Disclosure Preparation and Managers’ Explanations for Performance
Michael T. Durney*
Tippie College of Business
University of Iowa
mdurney@uiowa.edu
Kristina M. Rennekamp
SC Johnson College of Business
Cornell University
kmr52@cornell.edu
November 2021
Running Head: Disclosure Preparation and Managers’ Explanations for Performance
*Corresponding author. We thank Jeremy Bentley, Natasha Bernhardt, Rob Bloomfield,
Zhenhua Chen, Shana Clor-Proell, Melissa Ferguson, Brian Gale, Shannon Garavaglia, Nick
Guest, Erik Harvey, Joseph Johnson, Brad Kamrath, Bob Libby, Serena Loftus, Nick Seybert,
Derek Smith, Blake Steenhoven, Samatha Seto, Kyle Stubbs, Patrick Witz, and participants at
the 2017 BYU Accounting Research Symposium, the 2019 ABO Midyear Meeting, and the Fall
2021 University of Iowa PhD seminar for helpful comments on earlier versions of this paper.
Electronic copy available at: https://ssrn.com/abstract=3469813
Disclosure Preparation and Managers’ Explanations for Performance
ABSTRACT
There is considerable variation across firms and settings in the level of advance preparation for
disclosures. Differences in disclosure preparation result in different market outcomes, and less
preparation results in greater cognitive load for managers when issuing disclosures. Yet, little is
known about how greater cognitive load (i.e., lack of time, effort, or ability in disclosure
preparation) affects disclosure characteristics. Using two experiments and a survey, we investigate
how managers’ explanations for performance differ when issued under greater cognitive load. Our
experiments vary cognitive load and task performance and solicit reports explaining the reasons
for performance on abstract tasks. We find that participants provide more internal reasons for
performance when responding under greater cognitive load following both good and poor
performance. A follow-up survey of IROs documents reasons for different amounts of disclosure
preparation, as well as potential consequences. Our results shed light on why managers invest in
different levels of disclosure preparation and provide evidence on how cognitive load affects
disclosure characteristics.
Keywords: voluntary disclosure, spontaneity, causal attributions, private disclosure, cognitive
load
Data Availability: Contact the authors.
Electronic copy available at: https://ssrn.com/abstract=3469813
1. Introduction
Like investors, managers’ attention is limited (Hirshleifer and Teoh 2003; Land 2019), such
that managers must allocate time between various activities, including making operational
decisions, developing firm strategy, and preparing for disclosures. Managers prepare disclosures
for public conference calls and private meetings with investors by writing scripts, compiling
anticipated questions, rehearsing disclosures, and developing strategies to handle unanticipated
questions (Lee 2016; Brown et al. 2019; Amel-Zadeh et al. 2019; Durney 2021). Yet, allocating
time and resources to disclosure preparation is costly (Abenate 2018).
While direct evidence is sparse regarding managers’ disclosure preparation behavior (AmelZadeh et al. 2019), several recent studies document variations in the extent of preparation among
firms and across disclosure types. Brown et al. (2019) survey IROs about the importance of
different preparation methods and find that IROs find the following strategies to be “very
important”: scripting remarks (93 percent), preparing a list of anticipated questions and
corresponding responses (82 percent), developing a strategy to handle unanticipated questions
(60 percent), and conducting rehearsals for earnings conference calls (49 percent). Durney
(2021) also surveys IROs and finds that the extent of scripting is greater for earnings conference
calls than for private phone calls and face-to-face meetings with investors. Amel-Zadeh et al.
(2019) gather field data to show “that there is considerable variation in who prepares disclosures,
when they are prepared, and the amount of effort expended by different types of managers” and
that this variation correlates with actual differences in the “structure, style, and tone of 10-Ks and
conference calls.”
In this study, we provide causal evidence on how differences in disclosure preparation might
indirectly affect the explanations that firms provide for their performance by affecting managers’
cognitive load. Examining the effect of cognitive load on managers’ disclosures is important
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because the extent of disclosure preparation varies, which has downstream consequences for
managers’ cognitive load, but the corresponding effects on disclosure characteristics are not well
understood. Our study fills a gap in the literature by investigating how managers’ disclosures
change with cognitive load. We specifically examine how one disclosure characteristic differs:
managers’ causal attributions; or, in other words, their explanations for performance.
Understanding how disclosure preparation affects attributions for firm performance is important
given the prior literature showing that managers’ causal attributions affect market reactions (cf.
Baginski et al. 2000; Baginski et al. 2004; Kimbrough and Wang 2013)
Our predictions draw on psychology research that indicates one’s natural tendency is to
focus inwardly on oneself (Ross and Sicoly 1979; Gilovich et al. 2000; Savitsky et al. 2001;
Epley et al. 2002; Gilovich et al. 2002). Ross and Sicoly (1979) argue that this inward focus is
driven at least in part by more cognitively available information about oneself versus information
about others and the external environment. People are thus initially anchored on their own
experiences. Inclusion of external information in the attribution process involves effortful
adjustment, which is more difficult under cognitive load (Gilovich et al. 2000; Savitsky et al.
2001; Epley et al. 2002; Gilovich et al. 2002). We therefore predict that cognitive load will lead
managers to provide more internal attributions for performance. Our investigation helps resolve
some conflicting results in psychology research on causal attributions by providing evidence that
internal causal attributions occur more spontaneously than external causal attributions.
We test our prediction and provide evidence on the issue with two experiments and a survey.
Our first experiment uses a 2x2 between-participants design where participants report the reasons
for performance on an abstract task. The experiment manipulates cognitive load as high or low
and sorts participants into good or poor task performance conditions before soliciting free-
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response performance reports. Experimental results confirm our predictions as we find that
participants under greater cognitive load give more internal reasons for their performance
following both good and poor performance. This suggests that managers are more likely to
explain performance with an internal focus when they engage in less disclosure preparation and
face greater cognitive load. Consistent with theory, supplemental analyses indicate the
underlying psychological process is, at least in part, nonconscious.
Our second experiment again manipulates cognitive load and complements the first
experiment by ruling out an alternative explanation for results. Specifically, we rule out the
possibility that cognitive load simply makes it more difficult for managers to provide accurate
explanations for performance. Again, we find that participants under greater cognitive load give
more internal reasons for their performance. Thus, greater cognitive load in our experiments
prevents effortful adjustment needed to incorporate external information, resulting in
performance reports that are internally focused.
We complement the experiments with a survey of Investor Relations Officers (IROs), who
are known as “chief disclosure officers” (Brown et al. 2019). We conduct the survey to connect
our experiments with actual disclosure preparation by asking about (a) the reasons for, and (b)
the consequences of, managers’ disclosure preparation. Results suggest that disclosure
preparation (a) is motivated by a desire to reduce unfavorable disclosures and (b) reduces
disclosures that contain inconsistent messaging and unfavorable information. Unfavorable
disclosures in practice analogize to our experimental settings in the form of internal attributions
for poor performance and external attributions for good performance, consistent with prior
research (Barton and Mercer 2005; Kimbrough and Wang 2013).
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We use the comparative advantages of experimental and survey research to complement the
archival literature in this area. Although archival research has investigated managers’
explanations provided in firm disclosures, and market reactions to more versus less prepared
disclosures, the underlying process of preparing these disclosures is unobservable in archival
data. Our study provides evidence on the psychological process of disclosure preparation by
examining the effect of cognitive load on performance explanations analogous to those provided
in firm disclosures. As such, we provide one type of process evidence enumerated by Asay et al.
(2021) – namely, we provide a better understanding of the process behind the cause-effect
relationship of disclosure preparation on disclosure outcomes.
Our study makes three contributions to the literature. First, in response to Bloomfield’s
(2008) call for such research, we provide evidence on how differing levels of preparation might
affect disclosure characteristics. We do this by showing that managers’ explanations for
performance are more internally focused under more cognitive load, which maps to disclosure
settings with less preparation. While research indicates that variation in disclosure preparation
influences market outcomes (Lee 2016), little is known about how disclosure characteristics vary
based on differing levels of disclosure preparation.1 Our evidence on how cognitive load affects
disclosures also increases our understanding of the difference between public and private
disclosures. Durney (2021) finds that private disclosures involve less preparation than public
disclosures, suggesting that managers respond with more cognitive load in private settings,
ceteris paribus.
1
Recent working papers by Rennekamp, Steenhoven, and White (2021) and Asay, Clor-Proell, and Durney (2021)
offer some indirect evidence on the consequences of cognitive load for disclosures. Specifically, Rennekamp et al.
(2021) examine how managers’ attempts to suppress emotion and avoid unintentionally revealing information
(which increases cognitive load) affect investors’ perceptions of the manager. Asay et al. (2021) examine how IROs’
disclosure preparation (which decreases cognitive load) affects disclosure content in private meetings with investors.
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Second, we provide evidence on how and why managers prepare for disclosures. Evidence is
lacking regarding managers’ disclosure preparation (Amel-Zadeh et al. 2019). Our experimental
results suggest that disclosure preparation focused on explaining poor performance can avoid
unfavorable outcomes. Specifically, we show that managers may be more likely to blame poor
performance on themselves or the firm when explaining poor performance under more cognitive
load. Disclosure preparation might prevent such disclosure of unfavorable information by
devoting the cognitive resources necessary to incorporate external reasons for poor performance.
We also place our experimental results in the context of IROs’ perceptions of the reasons for,
and consequences of, disclosure preparation. IRO respondents in our survey indicate that
disclosure preparation is motivated by a desire to reduce unfavorable disclosures.
Combined with our experimental results, this evidence supports assumptions in Lee (2016)
about why managers prepare for disclosures. Lee (2016) argues that his finding of less
spontaneous disclosure following bad news is driven by a desire to reduce spontaneous
disclosure of bad (vs. good) news, but he admits that he cannot rule out an alternative
explanation that firms are simply better at anticipating investor questions with bad (versus good)
news. Our experimental results support Lee’s (2016) assertions by providing causal evidence that
more cognitive load results in unfavorable disclosures in the form of internal attributions for poor
performance. Thus, more disclosure preparation, in line with IROs reasons for disclosure
preparation from our survey and Lee’s (2016) assertions, can counteract the unfavorable
disclosures resulting from increased cognitive load.
Lastly, our findings contribute to the research on managers’ causal attributions, which
documents the market impact of the attributions that managers provide (cf. Baginski et al. 2000;
Baginski et al. 2004; Kimbrough and Wang 2013). The results of our paper suggest that
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managers’ causal attributions may not always be intentional, contrary to the typical assumption
in this literature (Staw et al. 1983; Salancik and Meindl 1984; Baginski et al. 2000; Baginski et
al. 2004; Barton and Mercer 2005; Osma and Guillamón-Saorín 2011; Chen 2012; Elliott et al.
2012; Libby and Rennekamp 2012; Kimbrough and Wang 2013; Libby and Emett 2014; Chen et
al. 2016). The assumption in this prior research that managers’ causal attributions are intentional
may be due to the fact that prior research largely focuses on textual disclosures, which are more
likely to contain strategic and intentional attributions due to the extent of preparation (AmelZadeh et al. 2019). However, other disclosure settings such as live interviews, earnings call
Q&As, and private meetings with investors, in which managers frequently participate, are more
susceptible to unintentional disclosures (Durney 2021). Our results speak to these other settings
and indicate that managers’ explanations for performance may be driven, at least in part, by
psychological processes outside managers’ awareness. Such unintentional disclosures provide at
least a partial explanation for prior archival results that show managers sometimes issue internal
attributions for poor performance despite contrary incentives (Lee et al. 2004; Chen 2012;
Kimbrough and Wang 2013; Zhou 2014; Chance et al. 2015).
The paper proceeds as follows. Section II reviews related literature and develops our
hypotheses. Section III presents the experimental method and results for our first experiment.
Section IV presents our second experiment, Section V presents the survey method and results,
and Section VI concludes.
2. Background and Hypothesis
In this section we develop our hypothesis with respect to how managers’ cognitive load
might affect the causal attributions that they provide to explain firm performance, starting with a
review of the broader literature on causal attributions.
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Causal Attributions
Causal attribution research began with Heider (1944, 1958) theorizing that people tend to
explain outcomes by attributing them to internal or external causes. On the level of the
individual, internal attributions refer to causes attributable to oneself such as ability or effort and
external attributions refer to environmental causes such as task difficulty or luck. In a corporate
setting, internal attributions might attribute cause to management or the firm, and external
attributions might attribute cause to the economy or the industry.
An early review by Miller and Ross (1975) focuses on self-serving causal attributions. Selfserving attributions attribute the causes of good performance to internal factors and the causes of
poor performance to external factors. In their review, Miller and Ross (1975) conclude that the
early studies find that individuals make self-serving attributions following good but not poor
performance (c.f., Schopler and Layton 1972; Beckman 1973; Wolosin et al. 1973). Duval and
Silvia (2002) find similar asymmetric evidence in their review of the literature nearly 30 years
later. More specifically, while researchers consistently find evidence of internal attributions after
good performance, the evidence is mixed regarding poor performance, with some studies finding
internal and some studies finding external attributions after poor performance (Duval and Silvia
2002). A meta-analysis supports these conclusions with consistently positive effects of good
performance on self-serving attributions but both positive and negative effects of poor
performance on self-serving attributions (Campbell and Sedikides 1999). Cross-cultural evidence
exhibits similar patterns of results (Mezulis et al. 2004; Sakaki and Murayama 2013). Overall,
prior research consistently finds internal attributions following good performance but either
internal or external attributions following poor performance.
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These conflicting results for attributions following poor performance might be partially
explained by research on the effect of cognitive load on causal attributions, a literature where the
evidence is still inconclusive (Sakaki and Murayama 2013). Responses issued under greater
cognitive load are characterized by less effortful control and are consistent with more
“automatic” responses investigated in the psychology literature (Bargh and Williams 2006).
Research on other egocentric biases provides insight into what automatic causal attributions
might look like. For example, Ross and Sicoly (1979) find that people overestimate their
individual contributions to group endeavors that result in both positive and negative outcomes.
The researchers argue that this occurs at least in part because information about the self is more
cognitively available than information about others or the external environment (Ross and Sicoly
1979). Later research builds on this concept and shows that people anchor on information about
themselves, and effortful adjustment is required to cognitively incorporate information about
others and the external environment (Gilovich et al. 2000; Savitsky et al. 2001; Epley et al. 2002;
Gilovich et al. 2002). While this prior research does not directly examine the effect of cognitive
load on causal attributions, the results suggest that attributions issued under greater cognitive
load will be more internally focused, regardless of prior performance, because internal reasons
for performance are more easily available than external reasons. This research leads us to predict
that managers under greater cognitive load will attribute performance internally, regardless of
performance, because coming up with external attributions requires more effortful adjustment.
Causal Attributions in Financial Reporting
A stream of literature in accounting and finance leverages the aforementioned psychology
research on individuals’ attributions about their own performance to make predictions about
managers’ attributions for firm performance, and corresponding consequences. This literature
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finds that managers’ narrative financial reports contain causal attributions that affect market
outcomes and investor decisions. Stock market reactions vary with the amount and type of
attributions for financial performance given by managers (Baginski et al. 2000; Baginski et al.
2004; Kimbrough and Wang 2013). Individual investors and analysts also exhibit different
judgments and decisions based on whether managers attribute performance internally or
externally (Barton and Mercer 2005; Elliott et al. 2012). Additionally, self-serving attributions
may contribute to the issuance of earnings forecasts (Libby and Rennekamp 2012).
The voluntary disclosure literature largely assumes that managers issue disclosures
intentionally. A recent review notes that “prior literature reveals two fundamental perspectives
regarding managers’ disclosure motives – the opportunistic perspective and the information
perspective” (Libby and Emett 2014, p. 414), both of which are intentional. Similarly, most of
the work on manager’s causal attributions assumes that managers’ causal attributions are
intentional (Staw et al. 1983; Salancik and Meindl 1984; Baginski et al. 2000; Baginski et al.
2004; Barton and Mercer 2005; Osma and Guillamón-Saorín 2011; Chen 2012; Elliott et al.
2012; Libby and Rennekamp 2012; Kimbrough and Wang 2013; Chen et al. 2016). Yet, prior
research suggests that causal attributions could be the result of unintentional (i.e., automatic)
processes. In fact, Aerts (2005) argues that unintentional motivations drive differences in
attributional patterns between listed and unlisted firms. More recently, Asay et al. (2018) find
that experienced financial managers think they use causal language in financial reports
differently than they actually do, indicating that managers may not always issue causal
attributions intentionally.
Since information for internal attributions is more readily available in individuals’ minds
(Ross and Sicoly 1979), we predict that managers automatically attribute both good and poor
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performance to internal causes. However, we expect managers to incorporate more external
attributions when additional cognitive resources are available. Managers’ advance preparation
for a disclosure affects the number of cognitive resources available in practice. With more
advance preparation, responses to questions can be easily recalled during a disclosure because
they may have already been considered. This reduces managers’ cognitive load during a
disclosure and allows them to expend greater cognitive resources on answering unanticipated
questions, or to provide richer information in their responses. On the other hand, a lack of
preparation requires more effortful thinking, and leads to greater cognitive load because it
reduces the cognitive resources available to managers. We therefore predict more internal
attributions when managers are under greater cognitive load because cognitive load reduces the
cognitive resources necessary to incorporate external attributions.
HYPOTHESIS: Narrative performance reports will include more internal attributions
with increased cognitive load.
Our hypothesis is a main effect prediction of cognitive load on the extent of internal vs.
external attributions. However, if the dependent variable is the extent of self-serving attributions,
then our hypothesis suggests an interactive effect of cognitive load and performance on the
extent of self-serving attributions. More specifically, our hypothesis implies that managers’
narrative performance reports will include more (less) self-serving attributions when managers
are under increased cognitive load following good (poor) performance.
Examining the extent of self-serving attributions in addition to the extent of internal or
external attributions is important to better connect with the causal attribution research in the
financial reporting and psychology literatures. In financial reporting, both the early (e.g., Staw et
al. 1983; Bettman and Weitz 1983) and more recent (e.g., Kimbrough and Wang 2013; Zhou
2014) research maintains a focus on the self-serving nature of managers’ attributions. This focus
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might be motivated by results showing the impact of managers’ self-serving attributions on stock
price (Baginski et al. 2004). Likewise, in psychology, both the early (e.g., Heider 1958; Miller
and Ross 1975; Miller 1976) and more recent research (e.g., Duval and Silvia 2002; Sakaki and
Murayama 2013) focuses on the self-serving nature of causal attributions. One reason for the
continued focus on self-serving attributions is the divergent findings regarding whether people
actually attribute causes in a self-serving way following poor performance (Miller and Ross
1975; Duval and Silvia 2002). Our paper sheds light on automatic causal attributions in response
to poor performance, which helps inform the discrepant findings regarding self-serving
attributions in psychology. In fact, one reason why some studies find internal attributions and
others find external attributions following poor performance could be due to differences across
studies in the cognitive resources available to participants when making attributions. We test this
directly in our experiment.
3. Experiment One
Participants
We recruit 138 MBA students at a highly ranked MBA program to participate in our
experiment2 Participants average 28 years of age and 37 (27 percent) identify as female. We use
MBA students to proxy for managers since we are examining a basic psychological mechanism
with an abstract task. Thus, MBA students are an appropriate match to the goals of our
experiment (Libby et al. 2002).
Design and Overview
We test our hypotheses using a 2x2 between-participants experiment that manipulates
cognitive load (High or Low) and varies task performance (Good Performance versus Poor
2
Approval for this experiment was obtained from the University’s Institutional Review Board.
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Performance) on an abstract trivia-question task. Following prior work (Libby and Rennekamp
2012; Asay 2018), we design the task to capture key elements of managers’ financial reporting
environment while holding constant confounding variables that prevent clean causal inferences.
Specifically, our manipulation of cognitive load reflects differences in managers’ disclosure
preparation. For example, more preparation prior to disclosure results in lower cognitive load.
However, disclosure preparation and cognitive load, along with performance, are potentially
confounded in practice with variables like ability, rendering a causal examination difficult.
Additionally, our abstract task allows participants to feel involved with the actual performance in
the task and provide corresponding attributions, which would not be possible with a more
context-rich task that looks more like a management disclosure context one might find in
practice.
In our experiment, participants first answer seven difficult binary-choice trivia questions.
Then, participants learn their score and that their performance is either better or worse than
average depending on whether participants answered 4-7 (i.e., Good Performance) or 0-3 (i.e.,
Poor Performance) questions correctly. After that, we ask participants to remember an 8- or 1digit number corresponding to High or Low cognitive load, respectively. Finally, participants
explain the reasons for their Good Performance or Poor Performance in a narrative freeresponse format, enter the 8- or 1-digit number they were asked to remember, and respond to
post-experimental questions. Our main dependent variable is the locus of (i.e., extent of internal
or external) causal attributions in participants’ narrative performance reports.
Independent Variables
We design our manipulation of cognitive load to reflect the extent of managers’ lack of time,
effort, or ability in disclosure preparation in practice. Increased cognitive load in our experiment
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should reduce participants’ available cognitive resources similar to how less preparation reduces
managers’ available cognitive resources. To craft this manipulation, we follow psychology
research in manipulating availability of cognitive resources. The specific operationalization of
our manipulation involves giving participants an 8- or 1-digit number to memorize. This method
is used in psychology research to examine more automatic psychological processes, which are
evident under greater cognitive load (Gilbert and Silvera 1996; Van Dillen et al. 2013).
Therefore, in our experiment we employ greater cognitive load (i.e., the 8-digit number) to
examine the kind of causal attributions that managers issue more automatically.
For our independent variable of task performance, we design seven trivia questions to sort
participants into conditions of good and poor performance. While we do not randomly assign
participants to performance conditions, we carefully structure the task to sort participants into
performance conditions while minimizing any potential influence from knowledge, ability,
effort, or other individual characteristics. Following prior literature (Libby and Rennekamp
2012; Asay 2018), we select questions from Trivial Pursuit (Hasbro 2009). We select 10 difficult
questions, modify the questions to be binary-choice, and administer the questions as a pretest to
34 MBA students from the same participant pool as our main sample.3 Our goal is to retain
questions where the correct response rate is no different from chance, in order to limit the
likelihood that performance on the task is driven by differences in knowledge, ability, effort, or
other individual characteristics. Results from this pretest reveal that responses to two of the
questions significantly differ from chance (both p<0.050, two-tailed, untabulated). We drop these
two questions and one additional question for which some participants indicate clear knowledge
via post-experimental feedback.
3
None of the students that participated in the pre-testing of questions were participants in the main experiment.
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With the final seven questions (shown in the Appendix), we examine the individual
characteristics for pretest participants who perform well (i.e., answer 4-7 questions correctly)
versus those who perform poorly (i.e., answer 0-3 questions correctly). While we do not
perfectly measure knowledge, ability, and effort, we do measure level of education, work
experience, age, and time spent on the trivia questions. Participants who perform well do not
have significantly more education or work experience and are not significantly older than
participants who perform poorly (all p > 0.400, two-tailed, untabulated). There are also no
differences between those who perform well and those who perform poorly when it comes to
perceptions of question difficulty, predicted accuracy, or time spent (all p > 0.250, two-tailed,
untabulated). These results suggest that ability, knowledge, effort, and other individual
characteristics do not affect task performance, on average.
Our independent variable of task performance is especially important in understanding the
psychology behind managers’ causal attributions. If we just examine instances of either good or
poor performance, we could not distinguish between different process explanations. We predict
cognitive load leads to more internal attributions because internal attributions are more readily
available (Ross and Sicoly 1979). However, another explanation might be that cognitive load
leads to increased motivational processes. For example, if we examined good performance alone,
then it would be more difficult to argue that results are due to information availability (Ross and
Sicoly 1979) compared to a motivational explanation to appear as a good performer.
Dependent Variables
To measure managers’ causal attributions, we ask participants to give the reasons for their
performance in a free-response text box. Using a free-response question for our primary
dependent variable allows us to better examine the psychological mechanism behind causal
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attributions compared to using a scale that explicitly asks about internal/external attributions
(Malle 2011a, 2011b; Böhm and Pfister 2015). Participants give reasons for their performance in
the free-response text box after we tell them to memorize a number but before we ask them to
recall the number. Blind to condition, two research assistants code participants’ performance
reports by placing each report in one of three buckets: mostly internal reasons, mostly external
reasons, or about the same amount of internal and external reasons. We assign ‘1’ to
performance reports with mostly internal reasons, ‘-1’ to reports with mostly external reasons,
and ‘0’ to the reports with about the same amount of internal and external reasons for
performance. The coders agreed on all ratings, which comprise our primary dependent measure.
For practical relevance and to connect with prior literature, we also examine the extent of
self-serving versus self-disserving reasons for performance. Self-serving reasons are internal
attributions for good performance and external attributions for poor performance, while selfdisserving reasons are external attributions for good performance and internal attributions for
poor performance. Transforming our main dependent variable to show the extent of self-serving
attributions involves multiplying the aforementioned coding for participant reports in the Poor
Performance conditions by ‘-1’. By doing so, we assign ‘1’ to reports with mostly self-serving
reasons for performance, ‘-1’ to performance reports with mostly self-disserving reasons, and ‘0’
to reports with about the same about of self-serving and self-disserving reasons for performance.
Compensation
We compensate participants in three ways. First, participants receive a flat $3 participation
fee. Second, participants earn an additional $1 for each trivia question that is answered correctly.
Lastly, we enter participants into a drawing for one of two $50 Amazon gift cards if they
correctly remember the 1- or 8-digit number from our cognitive load manipulation.
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Manipulation Checks
To check our manipulation of cognitive load, we examine participants’ recollection of the 1or 8-digit number. Most participants either remember the number exactly (72 percent) or at least
make an obvious attempt at remembering the number (91 percent), indicating a successful
manipulation of cognitive load. The remaining 9 percent of participants enter a number with a
significantly different number of digits (e.g., a 3-digit or a 4-digit number). Dropping these
participants strengthens our results, but we include all participants in all tests for completeness.
To check our manipulation of task performance, we examine participants’ perceptions of
their performance on the trivia question task using a 7-point scale with endpoints of 1=Very
poorly and 7=Very well and a midpoint of 4 =Neither poorly nor well. Consistent with
expectations, participants in the poor performance condition report feeling that they performed
significantly worse than participants in the good performance condition (2.91 vs. 4.15, p < 0.001,
one-tailed, untabulated).4
Hypothesis Tests
Our hypothesis predicts a main effect of cognitive load such that managers give more internal
reasons for performance when cognitive load is High versus Low, regardless of performance
condition. Table 1 presents descriptive statistics and tests of H1 and Figure 1 displays cell
means. As shown in Table 1 panel B, the main effect of load in our ANOVA is significant (p <
0.001, one-tailed) in support of our hypothesis that managers tend to automatically attribute
performance internally. Importantly, Figure 1 shows that the same patterns of results exist
following both Good Performance and Poor Performance, supporting our theoretical argument
4
We are primarily interested in the difference in perceived performance across the performance conditions, but for
completeness we also compare responses to the scale midpoint (4). The mean response of 2.91 (4.15) in the poor
(good) performance condition is (not) significantly different from the midpoint with two-tailed p<.001 (p=0.295).
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that these results can be explained by more cognitively available internal (vs. external)
attributions.
[INSERT TABLE 1 AND FIGURE 1 ABOUT HERE]
To connect with prior research in accounting and psychology, we also examine participants’
self-serving disclosures. As discussed in the previous section, this involves transforming the
dependent variable for participants in the Poor Performance conditions by multiplying by ‘-1.’
This new measure shows the extent of self-serving attributions (internal reasons for Good
Performance and external reasons for Poor Performance) in the performance reports. Our
prediction in H1 implies that this new measure will show an interaction between cognitive load
and task performance such that when cognitive load increases, managers attribute relatively more
self-servingly with Good Performance and relatively less self-servingly with Poor Performance.
Figure 2 displays this interaction, which is significant (p < 0.001, one-tailed, untabulated).
Follow-up simple effects also support the prediction in H1 as the effect of load given Good
Performance is to increase self-serving attributions (p = 0.009, one-tailed, untabulated) and the
effect of load given Poor Performance is to decrease self-serving (i.e., increase self-disserving)
attributions (p = 0.002, one-tailed, untabulated). Overall, these results suggest that managers tend
to attribute both good and poor performance to internal sources when under cognitive load.
[INSERT FIGURE 2 ABOUT HERE]
Supplemental Analyses
We complement our main results with additional tests regarding managers’ awareness of
their causal attributions. Our hypothesis development argues that the causal attribution process
is, at least in part, automatic, suggesting that managers might not exhibit conscious awareness of
their attributional locus (i.e., extent of internal vs. external attributions). We investigate this
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further with two sets of post-experimental questions, both of which require participants to make
explicit assessments of internal versus external attributions. We ask one question about
participants’ perceived amount of internal versus external attributions in general5 and we ask
four questions that ask participants to compare specific internal (effort and skill) and external
(luck and difficulty) reasons6 for their performance.
If managers' attribution process is entirely conscious, then their attributional locus should not
depend on whether they are simply giving reasons for performance, as in our main DV reported
above, or whether they are explicitly reporting on their attributional locus. However, if managers
lack conscious awareness of the attribution process, explicit assessments of attributional locus
will not result in responses that exhibit the same pattern as our main results.
For the first question, there is no significant effect of cognitive load (p = 0.405, two-tailed,
untabulated), contrary to results in Table 1 and consistent with the assumption that managers
lack conscious awareness of their attributional locus. However, a null result with frequentist
statistics cannot be considered confirmatory evidence (Kruschke 2011). So, we provide
confirmatory evidence by using Bayesian model comparison (Kruschke 2010, 2011, 2014;
Guggenmos and Bennett 2018) to show that participants’ perceived attributions do not differ
based on cognitive load. Specifically, we compare an intercept-only model (i.e., the null model)
to a model with a main effect of cognitive load. Results favor the null model with a Bayes factor
of 4.58, which can be interpreted as meaning our data indicate the null model is 4.58 times more
5
Specifically, the first question asks participants to rate themselves on the perceived extent of internal versus
external attributions in their performance reports on a 7-point scale anchored by “1 = All internal reasons” and “7 =
All external reasons” with the midpoint as “4 = About the same amount of internal and external reasons”.
6
For example, for the question asking participants to compare luck and skill, we state: “Just between (a) luck and (b)
skill, which played a bigger role in your performance on the trivia questions?” Participants respond on a 7-point
scale anchored by 1 = “Almost all due to luck” and 7 = “Almost all due to skill” with the midpoint as 4 = “Equal
amounts due to luck and skill”.
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likely to accurately describe the data than the model with a main effect of cognitive load.7 This
large a Bayes factor provides “substantial” or “positive” evidence supporting the null model
(Jeffreys 1961; Kass and Raftery 1995).
Results examining an average of the second set of questions yield similar results. The effect
of cognitive load is insignificant in an ANOVA (p = 0.407, two-tailed, untabulated) and the
corresponding Bayes factor comparing the null model to the model with a main effect of
cognitive load is 3.36, which is also substantial, positive evidence (Jeffreys 1961; Kass and
Raftery 1995) in favor of the null model.
Another way to investigate the extent of awareness of the attributional process is at a
participant level by comparing participants’ perceived attribution locus, from the first question
above, with participants’ actual attribution locus in their respective performance reports. If the
attributional process is, at least in part, automatic, we expect to see a significant lack of matching
between participants’ perceived attribution locus and the attribution locus of their actual
performance reports. We find that only 38 percent (53 participants) of all participants exhibit
such a match, which is not significantly different than chance matching of 33 percent (z=1.264, p
=0.206, two-tailed, untabulated).8 Taken together, these results suggest that participants do not
seem aware of their own causal attributions.
4. Experiment Two
To complement the results of our first experiment, we conduct a second experiment to rule
out an alternative explanation for our main results. Instead of just reducing the cognitive
resources necessary to incorporate external attributions, cognitive load might lead to less
7
We calculate Bayes factors using the BayesFactor package in the R statistical language (R Core Team, 2018).
Notably, this is a conservative test since the ex ante amount of matching that would indicate complete awareness is
100 percent whereas this test is comparing to chance matching of 33 percent.
8
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accurate attributions because tasks are more difficult under greater cognitive load. The task in
our first experiment was structured such that external attributions, like luck and chance, were
more “accurate”. Thus, the increase in internal attributions with increased cognitive load that we
found in our first experiment could be due to cognitive load leading to less accurate attributions.
To provide convergent evidence that cognitive load increases internal attributions rather than
increasing inaccurate attributions, we conduct another experiment wherein performance is driven
more by internal factors, like ability and effort.9 If cognitive load leads to less accurate
attributions rather than internal attributions, we would expect to see a positive association
between cognitive load and external attributions in this second experiment.
Design
We manipulate cognitive load (High or Low) while participants report on their performance
on a slider task. Participants first attempt to correctly position 10 sliders in 30 seconds. Then, just
as in experiment one, participants explain the reasons for their performance, which comprises
our dependent variable and is coded just as in experiment one. Half of all participants are subject
to High cognitive load and are asked to carefully listen for and remember the number of beeps
(the correct number is 19) they hear while they are reporting. These same participants are given a
chance to enter the number of beeps that they hear on the screen immediately following the
reporting screen. Low cognitive load participants are not asked to listen for, or count, any beeps.
Participants and Compensation
We recruit 178 participants on Amazon Mechanical Turk. Participants average 37 years of
age and 43 percent identify as female. All participants are paid a flat fee of $0.50 in addition to a
bonus of $0.10 for each correctly positioned slider and a $0.50 bonus for correctly remembering
9
In fact, Choi et al. (2019) argue and show that a slider task is especially effective at revealing differences in effort,
an internal factor.
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the number of beeps. To keep compensation equal between conditions, participants in the Low
cognitive load condition are randomly selected to receive a $0.50 bonus equal to the proportion
of participants in the High cognitive load that receive a bonus. Average compensation totaled
$1.29 per participant and average time to complete was 4 minutes and 19 seconds.10
Manipulation Check
To check our manipulation of cognitive load, we examine the proportion of participants in
the High cognitive load condition who appear to attempt to listen for and remember the number
of beeps. Fifty-six percent of participants enter ‘19’, which is the correct number of beeps and 88
percent are within three beeps. Restricting our analyses to just this 88 percent leaves inferences
identical, but we include all participants in all analyses for completeness.
Results
Experimental results are shown in Table 2. Consistent with the first experiment and in
support of our hypothesis that managers automatically attribute performance internally, we find
that participants’ performance reports include significantly more internal reasons for
performance when cognitive load is High versus Low (t176 = 2.81, p = 0.003, one-tailed,
untabulated).
[INSERT TABLE 2 ABOUT HERE]
5. Survey
To complement the experiments by placing the results in the context of actual disclosure
settings, we survey IROs to investigate perceptions of the (a) reasons for and (b) consequences of
different amounts of disclosure preparation. With more disclosure preparation, managers’
Approval for this experiment was obtained from the University’s Institutional Review Board. Completed
responses were obtained from 210 participants but 32 responses include indiscernible explanations for performance
(e.g., blank, “n/a,” or an unrelated copy-paste), leaving 178 responses for analysis.
10
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disclosures are likely more controlled and issued under less cognitive load. We survey IROs
because IROs are known as “chief disclosure officers” and are familiar and involved with firm
disclosures and disclosure preparation (Brown et al. 2019; Amel-Zadeh et al. 2019).
Our survey includes 114 IROs, 35 percent of which are female and 91 percent of which have
ten or more years of work experience in investor relations. We include the questions for the
current research as part of the survey instrument outlined in Durney (2021). We distribute the
survey through the National Investor Relations Institute (NIRI) via weekly newsletters sent to all
NIRI members, emails from NIRI chapter presidents, and posts on NIRI chapter LinkedIn pages.
We ask two questions, the first of which, outlined in Table 3, specifically asks participants to
rate the likelihood that scripting of disclosures is motivated by two different rationales. Both
rationales involve avoiding disclosure of information inconsistent with the issuer’s narrative, but
the first rationale involves inconsistent unfavorable information and the second reason involves
inconsistent favorable information. As Table 3 shows, IROs perceive the first reason to be a
likely motivator for disclosure preparation, with a mean response significantly greater than the
scale midpoint (p = 0.017, two-tailed). However, survey participants did not perceive the second
reason to be especially likely or unlikely, with a mean response that is not significantly different
than the scale midpoint (p = 0.188, two-tailed). Thus, IROs perceive the possible disclosure of
unfavorable (but not favorable) news to be a likely reason to engage in disclosure preparation.11
[INSERT TABLE 3 ABOUT HERE]
The results from this first question support our experimental results and complement existing
archival results. In our experiments, we provide evidence suggesting that managers automatically
11
Mean responses for the two reasons (4.81 vs. 4.45) are not significantly different from each other at conventional
levels of significance in a paired t-test (p = 0.145, two tailed, untabulated). This analysis involves removing one
observation because one IRO only answered for one of the reasons, making a paired t-test impossible unless that
observation is dropped. An unpaired t-test with all responses yields a p-value of 0.439 (two-tailed, untabulated).
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attribute performance in an unfavorable way when performance is poor – i.e., they automatically
attribute poor performance internally. These survey results suggest that managers are motivated
to prevent such disclosure of unfavorable information with disclosure preparation. These results
also complement results from Lee (2016) that indicate one type of disclosure preparation,
earnings call Q&A scripting, is more likely when managers have negative private information –
i.e., when performance is poor. Lee (2016) argues that his results are due to managers’ incentives
to “reduce the likelihood that bad news is unintentionally revealed” (p. 230). However, Lee
(2016) does not test the intentionality of managers’ disclosure decisions in his archival analysis.
Our experimental and survey results support Lee’s (2016) explanation of his results by
experimentally testing the intentionality of disclosures and showing that IROs agree with Lee’s
(2016) arguments.
The second survey question asks about the consequences of disclosure preparation by asking
IROs to rate the likelihood of two scenarios occurring in three specific disclosure venues:
earnings conference call Q&As, private phone calls, and private face-to-face meetings. The first
scenario is about responding to an unanticipated question with unfavorable information and the
second scenario is identical to the first except the disclosed information is favorable.
Importantly, this question asks about disclosures that are made in response to unanticipated
questions, which is a setting with less disclosure preparation where cognitive load is likely
higher.
Responses to this question, shown in Table 4, suggest three important takeaways regarding
the consequences of disclosure preparation. First, IROs’ responses indicate managers’ responses
to unanticipated questions are usually consistent with a firm’s overall narrative. The means of all
ratings are significantly below the scale midpoint (all p < 0.003, two-tailed), which is consistent
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with successful disclosure preparation – e.g., the anticipation of investor questions and
development of strategies to respond to unanticipated investor questions. Second, these results
hold for all three disclosure settings. Pairwise t-tests show that survey participants find no
difference between disclosure settings in the likelihood of the scenarios occurring (all p > 0.170,
two-tailed, untabulated).12 In fact, responses for the first and second scenario indicate good
internal consistency with values of Cronbach’s alpha of α = 0.80 and α = 0.81, respectively.
Finally, survey responses indicate that disclosure issue with less preparation more commonly
involve favorable information than unfavorable information, consistent with the implication of
Lee’s (2016) results that disclosure preparation, including prepping for unanticipated questions,
is more focused on unfavorable information. The likelihood of the second scenario occurring is
greater than the likelihood of the first scenario occurring (p < 0.001, two-tailed, untabulated).13
Collectively, these three takeaways suggest disclosure preparation reduces disclosures with
inconsistent messaging and unfavorable information.
[INSERT TABLE 4 ABOUT HERE]
6. Conclusion
This paper investigates how the cognitive load that results from different amounts of
disclosure preparation might affect managers’ explanations for performance. Two experiments
manipulate cognitive load and find that increased cognitive load results in more internal
attributions for performance, suggesting that internal explanations for performance are more
likely to occur spontaneously when firms spend less time on disclosure preparation. The survey
results complement these findings and suggest that managers use disclosure preparation to
12
We correct for multiple comparisons using the Bonferroni-Holm adjustment.
This analysis averages the three disclosure venues for each scenario, which is reasonable given the values of
Cronbach’s alpha, and compares the means for the two scenarios of 1.94 and 2.63.
13
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prevent the disclosure of unfavorable information and inconsistent messaging. The collective
results indicate that when under cognitive load managers are more likely to make unfavorable
disclosures in the presence of bad news. One corresponding implication is that managers’
disclosure preparation efforts are especially helpful when news is bad.
Our results provide three important contributions. First, we answer a call for research by
Bloomfield (2008) to investigate spontaneous disclosures (i.e., disclosures with less preparation),
which vary across firms and disclosure settings and affect market outcomes (Lee 2016; Durney
2021). This paper increases our understanding about how differing levels of disclosure
preparation affect disclosure characteristics. Second, our results provide evidence on how and
why managers prepare for disclosures. Our experimental and survey evidence complements
existing archival research by offering support for assumptions in Lee (2016) about why
managers prepare for disclosures. Lastly, our findings contribute to research on managers’ causal
attributions. Our results suggest that managers’ causal attributions may not always be intentional,
contrary to the general assumption in most of the prior literature (Staw et al. 1983; Salancik and
Meindl 1984; Baginski et al. 2000; Baginski et al. 2004; Barton and Mercer 2005; Osma and
Guillamón-Saorín 2011; Chen 2012; Elliott et al. 2012; Libby and Rennekamp 2012; Kimbrough
and Wang 2013; Libby and Emett 2014; Chen et al. 2016).
Our study is subject to limitations, which suggest avenues for future research. Our use of
abstract tasks increases internal validity in our experiments but may lack external validity and
generalizability. As such, our results suggest differences in managers’ actual disclosures in
different reporting settings that we do not test. For example, in the 10-K’s MD&A or in press
releases, managers are more likely to disclose only after substantial preparation – i.e., with less
cognitive load. On the other hand, in the earnings call Q&As, private meetings with investors, or
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in interviews with the press, managers’ disclosures are more likely to be issued under greater
cognitive load. To the extent that all explanations for firm performance are external to the
preparer of a given disclosure, this could limit the generalizability of our results. However,
consistent with prior work involving the application of attribution theory to managers (e.g., Staw
et al. 1983; Baginski et al. 2004; Barton and Mercer 2005; Elliott et al. 2012; Kimbrough and
Wang 2013), we believe that in most cases, explanations for firm performance that are internal to
the firm (e.g., that result from firm-level decisions) are more likely to be accessible than
explanations that are external to the firm.
Although our study does not directly test managers’ actual disclosures, future research could
investigate the differences in actual disclosures that occur due to differences in preparation or
cognitive load. Additionally, the generalizability of our results also depends on whether the
abstract tasks in our experiments reflect relevant features of the financial reporting setting. We
explicitly chose to abstract from reality in order to avoid unnecessary realism when examining a
psychological process (Libby et al. 2002; Libby et al. 2015; Griffith et al. 2016; Kadous and
Zhou 2017). As such, the current research complements studies that allow participants to draw
on their actual experience when explaining performance in a more realistic setting as in Asay et
al. (2018). Further research might incorporate various institutional elements of the financial
reporting setting and examine corresponding effects. Finally, we focus our investigation on
managers’ attributional locus, but future research might focus on other dimensions of managers’
causal attributions and other features of managers’ explanations that might differ if issued with
varying levels of preparation or cognitive load.
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Appendix: Trivia Questions
What animal is the second most common rabies carrier in the United States?
- Racoon
- Bat
Alexander the Great was a student of what Greek philosopher?
- Aristotle
- Socrates
How many total toes does an Ostrich have?
- Four
- Six
What was the name of the Apollo 11 lunar module?
- Odyssey
- Eagle
What is the longest book in the Harry Potter series?
- Harry Potter and the Order of the Phoenix
- Harry Potter and the Deathly Hallows
How long does it take a human blood cell to make a complete circuit of the human body?
- One minute
- Ten minutes
What are some wild gerbils in China fed in order to control their numbers?
- Arsenic
- Birth control pills
This appendix presents the trivia question that are used in experiment one. Questions and possible answers were
shown in random order.
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TABLE 1
Performance Condition
Experiment One Descriptive Statistics and ANOVA Results: Causal Attribution Locus in Participant
Performance Reports
Panel A: Descriptive statistics of causal attributions in participant performance reports: mean, (standard
deviation), and number of participants
Cognitive Load Condition
Low Load
High Load
Total
0.097
0.541
0.338
Good
(0.790)
(0.730)
(0.784)
Performance
n=31
n=37
n=68
-0.051
0.484
0.186
Poor
(0.857)
(0.677)
(0.822)
Performance
n=39
n=31
n=70
0.014
0.515
0.261
Total
(0.825)
(0.702)
(0.804)
n=70
n=68
n=138
Panel B: ANOVA results
Source
Load
Performance
Load x Performance
df
1
1
1
Mean
squares
8.178
0.358
0.071
Error
134
0.594
F- statistic
13.777
0.603
0.120
p-value
<0.001†
0.439
0.730
Notes: This table provides descriptive statistics and hypothesis tests for the effect of cognitive load and task performance on
participants’ use of causal attributions in their performance reports. The experiment varies cognitive load (high or low) and
performance on an abstract task (good or poor performance) in a 2x2 between-participants design. Participants provide
narrative performance reports after learning their task performance. The dependent variable shown here is the result of two
coders’ agreed-upon ratings (ranging from -1 to 1) of causal attribution locus (i.e., the extent of internal versus external
causal attributions) in participants’ performance reports. Coders assign ‘1’ to performance reports with mostly internal
attributions, ‘-1’ to reports with mostly external attributions, and ‘0’ to reports with an equal amount of internal and external
attributions.
†One-tailed equivalent due to directional prediction. All other p-values are two-tailed.
33
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TABLE 2
Experiment Two Descriptive Statistics: Causal Attribution Locus in Participant
Performance Reports
Descriptive statistics of causal attributions in participant performance reports: mean,
(standard deviation), and number of participants
Cognitive Load Condition
Low Load
High Load
Total
-0.082
0.259
0.073
(0.812)
(0.803)
(0.824)
97
81
178
Notes: This table provides descriptive statistics on experiment two’s main dependent variable. The
experiment varies cognitive load (high or low) on an abstract task in a 1x2 between-participants design.
Participants provide narrative performance reports after performing the task. The dependent variable
shown here is the result of two coders’ agreed-upon ratings (ranging from -1 to 1) of causal attribution
locus (i.e., the extent of internal versus external causal attributions) in participants’ performance reports.
Coders assign ‘1’ to performance reports with mostly internal attributions, ‘-1’ to reports with mostly
external attributions, and ‘0’ to reports with an equal amount of internal and external attributions.
34
Electronic copy available at: https://ssrn.com/abstract=3469813
TABLE 3
Likelihood of Reasons for Disclosure Preparation – IRO Survey Question
Without scripting, issuer representatives might
disclose unfavorable information inconsistent
with the issuer's narrative/message.
Without scripting, issuer representatives might
disclose favorable information inconsistent with
the issuer's narrative/message.
Mean
P-value of t-test for
difference from scale
midpoint
4.81
0.017
4.45
0.188
Notes: Table 3 shows IRO survey participant responses to the following: “Please rate the likelihood that the
following reasons are contributing to the scripting of issuer responses to anticipated questions from
investors/analysts.” The two reasons given are shown in the table above and participants rate the likelihood of both
reasons on 7-point scales with 1 = Very unlikely and 7= Very likely. P-values result from tests of whether the
average response is different than four, the scale midpoint. All p-values are two-tailed.
35
Electronic copy available at: https://ssrn.com/abstract=3469813
TABLE 4
Consequences of Disclosure Preparation – IRO Survey Questions
Mean
P-value of t-test for
difference from scale
midpoint
Scenario 1: In response to an unanticipated
question, an issuer representative discloses
unfavorable information inconsistent with the
issuer's narrative/message during the following:
Earnings conference call Q&As
Private face-to-face meetings
Private phone calls
1.970
1.853
2.030
<0.001
<0.001
<0.001
Scenario 2: In response to an unanticipated
question, an issuer representative discloses
favorable information inconsistent with the
issuer's narrative/message during the following:
Earnings conference call Q&As
Private face-to-face meetings
Private phone calls
3.030
2.471
2.441
0.003
<0.001
<0.001
Notes: Table 4 shows IRO survey participant responses regarding the likelihood of two scenarios in three
disclosure settings., which are shown in the table above. The likelihood of the scenarios is rated on 7-point scales
with 1= “Very unlikely” and 7= “Very likely.” P-values result from tests of whether the average response is
different than four, the scale midpoint. All p-values are two-tailed.
36
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Figure 1 The Effect of Load and Performance on the Locus of Causal Attributions in
Experiment One
1
0.5
Good Performance
0
Poor Performance
-0.5
-1
Low Load
High Load
Notes: This figure displays results for the effect of load and performance on participants’ locus (i.e., internal or
external) of causal attributions in their performance reports. The experiment varies cognitive load (high or low) and
performance on an abstract task (good or poor performance) in a 2x2 between-participants design. Participants
provide narrative performance reports after learning their task performance. The dependent variable shown here is
the result of two coders’ agreed-upon ratings (ranging from -1 to 1) of the extent of internal versus external causal
attributions in the performance reports. The dependent variable equals ‘1’ for performance reports with mostly
internal attributions, ‘-1’ for reports with mostly external attributions, and ‘0’ for reports with an equal amount of
internal and external attributions.
37
Electronic copy available at: https://ssrn.com/abstract=3469813
Figure 2 The Effect of Load and Performance on the Extent of Self-Serving Attributions in
Experiment One
1
0.5
Good Performance
0
Poor Performance
-0.5
-1
Low Load
High Load
Notes: This figure displays results for the effect of load and performance on participants’ use of self-serving
attributions in their performance reports. The experiment varies cognitive load (high or low) and performance on an
abstract task (good or poor performance) in a 2x2 between-participants design. Participants provide narrative
performance reports after learning their task performance. The dependent variable shown here is the result of two
coders’ agreed-upon ratings (ranging from -1 to 1) of the extent of internal versus external causal attributions in the
performance reports. Self-serving (self-disserving) attributions are internal (external) attributions with good
performance and external (internal) attributions with poor performance. The dependent variable equals ‘1’ for
performance reports with mostly self-serving attributions, ‘-1’ for reports with mostly self-disserving attributions,
and ‘0’ for reports with an equal amount of self-serving and self-disserving attributions.
38
Electronic copy available at: https://ssrn.com/abstract=3469813