Date of Award
Doctor of Philosophy (PhD)
This applied dissertation offered insights about the scientist-practitioner gap and examined the process of organizational decision making using a real case from a large United States manufacturing company. Historical records of employee work hours were extracted and compiled from several of the company's databases, along with recent plant performance metrics. These archival data were used to evaluate the relationship between the percentage of each plant's population working overtime (i.e., overtime utilization) and each plant’s performance on various manufacturing metrics. This comparison was made using a median split approach, such that the performance of plants with an overtime utilization above the median (i.e., the high-overtime group) was compared to the performance of pants with an overtime utilization falling below the median (i.e., the low-overtime group).
Post-hoc analyses were conducted to determine if a more rigorous methodological approach would have produced different findings. As such, a series of bivariate regressions (with overtime utilization as the predictor variable) was conducted, allowing the overtime variability that exists across plants to be taken into account in order to better understand the relationship between overtime utilization and the outcome metrics. Additionally, when available, financial metrics were leveraged instead of performance metrics so that results could be interpreted through the lens of the company’s bottom line. Ultimately, the more rigorous approach produced richer insights regarding how risky the decision to enact an overtime-reduction policy might be from a financial perspective and exemplified the value of leveraging confidence intervals to guide organizational decision making.
Reinecke, Olivia, "" (2021). Dissertation. 937.