
5
,p < 0005), and
they also show an (insignificant) trend of creating
fewer commitment contracts in the last month before
16
Note that we do not expect the 21st birthday to differ from other
birthdays (and do not find that it differs) when it comes to general
goal setting. The legal option to purchase alcohol may alter one’s
immediate inclination to exercise (Study 2) but it should not affect
one’s general inclination to set goals (Study 3).
their birthday relative to their half-birthday month
(= −304105,p > 0010).
Figure 3 illustrates that the magnitude of these effects
is quite large in comparison with the impact of ABC
News releasing a feature article about stickK in March,
2012 (see Farnham 2012), a benchmark event that we
would expect to dramatically increase attention to
stickK (indeed, this article significantly increased the
number of contracts created on the day of its release;
p < 0005). For example, the increase in an individual’s
probability of creating a contract right after a federal
holiday (relative to other more mundane days) is four
times as large as the effect of the release of this ABC
News article.
5.3.2. Commitment Contracts for Custom Goals.
It is important to address the possibility that some
of our findings in Studies 1 and 2 could be driven
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Dai, Milkman, and Riis: Temporal Landmarks Motivate Aspirational Behavior
2576 Management Science 60(10), pp. 2563–2582, © 2014 INFORMS
Figure 3 Changes in the Fitted Probability of Creating a Commitment Contract as a Function of the Date and Its Proximity to a Variety of
Temporal Landmarks
0.021%
0.004%
0.128%
0.080%
0.132%
0.030%
0.072%
0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140
The day after ABC News
released an article featuring stickK.com
In the first month following a birthday
(compared to the last month preceding it)
One-standard-deviation increase in
a holiday’s fresh start score
On the first workday
after a federal holiday
In the first month of the year
(compared to the last)
On the first day of the month
(compared to the 31st)
On the first day of the week
(compared to the last)
Change in fitted probability of creating a commitment contract (%)
Note. These effects are compared with the effect of ABC News releasing an article featuring stickK (see Farnham 2012) on the likelihood of creating a commitment
contract.
by overindulgence associated with certain types of
temporal landmarks (e.g., holidays, weekends, birth-
days), which might lead to subsequent compensatory
exercising and dieting. To address this possibility, we
investigate the patterns described above in §5.3.1 for
custom goals that are not health related. As described
above, when creating a custom goal, stickK provides a
list of goal subcategories and requires users to check
all subcategories that apply. The list of subcategories
encompasses a broad set of domains, including many
that are not directly related to health (specifically, these
include career, education and knowledge, money and
finance, personal relationships, green initiatives, home
improvement, religion, family, and relationships as
well as hobbies and recreation). Examples of health-
irrelevant custom goals that are featured on stickK.com
include “being on time,” “spending more time with
family,” “helping others,” “learning something new,”
and “reducing debt” (http://www.stickk.com, accessed
July 28, 2013). To ensure that the fresh start effect
is not simply the result of compensatory cutbacks
following overindulgence, we focus on custom goals for
which stickK users did not select any health-related
subcategories (N
contracts
=15,213,
Ndays =866
,N
users
=
10,074). Using the same OLS regression model specifi-
cations described in §5.2, we predict the total number
of contracts created each day for health-irrelevant
custom goals.
As predicted, health-irrelevant custom goal contracts
(see Models 11 and 12 in Table 4) are created more
frequently at the beginning of the week, month, and
year; following federal holidays; and particularly after
holidays rated as more like a fresh start compared with
other days. Although there is a trend whereby more
health-irrelevant custom goals are initiated following a
birthday, this trend is not significant (see Model 12
in Table 4). Models 13–15 in Table 5 report regression
results for the three most popular health-irrelevant
custom goals (career, education and knowledge, and
money and finance), which all show these same trends.
5.3.3. Robustness Across Goal Types. We find the
same basic patterns of results when we separately
analyze health-relevant custom goals as well as the
five types of standard goal contracts offered by stickK:
exercise regularly,lose weight,maintain weight,quit smok-
ing, and run a race. See Models 16–21 in Table 5 for
regression results broken down by goal type.
5.4. Discussion
Consistent with our hypothesis, Study 3 shows that
relative to baseline, people are more likely to commit to
their goals at the beginning of a new week (by 62.9%),
month (by 23.6%), or year (by 145.3%) and follow-
ing federal holidays (by 55.1%) as well as following
their birthdays (by 2.6%) (Model 10 in Table 4). Fur-
ther, Study 3 provides evidence that the fresh start
effect pertains to a broad set of health-irrelevant goals
(e.g., career, education and knowledge, and personal
relationships). This suggests that the increase in aspira-
tional behaviors following temporal landmarks that we
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Management Science 60(10), pp. 2563–2582, © 2014 INFORMS 2577
Table 5 Ordinary Least Squares Regressions to Predict Daily Creation of Commitment Contracts on stickK.com by Goal Category (Study 3)
Regression outcome variable: Daily number of contracts
Education and Money and Health-relevant Regular Weight Weight Smoking Running
Goal category: Career knowledge finance custom goals exercise loss maintenance cessation a race
Model 13 Model 14 Model 15 Model 16 Model 17 Model 18 Model 19 Model 20 Model 21
Generic calendar predictors
Days since the start of 0059∗∗∗ 0025∗∗∗ 00080095∗∗∗ 1003∗∗∗ 2030∗∗∗ 0004∗∗ 0017∗∗∗ 0009∗∗∗
the week (Monday) 400055 400065 400035 400135 400165 400415 400015 400035 400025
Days since the start of 0001 0002 0002000600070029∗∗ 201e−03 0001 801e−05
the month 400015 400015 400015 400035 400045 400095 4207e−035 400015 4400e−035
Months since the start of 0033∗∗∗ 0030∗∗∗ 0013∗∗∗ 1005∗∗∗ 1008∗∗∗ 2041∗∗∗ 0002∗∗ 0012∗∗∗ 0007∗∗∗
the year 400035 400035 400025 400075 400095 400225 4605e−035 400025 4908e−035
Work calendar predictors
First workday after 0016 1093∗∗ 00946077∗∗∗ 7037∗∗∗ 20010∗∗∗ 0005 1016∗∗ 0055
a federal holiday 400645 400735 400415 410545 410875 440805 400145 400355 400215
First workday ×Fresh start 1085∗∗ 0063 2021∗∗∗ 7048∗∗∗ 13022∗∗∗ 36064∗∗∗ 906e−03 1042∗∗∗ 0034
score of federal holiday 400595 400675 400375 410425 410725 440425 400135 400335 400205
Days since launch 103e−03 103e−03 900e−05 902e−03∗∗∗ 308e−03 408e−03 606e−04609e−04 604e−04
4107e−035 4200e−035 4101e−035 4100e−035 4501e−035 400015 4308e−045 4906e−045 4508e−045
Days since launch2700e−06∗∗∗ 505e−06102e−06 109e−06 904e−06 109e−06 607e−07 104e−07 803e−07
4109e−065 4202e−065 4102e−065 4100e−035 4507e−065 4105e−055 4403e−075 4101e−065 4604e−075
Observations 866 866 866 866 866 866 866 866 866
Number of stickK usersa3,068 2,944 1,399 8,493 9,695 20,273 327 1,329 700
R20031 0016 0013 0033 0027 0025 0003 0014 0010
Notes. Models 13–21 predict the daily number of commitment contracts associated with each of the three most popular health-irrelevant custom goals
(Models
13–15
), all health-irrelevant custom goals combined (Model 16), as well as each of the five standard goals (Models 17–21). Across all models,
independent variables include measures of a given day’s proximity to a variety of temporal landmarks.
a
This represents the number of stickK users who created at least one commitment contract in a corresponding goal category and thus were included in each
corresponding regression model.
p < 0010; p < 0005; ∗∗ p < 0001; ∗∗∗p < 00001.
document throughout this paper cannot be parsimo-
niously explained by the physiological need to offset
overindulgence.
6. General Discussion
Across three field studies, we find evidence of a fresh
start effect whereby people exhibit a higher likelihood
of engaging in aspirational behaviors following tempo-
ral landmarks such as the initiation of new calendar
cycles (e.g., the start of a new week, month, year, or
academic semester), holidays, and birthdays. We ana-
lyze a broad set of aspirational activities: web searches
for the term “diet,” gym attendance, and the creation
of commitment contracts to support a wide range of
different goals. The effects we document are large in
magnitude, suggesting that the fresh start effect has
meaningful implications for individual and societal
welfare.
The fresh start effect documented in this paper
is consistent with two psychological processes we
proposed to parsimoniously explain it. First, new
mental accounting periods as demarcated by temporal
landmarks psychologically distance the current self
from past imperfections, propelling people to behave
in line with their new, positive self-image. Second,
temporal landmarks interrupt attention to day-to-day
minutiae, causing people to take a big-picture view
of their lives and thus focus more on achieving their
goals. This paper relies on field data to demonstrate the
existence of the fresh start effect, but it does not offer a
direct test of the underlying mechanisms responsible
for this effect. Thus, future research documenting the
psychological processes that underlie the fresh start
effect would be extremely valuable. In the next section,
we discuss and provide evidence that helps rule out a
number of uninteresting alternative explanations for
our findings.
6.1. Alternative Explanations
One concern with our findings is that people tend
to engage in activities prior to (or during) temporal
landmarks that harm goal pursuit, and our findings
might simply reflect a rational attempt to offset these
bad behaviors after temporal landmarks. For example,
the fresh start effect could simply be attributed to the
desire to counteract excessive caloric intake associated
with weekends and holidays. We can rule out this
alternative explanation in a number of ways. First,
in Study 3, we rule out this alternative explanation
by showing that following temporal landmarks, com-
mitment contracts for health-irrelevant goals increase.
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Second, when we remove holidays that are particular
excuses for gluttony (Independence Day, Labor Day,
Thanksgiving, and Christmas), we still find a significant
uptick in aspirational behaviors immediately following
holidays and school breaks. Third, this compensatory
alternative explanation cannot account for our con-
sistent finding that aspirational behaviors are more
intense at the start of the month than at the close of
a month since neither the start nor the end of a new
month is associated with increased indulgence. Finally,
this alternative explanation suggests that engagement
in aspirational activities would be significantly lower
right before temporal landmarks than on other days.
We can directly test whether this is the case by explor-
ing whether people are indeed significantly less likely
to engage in aspirational behaviors immediately before
temporal landmarks than on other days across our
three field data sets.
Although we hypothesize that temporal landmarks
elevate the frequency of aspirational behaviors and
that these effects weaken as people perceive temporal
landmarks to be further away, our hypothesis does not
predict that engagement in aspirational behaviors will
be significantly lower in the short period immediately
preceding (or during) a temporal landmark than on
any other, typical day. Therefore, we created indicator
variables for weekends, the last seven days of each
month, the last seven days of each year, the seven days
preceding the first workday after each federal holiday
(Studies 1 and 3), the seven days preceding the first
school day after each school break (Study 2), the seven
days preceding each semester’s start (Study 2), and
the seven days immediately before and including a
person’s birthday (Studies 2 and 3). We then added
these additional predictor variables to our primary
regression models (Models 1 and 6–10). If our findings
were simply attributable to reduced engagement in
aspirational behaviors prior to temporal landmarks,
we would expect the coefficients on these new pre-
dictor variables to be significant and negative. In fact,
among 29 new predictor variables across six regression
models, only three predictor variables have a signif-
icant, negative coefficient at the 5% level, which is
not significantly more than the number that would be
expected by chance. In addition, the inclusion of these
predictor variables does not qualitatively change the
coefficients on our primary predictor variables, which
remain essentially the same in terms of magnitude
and statistical significance. Therefore, it is unlikely that
our findings are solely driven by people’s reduced
engagement in aspirational behaviors prior to temporal
landmarks.
Another alternative explanation for our findings
is that people do not have enough time and energy
to tackle their goals before temporal landmarks and
thus put off aspirational behaviors until after temporal
landmarks have passed. Such an alternative account
suggests that the period before a temporal landmark is
not a good time to initiate goal pursuit and thus should
be associated with a significant dip in the frequency of
aspirational behaviors, but the analyses described above
show that this is not the case. Further, although it is
likely that the arrivals of some new mental accounting
periods (e.g., following a wedding or a job change) are
accompanied by more free time to tackle goals than
the windows preceding them, people do not typically
have more free time to pursue aspirational activities
following most of the types of temporal landmarks
studied in this paper (e.g., the beginning of a new week,
the beginning of a new month, the first workday after
a holiday, or during the first few months following a
birthday) than before these temporal landmarks (e.g., on
the weekend, at the end of the month, before or during
a holiday, or in the few months preceding a birthday).
To further address this alternative explanation, however,
we recruited 53 participants online from Amazon’s
Mechanical Turk to participate in a survey about daily
activities. They were first asked to list three activities
that they had the tendency to put off doing until
a future date when they thought they would have
more time and energy. Next, participants were asked
to select the subset of activities from their list that
were not aspirational (see Appendix F in the electronic
companion for the exact questions). A research assistant
removed activities that fit our definition of “aspirational”
and then identified the most frequently listed activity
that participants tended to put off doing and that was
not aspirational in nature: “laundry.” Following the
procedures described in Study 1, we downloaded daily
Google search volume for this word from January 1,
2004, to June 30, 2012. We neither predict nor find
that searches for “laundry” systematically increase
following the temporal landmarks examined in Model 1
(see Model 4 in Table 1), suggesting that temporal
landmarks do not simply increase the interest in all
types of activities that require planning, time, and
energy.
There are several other potential explanations for the
documented fresh start effect besides the psychological
processes we propose that can be ruled out. First, it
could be argued that people generally embrace all types
of new activities at the beginning of new cycles. Study 3
helps address this alternative account by showing that
the fresh start effect is not confined to the adoption
of new habits. For example, temporal landmarks are
followed by an increase in the number of commitment
contracts created for smoking cessation, an aspirational
behavior that disrupts an existing habit (see Table 5).
To further address this alternative account, we recruited
another 49 participants online from Amazon’s Mechan-
ical Turk to list three “new” activities that they had
never engaged in before but would consider pursuing
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in the future. As in the survey we described above,
we again asked participants to indicate the subset of
activities on their list that were not aspirational (see
Appendix F in the electronic companion for the exact
questions) and asked a research assistant to remove
activities that fit our definition of “aspirational.” “Gar-
dening” was the most frequently listed “new” activity
that was not aspirational in nature. We did not find that
Google searches for “gardening” systematically increase
following the temporal landmarks examined in Study 1
(see Model 5 in Table 1), suggesting that temporal
landmarks do not induce increased engagement in all
types of new activities.
It is also important to note that some temporal
landmarks, particularly personally meaningful life
events (e.g., a wedding, a job change) tend to alter
one’s surroundings and daily routines, which in turn
trigger certain habitual actions. Past research has
shown that altering one’s surroundings and routines
can lead to behavior change (Wood et al. 2005). For
example, a move to a new residence may promote
a healthy lifestyle because recurring stimuli that cue
old, unhealthy habits no longer exist (e.g., a favorite
bakery is now far away). Alternatively, a move to a
new residence may promote an unhealthy lifestyle
because a favorite salad shop is no longer nearby and
instead an ice cream parlor is just down the street.
There are several reasons why we believe we can rule
out this explanation for our findings. First, although
many temporal landmarks do disrupt routines, many
of those we study (e.g., the start of a new week/month,
the celebration of a birthday) do not typically alter
routines significantly. In fact, weekly and monthly
cycles may actually reinforce routines. Second, this
past research on habit disruption does not clearly pre-
dict whether contextual shifts that may be induced
by certain types of temporal landmarks will lead to
increases in aspirational or harmful behaviors. In fact,
there is evidence that routine changes can disrupt
beneficial habits such as reading the newspaper (Wood
et al. 2005). Thus, past research on routines and habit
formation does not seem likely to explain the fresh
start effect detected in this paper.
It could be argued that some temporal landmarks
associated with relaxation, such as weekends and
holidays, might replenish self-regulatory resources,
restoring the self-control that people need to tackle
aspirational behaviors (Baumeister et al. 1998). Though
repletion could contribute to the elevated motivation
to pursue goals that we detect following weekends
and holidays and strengthen the impact of the psycho-
logical processes highlighted in §2, this account cannot
explain why people choose to engage in aspirational
activities at a higher rate following the start of a new
month or immediately following a birthday. Also, more
nuanced analyses of our field data suggest that the
observed fresh start effect is unlikely to be solely driven
by changes in self-regulatory resources. Specifically,
this alternative account predicts that the frequency of
aspirational behaviors should be higher on Saturday
and Sunday than Friday because having a day off from
work or school is relaxing. However, eight regression
models where we replace days since the start of the
week with six indicator variables—one for each day
of the week from Tuesday to Sunday (with Monday
omitted)—provide no consistent evidence that Friday
is associated with lower engagement in aspirational
behaviors than either Saturday or Sunday (see Mod-
els A1,
A6–A8
, and
A9–A12
in Appendices C, D, and E,
respectively, of the electronic companion). In concur-
rent research exploring the mechanism underlying
the fresh start effect through laboratory experiments,
Dai et al. (2014) show that people are more motivated
to pursue aspirational behaviors following more psy-
chologically meaningful temporal landmarks (e.g., a
meaningful birthday or job change) than objectively
commensurate but less psychologically meaningful
temporal landmarks (e.g., a typical birthday or job
change). These findings help rule out relaxation as
the sole explanation for the fresh start effect because
psychologically meaningful temporal landmarks would
not be expected to provide greater opportunities for
relaxation than would objectively identical but less
meaningful landmarks.
6.2. Implications
The fresh start effect has significant practical implica-
tions for individual decision makers, managers, and
policy makers. First, individuals can not only take
advantage of their fresh start feelings at naturally
arising temporal landmarks to follow through on good
intentions, but they may also be able to construct fresh
starts themselves by strategically “creating” turning
points in their personal histories, such as moving to a
new residence to start over (a previously named phe-
nomenon called “relocation therapy”; Kaufman 2013).
Second, our findings suggest new ways in which peo-
ple may be effectively “nudged” (Thaler and Sunstein
2008) to begin pursuing their aspirations. For example,
messages designed to promote aspirational behaviors
may be most impactful at fresh start moments (e.g.,
the beginning of a new month, right after holidays)
when message recipients will be more interested in
striving to achieve their long-term goals, as shown in
this paper. Further, marketers of products designed
to help people attain desirable objectives (e.g., retire-
ment counseling services, gym memberships, online
education programs) may best appeal to consumers’
desires for self-improvement by advertising at fresh
start moments.
Another implication of this research is that framing
certain days as opportunities for a fresh start (e.g., birth-
days, the start of a new week/month/year, etc.) may
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Dai, Milkman, and Riis: Temporal Landmarks Motivate Aspirational Behavior
2580 Management Science 60(10), pp. 2563–2582, © 2014 INFORMS
help people make choices that maximize their odds
of achieving their aspirations. For example, employ-
ers could potentially reframe transition points in the
workplace (e.g., a desk move or a return from vaca-
tion) to increase the adoption of aspirational activities
(e.g., attending training workshops or onsite biometric
screenings).
An important question related to the practical impli-
cations of fresh start effects is how long fresh start
feelings persist following the incidence of a temporal
landmark. Plots (see Appendix G in the electronic
companion) suggest that the elevated motivation we
document in this paper spikes on the first workday
after a federal holiday and declines rapidly thereafter,
whereas motivation wears off much more gradually
over the course of each week, month, year, and semester.
However, it is worth noting that even fleeting fresh
start feelings following temporal landmarks can poten-
tially be valuable for at least two reasons. First, the
abundance of fresh start opportunities throughout the
year offers repeated chances for people to attempt posi-
tive self-change, so even if they initially fail, they may
subsequently succeed (Polivy and Herman 2002). Sec-
ond, transient increases in motivation may be sufficient
to help people fulfill important one-shot goals such
as receiving a medical test or signing up for a 401(k)
account with monthly payroll deductions. In this paper,
we primarily study aspirational behaviors where the
end goal requires engaging in a series of goal-directed
actions (e.g., dieting, exercising, committing to a per-
sonal goal). It would be valuable for future research to
examine the extent to which temporal landmarks can
spur aspirational behaviors that only require a single
action (e.g., getting a vaccine, donating to a charity).
6.3. Limitations and Future Directions
The empirical evidence presented in this paper pri-
marily focuses on temporal landmarks associated with
socially constructed timetables (including the yearly
calendar, work calendar, and academic calendar). Birth-
days are the one exception and example of personally
relevant temporal landmarks studied here. Further, we
focus on the Gregorian calendar given its relevance
to the settings studied. Future research exploring and
comparing a broader set of temporal landmarks, includ-
ing temporal landmarks on different calendars (e.g.,
the Chinese New Year, the Jewish New Year) as well as
additional personal landmarks (e.g., religious conver-
sions, relocations, job changes, etc.) would be valuable.
We expect that the fresh start effect likely extends to all
temporal landmarks, not only those examined in this
paper, though certain types of temporal landmarks may
produce stronger effects than others (Dai et al. 2014).
In addition, the temporal landmarks highlighted
here are all associated with either neutral or positive
experiences. Temporal landmarks of negative valence
(e.g., a divorce, the death of a family member) may not
immediately increase motivation to pursue aspirations
if people need to first cope with stressful experiences
(Cohen and Hoberman 1983). It would be valuable for
future research to explore whether the fresh start effect
extends to temporal landmarks stained by negative
emotions such as grief, anger, and stress.
Our findings raise a number of other questions
worthy of exploration. One such question is how the
anticipation of a temporal landmark affects behavior.
Some recent work suggests that people might feel less
compelled to begin pursuing their goals when upcom-
ing landmark events are highlighted because the future
self (who will benefit from goal pursuit) feels more
disconnected from the current self (Bartels and Rips
2010, Bartels and Urminsky 2011, Tu and Soman 2014).
On the other hand, Peetz and Wilson (2013) contend
that when an intervening landmark event and a future
desirable state are both made salient, the discrepancy
between the current self and the future, desired self
is highlighted, which motivates beneficial behaviors.
Another possibility is that people may use upcoming
temporal landmarks as self-imposed deadlines and
attempt to bring ongoing goals to closure by these
deadlines (e.g., finish reading a book, complete an
assignment). Our research suggests two other possible
effects of anticipating an upcoming temporal landmark.
First, anticipated temporal landmarks might liberate
people to make goal-incongruent choices if they antici-
pate wiping the slate clean after an upcoming temporal
landmark (Zhang et al. 2007). Second, if a decision
maker foresees that a better opportunity to pursue her
aspirations will arise following an impending tempo-
ral landmark (e.g., after her next birthday), she may
strategically delay launching her plans until after the
landmark. Future research exploring these possibilities
would be valuable.
Further, future research could explore if and how
social influence reinforces the fresh start effect. For
example, a spike in goal pursuit on January 1 may
partly reflect a social bandwagon effect. Though other
fresh start moments highlighted in the current research
(e.g., the beginning of the week or month) attract less
attention, the fresh start effects we observe across
three studies could be magnified in part by a social
contagion process whereby others’ engagement in
aspirational activities stimulates increases in our own
goal motivation. Exploring this hypothesis in future
research would be valuable.
Supplemental Material
Supplemental material to this paper is available at http://dx
.doi.org/10.1287/mnsc.2014.1901.
Acknowledgments
For helpful feedback on this paper, the authors thank Maurice
Schweitzer, Uri Simonsohn, Joseph Simmons, Gal Zauberman,
Downloaded from informs.org by [128.91.111.13] on 07 October 2014, at 05:30 . For personal use only, all rights reserved.
Dai, Milkman, and Riis: Temporal Landmarks Motivate Aspirational Behavior
Management Science 60(10), pp. 2563–2582, © 2014 INFORMS 2581
Neeru Paharia, Sreedhari Desai, and Katie Shonk; partici-
pants at the Penn–Carnegie Mellon University 2012 Roybal
Center Retreat; participants at the Google PiLab Summit; and
participants at the Society for Consumer Psychology Winter
2013 Conference, the 2013 Annual Meeting of the Academy
of Management, the 2013 North American Association for
Consumer Research Conference, and the 2013 Society for
Judgment and Decision Making Conference. The authors
thank Alex Rogala, Elliot Tusk, Daniel Milner, Benjamin Kirby,
Kaity Moore, Nikila Venkat, and the Wharton Behavioral Lab
for help collecting data. They also thank stickK (in particular,
Victoria Fener, Jordan Goldberg, and Scott Goldberg) for
providing data. Finally, the authors thank the Wharton Dean’s
Research Fund, the Patient Engagement and Communication
Working Group at the University of Pennsylvania, and the
Wharton Risk Management and Decision Processes Center
for funding support.
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... Generally, people perceive time as continuously running from past to future. Still, continuous timelines are mentally separated into discrete periods by temporal landmarks that signal the start or end of a period (Dai et al., 2014(Dai et al., , 2015Peetz & Wilson, 2013. A previous study showed that fresh starts include socially constructed calendar time points such as the first day of a new year or the start of a semester, as well as personally significant events such as wedding days (Dai et al., 2015). ...
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This research examines the process behind the decision to undertake a multi-week hiking trip. It analyzes memoirs to understand people’s interest in, preparation for, and consumption of three long-distance thru-hiking trails – the Camino de Santiago, the Appalachian Trail, and the Pacific Crest Trail. The study reveals that the inspiration for long-distance hiking often arises as a vague notion, serendipitously through accidental exposure. This idea may incubate for years as people add the idea to a mental ‘bucket list’. Most typically, an important event triggers the decision to attempt the trail. Once people commit, preparatio requires information and intensive planning. The trip itself is typically a time of self-discovery. Finally, some hikers become devotees and serve as online evangelists to other potential hikers. These narratives provide an understanding of the psychological aspects behind the consumer journeys and the value of examining people’s narratives as a source of this understanding.
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Purpose Many managers and scholars focus on how to repair brand image after a corporate crisis. This research paper aims to propose that a fresh start mindset (FSM) and brand crisis type can jointly influence consumer forgiveness. Design/methodology/approach Three studies were conducted to examine the authors’ hypotheses. Study 1 is a 3 (FSM: high vs low vs control) × 2 (brand crisis: controllable vs uncontrollable) between-subjects factorial design to test the interaction effect of FSM and brand crisis type on consumer forgiveness. Study 2 is a 2 (FSM: high vs low) × 2 (brand crisis: controllable vs uncontrollable) between-subjects factorial design to identify the affective and cognitive mechanisms in the influence of FSM and brand crisis type on consumer forgiveness. Study 3 is a 2 (FSM: high vs low) × 2 (brand crisis: controllable vs uncontrollable) × 3 (strategy: defensive strategy vs accommodative strategy vs reticence) between-subjects factorial design, aimed to identify the possible boundary conditions of this effect and tested the moderating role of brand crisis response strategies. Findings Study 1 finds that the FSM interacts with brand crisis type to affect consumer forgiveness. Specifically, consumers faced with uncontrollable (vs controllable) brand crises tend to increase (vs decrease) consumer forgiveness after a corporate crisis. Study 2 identifies the underlying mechanism, such that two distinct mechanisms drive the interaction effect. Affective empathy and perceived responsibility mediate the interaction effect of FSM and brand crisis type on forgiveness. Study 3 replicates the findings of studies 1 and 2 and confirms the boundary condition of the effect, showing that crisis response strategy moderates the interaction between FSM and brand crisis type. Originality/value Theoretically, to the best of the authors’ knowledge, this research observes the interactive effect of FSM and brand crisis for the first time, thus extending the existing research on both FSM and brand crisis. This study also enriches existing empathy and responsibility literature by examining the mediating role of empathy and perceived responsibility. Practical implications for marketers are apparent, especially after a brand crisis occurs. Corporates can deal with different types of the brand crisis based on consumers’ FSM. Finally, future research with regards to the findings is discussed.
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This paper reports results from an inter-comparison effort involving different sensors and models used to measure the atmospheric boundary layer height (ABLH). The effort took place in the framework of the first Special Observing Period of the Hydrological Cycle in the Mediterranean Experiment (HyMeX-SOP1), with the Raman lidar system BASIL deployed in Candillargues (southern France) and operating in almost continuous mode over the time period September–November 2012. ABLH estimates were obtained based on the application of the Richardson number technique to Raman lidar and radiosonde measurements and to ECMWF-ERA5 reanalysis data. In the effort we considered radiosondes launched in the proximity of the lidar site, as well as radiosondes launched from the closest radiosonde station included in the Integrated Global Radiosonde Archive (IGRA). The inter-comparison effort also includes ABLH measurements from the wind profiler, which rely on the turbulence method, as well as measurements obtained from elastic backscatter lidar signals. The Richardson number approach applied to the on-site radiosonde data is taken as reference. Measurements were carried out throughout the month of October 2012. The inter-comparison is extended to both daytime and night-time data. Results reveal a very good agreement between the different approaches, with values of the correlation coefficient R2 for all compared data pairs in the range 0.94–0.98. Values of the slope of the fitting line in the regression analysis are in the range 0.91–1.08 for daytime comparisons and in the range 0.95–1.03 for night-time comparisons, which testifies to the presence of the very small biases affecting all five ABLH estimates with respect to the reference ABLH estimate, with slightly smaller bias values found at night. Results also confirm that the combined application of different methods to the sensors and model data allows us to get accurate and cross-validated estimates of the ABL height in a variety of weather conditions. Correlations between the ABLH measurements and other atmospheric dynamic and thermodynamic variables, such as CAPE (convective available potential energy), friction velocity and relative humidity, are also evaluated to infer possible mutual dependences.
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New technologies increasingly enable consumers to track their behaviors over time, making them more aware of their “streaks” – behaviors performed consecutively three or more times – than ever before. Our research explores how these logged streaks affect consumers’ decisions to engage in the same behavior subsequently. In seven studies, we find that intact streaks highlighted via behavioral logs increase consumers’ subsequent engagement in that behavior, relative to when broken streaks are highlighted. Importantly, this effect is independent of actual past behavior, and depends solely on how that behavior is represented within the log. This is because consumers consider maintaining a logged streak to be a meaningful goal in and of itself. In line with this theory, the effect of intact (vs. broken) logged streaks is amplified when consumers attribute a break in the streak to themselves rather than to external factors, and attenuated when consumers can “repair” a broken streak. Our research provides actionable insights for companies seeking to benefit from highlighting consumers’ streaks in various consequential domains (e.g., fitness, learning) without incurring a cost (e.g., reduced engagement or abandonment) when those streaks are broken.
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Mental accounting is the set of cognitive operations used by individuals and households to organize, evaluate, and keep track of financial activities. Making use of research on this topic over the past decade, this paper summarizes the current state of our knowledge about how people engage in mental accounting activities. Three components of mental accounting receive the most attention. This first captures how outcomes are perceived and experienced, and how decisions are made and subsequently evaluated. The accounting system provides the inputs to be both ex ante and ex post cost–benefit analyses. A second component of mental accounting involves the assignment of activities to specific accounts. Both the sources and uses of funds are labeled in real as well as in mental accounting systems. Expenditures are grouped into categories (housing, food, etc.) and spending is sometimes constrained by implicit or explicit budgets. The third component of mental accounting concerns the frequency with which accounts are evaluated and ‘choice bracketing’. Accounts can be balanced daily, weekly, yearly, and so on, and can be defined narrowly or broadly. Each of the components of mental accounting violates the economic principle of fungibility. As a result, mental accounting influences choice, that is, it matters. Copyright © 1999 John Wiley & Sons, Ltd.
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We describe a field experiment in which merely asking people about their goals prior to performance improved performance among experienced but not novice individuals. Whereas most previously-studied goal interventions involve externally-induced goals, our intervention targeted self-set goals. 1,758 marathoners were either asked or not asked to provide a time goal prior to their race. Although our manipulation did not influence the proportion of marathoners who established time goals, experienced marathoners who were asked about their goal in a pre- marathon survey ran 6.75 minutes faster than those who were not asked about their goal. The effect of our goal-asking manipulation on performance was mediated by the ambitiousness of marathoners’ time goals. We suggest that our manipulation increases goal ambitiousness by interrupting the typical decline in optimism as performance approaches.
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People often experience tension over certain choices (e.g., they should reduce their gas consumption or increase their savings, but they do not want to). Some posit that this tension arises from the competing interests of a deliberative "should" self and an affective "want" self. We show that people are more likely to select choices that serve the should self (should-choices) when the choices will be implemented in the distant rather than the near future. This "future lock-in" is demonstrated in four experiments for should-choices involving donation, public policy, and self-improvement. Additionally, we show that future lock-in can arise without changing the structure of a should-choice, but by just changing people's temporal focus. Finally, we provide evidence that the should self operates at a higher construal level (abstract, superordinate) than the want self, and that this difference in construal partly underlies future lock-in.
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Although observers of human behavior have long been aware that people regularly struggle with internal conflict when deciding whether to behave responsibly or indulge in impulsivity, psychologists and economists did not begin to empirically investigate this type of want/should conflict until recently. In this article, we review and synthesize the latest research on want/should conflict, focusing our attention on the findings from an empirical literature on the topic that has blossomed over the last 15 years. We then turn to a discussion of how individuals and policy makers can use what has been learned about want/should conflict to help decision makers select far-sighted options. © 2008, Association for Psychological Science. All rights reserved.
Conference Paper
Mental accounting is the set of cognitive operations used by individuals and households to organize, evaluate, and keep track of financial activities. Making use of research on this topic over the past decade, this paper summarizes the current state of our knowledge about how people engage in mental accounting activities. Three components of mental accounting receive the most attention. This first captures how outcomes are perceived and experienced, and how decisions are made and subsequently evaluated. The accounting system provides the inputs to be both ex ante and ex post cost-benefit analyses. A second component of mental accounting involves the assignment of activities to specific accounts. Both the sources and uses of funds are labeled in real as well as in mental accounting systems. Expenditures are grouped into categories (housing, food, etc.) and spending is sometimes constrained by implicit or explicit budgets. The third component of mental accounting concerns the frequency with which accounts are evaluated and 'choice bracketing'. Accounts can be balanced daily, weekly, yearly, and so on, and can be defined narrowly or broadly. Each of the components of mental accounting violates the economic principle of fungibility. As a result, mental accounting influences choice, that is, it matters. Copyright (C) 1999 John Wiley & Sons, Ltd.