A Comparative Study of Employee Churn Prediction Model

Alamsyah, Andry; Salma, Nisrina

doi:10.1109/icstc.2018.8528586

Cited by 35 publications

(21 citation statements)

References 10 publications

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“…The research mentioned in [8] aims to avoid this negative impact; therefore, their study builds the prediction model to predict future churners. For the comparison and evaluation of algorithms, the research implements the three algorithms and compares them.…”

Section: Data Mining Techniques For Predicting Employee Turnovermentioning

confidence: 99%

Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry

Naz¹,

Siddiqui

Koo

et al. 2022

Applied Sciences

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Employee churn analytics is the process of assessing employee turnover rate and predicting churners in a corporate company. Due to the rapid requirement of experts in the industries, an employee may switch workplaces, and the company then has to look for a substitute with the training to deal with the tasks. This has become a bottleneck and the corporate sector suffers with additional cost overheads to restore the work routine in the organization. In order to solve this issue in a timely manner, we identify several ML techniques that examine an employee’s record and assess factors in generalized ways to assess whether the resource will remain to continue working or may leave the workplace with the passage of time. However, sensor-based information processing is not much explored in the corporate sector. This paper presents an IoT-enabled predictive strategy to evaluate employee churn count and discusses the factors to decrease this issue in the organizations. For this, we use filter-based methods to analyze features and perform classification to identify firm future churners. The performance evaluation shows that the proposed technique efficiently identifies the future churners with 98% accuracy in the IoT-enabled corporate sector organizations.

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Section: Data Mining Techniques For Predicting Employee Turnovermentioning

confidence: 99%

Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry

Naz¹,

Siddiqui

Koo

et al. 2022

Applied Sciences

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“…Some turnover studies suggest using machine learning to design retention policies [8]. A study was carried out by [9] to predict employee turnover in one of Indonesia's renowned telecommunication company, comparing three classification algorithm (Naïve Bayes, Decision Tree and Random forest) based on 12 attributes. Another study by [10] implemented twelve features to predict employee turnover, while other use 19 attributes [8].…”

Section: Related Workmentioning

confidence: 99%

Predicting Employee Turnover in IT Industries using Correlation and Chi-Square Visualization

Priambodo¹,

Jumaryadi²,

Rahayu³

et al. 2022

IJACSA

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Employee turnover in the IT industry is among the highest compared to other industries. Knowing factors that influence the turnover may help reduce this issue in future. One of these factors is job satisfaction, which are composed of two important factors, status and seniority. In this study, the correlation and chi-square visualization are utilized to determine the factors that affect employee turnover. The experiment was carried out to predict turnover using a private IT consultant dataset comparing three classification algorithms (decision tree, Naïve Bayes, and Random Forest). The result shows that job duration and positioning are factors that influence employee turnover in a software company.

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“…Compliant with [8], churning is a notorious concern for organizations, given its frequent occurrence. When a worker presents their resignation, a set of complications arises: the need to find appropriate substitutes; the amount of time, effort, and associated costs implied by the latter; the delay in meeting deadlines, which oftentimes leads to unsatisfied clients, ultimately translates to huge losses for the said company [9]. Several authors have described churning as a multi-faceted concept with a multitude of associated definitions.…”

Section: The Origin Of Churning In Human Resourcesmentioning

confidence: 99%

The Theoretical-Conceptual Model of Churning in Human Resources: The Importance of Its Operationalization

Pirrolas

Correia

2021

Sustainability

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Given the current socio-economic context in which the labor market is set in, if we were to consider both employment opportunities in specific economic periods and the individual expectations workers have regarding one’s working conditions, it should be a matter of one’s individual right of choice to decide whether or not to stay or leave and change companies. The paper we present before you took into account the phenomenon of churning as a cyclical process. Our main goal was to understand the main causes leading to it in the context of human resources and, ultimately, what were the consequences emerging from it. In order to carry out this analysis, we put forward a conceptual-theoretical model of the phenomenon of churning, made possible through the analysis of both the currently available literature and the empirical studies and conclusions stemming from the latter. The aforementioned analysis allowed us to trace all main causes responsible for the origin of churning and also, to understand how it could be implemented further on, in order to minimize the costs associated with hiring new personnel and retaining qualified employees by resorting beforehand to the implementation of strategic measures of human resources retention. By applying the methodology based on grounded theory, this study allowed us to further contribute to the already available, yet limited, literature and definition of this multifaceted and greatly complex subject that is the phenomenon of churning.

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A Comparative Study of Employee Churn Prediction Model

Cited by 35 publications

References 10 publications

Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry

Predictive Modeling of Employee Churn Analysis for IoT-Enabled Software Industry

Predicting Employee Turnover in IT Industries using Correlation and Chi-Square Visualization

The Theoretical-Conceptual Model of Churning in Human Resources: The Importance of Its Operationalization

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