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Much emphasis has been placed on how the affordances and layouts of an office setting can influence co-worker interactions and perceived team outcomes. Little is known, however, whether perceptions of teamwork and team conflict are affected when the location of work changes from the office to the home. To address this gap, we present findings from a ten-week,in situ study of 91 information workers from 27 US-based teams. We compare three distinct work locations---private and shared workspaces at home as well at the office---and explore how each location may impact individual perceptions of teamwork. While there was no significant association with participants' perceptions of teamwork, results revealed associations of work location with team conflict: participants who worked in a private room at home reported significantly lower team conflict compared to those working in the office. No difference was found for the office and the shared workspace. We further found that the influence of work location on team conflict interacted with job decision latitude and the level of task interdependence among co-workers. We discuss practical implications for full-time work from home (WFH) on teams. Our study adds an important environmental dimension to the literature on remote teaming, which in turn may help organizations as they consider, prepare, or implement more permanent WFH and/or hybrid work policies in the future.

 

Working remotely from home during the COVID-19 pandemic has resulted in significant shifts and disruptions in the personal and work lives of millions of information workers and their teams. We examined how sleep patterns---an important component of mental and physical health---relates to teamwork. We used wearable sensing and daily questionnaires to examine sleep patterns, affect, and perceptions of teamwork in 71 information workers from 22 teams over a ten-week period. Participants reported delays in sleep onset and offset as well as longer sleep duration during the pandemic. A similar shift was found in work schedules, though total work hours did not change significantly. Surprisingly, we found that more sleep was negatively related to positive affect, perceptions of teamwork, and perceptions of team productivity. However, a greater misalignment in the sleep patterns of members in a team predicted positive affect and teamwork after accounting for individual differences in sleep preferences. A follow-up analysis of exit interviews with participants revealed team-working conventions and collaborative mindsets as prominent themes that might help explain some of the ways that misalignment in sleep can affect teamwork. We discuss implications of sleep and sleep misalignment in work-from-home contexts with an eye towards leveraging sleep data to facilitate remote teamwork.

 

Although some research highlights the benefits of behavioral routines for individual functioning, other research indicates that routines can reflect an individual's inflexibility and lower well-being. Given conflicting accounts on the benefits of routine, research is needed to examine how routineness versus flexibility in health-related behaviors correspond to personality traits, health, and occupational outcomes. We adopt a nonlinear dynamical systems approach to understanding routine using automatically sensed health-related behaviors collected from 483 information workers over a roughly two-month period. We utilized multidimensional recurrence quantification analysis to derive a measure of health regularity (routineness) from measures of daily step count, sleep duration, and heart rate variability (which relates to stress). Participants also completed measures of personality, health, and job performance at the start of the study and for two months via Ecological Momentary Assessments. Greater regularity was associated with higher neuroticism, lower agreeableness, and greater interpersonal and organizational deviance. Importantly, these results were independent of overall levels of each health indicator in addition to demographics. It is often believed that routine is desirable, but the results suggest that associations with routineness are more nuanced, and wearable sensors can provide insights into beneficial health behaviors. 

During the COVID-19 pandemic, millions of previously co-located information workers had to work from home, a trend expected to become much more commonplace in the future. We interviewed 53 information workers from 17 U.S. teams to understand how this unique extended work-from-home setting influenced teamwork and how they adapted to it. Using a grounded theory approach, we discovered that extended remote work highlighted diversity in team members' home-lives and daily work rhythms. Whereas these types of diversity played only marginal roles for teams in the co-located office, they had a more tangible impact in the work-from-home setting, from coordination delays and interruptions to conflicts related to workload fairness, miscommunication, and trust. Importantly, workers reported that their teams adapted to these challenges by setting explicit norms and standards for online communication and asynchronous collaboration and by promoting general social and situational awareness. We discuss computer-supported designs to help teams manage these latent diversities in an extended remote teamwork setting. 

Psychological science can benefit from and contribute to emerging approaches from the computing and information sciences driven by the availability of real-world data and advances in sensing and computing. We focus on one such approach, machine-learned computational models (MLCMs)—computer programs learned from data, typically with human supervision. We introduce MLCMs and discuss how they contrast with traditional computational models and assessment in the psychological sciences. Examples of MLCMs from cognitive and affective science, neuroscience, education, organizational psychology, and personality and social psychology are provided. We consider the accuracy and generalizability of MLCM-based measures, cautioning researchers to consider the underlying context and intended use when interpreting their performance. We conclude that in addition to known data privacy and security concerns, the use of MLCMs entails a reconceptualization of fairness, bias, interpretability, and responsible use.