In fact measuring productivity makes you even more motivated to put in more hours as you can (and often do) clearly see that the amount you derive from a business correlates to the hours you put into it.
I think it would be interesting if you described what your setup for measuring that was and, in rough terms, what your productivity was at various levels of labor per week.
Eric Ries had a phrase called "shadow belief": the unvoiced, unquestionable assumptions we make about the business world we're operating in. My day job had, for decades, a shadow belief about engineering productivity: namely, that it was a constant number per hour, and that therefore the number of kousuu (a unit of engineering production) produced could be computed as kousuu = constant * # engineers * # hours.
Then one day quite recently we sat down and said "Hey, we keep obsessive records of how many kousuu we produce. We keep obsessive records of how many hours each engineer works. Let's divide."
And we found that the constant changed. Some changes were not unanticipated: our best senior engineers were routinely more productive than our new company employees, OK, training period and whatnot. Some of the differences between expectations and results were so vast that I could not tell you them in good conscience.
Don't just say you're measuring. We did that, for decades. Measure. Then, act on it.
(I wish I could tell you what my day job found when actually started to measure. I think I can tell you this: there was audible skepticism and derision when we introduced a "temporary, experimental cost-cutting measure" obligating employees to leave at 5 PM two days a week. It was thought that, aside from the sheer un-Japaneseness of it all, it would throw schedules into disarray, necessitate extra crunching on the other days, and be widely ignored anyhow. That was months ago. On Friday, we received word that, on review of Actual Results, the experimental policy was henceforth merely Policy. 90% of the office was not there to read about the news, as the news arrived at 5:02.)
It would be really interesting to hear 1) how you measure - what data do you track, and what tools do you use to do so and 2) how you analyzed (simple Excel, something more sophisticated?). I imagine it's not simply RescueTime.
I think many, many people would love to have this to take to their employer and also for themselves.
I think it would be interesting if you described what your setup for measuring that was and, in rough terms, what your productivity was at various levels of labor per week.
Eric Ries had a phrase called "shadow belief": the unvoiced, unquestionable assumptions we make about the business world we're operating in. My day job had, for decades, a shadow belief about engineering productivity: namely, that it was a constant number per hour, and that therefore the number of kousuu (a unit of engineering production) produced could be computed as kousuu = constant * # engineers * # hours.
Then one day quite recently we sat down and said "Hey, we keep obsessive records of how many kousuu we produce. We keep obsessive records of how many hours each engineer works. Let's divide."
And we found that the constant changed. Some changes were not unanticipated: our best senior engineers were routinely more productive than our new company employees, OK, training period and whatnot. Some of the differences between expectations and results were so vast that I could not tell you them in good conscience.
Don't just say you're measuring. We did that, for decades. Measure. Then, act on it.
(I wish I could tell you what my day job found when actually started to measure. I think I can tell you this: there was audible skepticism and derision when we introduced a "temporary, experimental cost-cutting measure" obligating employees to leave at 5 PM two days a week. It was thought that, aside from the sheer un-Japaneseness of it all, it would throw schedules into disarray, necessitate extra crunching on the other days, and be widely ignored anyhow. That was months ago. On Friday, we received word that, on review of Actual Results, the experimental policy was henceforth merely Policy. 90% of the office was not there to read about the news, as the news arrived at 5:02.)