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DEFAULTS & DECISION TIME 1
The Limits of Defaults:
The Influence of Decision Time on Default Effects
Benjamin X. White, Duo Jiang, and Dolores Albarracín
University of Illinois at Urbana Champaign
Manuscript in press in Social Cogntion
DEFAULTS & DECISION TIME 2
Abstract
The stability of default effects to contextual features is critical to their use in policy. In this
paper, decision time was investigated as a contextual factor that may pose limits on the efficacy
of defaults. Consistent with the hypothesis that time constraints may increase reliance on
contextual cues, four experiments, including a preregistered one of a nationally representative
sample and a meta-analysis including three pilot experiments indicated that short decision times
increased the advantage of action-defaults (i.e., the default option automatically endorses the
desired behavior) and that the default advantage was trivial or nonexistent when decision times
were longer. These effects replicated for naturalistic as well as externally induced decision times
and were present even when participants were unaware that time was limited. This research has
critical implications for psychological science and allied disciplines concerned with policy in the
domains of public health, finance and economics, marketing, and environmental sciences.
Keywords: default effect, time, decision making, donation, evaluations, action and inaction
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The Limits of Defaults:
The Influence of Decision Time on Default Effects
If you want people to donate their organs, make donation the default decision when they
obtain a driver’s license! resonates with psychologists and policy makers alike. The action
default (i.e., the default option automatically endorses the desired behavior) is an easy way to
“channel” or “nudge” decision makers (Thaler & Sunstein, 2008), and has been portrayed as
guiding behavior in a powerful way (Johnson & Goldstein, 2003; Madrian & Shea, 2001; Thaler
et al., 2012). As shown by the quotes below, setting the recommended behavior as the default
option has been touted as the answer to increasing support for decisions like organ donation
(Johnson & Goldstein, 2003), pension-plan choices (Madrian & Shea, 2001), car insurance
(Johnson et al., 1993), and taxi tips (Haggag & Paci, 2014):
The United States could save a lot of lives if more people donated their organs. How can
donation rates be increased? You will not be stunned to hear that a switch in the default
rule would have a major impact. (Thaler & Sunstein, 2009, p. 159).
Empirically, default effects are both powerful and law-like (Smith, Goldstein, & Johnson,
2013, p.160).
Defaults and their effects are ubiquitous (Johnson & Goldstein, 2012, p.423).
A recent meta-analysis, however, found substantial heterogeneity in the efficacy of
setting an action such as donating as the default (I2 = 98%), with many studies documenting null
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or negative action default effects (Jachimowicz et al., 2019). Even among the majority of studies
that did find positive effects, those effects varied in size from small (d = 0.1) to large (d = 0.8),
and the inclusion of study characteristics, domain, and theoretical moderators reduced the
variance by only 6.7%. What could lead to such high levels of unexplained heterogeneity is thus
a critical question, particularly given recent failures of defaults to produce impact in contexts
such as vaccination (Reiter et al., 2012), organ donation (Parsons, 2018), and financial decisions
(Willis, 2013). One understudied factor that was not considered either in the meta-analysis or the
literature at large is time, arguably the elephant in the room of default effects. Considering how
often we end up “choosing” the default taxi tip because we run out of time to board a flight, who
could doubt that some of the most powerful effects of defaults ought to be due to limited time to
opt out of default choices? How could such a consequential factor be considered trivial enough
to be have been ignored in the literature? We believe that having an answer to this question is
key to policy in the domains of public health, finance and economics, marketing, and
environmental sciences.
The Default Effect and Time
Plenty of evidence supports advantages of action defaults when policy makers desire to
steer a reluctant audience in the “right direction.” For example, participants in an online
experiment were asked whether they would be organ donors if they were to move to a new state.
They were given (a) the default to donate, (b) the default to not donate, or (c) a neutral format
that required active choice (Johnson & Goldstein, 2003). In this study, 82% of participants chose
to donate when the default was to donate, and 79% chose to donate with the neutral condition of
active choice. However, only 42% chose to donate when the default was to not donate. Similarly,
86% of new employees signed up for a retirement plan when the plan was set as the default,
DEFAULTS & DECISION TIME 5
whereas only 50% chose the plan when it was not preselected (Madrian & Shea, 2001). This
large difference between action defaults and no-action defaults implies an action default
advantage with potentially major policy implications.
Despite these earlier promising results, recent research has found defaults effects to be
smaller and less consistent across situations. As mentioned before, the meta-analysis by
Jachimowicz et al.’s (2019) yielded an overall positive effect for defaults (d = 0.68) but negative,
null, and positive effects ranging from -0.5 to 2 across studies and sizeable unexplained
heterogeneity (over 90% accounting for all moderators). Furthermore, in recent policy studies,
instituting donation defaults did not increase donation (Parsons, 2018), and instituting Human
Papilloma Virus vaccination defaults had either not significant or opposite effects (Reiter et al.,
2012). These inconsistencies are also reflected in a growing interest in why nudges might fail
(e.g., Sunstein, 2017) and a need to identify their boundary conditions.
We propose that action defaults are present in contexts that prevent deliberation (for
theories about the effects of such contexts, see Kahneman & Egan, 2011; Petty & Cacioppo,
1986), such as when people have limited time to make a decision. Thaler and Sunstein have
recognized that “a change entails time and effort, and many people seem to prefer to avoid both
of these” (p.177, 2003). More generally, researchers have emphasized the need to use action
defaults while at the same time preventing decision procrastination, often by imposing financial
penalties to delays. For example, O’Donoghue and Rabin (1998) wrote that “a person will
procrastinate in preparing for retirement unless the cost of a short delay is sufficient to overcome
the desire to put in the effort sometime in the future” (p. 26). In agreement with this point, Carrol
(2009) stated that defaults may be advantageous because an “active decision mechanism compels
DEFAULTS & DECISION TIME 6
agents to struggle with a potentially time-consuming decision—which they may not be qualified
to make.” (p. 2).
Even though default effects have been predicted to operate in conjunction with
mechanisms that accelerate decision making (e.g., ease or effort; Johnson & Goldstein, 2003;
Dinner et al., 2011), surprisingly little prior research has properly investigated the effect of
decision time. When people encounter a decision, they need time to make it. Hence, we
hypothesized that following an action default is one way people use to cope with having limited
time to make their decision. The effect of defaults for taxi tips is one scenario – if people want to
tip and are in a rush, they should be more likely to donate the default amount to conserve effort
while leaving the cab (Haggag & Paci, 2014). In comparison, the effects of defaults in scenarios
that typically involve long decision times, like deciding whether to vaccinate a child, sharply
contrast with typical findings as was found when the effect was reversed when implementing
defaults to increase the HPV vaccine uptake (75% with a No-Action Default vs. 52% with an
Action Default; Reiter et al., 2012).
The hypothesis that decision time is important to the decision-making process is common
across multiple research areas. For instance, people who encounter decisions with too many
options experience decision overload when they have a shorter (vs. longer) time to make a
decision (Chernev, Böckenholt, & Goodman, 2015; Mcshane & Böckenholt, 2018). Similarly,
people who have a short time to examine information and make a decision are more likely to use
heuristics (Dhar & Nowlis, 1999; Weenig & Maarleveld, 2002; Wright, 1974; for a review, see
Ariely & Zakay, 2001). For example, people who lack decision ability frequently rely on such
heuristics as using price as indicator of product quality (Suri & Monroe, 2003), or basing a
judgment on the mood they experience for unrelated reasons (Schwarz & Clore, 2004, 1983).
DEFAULTS & DECISION TIME 7
Considering these findings, the decision time allowed by the situation should be important for
default options as well, particularly because the default provides a decision in the event the
people fail to make up their mind.
Indeed, past scholarship on defaults has proposed that decision time is likely to moderate
the effect (Johnson & Goldstein, 2003). Yet, Jachimowicz et al. (2019) meta-analytically tested
if default effects varied by how difficult it was to make a decision and found no effect of ease of
decision making (b = -.05, SE = 0.15, p = .75). This finding would suggest that difficult contexts
such as not having sufficient time might produce similar effects as those without such
constraints. However, they defined ease as playing a role only when participants selected the
“defaulted choice option because it iseasier to stay with the pre-selected option than to choose a
different option (p.172)” and could include things outside of having to make a quick decision
Additionally, to the best of our knowledge, the only two studies that directly examined the
influence of time on defaults produced null effects. First, in a study examining the use of defaults
in the selection of light bulbs, whether participants took a shorter or longer time to make
decisions made no difference for the impact of the action default (Experiment 2; Dinner et al.,
2011). Second, in a study examining the use of defaults in the selection of hotel amenities,
experimentally manipulating suggested decision time had no association with default
endorsement (Steffel et al., 2014). Thus, despite other areas finding effects of decision time on
decision making, evidence from these studies would suggest that time is unrelated to the impact
of the action default.
One possible reason why decision time had null effects in the Dinner et al.'s (2011) and
Steffel et al.’s (2014) experiments is that the times were not properly calibrated to detect effects
that may well be present in common decisions. In the Dinner et al.’s study, participants’
DEFAULTS & DECISION TIME 8
decisions were slow and wide ranging, with quick decision makers taking approximately 20
seconds and slow ones taking approximately one minute. In the Steffel et al.’s study, participants
were recommended to either take at least one minute or were given no such recommendation, but
participants in both conditions took close to 5 minutes to make their decision (i.e., 4.5 minutes
vs. 5 minutes). Thus, neither study experimentally manipulated actual decision time (for an
analysis of problems with naturally occurring decision times, see Krajbich, Bartling, Hare, &
Fehr, 2015), and neither study had a condition with a sufficiently short decision time to provide a
valid test of the effect of decision times.
Even though the two previously mentioned studies that have empirically analyzed
decision time did not find effects, it is important to look at descriptions of other default
experiments to see if the action default advantage prevails for quickly made decisions. Even if
explicit time constraints are not placed on participants, an experiment’s context could still lead
participants to make quick decisions. We present examples in Table 1. Field studies with
decisions typically requiring little time, such as street petitions and taxi tips (e,g., Haggag &
Paci, 2014; Johnson & Goldstein, 2003; Johnson et al., 1993), have tended to show action default
advantages. In contrast, studies with decisions typically requiring more time, such as healthcare
and financial decisions, (e.g., Brown & Krishna, 2004; Di Guida, Marchiori, & Erev, 2012;
Keller, Harlam, Loewenstein, & Volpp, 2011; Shepherd & O’Carroll, 2013) have found null
effects or even reversals of the action default advantage (see Table 1). These data illustrate the
possibility of decision time may have an effect based simply on the natural time constraints of
the situation. Whether this effect occurs experimentally was directly investigated in the
experiments we conducted.
DEFAULTS & DECISION TIME 9
Another important question is whether decision time exerts effects due to reductions in
ability. If a shorter decision time simply limits people’s ability to make decisions, time limits
should increase the action-default advantage because the format channels a particular decision
irrespective of whether people perceive time pressure. In this case, people may select the default
choice because they cannot consider the alternative or cannot swap their choice within the
allotted time. In contrast, people may be aware that their time is limited and still choose the
default. In a study manipulating cognitive load, ability to deliberate on decisions did not
influence selection of the default option (Van Gestel et al., 2020). Hence, we manipulated actual
and perceived time limits in one of our experiments to tease out the impact of actual and
perceived time. If perceived time has an impact, then perceiving a time limit should reduce the
action default advantage. If actual time has an impact, then actual time should reduce the action
default advantage.
The Present Experiments
Research investigating the influence of decision time on the effects of no-action defaults
has suffered from the limitation of either not manipulating decision time or doing so in a way
that might have obscured effects. The present research was designed to estimate decision time
effects both naturalistically (Study 1) and experimentally (Studies 2-4). We hypothesized that
donation defaults would have stronger influences when the time to make a decision is short. We
measured donation evaluations because defaults bias evaluations in line with the pre-selected
default (Experiment 1; Dinner et al., 2011; for the general notion of biased scanning, see
Albarracín & Wyer Jr, 2000). We also measured social norms because defaults can affect
perceptions of social norms (Everett et al., 2015; Huh, Vosgerau, & Morewedge, 2014) and
because social norms guide behavior when people are under time pressure (Rand et al., 2014).
DEFAULTS & DECISION TIME 10
We conducted four experiments to examine the effects of decision times on donation to a
charitable or service organization using an action or no-action-default format. Experiment 1 used
a naturalistic measure of decision time and estimated associations with donation choices for
different defaults. Experiments 2, 3, and 4 experimentally manipulated choice time to exclude
the possibility of reverse causality and confounds related to naturally varying decision times
(e.g., educational level). The manipulations of Experiments 2 and 3 combined actual and
perceived time, such that when participants were given only 2 seconds, they knew that they only
had 2 seconds. Thus, to clarify whether actual or perceived time matters, Experiment 3 assessed
if perceived decision time was responsible for the effect of our earlier time manipulation, and
Experiment 4 experimentally separated actual and perceived time. Finally, a meta-analysis
synthesized these four experiments along with three additional pilot experiments, for
completeness and to reach overarching conclusions. All statistical analyses involved two-sided
tests of statistical significance. All data presented can be accessed from the online repository
(https://osf.io/b4kyq/?view_only=d9bec36d56a9458abf0b0520bb3d2bfa). The last experiment,
which as conducted with a nationally representative sample, was preregistered
https://aspredicted.org/k6id6.pdf
Experiment 1: Measuring Associations with Naturally Occurring Decision Times
We predicted that the action-default format would lead to higher likelihood of donation
than the no-action-default format, particularly when participants spend relatively less time
making their decision. Therefore, in Experiment 1, we gave participants the option of donating
money to a charity. After being awarded extra money for participating in an unrelated prior task
(i.e., sorting household goods into self-determined groups), participants were asked if they
wanted to donate these earnings to a charity ($0.10). They learned either that they would donate
DEFAULTS & DECISION TIME 11
unless they indicated that they did not want to (Action-Default Condition), or that they would not
donate unless they indicated that they did want to (no-action-default condition). We measured
the time participants spent making the decision as a critical factor expected to interact with our
choice-format manipulation.
Method
One hundred and twenty-two participants from the Amazon Mechanical Turk website
(53% female, Mage = 38.07, SDage = 13.00, age range from 20 to 74, 98% native speakers of
English, 80% Caucasian, 7% African American, 5% Hispanic, 5% Asian, 4% Other Ethnicity)
were recruited to participate in this study in exchange for $0.50. Sample sizes of all three
experiments were based on past research (e.g., Jachimowicz et al., 2019). We employed a 2-
choice format (action default vs. no-action-default) × continuous decision time in which choice
format was a between-subjects manipulation and time was a subject variable. Using the pwr
package in R (Champely et al., 2018) and given the meta-analytic estimate of d = 0.68
(Jachimowicz et al., 2019), we expected to detect a main effect of choice format with 95%
power. There were 60 subjects in the action-default condition and 62 subjects in the no-action
default condition.
Participants first completed a sorting task and were then told that they would receive an
additional $0.10 payment to reward their performance. We chose $0.10 because small donations
are becoming increasingly common in everyday life, from round-up donations at checkouts
(Hwang, Choi, & Mattila, 2020) to small donations to crowdsourced projects or political
campaigns (Center for Responsive Politics, 2020). They were then directed to a second page
(denoted page 2) where they read about a donation campaign for a child cancer nonprofit charity
and told that the researcher was conducting a fundraiser to generate donations for this charity.
DEFAULTS & DECISION TIME 12
Below this description on the same page, participants were then told they would be asked to
choose whether or not they wanted to donate their extra earnings to the charity in a future
question. Half of the participants were told that they would donate unless they clicked on a mark
(a smiley face; see Figure 1 action-default condition). The other half were told that they would
not donate unless they clicked on the smiley face on the next page (no-action-default condition).1
Further below these first two points on this same page, all the participants were asked to write
down their understanding of the instructions to ensure successful manipulation of the choice
format. Ninety six percent of the participants understood the choice format correctly (indicating
both what the decision was and what option would be pre-selected), suggesting that the
manipulation was successful.
After this manipulation check, participants proceeded to the next page where they were
presented with a question corresponding to their assigned default regarding if they would donate
their additional funds. The time they spent making the choice on this page was recorded in
seconds. Then, participants reported demographic information. At the end of the study,
participants who chose not to donate received their extra $0.10 in addition to their original
payment obtained for their participation in the sorting task. Any amount that was donated by
participants was donated to the organization. In addition, we measured evaluations of donation
behavior using the following scale (see Supplement for screenshots of participant perspective).
Evaluations. Participants answered four items regarding their donation evaluations. We
introduced the items with “Please indicate to what extent you agree with the following
statements.” The four items used the scale 0: Not at all – 10: Very Much and were: (a) Donating
1 We utilized a smiley face to allow for easy processing of the choice format. Later studies established
generalizability by using other formats.
DEFAULTS & DECISION TIME 13
money is something I dislike, (b) When I was considering my decision, I liked the idea of
donating money, (c) When I was considering my decision, I thought donating money was the
right choice, and (d) When I was considering my decision, I was not interested in donating
money. The full scale displayed strong internal consistency (α = .92). The average of the four
items was used as a measure of evaluations.
Results
We predicted that participants who made faster choices would donate more with the action-
default format than with the no-action-default format. For analysis, choices were recorded to
represent choice to donate (1 = donation, 0 = no donation) regardless of format. As decision time
can be heavily skewed, we assessed the normality of the distribution and the impact of outliers.
We found the data to be moderately skewed (M = 3.75, SD = 3.90, Min = 0.44, Max = 30.86).
We checked if the two conditions were different and found moderate, non-significant mean
differences in decision time between the conditions: Mean of 3.18 seconds and SD of 3.89 in the
No-Action Default condition vs. 4.30 seconds and SD of 3.86 in the Action Default condition (t
= 1.59, p = .114). However, the distributions were similar in both groups. We assessed methods
of normalizing the data using Tukey’s ladders of power and found that a log transformation best
normalized the data.2
We first tested whether there was a main effect of default condition on donation rates. A
logistic regression comparing Action Defaults (50% donation rate) to No-action Defaults (48%
donation rate) showed no evidence that the default manipulation affected donation rates (B =
2 This transformation does not change the findings in a substantive way. We report the un-transformed data with
analyses in the Supplement. Overall, all coefficients were still significant and had the same direction, but the model
displayed better AIC/BIC/ χ2 compared to the untransformed model.
DEFAULTS & DECISION TIME 14
0.067, SE = 0.36, p = .85). We then performed a logistic regression with donation choice as the
outcome and choice format, log decision time, and their interaction as predictors. Donation
evaluations were controlled for in our analyses. As shown in Table 2, the interaction between
choice format and decision time was significant, (dinteraction = 0.61, 95%CI: [0.25,0.97]), as were
the main effects of choice format and time. To probe the interaction, we used the Johnson-
Neyman technique as shown in Figure 2 (i.e., floodlights analysis; Spiller, Fitzsimons, Lynch, &
McClelland, 2013). This analysis revealed that the action default led to more donation when
participants spent less than 1.60 seconds making the decision (BJN = 0.999, SE = .510, p = .05),
In contrast, when participants spent a longer time making the decision, they actually donated less
with the action-default format than with no-action-default format after 3.90 seconds (BJN =
−1.119, SE = .571, p = .05).
Discussion
Experiment 1 showed that, when participants spent less time making a decision, the action-
default format was associated with more donations than the no-action-default format. Although
these results were consistent with our predictions, the use of a subject variable to analyze the
effect of time introduces interpretational problems because correlation is not causation. There is
fundamental difference between people making naturally quick decisions and being constrained
to make quick decisions, although we take the results of Experiment 1 as an initial indication of
the plausibility of our hypothesis. Therefore, it was desirable to replicate our results within an
experiment in which decision time was also experimentally manipulated. In addition, the use of a
smiley face is not necessarily conventional for these kinds of decisions, so all further studies
involved selecting between two bullet choices of which one was initially selected.
DEFAULTS & DECISION TIME 15
Experiment 2: Manipulating Decision Times
In Experiment 2, we directly manipulated decision time and expected that, when the time
to make the presented decision is short, the action-default format would lead to more donation
than the no-action-default format. In contrast, when decision time is longer (i.e., unlimited), the
action-default format may lead to more compliance than (see Experiments 1) or no advantage
over the no-action-default format. In addition to measuring decisions, we also measured conation
evaluations. Furthermore, as explained presently, the manipulation involved actual differences in
time, but participants knew how much time they had. This issue received further attention in the
subsequent experiments.
Method
One hundred and nineteen participants from the Amazon Mechanical Turk website (43%
female, Mage = 35.01, SDage = 11.85, age 19 to 66, 98% native speakers of English, 73%
Caucasian, 11% African American, 7% Hispanic, 7% Asian, 3% Other Ethnicity) were recruited
to participate in this study in exchange for $0.50 payment. We used a 2-choice format (action
default vs. no-action-default) × 2 decision time (shorter vs. longer) between-subjects design. (We
did not test the continuous effects of time in the longer time group due to the reduced sample size
compared to Experiment 1.)
As in Experiment 1, participants were awarded additional $0.10 in earnings for a prior
unrelated task. Unlike Experiment 1, which used real donations, participants in Experiment 2
were asked to imagine that they had the opportunity to donate the additional money to the child
cancer nonprofit charity presented in Experiment 1. The action-default format was manipulated
in the same way as in Experiment 1, and a manipulation check showed that 97% of participants
understood the choice format correctly. Furthermore, participants were told that they had either 2
seconds (Shorter Time Condition) or unlimited time (Longer Time Condition) to make the
DEFAULTS & DECISION TIME 16
decision. We chose 2 seconds to mimic the naturalistic findings from Experiment 1. Unlike in
Experiment 1, participants chose between two bullet point options rather than clicking on a
smiley face to make their decision. The option order (top vs. bottom choice) was randomized
between participants to eliminate order effects. At the end of the study, all the participants
received their $0.10 earnings in addition to their original payment. Given our sample size, we
calculated that our study had 80% power to find an interaction of size d = 0.74. The distribution
across conditions was: 30 in no-action default & longer time; 30 in action default & longer time;
29 in no-action default & shorter time; 30 in action default & shorter time. As in Experiment 1,
we used the following donation evaluation scale as a control in our analyses:
Evaluations. Participants answered four items regarding their donation evaluations. We
introduced the items with “Please indicate to what extent you agree with the following
statements.” The four items used the scale 0: Not at all – 10: Very Much and were: (a) Donating
money is something I dislike, (b) When I was considering my decision, I liked the idea of
donating money, (c) When I was considering my decision, I thought donating money was the
right choice, and (d) When I was considering my decision, I was not interested in donating
money. The full scale displayed strong internal consistency (α = .84). The average of the four
items was used as a measure of evaluations.
Results
Confirming our manipulation, participants in the shorter time conditions made a decision
more quickly (M = 1.71 , SD = 0.46) than those in longer time conditions (M = 5.74 , SD = 4.87;
t = 8.77 , p < .001).We performed a logistic regression with donation behavior as the outcome
and choice format, decision time (shorter vs. longer), and their interaction as predictors. As
shown in Table 3, the interaction between choice format and decision time was significant. When
DEFAULTS & DECISION TIME 17
decision time was only 2 seconds, the action-default format (73%) led to more donations than the
no-action-default format (31%; p < .001). In contrast, when decision time was longer, the action-
default format (50%) had no significant advantage over the no-action-default format (57%, p =
.605; see Table 4 and Figure 3; dinteraction = 1.18, 95%CI: [0.62,1.73]).
Discussion
Experiment 2 included a manipulation of decision time, which, like the natural variation in
decision times in Experiment 1, produced a larger action default advantage when decision time
was shorter. Experiment 2 was designed to produce a replication of this effect. Furthermore,
Experiment 2 tested whether the effect of our omnibus manipulation disappeared after
controlling for participants’ perceptions that the time was limited. Such evidence would suggest
that the effect of our decision time manipulation was due to motivation rather than actual ability
to make a decision. In addition, we controlled for social norms and evaluations regarding
donation, as defaults can change these perceptions often predict donation.
Experiment 3: Manipulating Decision Time
Two hundred and twenty-two participants from the Amazon Mechanical Turk website
(43% female, Mage = 32.77, SDage = 10.5, age 19 to 76, 98% native speakers of English, 73%
Caucasian, 4% African American, 7% Hispanic, 6% Asian, 10% Other Ethnicity) were recruited
to participate in this study in exchange for $0.40 payment. We used the same 2-choice format
(action default vs. no-action-default) × 2 decision time (shorter vs. longer) between-subjects
design as Experiment 3. Finally, participants also answered questions about the perceived time
they had to make a decision, social norms regarding the decision, and their donation evaluations
immediately after making their decision. Given our sample size, we had 80% power to find an
interaction of size d = 0.54. The distribution across conditions was: 56 in no-action default &
DEFAULTS & DECISION TIME 18
longer time; 54 in action default & longer time; 56 in no-action default & shorter time; and 56 in
action default & shorter time.
Method
Experimental procedures were the same as in Experiment 2. For self-reported measures,
we used a total of 10 items to measure perceived time, social norms, and evaluations. We
assessed measurement validity using both internal consistency (Cronbach’s Alpha) and
Confirmatory Factor Analyses of all items. Factor loadings and model fit were sufficient for the
full model (RMSEA = .083, 95% CI: [.032, .129], CFI = .95; See Supplement for full details).
Decisions regarding final scale items were made using information from both statistical analyses.
Perceived time. Participants answered three items regarding the perceived time they had
to make a decision. We introduced the items with “Please indicate to what extent you agree with
the following statements” and each item used the scale [0: Not at all – 10: Very Much]. The
items were: (1) I felt prepared to make a decision when I was asked to, (2) I was able to make the
decision I wanted to when the choice was presented, and (3) I did not have enough time to make
my decision. The third item was reverse-scored and the average of the three items was sued as an
index of perceived time. The scale displayed sufficient internal consistency (α = .80).
Social norms. Participants answered three categorical items regarding perceived social
norms. The questions were: (a) When you made your decision, what did you think the social
norm was, (b) When you made your decision, what did you think others might do in this situation.
Each item had three response categories. Each question had three categorical answers: (a) “To
donate”, (b) “To not donate”, or (c) “Unsure”. For relevant analyses, we removed these third
responses as endorsing these options was uncorrelated between items. The two items displayed
moderate internal consistency (α = .69).
DEFAULTS & DECISION TIME 19
Evaluations. Participants answered six items regarding their donation evaluations. We
introduced the items with “Please indicate to what extent you agree with the following
statements.” The first five items used the scale 0: Not at all – 10: Very Much and were: (a)
Donating money is something I value, (b) Donating money is something I enjoy, (c) When I was
considering my decision, I liked the idea of donating money, (d) When I was considering my
decision, I thought donating money was a good idea, and (e) When I was considering my
decision, I was not interested in donating money. We also asked them to complete a bipolar scale
item “Donating money is [0: Unpleasant – 10: Pleasant]. The full scale displayed moderate
internal consistency (α = .67), which increased to α = .86 after removing When I was considering
my decision, I was not interested in donating money. The average of the remaining five items
was used as a measure of evaluations, although results replicated with the full scale.
Results
Once again, participants in the shorter time conditions decided more quickly (M = 2, SD
= 0.2) than those in longer time conditions (M = 6.1 seconds, SD = 6.7). We first checked for a
main default effect. A logistic regression comparing Action Defaults (80% donation rate) with
No-action Defaults (45% donation rate) showed no main effect of the default manipulation on
donation rates (B = 1.44, SE = 0.35, p < .001). We then performed a logistic regression with
donation behavior as the outcome and choice format, decision time (shorter vs. longer), and their
interaction as predictors. In addition to donation evaluations, we also controlled for donation
norms and perceived time to make a decision. As shown in Table 4, the interaction between
choice format and decision time was significant, as was the main effect of choice format. When
decision time was 2 seconds, the action default choice format (86%) led to more donations than
the no-action-default choice format (32%), p = .001. In contrast, when the decision time was
DEFAULTS & DECISION TIME 20
longer, the action default choice format (74%) had a marginal advantage over the no-action-
default choice format (57%), p = .064 (dinteraction = 1.05, 95%CI: [0.652,1.446]).
Finally, we conducted analyses to determine if the perceived aspects of our manipulation
contributed to the effect of the decision time manipulation. We ran a linear regression with self-
reported perceived time to respond as the outcome and choice format, decision time (shorter vs.
longer), and their interaction as predictors. There was neither a main effect of limited time
condition (B = 0.117, SE = 0.294, p > .05), nor an interaction effect (B = -0.469, SE = 0.418, p >
.05). This pattern of results was present when the analyses were repeated with the individual
perceived time items instead of the complete measure of perceived time. In conclusion, across
decision-time conditions, participants did not report large differences in the perceived time to
make a decision, suggesting that actual rather than perceived time was likely at play.
Discussion
Experiment 3 had two goals. First, we were interested in replicating our finding that the
action-default advantage is more prevalent when people have a short time to make a decision and
did in fact replicate this finding. Second, we tested whether the effect of decision time
disappeared when controlling for the effects of perceived decision time. The results suggested
that our effects were due to actual rather than perceived time even when both were introduced
together in our manipulation. However, it was desirable to separate the actual/perceived time
manipulations through another study. Therefore, in the following experiment (Experiment 4), we
crossed a manipulation of actual time with a manipulation of awareness of time. Both could have
independent effects and a factorial designed allowed us to examine this possibility.
As discussed previously, participants’ perception that they have limited time could alter
their decision-making process. Participants may be more motivated to make a decision if they
DEFAULTS & DECISION TIME 21
know they need to do so quickly (Amabile et al., 1976). If motivation is the key, and not actual
time, then perceived time should be more important actual time. An additional possibility is that
people may also see the use of a default as a manipulative attempt to favor the default option,
thus increasing bias correction (e.g., Wegner & Petty, 1997). In the following experiment, we
therefore separated participants having limited time to make a decision from perceiving that
there was a time limit. We also pre-registered Experiment 4 https://aspredicted.org/k6id6.pdf; for
the advantages of preregistration, see van ’t Veer & Giner-Sorolla, 2016).
Experiment 4: Preregistered Test of the Impact of Actual and Perceived Time
The primary purpose of this pre-registered experiment was to further demonstrate that
time limits promote default effects because of the actual time constraints of the situation instead
of because of an increase in the motivation to make a decision earlier. Our previous experiments
had manipulated actual decision time but, at the same time, participants had been told that they
had either 2 seconds (Shorter Time Condition) or unlimited time (Longer Time Condition) to
make the decision. Hence, in addition to the previous four conditions used in Experiments 2 and
3, we added four additional conditions in which participants were unaware of how long they
would have to make their decision. We had three hypotheses: Hypothesis 1: across the board,
participants would be more likely to select the default option, leading to more donations in the
Action Default condition than the No-Action Default condition (main effect of choice format);
Hypothesis 2: participants would be more likely to select the default option in actual shorter-time
conditions, producing a greater advantage of the Action default in shorter time situations (i.e., an
interaction between default type and actual time condition); and (c) Hypothesis 3: participants
would be more likely to select the default option, producing a larger Action-default advantage,
when they perceived that time was shorter rather than longer (i.e., an interaction between choice
format, actual time, and perceived time).
DEFAULTS & DECISION TIME 22
Method
Six hundred and thirty-seven participants were recruited through Dynata, a data platform
that provides a representative sample of U.S. respondents (53% female, Mage = 45.7, SDage =
16.6, age 18 to 88, 94% native speakers of English, 75% Caucasian, 13% African American, 9%
Hispanic, 3% Asian, 0% Other Ethnicity) for industry-standard payment (see pre-registration file
for review).3 We used a 2-choice format (action default vs. no-action default) × 2 actual decision
time (shorter vs. longer) × 2 perceived decision time (aware vs. not aware of the available decision
time) between-subjects design.
As in all previous experiments, participants were awarded additional $0.10 in earnings
for a prior unrelated task. The default manipulations were the same as in Experiments 2 and 3.
The actual decision time manipulation involved participants being allocated to either having two
seconds or unlimited time to make their decision. The perceived time limit manipulation
involved telling or not telling participants how long they had to make their decision. The
conditions with time perception replicated Experiments 2 and 3. The conditions without time
perception had the same decision times but our description of the task did not mention how long
the participant had time to make their decision. That is, participants in the action-default, actual
short time, no time perception condition were not told that they had two seconds to make their
decision. Likewise, participants in the action-default, actual long time, no time perception
condition were not told that they had unlimited time to make their decision.
3 Our pre-registration stated we would recruit 400 participants. Dynata obtained more participants than planned due
to the possibility of incomplete cases and data problems. However, we did not receive the dataset until all responses
had been completed, and had no opportunity to check results or stop data collection prior to receiving the data used
in these analyses.
DEFAULTS & DECISION TIME 23
The other procedures were the same as in Experiments 2 and 3. At the end of the study,
participants who chose not to donate received their extra $0.10 in addition to their original
payment obtained for their participation in the sorting task. Any amount that was donated by
participants was donated to the organization. Given our sample size, we calculated that we had
80% power to detect a two-way interaction of size d = 1.31. The distribution of participants
across conditions was: 77 in no-action default, actual longer time, aware of time; 77 in action
default, longer actual time, perception of time; 79 in no-action default, shorter actual time,
perception of time; 81 in action default, shorter actual time, perception of time; 82 in no-action
default, longer actual time, no perception of time; 80 in action, longer actual time, no perception
of time; 82 in no-action default, shorter actual time, no perception of time; and 79 in action
default, longer actual time, no perception of time.
Results
Similar to our previous experiments, we ran a logistic regression predicting the likelihood
of donating from all the main effects and interactions involving our three experimental
manipulations. As in all previous experiments, participant donation evaluations were included as
a control variable. Table 5 presents a progression of models testing our four hypotheses. In line
with Hypothesis 1, participants were more likely to select the default option than the alternative
option. A logistic regression comparing Action Defaults (76% donation rate) with No-action
Defaults (47% donation rate) revealed an overall positive effect of the action-default on donation
rates (B = 1.42, SE = 0.319 p < .001). In addition, in line with Hypothesis 2, participants were
more likely to donate if they were in Action Default condition and had a shorter (vs. longer)
actual time to make a decision (86% with shorter time vs. 66% with longer time). When time
was longer, there was no significant difference between the action and no-action defaults (66%
DEFAULTS & DECISION TIME 24
with action default vs. 64% with no-action default, p = 0.506). This pattern was supported by a
significant interaction between default condition and actual time dinteraction = 1.56, 95%CI:
[1.33,1.79].
To test Hypothesis 3, we ran a logistic regression with choice format, actual time,
perceived time, and all interactions predicting likelihood of donation. Counter to Hypothesis 3,
participants were not more likely to select the default option when they perceived that time was
shorter than longer (for actual short time, an Action Default advantage of 46% for perceived time
vs. an advantage of 65% for not perceived time; Table 5, panel “Hypothesis 3”). As can be seen
in the left panel of Figure 5, the Action Default advantage was actually greater when participants
in the short actual time condition were unaware of how much time they had. This result implies
that the time perception did not cause the default advantage, and if anything, it diminished it.
Finally, it is worth noting that perceived ability to respond was controlled for in all
analyses and that the effects remained the same despite this statistical control. Furthermore, in a
linear regression, we found no effect suggesting that those in the low actual time and no
perception of the time they had reported differences in their ability to make a response.
Therefore, this effect is at least not reportedly due to participants being shocked that the page
advanced without them being able to make a decision (B = 0.166, SE = 0.477, p = .728).
Discussion
Experiment 4 examined whether the interaction between default type and decision time
was due to the actual time decision makers have or to how much time decision makers perceived
they had to make their decision. First, like in the earlier experiments, we found that participants
were more likely to donate with an Action Default than a No-Action Default. Second, this
difference was large when the actual time to make a decision was shorter and absent when it was
DEFAULTS & DECISION TIME 25
longer. Third, counter to our Hypothesis 3, participants were not more likely to select the default
option when they perceived that time was shorter. In fact, they donated less compared to their
counterparts who were unaware of how long they had to make a decision when decision time
was short. This finding suggests that calling attention to the time limit may motivate participants
to correct for the manipulation as a way of avoiding an unwanted influence of the response
format (Schwarz & Clore, 1983; Wegener & Petty, 1997). Finally, as expected, we did not see
any effect of perceived time on those who had unlimited time to make their decision.
To conclude and to provide the most accurate estimate of the effects of decision times on
the action default advantage, we conducted an individual data meta-analysis using all the
experimental data we collected, including several pilots and related experiments, and the
previously presented experiment. This meta-analysis was conducted to determine replicability
and to estimate an average effect size (Mcshane & Böckenholt, 2017).
Meta-Analytic Synthesis
In addition to the studies reported in the main text of this article, we ran several
additional studies to develop the ideas we presented in this paper. These studies were
predominantly pilots that used either modified or different manipulations than reported in the
main text. For instance, two studies used a longer decision time (Studies S3 and S4) that
produced a non-significant default effects although its effect was directionally consistent to those
reported in Experiments 2-4. We therefore thought it was appropriate to include these as part of
the meta-analytic synthesis as they did manipulate both decision time and defaults. This analysis
uses data from all studies collected that manipulated default choice. Not all studies manipulated
decision time. If a study did not, participants in these conditions were labeled as “Longer Time”
participants as they matched the treatment given to participants in this condition for Experiments
DEFAULTS & DECISION TIME 26
2-4. If a study contained both shorter-time and longer-time decisions (e.g., Experiments 2-4), all
information was used and coded according to condition.
Method
Our meta-analysis contained data from fourteen independent experiments containing
seven effect sizes for the format and decision time interaction (total N = 2359; 795 Short-time
decisions, 1564 longer-time decisions). Choice format was randomized in each study, but studies
differed in both choice format and decision time. Therefore, some variability is due to between-
study effects. A complete list of details for all studies included in the analysis is available in
Table S8, and we report full analyses for the three additional experiments in the supplement. As
we had access to all participant data, we utilized an individual data meta-analysis (IDM) to
estimate our effects. An easy to interpret method of conducting IDM is to treat the data as a
mixed-effect hierarchical model with conditions nested within studies. We included both a
random effect for experiment as well as experiment × condition interaction, thus capturing
variance between studies while providing an estimate for condition effects (Stewart et al., 2012).
We chose not to calculate a measure of heterogeneity (I2 or Q) due to both of these statistics
having undesirable biases when the number of studies in the analysis is small (Hoaglin, 2017).
We also report the standardized mean difference representing the interaction effects along with
their confidence intervals in Figure 6.
Results
We first performed a multilevel logistic regression with donation as the outcome
measure, a random effect for each study and condition, and fixed effects for choice format. We
found greater donation rates in action default compared to no-action default conditions (78% vs
61%, p < .001) We then performed a multilevel logistic regression with donation as the outcome
DEFAULTS & DECISION TIME 27
measure, a random effect for each study, and fixed effects for choice format, decision time, and
their interaction. The results from these analyses appear in Table 6. The interaction effect was
significant (p < 0.001), with shorter-time conditions having increased acceptance of the default
option relative to longer time conditions (see Figure 4). Overall estimates for the action default
were 78% vs. 61% for shorter- and longer-time conditions, in contrast to 33% vs. 51% for the
no-action-default donations (dinteraction = 1.19, 95%CI: [1.06,1.32]).
General Discussion
The role of time in producing the action default advantage has remained understudied
even though processing time is a critical factor in the broader judgment and decision-making
literature (Ariely & Zakay, 2001). Across four experiments measuring effects on donation, we
examined whether an action-default format is more effective when decision time is shorter than
longer. In Experiment 1, we found that defaults have strong effects among those that
spontaneously make quick decisions. We then manipulated decision time in Experiments 2, 3,
and 4 to assess causality and the robustness of this effect. We found that participants who had
their decision time constrained to be short were more likely to donate in response to action-
default options in Experiments 2-4. The size and significance of the default effect was
consistently small when decision time was unconstrained, with a null difference between defaults
in all four experiments presented here. A significant effect was only found when pooling
participants from all studies in our meta-analysis (N = 1564). We then correlationally separated
the effects of participant’s perceived time to make a decision from their actual time (Experiment
3) and found that actual decision time appeared to be the primary driver of our effect. Finally, we
experimentally manipulated perceived decision time (Experiment 4) and confirmed our
conclusion from Experiment 3. Our primary effect that quicker decisions creates the Action
Default advantage replicated in all experiments. Moreover, we found no statistical evidence of a
DEFAULTS & DECISION TIME 28
default effect in individual studies when people had as long as they liked to make their decision.
Rather, the overall effect of the default was only positive and significant when pooling data
across all collected studies.
Several implications are noteworthy. First, situations that constrain how long people have
to make a decision will favor the default option. Across all studies, we found a stable, large
difference in donations when people had less time to make their decision. This difference
occurred in spite of participants reporting they had time to make a decision (Experiment 3). In
comparison, the default advantage was not present when participants had unlimited time to make
their decision in individual studies. In some cases, we even saw reversals of the effect (e.g.,
Experiment 2), and the meta-analysis found only a small action default advantage when pooled
across a large number of studies. This finding has been suggested previously in the literature, but
only null results had been reported prior to our research. Second, this effect is stronger when
people are unaware of how long they have to make their decision (e.g., an unexpected deadline)
as shown in Experiment 4. This finding is not surprising but has yet to be documented and is an
important reminder for policy makers to consider if the targeted situation has time constraints
and if the selected default could produce negative effects in some circumstances. Finally,
participants in who perceived they were in time limited conditions appeared to use some form of
bias correction (Schwarz & Clore, 2004) that attenuated the default effect. Specific to our
experiments, this finding would suggest that participants took the decision seriously and exerted
some effort even though the money they donated was little. More broadly, this finding also
suggests that situations that may make defaults appear manipulative will be less successful in
producing sizeable effects.
DEFAULTS & DECISION TIME 29
In the case of donation, individual donors constitute the primary contributors to charitable
giving in the USA, contributing about 70% of all the U.S. donations, totaling more than $200
billion in recent years (Giving USA, 2015). Given the importance of individual contributions to
the public good, how to get people to donate is an important question that interests both
fundraisers trying to increase contributions and social scientists trying to understand behavior
(Ariely et al., 2009). One line of research claims that the intrinsic motivation of beneficence
drives the donation behavior (Meier, 2006). As a result, individual giving may increase in
response to communications that highlight the significance of giving. A different line of
research, however, suggests that philanthropy often depends on such extrinsic, seemingly
inconsequential channeling cues as thank-you wristbands and tax breaks (Ariely et al., 2009).
This second view contends that minor contextual factors, and thus simple interventions like the
defaults and time constraints proposed here, are likely to shape giving behavior.
A final point is that we produced this effect with a small donation amount ($0.10). As
previously discussed, this donation amount is within the normal bounds and one that is
encountered regularly in everyday life via micro-donations (Center for Responsive Politics,
2020). Policy changes around defaults at checkouts may lead to increases in donations in
situations that impose naturalistic time constraints and that involve similarly small donation
amounts. Furthermore, even though motivation to actively make a decision with such small sums
may be questionable, participants clearly showed that they cared about making even these small
donations as they engaged in motivated bias correction. We think that larger donation amounts
may produce different effects and suggest future replications varying the donation amount.
To summarize, we examined and found that the amount of time to make a decision is a
critical factor driving default effects, with reliance on defaults when people need to make
DEFAULTS & DECISION TIME 30
decisions quickly. We found evidence for this effect across four experiments in which
participants relied on default choices more when decision time was short (vs long). Our finding
dovetails well with prior work suggesting that certain contexts can enhance or undermine
defaults (Ariely et al., 2009; Jachimowicz et al., 2019). In this light, defaults are not universally
or consistently effective, but rather are successful in specific scenarios (e.g., perhaps taxi tips and
emergency room protocols) that should be carefully identified when considering implementation.
DEFAULTS & DECISION TIME 31
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Table 1.
Summary of Sample Articles by time on the Default Effect
Note. Positive effects indicate an Action Default advantage, whereas negative effects indicate a No-Action
Default Advantage
# Short reference Description of participants and decision Likely
decision
time
Observed
effect
1 Johnson et al.
(1993)
Drivers deciding whether or not to acquire the
right to sue when purchasing insurance
Short Default
effect
2 Johnson &
Goldstein (2003)
Driver license applicants deciding whether or
not to become organ donors
Short Default
effect
3 Madrian & Shea
(2001)
Employees deciding whether or not to enroll
in a retirement plan
Long Default
effect
4 Araña et al. (2013) Individuals deciding whether or not to pay
additional taxes on vacation expenditures to
help prevent global warming
Long Default
effect
5 Haggag & Paci
(2014)
Taxi passengers deciding whether or not to tip
the default percentage on a taxi ride
Short Default
effect
6 Reiter et al. (2012) Parents deciding whether or not to have their
sons receive the vaccine against the Human
Papillomavirus
Long Reverse
effect
7 Di Guida et al.
(2012)
Experimental participants deciding whether or
not to switch to a new task in the midst of the
experimental session
Long Null effect
8 Keller et al. (2011) Participants deciding whether or not to
receive a reminder to be vaccinated against
the flu
Long Reverse
effect
9 Shepherd &
O'Carroll (2013)
Participants deciding whether or not to be
organ donors
Long Null effect
10 Brown & Krishna
(2004)
Consumers deciding whether or not to accept
the default settings for specific products (e.g.,
keyboards, computers, and vacation
packages)
Long Reverse
effect when
people were
skeptical
DEFAULTS & DECISION TIME 36
Table 2.
Effects on Donations: Experiment 1
Donation Behavior
B SE p
Constant -2.599 1.584 .101
Choice Format 2.979 0.988 <.001
Log Decision time 2.997 0.838 <.001
Choice Format × Decision time -3.174 0.935 <.001
Donation Evaluations -0.027 0.224 .904
Null Deviance/Residual Deviance 169.1/147.6
AIC 157.6
Note. Logistic regression predicting donation behavior from choice format (action default vs. no-action-default),
decision time, and their interaction. B is the estimated logit coefficient. SE is the standard error of the coefficient. p
is the significance level of the coefficient. Choice Format was coded as (0 = no-action-default; 1 = action default).
DEFAULTS & DECISION TIME 37
Table 3.
Effects on Donations: Experiment 2.
Donation Behavior
B SE p
Constant -1.012 1.147 .377
Choice Format -0.315 0.523 .547
Decision time -1.119 0.550 .042
Choice Format × Decision time 2.143 0.784 .006
Donation Evaluations 0.214 0.181 .239
Null Deviance/Residual Deviance 164.5/152.9
AIC 161.9
Note. Logistic regression predicting donation behavior from choice format (no-action-default vs. action default),
Decision time condition (Longer or Two-seconds), and their interaction. B is the estimated logit coefficient. SE is
the standard error of the coefficient. Choice format coded as (0 = no-action-default; 1 = action default) and Decision
time coded as (0 = Longer Time; 1 = Shorter Time).
DEFAULTS & DECISION TIME 38
Table 4.
Effects on Donations and Potential Mediators: Experiment 3.
Donation Behavior
B SE p
Constant -6.834 2.200 .002
Choice Format 0.530 0.676 .433
Decision time -1.555 0.657 .018
Choice Format × Decision time 3.467 1.184 .003
Perceived Time -0.120 0.233 .606
Donation Evaluations 0.837 0.223 <.001
Donation Norms 2.374 0.848 .005
Null Deviance/Residual Deviance 162.6/98.4
AIC 112.4
Note. Top panel is a logistic regression predicting donation behavior from choice format (no-action-default vs.
action default), Decision time condition (Longer or Two-seconds), and their interaction. Second and third panel are
linear regression predicting specified mediator same model as top panel. B is the estimated logit coefficient. SE is
the standard error of the coefficient. Choice Format coded as (0 = no-action-default; 1 = action default), Decision
Time is coded as (0 = Longer Time ;1 = Shorter Time). Donation Norm is coded such that low values imply not
donating is the norm, whereas high values indicate donating is the norm. Greater Evaluation means indicate more
favorable evaluations of donation.
DEFAULTS & DECISION TIME 1
Table 5.
Effects on Donations: Experiment 4.
Donation Behavior Hypothesis 1 Hypothesis 2 Hypothesis 3
B SE p B SE p B SE p
Constant -2.049 0.281 <.001 -1.480 0.307 <.001 -1.506 0.345 <.001
Choice Format 1.421 0.189 <.001 0.122 0.255 .632 -0.031 0.349 .929
Actual Decision Time - - - -1.492 0.259 <.001 -1.521 0.363 <.001
Perceived Decision Time - - - - - - 0.023 0.359 .950
Choice Format × Actual
Decision time
- - - 2.875 0.410 <.001 4.417 0.749 <.001
Choice Format × Perceived
Time Limit
- - - - - - 0.336 0.514 .514
Actual Decision Time ×
Perceived Decision Time
- - - - - - 0.053 0.514 .918
Choice Format × Actual
Decision Time × Perceived
Decision Time
- - - - - - -2.439 0.921 .008
Donation Evaluations 0.264 0.034 <.001 0.287 0.410 <.001 0.289 0.037 <.001
Null Deviance/Residual Deviance 829.6/702.3 829.6/645.4 829.6/632
AIC 708.31 655.38 650.02
Note. Logistic regression predicting donation behavior from choice format (no-action-default vs. action default), Actual Decision time condition
(unlimited or two-seconds), Perceived Time (aware vs. unaware of available time), and all their interaction. B is the estimated logit coefficient. SE
is the standard error of the coefficient. Choice format coded as (0 = no-action-default; 1 = action default), Actual Decision time coded as (0 =
Unlimited Time; 1 = Two seconds), and Perceived Decision Time coded as (0 = unaware of allowed decision time, 1 = aware of allowed decision
time). Hypothesis 1: Participants will be more likely to select whichever default option they are given. Hypothesis 2: Participants will be more
likely to select the default option if they are in situation with limited time. Hypothesis 3: Participants will be more likely to select the default
option if they perceive the situation is limited in time.
DEFAULTS & DECISION TIME 1
Table 6.
Results from Meta-analysis.
Donation Behavior
B SE p
Experiment (Random Effect) 0.300
0.
545
Experiment × Condition
(Random Effect)
0.134 0.366
Constant -1.058 0.209 .001
Choice Format 2.102 0.221 <.001
Decision time 0.951 0.148 <.001
Choice Format × Decision time -1.647 0.212 <.001
Note. Multilevel Logistic Regression predicting Donation from choice format (no-action-default vs. action default),
Decision time condition (Longer or Two-seconds), and their interaction. Studies assumed to have random
intercepts and fixed slopes given similar samples. B is the estimated logit coefficient. SE is the standard error of the
coefficient. k = 8 independent studies. Choice format coded as (0 = no-action-default; 1 = action default) and
Decision time coded as (0 = Longer Time; 1 = Shorter Time)
DEFAULTS & DECISION TIME 41
Figure 1. Choice Format for Experiments
DEFAULTS & DECISION TIME 42
Figure 2. Spotlight Analysis of Experiment 1 Results
Note. Figure 2 shows regions of significance for Decision Time values on donation behavior. Left Area is
before 1.60 seconds and displays an action-default advantage. Middle Area from 1.6 to 3.9 seconds
displays no significant difference in effects. Right Area from 3.9 seconds onward displays a No-Action
Default advantage
DEFAULTS & DECISION TIME 43
Figure 3. Percentage Probability of Donating by Interaction of Time and Default Type: Experiment 2
Note. Y-axis is marginal probability of a participant donating to a non-profit organization in each
condition. Longer Time were conditions where participants had no restrictions on their decision time.
Shorter Time conditions were conditions where participants had two seconds to make their decision.
Graph presents corrected means and 95% confidence intervals accounting for participant attitudes to
donation behavior.
DEFAULTS & DECISION TIME 44
Figure 4. Probability of Donating by Interaction of Time and Default Type: Experiment 3
Note. Y-axis is marginal probability of a participant donating to a non-profit organization in each
condition. Longer Time were conditions where participants had no restrictions on their decision time.
Shorter Time conditions were conditions where participants had two seconds to make their decision.
Graph presents corrected means and 95% confidence intervals accounting for participant attitudes to
donation behavior.
DEFAULTS & DECISION TIME 45
Figure 5. Marginal Means of Donation by Experimental Condition in Experiment 4
Note. Y-axis is marginal probability of a participant donating to a non-profit organization in each
condition. Upper Labels reference the actual decision time participants have. Bottom X-axis labels
indicate if the Available Time is Perceived or Not Perceived Graph presents corrected means and 95%
confidence intervals accounting for participant attitudes to donation behavior.
DEFAULTS & DECISION TIME 46
Figure 6. Meta-analytic estimates of donation as a function of choice format and decision time
Note. Y-axis is marginal probability of a participant donating to a non-profit organization in each
condition across all studies included in the meta-analysis, accounting for random effects. Graph presents
corrected means and 95% confidence intervals accounting for participant attitudes to donation behavior.
Synthesis includes data from all reported studies in main text and supplement that manipulated both
decision time and default format.
DEFAULTS & DECISION TIME 47
