DOI

10.17077/etd.3x1x-s7p3

Document Type

Dissertation

Date of Degree

Summer 2019

Access Restrictions

Access restricted until 09/04/2021

Degree Name

PhD (Doctor of Philosophy)

Degree In

Business Administration

First Advisor

Pant, Gautam

First Committee Member

Blackhurst, Jennifer

Second Committee Member

Sheng, Olivia

Third Committee Member

Wang, Tong

Fourth Committee Member

Zhao, Kang

Abstract

Applying data analytics for talent acquisition and retention has been identified as one of the most urgent challenges facing HR leaders around the world; however, it is also one of the challenges that firms are least prepared to tackle. Our research strives to narrow such a capability gap between the urgency and readiness of data-driven human resource management.

First, we predict interfirm competitors for human capital in the labor market utilizing the rich information contained in over 89,000 LinkedIn users' profiles. Using employee migrations across firms, we derive and analyze a human capital flow network. We leverage this network to extract global cues about interfirm human capital overlap through structural equivalence and community classification. The online employee profiles also provide rich data on the explicit knowledge base of firms and allow us to measure the interfirm human capital overlap in terms of similarity in their employees' skills. We validate our proposed human capital overlap metrics in a predictive analytics framework using future employee migrations as an indicator of labor market competition. The results show that our proposed metrics have superior predictive power over conventional firm-level economic and human resource measures.

Second, we estimate the effect of skilled immigrants on the native U.S. workers' turnover probability. We apply unsupervised machine learning to categorize employees' self-reported skills and find that skilled immigrants disproportionately specialize in IT. In contrast, the native workers predominantly focus on management and analyst skills. Utilizing the randomness in the H-1B visa lottery system and a 2SLS design, we find that a 1 percentage point increase in a firm's proportion of skilled immigrant employees leads to a decrease of 0.69 percentage points in a native employee's turnover risk. However, this beneficial crowding-in effect varies for native workers with different skills. Our methodology highlights the need to account for a multifaceted view of the skilled immigration's effect on native workers. Finally, we also propose a set of features and models that are able to effectively predict future employee turnover outcomes. Our predictive models can provide significant utility to managers by identifying individuals with the highest turnover risks.

Keywords

Competitor Analysis, Employee Turnover, Human Capital, Machine Learning, Network Analysis, Topic Modeling

Pages

xiii, 181 pages

Bibliography

Includes bibliographical references (pages 173-181).

Copyright

Copyright © 2019 Yuanyang Liu

Available for download on Saturday, September 04, 2021

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