Vibes is a great term for describing a mood, but it's not a strong basis for setting policy. What helps is data -- the kind of data collected in experiments where the results are replicable, repeatable and reproducible.
That's why a recent study conducted by Nicholas Bloom, James Liang and Ruobing Han is so intriguing. You all may know Bloom as the Stanford economics professor who's been studying management practices and how the last few years of remote and hybrid work have affected the practices. Han recently finished his PhD in economics at Stanford, and Liang is a co-founder of Trip.com. Liang's co-founder status made it possible to conduct an experiment with that company.
The researchers recruited 1,600 China-based employees in marketing, finance, accounting and engineering, then randomized them. For a six month period, the control group went into the office five days a week while the treatment group, went to the office only on Mondays, Tuesdays and Thursdays. The researchers analyzed data from the experiment after six months, then performance reviews for the next two years. What did they find after two years? According to the team's writeup of the experiment in the Harvard Business Review, "The two groups showed no differences in productivity, performance review grade, or promotion."
Moreover, the researchers continued:
Before the experiment, managers estimated hybrid would reduce productivity by 2.6%. After the six-month experiment they estimated it increased productivity by 1%. Those working under the hybrid model had a higher satisfaction rate, and 35% lower attrition. Quit-rate reductions were largest for female employees. Non-managers and those with the long commutes greater than 1.5 hours also had significantly reduced quit rates under hybrid.
So there were quantifiable differences in both productivity and employee retention between the control and hybrid work groups. There were also cost savings; the researchers estimated that every employee departure costs a company 50% of the departing employee's annual salary, so by reducing employee turnover, the company saved millions of dollars annually.
Company culture made this experience possible, the team reported. The researchers point out that the company's habit of doing performance reviews every six months, based on employee performance data, as well as feedback from co-workers, clients, direct reports and managers, allows managers to identify their team's highest performers and keep an eye on ones whose performances needs to improve. Additionally, managers were responsible for fostering a clear, coordinated schedule for in-office days, the better to reduce frustration on the part of employees who come into an office only to sit on Teams calls all day. The attention to performance and clear communication around time expectations are also the result of sharing and acting on data.
Not every company has the wherewithal to split its workforce into two groups and A/B test optimal schedules. But this experiment does show how important it is to be able to use data to assess how different work schedules will affect a company's bottom line. Smart workplace strategists and managers should be asking how they can get the data they need to make the best decision.