Specialization vs. Generalization: Analyzing Skill Transferability for Predicting Career Trajectories in High-Tech

45 Pages Posted: 8 Feb 2018 Last revised: 6 Jun 2019

See all articles by Rajiv Garg

Rajiv Garg

University of Texas at Austin - Department of Information, Risk and Operations Management

Bryan Stephens

University of Texas at Austin - Red McCombs School of Business

John Sibley Butler

University of Texas at Austin - Red McCombs School of Business

Date Written: January 30, 2018

Abstract

For decades, career transition counseling and research have focused on myopic insights on the next job versus understanding optimal career path. While the research mind-set was well placed to understand the evolution of skills to suggest possible terminal job outcomes, a lack of relevant data and computation resources posed challenges in developing valuable career transition insights. Furthermore, because of the data limitations, the extant research focused on the role of generalizable and specialized skills in highly heterogeneous career transitions. Technology, big data, and analytics have now enabled organization and management science research to investigate these questions. This paper analyzes 67,000 career profiles for entrepreneurs, executives, and senior managers from a popular social networking platform to predict career outcomes for these professionals. We built career trajectories as network graphs that provide insights on career paths of entrepreneurs, executives, and senior managers. In these graphs, each node is a job consisting of an industry-level and seniority-level classification. Focusing on the high-tech industry, we found that individuals with certain transferable skills—notably technical, management, mixed, or boundary-spanning experience—can successfully capitalize on these skills when transitioning across industries. During the process we determined common patterns in career trajectories for entrepreneurs, executives, and persons in senior-level roles within firms. Furthermore, we tested the validity of these insights by developing and refining a learning model for predicting individual career transitions of 10,000 individuals and achieved 48% accuracy for 204 job choices. Managerially, these insights can help in recruitment (candidate identification), career planning, and job search.

Keywords: careers, work, technology, executives, entrepreneurs, skills, human capital

JEL Classification: J62, C45, J24, J01

Suggested Citation

Garg, Rajiv and Stephens, Bryan and Butler, John Sibley, Specialization vs. Generalization: Analyzing Skill Transferability for Predicting Career Trajectories in High-Tech (January 30, 2018). Available at SSRN: https://ssrn.com/abstract=3113843 or http://dx.doi.org/10.2139/ssrn.3113843

Rajiv Garg

University of Texas at Austin - Department of Information, Risk and Operations Management ( email )

CBA 5.202
Austin, TX 78712
United States

HOME PAGE: http://www.RajivGarg.org

Bryan Stephens (Contact Author)

University of Texas at Austin - Red McCombs School of Business ( email )

Austin, TX 78712
United States

John Sibley Butler

University of Texas at Austin - Red McCombs School of Business ( email )

Austin, TX 78712
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
106
Abstract Views
530
rank
256,752
PlumX Metrics