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PurposeThe purpose of this paper is to examine the claim that the application of human factors (HF) knowledge can improve both human well‐being and operations system (OS) performance.Design/methodology/approachA systematic review was conducted using a general and two specialist databases to identify empirical studies addressing both human and OS effects in examining manufacturing OS design aspects.FindingsA total of 45 empirical studies were found, addressing both the human and system effects of OS (re)design. Of those studies providing clear directional effects, 95 percent showed a convergence between human effects and system effects (+, + or −,−), 5 percent showed a divergence of human and system effects (+,− or −,+). System effects included quality, productivity, implementation performance of new technologies, and also more "intangible" effects in terms of improved communication and co‐operation. Human effects included employee health, attitudes, physical workload, and "quality of working life".Research limitations/implicationsFuture research should attend to both human and system outcomes in trying to determine optimal configurations for OSs as this appears to be a complex relationship with potential long‐term impact on operational performance.Practical implicationsThe application of HF in OS design can support improvement in both employee well‐being and system performance in a number of manufacturing domains.Originality/valueThe paper outlines and documents a research and practice gap between the fields of HF and operations management research that has not been previously discussed in the management literature. This gap may be inhibiting the design of OSs with superior long‐term performance.

AbstractThis article discusses the importance of reusing existing data in research. In addition to reuse data for replication of earlier findings and for answering extended or new research questions, we propose a third application of data reuse: studying the phenomenon from an alternative causal perspective. We focus on the reuse of data with a necessity causal perspective ("if not X, then not Y") as employed in necessary condition analysis (NCA). Such reuse of data offers additional insights compared with those obtained from the conventional probabilistic causal perspective ("if X, then probably Y") as employed in regression analysis. NCA is gaining recognition in various fields, including strategic management. Reusing data for conducting NCA is an efficient way to get new causal insights. We provide recommendations on how to use NCA with existing data and emphasize the importance of transparency when reusing data.

PurposeThe 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/approachThe 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.FindingsNecessary 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/valueBecause 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.