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Can artificial neural networks predict lawyers’ performance rankings?

Can artificial neural networks predict lawyers’ performance rankings? PurposeThe purpose of this paper is to propose a predictive model that could replace lawyers’ annual performance rankings and inform talent management (TM) in law firms.Design/methodology/approachEight years of performance rankings of a sample of 140 lawyers from one law firm are used. Artificial neural networks (ANNs) are used to model and simulate performance rankings over time. Multivariate regression analysis is used to compare with the non-linear networks.FindingsWith a lag of one year, performance ranking changes are predicted by the networks with an accuracy of 71 percent, over performing regression analysis by 15 percent. With a lag of two years, accuracy is reduced by 4 percent.Research limitations/implicationsThis study contributes to the literature of TM in law firms and to predictive research. Generalizability would require replication with broader samples.Practical implicationsNeural networks enable extended intervals for performance rankings. Reducing the time and effort spent benefits partners and lawyers alike, who can instead devote time to in-depth feedback. Strategic planning, early identification of the most talented and avenues for tailored careers become open.Originality/valueThis study pioneers the use of ANNs in law firm TM. The method surpasses traditional static study of performance through its use of non-linear simulation and prediction modeling. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Productivity and Performance Management Emerald Publishing

Can artificial neural networks predict lawyers’ performance rankings?

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1741-0401
DOI
10.1108/IJPPM-08-2017-0212
Publisher site
See Article on Publisher Site

Abstract

PurposeThe purpose of this paper is to propose a predictive model that could replace lawyers’ annual performance rankings and inform talent management (TM) in law firms.Design/methodology/approachEight years of performance rankings of a sample of 140 lawyers from one law firm are used. Artificial neural networks (ANNs) are used to model and simulate performance rankings over time. Multivariate regression analysis is used to compare with the non-linear networks.FindingsWith a lag of one year, performance ranking changes are predicted by the networks with an accuracy of 71 percent, over performing regression analysis by 15 percent. With a lag of two years, accuracy is reduced by 4 percent.Research limitations/implicationsThis study contributes to the literature of TM in law firms and to predictive research. Generalizability would require replication with broader samples.Practical implicationsNeural networks enable extended intervals for performance rankings. Reducing the time and effort spent benefits partners and lawyers alike, who can instead devote time to in-depth feedback. Strategic planning, early identification of the most talented and avenues for tailored careers become open.Originality/valueThis study pioneers the use of ANNs in law firm TM. The method surpasses traditional static study of performance through its use of non-linear simulation and prediction modeling.

Journal

International Journal of Productivity and Performance ManagementEmerald Publishing

Published: Nov 19, 2018

References