Measure Outcomes if You Can, But Can You?

Productivity measurement as it applies to intellectual or professional work is notoriously hard to measure, if it is truly possible at all. For starters, Person's Law and the Hawthorne Effect  illustrate the concept that people who know they are being measured will perform better than they would if they are not being measured. 

Pearson's Law states that “when performance is measured, performance improves. When performance is measured and reported back, the rate of improvement accelerates.” In other words, productivity metrics improve when people know they are being measured, and even more when people know the results are reported to managers. 

Performance feedback is similar to the Hawthorne Effect. Increased attention from experimenters tends to boost performance. In the short term, that could lead to an improvement in productivity.

In other words, “what you measure will improve,” at least in the short term. It is impossible to know whether productivity--assuming you can measure it--actually will remain better over time, once the near term tests subside. 

 

Many argue, and studies maintain that remote work at scale did boost productivity. One might argue we actually have no idea, most of the time. 

source: Deloitte 

That workers say they are more productive is not to say they actually are more productive. 

Also, worker satisfaction is not the same thing as productivity. Happy workers can be less productive; unhappy workers can be more productive. This is an apples compared to oranges argument, in all too many cases.  

The other issue is that we equate worker attitudes with outcomes. Happier workers might, or might not, be more productive. All we can measure is a subjective attitude. More happy or less happy does not necessarily correlate with outcomes. 

In principle, one could have happier but less productive workers; less happy but more productive workers. And most workers prefer remote work; hence the obvious subjective reporting that “we are more productive when working remotely.” Maybe so, maybe not. The point is that a subjective statement is not a measurable fact. 

Now some argue  the traditional focus on measuring inputs makes less sense than ever. There is merit to measuring outputs, rather than inputs. Either way, as we shift from quantifiable outcomes to qualitative outcomes, our measurement tasks do not become easier. 

“Previous models of productivity-focused on several now-outdated approaches to measuring the ‘quantifiable’ results of an employee’s work,” Deloitte consultants say. “This included measurement of outputs, such as units and/or deliverables completed, and production line management’s (and its white-collar equivalents) assessments of hours worked by watching over a sea of workers, cubicles, and offices.”

Still, measuring inputs--for the most part--tends to overshadow business outcomes, as difficult as that may be. 

But when “outcomes” can be defined in ways that--though believed to contribute to business outcomes--might or might not do so. 

Perhaps it sounds logical enough to substitute “desired outcomes and outputs of work visible to employees in order to achieve them.” The hard part is ensuring that the substituted outcomes really have some causal relationship to business outcomes. 

Perhaps not everything an entity produces can be measured quantitatively and directly. Perhaps everything is not “the bottom line.” Qualitative outcomes matter, even if we cannot measure them. 

So, yes, outcomes matter more than measurement of inputs. The problem remains: intellectual or non-tangible output is hard to measure, if we can measure it at all. By all means, measure outcomes if you can. 

The issue is whether we can, and to what extent.