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Int. Journal of Business Science and Applied Management, Volume 16, Issue 1, 2021
Effects of Time Pressure on the Amount of Information
Acquired
Peter Letmathe
RWTH Aachen University, Faculty of Business and Economics, Chair of Management Accounting
Templergraben 64, 52062 Aachen, Germany
Tel: +49 241 80 96164
Email: peter.letmathe@rwth-aachen.de
Elisabeth Noll
RWTH Aachen University, Faculty of Business and Economics, Chair of Management Accounting
Templergraben 64, 52062 Aachen, Germany
Tel: +49 241 80 96164
Email: elisabeth.noll@rwth-aachen.de
Abstract
We tested the influence of time pressure and to what extent time pressure interacts with the contextual
factors (‘payoff scheme’ and ‘level of costs for information that can be acquired’) in three laboratory
experiments. Participants had to decide how many pieces of information they wanted to purchase non-
sequentially in order to make a decision under uncertainty. Our findings indicate that under time
pressure, individuals acquire less information. Moreover, while we found no effect of time pressure
with a negative payoff scheme, higher levels of information costs suppress the willingness to acquire
information.
Keywords: information acquisition, decision-making, amount of information, time pressure, payoff
scheme, information costs
Peter Letmathe and Elisabeth Noll
87
1. INTRODUCTION
Many economic decisions are accompanied by time restrictions (e.g., Kocher et al., 2019), such as
trading, purchasing and sales, or production decisions. Particularly in the times of digital media, more
information is available for decision-making and needs immediate processing (e.g., Gawryluk and
Krawczyk, 2017). In organizational contexts, where time plays a crucial role, questions about the
behavioral control of how much information should be acquired and used in decision-making processes
are highly relevant. Even though prior research has already investigated the role of time pressure for
sequential information acquisition behavior (Mann & Tan, 1993), research on information acquisition
has not yet examined its influence when information acquisition is non-sequential. The examination of
non-sequential information acquisition under time pressure is highly relevant, since the argument that
the given time frame hinders the decision maker from inspecting all the information is most important
for many work situations. Further, through digitization, more and more information is becoming
available and can be used as a basis for decision-making (Saxena & Lamest, 2018). Therefore, it is
often unrealistic to inspect all the information available so that the decision about the amount of
information to be inspected (and acquired) is often made at the beginning of the decision-making
process. Up to now, most studies have manipulated time as the only variable across treatments even
though it is possible that this variable interacts with other contextual factors (Spiliopoulos and Ortmann,
2018). Hence, the present study aims to identify how time pressure by itself and in conjunction with
further contextual factors affects the amount of information acquired in a decision-making process.
For three reasons, the factor of primary interest in this paper is that of time pressure: First, time
pressure is an important topic in everyday work life (Lallement, 2010). Second, time pressure is often
strongly related to decision-making processes due to the sheer amount of decisions that have to be
made on a regular basis and that are often accompanied by strict deadlines (Geisler and Allwood, 2018).
Third, time pressure is not only a natural factor within organizations, it is also a factor that can be
artificially invoked by managers and it can therefore be used as a control instrument. For these reasons,
we investigate the following research questions: (1) What is the effect of time pressure on the amount
of information acquired by individual decision makers? (2) Does the effect of time pressure on the
amount of information acquired depend on different payoff schemes in a decision-making task? (3) Is
the effect of time pressure on the amount of information acquired different for various levels of
information acquisition costs? To answer these research questions, we conducted laboratory
experiments and employed multivariate analyses.
The paper is structured as follows: In the next section, we summarize existing research on
information acquisition in decision-making. We review related research on time pressure effects in the
decision-making literature, as well as on payoff schemes and information costs. Then, we explain the
protocols of the experiments we conducted, the measures used, and the methodology of the analysis.
Subsequently, we present and then discuss the results of the three experiments. Finally, we summarize
the theoretical and practical implications as well as the limitation of our research and we outline future
research implications.
2. THEORY AND HYPOTHESES
2.1 Information acquisition research
Prior literature on the amount of information acquired for decision-making (e.g., San Miguel, 1976;
Mann and Tan, 1993; Kerstholt, 1996) can be divided into two parts: the acquisition of sequential and
of non-sequential information. The former investigates pieces of information that are acquired one after
another. The latter deals with the process of obtaining varying amounts of information at only one point
in time and is the underlying form of information acquisition in the present study. Studies in this field
deal with a broad range of topics: San Miguel (1976) examined, among other things, the effect of
psychological traits on the amount of information purchased before decision-making. This study shows
that the mean amount of information purchased is higher for individuals low in flexibility (also
described as being intolerant of ambiguity) and is very similar between different levels of intellectual
efficiency, which describes how efficiently an individual uses her / his intellectual resources (Gough,
2000). In the experimental task by Fischer, Schulz-Hardt, and Frey (2008) participants made a decision
on a legal case and each participant was requested to choose one piece of information that was
consistent or inconsistent with her or his prior decision between a set of two or ten pieces of
information (half of the set of two and half of the set of ten pieces of information was consistent and
the other half was inconsistent). The results show a preference for inconsistent information when
participants were faced with two pieces of information and for consistent information when they were
faced with ten. In the experiment by Uecker (1978), participants had to choose an amount of
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information in the form of a random sample size to be drawn from an urn containing a total of 100
marbles, some of them black and some of them white (as information systems). The urn was selectable
from a set of 10 urns and the ratio of black to white marbles in the selected urn was unknown to the
participants. They only knew that out of the 10 urns, 2 of them contained 90 black and 10 white
marbles, 4 urns contained 70 black and 30 white marbles, 3 urns contained 50 black and 50 white
marbles, and 1 urn contained 30 black and 70 white marbles. After specifying a random sample size,
the marbles were shown to a simulated decision maker, executed through a computer, and programmed
with either a Bayesian or a conservative decision model. Based on the sample results and its prior
probability assessments, the simulated decision maker estimated the ratio of black to white marbles in
that urn. The optimal information system for the Bayesian model comprised 16 marbles and, for the
conservative model, the sample size was 24 marbles. The participants were provided with a budget of
$3.00, and the cost for sampling a marble was $0.01. In the case of a decision maker making a correct
decision, each participant received $0.50 less the cost for the marbles in the specified sample. In the
case of a decision maker making an incorrect decision, each participant lost $0.50 plus the cost of the
marbles in the specified sample. However, this study was not primarily interested in the amount of
information acquired. It was more interested in discovering whether the participants were able to
choose an optimal information system for the decision maker, i.e. if he / she specified the sample size
in accordance with the normative theory of information evaluation. Overall, there was no significant
convergence on the optimal number of marbles with the decision models. Moreover, participants
specified smaller sample sizes than optimal for the conservative model. In the present study, we aim to
identify contextual factors that influence acquisition behavior rather than the way in which people
deviate from an optimal information level.
2.2 Time pressure and its effects on decision-making
Time pressure plays a crucial role in information acquisition and decision-making (e.g., Payne et
al., 1988) and is often induced through the imposition of severe time restrictions within the decision-
making process. Overall, much of the previous research explored the effects of time pressure on
information processing (for a review, see Lallement, 2010). In particular, the following phenomena
play a role when processing information under time pressure: individuals accelerate the decision
process, pay more attention to negative information, and may selectively screen information since they
are focusing on aspects they regard as being important (Ben Zur and Breznitz, 1981; Wright, 1974).
For example, in the experiment by Mann and Tan (1993), participants were confronted with a decision
dilemma. Before making a choice between two options, they had the possibility to inspect information
sequentially in an information booklet. Results show that participants who were pressed for time read
less information in the booklet because they focused on information that they perceived as being
important.
Apart from the effects of time pressure on information processing in general, studies highlight its
negative effects, the fact that it influences individuals differently and they outline its positive effects as
well. Time restrictions leading to time pressure are often seen as factors that increase task complexity
or difficulty (e.g., Chernev et al., 2015). Furthermore, it is assumed that individuals under time pressure
tend to disregard relevant aspects and to use heuristic methods (e.g., Kruglanski and Freund, 1983).
Besides these assumptions, time pressure may lead to disruptions because the remaining time is
inspected visually (Mann and Tan, 1993) and has been found to lead to lower decision performance
(e.g., De Paola and Gioia, 2016). In addition to this, it is common sense that perceived time pressure
induces stress (e.g., Keinan et al., 1987). Importantly, Conte et al. (2015) argue that when time pressure
is present, the performance of the majority of individuals may be negatively affected but not of all of
them. This finding is also supported by the study results of Kocher et al. (2019), who, in risky decision-
making tasks, found that individuals with the ability to cope with time restrictions perform differently
when perceiving time pressure from those individuals without this ability. Apart from these negative
time pressure effects dependent on individual traits, Lindner and Rose (2017) found that time pressure
leads to less present-bias, which means that individuals pay more attention to the amount of payment
instead of the immediateness of the decision. Additionally, Ordóñez et al. (2015) infer from the
literature that people work more smartly when a deadline is in place, thereby increasing efficiency.
The dual-system approach (Kahneman and Frederick, 2002; Stanovich and West, 2000) can help
us to understand how time pressure impacts decision-making. It assumes that two systems of
information processing and decision-making exist. These are defined, in particular, due to their
characteristics of rapidity and controllability (Kahneman and Frederick, 2002). System 1 is described
by traits such as automatic, intuitive, or fast. In contrast, System 2 is characterized by traits such as
controlled, deliberative, or slow. The interaction of the two systems is described in the literature as
follows: System 1 suggests intuitive solutions immediately, while System 2 monitors and, if necessary,
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remediates these. Responses of an individual evolve either through automatic (System 1) or controlled
(System 2) cognitive processes (e.g., Kahneman and Frederick, 2002). The operations of System 2 can
be disrupted by external factors, such as time pressure, since there is less time for thinking deliberately
and the remaining time needs to be monitored. Thus, the function of System 2 is weakened through the
presence of time pressure and leads individuals to filter for only those aspects that appear to be most
striking (Maule et al., 2000). As a result, judgement biases, which are not necessarily remediated by
System 2, can occur and can interrupt decision-making. Importantly, Glöckner and Betsch (2008) note
that even if the presence of time pressure inhibits deliberate considerations, this does not need to
impact automatic processes negatively. In the same vein, Kahneman (2003) argues that automated
decisions often lead to good results and can even be superior to System 2 thinking.
Derived from these theoretical lines or argumentation (e.g., Lallement, 2010; Glöckner and Betsch,
2008), we assumed that participants under time pressure decided quickly, without thinking deliberately
about how much information to purchase. Based on prior literature in this research field (e.g., Mann
and Tan, 1993), we argue that fewer pieces of information are selected non-sequentially under time
pressure, and we therefore tested the following hypothesis:
H1: Participants under time pressure acquire less information than participants without time
restrictions.
Kahneman (2003) regards the accessibility of information, which he describes as “the ease (or
effort) with which particular mental contents come to mind” (Kahneman, 2003: 699), to be dependent
on properties of the cognitive constitution and the context. Therefore, we explore not only the influence
of time pressure but also the influence of its interactions with the following contextual factors: payoff
scheme and information costs on the amount of information acquired.
2.3 Payoff schemes in decision-making under time pressure
In general, payoff schemes describe the decision-based and environmentally condition-based
payments for the decision maker and they therefore consist of various outcome options. These
outcomes are often based on the information used to make decisions and can include positive
information on potential positive outcomes (gains) and negative information on potential losses.
The literature has revealed that a negativity bias exists i.e., negative information, also labeled as
entity, event, stimuli, or aspect of an object, has a greater effect than positive information (Kanouse and
Hanson, 1987; Baumeister et al., 2001; Rozin and Royzman, 2001; Peeters and Czapinski, 1990).
Negative information comprises, for example, information about potentially losing money, or being
criticized, whereas positive information, for example, refers to winning money or receiving
acknowledgments (Baumeister et al., 2001). The literature also links the negativity bias to cognitive
processes and states that negative information involves more (thorough and conscious) processing than
positive information does, so that an individual’s definite impression builds more strongly on negative
information (Baumeister et al., 2001; Ito and Cacioppo, 2000; Peeters and Czapinski, 1990). Although
many studies provide evidence for a negativity bias, the strength of the evidence depends on various
issues (Baumeister et al., 2001) and it is therefore not a generic bias (Rozin and Royzman, 2001).
Payoff schemes in conjunction with time pressure have mainly been researched in the context of
risky gambles. For example, Ben Zur and Breznitz (1981) found that participants under time pressure
paid more attention to possible losses compared to gains and Gawryluk and Krawczyk (2017) found
that more deliberation time leads to a more accurate weighting of the options in risky gambles.
Moreover, studies have examined risk preferences under time pressure (e.g., Kirchler et al., 2017).
Even though risk preferences for lotteries do not play a role in the context of the present study, the
important take away is that time pressure affects lottery choices where payoff schemes play an
important role. The study by Haesevoets et al. (2019) supports this notion, since they found that the
payoff structure (i.e. endowment size) significantly influences choice behavior. This leads to the
assumption that different payoff schemes invoke different information acquisition behaviors.
Whereas some studies have already shown that under time pressure individuals give more weight
to negative outcomes (Ben Zur and Breznitz, 1981; Wright, 1974; Huber and Kunz, 2007), prior
research studies have not yet investigated the effects of different payoff schemes, i.e. with either
negative or positive expected values, in conjunction with time pressure on information acquisition
behavior. When participants faced a positive payoff scheme, we assumed that participants under time
pressure would make use of System 1 thinking and that they would acquire significantly less
information. Based on previous studies in this research field (e.g., Haesevoets et al., 2019; Ben Zur and
Breznitz, 1981), we expected that a higher negative payment in possible negative outcomes would lead
to a focus on this negative information and to a shift away from intuitive processes (induced by time
pressure) towards more deliberate cognitive processes. In particular, we hypothesized the following:
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H2: Participants confronted with a negative payoff scheme and who are placed under time
pressure acquire more information than participants confronted with a positive payoff scheme and who
are placed under time pressure.
2.4 Information costs in decision-making under time pressure
Information costs describe the (monetary) value relevant to acquiring information and this is very
often linked to a certain amount of costs to be paid. From the rational perspective, the cost for
information must be compared with the benefit of its diagnostic value (Connolly and Thorn, 1987) and
higher costs should lead people to purchase only the amount of information that has a higher or equal
utility than related costs (Kraemer et al., 2006).
When sequential information is considered, only the study of Kerstholt (1996), at least to our
knowledge, has investigated the effect of different levels of information costs under time pressure in a
dynamic task. The study has shown that under time pressure, relatively low information costs lead
people to acquire more information compared to relatively high information costs. Importantly, this
result was found when it would have been better (because of a higher expected outcome) to apply an
action immediately instead of requesting any (further) information. They inferred that people tend to
decide based on a direct comparison of the costs for information and action and that further factors are
not considered. Additionally, they concluded that relatively low information costs lead people to
acquire information sooner in that task.
The role of information costs was also the subject of the following studies which did not consider
the influence time pressure: The experimental study by Baethge and Fiedler (2016), where participants
were involved in an investment task with either free or costly information, has shown that when
confronted with information costs, significantly less information was acquired. The researchers also
concluded that information costs lead people to spend more time on analyzing a certain piece of
information. The study by Ambuehl et al. (2018) investigated the information acquisition behavior
when information costs are non-monetary and are measured through experimental variation in the
amount of calculations to be checked and participants’ psychological costs, such as cognitive ability.
They showed that higher non-monetary information costs lead people to acquire less information
before making a decision. Kraemer, Nöth, and Weber (2006) experimentally examined the information
acquisition and Bayesian updating behavior of individuals. Participants were shown decisions by their
predecessors and were allowed to acquire further information at a certain cost (without manipulating its
level). The results of this study revealed that half of the participants did not decide rationally and
purchased too much further information.
Nonetheless, we are not interested in the deviations from an optimal amount of information.
Rather, we want to know in what way costs influence non-sequential individual acquisition behavior
when time pressure is present. Based on theory and prior studies in this field (e.g., Kerstholt, 1996;
Kraemer et al., 2006), we assumed that costs would be an important factor for determining the amount
of information acquired and that they would lead participants to neglect other aspects of the choice
context, such as time pressure. Moreover, when information costs are low, it is reasonable to assume
that the cost factor would override time pressure effects. Accordingly, we hypothesized the following:
H3: Participants facing relatively low information costs and who are placed under time pressure
acquire more information than participants facing relatively high information costs and who are
placed under time pressure.
3. EXPERIMENTAL PROTOCOLS
3.1 Procedure and task structure
We tested the influence of time pressure and its interactions with the contextual factors payoff
scheme and information costs on the amount of information acquired non-sequentially in a decision-
making task. Therefore, we conducted three laboratory experiments. Experiment 1 served as the basis
and comprised a so-called positive payoff scheme and relatively high information costs. Compared to
Experiment 1, we altered the choice context in Experiment 2 due to the payoff scheme (a so-called
negative payoff scheme was presented) and in Experiment 3 due to the level of information costs
(participants faced relatively low information costs). In each experiment, we varied the presence of
time pressure (present, not present). Altogether, we had 6 treatments. Table 1 gives an overview of the
factors considered in every experiment.
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Table 1. Overview of experiments
Experiment Treatment Time pressure Payoff scheme Information costs
1
1
yes
positive relatively high
2
no
2
3
yes
negative relatively high
4
no
3
5
yes
positive relatively low
6
no
The use of experiments is most reasonable because we can implement manipulations of the
explanatory factors directly, which, in turn, minimizes problems with reverse inference (Croson and
Gächter, 2010). The experiments were programmed in z-Tree (Fischbacher, 2007) and were conducted
at a large German university. All participants were students and were assigned randomly to the
different treatments. All experiments consisted of the same procedure and the same task structure,
which was divided into four parts. Appendix A displays the full experimental instructions.
In the introduction, participants learned about the main task and about the payment modalities.
The main task was a decision task under uncertainty, inspired by Connolly and Thorn (1987). In our
study, participants were told that they would be the production manager in a cake factory and would
have to decide about whether an unused machine should be operated again. Re-operating made sense in
case of a high future demand. As long as the test persons had no further information about the future
demand, there was an equal likelihood of a high or a low demand. Pieces of information about the
future demand trend for cakes were purchasable from eleven distribution centers, each providing
exactly one piece of information, which was drawn at random. If at least six distribution centers
forecasted a high demand, the demand was then considered to be high, otherwise to be low. We chose
to provide eleven pieces of information in order to have a complex task regarding the calculations of
conditional expectations so that an optimal amount of information was not obvious. After one practice
round, the task was repeated for twenty independent rounds. To perform the task, each participant
received a budget of 15,000 Experimental Currency Units (ECU) in every round. From this budget, a
participant was able to buy information non sequentially from the distribution centers at a certain cost.
From the remaining budget, an additional payment was added or deducted. This additional payment,
which was shown in the form of a payoff matrix, was based on a participant’s decision to operate the
machine (yes, no) and the overall future demand. The level of the task-based payment was dependent
on the experimental condition. Similarly to prior studies, we implemented time pressure by restricting
the time available for each round. If participants needed more than 13 seconds for a round, the system
made a random decision. The level of time pressure was chosen based on prior literature in this
research field (e.g., Kerstholt, 1996) and on the pre-tests conducted. After the main task, participants
had to fill out a questionnaire. Afterwards, participants had to perform as many calculations as possible
in an arithmetic problem task (Ekstrom et al., 1976) for 5 minutes. After completing the experiment,
participants received their individual payment, consisting of their performance in the decision task (one
round was selected by lottery) and in the arithmetic problem task (depending on the correctly answered
arithmetical operations) at an exchange rate of 2,000 ECU = 1 Euro. 219 students took part in the
experiments (88 females, 131 males). The average age of the participants was 23.44 years (SD = 3),
ranging from 19 to 37 years. 155 participants were enrolled in different engineering disciplines and 64
students were studying economics, business administration, or political science. On average, the
experimental sessions lasted for 47.58 minutes and the average payment was 9.46 Euros.
3.1.1 Experiment 1
The aim of Experiment 1 was to test the influence of time pressure on the amount of information
acquired in order to test Hypothesis 1. Consequently, one half of the participants was randomly
assigned to the time pressure treatment (Treatment 1), whereas the other half was not (Treatment 2).
Based on the procedure and the task described above, participants in Experiment 1 were incentivized
by the payoff scheme depicted in table 2. This payoff scheme describes the additional payment that was
added to the budget of 15,000 ECU less information costs for the acquired information. In the case of
the decision to operate the machine, the additional payment was 10,000 ECU for a high future demand,
and -8,000 ECU for a low future demand, i.e. 8,000 ECU would be subtracted from their remaining
budget. Since the probabilities for both conditions were 0.5 when deciding to operate the machine
without further information, the expected value was 1,000 ECU, which is why we term this payoff
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scheme ‘positive’. If the participants decided that the machine should not be operated, the additional
payment was 0 ECU.
Table 2. Positive payoff scheme
The direct costs for an additional piece of information increased with every piece of information
by 68 ECU. To acquire a certain amount of information, the direct costs were added together. For
example, to acquire two pieces of information the total costs were 204 (= 68 + 136) ECU. We chose
these values so that the total costs for eleven pieces of information, which were 4,488 ECU, would be
slightly under the expected value for the additional payoff if participants acquired all the information.
This expected additional payoff was 5,000 ECU, since the reasonable outcomes in the payoff matrix for
participants with complete information about the future demand were either 10,000 ECU (the decision
to operate the machine and a high future demand with a probability of 50 percent) or 0 ECU (the
decision that the machine should not be operated).
3.1.2 Experiment 2
We altered the task conducted in Experiment 1 by varying the payoff scheme due to the negative
possible outcome option in Experiment 2: If participants decided to operate the machine and the overall
future demand was low, their additional payment was -12,000 ECU instead of -8,000 ECU. This
variation led to a negative expected value of -1,000 ECU (0.5*10,000 ECU - 0.5*12,000 ECU) when
no information was acquired, i.e. leading to a negative payoff scheme. In Experiment 2, participants
performed the same procedure and tasks as in Experiment 1. Again, in one treatment, students were
placed under time pressure (Treatment 3) and in the other one they had no time restrictions (Treatment
4). In contrast to the positive payoff scheme before, participants were paid according to the negative
payoff scheme, as illustrated in table 3. This means that if they decided to operate the machine and the
overall future demand was low, their additional payment would be -12,000 ECU. All other values in
the payoff scheme remained unchanged.
By taking the data of Experiment 1 and 2 together, we were able to test the influence of the
interaction effect of time pressure and a negative payoff scheme on the amount of information acquired
and to investigate Hypothesis 2.
Table 3. Negative payoff scheme
3.1.3 Experiment 3
In Experiment 3, we altered the task conducted in Experiment 1 by varying the level of
information costs for acquiring information. They were reduced by fifty percent compared to
Experiment 1. Participants in Experiment 3 had to perform the same procedure and tasks as in the
previous Experiments. Again, one treatment was pressed for time (Treatment 5), whereas the other was
not (Treatment 6). In order to test the influence of time pressure in conjunction with relatively low
information costs on the amount of information acquired within regression analyses and to investigate
Hypothesis 3, we took the data of Experiment 1 and 3 together.
3.2 Measures
The dependent variable in the present paper is the amount of information acquired, ranging from 0
to 11. The independent variable of main interest in this study is time pressure, measured binarily (1 if
present, 0 otherwise). In Experiment 2, we additionally included the variable negative payoff scheme
(1 if confronted with a negative payoff scheme, 0 otherwise). Only in Experiment 3 did we add the
your additional payment in the case of a
high demand
low demand
your decision:
Machine will operate
10,000
-8,000
Machine will not operate
0
0
your additional payment in the case of a
high demand
low demand
your decision:
Machine will operate
10,000
-12,000
Machine will not operate
0
0
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variable low costs (1 for low information costs, 0 otherwise). To control whether the acquisition
decisions are influenced by an individual’s general risk preference, we included the construct general
risk aversion (Mandrik and Bao, 2005). The items were measured using a Likert scale, ranging from 1
to 7. A confirmatory factor analysis revealed factor loadings ranging from .4602 to .7284. The
Cronbach’s alpha coefficient was .7668. We included the variable round, with values ranging from 1 to
20, to account for learning effects. To test whether ‘smart-decisions’ influence the amount of
information acquired, we generated two variables: decision rule and uneven number. Decision rule
describes the application of a decision rule that would maximize the expected payoff, which means that
the participant decided to operate the machine when more information forecasted a high rather than a
low demand. If an equal amount of information indicated a high or low demand, participants
confronted with a positive payoff scheme would maximize their expected payoff if they decided to
operate the machine. In contrast, participants confronted with a negative payoff scheme would then
decide against operating the machine. The control variable uneven number indicates whether the test
person acquired one, three, five, seven, nine, or eleven pieces of information. This selection is ‘smart’
because the actual information value is higher when a majority of positive or negative information
exists. Decision rule and uneven number are measured binarily (1 if the decision rule was applied / an
uneven number was acquired, 0 otherwise). Further, we included the control variables score, age, and
field of study. Score is the sum of all points (measured in ECU) achieved in the arithmetic problem task.
This variable was included to test whether quick calculation abilities play a role. As the literature
assumes that differences due to time pressure perceptions exist between older and younger decision
makers (Ordóñez et al., 2015), we controlled for age (measured in years) in our analyses. The field of
study of the participants was (reflecting the nature of the university in question) either engineering or
other fields of studies (measured binarily).
3.3 Analysis Methodology
For each experiment, we started with a descriptive data analysis and used two-sample t-tests for
independent samples to test whether the average amount of information acquired was different between
the treatments. Because the experimental tasks include repeated measures, we applied the Generalized
Estimating Equations (GEE) method. We specified the ‘identity’ link function, which is applied for
data that are normally distributed (Ballinger, 2004). We also employed an autoregressive correlation
structure, which is applicable for time-dependent correlations (Ballinger, 2004). We performed
supplementary analyses, where we excluded subjects who acquired zero information over twenty
rounds. In doing so, we checked for the robustness of the results because we cannot rule out the
possibility that selecting zero information means that some of the individuals did not participate in the
task seriously.
4. RESULTS
4.1 Results of Experiment 1
73 students participated in Experiment 1. Of these, 35 students (16 female, 19 male) were in the
time pressure treatment and 38 students (11 female, 27 male) were in the no time pressure treatment.
The participants were 19 to 37 years old (M = 24.27, SD = 3.13). 51 participants were enrolled in
different engineering disciplines and 22 students studied economics, business administration, or
political science.
In the time pressure treatment, the mean amount of information acquired was 5.39 (SD = 3.22). In
contrast, the amount was 6.18 (SD = 2.75) in the no time pressure treatment. To test whether the mean
amount of information acquired is different between the time pressure and the no time pressure
treatment, we executed a two-sample t-test for independent samples, which indicates a statistically
highly significant difference (p < .001). Accordingly, participants in the time pressure treatment
acquired significantly less information.
Descriptive statistics of the decision rule and uneven number variables are reported in the
respective columns in Appendix B. The results show that participants without time pressure in
Experiment 1 made use of the decision rule and selected an uneven number more frequently. The
average time per round the participants needed to select information and to make the decision was 5.81
seconds in the time pressure treatment and 8.33 seconds in the no time pressure treatment. Only in five
out of 700 rounds did participants fail to make a decision within the given time frame (only relevant for
the time pressure treatment).
The results of the GEE regressions used to examine the influence of the independent variables,
specifically of time pressure, on the amount of acquired information are presented for models 1 and 2
in the ‘all subjects’ column of table 4. When investigating the effect of time pressure only (model 1),
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the results reveal that significantly less information is acquired (p < .1). When including control
variables additionally in the regression analysis (model 2), the results show that time pressure still
significantly decreases the amount of information acquired (p < .05). Accordingly, we find support for
Hypothesis 1. Interestingly, neither the general risk aversion construct nor the interaction with time
pressure is significant. The round variable also shows no significant influence. Applying the decision
rule and selecting an uneven number both influence significantly and positively the amount of
information acquired (p < .001; p < .001). Furthermore, score has no effect, age has a significant
positive effect (p < .01), and engineering studies shows no effect on the amount of information
acquired.
We report the results of the supplementary analyses for models 3 and 4 in the ‘without subjects
that acquired zero information over twenty rounds’ column of table 4. Altogether, the results show
identical patterns. However, the time pressure effect is insignificant when regressed solely on the
amount of information acquired (model 3) but remains significant when other variables are included in
the regression analysis (model 4), even though the significance level becomes weaker (p < .1). Thus,
these results support Hypothesis 1 only to a limited extent. In addition to this, the effect of age remains
at the significance level of 5%.
Table 4. GEE regression analyses on the amount of information acquired – Experiment 1
Hypotheses
All subjects
Without subjects who acquired 0
information over 20 rounds
Prediction
Finding
Model 1
Model 2
Finding
Model 3
Model 4
Time
pressure
H1 (-)
✓
-0.767
(0.0587)
-0.896
(0.0137)
(
✓
)
-0.432
(0.1712)
-0.575
(0.0588)
General
risk
aversion
-0.0739
(0.8072)
-0.140
(0.5701)
Time
pressure *
general risk
aversion
0.363
(0.3757)
0.463
(0.1721)
Round
-0.0109
(0.6237)
-0.0111
(0.5849)
Decision
rule
0.627
(0.0000)
0.632
(0.0000)
Uneven
number
1.136
(0.0000)
1.074
(0.0000)
Score
0.000190
(0.1011)
0.000102
(0.2871)
Age
0.173
(0.0028)
0.113
(0.0178)
Engineering
studies
0.143
(0.7201)
0.234
(0.4818)
Constant
6.105
(0.0000)
0.325
(0.8190)
6.469
(0.0000)
2.294
(0.0518)
N
1460
1460
1340
1340
Note. The first observation in each cell is the estimate and the second observation (in parentheses) is the two-sided
p-value.
4.2 Results of Experiment 2 (in combination with Experiment 1)
70 students took part in the second experiment. 35 students were assigned to the time pressure
treatment (11 female, 24 male) and 35 students were assigned to the no time pressure treatment (18
female, 17 male). Participants ranged in age from 19 to 32 years (M = 23.41, SD = 2.98). 49 students
were enrolled in different engineering disciplines, 21 students reported that they were studying
economics or business administration.
Peter Letmathe and Elisabeth Noll
95
Descriptive statistics according to the amount of information acquired reveal the following: The
mean amount is 5.52 pieces (SD = 3.01) for the time pressure treatment (Treatment 3) and 5.85 (SD =
2.73) for the no time pressure treatment (Treatment 4). We also employed a two-sample t-test, which
shows a significant difference between these two treatments (p < .05), with the time pressure treatment
stimulating the participants to buy significantly less information. Taking the treatments of Experiment
1 and 2 together, we again performed two-sample t-tests. The test for differences in the amount of
information acquired between the time pressure treatments (Treatments 1 and 3) and the no time
pressure treatments (Treatments 2 and 4) again indicates that the time pressured participants bought
significantly less information (p < .001). In addition to this, we executed a two-sample t-test based on
the time pressure treatments (Treatments 1 and 3) only. The test for differences in the amount of
information acquired between time pressured participants confronted with the positive payoff scheme
(Treatment 1) versus negative payoff scheme (Treatment 3) indicates no significant difference.
Descriptive statistics of the decision rule and uneven number variables in the treatments of
Experiment 2 are reported in Appendix B. As in Experiment 1, participants in the no time pressure
treatment decided in line with the decision rule and chose an uneven number of pieces of information
more frequently. The average time participants needed for every round was 6.1 seconds for the time
pressure treatment and 8.21 seconds for the no time pressure treatment. Only in eight out of 700 rounds
did participants fail to make a decision within the given time frame (only relevant for the time pressure
treatment).
To examine the effects of the independent variables, specifically of time pressure in conjunction
with a negative payoff scheme, we based the GEE regression analyses on the data from Experiments 1
and 2 together. The results are provided for models 5 and 6 in the ‘all subjects’ column of table 5.
Model 5 covers the time pressure, negative payoff scheme, and the interaction of time pressure and
negative payoff scheme variables. In model 6, control variables were additionally included. The effect
of time pressure on the amount of information acquired is significantly negative in model 5 and 6 (p
< .1; p < .05). The results show no significance for the effect of a negative payoff scheme alone in
either model. The effect of the interaction of time pressure and negative payoff scheme is also not
significant in models 5 and 6. Hence, Hypothesis 2 is not supported. An examination of the control
variables allows us to draw the following conclusions: As in Experiment 1 alone, general risk aversion
as well as the interaction with time pressure indicate no significant effects. Round has no significant
effect again, but the decision rule and uneven number variables significantly increase the amount of
information acquired (p < .001; p < .001). Score shows a significantly positive effect (p < .05), age
indicates no effect, and engineering studies indicates a significantly positive effect (p < .05) as well.
Table 5. Regression analyses on the amount of information acquired – Experiment 2
Hypotheses All subjects
Without subjects who acquired 0
information over 20 rounds
Prediction
Finding
Model 5
Model 6
Finding
Model 7
Model 8
Time pressure
-0.764
(0.0718)
-0.777
(0.0490)
-0.418
(0.2645)
-0.472
(0.1919)
Negative
payoff scheme
-0.218
(0.6072)
-0.154
(0.6944)
-0.403
(0.2705)
-0.416
(0.2371)
Time pressure
* negative
payoff scheme
H2 (+)
✗
0.480
(0.4287)
0.461
(0.4116)
✗
-0.0478
(0.9276)
0.101
(0.8412)
General risk
aversion
-0.0504
(0.8357)
0.0161
(0.9408)
Time pressure
* general risk
aversion
0.134
(0.6867)
0.0969
(0.7435)
Round
-0.00531
(0.7459)
-0.00484
(0.7569)
Decision rule
0.424
(0.0000)
0.429
(0.0000)
Uneven
0.906
0.888
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96
number
(0.0000)
(0.0000)
Score
0.000203
(0.0442)
0.000140
(0.1191)
Age
0.0539
(0.2596)
0.00553
(0.8970)
Engineering
studies
0.714
(0.0232)
0.712
(0.0118)
Constant
6.093
(0.0000)
2.925
(0.0114)
6.447
(0.0000)
4.604
(0.0000)
N
2860
2860
2720
2720
Note. The first observation in each cell is the estimate and the second observation (in parentheses) is the two-sided
p-value.
We also ran regressions with the data from Experiment 2 only and can report significant
differences compared to the regressions based on Experiments 1 and 2 together. However, we do not
report these results in detail due to space limitations. An important difference in the regression results
based on data from Experiment 2 only is that the effect of time pressure is not significant any more.
This is reasonable, since the effect of the negative payoff scheme as well as its interaction with time
pressure were already identified as being insignificant.
Models 7 and 8 in the ‘without subjects who acquired zero information over twenty rounds’
column in table 5 report the results of the supplementary analyses. Differences compared to models 5
and 6 are that time pressure is not significant any more in models 7 and 8. Apart from this, the effect of
score diminishes and the effect of engineering studies remains at the significance level of 5%.
4.3 Results of Experiment 3 (in combination with Experiment 1)
76 students participated in Experiment 3. Of these, 37 students were randomly assigned to the time
pressure treatment (10 female, 27 male) and 39 students to the no time pressure treatment (22 female,
17 male). The age of the students ranged from 19 to 33 years (M = 22.67, SD = 2.65). 55 students
indicated that they were studying an engineering discipline, 21 students indicated that they were
studying economics or business administration.
Descriptive statistics for the amount of information acquired indicate the following: The mean
number of acquired pieces of information is 6.7 (SD = 2.74) for the time pressure treatment (Treatment
5) and 6.34 (SD = 3.35) for the no time pressure treatment (Treatment 6). We performed a two-sample
t-test. This shows, in contrast to the prior experiments, that the no time pressure treatment acquired
significantly less information (p < .05). Taking the data of Experiment 1 and 3 together, the test for
differences in the amount of information acquired between the time pressure treatments (Treatments 1
and 5) and the no time pressure treatments (Treatments 2 and 6) indicates that participants in the time
pressure treatments acquired significantly less information (p < .05). For the time pressure treatments
(Treatments 1 and 5) only, the t-test for differences in the amount of information acquired between the
treatments with relatively high information costs (Treatment 1) and relatively low information costs
(Treatment 5) reveals that participants facing relatively low information costs acquired significantly
more information (p < .001).
As in the previous studies, descriptive statistics of the decision rule and uneven number variables
for the treatments of Experiment 3 are reported in Appendix B. Unlike before, the participants of
Treatments 5 and 6 used the decision rule almost equally frequently and participants under time
pressure (Treatment 5) selected an uneven number more frequently. On average, participants under
time pressure needed 6.69 seconds and those under no time pressure 9.36 seconds for every round in
the decision task. In seven out of 740 rounds, individuals did not decide within the given time frame
(again, only relevant for the time pressure treatment).
To identify the effects of time pressure in conjunction with relatively low information costs, we
took the data of Experiments 1 and 3 together to calculate the respective GEE regressions. The results
are displayed for models 9 and 10 in the ‘all subjects’ column of table 6. Model 9 comprises the
variables time pressure, low costs as well as its interaction term with time pressure. Control variables
were included additionally in model 10. We found a significantly negative time pressure effect on the
amount of information acquired in models 9 and 10 (p < .1; p < .05). Although no effect of low costs
can be found in either model, we can find a significantly positive effect of the interaction term of time
pressure and low costs in models 9 and 10 (p < .05; p < .05), i.e. the combination of time pressure and
Peter Letmathe and Elisabeth Noll
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low costs leads to more information acquisition. Hypothesis 3 is thus supported. Examining the
influence of the control variables reveals the following: As in both previous experiments, general risk
aversion by itself and in conjunction with time pressure, as well as round, are insignificant. Again, the
decision rule and uneven number variables significantly increase the amount of information acquired
(p < .001; p < .001). Score has a significantly positive effect (p < .01) and age and engineering studies
both have no significant influence.
Again, we also ran regressions with data from Experiment 3 only and report conspicuous changes
compared to the results obtained from the data of Experiments 1 and 3 together: The effect of time
pressure is not significant any more. This is likely to be caused by the low information costs, which
were halved, i.e. information costs were so low that the effect of time pressure faded in Experiment 3.
The supplementary analyses, depicted for models 11 and 12 in the ‘without subjects who acquired
zero information over twenty rounds’ column of table 6, show that the effects of time pressure become
insignificant in both models. Further, the influences of time pressure in interaction with low costs are
not significant in models 11 and 12, so that Hypothesis 3 is not supported any more. The effect of score
remains significant at the 1% level.
Table 6. Regression analyses on the amount of information acquired – Experiment 3
Hypotheses
All subjects
Without subjects who acquired 0
information over 20 rounds
Prediction
Finding
Model 9
Model 10
Finding
Model 11
Model 12
Time
pressure
-0.768
(0.0559)
-0.866
(0.0198)
-0.423
(0.2045)
-0.512
(0.1145)
Low costs
0.177
(0.6513)
0.302
(0.4056)
0.174
(0.5849)
0.260
(0.4025)
Time
pressure *
low costs
H3 (+)
✓
1.122
(0.0460)
1.066
(0.0399)
✗
0.621
(0.1779)
0.532
(0.2324)
General risk
aversion
0.183
(0.3822)
0.149
(0.3988)
Time
pressure *
general risk
aversion
0.229
(0.4349)
0.0942
(0.7110)
Round
0.00738
(0.6512)
0.00759
(0.6147)
Decision
rule
0.559
(0.0000)
0.584
(0.0000)
Uneven
number
1.098
(0.0000)
1.041
(0.0000)
Score
0.000204
(0.0062)
0.000206
(0.0055)
Age
0.0547
(0.2275)
0.00414
(0.9157)
Engineering
studies
-0.0445
(0.8795)
0.210
(0.4043)
Constant
6.109
(0.0000)
3.000
(0.0072)
6.467
(0.0000)
4.445
(0.0000)
N
2980
2980
2800
2800
Note. The first observation in each cell is the estimate and the second observation (in parentheses) is the two-sided
p-value.
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5. DISCUSSION OF RESULTS
The results of the regression analyses of the experiments show that time pressure, when
considered alone, reduces the amount of information acquired. Based on the literature, we assume that
participants under time pressure perceived stress, decided quickly, had no time to process information
deliberately, and thus relied on System 1 processes. The fact that people under time pressure needed on
average less time to make the decisions supports these conclusions.
No significant effect was found for participants who were placed under time pressure and who
were influenced by a negative payoff scheme. The findings also reveal that being confronted with a
negative payoff scheme instead of a positive payoff scheme has no effect on the amount of information
acquired. These results suggest that the previous empirical findings from the literature that have
supported this effect should be treated with caution.
Apart from this, the findings imply that being confronted with relatively low information costs
under time pressure increases the amount of information acquired. Since the low information costs
variable alone has no significant effect on the amount of information acquired, we infer that the
weighting of low information costs has a significant effect only if time pressure is present. Under time
pressure, i.e. when people do not think deliberately, relatively low information costs might appear to be
so cheap that people will purchase more information (than is reasonable). Moreover, we conclude that
the effect of time pressure, which leads people to acquire less information, is diminished through
relatively low information costs.
In addition to this, in Experiments 1 and 2, subjects who were not pressed for time (Treatments 2
and 4) made use of the decision rule and selected an uneven number more frequently than subjects with
a time budget, suggesting that the former thought more consciously about a strategy for making a
sound decision. In contrast to this, in Experiment 3, participants under time pressure (Treatment 5)
made use of the decision rule equally often and selected an uneven number more often than participants
without time pressure (Treatment 6). The cost factor might have been so strong that it led participants
in both treatments (Treatments 5 and 6) to use different acquisition patterns or that it outweighed the
effect of time pressure.
With regard to the control variables, we can conclude that these do play a role in the present
context, at least to some extent. People with calculation skills, older participants, or students of
engineering studies might have thought more deliberately before making a decision, e.g. by trying to
compute an economically reasonable solution.
6. CONCLUSIONS AND IMPLICATIONS
We examined the effects of time pressure on the amount of information acquired non-sequentially
in a decision-making process. Experiment 1 served as the basis and the results show that under time
pressure, less information is acquired. We altered the choice context in Experiment 2 due to the payoff
scheme, which yields a higher negative payment for the experiments’ negative outcome (low demand)
compared to Experiment 1. As a result, we found no significant effect of time pressure in conjunction
with a negative payoff scheme on the amount of information acquired. In Experiment 3, we halved the
information costs compared to Experiment 1. We found that being pressed for time and faced with
relatively low information costs simultaneously leads people to purchase more pieces of information
that can be used for decision-making.
The present study contributes to theory and practice in several ways. First, it provides further
insights into the psychological processes of individuals during the decision-making processes which are
often accompanied by time limits (Geisler and Allwood, 2018): We discovered in the regression
analyses that under time pressure by itself, less information is acquired non-sequentially and that
participants under time pressure needed on average less time per round to select information and to
make the decision. Due to our results and previous results from the literature, we assume System 1
thinking to be the underlying psychological process.
Second, while many studies have examined contradictory performance effects of time pressure
(e.g., De Paola and Gioia, 2016; Glöckner and Betsch, 2008), we did not focus on an optimal amount of
information. Rather, we identified how to control the amount of information acquired non-sequentially
in decision-making processes under time pressure. Additionally, we investigated interaction effects of
time pressure with contextual factors. We found, in fact, that the effects of time pressure are influenced
by these contextual factors and that it cannot be concluded that time pressure always reduces the
number of pieces of information acquired. In this vein, we found time pressure effects to be conditional
on the level of information costs. Under time pressure, individuals confronted with relatively low
information costs acquired more pieces of information than participants facing relatively high
information costs. With this, prior studies on information costs are supported (Ambuehl et al., 2018;
Peter Letmathe and Elisabeth Noll
99
Baethge and Fiedler 2016; Kerstholt, 1996) and we expanded research by investigating information
costs in conjunction with time pressure when information is acquired non-sequentially. Under time
pressure, people seem to set another focus or to weight contextual factors differently, leading to
different cognitive processes. Particularly, information costs seem to be such an important factor that
they might reverse the primary time pressure effect.
Third, the result that less information is acquired under time pressure has been based on sequential
information acquisition only (Mann and Tan, 1993). With the present study, we have transferred this
conclusion to the non-sequential domain. The important difference between the two domains is that the
argument that fewer pieces of information are purchased because participants are not able to inspect all
the information and they need a fast closure cannot be raised in our non-sequential acquisition context.
Fourth, for the first time we investigated the effects of a payoff scheme with negative versus
positive expected values in conjunction with time pressure on information acquisition behavior.
Previously, payoff schemes have mainly been researched in the context of risky gambles (e.g.,
Gawryluk and Krawczyk, 2017). While previous research has already shown that the payoff structure
(i.e. endowment size) significantly influences choice behavior (Haesevoets et al., 2019), our
contribution to research is that we introduce payoff schemes as influencing factors to information
acquisition research and that we uncover the need for research in this domain.
Fifth, we found that individual factors of the decision maker also influence acquisition behavior.
Thus, research should pay attention to these when further studying information acquisition behavior.
In addition, our study has several practical implications. In a setting in which a superior wants a
subordinate to acquire just a few pieces of information before decision-making, the superior can control
this requested behavior by influencing the information costs or the time frame within which a decision
has to be made. Importantly, the superior should pay attention to the contextual as well as the
individual factors of the subordinate in order to influence acquisition behavior in a certain direction. In
particular, information costs can influence the amount of information fundamentally acquired under
time pressure.
However, our paper has some limitations. The experiments were conducted with a student sample
in a laboratory setting and the task is hypothetical. In line with previous works (Peterson, 2001), we
believe that students and decision makers at the workplace are highly comparable with regard to their
information acquisition patterns since they have gone through a similar education - specifically in
Germany where the dual education system is established (BMBF, 2015). In addition to this, a field
experiment with employees would reduce some of the experiment’s internal validity through
difficulties in the controllability of job-specific experiences. It was these arguments especially that
convinced us to use a student sample for the experiment. Nonetheless, we recognize that the use of an
employee sample in a field experiment would have the potential to further increase the validity of the
study’s results and to test the replicability of the results in a natural environment. Besides this, our
sample is relatively well educated. Since cognitive abilities, i.e. how sophisticated information can be
processed, have been found to determine how people cope with time pressure (Kocher et al., 2019), a
sample composition with different characteristics might lead to different results. However, the
educational background of people making economic decisions should be comparable to the students
who took part in the experiments. In the present study, we found neither an effect of the interaction of
time pressure with a negative payoff scheme nor an effect of a negative compared to a positive payoff
scheme. Perhaps the difference between the two schemes was not distinct enough to yield significant
effects. Future research should use payoff schemes that are more different in order to make inferences
about a shift towards negative factors under time pressure. Future studies could investigate the effect of
experience with time pressure and clarify its influence on acquisition behavior. Moreover, this could
also be examined in longitudinal studies. Other contextual factors could be taken into account in future
investigations as well, for example the amount of available information or the working environment.
Int. Journal of Business Science and Applied Management / Business-and-Management.org
100
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APPENDIX A: Experimental Instructions
Abbreviations for the six experimental treatments:
TP-POS-HCOSTS:
Time pressure was present, participants were facing a ‘positive
payoff scheme’, and information costs were relatively high
NOTP-POS-HCOSTS:
Time pressure was not present, participants were facing a ‘positive
payoff scheme’, and information costs were relatively high
TP-NEG-HCOSTS:
Time pressure was present, participants were facing a ‘negative
payoff scheme’, and information costs were relatively high
NOTP-NEG-HCOSTS:
Time pressure was not present, participants were facing a ‘negative
payoff scheme’, and information costs were relatively high
TP-POS-LCOSTS:
Time pressure was present, participants were facing a ‘positive
payoff scheme’, and information costs were relatively low
NOTP-POS-LCOSTS:
Time pressure was not present, participants were facing a ‘positive
payoff scheme’, and information costs were relatively low
Notes:
The instructions have been translated from the German; the original instructions are available upon request.
Text in red lettering displays differences between the TP and NOTP treatments.
Text in blue lettering displays differences between the POS and NEG treatments.
Text in green lettering displays differences between the HCOSTS and LCOSTS treatments.
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APPENDIX B: Descriptive statistics
Experiment 1 Experiment 2 Experiment 3
Independent
variable
Treatments
1*
2**
3*
4**
5*
6**
Decision rule
N
700
760
700
700
740
780
Frequency in %
1
76.43
82.11
85.43
86.43
87.97
87.95
0
23.57
17.89
14.57
13.57
12.03
12.05
Uneven
N
700
760
700
700
740
780
number
Frequency in %
1
42.86
51.84
45.71
52.00
62.57
53.59
0
57.14
48.16
54.29
48.00
37.43
46.41
Note. * time pressure; ** no time pressure
