This is Part 2 of our two-part series explaining the Cultivate 7—the seven factors that the Cultivate platform measures to illuminate company culture in the Cultivate Culture Report. To refresh your memory, the Cultivate Culture Report objectively measures facets of company culture and can help organizations correlate them to other success metrics such as engagement, retention or revenue. This helps them understand their company culture, see how they are meeting (or not meeting) culture goals, and decide on ways they might improve.
You can read Part 1, which covered the first three factors here. This post will explain the remaining four factors: digital accommodation, after-hours work, response time and response density.
Factor Four: Digital Accommodation
This factor measures how similarly managers and direct reports communicate in terms of politeness, sentiment, and style. If a manager and their team are all polite, engage in small talk before getting down to business, and tend to use positive language and emojis (yes, we can measure emoji usage too), then they will score highly on this factor. If the manager is blunt and optimistic, but their team is chatty and pessimistic, then they will score lower on this factor. In our data set, this factor was correlated with one on one meetings, collaboration, and sharing and gathering information. It seems that in our data, managers that have a lot of meetings and communication with their teams tend to adopt a similar communication style (either the team matches the manager, the manager matches the team, or they meet somewhere in the middle).
Factor Five: After-Hours Work
This factor measures the amount of digital communication (emails and chat messages) that a user sends and receives after normal working hours. This includes requests that managers send after hours, and specific requests for a team member to do something after hours (the Cultivate platform can surface both of these). These requests can create more pressure on team members to be working late at night, and can contribute to a culture of always being available. In our data set, this factor has a strong negative correlation with sending and receiving information. Perhaps unsurprisingly, users who work late don’t coordinate with their teams as much.
We don’t make any value judgements while measuring company culture. If our platform finds high levels of after-hours communication at an organization or on a certain team, it is up to the users to decide if this is effective and if it fits with their larger people strategy and organizational values. Our role is just providing data—we show users the patterns that their employees are exhibiting and they can decide what, if anything, should be done about it.
Factor Six: Response Time
This measures how quickly a user responds to messages. This includes response time when making a commitment, giving feedback on someone else’s idea, sharing their opinion in response to a message from a team member, or following up on messages they already sent. In our initial data sets, this factor tended to have small, positive correlations with most of the other factors, which could indicate that managers who are active communicators in general (they share more info with their teams, have more 1v1 meetings, etc.) also tend to respond more quickly.
Again, this behavior can be either positive or a negative depending on the team and organization in question. A responsive manager can be very helpful if they ensure their team members all have the information they need to do their jobs. But if they spend all their time glued to email and their other work suffers for it, that could have negative effects. Looking at this factor in relation to other factors and correlating it with other KPIs can help organizations determine what the right level of manager responsiveness is for them.
Factor Seven: Response Density
This factor measures the length of responses. A user with a high response density will write longer messages and emails. In our data set, this factor was positively correlated with information sharing and gathering (which makes sense – sharing more information means that messages tend to be longer), and also positively correlated with response time. You might expect that people who respond to messages quickly are more likely to dash off shorter replies quickly, but our data suggests the opposite (although different organizations might find a different relationship here). It’s also slightly negatively correlated with after-hours work in our data set, so working late does seem to encourage shorter messages in some way.
We’re very excited about the possible uses of this new way to measure and quantify company culture. Our report can help users get a clearer understanding of the strengths and pain points of their organization and teams. In collaboration with the customers, we can then highlight successes where they are already meeting their digital communication goals and pinpoint areas where they could improve. In the future, we can correlate these cultural elements with performance (for example, determining which elements an organization’s high-performing teams display) to show the relationship between communication, culture, and business outcomes in greater detail. That’s incredibly valuable information for any organization that wants to empower their employees and improve their overall performance.