Company culture refers to behavioral norms, values and expectations—both implicit and explicit—that drive an organization. There is no single template for a good company culture, but most successful companies have strong organizational cultures and buy-in. Good organizational culture is linked to a whole host of positive outcomes, including innovation, employee retention and company effectiveness. Despite its importance, culture is notoriously difficult to measure, especially passively. At Cultivate, we were able to objectively measure a facet of company culture using observed digital behaviors. Specifically, we isolated seven groups of behaviors using factor analysis, which together give a clear understanding of a company’s digital norms.
Understanding digital behavior norms, whether explicit or implicit, is important for a variety of reasons. Companies can use this information to decide whether their current culture meets their expectations or needs to be addressed. For example, a company could have an explicit norm to limit after hours messaging, and seeing their digital behaviors laid out can help confirm whether that norm is being met. Similarly, a company may expect messages to be answered quickly; seeing hard evidence of what is really happening can confirm the expectation is being met or allow for corrective action.
What is factor analysis?
Factor analysis is a statistical technique used to discover patterns in data. In our case, we wanted to see if there were sets of management behaviors that generally occur together. For instance, does a manager who asks for a lot of feedback also respond to messages quickly? Or does a manager who has lots of one on ones with their direct reports send less dense messages over digital media?
The Cultivate 7
Through factor analysis, we discovered seven core groups of behaviors that define most managers in their digital interactions. In no specific order, they include:
- Sharing and gathering information – Giving opinions, recognizing direct reports, informing direct reports about completed work, requesting action from direct reports, requesting ad hoc meetings with direct reports, requesting feedback from direct reports.
- Response time – Response time when making a commitment, giving feedback, following up to messages, and giving opinions.
- One on one meetings – The number and duration of one on one meetings.
- Collaboration – The number of duration of total meetings together.
- Digital accomodation – How similarly managers and direct reports communicate regarding politeness, sentiment, and style.
- After hours work – Total after hours communication, after hours requests, and after hours action requests.
- Response density – Length of responses.
To begin, we can look at how these factors relate to one another. Blue means a positive correlation and red means a negative correlation. The bigger the circle, the more strongly correlated the two factors are. For instance, you can see that people who are communicating information to or requesting information from their direct reports more often are also levying more after hours requests, potentially resulting in poorer work life balance. On the positive side, those same managers who request and share information hold more one-on-one meetings than less communicative managers.
These factors allow us to easily visualize things like company culture. In the heat map below, darker squares indicate a relatively “stronger” culture on that dimension as compared to the other companies. For instance, darker “after hours work” squares mean that managers are sending fewer after hours requests, while darker “one on ones” squares mean that managers are having more one on one meetings with their team. You can easily see how company culture differs for a random sample of seven companies we currently have data from.
Walking through the data for Company 4, for instance, you can see that managers there give and ask for more information than at any other company, and they respond very quickly, but those behaviors seem to come at the expense of work-life balance, which is the lowest there compared to all the other companies. The rest of Company 4’s behaviors seem to fall in the middle of the spectrum.
We can also measure similarity on all of those factors from company to company. In the matrix below, blue circles again indicate positive correlations (similar cultures) and red circles indicate negative correlations (dissimilar cultures). The size and darkness of the circle tells you how similar or dissimilar the cultures are. A very small, very light circle would indicate that cultures are neither similar nor dissimilar. You can see, for instance, that Company 2 and Company 3 have very similar cultures, while Company 6 and Company 4 have opposite cultures. Meanwhile, Company 5 and Company 1 have very little overlap in company culture.
Via factor analysis, Cultivate has uncovered a new way to measure and quantify company culture. We can look at similarities and differences between companies and get a clearer understanding of their strengths and pain points. In collaboration with the customers, we can then highlight successes where they are already meeting their digital communication goals and pinpoint areas to focus on to improve. In the future, we may be able to correlate these cultural elements to performance (for example, determining which elements an organization’s high-performing teams display), or perhaps even benchmarking a company’s culture against other companies in their industry or against P/E ratio and stock performance. It’s exciting stuff – keep checking our blog and social media feeds for more updates on how we’re pushing the envelope on people analytics and company culture.
Rachel is the Senior People Scientist at Cultivate. As a psychologist, she’s always been interested in people: how we think, grow, evolve, and interact. She is excited to help Cultivate users interpret their behaviors through a scientific, research-based lens.