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Reproducibility in Management Science

  • the Management Science Reproducibility Collaboration
  • Masaryk University
  • Vienna University of Economics and Business
  • University of New South Wales
  • University of Texas at Dallas
  • Université Côte d’Azur
  • University of Reading
  • University of Virginia
  • National University of Singapore
  • Cornell University
  • WZB Berlin Social Science Center
  • Indian Institute of Management Ahmedabad
  • University of Toronto
  • HEC School of Management
  • CNRS
  • Harvard University
  • University of Technology Sydney
  • Université catholique de Lille
  • University of Mannheim
  • London Business School
  • George Washington University
  • University of Minnesota Twin Cities
  • Indian School of Business
  • University of Göttingen
  • Max Planck Institute for Human Development
  • Simply Rational - The Decision Institute
  • Berlin International University of Applied Sciences
  • University of Luxembourg
  • University of Michigan, Ann Arbor
  • SKEMA Business School
  • Pennsylvania State University
  • North Dakota State University
  • Oklahoma State University
  • Nanyang Technological University
  • INSEAD
  • Catholic University of Portugal
  • Saint Josephs University
  • University of Wisconsin-Madison
  • Claremont McKenna College
  • Télécom Ecole de Management
  • Temple University
  • University of Kansas
  • Erasmus University Rotterdam
  • Western University
  • Gebze Technical University
  • Paris-Dauphine University
  • University of Lille
  • iRisk Research Center on Risk and Uncertainty
  • Université Paris Nanterre
  • Norwegian University of Science and Technology
  • Washington University St. Louis

Research output: Contribution to journalArticlepeer-review

32 Scopus citations

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 hardware 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 29% of articles, at least part of the data set was not accessible to the reviewer. Considering all articles in our sample reduces the share of reproduced articles to 68%. These figures represent a significant increase compared with the period before the introduction of the disclosure policy, where only 12% of articles voluntarily provided replication materials, of which 55% could be (largely) reproduced. Substantial heterogeneity in reproducibility rates across different fields is mainly driven by differences in data set accessibility. Other reasons for unsuccessful reproduction attempts include missing code, unresolvable code errors, weak or missing documentation, and software 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.

Original languageEnglish
Pages (from-to)1343-1356
Number of pages14
JournalManagement Science
Volume70
Issue number3
DOIs
StatePublished - Mar 1 2024

Keywords

  • crowd science
  • replication
  • reproducibility

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