Overtime and Paid Leave Effects on Burnout: Conceptualizing an Anthroengineering Methodology to Predict Turnover
Mentor 1
Matthew Petering
Start Date
10-5-2022 10:00 AM
Description
The COVID-19 pandemic has severely stressed the American workforce, with many looking for a new job or leaving the workforce altogether in 2021. Two of the most common reasons cited for this high turnover are burnout and work-life balance. Employee turnover, or exit from an organization, is expensive for businesses because of the costs associated with hiring and training. In response to this, businesses have been looking for ways to increase engagement or loyalty to retain employees. At an even deeper level, we might ask: how did we get to this point? This research considers how work might rearrange in response to turnover and how these rearrangements have historically shaped the current cultures of work. The goal of this research is two-fold, firstly, this work is used to further develop our nascent Labor Gap Response Theory which is a framework that utilizes concepts from both industrial engineering and sociocultural anthropology to qualitatively recognize workplace dynamics. Secondly, we are focused on conceptualizing a legitimate and uniquely anthroengineering methodology aimed at quantifying and predicting employee burnout. Ethnographic techniques will be leveraged to better understand the factors contributing to the current attitudes and behaviors of manufacturing workplaces. These factors will then be targeted in a survey and the results will be analyzed via fuzzy logic, which can handle vague or fuzzy data as opposed to strictly binary data. This will allow us to produce a visualized output of our results that we may refer to as a “topography of burnout” which will make visible the thresholds past which employees are likely to quit. We expect to find that the amount of overtime worked, paid sick time offered, and vacation time offered are correlated to the feeling of burnout and that the factors which contribute most to burnout differ between hourly and salary workers.
Overtime and Paid Leave Effects on Burnout: Conceptualizing an Anthroengineering Methodology to Predict Turnover
The COVID-19 pandemic has severely stressed the American workforce, with many looking for a new job or leaving the workforce altogether in 2021. Two of the most common reasons cited for this high turnover are burnout and work-life balance. Employee turnover, or exit from an organization, is expensive for businesses because of the costs associated with hiring and training. In response to this, businesses have been looking for ways to increase engagement or loyalty to retain employees. At an even deeper level, we might ask: how did we get to this point? This research considers how work might rearrange in response to turnover and how these rearrangements have historically shaped the current cultures of work. The goal of this research is two-fold, firstly, this work is used to further develop our nascent Labor Gap Response Theory which is a framework that utilizes concepts from both industrial engineering and sociocultural anthropology to qualitatively recognize workplace dynamics. Secondly, we are focused on conceptualizing a legitimate and uniquely anthroengineering methodology aimed at quantifying and predicting employee burnout. Ethnographic techniques will be leveraged to better understand the factors contributing to the current attitudes and behaviors of manufacturing workplaces. These factors will then be targeted in a survey and the results will be analyzed via fuzzy logic, which can handle vague or fuzzy data as opposed to strictly binary data. This will allow us to produce a visualized output of our results that we may refer to as a “topography of burnout” which will make visible the thresholds past which employees are likely to quit. We expect to find that the amount of overtime worked, paid sick time offered, and vacation time offered are correlated to the feeling of burnout and that the factors which contribute most to burnout differ between hourly and salary workers.
