A Replication Study of Operations Management Experiments in Management Science

Author:

Davis Andrew M.1ORCID,Flicker Blair2ORCID,Hyndman Kyle3ORCID,Katok Elena3ORCID,Keppler Samantha4ORCID,Leider Stephen4ORCID,Long Xiaoyang5ORCID,Tong Jordan D.5ORCID

Affiliation:

1. Samuel Curtis Johnson Graduate School of Management, Cornell University, Ithaca, New York 14853;

2. Darla Moore School of Business, University of South Carolina, Columbia, South Carolina 29208;

3. Naveen Jindal School of Management, University of Texas at Dallas, Richardson, Texas 75080;

4. Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109;

5. Wisconsin School of Business, University of Wisconsin–Madison, Madison, Wisconsin 53706

Abstract

Over the last two decades, researchers in operations management have increasingly leveraged laboratory experiments to identify key behavioral insights. These experiments inform behavioral theories of operations management, impacting domains including inventory, supply chain management, queuing, forecasting, and sourcing. Yet, until now, the replicability of most behavioral insights from these laboratory experiments has been untested. We remedy this with the first large-scale replication study in operations management. With the input of the wider operations management community, we identify 10 prominent experimental operations management papers published in Management Science, which span a variety of domains, to be the focus of our replication effort. For each paper, we conduct a high-powered replication study of the main results across multiple locations using original materials (when available and suitable). In addition, our study tests replicability in multiple modalities (in-person and online) due to laboratory closures during the COVID-19 pandemic. Our replication study contributes new knowledge about the robustness of several key behavioral theories in operations management and contributes more broadly to efforts in the operations management field to improve research transparency and reliability. This paper was accepted by David Simchi-Levi, operations management. Funding: The authors gratefully acknowledge financial support from Cornell University, the University of South Carolina, the University of Texas at Dallas, the University of Michigan, and the University of Wisconsin-Madison. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4866 .

Publisher

Institute for Operations Research and the Management Sciences (INFORMS)

Subject

Management Science and Operations Research,Strategy and Management

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