Workforce Analytics in Teleworking

The recent COVID-19 pandemic has accelerated the interest in new software tools to monitor the computer-based activities of employees working remotely (teleworking), and the demand for better analytics functionalities to be offered, focusing on employees’ performance and work-life balance...

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Autores principales:	Claudiu Vasile Kifor, Sergiu Stefan Nicolaescu, Adrian Florea, Roxana Florenta Savescu, Ilie Receu, Anca Victoria Tirlea, Raluca Elena Danut
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Computerized monitoring workforce analytics employee performance data processing data engineering data analytics Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/b3fffca7982c49d9b8f8bcc21b53ab8d
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spelling	oai:doaj.org-article:b3fffca7982c49d9b8f8bcc21b53ab8d2021-12-02T00:00:25ZWorkforce Analytics in Teleworking2169-353610.1109/ACCESS.2021.3129248https://doaj.org/article/b3fffca7982c49d9b8f8bcc21b53ab8d2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9620096/https://doaj.org/toc/2169-3536The recent COVID-19 pandemic has accelerated the interest in new software tools to monitor the computer-based activities of employees working remotely (teleworking), and the demand for better analytics functionalities to be offered, focusing on employees’ performance and work-life balance. In this paper, we aim to analyze the habits of teleworking employees based on their interaction with the computer: how the employees are involved in different types of activities (actual work, recreation, documentation), and which are the most intensive periods. A conceptual framework for workforce analytics was developed for this purpose, together with tools and applications, that can provide useful information on different categories of activities where employees are involved. Knowledge generation is performed in four phases: collecting, processing, organizing, and analyzing the data to create valuable insights for the organization. Based on this framework, we developed a case study in an IT company, where two categories of employees, developers and software consultants, were monitored for 114 days, with 3.5 million events being generated and processed. The results showed different habits for consultants and developers, in terms of working activity structure, working schedule, inactivity time and interaction with the computer. Differences were also identified when we compared our results with previous research that monitored software developers working in-house: remote workers tend to organize their program for a longer period during the workday, and spend less time on meetings but longer time for programming. On the other hand, both categories of employees (in-house and teleworkers) show highly fragmented work, switching windows after very short periods of activity, with a potential negative impact on productivity, progress on tasks, and quality of output. The research results can be used in future employee productivity studies when searching answers to a fundamental question for workforce analytics – why are some employees more productive than others?Claudiu Vasile KiforSergiu Stefan NicolaescuAdrian FloreaRoxana Florenta SavescuIlie ReceuAnca Victoria TirleaRaluca Elena DanutIEEEarticleComputerized monitoringworkforce analyticsemployee performancedata processingdata engineeringdata analyticsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 156451-156464 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computerized monitoring workforce analytics employee performance data processing data engineering data analytics Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Computerized monitoring workforce analytics employee performance data processing data engineering data analytics Electrical engineering. Electronics. Nuclear engineering TK1-9971 Claudiu Vasile Kifor Sergiu Stefan Nicolaescu Adrian Florea Roxana Florenta Savescu Ilie Receu Anca Victoria Tirlea Raluca Elena Danut Workforce Analytics in Teleworking
description	The recent COVID-19 pandemic has accelerated the interest in new software tools to monitor the computer-based activities of employees working remotely (teleworking), and the demand for better analytics functionalities to be offered, focusing on employees’ performance and work-life balance. In this paper, we aim to analyze the habits of teleworking employees based on their interaction with the computer: how the employees are involved in different types of activities (actual work, recreation, documentation), and which are the most intensive periods. A conceptual framework for workforce analytics was developed for this purpose, together with tools and applications, that can provide useful information on different categories of activities where employees are involved. Knowledge generation is performed in four phases: collecting, processing, organizing, and analyzing the data to create valuable insights for the organization. Based on this framework, we developed a case study in an IT company, where two categories of employees, developers and software consultants, were monitored for 114 days, with 3.5 million events being generated and processed. The results showed different habits for consultants and developers, in terms of working activity structure, working schedule, inactivity time and interaction with the computer. Differences were also identified when we compared our results with previous research that monitored software developers working in-house: remote workers tend to organize their program for a longer period during the workday, and spend less time on meetings but longer time for programming. On the other hand, both categories of employees (in-house and teleworkers) show highly fragmented work, switching windows after very short periods of activity, with a potential negative impact on productivity, progress on tasks, and quality of output. The research results can be used in future employee productivity studies when searching answers to a fundamental question for workforce analytics – why are some employees more productive than others?
format	article
author	Claudiu Vasile Kifor Sergiu Stefan Nicolaescu Adrian Florea Roxana Florenta Savescu Ilie Receu Anca Victoria Tirlea Raluca Elena Danut
author_facet	Claudiu Vasile Kifor Sergiu Stefan Nicolaescu Adrian Florea Roxana Florenta Savescu Ilie Receu Anca Victoria Tirlea Raluca Elena Danut
author_sort	Claudiu Vasile Kifor
title	Workforce Analytics in Teleworking
title_short	Workforce Analytics in Teleworking
title_full	Workforce Analytics in Teleworking
title_fullStr	Workforce Analytics in Teleworking
title_full_unstemmed	Workforce Analytics in Teleworking
title_sort	workforce analytics in teleworking
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/b3fffca7982c49d9b8f8bcc21b53ab8d
work_keys_str_mv	AT claudiuvasilekifor workforceanalyticsinteleworking AT sergiustefannicolaescu workforceanalyticsinteleworking AT adrianflorea workforceanalyticsinteleworking AT roxanaflorentasavescu workforceanalyticsinteleworking AT iliereceu workforceanalyticsinteleworking AT ancavictoriatirlea workforceanalyticsinteleworking AT ralucaelenadanut workforceanalyticsinteleworking
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Workforce Analytics in Teleworking

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