Modeling the Impact of Person-Organization Fit on Talent Management With Structure-Aware Attentive Neural Networks



Employee Layoff Prediction using Recurrent Neural Network


Person-Organization fit (P-O fit) refers to the compatibility between employees and their organizations. The study of P-O fit is important for enhancing proactive talent management. While considerable efforts have been made in this direction, it still lacks a quantitative and holistic way for measuring P-O fit and its impact on talent management. To this end, in this paper, we propose a novel data-driven neural network approach for dynamically modeling the compatibility in P-O fit and its meaningful relationships with two critical issues in talent management, namely talent turnover and job performance. Specifically, inspired by the practical management scenarios, we creatively propose a novel neural-network-based P-O fit model. We first designed three kinds of organization-aware compatibility features extraction layers for measuring P-O fit. Then, to capture the dynamic nature of P-O fit and its consequent impact, we further exploit an adapted Recurrent Neural Network with attention mechanism to model the temporal information of P-O fit. Finally, we compare our approach with a number of state-of-the-art baseline methods on real-world talent data. Experimental results clearly demonstrate the effectiveness in terms of turnover and job performance prediction. Moreover, we show some interesting indicators of talent management through the visualizing some network layers.


The increasing complexity of today’s business environment has led organizations to seek innovative solutions for workforce management, with a particular focus on predicting and mitigating employee layoffs. This study proposes a novel approach to layoff prediction using machine learning, specifically leveraging Recurrent Neural Networks (RNNs). RNNs are well-suited for modeling sequential data, making them ideal for capturing the temporal dynamics inherent in workforce-related datasets.

In this research, historical workforce data, including variables such as employee performance metrics, tenure, departmental changes, and economic indicators, are employed to train and fine-tune the RNN model. The model’s ability to learn patterns and dependencies within the data enables it to make accurate predictions regarding potential layoffs.

The study explores various RNN architectures and hyperparameter configurations to optimize predictive performance. Additionally, feature importance analysis is conducted to identify the key factors influencing layoff predictions, providing valuable insights for organizational decision-makers. The proposed model’s performance is benchmarked against traditional machine learning approaches to highlight its superiority in capturing nuanced temporal relationships.


Software Requirements:

  • Front End – Anaconda IDE
  • Backend – SQL
  • Language – Python 3.8

Hardware Requirements

  • •Hard Disk: Greater than 500 GB
  • •RAM: Greater than 4 GB
  • •Processor: I3 and Above


Including Packages


  • * Base Paper
  • * Complete Source Code
  • * Complete Documentation
  • * Complete Presentation Slides
  • * Flow Diagram
  • * Database File
  • * Screenshots
  • * Execution Procedure
  • * Readme File
  • * Addons
  • * Video Tutorials
  • * Supporting Softwares

Specialization =======================

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  • * Remote Connectivity
  • * Code Customization
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