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Enhancing Promotion Decisions using Classification and Network-based Methods

When it comes to making promotions, companies rely upon a variety of metrics and rating systems to support their decisions. However, are they looking at the most important metrics and more broadly, how should they identify employees to promote? The literature predominantly focuses on the measurement of performance, but businesses also need instruments that can predict management potential for promotional decision-making. This paper utilizes the data contained in the Human Resources Information System (HRIS) of a company to analyze drivers of potential for promotion among a sample of its workers. Numerous prior studies have been conducted of human resource variables in a variety of organizations. These studies share in common the use of linear models to report which explanatory variables are statistically significant determinants of the dependent variable – in most cases the performance of employees with a focus on the individual’s output. What they do not deliver, and what this study provides, in addition to regression studies on employee performance, is an analysis of the drivers of promotion potential for management roles. The perspective of our analysis diverges from others in that its primary focus is to identify future leaders of a company rather than identifying strong individual contributors. The methods used consist of basic statistical procedures, multiple classification methods and graph theory analysis. In our study of managerial potential drivers, the logistic regression model performs with the best predictive accuracy and recognizes which factors in a manager reveals leadership potential. In our study of promotion potential from a teamwork perspective, we show that graph network-based methods adapt well to employee data containing several bilateral relationships while preserving the hierarchy of an organization and providing defensible accuracy.