Designing graduate business curricula by utilizing a market-driven skills extraction approach

Traditionally, academic institutions have relied on interviews with industry representatives and alumni surveys to gauge market demand, an approach that often results in dated and limited information. In this article, we show that using real-time data from job postings in professional sites provides more direct, rich, and timely insights regarding the current demand and skill requirements. We propose a novel machine-learning based framework for detecting the skillsets for positions requiring Master of Business and Administration (MBA). Utilizing LinkedIn job-posting data in the US state of Pennsylvania, our analysis reveals 20 distinct functional areas. While some of these functional areas (e.g., people management) are predictable, others (e.g., supply chain project management) were not anticipated to be high in demand in the given market. Our results also identify the most sought-after skillsets (e.g., resource allocation). Most importantly, we observe that the top skillsets span multiple functional areas. Taken together, our results can help business school program directors update and customize curricula to meet market demand.