Working From Home: Are You Overworked, Overwhelmed or Overjoyed?
As we settle into our new normal, a plethora of survey-based research addressing productivity gains or losses has found those working from home are experiencing a range of emotions. Common themes from the research include an increase in working hours, though at a lower intensity than a day in the office. We are overjoyed with the loss of the daily commute and the increased flexibility that working from home (WFH) affords us. Management and supervisors are finding it more difficult to keep track. And we are all having too many online meetings.
We're seeing innovative objective approaches to research, like Microsoft’s use of brainwave analyses of people during video meetings, chatting, handling emails and working online versus working in the office in general. Organizational Network Analysis (ONA) academic Rob Cross has written extensively on collaboration-induced overload. Cross found much of staff overload is self-inflicted, and attributed this to many of us wanting to help our colleagues.
Neuroeconomist Paul Zac studies the effects of peptide hormone Oxytocin on how people socially connect, collaborate and build trust. He builds a link between Oxytocin levels and organizational trust by encouraging staff to socially connect, bond, show empathy and collaborate in a virtuous cycle that creates even more Oxytocin. Zac identifies two forms of stress: challenge stress, the stress we experience when pursuing a purposeful team challenge, and the more acknowledged chronic stress, which is debilitating and leads to less bonding, collaboration and ultimately, trust. The former is good stress, the latter is something to avoid.
We believe we can identify “good stress” by the level of online reciprocated individual experiences (“I reply to you and you reply to me”), independent of time of day. Zac identifies trust (with our proxy being reciprocation levels) as a buffer against chronic stress from overwork.
Analyzing Stress Levels in Microsoft Teams
We chose one large organization to test our theory on. It conveniently has most of its staff in a single time zone and is a heavy user of Microsoft Teams for day to day work. Teams has a collection of asynchronous (Team channels, chat) and synchronous (calls, meetings) functions. We limited our analysis to Teams channels, being the function that can be directly attributed to a specific team.
We chose just one month of activity, which provided over two million interactions from more than 20,000 participants. We know, however, that digital platforms usage has a long tail distribution, with some extremely active participants and a long tail of modest users. In this case, our most active Teams participant was interacting on the team channels once every two to three minutes — and that doesn’t include chat messages.
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A Comparison of Team Level Results
During the selected period some 5,178 teams were active. We selected three very active teams to analyze for their hourly interactions:
The blue bars show the number of team members actively participating during each hourly time slice. The orange line tracks the reciprocity that exists between the network of all staff members interacting within the team.
Though we have only explored three active teams, there are some clear insights:
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- Teams A and B are relatively large, with up to 80 members participating in any given hour. These larger teams are active into the evening.
- For Teams A and B the participant levels may have dropped off in the evenings, but those that are active are interacting and reciprocating within the hourly periods, i.e., are fully engaged online.
- The overall reciprocation levels were in the 70% to 80% level, which is relatively high for teams of this size.
- Team C is contrasting. It is smaller, with a maximum of 20 members participating in any hour. The reciprocity levels were in the 80% to 90% levels, which is very high.
- Significantly, Team C manages its work hours. It is easier with small teams to establish team norms that people comply with. This is another argument for keeping teams smaller.
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Individual Workplace Stress Analysis
We identified 331 of the most active staff to look at their individual network performance (reciprocity within their personal network vs. diversity of experience, measured as active participation across multiple teams). Network science tells us that highest network performance occurs when both Reciprocity/Cohesion and Diversity can be maximized.
In this analysis, we explored Zac’s proposition that trust and, therefore, reciprocity provide a buffer for workplace stress. When it is present, stress is avoided. When it is absent, stress can be amplified.
The above graphic plots individuals positioned on the network performance chart. The size of the bubbles reflect the relative size of their personal networks. The quadrants are partitioned by the average scores for reciprocity and diversity. We can draw the following insights from the above data:
- The “Challenge” quadrant contains staff who have high reciprocity in their personal networks, while at the same time sharing their attention across more than five different teams. These employees likely experience “good stress,” sustaining high levels of reciprocations, despite interacting with multiple teams and for some, reasonably sized personal networks.
- The “High Engagement” quadrant has individuals who experience high levels of reciprocity, within a limited number of teams and smaller personal networks. This is the lower stress quadrant, and potentially the place to find “workplace joy.”
- Those in the “Chronic Stress” quadrant experience lower levels of reciprocity, while struggling with interacting across multiple teams and larger personal networks. Are these the typical “line managers” trying to manage across multiple teams and their staff? Thankfully no staff are at the extremes of this quadrant.
- The “Low Engagement” quadrant shows staff with relatively low reciprocity in their personal networks, even though they operate across a smaller number of teams. Their network sizes are marginally larger than those in the high engagement quadrant. A level of stress can exist, especially for those with the lowest levels of reciprocity across larger personal networks.
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Stress Depends on Many Factors
Our analysis shows it is misleading to simply infer longer hours maps to greater workplace chronic stress. The real situation is more nuanced and heavily impacted by the levels of trust and reciprocity that exist in the organization. Self-awareness is key. This is where analytics have a critical role to play; to help identify and amplify the good “challenge stress” and dampen the debilitating “chronic stress.”
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About the Author
Laurence Lock Lee is the co-founder and chief scientist at Swoop Analytics, a firm specializing in online social networking analytics. He previously held senior positions in research, management and technology consulting at BHP Billiton, Computer Sciences Corporation and Optimice.