1. What are the benefits of alternative work arrangements?
Alternative work arrangements, such as compressed workweek and hybrid work, offer potential solutions to promote productivity and work-life balance. They have been implemented in many workplaces to enhance employees' work-life balance, which is directly linked to organizational and social sustainability. Countries and companies are exploring the impacts of these arrangements, with examples like the 4-day workweek in Spain, Unilever in New Zealand, Shopify in Canada, and Microsoft in Japan. Studies have shown that alternative work arrangements can improve employee well-being and contribute to long-term business success. These arrangements also align with the changing needs of employers and employees in the 21st century, making them a critical consideration for modern workforce policies and practices.
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2. What was the study design used?
The study utilized a fully within-subjects design, with day of the week as the independent variable and computer output metrics as the dependent variables. This design aimed to control for artifacts and differences between individuals, examining changes within participants over a two-year period. By focusing on stable fluctuations within-person over time, the study aimed to isolate factors that contribute to overall productivity. This approach also accounted for variations in job types and duties among participants. The research complied with ethical guidelines and obtained informed consent from all participants.
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3. What software was used to collect computer usage metrics?
RSIGuard software version 6 was used to collect computer usage metrics. This software, developed by Cority Enviance, has been previously validated to measure computer usage and productivity. It was installed on employees' computers to track metrics such as total active hours, keyboard and mouse hours, total number of words typed, mouse distances, and mouse clicks and scrolls. The dataset included 130,681 observations from 789 individuals, collected daily between January 1, 2017, and December 31, 2018, excluding data from August 1 to September 30, 2017, due to Hurricane Harvey's impact. Observations with zero recorded metrics or missing values were excluded, resulting in 111,719 observations used for analysis.
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4. What factors were included in the model for analyzing mouse scrolls?
In the model for analyzing mouse scrolls, the primary independent variable was the day of the week. Separate models were built for each dependent variable, such as words typed. The total number of words typed was included to adjust for the pattern of typos. Time of day (AM vs. PM) was also included as an independent variable, with its interaction with the day of the week analyzed. Additionally, total mouse and keyboard computer activity were controlled for in further analyses. Post hoc pairwise multiple comparisons were conducted using Tukey's method, and the least squared geometric means (LSGM) of the computer metrics were calculated. The data were completely anonymous, and demographic factors were not collected, but it was assumed that they would be evenly distributed among the large sample size. Statistical analyses were conducted using SAS software, and significance was declared at p < 0.01.
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