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Reproducibility in Management Science *
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Reproducibility in Management Science∗
Miloš Fišar, Ben Greiner, Christoph Huber, Elena Katok, Ali I. Ozkes,
and the Management Science Reproducibility Collaboration†
November 1, 2023
Abstract
With the help of more than 700 reviewers we assess the reproducibility of nearly 500 articles
published in the journal Management Science before and after the introduction of a new Data and
Code Disclosure policy in 2019. When considering only articles for which data accessibility and
hard- and software requirements were not an obstacle for reviewers, the results of more than 95%
of articles under the new disclosure policy could be fully or largely computationally reproduced.
However, for almost 29% of articles at least part of the dataset was not accessible for the reviewer.
Considering all articles in our sample reduces the share of reproduced articles to 68%. The
introduction of the disclosure policy increased reproducibility significantly, since only 12% of articles
accepted before the introduction of the disclosure policy voluntarily provided replication materials,
out of which 55% could be (largely) reproduced. Substantial heterogeneity in reproducibility rates
across different fields is mainly driven by differences in dataset accessibility. Other reasons for
unsuccessful reproduction attempts include missing code, unresolvable code errors, weak or missing
documentation, but also soft- and hardware requirements and code complexity. Our findings
highlight the importance of journal code and data disclosure policies, and suggest potential avenues
for enhancing their effectiveness.
Keywords: reproducibility, replication, crowd science
∗
We thank the members of the Management Science Reproducibility Collaboration for their contributions, Matthew D.
Houston and Lucas Unterweger for research support, and Anna Dreber, Susann Fiedler, and Lars Vilhuber for helpful
comments.
†
Fišar: Masaryk University, e-mail: milos.fisar AT econ.muni.cz.
Greiner: Wirtschaftsuniversität Wien, e-mail: bgreiner AT wu.ac.at, and University of New South Wales.
Huber: Wirtschaftsuniversität Wien, e-mail: christoph.huber AT wu.ac.at.
Katok: University of Texas at Dallas, e-mail: ekatok AT utdallas.edu.
Ozkes: SKEMA Business School, Université Côte d’Azur (GREDEG), e-mail: ali.ozkes AT skema.edu, and Université
Paris-Dauphine - PSL (LAMSADE).
A complete list of the members of the Management Science Reproducibility Collaboration is included in Appendix A.

I Introduction
To be relevant and credible, scientific results have to be verifiable. The integrity of academic endeavors
rests upon reproducibility, wherein independent researchers obtain consistent results using the same
methodology and data, and replicability, which involves the application of similar procedures to new
data.
The significance of these twin principles for scientific research is commonly agreed upon. Yet, recent
assessments of empirical studies in the social sciences suggest a concerning rate of non-reproducibility or
non-replicability (e.g., Ioannidis, 2005; Ioannidis and Doucouliagos, 2013; Open Science Collaboration,
2015). A replicability crisis does not only erode the confidence in individual studies, but casts a shadow
over entire fields and literatures, and may potentially compromise business and policy decisions based
on these findings. Assessing and addressing these issues is imperative to maintain the credibility of
social science research, including management, psychology, economics, sociology, and political science,
and its subsequent applications in economic policies and management strategies, guiding societal
progress.
Several reasons are cited in the literature as contributing to reduced replicability, such as publication
bias (De Long and Lang, 1992), undisclosed analysis flexibility (Simmons et al., 2011), p-hacking
(Brodeur et al., 2016), and plain fraud (John et al., 2012; List et al., 2001). Ensuring that published
results can be reliably reproduced is a necessary foundation for addressing these issues. While tackling
the underlying reasons of limited replicability may be difficult, the ability to reproduce results based
on the original data and analyses can be seen as a minimum criterion for scientific credibility to
be expected from all published research (Christensen and Miguel, 2018; Nagel, 2018; Welch, 2019).
Indeed, if published results cannot be reproduced because data are unavailable, or code used for data
or numerical analysis is missing, poorly documented, or error-ridden, then the replicability crisis is
partly also a reproducibility crisis.
In this study, we directly assess the reproducibility of results reported in nearly 500 research
articles published in Management Science, a premier general interest academic journal that comprises
14 departments covering a broad variety of areas in business and management. In 2019, the journal
introduced a new Policy for Data and Code Disclosure,1 which stipulates that “Authors of accepted
papers ... must provide ... the data, programs, and other details of the experiment and computations
sufficient to permit replication.” While our focus is primarily on assessing the reproducibility of work
published since the disclosure policy went into effect, we also analyze articles accepted prior to May
2019, for comparison.
In order to reproduce results in articles from a variety of sub-fields of the journal such as Finance,
Accounting, Marketing, Operations Management, Organizations, Strategy, and Behavioral Economics,
we use a crowd-science approach (Nosek et al., 2012; Uhlmann et al., 2019) to leverage the expertise
of many researchers in these different sub-fields. Overall, 731 volunteers joined the Management
1
Retrieved on August 22, 2023, from https://pubsonline.informs.org/page/mnsc/datapolicy.
2

Science Reproducibility Collaboration as reproducibility reviewers (see Appendix A for all names and
affiliations), who together reportedly spent more than 6,500 hours on attempting to reproduce the
results reported in the articles, using the replication materials and information provided by the article
authors.
For articles subject to the 2019 disclosure policy, we find that when the reviewers obtained all
necessary data (because they were included, could be accessed elsewhere, or no data were needed) and
managed to meet the soft- and hardware requirements of the analysis, then results in the vast majority
of articles (95%) were fully or largely reproduced.2 However, in approximately 29% of the articles,
data were unavailable either because it was proprietary or under a non-disclosure agreement (NDA), or
because it originated in subscription data services to which reviewers did not have access. If we consider
all assessed articles under the disclosure policy, then about 68% could be at least largely reproduced.
Since data availability was by far the largest impediment to reproduce results, the methodology used in
the article is strongly correlated to its reproducibility. Namely, computational and simulation studies
as well as online and laboratory experiments are more likely to be reproducible than empirical studies,
field experiments, and surveys. These differences in methodology and data availability are also the
main drivers for substantial heterogeneity in reproducibility across the 14 departments of the journal.
Comparing these results to the period before the introduction of the mandatory disclosure policy,
we observe a substantial increase in reproducibility. When code and data disclosure was voluntary,
only 12% of article authors provided replication materials. Out of these selected articles, 55% could
be (largely) reproduced.
The share of fully and largely reproduced results in our study appears high, in particular considering
that the Code and Data Editorial team at the journal primarily assesses the completeness of replication
materials, but does not attempt reproduction of the results themselves. That said, in addition to
limited data availability, some replication materials suffered from insufficient documentation, missing
code, or errors in the code, making reproduction impossible. For some studies, reviewers obtained
different results and were not able to make out the reasons for these discrepancies. This implies that
there is still room for improvement. We discuss implications for disclosure policies and procedures at
Management Science and other journals in Section IV of this paper.
Our results complement findings in a recent literature on reproducibility and replicability in the
social sciences. The definitions of these terms vary somewhat across studies, with some overlaps in
their meaning (e.g., Christensen and Miguel, 2018; Dreber and Johannesson, 2023; Pérignon et al.,
2023; Welch, 2019). “Replication” typically refers to verifying the results of a study using different
datasets and different methods, thus exploring the robustness of results. The term “computational
reproducibility” comes closest to the scope of our study, and is defined as the extent to which results
in studies can be reproduced based on the same data and analysis as the original study.3 Other types
2
We use the term “largely reproduced” when only minor issues were found and the conclusions from the analysis were
not affected.
3
Other scholars refer to computational reproduction also as verification (Clemens, 2017), verifiability (Freese and
Peterson, 2017), or pure replication (Hamermesh, 2007; for an overview see also Ankel-Peters et al., 2023).
3

of reproducibility may consider recreation of analysis and data, or explore robustness to alternative
analytical decisions (Dreber and Johannesson, 2023).4
Recent systematic replication attempts of published results in the social sciences yielded replication
rates of 36% in psychology (Open Science Collaboration, 2015, N = 100), 61% in laboratory
experiments in economics (Camerer et al., 2016, N = 18), 62% in social science experiments published
in Nature and Science (Camerer et al., 2018, N = 21), and 80% in behavioral operations management
studies published in Management Science (Davis et al., 2023, N = 10).
In the field of economics, a number of studies targeting different sub-fields have set out to evaluate
the computational reproducibility of results. The Journal of Money, Credit and Banking (JMCB)
was one of the first journals to introduce a “data availability policy”, and one of the first ones to
be evaluated. Dewald et al. (1986) assess the first 54 studies subject to the policy. Only 8 studies
(14.8%) submitted materials that were deemed sufficient to attempt a reproduction, and only 4 of
these studies could be reproduced without major issues. As the authors put it, “inadvertent errors ...
are a commonplace rather than a rare occurrence” (Dewald et al., 1986, p. 587). McCullough et al.
(2006) examine JMCB articles published between 1996 and 2002, and successfully reproduce 22.6% of
62 examined works with a code and data archive, and only 7.5% considering all 186 relevant empirical
articles in the journal. McCullough et al. (2008) report that for articles published between 1993 and
2003 in the Federal Reserve Bank of St. Louis Review, only 9 out of 125 studies (7.2%) with an archive
could be successfully reproduced.
One of the top journals in economics, the American Economic Review, introduced a data and
code availability policy in 2004, and other top journals followed. In examining this policy for studies
published between 2006 and 2008, Glandon (2011) reports that 5 out of 9 studies (55.6%) under
consideration, which contained sufficient data archives, could be reproduced without major issues.
Only 20 out of 39 sampled studies (51.3%), however, contained a complete archive, and for 8 studies
(20.5%) a reproduction was not feasible without contacting the authors.
More recently, Chang and Li (2017) attempt to reproduce articles in macroeconomics published
between 2008 and 2013 across several leading journals, and successfully reproduce 22 out of 67 studies
(32.8%). Gertler et al. (2018) examine the reproducibility of 203 empirical studies published in 2016
that did not contain proprietary or otherwise restricted data, and can reproduce 37% of them (but only
14% from the raw data). For 72% of the studies in the sample, code was provided, but executed without
errors in only 40% of the attempts. Herbert et al. (2023) ask undergraduate economics students to
attempt to reproduce 303 studies published in the American Economic Journal: Applied Economics
between 2009 and 2018. Only 162 studies contained non-confidential and non-proprietary data. For
these, 68 reproduction attempts (42.0%) were successful and another 69 (42.6%) were deemed partially
successful. Pérignon et al. (2023) leverage a set of 168 replication packages produced in the context
4
Note that a study may be reproducible but not replicable (e.g., the results can be obtained with the same dataset
but not with a new dataset generated in a different context), and a study may not be reproducible but replicable (e.g.,
the original dataset may be unavailable so the code cannot be applied, but results with data obtained from a different
source show the same effects).
4

of an open science multi-analyst study in empirical finance (see Menkveld et al., 2023). Out of 1,008
hypothesis tests across all materials, 524 (52.0%) were fully reproducible, with another 114 (11.3%)
yielding only small differences to the original results.
Reproducibility studies in other related fields show similarly limited reproducibility. For a sample
of 24 studies subject to the Quarterly Journal of Political Science’s data and code review, Eubank
(2016) finds that only 4 (16.7%) did not require any modification in order to reproduce the results.
In genetics, Ioannidis et al. (2009) report that only 8 out of 18 microarray gene expression analyses
(44.4%) were reproducible. An analysis of biomedical randomized controlled trials yields 14 out of
37 (37.8%) successfully reproduced studies (Naudet et al., 2018). Artner et al. (2021) attempt to
reproduce the main results from 46 published articles in psychology with the underlying data but no
code, and were successful in 163 out of 232 statistical tests (70.3%). Xiong and Cribben (2023) examine
reproducibility of 93 articles using fMRI published in prominent statistics journals between 2010 and
2021, of which only 23 (24.7%) included the actual dataset, and 14 (15.1%) could be fully reproduced.
A comparison of reproducibility rates across different studies is difficult. Different studies often
apply different definitions and standards of reproducibility, and reasons for non-reproducibility may
differ between different journals due to different policies and enforcement procedures, and different
methods and data availability conditions in their fields. For example, our share of 95% of (largely)
reproduced articles (conditional on data being available to the reviewer and hard- and software
requirements being met) appears to be in a similar ballpark as the 85% of at least partially successful
reproductions at the AEJ: Applied Economics. However, while both journals have similar disclosure
policies, in the respective time periods replication materials of articles at AEJ:AE only underwent a
cursory review while the Code and Data Editorial Team at Management Science checked all replication
packages for completeness.
In recent years, there has been significant movement in the institutional arrangements for
reproducibility of journal articles. For economics, Vlaeminck (2021) reports that in a sample of 327
journals, 59% have data availability policies, a significant increase compared to 21% in the year 2014.
Similar developments are present in the fields of business and management. For example, several
other journals published by INFORMS have adopted similar code and data disclosure policies after
Management Science took the lead in 2019. At the time of writing this paper, 20 out of the 24 journals
used for the UT Dallas Business School rankings have a code/data disclosure policy, but only 10 made
code/data sharing compulsory, and only two have a code and data editor enforcing the policy.5
The ability to reproduce results reported in published articles by executing the code on the data,
both provided by the authors, does not, by itself, guarantee that results are replicable. But it does
provide a useful baseline. It increases confidence that reported results could, in principle, be replicated.
Allowing access to original code and data also makes it possible for independent research teams to
5
For comparison, out of the top 25 journals in the 2022 Scimago ranking in Economics and Econometrics, 23 have
code/data policies, 17 require that code/data are shared, and 6 have code/data editors. There is some overlap of this set
of journals with the UT Dallas list. See also Colliard et al. (2023) for a discussion of journals’ incentives with respect to
reproducibility, and Höffler (2017) for evidence that journals with disclosure policies are more often cited than journals
without such policies.
5

scrutinize robustness, conduct their own analysis including meta-analytical work spanning multiple
studies and datasets, reuse code in other research, and either build on the results or design studies
to show the limitations of original results. The ability to do this promotes scientific discourse, and,
importantly, also decreases incentives for academic fraud and data falsification.
II Study design and procedures
II.A Procedures
Prior to 2019, Management Science encouraged but did not require the disclosure of data for
submitted/accepted manuscripts. In June 2019, a new policy was established, which applied to all
newly submitted manuscripts and is still in effect at the time of this writing. The policy requires that
all code and data associated with accepted manuscripts at Management Science have to be provided
before the manuscript goes into production, but it also allows for a number of exceptions, in particular
licensed data (Compustat, CRSP, Factset, WRDS, etc.), proprietary data, or confidential data under
NDA. In these cases, detailed descriptions of data provenance and dataset creation are expected. The
journal established the position of a Code and Data Editor (CDE) and consequently positions of Code
and Data Associate Editors (CDAEs), who review all replication packages for completeness before an
article goes into production. However, the CDE and CDAEs are volunteer positions, so there are limits
to a complete check of the packages of all accepted articles for reproduction.6
Our study, pre-registered at the Open Science Framework,7 attempts to assess the reproducibility
of articles published in Management Science before and after the introduction of the 2019 policy,
based on the materials provided by the authors. For the period after the policy change, our initial
sample consists of 447 articles8 that fell under the disclosure policy introduced in June 2019, had
been reviewed by the CDE team through January 2023, and were published (with their compulsory
replication package) on the journal’s website. As a comparison sample we chose all 334 articles that
were accepted at the journal between January 2018 and April 2019, and would have fallen under the
disclosure policy (i.e., include code or data) but were accepted before the announcement of the policy
and were thus not subject to the policy (which only applied to articles initially submitted after June
1, 2019).9 Out of those 334 articles, for 42 the authors had voluntarily provided a replication package,
which entered our project reviews. Thus, the size of our initial sample of replication packages to be
reproduced is 489.
6
If code and data are included, the CDE team also attempts to run the code, but without verifying outputs. As a
contrasting example, the American Economic Association employs a different model with a paid Data Editor position
including a budget for administrative and research assistants, where all replication packages for all AEA journals are
fully reproduced before a final acceptance decision is made.
7
The pre-registration can be found at URL https://osf.io/mjqg5. Unless otherwise noted, we followed our pre-
registered procedures.
8
In our pre-registration we mention 450 articles, but during the review phase we noted that 3 of these articles did
not fall under the disclosure policy, reducing the initial sample to 447.
9
Note that we thus deliberately did not include articles in our study that were accepted after the introduction of
the 2019 policy but were not subject to it because they were originally submitted before the introduction. For these
articles, their authors could have falsely assumed that the new disclosure policy applies while it did not, thus biasing our
assessment of the effect of the policy.
6

On January 12, 2023, the Editor-in-Chief of Management Science wrote an email to all 9,762
reviewers who provided a review to the journal in the past 5 years, introducing the project and inviting
them to serve as reproducibility reviewers (see Appendix E.1). In addition, the invitation to participate
in the project was sent via professional mailing lists (e.g., Behavioral Economics, Finance, Marketing).
In total, 927 researchers completed an initial reviewer survey asking for their research fields (namely,
to which Management Science departments they would typically submit their manuscripts) and their
familiarity with different analysis softwares/frameworks and databases (see Appendix E.2).
The assignment of articles to reviewers proceeded over two main assignment rounds and a
consecutive third round. In the first assignment round at the beginning of February 2023, we attempted
to find a reviewer for each of the 489 packages out of the 927 reviewers. We applied the Hungarian
method (Kuhn, 1955) that tries to maximize the match with penalties for mismatches in department,
software skills, and database access, and random resolution of ties (see Hornik, 2005, for the R
implementation). These matches were then manually assessed for potential conflicts of interest (e.g.,
reviewer and author in the same department), in which case article and reviewer were removed from the
match and re-entered the “pools” of articles and reviewers. Once the match was completed, all reviewers
received an email informing them of their assignment, with links to the article, the supplementary
materials page, and to guidelines for reviewers. Reviewers were also asked to either confirm their
assignment, or to contact us to indicate any conflicts of interests or other reasons that they could not
provide a report for the assigned article. These cases were also added back to the pool.
After two weeks, we ran a second assignment round. For articles, the samples consisted of previously
unmatched articles (which received priority) and a second set of all articles (to find a second reviewer
for many of them). For reviewers, all reviewers with no assignment yet entered the match. We once
again used the Hungarian method with moderate penalties for department and software mismatches
and prohibitive penalties for assignments of the same article or previous assignments, and random
resolution of ties. The resulting match was screened for conflicts of interests. As before, reviewers
received their assignment by email, and any reported mismatches or conflicts were tracked. A few
dropouts of reviewers were recorded, otherwise articles and reviewers re-entered the “pool”. Reviewers
who did not confirm their assignment in the first or second round received a reminder email at the end
of February.
The third round of assignments, from the beginning of March 2023, was run continuously in several
waves and mostly manually. Once a sufficient mass of articles (rejections of assignments, leftover
articles who have not received their second assignment yet) and reviewers (unmatched reviewers, or
reviewers available for another report) was reached, for each article a list of all possible compatible
reviewer matches was compiled, and out of these one reviewer was assigned. As before, reviewers were
informed about their match and asked to confirm their assignment.
Reviewers were asked to make an honest attempt to a reproduction of the article’s main results
(figures, tables, other results in the main manuscript) solely on the basis of the provided replication
materials (and not to contact the original authors of the articles, see also McCullough et al. 2006, for
7

similar approaches), and to provide their report within about 5 weeks (though we also accepted late
entries). Reviewers submitted their report through a structured survey implemented in Qualtrics (see
Appendix E.3). They also received detailed guidelines (see Appendix E.4), providing definitions for
different reproducibility assessment outcomes and explanations for all survey fields. The survey asked
for an overall assessment, information about the content of the replication package (readme, data, code,
etc.) and their quality, individual reproducibility assessment of all results tables and figures as well as
other results reported in the manuscript, as well as assessments of time spent, of their own expertise
in research field and analysis methods, and of their expectation of the replicability (as opposed to
reproducibility) of the article. Reviewers were also asked to provide evidence of their reproduction
attempts in the form of log files or screenshots.
During the whole review period, we answered any questions by reviewers by email. Once a
significant number of reviews had been collected, we checked them for completeness and consistency.
Where necessary, we followed up with reviewers to clarify questions and resolve inconsistencies.10 All
in all, we followed up on about 13% of all reports.
In late September 2023, we wrote emails to all corresponding authors of the articles for which we
obtained reports, and provided them with the reports (redacted for anonymity). Authors could submit
a short comment of up to 2,000 characters on each report, which was then included in our dataset.11
115 authors or author teams made use of this possibility and submitted comments.
II.B Final Sample
In total, we received 753 reports from 675 reviewers and reviewer teams, who spent in total more than
6,500 hours on this project.12 We allowed reviewers to enlist the help of a colleague as a secondary
reviewer, so for 61 reports reviewers are actually teams of two persons. While 599 reviewers provided
one report each, 74 reviewers provided reports for 2 different articles, and two reviewers for 3 articles.
Table 1 shows that a majority of reviewers are in the midst of their academic career, at the Associate
Professor, Assistant Professor, or Postdoc level. About one in seven reviewers was a full professor, and
about the same number are PhD students. In addition, there are reviewers working in other roles at
research and professional institutions. Across these career levels, reviewers differ in their frequency to
have enlisted a secondary reviewer (with Full or Associate Professors being more likely to do so, while
almost all PhD students worked alone) and the time spent (differences there are mainly driven by
whether it was a team or not). However, they do not differ much in their self-assessed expertise in the
10
E.g., a reviewer may indicate that log files are provided, but did not verify whether they are consistent with the
results. In other cases, the overall assessment of a replication package may not have been consistent with the individual
assessments of tables and figures. Some reviewers could initially not find the replication package because the respective
link was missing on the journal’s webpage, and we provided them with the correct links.
11
In addition, the journal allows authors to submit an improved replication package, which will replace the previous
(reviewed) replication package on the journal’s replication server. We note, however, that our analysis is only based on
the original replication materials.
12
Two reviewers entered unrealistically high numbers of more than 160 hours (4 working weeks); we set these
observations to “missing” in our dataset. The median reviewer spent 4 hours.
8

method or topic of the article. In our analysis below, we also did not find any systematic differences
across reviewer characteristics in terms of assessment outcomes or other report characteristics.
TABLE 1: Reviewer characteristics
N = 675 Share Enlisted 2nd Avg. Hours Avg. Expertise Avg. Expertise
reviewer Spent Method (0-100) Topic (0-100)
Professor 14% 21% 13.1 84.3 60.8
Associate Professor 26% 11% 8.3 83.2 61.5
Assistant Professor/Postdoc 40% 6% 8.4 84.1 58.7
PhD student 16% 1% 9.0 83.8 59.2
Other 4% 3% 6.1 82.8 52.7
Table 2 gives an overview on our final sample of assessed articles. Out of the 781 articles, 292
from before introduction of the 2019 policy had no replication package, so are not assessed. For 30
articles with replication packages, we could not find a suitable reviewer, and thus cannot report any
reproducibility results.13
TABLE 2: Initial and final sample of articles and reports
Before 2019 policy After 2019 policy Total
Initial sample of articles 334 447 781
Replication package available 42 447 489
No report 2 28 30
1 report 16 149 165
2 reports 24 270 294
In Table 3 we list the Management Science departments at which the articles in our final sample
appeared.14 This distribution is representative of the distribution of articles in the journal, with
Finance, Behavioral Economics and Decision Analysis, Accounting, and Operations Management being
the largest fields. To facilitate the matching of reviewers and articles, upon registration we asked
reviewers to which department(s) they would most likely send one of their articles. Table 3 shows
the distribution of the first-named department. This distribution follows largely the distribution
13
These 30 articles are not part of the analysis. We observe little evidence of selection issues. Table B.1 in the
Appendix B compares software requirements of the 30 articles without a report and the 459 articles with at least one
report. It seems that articles where we could not find a suitable reviewer were less likely to use the most common software
Stata and more likely to use one of the less often used softwares, but these differences are statistically not significant at
the 5%-level (Fisher Exact test, two-sided, on frequency of Stata and frequency of “Other” softwares).
14
There have been some changes in the structure of departments at the journal over the past years. In case departments
were changed or merged, we classified articles by the current (successor) department.
9

TABLE 3: Fields of assessed articles and reviewers
Management Science Department Abbr. Share of Articles Share of Reviewers
(N = 489) (N = 675)
Finance FIN 27.4% 24.3%
Behavioral Economics and Decision Analysis BDE 18.4% 30.1%
Accounting ACC 12.5% 8.2%
Operations Management OPM 9.2% 7.1%
Marketing MKG 5.7% 6.5%
Revenue Management and Market Analytics RMA 4.7% 0.7%
Information Systems INS 4.3% 4.0%
Business Strategy BST 3.3% 4.6%
Healthcare Management HCM 3.3% 1.9%
Big Data Analytics/Data Science BDA 3.1% 3.4%
Organizations ORG 3.1% 3.6%
Entrepreneurship and Innovation ENI 2.3% 4.0%
Optimization OPT 1.4% 1.2%
Stochastic Models and Simulations SMS 1.4% 0.4%
of articles, with the exception that researchers from Behavioral Economics and Decision Analysis
contribute disproportionately.15 During code and data review the CDE team usually classifies articles
into one of five categories according to their main methods. While about one-fifth of the articles in the
sample mainly use simulations or computations (and thus often do not rely on data), almost 60% of the
articles in our sample are based on empirical data, with the remaining articles discussing laboratory
or online experiments (14%), field experimental data (4%), or data from surveys (3%).
II.C Reviewer consistency and aggregation
In order to obtain information on potential variability in reproducibility assessments, we aimed to
get not just one but two reports for as many articles/replication packages as possible. We succeeded
in obtaining 2 reproducibility reports for 294 articles. In 59% of the articles, both reviewers chose
the exact same overall assessment. For 93% of the articles, the two reviewer assessments were in
neighboring assessment classifications.16 When only considering whether a reviewer classified an article
as at least largely reproducible, or not, then the agreement rate is 86%. For the overall assessment of
reproducibility, reviewers seem mostly to differ on whether some minor issues are worth mentioning (in
generally reproducible studies), and whether a few results that can be recovered are sufficient to deem
a study “Largely reproduced” rather than “Not reproduced.” Otherwise, differences may result from
15
One reason for this might be a higher awareness for the issues of reproducibility and replicability in this field.
Another reason could be that most of the primary authors of this reproducibility study come from this research area.
16
By “neighboring assessment classifications,” we refer to pairs of adjacent classifications such as “Fully reproduced”
and “Largely reproduced,” “Largely reproduced” and “Largely not reproduced,” and “Largely not reproduced” and “Not
reproduced.”
10

whether reviewers obtained access to datasets, managed to run the code in the appropriate software
environment, or how much effort they put into the reproduction.17
In our analysis presented in the next section, we aggregated assessments at the article level.
Specifically, if we have two reports for an article, we select the report with the higher reproducibility
assessment. This approach is in line with other reproducibility studies, e.g., Herbert et al. (2023). If
two reproducibility assessments yield different results, it seems more likely that the lower assessment
is based on idiosyncratic difficulties (e.g., to obtain the dataset) and other random artifacts of a
reviewer, rather than the higher-assessment reviewer overstating their result. If both reviewers chose
the same overall assessment, we select one report randomly. At the end of the next section we discuss
the robustness of our results to analyzing the data at the report level, or at the level of individual
figures and tables, with detailed results included in Appendix B.
III Results
III.A Main results
In addition to individual reproducibility assessments of tables, figures, and other results, we asked
reviewers for an overall assessment of their reproduction attempt. According to the guidelines given
to reviewers, an assessment of “Fully reproduced.” means that the output of the reproduction analysis
shows the exact same results as reported in the article, for all results reported in the main manuscript.
“Largely reproduced, with minor issues.” means that there may be minor differences in the reproduction
output compared to the results in the original article, but the article’s conclusions and learnings stay
the same. “Largely not reproduced, with major issues.” means that there are major differences in the
output compared to the results in the article, such that the reproduction results could not be used to
support the conclusions of the original article. An assessment of “Not reproduced.” means that the
results from the reproduction cannot support the conclusions drawn in the paper, either because the
output is different, or because the results cannot be produced at all because of missing data or non-
recoverable code. We note, however, that equipped with these guidelines, the eventual categorization
of the article remains subjective to the reviewer.
For all overall assessments of “Largely not reproduced.” and “Not reproduced.”, we reviewed the
individual reports to distill the main reasons for limited reproducibility. Consequently, cases where the
reviewer was not able to get access to a required dataset or could not meet the software and hardware
requirements of the analysis were labeled “Not verifiable” and “Largely not verifiable” rather than “Not
reproduced” and “Largely not reproduced”, respectively.18
Based on these classifications, Figure 1 presents our main outcomes. The upper two panels show
reproducibility assessments for articles that were subject to the disclosure policy introduced in 2019,
17
In Appendix D we provide more details on variability in reviewer assessments.
18
We note that this qualification of assessments was not yet anticipated in our pre-registration.
11

FIGURE 1: Overall article reproducibility assessments, by policy
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while the lower two panels pertain to articles that appeared before that policy. The first panel shows
the distribution of assessments conditional on reproducibility being verifiable. Among these articles,
95.3% could be classified as fully reproduced or largely reproduced.
However, for 29% of assessed articles, reviewers could not obtain the dataset, and in 1% the hard-
and software requirements could not be met (e.g., software could not be installed, or the code would
run for an untenable amount of time). Also in these cases, reviewers were not able to reproduce the
results. The second panel in Figure 1 includes these cases, displaying results for all assessed articles.
The share of articles that our reviewers were able to fully or largely reproduce is 67.5%.
The third panel of Figure 1 shows the overall assessments for the 40 articles from the time
before the 2019 disclosure policy was introduced, for which replication materials were available. Our
reviewers could reproduce or largely reproduce the results of 55% of these articles.19 In the fourth
panel of Figure 1, we include all 332 articles from our sample of articles accepted before the 2019
disclosure policy. Considering those articles that do not voluntarily provide replication materials as
not reproducible reduces the share of at least largely reproduced articles to 6.6%.
Results from linear probability models, displayed in Table 4, lend statistical support to the positive
effect of introducing the data and code disclosure policy. In Model 1 we regress whether an article
could be at least largely reproduced or not on the policy dummy for all articles in our sample (i.e., we
19
We note, however, that these 40 out of 332 articles are heavily selected: authors voluntarily provided a replication
package while being encouraged but not required by the journal. More than 50% of these articles were published in the
BDE department, and none of them belonged to the Finance department, indicating selection also on availability of data.
12

are comparing the second and the fourth panels in Figure 1), indicating that after the introduction of
the policy, a randomly chosen article is 61% more likely to be reproduced. In Model 2 we restrict our
attention to the sample of articles for which a replication package was provided (i.e., comparing the
second and the third panel in Figure 1). In this regression, the coefficient for the policy is positive but
statistically not significant (p = 0.109). Finally, Model 3 focuses on all articles which are considered
verifiable (i.e., comparing the second and the third panel in Figure 1 but without the non-verifiable
articles). The policy coefficient indicates that conditional on data being available and hard- and
software requirements being met, articles are 19% more likely to be reproducible after the introduction
of the disclosure policy.20
TABLE 4: Regressing reproducibility on disclosure policy existence
Model (1) (2) (3)
Sample of articles All incl. no package All with package All verifiable
Coeff StdErr Coeff StdErr Coeff StdErr
Constant 0.066∗∗∗ (0.021) 0.550∗∗∗ (0.075) 0.759∗∗∗ (0.045)
Disclosure Policy 0.609∗∗∗ (0.028) 0.125 (0.078) 0.194∗∗∗ (0.047)
Observations 751 459 326
R2 0.379 0.006 0.051
Note: *, **, *** indicate significance at the 10%, 5%, and 1% level, respectively.
The unavailability of data is one of the major impediments for reviewers to reproduce an article.
A dataset may be unavailable, for example, because the reviewer does not have a subscription to the
commercial provider, because the dataset was collected under NDA with the involved company, or
because the dataset contains sensitive information (e.g., on personal health or illegal activity). For
the sample of 136 reviewed articles falling under the disclosure policy that were classified as either
“Not reproduced” or “Largely not reproduced”, Figure 2 displays the main reasons we identified for the
reviewers’ failure to reproduce.21
Limited access to the dataset was a reproducibility barrier for 88% of non-reproducible articles,
and the time needed to run the code, complexity of the code, or issues with installing the software
environment were behind non-reproducibility of another 3%. Other reasons included the non-
availability of code or functions (12%), insufficient or missing documentation (7%), or unresolvable
errors when executing the code (5%). For 4% of the non-reproducible or largely not reproducible
20
We obtain the same conclusions employing corresponding Probit/Logit models or Fisher Exact tests. We note
that strictly speaking, our data does not allow to imply a causal effect of the disclosure policy. Authors’ attitudes
towards making their research reproducible may have independently changed over time, just as the intensity of policy
enforcement at the journal may have varied. Older replication packages may be less reproducible due to software changes.
The introduction of the policy does not have features of a natural experiment, and our sample only spans a relatively
short (and interrupted, see Footnote 9) time period.
21
Note that multiple issues may apply to the same article.
13

FIGURE 2: Reasons for non-reproducibility for articles since 2019 policy
88.2 %
2.9 %
12.5 %
7.4 %
5.1 %
4.4 %
No access to dataset.
Issues with software/hardware requirements.
Code or parts of code/functions missing.
Insufﬁent documentation, missing information.
Unresolvable errors when executing code.
Reproduction yields (partly) different results.
0 20 40 60 80
articles, the main reason for this assessment was that the reproduction yielded partly different results
than reported in the article.22
Since many authors cannot include the original data in their replication packages for various reasons,
in such cases the Code and Data Editor at the journal started to encourage the provision of log files
that can show that the analysis code works and produces the desired results. Correspondingly, about
47% of the articles classified as “Not verifiable” or “Largely not verifiable” included log files for all
results in the replication package, and further 25% included log files for at least some results. As a
consequence, 51% of (largely) not verifiable articles were assessed as “Not reproduced but consistent
with log files” (84% of those which provided all log files, and 66% of those which provided at least
some logs).
III.B Variation in reproducibility
Our data allows us to break down the reproducibility of articles published under the disclosure policy
to the level of research fields and types of research. Figure 3 shows the reproducibility assessments
across the 14 Management Science departments. We observe considerable heterogeneity in the share of
reproduced or largely reproduced articles across the different fields, ranging from 42% to 100%. Note,
however, that there are substantial differences in the number of published articles across departments.
Also, data availability may vary drastically between different fields.
While many studies in the department Behavioral Economics and Decision Analysis (BDE) rely
on primary data from experiments, other fields often use proprietary data from subscription databases
(e.g., Compustat, CRSP, WRDS), or confidential and sensitive data which cannot be shared with other
researchers (e.g., field experiments with companies, health care data, surveys, etc.). In Figure 4, we
distinguish reproducibility outcomes by the primary type/method of the article, as classified during
22
In Table B.2 in Appendix B we contrast these numbers with the reasons for non-reproducibility for articles which
voluntarily provided replication packages before the 2019 disclosure policy took effect. Although the sample size for this
period is low (N = 18), it appears that reasons for non-reproducibility of voluntarily provided packages are less likely to
be missing data and more likely to be issues with missing or non-working code.
14

FIGURE 3: Overall reproducibility assessments by journal department
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Note: Department acronyms are SMS: Stochastic Models and Simulations, BDE: Behavioral Economics and Decision
Analysis, ENI: Entrepreneurship and Innovation, RMA: Revenue Management and Market Analytics, ACC: Accounting,
OPM: Operations Management, OPT: Optimization, BDA: Big Data Analytics/Data Science, FIN: Finance, HCM:
Healthcare Management, INS: Information Systems, MKG: Marketing, ORG: Organizations, BST: Business Strategy.
FIGURE 4: Overall reproducibility assessments by article type/method
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15

the journal’s code and data review. We indeed observe significant differences in the reproducibility
outcomes across articles employing different methods. All studies reporting on laboratory and online
experiments include their dataset, making them highly reproducible. Most studies running simulations
or other computations, mostly embedded in theoretical articles, do not rely on datasets, making them
highly reproducible. On the other hand, many empirical studies rely on proprietary or subscription
data, making them less reproducible if reviewers have no access to these datasets. Field experiments
in business fields often run under NDAs, and survey studies may include sensitive data that cannot be
shared (sometimes even ethics committees restrict the publication of datasets).23
TABLE 5: Regressing reproducibility on journal department and article type
Model (1) (2) (3)
Constant 0.629∗∗∗ (0.041) 0.600∗∗∗ (0.138) 0.630∗∗∗ (0.146)
SMS 0.371∗ (0.209) 0.034 (0.207)
BDE 0.250∗∗∗ (0.070) 0.019 (0.087)
ENI 0.171 (0.151) 0.215 (0.143)
RMA 0.160 (0.113) −0.110 (0.118)
ACC 0.073 (0.073) 0.128∗ (0.070)
OPM 0.055 (0.085) −0.049 (0.083)
OPT 0.038 (0.192) −0.299 (0.191)
BDA 0.014 (0.129) −0.323∗∗ (0.137)
HCM −0.067 (0.122) −0.059 (0.115)
INS −0.103 (0.113) −0.073 (0.108)
MKG −0.129 (0.111) −0.118 (0.106)
ORG −0.167 (0.134) −0.120 (0.127)
BST −0.212 (0.139) −0.188 (0.134)
Lab/Online Experiments 0.384∗∗ (0.149) 0.336∗∗ (0.153)
Simulation/Computation 0.254∗ (0.146) 0.336∗∗ (0.155)
Field experiment −0.044 (0.172) −0.009 (0.173)
Empirical study −0.051 (0.141) −0.087 (0.143)
Observations 419 419 419
R2 0.072 0.140 0.180
Notes: Baseline is the Finance department, and survey studies. *, **, *** indicate significance at the
10%, 5%, and 1% level, respectively. Department acronyms are SMS: Stochastic Models and Simulations,
BDE: Behavioral Economics and Decision Analysis, ENI: Entrepreneurship and Innovation, RMA: Revenue
Management and Market Analytics, ACC: Accounting, OPM: Operations Management, OPT: Optimization,
BDA: Big Data Analytics/Data Science, FIN: Finance, HCM: Healthcare Management, INS: Information
Systems, MKG: Marketing, ORG: Organizations, BST: Business Strategy.
23
Table B.3 in Appendix B demonstrates the variation of paper types/methods across the different departments of
the journal. In the table, we ordered departments and methods by their reproducibility to highlight the correlation.
16

In Table 5 we report three linear probability models in which we assess this heterogeneity
statistically. The outcome variable in all three models is a dummy indicating whether an article is
classified as fully or largely reproduced, or not. In Model (1), we regress reproducibility on department
fixed effects, with the baseline being the Finance department (FIN), with a sizable sample size and
close to the average reproducibility level. We observe that the SMS and BDE departments have
significantly higher reproducibility rates than the Finance department, while the other departments
do not differ significantly from Finance. In Model (2), we regress the same outcome on article type
fixed effects, with articles based on surveys as the baseline. We find that while field experiments and
empirical studies do not differ from survey studies in their reproducibility, lab/online experiments and
articles featuring simulation/computation are significantly more likely to be reproducible. Finally, in
Model (3), we include both department and article type fixed effects. The coefficients for article type
are not much affected by including department fixed effects, while vice versa there are some sizable
changes. Once accounting for the article type/method used, articles in departments SMS and BDE
are not significantly more reproducible anymore compared to other departments, namely Finance. On
the other hand, controlling for methods, articles in the Accounting (ACC) department are significantly
more reproducible than articles in Finance (more often including the data set), and articles in the field
of Big Data Analytics (BDA) are less reproducible (as datasets are often not included or accessible).
III.C Robustness
In the analysis above we only considered reproducibility assessments at the article level, taking the
higher assessment if two reports were available for an article. To examine the robustness of our results,
we also examine the reproducibility at the level of individual reports, and at the level of tables, figures,
and other results.
Appendix C shows versions of Figure 1 and Table 4 based on all reports rather than just one report
per article. Since in our aggregation above we selected the report with the higher reproducibility
assessment, these data show somewhat lower reproducibility levels. Namely, ignoring reports which
found that articles are not verifiable due to limited data access or code complexity, 93.7% of reports
provided a “Fully reproduced” or “Largely reproduced” assessment. Including reports on (largely) non-
verifiable articles as (largely) “not reproducible”, this share goes down to 62.4%. That said, the same
reproducibility patterns emerge: the main reason for non-reproducibility is data access, departments
differ widely in their reproduction rates, but that is to a large extent driven by different methods used
across departments.
Appendix C also reports and discusses the assessment results for individual tables, figures, and other
results (e.g., statistical tests reported in the manuscript texts). As to be expected, these individual
results are highly correlated with overall assessments. For example, in reports that reached an overall
assessment of “Fully reproduced”, 99.1% of individual tables and 99.7% of individual figures were
classified as largely or fully reproduced. When the overall assessment was “Not reproduced”, only 2.7%
of tables and 7.5% of figures could be reproduced, on average.
17

IV Discussion and Conclusion
In this study we undertake a comprehensive assessment of the reproducibility of results in Management
Science. With the collaborative efforts of over 700 reviewers we examine nearly 500 articles to assess
the computational reproducibility of their results. For articles published since the introduction of
the 2019 disclosure policy, the good news is that more than 95% of articles could be fully or largely
computationally reproduced, when data accessibility and hardware/software requirements were not
obstacles for reviewers. This appears commendable. However, reviewers faced data accessibility
challenges for approximately 29% of the articles in our sample, and the overall rate of successful
reproduction is reduced to 68% when considering such articles as non-reproducible. Relatedly,
differences in methods and dataset accessibility also drive heterogeneity in reproducibility rates across
different fields.
This makes data availability a central issue in reproducibility. To improve the credibility of research
within business and management, efforts should be directed toward facilitating data access and sharing.
Strictly restricting a journal in the area of business, economics, and management to only articles that
can freely share their data seems not realistic and would exclude valuable research from being published.
Instead, other arrangements may need to be found for such cases. Approaches could include, among
others,
• the inclusion of de-identified data in the replication package, only useful for reproduction but not
for new original research;
• agreements with subscription databases for access for reproduction purposes via the journal;
• providing access to datasets through special infrastructure that limits use to specific purposes
(similar to platforms used by government agencies to provide micro data); or
• sharing data only with a journal’s code and data editor or with a third-party agency which then
certifies reproducibility.
In addition, human subjects ethics committees may need to be sensitized to also consider the ethics
of research transparency in their deliberations, to find compromises that at the same time ensure
human participant privacy and allow for full reproduction of research results. Data access limitations
also touch upon important questions of fairness and bias: with proprietary, non-open datasets, certain
research results may only be obtained by privileged researchers, with the data provider serving as a
gate-keeper with potential conflicts of interest.
Our study underscores the value of large-scale reproducibility assessment projects. We provide an
assessment of the current state of affairs in the field of business and management, and thus contribute to
drawing a realistic picture of the overall credibility of research in the field. Repeating such assessments
will serve as a form of quality control for newly developed journal policies and procedures. The project
18

showcases best practices and may help developing standards for replication materials, but also identifies
major gaps and weaknesses in current policies that need to be addressed. Our results can influence
journal and funding agency policy decisions. The active participation of more than 700 reviewers who
invested significant time and effort in reproducing results highlights the commitment in the community
to improving scientific rigor. In an ex-post survey, quite a few of our reviewers reported that their
participation was a great learning experience, in particular with respect to preparing their own future
replication packages. Informed about the assessments of their articles, most authors appreciated the
reviewers’ comments, and many voluntarily provided improved versions of their replication packages
which address the reviewer comments. Thus, this project also raised awareness of reproducibility issues,
furthering a culture of open science, and potentially also the quality of (existing and future) replication
materials.
That said, our study also sheds light on the significance of journal code and data review procedures.
We observe that the introduction of the 2019 disclosure policy is associated with a significant increase in
the reproducibility of articles in Management Science. When code and data disclosure was voluntary,
only 12% of authors submitted replication materials (out of which 55% could be at least largely
reproduced). Thus, the policy’s effect is largely driven by increasing the mere verifiability of articles.
However, there is still room for significant improvement. Smaller scale changes could be targeted
towards improving the current process, such as increasing incentives for authors to provide proper
replication packages right away by making the acceptance decision conditional on replication package
approval; or integrating the code and data review process into the manuscript handling system to make
it more efficient and transparent.
A more comprehensive reevaluation of code and data review procedures, however, may foster the
pivotal role that code and data review plays in ensuring research reproducibility more effectively.
In particular, large-scale reproducibility projects such as the present study may become obsolete if
the journal puts resources and processes into verifying reproducibility already upon publication of
an article. In the current institutional setup, the Code and Data Editor at Management Science and
his team of Associate Editors are volunteers with naturally limited capacity to conduct comprehensive
reproduction. To that end, different institutional arrangements may be advisable:
• Similar to the institutional setup at the American Economic Association (see Vilhuber, 2019),
code and data review could be professionalized by introducing the position of a (half- or full-time)
paid Code and Data Editor, with appropriate budget for assistance and software and data access.
• Code and data review, and reproducibility certification could be delegated to a third-party agency
which undertakes these activities for a fee (such as, for example, the Odum Institute used by the
American Journal of Political Science, or CASCaD, see Pérignon et al., 2019).
• The fact that more than 700 reviewers participated in this project indicates that there is sufficient
willingness and expertise in the community to integrate the code and data review into the peer
19

review cycle of a manuscript, with low direct costs. E.g., in a last minor revision round, one
reviewer could be assigned by the Department or Associate Editor to review the replication
materials and certify reproducibility.
In conclusion, our study illuminates the critical importance of reproducibility in maintaining the
integrity and credibility of scientific research in Management Science and related fields. By addressing
data availability challenges and refining journal code and data review procedures, the academic
community can work collaboratively to improve reproducibility. These efforts are essential to ensuring
that robust research findings continue to guide decision-making and contribute to the advancement of
knowledge.
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22

Online Appendix
A The Management Science Reproducibility Collaboration
The following co-authors lent their time and expertise as reproducibility reviewers to the
Management Science Reproducibility project and are credited as “Management Science Reproducibility
Collaboration” in the author string.
Diya Abraham, University of Reading
Gabrielle S. Adams, University of Virginia
Arzi Adbi, National University of Singapore, Business
School
Jawad M. Addoum, Cornell University
Maja Adena, WZB Berlin
Laxminarayana Yashaswy Akella, Indian Institute of
Management Ahmedabad
Pat Akey, University of Toronto
Olivier Akmansoy, HEC Paris; CNRS
Andres Alban, Harvard University, Harvard Medical
School
Vitali Alexeev, University of Technology Sydney
Azizjon Alimov, University of Lille, IESEG School
of Management, LEM - Lille Economie Management;
CNRS
Argun Aman, University of Mannheim
Ali Aouad, London Business School
Gil Appel, George Washington University, School of
Business
Nick Arnosti, University of Minnesota
Kashish Arora, Indian School of Business
Thibaut Arpinon, Georg-August Universität Göttingen
Florian M. Artinger, Max Planck Institute for Human
Development; Simply Rational - The Decision Institute;
Berlin International University of Applied Sciences
Joachim Arts, University of Luxembourg
Lennart Baardman, University of Michigan, Ross School
of Business
Zakaria Babutsidze, SKEMA Business School
Golnaz Bahrami, Pennsylvania State University
Somnath Banerjee, North Dakota State University
Chenzhang Bao, Oklahoma State University
Te Bao, Nanyang Technological University, School of
Social Science
Opher Baron, University of Toronto, Rotman School of
Management
Xabier Barriola, INSEAD
Pedro Monteiro E Silva Barroso, Universidade
Católica Portuguesa
Ernest Baskin, Saint Joseph’s University
Robert J. Batt, University of Wisconsin-Madison,
Wisconsin School of Business
George Batta, Claremont McKenna College
Anahid Bauer, Institut Mines-Télécom Business School,
LITEM, Paris Saclay
Konstantin Bauman, Temple University, Fox School of
Business
William Bazley, University of Kansas
Michael Becker-Peth, Erasmus University, Rotterdam
School of Management
Mehmet Begen, Western University, Ivey Business School
Nazire Begen, Gebze Technical University
Sylvain Benoit, Université Paris Dauphine - PSL
Loic Berger, University of Lille, IESEG School of
Management, LEM - Lille Economie Management;
CNRS; iRisk Research Center on Risk and Uncertainty
Noémi Berlin, CNRS, EconomiX, Université Paris
Nanterre
Lars Peter Berling, Norwegian University of Science and
Technology
Anna Bernard, Catolica Lisbon School of Business and
Economics
Jeremy Bertomeu, Washington University in St. Louis
Jedrzej Bialkowski, University of Canterbury
Pawel Bilinski, City University of London, Bayes
Business School
Jannis Bischof, University of Mannheim
Jeffrey R. Black, University of Memphis
Hayley Blunden, American University
Dion Bongaerts, Erasmus University, Rotterdam School
of Management
Felix Bönisch, WZB Berlin
Marieke Bos, Swedish House of Finance
Ciril Bosch-Rosa, Technical University of Berlin
Sylvain Bourjade, TBS Business School
23

Andrew Boysen, University of North Carolina at Chapel
Hill, Kenan-Flagler Business School
Craig Brimhall, University of California Los Angeles,
Anderson School of Management
Zuzana Brokesova, University of Economics in Bratislava
J. Paul Brooks, Virginia Commonwealth University
Stephan B. Bruns, Hasselt University
Georgia Buckle, UK Office for National Statistics
Guido Buenstorf, University of Kassel
Gordon Burtch, Boston University
Benjamin Bushong, Michigan State University
Sabrina Buti, Université Paris Dauphine - PSL
Patrick Callery, University of Vermont
Mehmet Canayaz, Pennsylvania State University
Jie Cao, Hong Kong Polytechnic University
Wei Cao, Shanghai University of Finance and Economics
Xinyu Cao, The Chinese University of Hong Kong
Martin Carree, Maastricht University, School of Business
and Economics
Vincent Castellani, Pennsylvania State University
Yann Joel Cerasi, Norges Bank
Hannah H. Chang, Singapore Management University
Jin Wook Chang, Korea University Business School
Michelle Chang, Nanyang Technological University
Yanru Chang, City University of New York, Baruch
College
Aadhaar Chaturvedi, University of Auckland Business
School
Jasmina Chauvin, Georgetown University
Daniel E. Chavez, University of Tennessee
Christopher Chen, Indiana University
Fadong Chen, School of Management
Neuromanagement Lab, Zhejiang University
Josie I Chen, National Taiwan University
Peng-Chu Chen, University of Hong Kong
Roy Chen, RWTH Aachen University
Wei Chen, University of Connecticut
Wei James Chen, National Taiwan University,
Department of Agricultural Economics
Yuanyuan Chen, University of Alabama
Zepeng Chen, Hong Kong Polytechnic University
Zhuoqiong Chen, Harbin Institute of Technology,
Shenzhen
Lydia Chew, Harvard University, Harvard Business
School
Param Pal Singh Chhabra, University of Alberta
Sai Chand Chintala, Cornell University
Ga-Young Choi, City University of London
Seungho Choi, Hanyang University; Queensland
University of Technology
Vivek Choudhary, Nanyang Technological University,
Nanyang Business School
Vincent Tsz Fai Chow, Hong Kong Polytechnic
University, Faculty of Business
Katherine L. Christensen, Indiana University, Kelley
School of Business
Doug Chung, University of Texas at Austin
Melissa Cinelli, University of Mississippi
Lubomír Cingl, Prague University of Economics and
Business
Andre Augusto Cire, University of Toronto, Rotman
Jeffrey Clark, Stockholm School of Economics
Jeffrey Clement, Augsburg University
John Clithero, University of Oregon
Héloïse Cloléry, Ecole Polytechnique IP Paris, CREST
David R. Clough, University of British Columbia
Nicholas Clyde, Washington University in St. Louis
Andrea Coali, Bocconi University
Irene Comeig, University of Valencia
Nikolai Cook, Wilfrid Laurier University
Joao Correia-da-Silva, University of Porto
Elaine Costa, University of Utah
Alexander Coutts, York University
Ivor Cribben, University of Alberta, Alberta School of
Business
Carina Cuculiza, Oklahoma State University
Zimeng (Simon) Cui, University of Utah
Colleen Cunningham, University of Utah, Eccles School
of Business
Peter Cziraki, Texas AM University
Étienne Dagorn, National Institute of Demographic
Studies (INED)
Rui Dai, University of Pennsylvania, The Wharton School
Jason Dana, Yale University, Yale School of Management
Nicholas Patrick Danks, Trinity College Dublin, Trinity
Business School
Alper Darendeli, Nanyang Technological University
Simon Dato, EBS Universität für Wirtschaft und Recht
Nebojsa Davcik, EM Normandie Business School, Metis
Lab
Charles de Grazia, Léonard de Vinci Pôle Universitaire,
Research Center
Jose De Sousa, Université Paris Panthéon-Assas
24

Jelle De Vries, Erasmus University, Rotterdam School of
Management
Martijn De Vries, Vrije Universiteit Amsterdam
Oleg Deev, Masaryk University
Ryan DeFronzo, California State University, Fullerton
Lennart Dekker, De Nederlandsche Bank
Arthur Delarue, Georgia Institute of Technology,
H. Milton Stewart School of Industrial Systems
Engineering
Elif E. Demiral, Austin Peay State University
Cem Demiroglu, Koc University
Aishwarrya Deore, Georgetown University
Andrew Detzel, Baylor University
Azamat Devonaev, University of Luxembourg
Archana Dhinakar Bala, National University of
Singapore
Eugen Dimant, University of Pennsylvania
Drew Dimmery, University of Vienna
Stephen G. Dimmock, National University of Singapore
Cheng Ding, Emory University
Likang Ding, University of Alberta
Tingting Ding, James Madison University; Shanghai
University of Finance and Economics
Yuheng Ding, University of Maryland
Lu Dong, Southern University of Science and Technology
Karen Donohue, University of Minnesota, Carlson School
of Management
Andreas Drichoutis, Agricultural University of Athens
Shaoyin Du, University of North Carolina at Charlotte
Ying Duan, Simon Fraser University
Teodor Duevski, HEC Paris
Huu Nhan Duong, Monash University
Merle Ederhof, University of Zurich, Stanford University
Hussein El Hajj, Santa Clara University, Leavey School
of Business
Martin Ellison, University of Oxford
Jonas Nygaard Eriksen, Aarhus University
Miguel Espinosa, Bocconi University
Francesco Fallucchi, University of Bergamo
Xiaohua Fang, Florida Atlantic University
Valeria Fanghella, Grenoble Ecole de Management
Matilde Faralli, Imperial College London
Saleh Farham, University of Alberta
Felix Fattinger, Vienna University of Economics and
Business
Stephanie Feiereisen, Montpellier Business School
Yiding Feng, Microsoft Research
Elia Ferracuti, Duke University
Antonio Filippin, University of Milan
Adrien Fillon, University of Cyprus, SInnoPSis
Stefano Fiorin, Bocconi University
Geoffrey Fisher, Cornell University
Matthew Fisher, Southern Methodist University
Christoph Flath, University of Würzburg
Jens Foerderer, Technical University of Munich
Vincenz Frey, University of Groningen, Department of
Sociology
Christoph Fuchs, University of Vienna
Nicolas Fugger, University of Cologne
Sebastian Gabel, Erasmus University Rotterdam,
Rotterdam School of Management
Fabian Gaessler, Universitat Pompeu Fabra
Bernhard Ganglmair, University of Mannheim
Manish Gangwar, Indian School of Business
Pedro Angel Garcia Ares, Instituto Tecnologico
Autonomo de Mexico
Rajiv Garg, Emory University
José Miguel Gaspar, ESSEC Business School
Chiara Gastaldi, Free University of Bozen-Bolzano
Romain Gauriot, Deakin University
Alan De Genaro, Sao Paulo School of Business
Administration (FGV-EAESP)
Yuxin Geng, Tsinghua University
Konstantinos Georgalos, Lancaster University
Management School
Diogo Geraldes, University College Dublin, School of
Economics; Geary Institute for Public Policy
Leonie Gerhards, King’s College London
William Gerken, University of Kentucky
Mike Gibson, University of Maryland, Agricultural and
Resource Economics Department
Joren Gijsbrechts, Esade; Ramon Llull University
Sebastian Goerg, Technical University of Munich
Daniel Goetz, University of Toronto, Rotman School of
Management
Jim Goldman, University of Warwick
Filip Gonschorek, ZEW Leibniz Centre for European
Economic Research
Victor Gonzalez-Jimenez, Erasmus University
Rotterdam
Jorgo T.G. Goossens, Radboud University Nijmegen,
Institute for Management Research; Tilburg University,
Department of Econometrics and Operations Research
Michael Gordy, Federal Reserve Board
25

Paul M. Gorny, Karlsruhe Institute of Technology
Indranil Goswami, University at Buffalo
Amit Goyal, University of Lausanne
Ruslan Goyenko, McGill University
Tom Grad, Copenhagen Business School
Wesley Greenblatt, Massachusetts Institute of
Technology, Sloan School of Management
Martin Gregor, Charles University
Daniela Grieco, University of Milano
Manuel Grieder, UniDistance Suisse; Zurich University
of Applied Sciences (ZHAW)
Max R. P. Grossmann, University of Cologne
Sven Grüner, University of Rostock
Sreyaa Guha, Universidade NOVA de Lisboa, Nova
School of Business and Economics
Audrey Guo, Santa Clara University
Gang Guo, National University of Singapore
Haihao Guo, Washington University in St. Louis
Lewen Guo, University of Memphis
Dominik Gutt, Erasmus University Rotterdam
Andre Gygax, University of Melbourne
Isaac Hacamo, Indiana University
Simone Haeckl, University of Stavanger
Thomas C. Hagenberg, Northwestern University,
Kellogg School of Management
David Hagmann, The Hong Kong University of Science
and Technology
Jacob Haislip, Texas Tech University
Eojin Han, Southern Methodist University, Operations
Research and Engineering Management
Jiatong Han, Zhejiang University; School of Management
Neuromanagement Lab
Joseph Earle Harvey, Consumer Financial Protection
Bureau
Olena Havrylchyk, Université Paris 1 Panthéon-
Sorbonne, Centre d’Economie de la Sorbonne
Sonali Hazarika, City University of New York, Baruch
College
Leshui He, Bates College
Yuhang He, Nanyang Technological University, Nanyang
Business School
William Hedgcock, University of Minnesota
Irina Heimbach, WHU Otto Beisheim School of
Management
Brian Henderson, George Washington University
Jurian Hendrikse, Tilburg University
Erin Henry, University of Arkansas
Bradford Hepfer, The University of Iowa
Roberto Hernan, Burgundy School of Business
Holger Herz, University of Fribourg
Anthony Heyes, University of Birmingham
Christian Hildebrand, University of St. Gallen, Institute
of Behavioral Science Technology
Adrian Hillenbrand, Karlsruhe Institute for Technology;
Leibniz Centre For European Economic Research
Alexander Hillert, Goethe University Frankfurt; Leibniz
Institute for Financial Research SAFE
Michael Hilweg, University of Mannheim
Erik Hjalmarsson, University of Gothenburg
Seth Hoelscher, Missouri State University
Peter Hoffmann, European Central Bank
Brett Hollenbeck, University of California Los Angeles,
Anderson School of Management
Niels Holtrop, Maastricht University
Felix Holzmeister, University of Innsbruck, Department
of Economics
Swarnodeep Homroy, University of Groningen
Mallick Hossain, Federal Reserve Bank of Philadelphia
Leon Houf, Heidelberg University
Taeya Howell, Brigham Young University, Marriott
School of Business
Kejia Hu, University of Oxford
Allen Huang, Hong Kong University of Science and
Technology
Jing-Zhi Huang, Pennsylvania State University
Lingbo Huang, Shandong University
Sterling Huang, Singapore Management University
Stefanie J. Huber, University of Bonn
Stanton Hudja, University of Toronto
Jacquelyn Humphrey, University of Queensland
Paul Hünermund, Copenhagen Business School
William Reuben Hurst, University of Michigan, Ross
School of Business
Carlos Hurtado, University of Pittsburgh
Kim P. Huynh, Bank of Canada
Kyle Hyndman, University of Texas at Dallas
Armann Ingolfsson, University of Alberta
Panos Ipeirotis, New York University
Ayelet Israeli, Harvard University, Harvard Business
School
Alexey Ivashchenko, Vrije Universiteit Amsterdam
Wael Jabr, Pennsylvania State University
Pankaj K. Jain, University of Memphis
26

Ainhoa Jaramillo-Gutierrez, University Jaume I
Castellon
Nahid Javadinarab, University of Luxembourg
Yonghua Ji, University of Alberta
Mofei Jia, Xi’an Jiaotong-Liverpool University
Hansheng Jiang, University of Toronto
Houyuan Jiang, University of Cambridge, Judge Business
School
Jiashuo Jiang, Hong Kong University of Science and
Technology
Jingdan Tan, Nanyang Technological University
Michal Jirásek, Masaryk University
Brandon Julio, University of Oregon
Heejung (HJ) Jung, Imperial College London, Business
School
Daniel Marcel te Kaat, University of Groningen
Jonathan Kalodimos, Oregon State University
Mark Kamstra, York University, Schulich School of
Business
Hyo Kang, University of Southern California
Qiang Kang, Florida International University
Salpy Kanimian, Rice University
Martin M. Kapons, University of Amsterdam
Egle Karmaziene, Vrije Universiteit Amsterdam;
Swedish House of Finance; Tinbergen Institute
Asad Kausar, American University
Patrick J Kelly, University of Melbourne
Saravanan Kesavan, University of North Carolina at
Chapel Hill
Menusch Khadjavi, Vrije Universiteit Amsterdam; Kiel
Institute for the World Economy
Hamid Khobzi, University of Sussex
Robizon Khubulashvili, University of San Francisco
Alex G. Kim, University of Chicago
Byungyeon Kim, University of Minnesota
Chungyool Kim, University of Iowa
Dong Soo Kim, Ohio State University
Sehoon Kim, University of Florida
Seojin Kim, Drexel University
Seung Hyun Kim, Yonsei University, School of Business
Soohun Kim, Korea Institute of Advanced Science and
Technology
Margarita Kirneva, Ecole Polytechnique, CREST;
ENSAE Paris
Andrea Kiss, Carnegie Mellon University
Leonardo Mayer Kluppel, Ohio State University
Özgecan Koçak, Emory University
Christoph Kogler, Tilburg University
Christian König-Kersting, University of Innsbruck
Anita Kopányi-Peuker, Radboud University Nijmegen,
Institute for Management Research
Lina Koppel, Linköping University
Sharon Koppman, University of California Irvine
Orestis Kopsacheilis, Technical University of Munich
Laura J. Kornish, University of Colorado Boulder, Leeds
School of Business
Anne Krahn, Tufts University
Ondřej Krčál, Masaryk University
Srinivasan Krishnamurthy, North Carolina State
University
Philipp Kropp, University of Munich
Santanu Kundu, University of Mannheim
Michael Kurschilgen, UniDistance Suisse
David J. Kusterer, Erasmus University Rotterdam,
Rotterdam School of Management
Samet Kutuk, Vrije Universiteit Amsterdam
Olga Kuzmina, New Economic School
Ellie Kyung, Babson College
Camille Lacan, CRESEM; IAE School of Management;
University of Perpignan Via Domitia
Adrian Lam, University of Pittsburgh
Thomas Lambert, Erasmus University Rotterdam
Lauren Lanahan, University of Oregon
Mike Langen, CPB Netherlands Bureau for Economic
Policy Analysis
Nadzeya Laurentsyeva, Ludwig-Maximilians-
Universität München
Kelvin K. F. Law, Nanyang Technological University
Quoc Thai Le, University of Trento, Department of
Economics and Management
Choonsik Lee, University of Rhode Island
Daniel Lee, University of Delaware
Kyeong Hun Lee, University of Alabama, Culverhouse
College of Business
Sunkee Lee, Carnegie Mellon University, Tepper School
of Business
Yeonjoo Lee, University of Minnesota, Carlson School of
Management
Murray Lei, Queen’s University
Zhou Lei, Nanyang Technological University, Nanyang
Business School
Stephan Leitner, University of Klagenfurt
Gabriele Mario Lepori, University of Southampton
27

David E. Levari, Harvard University, Harvard Business
School
Ben William Lewis, Brigham Young University
Benjamin T. Leyden, Cornell University
Chenghuai Li, Duke University, Fuqua School of Business
Jiasun Li, George Mason University
King King Li, Shenzhen University, Shenzhen Audencia
Financial Technology Institute
Linfeng Li, University of Michigan
Meng Li, University of Houston
Shukai Li, Northwestern University
Shuo Li, Singapore Management University
Ye Li, University of California Riverside
Yushen Li, Jinan University, Institute of Industrial
Economics
Chuchu Liang, University of California, Irvine
Stanley Lim, Michigan State University
Mingfeng Lin, Georgia Tech
Po-Hsuan Lin, California Institute of Technology
Yunduan Lin, University of California Berkeley
Sera Linardi, University of Pittsburgh
William Lincoln, Claremont McKenna College
Michaela Lindenmayr, Technical University of Munich
Martina Linnenluecke, University of Technology Sydney
Ariel Listo, University of Maryland
Robin Litjens, Tilburg University
Chengwei Liu, European School of Management and
Technology
Dingyue (Kite) Liu, University of California Santa
Barbara
Fang Liu, University of the Chinese Academy of Sciences
Haibo Liu, Claremont Colleges, Keck Graduate Institute
Haiyang Liu, Nanyang Technological University
Jiaxin Liu, Morgan State University
Kaiqi Liu, Maastricht University, Department
Microeconomics and Public Economics
Nan Liu, Boston College
Sheng Liu, University of Toronto
Xiaojin Liu, Virginia Commonwealth University
Neta Livneh, Tel Aviv University
Tatiana Lluent, European School of Management and
Technology
Nils Loehndorf, University of Luxembourg
Matthijs Lof, Aalto University, School of Business
Youenn Loheac, Rennes School of Business
Paul Lohmann, University of Cambridge, Judge Business
School
Luis Arturo Lopez, University of Illinois at Chicago
Matej Lorko, University of Economics in Bratislava;
Prague University of Economics and Business
Francesca Lotti, Bank of Italy, DG Economics, Statistics
and Research
Joy Lu, Carnegie Mellon University
Xinyu Lu, HEC Paris
Jonathan Luffarelli, Montpellier Business School
Wolfgang J. Luhan, University of Portsmouth
Hoang Luong, University of Queensland
Guodong Lyu, Hong Kong University of Science and
Technology
Liang Ma, San Diego State University
Leonardo Madio, University of Padova
Kai Maeckle, University of Mannheim
Mahdi Mahmoudzadeh, University of Auckland
Business School
Patrick Maillé, IMT Atlantique
Vincent Mak, University of Cambridge, Cambridge Judge
Business School
Antoine Malézieux, Burgundy School of Business
Shawn Mankad, North Carolina State University
César Mantilla, Universidad del Rosario
Benny Mantin, University of Luxembourg
Marco Mantovani, Università degli Studi di Milano
Bicocca, Dipartimento di Economia
Giacomo Marchesini, Copenhagen Business School
Juri Marcucci, Bank of Italy
Diego Marino Fages, Durham University
Aidas Masiliunas, University of Sheffield
Sébastien Massoni, Université de Lorraine; Université de
Strasbourg; CNRS; BETA
Nunez Matias, Ecole Polytechnique, CREST; CNRS
Thomas Matthys, University of Technology Sydney
Martin Mattsson, National University of Singapore
Thomas Andreas Maurer, University of Hong Kong
Patrick Maus, University of Nottingham
Merve Mavuş Kütük, University of Amsterdam
Malte M. Max, Vrije Universiteit Amsterdam
Christoph Meinerding, Deutsche Bundesbank
Matt Meister, University of Colorado Boulder; University
of San Francisco
Dong Meitong, University of Hong Kong
Eduardo Melero, Universidad Carlos III de Madrid
Diogo Mendes, Stockholm School of Economics
Tyler Menzer, University of Iowa
Christoph Merkle, Aarhus University
28

Jason Merrick, Virginia Commonwealth University
Steffen Meyer, Aarhus University; Danish Finance
Institute
Tomáš Miklánek, Prague University of Economics and
Business
Wladislaw Mill, University of Mannheim
Stefan Minner, Technical University of Munich
Emil Mirzayev, University College London, School of
Management
Sergio Mittlaender, Fundação Getulio Vargas Law
School in São Paulo; Max Planck Institute for Social Law
and Social Policy
Stig Vinther Møller, Aarhus University
Andras Molnar, University of Michigan, Department of
Psychology
David Moore, Loyola Marymount University
Sandra Mortal, University of Alabama
Giovanni Moscariello, Stockholm School of Economics
Yuting Mou, Southeast University
Jifeng Mu, Alabama AM University
Clemens Mueller, University of Mannheim
Anirban Mukherjee, Cornell University; INSEAD
Sara Mustafazade, University of Montpellier
Kumar Muthuraman, University of Texas-Austin
Alper Nakkas, University of Texas at Arlington
Jim Naughton, University of Virginia
Hunter Boon Hian Ng, City University of New York,
Baruch College
Lily Nguyen, University of Queensland
Mike Nguyen, University of Southern California
Ngoc Phuong Anh Nguyen, University of Technology
Sydney
Thi Thuy Tien Nguyen, University of Auckland
Amy Nguyen-Chyung, University of California San
Diego, Rady School of Management
Nicos Nicolaou, University of Warwick
Sven Nolte, Radboud University Nijmegen
Arjan Non, Erasmus University Rotterdam
Bernt Arne Ødegaard, University of Stavanger
Yuval Ofek-Shanny, Friedrich-Alexander-Universität
Erlangen-Nürnberg
Chang Hoon Oh, University of Kansas
Christopher Yves Olivola, Carnegie Mellon University
Thomas C. Omer, University of Nebraska-Lincoln
Andreas Orland, Corvinus University of Budapest
Tizian Otto, Yale University; University of Hamburg
Manlu Ouyang, New York University, Stern School of
Business
Hakan Ozyilmaz, Toulouse School of Economics
Nicholas A. Pairolero, United States Patent and
Trademark Office
Stefan Palan, University of Graz
Navya Pandit, University of Cologne
Dominik Papies, University of Tuebingen, School of
Business and Economics
Jiyong Park, University of North Carolina at Greensboro
Tae-Youn Park, Sungkyunkwan University
Chris Parker, American University
Vinay Patel, University of Technology Sydney
Grzegorz Pawlina, Lancaster University
Elise Payzan-Le Nestour, University of New South
Wales
Graeme Pearce, Bangor University
Thomas Peeters, Erasmus University Rotterdam,
Erasmus School of Economics; Tinbergen Institute;
Erasmus Research Institute in Management
Jana Peliova, University of Economics in Bratislava
Zhuozhen Peng, Central University of Finance and
Economics
Christophe Pérignon, HEC Paris
Noemi Peter, University of Groningen
Christian Peukert, University of Lausanne, Faculty of
Business and Economics (HEC)
Hieu Phan, University of Massachusetts Lowell
Aviva Philipp-Muller, Simon Fraser University
Kenny Phua, University of Technology Sydney
Matthew Pierson, University of Pennsylvania, The
Wharton School
Tomáš Plíhal, Masaryk University
Matteo Ploner, University of Trento, Department of
Economics and Management
Simon Porcher, Université Paris Panthéon-Assas
Matthieu Pourieux, Rennes School of Business; Univ
Rennes, CNRS, CREM-UMR6211
Susanne Preuss, University of Amsterdam
Jakub Procházka, Masaryk University, Faculty of
Economics and Administration
Shaolin Pu, University of Kansas, School of Business
Žiga Puklavec, Tilburg University
Hanzhang Qin, Amazon; National University of
Singapore
Tian Qiu, University of Alabama
Xincheng Qiu, University of Pennsylvania
29

Rima-Maria Rahal, Max Planck Institute for Research
on Collective Goods
Amin Rahimian, University of Pittsburgh
Mohammadreza Rajabzadeh, York University, Schulich
School of Business
Oliver Randall, University of Melbourne
Soumya Ray, National Tsing Hua University, Institute of
Service Science
Oliver Rehbein, Vienna University of Economics and
Business
Jurij-Andrei Reichenecker, University of Strathclyde
Nicholas Reinholtz, University of Colorado Boulder
J. Philipp Reiss, Karlsruhe Institute of Technology
Jean-Paul Renne, University of Lausanne
Sadat Reza, Nanyang Technological University
Paul Richardson, Pennsylvania State University
Steven Riddiough, University of Toronto
Marc Oliver Rieger, University of Trier; University of
Economics Ho Chi Minh City
Cesare Righi, Universitat Pompeu Fabra, Department
of Economics and Business; UPF Barcelona School of
Management; Barcelona School of Economics
Rainer Michael Rilke, WHU Otto Beisheim School of
Management
Julio Riutort, Universidad Adolfo Ibáñez
Cesare Robotti, University of Warwick
Nathalie Römer, Leibniz University Hannover
Paul Romser, Ludwig-Maximilians-Universität München
Julia Rose, Erasmus University Rotterdam, Erasmus
School of Economics; Tinbergen Institute
Michael Rose, Max Planck Institute for Innovation and
Competition
Federico Rossi, Purdue University
Borzou Rostami, University of Alberta
Kasper Roszbach, Norges Bank; University of Groningen
Kristian Rotaru, Monash University, Monash Business
School
Yefim Roth, University of Haifa
Daniele Rotolo, University of Sussex; Technical
University of Bari
Christina Rott, Vrije Universiteit Amsterdam; Tinbergen
Institute
Bryan Routledge, Carnegie Mellon University
Brian Rubineau, McGill University
Hannes Rusch, Maastricht University
Ilya O. Ryzhov, University of Maryland
Pedro Saffi, University of Cambridge, Judge Business
School
Mehmet Saglam, University of Cincinnati
Margaret Samahita, University College Dublin
Panagiotis Sarantopoulos, Athens University of
Economics and Business; University of Manchester
Vahid Sarhangian, University of Toronto
Secil Savasaneril, Middle East Technical University,
Industrial Engineering Department
Harald Scheule, University of Technlogy Sydney
Maximilian Schleritzko, Vienna Graduate School of
Finance
Max Schnidman, University of Virginia
Daniela Stephanie Schoch, emlyon business school
Marina Schröder, Leibniz University Hannover
Erik Christian Montes Schütte, Aarhus University;
Danish Finance Institute
Daniel Schwartz, University of Chile
Frederik Schwerter, Frankfurt School of Finance and
Management
Robert Seamans, New York University
Matthias Seifert, IE University, IE Business School
Tom Servranckx, Ghent University, Faculty of Economics
and Business Administrations
Nagarajan Sethuraman, University of Kansas
Victoria Sevcenko, INSEAD
Divyesh Rajendra Shah, University of Toronto
Rachna Shah, University of Minnesota
Kartikey Sharma, Zuse Institute Berlin
Padma Sharma, Federal Reserve Bank of Kansas City
Amy Sheneman, Ohio State University
Yunting Shi, Shanghai Jiao Tong University, Antai
College of Economics and Management
Ling Shuai, Tianjin University
Simon Siegenthaler, University of Texas at Dallas
John Silberholz, University of Michigan
Rui Silva, University of East Anglia
Katherine Silz-Carson, U.S. Air Force Academy
Felipe Simon, University of Minnesota
Raghav Singal, Dartmouth College, Tuck School of
Business
Nitish Ranjan Sinha, Board of Governors of the Federal
Reserve System
Spyros Skouras, Athens University of Economics and
Business
David Smerdon, University of Queensland
30

Katrin Smolka, University of Warwick, Warwick Business
School
Adriaan Soetevent, University of Groningen
Elvira Sojli, University of New South Wales
Konstantin Sokolov, University of Memphis
Jeeva Somasundaram, IE Business School
Yoonseock Son, University of Notre Dame
Ju Myung Song, University of Massachusetts Lowell
Vikas Soni, University of South Florida
Doron Sonsino, University of Limassol, Cyprus
Matthew Souther, University of South Carolina
Christophe Spaenjers, University of Colorado Boulder
Martin Spann, Ludwig-Maximilians-Universität
München, LMU Munich School of Management
Eirini Spiliotopoulou, Tilburg University
Jeffrey Starck, University of Cologne
Austin Starkweather, University of South Carolina
Dayton Steele, University of Minnesota, Carlson School
of Management
Matthias Stefan, University of Innsbruck
Frauke Stehr, Maastricht University
Eva Steiner, Pennsylvania State University
Lucas Stich, Julius-Maximilians-Universität Würzburg
Thomas Stoeckl, MCI The Entrepreneurial School
Jan Stoop, Erasmus University Rotterdam, Erasmus
School of Economics
Karoline Ströhlein, University of Regensburg
Robert Stüber, New York University Abu Dhabi
Jason Sturgess, Queen Mary University of London
Yuhan Su, Tianjin University
Yuxin Su, SKEMA Business School
Rémi Suchon, Université Catholique de Lille
Mengtian Sui, City University of New York, Baruch
College
Sandra Sülz, Erasmus University Rotterdam, Erasmus
School of Health Policy Management
Elie Sung, HEC Paris
Marta Szymanowska, Erasmus University, Rotterdam
Giovanni Alberto Tabacco, Freelance researcher
David Tannenbaum, University of Utah
Necati Tereyagoglu, University of South Carolina, Darla
Moore School of Business
Chloe Tergiman, Pennsylvania State University
Marco Testoni, Miami Herbert Business School,
University of Miami
Richard Thakor, University of Minnesota; Massachusetts
Institute of Technology, Laboratory for Financial
Engineering
Wing Wah Tham, University of New South Wales
Samuel Thelaus, London School of Economics
Simon Thielen, MCI The Entrepreneurial School
Lu Tong, Southwestern University of Finance and
Economics
Ozlem Tonguc, Binghamton University
Mirco Tonin, Free University of Bozen-Bolzano
Sinem Yagmur Toraman, Johns Hopkins University,
Department of Economics
Marco Tortoriello, Bocconi University
J. Dustin Tracy, Augusta University
James Tremewan, IESEG School of Management
Muktak K. Tripathi, Temple University
Gunseli Tumer-Alkan, Vrije Universiteit Amsterdam
Danko Turcic, University of California Riverside
Theodore Turocy, University of East Anglia
Hanu Tyagi, University of Minnesota
Maximiliano Udenio, KU Leuven
Sezer Ulku, Georgetown University, McDonough School
of Business
Michael Ungeheuer, Aalto University
Steven Utke, University of Connecticut
Cihan Uzmanoglu, SUNY, Binghamton University
Matteo Vacca, Aalto University, School of Business
Philip Valta, University of Bern
Michel Van Der Borgh, Copenhagen Business School
Jesse Van Der Geest, Tilburg University
Milan Van Steenvoort, Maastricht University
Roel Van Veldhuizen, Lund University
Prasad Vana, Dartmouth College, Tuck School of
Business
Mario Vanhoucke, Ghent University; Vlerick Business
School; University College London
Bart Vanneste, University College London
Joseph Vecci, Gothenburg University
Sriram Venkataraman, University of South Carolina,
Darla Moore School of Business
Marcella Veronesi, Technical University of Denmark;
University of Verona
Sergio Vicente, University of Luxembourg
Sebastian Villa, University of New Mexico
Marta Villamor Martin, University of Maryland
Lynne Vincent, Syracuse University
31

Theodor Vladasel, Universitat Pompeu Fabra, Barcelona
School of Economics
Stefan Voigt, University of Copenhagen
Joachim Vosgerau, Bocconi University
Christian A. Vossler, University of Tennessee
Angela Vossmeyer, Claremont McKenna College
Hannes F. Wagner, Bocconi University
David M. Waguespack, University of Maryland
Edward Walker, University of California Los Angeles
Matthew Walker, Newcastle University
Markus Walzl, University of Innsbruck
Zhixi Wan, University of Hong Kong
Charles C.Y. Wang, Harvard University, Harvard
Business School
Joseph Tao-Yi Wang, National Taiwan University,
Kanix Wang, University of Cincinnati
Victor Xiaoqi Wang, California State University Long
Beach
Xiaohong Wang, University of Pittsburgh
Yiwei Wang, Zhejiang University
Xavier S. Warnes, Stanford University
Lilia Wasserka-Zhurakhovska, University of Duisburg-
Essen
Wei Wei, University of Oklahoma
Stefan Weiergraeber, Indiana University, Department of
Economics
Patrick Weiss, Reykjavik University
Jingjing Weng, Temple University
Wei-Chien Weng, National Taiwan University
James Weston, Rice University
Joshua Tyler White, Vanderbilt University
Matthias Wibral, Maastricht University
Jared Williams, University of South Florida
Ole Wilms, Hamburg University; Tilburg University
Franz Wirl, University of Vienna
Adrian Wolanski, University of California San Diego,
M.H. Franco Wong, University of Toronto
Daniel John Woods, University of Innsbruck
Biyu Wu, University of Nebraska-Lincoln
Yiran Wu, Vrije Universiteit Amsterdam
Ziye Wu, National University of Singapore
David Wuttke, Technical University of Munich, TUM
School of Management, TUM Campus Heilbronn
Yuze Xia, Northwestern University, Kellogg School of
Management
Jingui Xie, Technical University of Munich
Wen Xie, City University of New York, Baruch College
Feiyu Xu, Hong Kong University of Science and
Technology
Luze Xu, University of California Davis
Sikun Xu, Washington University in St. Louis
Simon Xu, Harvard University, Harvard Business School
Yilong Xu, Utrecht University School of Economics,
Utrecht University
Rui Xue, La Trobe University
Beril Yalcinkaya, University of Maryland
Ruijing Yang, Chinese University of Hong Kong
Yadi Yang, Nanjing Audit University
Huang Yao, Central South University, Business School;
Hunan Agricultural University, College of Economics
Shiqing Yao, Monash University
Yaojun Ke, Nanyang Technological University
Ozge Yapar, Indiana University, Kelley School of Business
Eduard Yelagin, University of Memphis
Ira Yeung, University of British Columbia
Erdem Dogukan Yilmaz, Erasmus University
Rotterdam
Levent Yilmaz, Turkish-German University
Woongsun Yoo, Central Michigan University
Simon (Seongbin) Yoon, University of California Irvine
Sora Youn, Texas AM University
Alex Young, Hofstra University
Jin Yu, Monash University
Jungju Yu, Korea Advanced Institute of Science and
Technology
Junhao Vincent Yu, Miami University, Farmer School of
Business
Lizi Yu, University of Queensland
Huaiping Yuan, The Chinese University of Hong Kong-
Shenzhen, SME and SFI
Yuan Yuan, Purdue University
Lei Yue, University of California Santa Barbara
Anita Zednik, Vienna University of Economics and
Business
Yasser Zeinali, University of Alberta
Shenghui Zhai, University of the Chinese Academy of
Sciences
Xintong Zhan, Fudan University
Aiqi Zhang, Wilfrid Laurier University, Lazaridis School
of Business and Economics
Chengyu Zhang, McGill University
Huanan Zhang, University of Colorado Boulder
32

Huanren Zhang, University of Southern Denmark
Hulai Zhang, Tilburg University; ESCP Business School
Jack H. Zhang, Nanyang Technological University
Le (Lyla) Zhang, Macquarie University
Quan Zhang, Nanyang Technological University
Renyu Zhang, Chinese University of Hong Kong
Ruishen Zhang, Shanghai University of Finance and
Economics
Shu Zhang, Shanghai University of Finance and
Economics
Sili Zhang, Ludwig-Maximilians-Universität München
Walter W. Zhang, University of Chicago, Booth School
of Business
Zhiqi Zhang, Washington University in St. Louis, Olin
Business School
Jiayu (Kamessi) Zhao, Massachusetts Institute of
Technology, Operations Research Center
Xiaofei Zhao, Georgetown University
Zhongyu Zhao, University of Hong Kong
Jiakun Zheng, Renmin University of China, School of
Finance
Yaping Zheng, McGill University
Zhanzhi Zheng, University of North Carolina at Chapel
Hill, Kenan–Flagler Business School
Aner Zhou, San Diego State University
Hongyi Zhu, University of Texas at San Antonio
Jason Zhu, Microsoft
Yayongrong Zhu, University of Queensland
Christian Zihlmann, University of Fribourg, Berne
Business School
Marius Zoican, University of Toronto
Ro’i Zultan, Ben-Gurion University of the Negev
Zhuan Zuo, University of the Chinese Academy of
Sciences
33

B Additional tables and figures
TABLE B.1: Software used in articles
with and without report
Has Report No Report
(N = 459) (N = 30)
Stata 60.1% 43.3%
R 19.2% 23.3%
Matlab 17.9% 26.6%
SAS 12.9% 13.3%
Python 10.7% 13.3%
Mathematica 1.7% 6.7%
SPSS 1.3% 0.0%
Other 5.7% 13.3%
TABLE B.2: Reasons for non-reproducibility for articles
with replication package, by policy
Before 2019 Since 2019
policy policy
(N = 18) (N = 136)
No access to dataset. 61.1% 88.2%
Issues with software/hardware requirements. 5.6% 2.9%
Code or parts of code/functions missing. 55.6% 12.5%
Insuffient documentation, missing information. 11.1% 7.4%
Unresolvable errors when executing code. 11.1% 5.1%
Reproduction yields (partly) different results. 11.1% 4.4%
34

TABLE B.3: Distribution of article types/methods
for each journal department, since 2019 policy
Theory
Lab/online /Simulation Survey Field Empirical
experiment /Computation study experiment data
SMS (N = 5) 0 100 0 0 0%
BDE (N = 66) 70 3 5 8 15%
ENI (N = 10) 10 0 0 0 90%
RMA (N = 19) 0 84 0 0 16%
ACC (N = 57) 7 0 2 0 91%
OPM (N = 38) 11 32 5 11 42%
OPT (N = 6) 0 100 0 0 0%
BDA (N = 14) 0 100 0 0 0%
FIN (N = 124) 5 15 1 1 78%
HCM (N = 16) 0 19 0 0 81%
INS (N = 19) 0 11 5 11 74%
MKG (N = 20) 10 5 0 15 70%
ORG (N = 13) 0 8 8 0 85%
BST (N = 12) 0 8 8 25 58%
Total (N = 419) 15 20 2 4 59%
Note: Department acronyms are SMS: Stochastic Models and Simulations, BDE: Behavioral Economics
and Decision Analysis, ENI: Entrepreneurship and Innovation, RMA: Revenue Management and Market
Analytics, ACC: Accounting, OPM: Operations Management, OPT: Optimization, BDA: Big Data
Analytics/Data Science, FIN: Finance, HCM: Healthcare Management, INS: Information Systems,
MKG: Marketing, ORG: Organizations, BST: Business Strategy.
C Robustness analyses
In Figure C.1 and Table C.1 we replicate our main results reported in Section III (Figure 1 and Table 4)
based on a sample of all submitted reports. The first panel of Figure C.1 only considers reports for
verifiable articles (i.e., where data was available if needed, and soft- and hardware requirements were
met) that were subject to the 2019 disclosure policy. The second panel also includes reports for
non-verifiable articles, and the third panel focuses on reports on articles that were accepted before
the disclosure policy was introduced and that voluntarily provided replication materials. (We do not
replicate the fourth panel of Figure 1 in Figure C.1, since the focus here is on reports, and articles
without any package that did not enter our review sample.) Our results at the report level largely
mimic results at the article level reported in the main text. Reproducibility levels are necessarily
somewhat lower, since at the article level we only considered the better of two reports (if there were
two reports), but are in the same ballpark. Namely, for verifiable articles, 93.7% of reports assess that
results are fully or largely reproduced (compared to 95.3% at the article level). Including non-verifiable
articles, this share is 62.4% at the report level (compared to 67.5% at the article level). Similarly, for
35

FIGURE C.1: Overall reproducibility assessments at report level, by policy
!#$%
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voluntarily provided replication packages from the pre-policy period, at the report level 54.7% can be
at least largely reproduced compared to 55% at the article level. The regressions reported in Table C.1,
assessing the disclosure policy effect at the report level, replicate our results reported in Table 4 in the
main text at the article level.
TABLE C.1: Regressing reproducibility on disclosure policy existence, report level
Model (1) (2) (3)
Sample of articles All incl. no package All with package All verifiable
Constant 0.098∗∗∗ (0.020) 0.547∗∗∗ (0.077) 0.778∗∗∗ (0.069)
Policy 0.526∗∗∗ (0.031) 0.077 (0.081) 0.159∗∗ (0.070)
Report observations 1,045 753 504
R2 0.251 0.002 0.029
Note: Standard errors are clustered at the article level. *, **, *** indicate significance at the 10%, 5%,
and 1% level, respectively.
36

In addition to an overall assessment, we asked our reviewers to provide individual assessments
for each table and figure in the article that are based on code and/or data analysis, and a summary
assessment of other analysis reported in the manuscript (that is, how many of those results they could
reproduce). Many reviewers did so, but not all. Some articles only included figures and/or tables that
were not based on code or data analysis. As a result, the sample size in terms of articles is slightly
lower for this analysis.
Table C.2 shows that, as to be expected, overall assessments and individual assessments are highly
correlated. If an article was overall classified as fully reproduced, then more than 99% of tables and
figures and more than 92% of other results could be reproduced. If an article was overall classified
as Not reproduced, the shares of reproduced tables, figures, and other results are 3%, 8%, and 25%,
respectively.
TABLE C.2: Share of tables, figures, and other results assessed as at least largely
reproducible, by overall reproducibility assessment, since 2019 policy
Tables Figures Other Results
(N = 374) (N = 301) (N = 145)
Fully reproduced 99.1 % 99.7 % 92.3 %
Largely reproduced, with minor issues 86.6 % 84.9 % 63.4 %
Largely not reproduced, with major issues 12.0 % 30.5 % 0.0 %
Not reproduced 2.7 % 7.5 % 24.7 %
Figures C.2, C.3, and C.4 show the distribution of assessment outcomes for tables, figures, and other
results, respectively, for different samples. The first panel of each figure displays the distributions over
all tables, all figures, and all other results, respectively. To account for the fact that articles differ
substantially in the number of included tables and figures, for the second panel of each figure we first
calculate the distribution of assessment outcomes for each article (using the report with the higher
overall assessment, as above), and then average over all articles. In the third panel, we only consider
articles which have been deemed verifiable (i.e., for which the dataset was available to the reviewer
and soft- and hardware requirements could be met).
We find that it makes little difference how we aggregate individual results, in particular for tables
and figures. The share of at least largely reproduced tables is 58-62% (depending on the aggregation
method) for all articles, and 88% when considering verifiable articles only. For figures, these shares are
68-70% for all articles and 90% for verifiable articles. For other results we only distinguish between
reproducible and not reproducible and results are based on a smaller sample (not all articles report
other results, and not all reviewers assessed other results). The respective numbers here are 66-83%
for all articles and 75% for verifiable articles.
37

FIGURE C.2: Reproducibility assessments of tables, since 2019 policy
!#$
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!#$%'%()*( +,%-./$0#$%'%()*( +,%-./$%'%()*(1$23#4$530%$366)6 7)../$%'%()*(
FIGURE C.3: Reproducibility assessments of figures, since 2019 policy
!!#
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!#$%'%()*( +,%-./$0#$%'%()*( +,%-./$%'%()*(1$23#4$530%$366)6 7)../$%'%()*(
FIGURE C.4: Reproducibility assessments of other Results, since 2019 policy
!#$%
#'%
()#%
)#$%
**#!%
'!#)%
! #! $! %! ! '! (! )! *! +! #!!
+,-./01201310
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38

D Reviewer consistency
For articles for which we were able to obtain two reviews, Table D.1 displays the assessments of the
reviewer with the higher assessment and the second reviewer (with the same or lower assessment).
Among the 120 reviewer pairs with different assessments, the reviewer with the lower assessment of
reproducibility rated the straightforwardness of the reproduction lower (avg. of 71.7 vs. 80.9 on
a scale 0-100, p 0.001), was (weakly significantly) less likely to rate the readme file as sufficient
(p = 0.063), and rated their own methodological expertise as lower (avg. of 80.9 vs. 84.8 on a scale
0-100, p 0.001). No differences between reviewers with lower and higher rating were found with
respect to time spent on the review (9.2 vs. 10.4 hours, p = 0.478), and for their self-assessed expertise
in the topic of the article (p = 0.842).
TABLE D.1: Reviewer consistency
Reviewer with (weakly) higher assessment
Reviewer with (weakly) lower assessment Fully Largely Largely not Not
Fully reproduced. 31
Largely reproduced, minor issues. 64 65
Largely not reproduced, major issues. 5 20 8
Not reproduced. 2 13 16 70
39

E Project documentation
E.1 Reviewer Invitation Emails
Invitation email to Management Science reviewers
Dear First Name,
As you may know, recently Management Science initiated the Management Science Reproducibility
Project (ManSciReP). In this project, we assess the computational reproducibility of studies published
in the journal. Since 2020, the Code Data Editor verifies that replication materials are provided
but does not attempt reproduction itself. In this project, we aim to quantify the reproducibility of
results published in Management Science articles before and after the new Data and Code Disclosure
Policy came into effect.
I am writing to see if you would be willing to review a replication package of a paper recently accepted
for publication in Management Science. You are receiving this email because you have served as a
reviewer for Management Science before.
If you are willing to review, we would assign you a paper from your own field of research,
and using software that you are familiar with. We would then ask you to report back within 4-6
weeks to what extent you were able to reproduce the paper’s main results, and what the obstacles were.
This call for reviewers is open to any researcher in the community, including advanced Ph.D. students.
Please feel free to forward this call to colleagues and students.
All participating reviewers who submit a report will become members of a “consortium co-authorship”
for the final publication that reports the outcomes of the project. This consortium, the “Management
Science Reproducibility Collaboration,” will be listed as a co-author on the front page of the article,
with all members listed by name and affiliation in the paper’s appendix.
If you are willing to participate as a reviewer, we ask you to complete this short survey (before January
15, 2023), so we can match you with a paper from your field.
Begin Survey
In case of any questions, please contact the project team at ManSciReP@informs.org.
Sincerely,
David Simchi-Levi
Editor-in-Chief, Management Science
40

Invitation email to others
Dear Researcher:
We would like to draw your attention to an opportunity to join a new project on the reproducibility
of studies published in Management Science as a reviewer.
In the Management Science Reproducibility Project (ManSciReP), we assess the computational
reproducibility of studies published in the journal. Since 2020 the Code Data Editor verifies that
replication materials are provided but does not attempt reproduction itself. In this project, we aim to
quantify the reproducibility of results published in Management Science articles before and after the
new Data and Code Disclosure Policy came into effect.
If you would be willing to review, we would assign you a paper from your own field of research,
and using software that you are familiar with. We would then ask you to report back within 4-6
weeks to what extent you were able to reproduce the paper’s main results, and what the obstacles were.
This call for reviewers is open to any researcher in the community, including advanced PhD students.
Please feel free to forward this call to colleagues and students.
All participating reviewers who submit a report will become members of a consortium co-authorship
for the final publication that reports the outcomes of the project. This consortium, the “Management
Science Reproducibility Collaboration”, will be listed as a co-author on the front page of the article,
with all members listed by name and affiliation in the paper’s appendix.
If you are willing to participate as a reviewer, we ask you to complete this short survey, so we can
match you with a paper from your field.
Survey link
In case of any questions, please contact the project team at ManSciReP@informs.org.
Sincerely,
David Simchi-Levi
Editor-in-Chief, Management Science
Miloš Fišar, Ben Greiner, Christoph Huber, Elena Katok, and Ali Ozkes
Project coordinators
41

E.2 Reviewer registration survey
42

E.3 Reproducibility report survey
47

E.4 Reviewer guidelines
Management Science Reproducibility Project
Reviewer Guidelines
Scope
We ask you to attempt to reproduce the results in the main manuscript of the paper. Results include
tables and figures that are based on data or code, as well as results only reported verbally in the text
(e.g., statistical test results not reported in tables and figures). You can ignore results reported in the
appendix or in footnotes. Note that this assessment is purely about reproducibility, not about the
appropriateness, soundness, or robustness of applied methods.
Some packages, in particular older ones submitted before the new code and data disclosure policy took
effect, may not include data or code, or provide only limited documentation. In any case, please make an
honest attempt to reproduce the results based on the information provided in the paper, appendix, and
replication package. Report any barriers to reproduce the results in the final report survey.
If reproduction is not possible, some reviews may be completed very quickly. In these cases you can
indicate your availability to review another article / replication package in the report survey, and we will
be happy to assign you another one.
Anonymity
Please do not communicate with authors directly. We want to keep strict reviewer anonymity. The goal
of this reproducibility project is to establish how many articles can be reproduced based only on the
information provided in the paper, the appendix, and the replication package, i.e., without having to
contact the authors in the process.
Conflicts of interest
Please apply the same ethical standards to this review as you would to a regular manuscript review at
Management Science. In particular, there is a conflict of interest if one of the authors is/was your advisor
or student, works at the same institution as you, is/was a co-author during the last 5 years, or if you have
otherwise an interest in the outcome of the reproduction attempt. Please report any conflict of interest
to us, and we will assign you to a different article/replication package.
Documentation
Please document your reproduction attempts. You can either produce log files that show your output, or
make screenshots, or use any other method of documentation. In the report survey you will be asked to
upload a zip file of your documentation.
57

The Report Survey
A full printout of the report survey is included at the end of this document. A personalized link to the
survey is provided in your assignment email.
Paper/reviewer details: The first part of the survey just asks to identify yourself and the
article/replication package you reviewed.
Overall assessment: We then ask for your overall assessment of the reproducibility of the whole article.
Similar to the table-by-table, figure-by-figure results below, we ask you to select one of six possible
assessment outcomes.
- “Fully reproduced” means that the output of your analysis shows the exact same results as
reported in the paper, for all results reported in the main manuscript. You can ignore
non-essential issues such as colors/line types in figures or similar.
- “Largely reproduced, with minor issues” means that there may be minor differences in your
output compared to the results in the paper, but the paper’s conclusions and learnings stay the
same.
- “Largely not reproduced, with major issues” means that there are major differences in your
output compared to the results in the paper (because you get different numbers or you are
unable to reproduce the results because of missing data etc.), such that the reproduction results
could not be used to support the conclusions of the paper.
- “Not reproduced” means that the results from the reproduction cannot support the conclusions
drawn in the paper, either because the output is different, or because the results cannot be
produced at all because of missing data or non-recoverable code.
- “Not reproduced but consistent with log files” means that you cannot reproduce the results
based on running code on data, but that log files are included in the replication package, and the
log files are fully consistent with the results reported in the paper.
- “Not based on any data analysis, simulation, or code” means that the paper does not include any
analysis that would fall under the Code and Data Disclosure policy, i.e., analysis that is based on
data, and does not use simulations or other code based-analysis. This typically only applies to
pure theory papers.
Package documentation: The next part asks about the quality of documentation in the replication
package, i.e., whether a README file is provided and whether it was sufficiently helpful in your
reproduction attempt.
Data: The next part asks about the amount and quality of data included in the replication package, i.e.,
whether data, partial data, synthetic data or sample data is included or not, whether you could obtain
non-included data from publicly available, private, or subscription sources, which data sources the study
is based on, and whether in the end you had sufficient data to continue with the reproduction. It also
asks whether log files are provided in the replication package.
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Code: The next part asks whether code was included in the replication package and which type of code.
Tables/Figures: We then turn to the individual tables and figures in the main manuscript. First, we ask
how many tables and figures there are overall in the manuscript, such that subsequently we can ask you
for each single one of them, first for all tables, then for all figures. Please ignore tables and figures in the
appendix.
You will see a table with one row per table in the manuscript. For each manuscript table, we ask via a
dropdown field whether the manuscript table could be reproduced (fully, largely, largely not, not),
whether there are log files consistent with the table, or whether the manuscript table was not based on
data/analysis (e.g., a list of conditions, experimental design), and for details or comments.
In the dropdown field,
- “Fully reproducible” means all numbers / all output is the same in your output as reported in the
paper (ignoring non-essential differences like color or line type in figures).
- “Largely reproducible, with minor issues” means that there may be small quantitative
differences in reported numbers / output (e.g., due to rounding errors, different software
versions, different random seeds, typos) but the qualitative conclusions and learnings from the
table/figure stay the same.
- “Largely not reproducible, with major issues” means that there are significant quantitative
differences in reported numbers / output such that different qualitative conclusions and
learnings would be drawn, or that important parts of the table/figure cannot be produced at all.
For example, while some models in a regression table can be reproduced, others yield
completely different numbers.
- “Not reproducible” means that the results from the reproduction cannot support the
conclusions drawn in the paper from the table/figure, either because the output is different, or
because the table/figure/result cannot be produced at all because of missing data or
non-recoverable code.
- “Not reproducible but consistent with provided log file” means that you cannot reproduce the
results based on running code on data, but that log files are included in the replication package,
and the log files are fully consistent with the results reported in the paper.
- “Table/Figure not based on data/analysis” means that this table or figure is not based on results
from analyzing data or otherwise running code, such that they do not need to be documented.
Examples include tables outlining experimental designs, showing a timeline of events, or listing
variables, or figures providing screenshots or illustrations, or visualizing a conceptual model.
In the comments, please provide a short description of details in case you were not able to fully
reproduce some results, e.g., denoting the column or cells where differences appear, or commenting
which errors in the code prevent you from running a model, etc.
59

After tables, we ask about figures. As for manuscript tables, you will see a table with one row per
manuscript figure, and for each figure, we ask via a dropdown field whether the figure could be
reproduced (fully, largely, largely not, not), whether there are log files consistent with the figure, or
whether the figure was not based on data/analysis (e.g., an illustration or picture). Please use the
comment field to provide details on reproduction issues.
Other results: Next we ask about other results reported in the text of the main manuscript, e.g., p-values
from statistical tests not yet reported in the tables/figures. For these results, we only ask for a summary
report: how many results you identified, and how many you could reproduce. You can ignore results
reported in the appendix or in footnotes.
Review documentation: After having reported your reproduction results, we ask you to upload log files,
screenshots, or output files that you compared to the results reported in the paper. Please include all
logs/screenshots in one single file (pdf, zip, etc.).
Review experience: The last part of the survey asks about your experience when reviewing the
replication package. Namely, we would like to know if you needed to fix/change any code or datasets in
order to be able to run the reproduction, how much time you invested, how
complicated/straightforward the reproduction was, and how you assess your own expertise in terms of
the article’s topic and the applied methods/software. We also ask for your view on the replicability (as
opposed to reproducibility) of the article.
Review availability: The final question asks whether you would be available to do another
reproducibility review of a different article/replication package.
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