research-article

A Data-driven Method for Competency Evaluation of Personnel

Authors:

Zhongshan ZhangAuthors Info & Claims

DSIT 2020: Proceedings of the 3rd International Conference on Data Science and Information Technology

Pages 93 - 104

https://doi.org/10.1145/3414274.3414489

Published: 26 August 2020 Publication History

Abstract

Nowadays, human capital is very important for organizations. The organizations with more outstanding talents can often gain a foothold in the fierce competition. As a tool to measure talents' performance, competency model often plays a vital role in identifying and evaluating talents. Traditionally, the process of building competency model needs to invest a lot of manpower and time. Also it often lacks of objectivity to some extent. Meanwhile, the original data in the human resource (HR) database of organizations is often not fully utilized. This paper proposes a data-driven method to build competency model, in order to convert original data in database to more valuable information for evaluating talents. Firstly, a data preprocessing framework is designed to facilitate the use of HR data in subsequent analysis. Then 9 methods are designed to construct a set of features that can objectively reflect the situation and abilities of personnel. Data analytics and machine learning are mainly used to construct and verify the competency model. A case study is also included in this paper, a competency model of the middle-level manager (MLM) of a Chinese state-owned enterprise is obtained based on the proposed method. This competency model is also verified by the validation mechanism designed in this paper.

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Cited By

Liao CTodo YWang MPan Y(2024)Machine Learning for Talent Analytics: Unveiling Competency Indicators in Live Streamer2024 IEEE International Conference on Cognitive Computing and Complex Data (ICCD)10.1109/ICCD62811.2024.10843456(189-194)Online publication date: 28-Sep-2024
https://doi.org/10.1109/ICCD62811.2024.10843456
Liu JWang TYao FPedrycz WSong YHe R(2024)Assessing growth potential of careers with occupational mobility network and ensemble frameworkEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107306127:PAOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107306
Thakurta R(2024)A work system theory perspective on talent management and systemsSystems Research and Behavioral Science10.1002/sres.3007Online publication date: 3-Apr-2024
https://doi.org/10.1002/sres.3007
Show More Cited By

Index Terms

A Data-driven Method for Competency Evaluation of Personnel
1. Applied computing
  1. Enterprise computing
    1. Enterprise data management

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cover image ACM Other conferences

DSIT 2020: Proceedings of the 3rd International Conference on Data Science and Information Technology

July 2020

261 pages

ISBN:9781450376044

DOI:10.1145/3414274

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Natl University of Singapore: National University of Singapore
SKKU: SUNGKYUNKWAN UNIVERSITY

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 August 2020

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DSIT 2020

DSIT 2020: 2020 3rd International Conference on Data Science and Information Technology

July 24 - 26, 2020

Xiamen, China

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DSIT 2020 Paper Acceptance Rate 40 of 97 submissions, 41%;

Overall Acceptance Rate 114 of 277 submissions, 41%

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Cited By

Liao CTodo YWang MPan Y(2024)Machine Learning for Talent Analytics: Unveiling Competency Indicators in Live Streamer2024 IEEE International Conference on Cognitive Computing and Complex Data (ICCD)10.1109/ICCD62811.2024.10843456(189-194)Online publication date: 28-Sep-2024
https://doi.org/10.1109/ICCD62811.2024.10843456
Liu JWang TYao FPedrycz WSong YHe R(2024)Assessing growth potential of careers with occupational mobility network and ensemble frameworkEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107306127:PAOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107306
Thakurta R(2024)A work system theory perspective on talent management and systemsSystems Research and Behavioral Science10.1002/sres.3007Online publication date: 3-Apr-2024
https://doi.org/10.1002/sres.3007
Huang JYao FWang TLi JShen DWang H(2021)Evaluation of Researchers' Academic Influence Based on Rank Aggregation Method2021 7th International Conference on Big Data and Information Analytics (BigDIA)10.1109/BigDIA53151.2021.9619658(205-212)Online publication date: 29-Oct-2021
https://doi.org/10.1109/BigDIA53151.2021.9619658

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