Pressure Test: Quantifying the impact of positive stress on companies from online employee reviews
Sanja \v{S}\'cepanovi\'c, Marios Constantinides, Daniele Quercia,, Seunghyun Kim

TL;DR
This study uses deep learning to analyze online employee reviews, quantifying positive stress in workplaces and linking it to company growth and economic indicators over a decade.
Contribution
Introduces a novel methodology to classify companies based on stress levels from employee reviews and links positive stress to higher growth and economic trends.
Findings
Positive stress companies grew 5.1 times over 10 years
Stress scores from reviews track U.S. unemployment rates
Automated language analysis can quantify workplace stress
Abstract
Workplace stress is often considered to be negative, yet lab studies on individuals suggest that not all stress is bad. There are two types of stress: distress refers to harmful stimuli, while eustress refers to healthy, euphoric stimuli that create a sense of fulfillment and achievement. Telling the two types of stress apart is challenging, let alone quantifying their impact across corporations. By leveraging a dataset of 440K reviews about S&P 500 companies published during twelve successive years, we developed a deep learning framework to extract stress mentions from these reviews. We proposed a new methodology that places each company on a stress-by-rating quadrant (based on its overall stress score and overall rating on the site), and accordingly scores the company to be, on average, either a low stress}, passive, negative stress, or positive stress company. We found that (former)…
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Taxonomy
TopicsSupply Chain Resilience and Risk Management
