Metacognition-based process improvement practices

Young Sik Cho, Kevin Linderman

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


While cognition is the knowledge structure used by people to make an assessment or decision, metacognition is the higher-order process controlling that existing knowledge structure. Thus, the functional role of metacognition in process improvement practices (PIP) is quite different from the role of cognition. Metacognition has been studied in several disciplines, including education, psychology, and neuroscience; however, research has yet to address the role of metacognition in an operations management context. Thus, the present study explored how a process improvement leader's metacognitive awareness influences PIP effectiveness. A survey was conducted with process improvement leaders from U.S.-based firms. Results revealed that managerial metacognition was positively related to adaptive PIP performance within the organization. There was also a positive synergistic relationship between managerial metacognition and PIP implementation on a firm's competitive advantage. We discussed practical and theoretical implications as well as future research agendas for how managerial metacognitive skills can be utilized for advantageous operational activities. This study not only contributes to the operations literature by providing new insights into the relationship between psychological and methodological mechanisms in PIP implementation but also provides the first step toward theory-building in terms of metacognition concepts in operations management.

Original languageEnglish (US)
Pages (from-to)132-144
Number of pages13
JournalInternational Journal of Production Economics
StatePublished - May 2019

Bibliographical note

Funding Information:
We would like to thank Dr. Brent Moritz (Pennsylvania State University), Dr. Barb Flynn (Indiana University), Dr. Thomas Choi (Arizona State University), Dr. Joo Jung (University of Texas - RGV), and anonymous IJPE reviewers for their insightful comments on an earlier draft of this paper. Dr. Young Sik Cho is Assistant Professor of Operations Management at the Jackson State University's College of Business. He holds BA from Kyung Hee University in South Korea, MBA from the University of Wisconsin - Milwaukee, and Ph.D. in Business Administration from the University of Texas - Pan American. His research to date focuses on three broad areas: (i) Process Improvement Practices, (ii) Knowledge Creation in Supply Networks and (iii) Managerial Metacognition. He aims to construct a single new research discipline, “Metacognition-based Operations and Supply Chain Management,” that integrates the fields of OSCM, Cognitive Psychology, and Neuroscience. His research has appeared in International Journal of Operations and Quantitative Management, Academy of Entrepreneurship Journal, Academy of Management Proceedings , International Journal of Supply and Operations Management , International Journal of Quality & Reliability Management, International Journal of Production Economics, and so on. Dr. Kevin Linderman is the Curtis L. Carlson Professor in Supply Chain and Operations at the University of Minnesota's Carlson School of Management. He has received two National Science Foundation grants to study quality management, knowledge management, and sustaining high performance. His research focus is in the areas of process improvement and innovation, knowledge management, environmental operations, and managing complex systems. His research has been published in several leading journals including Journal of Operations Management , Decision Sciences , Management Science , International Journal of Production Economics , and Production and Operations Management to name a few. Prof. Linderman received a number of scholarly awards including the 2016 Operations Management Scholar Award (Academy of Management).

Publisher Copyright:
© 2019 Elsevier B.V.


  • Behavioral operations
  • Cognitive psychology
  • Managerial metacognition
  • Process improvement practices
  • Structural equation modeling


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