HiSPEED: a system for mining performance appraisal data and text
Performance appraisal (PA) is a crucial HR process that enables an organization to periodically measure and evaluate every employee’s performance and also to drive performance improvements. In this paper, we describe a novel system called HiSPEED to analyze PA data using automated statistical, data...
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Published in | International journal of data science and analytics Vol. 8; no. 1; pp. 95 - 111 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Cham
Springer International Publishing
01.07.2019
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Subjects | |
Online Access | Get full text |
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Summary: | Performance appraisal (PA) is a crucial HR process that enables an organization to periodically measure and evaluate every employee’s performance and also to drive performance improvements. In this paper, we describe a novel system called HiSPEED to analyze PA data using automated statistical, data mining and text mining techniques, to generate novel and actionable insights/patterns and to help in improving the quality and effectiveness of the PA process. The goal is to produce insights that can be used to answer (in part) the crucial “business questions” that HR executives and business leadership face in talent management. The business questions pertain to (1) improving the quality of the goal setting process, (2) improving the quality of the self-appraisal comments and supervisor feedback comments, (3) discovering high-quality supervisor suggestions for performance improvements, (4) discovering evidence provided by employees to support their self-assessments, (5) measuring the quality of supervisor assessments, (6) understanding the root causes of poor and exceptional performances, (7) detecting instances of personal and systemic biases and so forth. The paper discusses specially designed algorithms to answer these business questions and illustrates them by reporting the insights produced on a real-life PA dataset from a large multinational IT services organization. |
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ISSN: | 2364-415X 2364-4168 |
DOI: | 10.1007/s41060-018-0142-x |