The COVID-19 pandemic has forced much of the country’s workforce into remote work arrangements. With the need for social distancing, the ability to continue essential business functions through effective remote work arrangements is a key means for addressing the global health crisis. However, many organizations are unprepared to accommodate a remote workforce and likewise lack insight into best practices as to how to promote continued productivity and well-being of the workforce in such arrangements. Although there is a large body of extant research on remote work arrangements, numerous questions remained under investigated. This project will address these gaps by studying the impact of several organizational, individual, technological, and supervisor characteristics on remote worker adjustment, well-being, and productivity. Findings from the project will provide evidence-based best practices that many large and small businesses can use both during future pandemics and other extreme events, but also going forward in normal work environments that may increasingly want to support remote work.
The COVID-19 pandemic has suddenly promoted the need for remote work arrangements on a vast scale, but we lack key information regarding what makes such work productive and sustainable. This project will obtain data from 500 full-time employees who are working remotely during the response to COVID-19 but were not doing so previously. The first phase of the project will collect a baseline survey that captures characteristics and experiences prior to the pandemic and remote work transition and general perceptions of the adjustment to remote work process. Phase 2 involves a 30-day experience sampling study administered daily at the end of each workday that will capture day-to-day experiences, attitudes, and performance of remote workers. Benefits of this panel design are: 1) provides insights into how multiple dynamic changes influence outcomes; 2) enhances ecological validity; 3) allows researchers to examine both within- and between-person processes; and 4) reduces retrospective recall biases. The project will partner with several work organizations to collect the data. Data from Phase 1 will be analyzed using regression and dominance analysis; data from Phase 2 will be analyzed using multi-level modeling which controls for nested structures, adjusting standard errors to take into account the lack of independence. These analyses also control for between-subject variables and previous measurements while also accounting for missing data. Findings from the project will inform organizational theories involving the effects of organizations, individuals, work, and technology on workers and work outcomes.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
