(Georgia Institute of Technology, 2023-04-27)
Green, Christopher M.
The three essays in this dissertation focus on understanding how the structural policies and organizational procedures set by management may impact the performance of the workforce. Specifically, this dissertation: (i) employs analytical (e.g. game-theoretical and optimization models) and experimental techniques and (ii) encompasses the following three areas: performance management systems, rating systems, and sequential selection.
Performance and Talent Management: The U.S. Army is at an inflection point with its talent
management process. The Secretary of the Army has stated that a focused priority of the Army
is to revolutionize the decades old process that was established in the 1980s under the Defense Officer Personnel Management Act (DOPMA) which has fallen woefully behind current talent and personnel management programs. One of the most promising areas for change is in the arena of performance management. To support this effort, I review performance management and evaluation systems studied in literature and currently in use within industry to identify how organizational and structural characteristics impact their effectiveness. I then apply this analysis to the unique case of the U.S. Army. The procedural limitations and legal constraints set on the military services make understanding and incorporating the factors of the external environment and organization structure critical to constructing a performance management system that meets the unique needs of these organizations.
Rating Systems: Performance evaluation has long been a key mechanism for supervisors to grade the proficiency of employees at executing the tasks associated with their jobs. To conduct these evaluations firms often rate workers compared to their peers or against an objective standard. Which of these rating systems leads to higher workforce performance? To answer this question, I construct game-theoretic models of two performance rating systems. First, for a Relative rating system where workers compete with each other for a constrained number of high ratings. Second, for an Absolute rating system where workers are awarded high ratings by performing at or above a standard threshold. I derive the workers’ equilibrium performance as a function of their ability and the characteristics of the rating pool. From a firm’s perspective, I find that an Absolute rating system can lead to higher performance than a Relative rating system when the rating pool size is small or the workers’ cost of effort relative to their efficiency rate is low, and the reverse holds true otherwise. When considering the workers’ perspective, I find that higher ability workers prefer an Absolute system due to its predictable nature, while lower ability workers prefer a Relative system as it provides them an opportunity to outperform other workers.
Sequential Selection: Enhancing workforce performance is the key to success for professional
firms. Many firms employ competitive rating systems where supervisors can only award promotions or bonuses to a certain percentage of their subordinates. In many cases, such as the evaluation system of the U.S. Army, supervisors evaluate subordinates' performances over time and in sequence (e.g., based on employee’s work anniversary). As such, supervisors must make decisions based on incomplete information due to the temporal nature of the evaluation process. In this paper, I study how managers react under such sequential evaluation systems. I construct a theoretical model of a sequential selection problem to generate the optimal solution. I then conduct a set of experimental studies and evaluate the impact of pool size on the accuracy of each participant's decisions as compared to the state dependent optimal solutions. Despite theoretical increases in performance with larger pools, experimental performance did not yield an increase. Indeed, the average performance of subjects was the highest in the treatment that had the smallest pool size. I conduct multiple decision mechanism analyses to provide insights about the approaches subjects take and the nature of the behavioral traits leading to sub-optimal outcomes. Those comparisons suggest that the search fatigue mechanism may account for subjects’ sub-optimal behavior across treatments.