ARROW: A web-based employee turnover analysis tool for effective human resource management in large-scale organizations

Weeramanthrie, T. T.; Thilakumara, C. N.; Wijesiri, K. N. A. C.; Fernando, Nimalika; Thelijjagoda, Samantha; Gamage, Anjalie

doi:10.1109/nitc.2017.8285665

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…When a software company wants to lower the churn rate, it needs to analyze the churn factors and understand the reasons. Some organizations provide researchers with facilities to examine the turnover pattern from their HR data [2]. The researchers present their conclusion that the sustainability of employees is a challenging job for the companies, because nowadays, more worthy employees are tempted globally by other organizations [3].…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…In 2017, Weeramanthrie [22] proposed a HRM web software. The aim of presented sytstem was to estimate the turnover employees in businesses and organizations, the system works on historical data to fill some gaps among admin's abilities and staff behaviors in the past, current and even future of the association.…”

Section: Related Workmentioning

confidence: 99%

An HRM System for Small and Medium Enterprises (Sme)s Based on Cloud Computing Technology

Abdullah

Zeebaree

Jacksi

et al. 2020

Int. J. Res. Granthaalayah

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Technology has changed our life and the way we work; however, technology has affected several methods of working in Small and Medium Enterprises (SME)s. Human Resource (HR) is one of the core components in businesses, and nowadays most businesses are using technology for daily basis tasks. However, it still is not used all over the world. In Kurdistan Region-Iraq (KRI), most of the SMEs still use the old way of working and follow the paper-based method for their daily basis tasks. According to a survey, more than seventy percent of SMEs in Kurdistan are not using software to manage human resource management tasks. However, some big companies are using HRMS; but even then, there is a lack of use of Cloud Technology. In this study, a model of the Enterprise Human Resource Management System (EHRMS) is proposed and implemented to solve the HR problems in this area using Cloud Technology. The proposed system consists of sixteen standard modules which used usually with famous HRM systems. The system has been developed by using several technologies such as CodeIgniter as a software framework. The system is launched and deployed on Amazon Web Service (AWS) Elastic Compute Cloud (EC2).

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“…Weeramanthrie, Thilakumara [2], proposed a unique web application called (ARROW) which aims to predict PEN Vol. 7, No.…”

Section: Fig2mentioning

confidence: 99%

“…It was presented as a modern management method in the 1960s [1]. Currently, human beings are considered assets rather than resources [2]. HR consists of administrators, employees, users, and staff.…”

Section: Introductionmentioning

confidence: 99%

Human resource management systems for enterprise organizations: A review

Zeebaree

Shukur

Hussan

2019

PEN

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Human Resources HRs are one of the most important elements in the organizations, but it is difficult to manage a large number of human resources, especially in large companies. For this purpose, companies have tended to manage these resources taking advantages of the technology and the Internet. In this research, we will focus on human resources management in different companies and different countries and explore the factors that motivate companies to adopt Electronic Human Resource Management E-HRM as well as the factors that affect its adoption. The results showed that HRM plays a vital role in facilitating organizational processes, save cost and time, it also beneficial for competitive advantages. The findings also explored that E-HRM aspects and factors impact E-HRM adoption are varied from firm to another and from country to another. However, the companies moved towards adopting E-HRM because it mitigates the efforts of HR managers to take a decision.

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ARROW: A web-based employee turnover analysis tool for effective human resource management in large-scale organizations

Cited by 8 publications

References 2 publications

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

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

An HRM System for Small and Medium Enterprises (Sme)s Based on Cloud Computing Technology

Human resource management systems for enterprise organizations: A review

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