Search results

The 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.

Eight 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.

With 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.

This study contributes to the literature of TM in law firms and to predictive research. Generalizability would require replication with broader samples.

Neural 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.

This 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.

The purpose of this paper is to propose a new approach to talent management that consists of averaging performance appraisal and assessment center ratings for in-depth identification of lawyers’ talents.

The approach’s adjustment was examined using a 61 senior-lawyer sample from a Portuguese law firm. Comparisons between assessment center and performance appraisal ratings were analyzed using paired-sample t-tests and a kernel density function, and predictive validity was assessed with Pearson correlations. Evidence of both a general performance factor and two additional factors was verified using principal component analysis. Varimax rotation was used to verify three broad factors with job profile’s three broad areas.

Results suggest support for the assessment center’s predictive validity. Its lower and more variable ratings overcome performance appraisal rating bias. Adjustment of the new approach to lawyers’ overall talent identification (the general factor) and each lawyer’s relative talents (three broad factors) was observed.

This study contributes to the body of knowledge regarding the substantive existence of a general performance factor, and adds to empirical research concerning talent management, which is lacking. However, generalizability requires broader samples and replication.

The approach is a methodology that informs career management, high-flyers’ identification, talent mapping, development, succession planning, team composition, and diversity analysis. For lawyers, objective feedback allows benchmarking talent and managing one’s career.

This study pioneers empirical research that develops methods for identifying talent in law firms, vital for firm sustainability.