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E-mail communication patterns and job burnout

机译:电子邮件通信模式和工作倦怠

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摘要

A considerable body of research has documented the negative effects of job burnout on employees and their organizations, emphasizing the importance of the identification of early signs of the phenomenon for the purposes of prevention and intervention. However, such timely identification is difficult due to the time and cost of assessing the burnout levels of all employees in an organization using established scales. In this paper, we propose an innovative way to identify employees at risk of job burnout by analyzing their e-mail communication patterns. Building on the Job Demands–Resources model, we theorize about the relationship between e-mail communication patterns and levels of employee exhaustion and disengagement (two dimensions of burnout). We analyzed 52,190 e-mails exchanged between 57 employees of a medium sized R&D company over a five-month period. We then related these employees’ communication patterns to their levels of burnout, collected using an established scale (the OLBI–Oldenburg Burnout Inventory). Our results provide support for the overall proposition of the paper, that e-mail communications can be used to identify individuals at risk of job burnout. Our models explain up to 34% of the variance of burnout and up to 37% and 19% respectively of the variance of exhaustion and disengagement. They also successfully distinguish between employees with a higher risk of burnout and those with lower levels of risk (F1 score of 84% with recall of 100% and 73% precision). We discuss the implications of our results and present suggestions for future research.
机译:大量研究记录了工作倦怠对员工及其组织的负面影响,强调了为预防和干预目的,识别现象的早期迹象的重要性。但是,由于使用既定的量表来评估组织中所有员工的倦怠水平的时间和成本,这样的及时识别很困难。在本文中,我们提出了一种创新的方法,可以通过分析员工的电子邮件通信模式来确定有工作倦怠风险的员工。在工作需求-资源模型的基础上,我们对电子邮件通信模式与员工疲惫和疏散程度(倦怠的两个维度)之间的关系进行了理论化。我们分析了一个中型研发公司的57名员工在五个月内交换的52,190封电子邮件。然后,我们将这些员工的沟通模式与他们的倦怠程度相关联,并使用既定规模(OLBI-奥尔登堡倦怠量表)进行收集。我们的结果为该论文的总体主张提供了支持,即电子邮件通信可用于识别有工作倦怠风险的个人。我们的模型解释了高达34%的倦怠差异以及分别高达37%和19%的疲劳和脱离啮合差异。他们还成功地区分了倦怠风险较高的员工和风险水平较低的员工(F1分数为84%,召回率分别为100%和73%)。我们讨论了结果的含义,并提出了对未来研究的建议。

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