Capturing and Analysing Employee Behaviour: An Honest Day’s Work Record

IF 2.7 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Data & Knowledge Engineering Pub Date : 2024-08-31 DOI:10.1016/j.datak.2024.102350
Iris Beerepoot, Tea Šinik, Hajo A. Reijers
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Abstract

For a range of reasons, organisations collect data on the work behaviour of their employees. However, each data collection technique displays its own unique mix of intrusiveness, information richness, and risks. For the sake of understanding the differences between data collection techniques, we conducted a multiple-case study in a multinational professional services organisation, tracking six participants throughout a workday using non-participant observation, screen recording, and timesheet techniques. This led to 136 hours of data. Our findings show that relying on one data collection technique alone cannot provide a comprehensive and accurate account of activities that are screen-based, offline, or overtime. The collected data also provided an opportunity to investigate the use of process mining for analysing employee behaviour, specifically with respect to the completeness of the collected data. Our study underlines the importance of judiciously selecting data collection techniques, as well as using a sufficiently broad data set to generate reliable insights into employee behaviour.

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捕捉和分析员工行为:诚实的日常工作记录
出于各种原因,企业会收集员工的工作行为数据。然而,每种数据收集技术都有其独特的侵入性、信息丰富性和风险性。为了了解数据收集技术之间的差异,我们在一家跨国专业服务机构开展了一项多案例研究,使用非参与者观察、屏幕记录和时间表技术对六名参与者的整个工作日进行跟踪。由此获得了 136 个小时的数据。我们的研究结果表明,仅仅依靠一种数据收集技术无法全面准确地描述基于屏幕、离线或加班的活动。收集到的数据还为研究如何利用流程挖掘来分析员工行为提供了机会,特别是在所收集数据的完整性方面。我们的研究强调了明智选择数据收集技术的重要性,以及使用足够广泛的数据集对员工行为进行可靠洞察的重要性。
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来源期刊
Data & Knowledge Engineering
Data & Knowledge Engineering 工程技术-计算机:人工智能
CiteScore
5.00
自引率
0.00%
发文量
66
审稿时长
6 months
期刊介绍: Data & Knowledge Engineering (DKE) stimulates the exchange of ideas and interaction between these two related fields of interest. DKE reaches a world-wide audience of researchers, designers, managers and users. The major aim of the journal is to identify, investigate and analyze the underlying principles in the design and effective use of these systems.
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