Work and wellbeing: measurement, intervention, and performance

工作与福祉：测量、干预和绩效

• 批准号: 2095311
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2095311
• 关键词: Work; wellbeing; measurement; intervention; performance

I intend to successively study the following stages in the next three years.Study 1: How are wellbeing and workplace characteristics interlinked? Fortunately, there are now more data available on wellbeing than ever before (Powdthavee, 2015). Descriptive correlations have already been established using large-scale datasets like the European Social Survey (Ward & De Neve, 2017). However, there is some more potential here with new approaches and other datasets. For example, regression discontinuity designs in Gallup Daily data promise access to causal cues. In addition, social media analyses and machine learning algorithms such as natural language processing are potential techniques to be used in this research. This would increase the chances of uncovering new causal effects. For example, there is a good chance of using non-traditional social network data like the one used by Johannes Eichstaedt at the University of Pennsylvania (Kern et al., 2016). His freely available code can be used for my purposes and is familiar to me given my background in data science including machine learning and programming. All in all, the main purpose of stage 1 is the formulation of hypotheses for the later stages that look at the noteworthy observed relationships in more detail.Study 2: Based on the results in Study 1 and general results from the literature, the initial step in this research would be a lab study. The Nuffield Centre for Experimental Social Sciences stands out as a place in Oxford with the funding and the facilities for this research. The field has seen numerous lab studies, for example by Oswald et al. (2015): in their study, the authors increased the happiness levels of participants in an RCT-like intervention, by showing them entertaining videos. They then showed that workers in the experimental group performed relatively better on tasks than those in the control group. I have already been able to gain experience in lab studies during my time at the Haas School of Business in the lab of Juliana Schroeder (Management of Organizations group). In my role as Summer Research Assistant I took part in the design and execution of several lab, field, and online experiments. The unique advantage of the lab experiment in Study 2 is that it allows for custom situations. For example, boundary condition experiments can be used to establish more information about a relationship. Another comparatively cheap and easy way of doing testing before Study 3 would potentially be the following study design, which I got to know at the Haas School of Business: with the help of crowdsourcing sites such as Amazon Mechanical Turk one can get a large number of people to work on an experiment relatively quickly (Brooks et al., 2016).Study 3: The last stage follows from Studies 1 and 2. It would consist of conducting a field experiment on a hopefully large scale. The observed most meaningful relationships in Studies 1 and 2 would thus go into the field to be tested. Another criterion to keep in mind is the cost-effectiveness of the implemented measures. This field experiment can be particularly effective with companies using a complete feedback cycle, such as call centres or other highly automated and digitalised businesses. It can also touch on other topics of interest, such as the aforementioned link between wellbeing and firm success. An example of a well-implemented field experiment looking at the relationship between employee recognition and performance was executed with a sample of some 300 students executing data tasks (Bradler et al., 2016).All in all, this study would allow for a detailed dissection of various effects, e.g. exactly what types of workplace characteristics influence wellbeing in which ways.

我打算在接下来的三年里陆续研究以下几个阶段。研究一：幸福感和工作场所特征是如何相互联系的？幸运的是，现在关于幸福感的数据比以往任何时候都多(Powdthavee，2015)。已使用欧洲社会调查(Ward&De Neve，2017)等大规模数据集建立了描述性相关性。然而，新的方法和其他数据集在这里有更多的潜力。例如，盖洛普每日数据中的回归不连续性设计承诺获得因果线索。此外，社交媒体分析和机器学习算法，如自然语言处理，是这项研究中潜在的技术。这将增加发现新的因果关系的机会。例如，很有可能使用非传统的社交网络数据，比如宾夕法尼亚大学的Johannes Eichstaedt使用的数据(Kern等人，2016)。他的免费代码可以用于我的目的，考虑到我在数据科学方面的背景，包括机器学习和编程，我对他很熟悉。总而言之，第一阶段的主要目的是为更详细地考察值得注意的观察到的关系的后期阶段制定假设。研究2：根据研究1的结果和文献的一般结果，本研究的第一步将是实验室研究。纳菲尔德实验社会科学中心作为牛津大学的一个地方脱颖而出，为这项研究提供了资金和设施。该领域已经看到了许多实验室研究，例如奥斯瓦尔德等人的研究。(2015)：在他们的研究中，作者通过向参与者播放有趣的视频来提高参与者的幸福感水平。然后，他们显示，试验组的员工在任务中的表现相对好于控制组。我在哈斯商学院朱莉安娜·施罗德(Juliana Schroeder)的实验室工作期间，已经获得了实验室研究的经验。作为暑期研究助理，我参与了几个实验室、现场和在线实验的设计和执行。研究2中的实验室实验的独特优势是它允许自定义情况。例如，边界条件实验可用于建立有关关系的更多信息。另一种在研究3之前进行测试的相对廉价和简单的方法可能是以下研究设计，这是我在哈斯商学院了解到的：在亚马逊机械土耳其人等众包网站的帮助下，人们可以相对较快地让大量人参与实验(Brooks等人，2016)。研究3：研究1和研究2的最后阶段。它将包括进行有望大规模的实地实验。因此，在研究1和研究2中观察到的最有意义的关系将进入现场进行测试。另一个需要牢记的标准是所实施措施的成本效益。这种实地试验对使用完整反馈周期的公司尤其有效，例如呼叫中心或其他高度自动化和数字化的企业。它还可以触及其他感兴趣的话题，例如前面提到的幸福感和公司成功之间的联系。一项关于员工认可度和绩效之间关系的现场实验实施得很好，其样本约为300名执行数据任务的学生(Bradler等人，2016)。总而言之，这项研究将允许详细剖析各种影响，例如，确切地说，哪种类型的工作场所特征以哪些方式影响幸福感。