January 22, 2021
This is a part of a series that briefly introduces 20 different HR metrics that are based on passive data extracted from digital communication and collaboration platforms.
Overtime is usually defined as an amount of time spent at work before and after official working hours. Many countries designate all work time surpassing 8 hours in a day or 40 hours in a week as overtime. Now that so many of us have switched to remote work, without the office to physically separate employees' professional and personal lives, it is more appropriate to measure overtime as a proportion of working activities taking place on various digital collaboration platforms after hours. (See Fig. 1 for an illustration of overtime, as such, on three three different collaboration platforms - calendars, emails and Slack).
Fig. 1: Screenshot from Time is Ltd.’s analytics platform showing the distribution of overtime on three collaboration platforms (calendars, emails and Slack) across departments. In the case of meetings, overtime is computed as the proportion of the employees’ total meeting time which takes place after hours (defined as a time outside working time from Monday to Friday between 8AM and 6PM). In the case of emails and Slack messaging, the overtime is computed as a proportion of emails and messages, respectively, that were sent after hours.
Work-life balance is one of the most important factors of the employee experience. If your working environment relies on excessive overtime, employee satisfaction can plunge - endangering employee retention and occupational health. Given both direct and indirect costs associated with unsatisfied, absent and/or leaving employees, it is crucial for management to follow how this aspect of employee experience evolves over time and across teams/departments, so they can take well targeted, proactive countermeasures.
Get to grips with what your data shows you to determine when and where changes need to be made. Put the metrics into context: compare the proportion of after-hours collaboration activities with the average amount of time these activities take up, per employee in a given team/department. In this way management can focus its attention and potentially intervene in departments that are in dangerous waters (defined by combination of high activity on a given collaboration platform and frequent after hours collaboration activity - see Fig. 2 for an illustration of this contextualization).
Fig. 2: Screenshot from Time is Ltd.'s analytics platform showing the graph that displays information about the proportion of time employees spent in meetings after hours (x-axis) and about the typical monthly number of hours spent in meetings per capita (y-axis) for each department represented by individual bubbles. Bubble size represents departments’ respective headcount. The red circle highlights two departments that demand management’s attention the most, given that they use meetings for collaboration a lot and often after hours.
Overtime is a relative term, it would be reductive to place a blanket rule on what constitutes as excessive overtime across different industries, companies, and even across departments/teams within each company. It depends upon specific circumstances and roles. These differences should be taken into account when specifying how overtime is computed and reported in a company.
Besides organizational specifics you cannot forget to consider that in internationally/globally operating companies employees work in different time zones. While making a Slack call at 3PM from Prague is perfectly fine for employee’s well-being and work-life balance, for her colleague in Seattle such a call at the corresponding 6AM is less suitable from this perspective. Unfortunately, time zone information is not always part of the data exported from some collaboration platforms and must be provided externally, e.g. together with information about organizational structure of the company, to make sense of the collaboration data.
There are a variety of ways to manage overtime. The proper solutions can vary based on industry, company size, work environment, and many other factors, but among some of the effective means belong the following actions:
When defining a proper policy for dealing with overtime, keep in mind that, as with so much in life, overtime is not a simple matter of “good or bad”. It has rather ambivalent implications for various organizational outcomes.
As summarized by Young Jin Ko and Jin Nam Choi in their research article on this topic, “The firm overtime level is negatively related to employee satisfaction but positively related to firm productivity and curvilinearly [(inverted U‐shaped)] related to firm innovation. Moreover, the effects of the firm overtime level on firm productivity and innovation are positively moderated by the organizational trust. In conclusion, our findings reveal considerable perils and promises that result in firm‐level overtime as a prevailing tool for utilizing human capital and highlight the critical contingency of organizational trust.” In other words, we can compare overtime to a knife: both can be useful, but they demand caution as they can also cause serious harm if (mis)used in improper situations and with improper intentions.