BEHAVIORAL OPERATIONS

Mission Statement

The department seeks papers that further our understanding of operations and its best practices by explicitly accounting for empirically observed influences of human behavior on operations.  For a paper to be relevant for our department, it needs to be (a) relevant for operations managers, (b) focused on individuals or small groups of individuals, and (c) allow for the possibility of non-rational behavior of individuals.

 

Behavioral issues naturally arise in many operational contexts. Relevant behavioral influences can originate from a variety of sources, including stakeholders such as suppliers, employees, customers, workers, regulators, and managers. Possible relevant contexts include manufacturing and service processes, supply chain management, procurement, revenue management, product development, human-AI interaction, and technology management.

 

The department is particularly interested in research that uncovers behavioral regularities relevant to operational settings, provides a theory on how these regularities affect operational performance, or examines the effectiveness of alternative institutional mechanisms or tools in improving operational performance. The department is open to a broad range of methodologies, including laboratory experiments, field studies, system dynamics, and analytical models of human behavior. The chosen method should be well-motivated and executed with the highest rigor. Empirical studies that rely primarily on unincentivized choice or judgment data need to justify why incentivizing participants in their research was not feasible.

The review process is dedicated to providing both fast and accurate feedback. We encourage focused papers and consider efficient use of page space necessary for publication. Novel ideas must be supported with data and arguments, but are granted some leeway. Papers must be well written with a clear statement of their contribution to theory, practice, or institutional design.

 

Departmental Editors

 

Professor Gary Bolton
University of Texas at Dallas
gbolton@utdallas.edu

 

 

 

Professor Enno Siemsen
University of Wisconsin–Madison
esiemsen@wisc.edu

 

 

 

 

Senior Editors

Kay-Yut Chen, University of Texas at Arlington
Ernan Haruvy, McGill University
Kyle Hyndman, University of Texas at Dallas

Xiaoyang Long, University of Wisconsin–Madison

Anton Ovchinnikov, Queen's University
John Sterman, Massachusetts Institute of Technology

Xuanming Su, University of Pennsylvania
Jordan Tong, University of Wisconsin-Madison
Yaozhong Wu, National University of Singapore