Research: Optimizing Internal Mobility Through Skills-Taxonomy Mapping and Data-Driven ...

Research question: How can skills-taxonomy mapping combined with machine learning improve the accuracy and effectiveness of internal mobility recommendations within large organizations?

Background & Motivation: Internal mobility is increasingly recognized as a key strategy for talent retention, employee engagement, and organizational agility. Effective movement of employees within a firm relies on accurately matching individuals’ skills with relevant open positions. Traditional approaches often overlook the complexity and evolving nature of skill requirements across roles.

Research Gap / Question: Despite the proliferation of competency frameworks and HR data, there is limited empirical research on leveraging advanced skills-taxonomy mapping and analytics for personalized, scalable internal mobility recommendations. The central question explores whether integrated taxonomy mapping and machine learning can enhance recommendation accuracy over existing methods.

Approach & Expected Contribution: This study will systematically review literature on skills taxonomies and internal mobility, analyze anonymized HR and LinkedIn datasets, and apply natural language processing and supervised learning to map employee skills to roles. Experimental evaluation will compare recommendation quality against baseline heuristics. Findings will provide actionable evidence for HR practitioners and contribute novel methodology for talent analytics research.

Why It Matters: As organizations face rapid skill shifts and competitive talent markets, evidence-based internal mobility frameworks can help unlock workforce potential, reduce attrition, and support strategic talent planning.