Necessary condition hypotheses in operations management
Purpose – The purpose of this paper is to show that necessary condition hypotheses are important in operations management (OM), and to present a consistent methodology for building and testing them. Necessary condition hypotheses (“X is necessary for Y”) express conditions that must be present in order to have a desired outcome (e.g. “success”), and to prevent guaranteed failure. These hypotheses differ fundamentally from the common co-variational hypotheses (“more X results in more Y”) and require another methodology for building and testing them. Design/methodology/approach – The paper reviews OM literature for versions of necessary condition hypotheses and combines previous theoretical and methodological work into a comprehensive and consistent methodology for building and testing such hypotheses. Findings – Necessary condition statements are common in OM, but current formulations are not precise, and methods used for building and testing them are not always adequate. The paper outlines the methodology of necessary condition analysis consisting of two stepwise methodological approaches, one for building and one for testing necessary conditions. Originality/value – Because necessary condition statements are common in OM, using methodologies that can build and test such hypotheses contributes to the advancement of OM research and theory.
|Keywords||condition monitoring, critical success factors, operations management|
|JEL||Business Administration and Business Economics; Marketing; Accounting (jel M), Production Management (jel M11), Innovation and Invention: Processes and Incentives (jel O31), Management of Technological Innovation and R&D (jel O32)|
|Persistent URL||dx.doi.org/10.1108/01443571011087378, hdl.handle.net/1765/21222|
|Series||ERIM Top-Core Articles|
|Journal||International Journal of Operations and Production Management|
Dul, J, Hak, A, Goertz, G, & Voss, C. (2010). Necessary condition hypotheses in operations management. International Journal of Operations and Production Management, 30(11), 1170–1190. doi:10.1108/01443571011087378