Role of Behavioural Factors on Process Performance: An Experimental Study

The intense competition is forcing the business entities to continually change their processes, systems, technology, skills, and products. Operations management (OM) as a field is replete with lots of mathematical decision support models with sufficient data availability. Yet literature shows that OM studies till recently have neglected “human resources†who are critical to efficiency and throughput in production processes. Literature suggests that the performance of a worker changes during the execution of tasks due to the variation of numerous factors affecting human behaviour. The success of OM tools, techniques, and theories relies heavily on how well we are able to understand human behaviour. This article undertakes an experimental study of behavioural factors that affect worker’s productivity in production processes. Initially, 15 factors affecting human performance are selected. Using failure mode and effects analysis, six factors are shortlisted and later classified in two groups using the affinity diagram. Our study uses the design of experiments (DOEs) method to analyse the data collected. Finally, Taguchi’s DOE is conducted on the basis of the outcomes of the first two experiments. We then predict significant factors and their levels to achieve the desired outcome. A confirmatory test is also done to confirm the results. Understanding and modelling the behavioural factors in a complex environment can be quite a challenging task. Our work demonstrates that instead of using complicated experiments, researchers can obtain meaningful insights by adopting relatively simpler tasks with the benefit of savings of cost and time while conducting behavioural studies. The results and findings highlight significant implications for researchers, practitioners, and managers to re-design and improve workplaces besides improving processes and productivity. The significant theoretical contributions include proposing a modified learning and decision-making model for building better behavioural OM models.