TOWARD IDENTIFYING A TOP EMPLOYER USING MACHINE LEARNING

Abstract

A firm's reputation, a measure of employee satisfaction, is valuable because, among other benefits, it can attract and motivate good employees, and this in turn, can create labor resource efficiency advantages. There were many efforts to quantify corporate reputation, with fewer attempts to identify the drivers of a firm's reputation, and none used machine learning algorithms. This research work investigates the drivers, in both human and operational practices, that determine performance and increase a firm’s reputation as a top employer. Using top employers from Fortune 100 Best Companies to Work For survey, we examine whether a company's financial and operational data in Compustat can foretell its corporate reputation by testing several machine learning algorithms. We provide mathematical evidence that increased spending on R&D and employees’ benefits, such as salaries, retirement plans, and insurance packages, does help companies achieve this reputation. Thus, we provide insights on the indicators that help classify a company as a top employer and build recommendations both for general and industry-specific to follow towards becoming a top employer. This research presents a methodological framework to guide employers into becoming a top employer by advancing their internal operational processes and adopting the recommended strategies.