Date of Award
Doctor of Philosophy (PhD)
Contemporary Information Systems literature is recognizing the exponential growth of data made possible by the perfect storm of increasing hardware capabilities, decreasing hardware cost and introduction of ever increasing data points from social media through to regulatory reform. America in 2012 is a society of "big data". The challenge presented is how to move from the collection stage of data to the data driven decision making platforms by providing meaningful insight that contributes to gaps in existing knowledge bases. In this light Information Systems is uniquely positioned to illustrate how existing techniques can be introduced into new domains to help to leverage their "big data" into daily utilization. This is particularly important in stimulating new research that continues to further our understanding of how co-mingling of existing information systems tools and techniques can provide new and interesting interpretations and contributions.
Thus this research focuses on one specific domain where "big data" resides, that being Human Resources (HR). The potential of HR data has recently been explored by the likes of Davenport et al. and by progressive technology companies such as Google. This dissertation concentrates on filling the gap in the current knowledge base by focusing on how decision support systems techniques can be extending into the HR domain in a proactive manner by employing the current data captured in Human Resources Information Systems to enable proactive talent analytics.
This research contributes insights into the Data Mining techniques that are applicable in the context of solving one of the most challenging and expensive decisions any organization makes - its' human capital. Hiring the right person can reap huge rewards, hiring the wrong person can be poison to a team, expensive to an organization and can risk the brand reputation. Therefore this dissertation looks at a large present day workplace reviewed current and existing HR "big data" in different and creative ways, using existing techniques and systems tools in new and innovative combinations. The objective of this research is to contribute value in how Data Mining can create a multi-fit talent assessment, that captures not only a singular aspect of fit like most current talent assessment techniques, but can capture multiple aspects of fit like person-job fit and person-organization fit.
Vizecky, Karen, "TALENT ANALYTICS: HOW DATA MINING CAN IMPROVE TALENT ASSESSMENT" (2012). Masters Theses & Doctoral Dissertations. 411.